selected problems of soil tillage systems and operations
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selected problems of soil tillage systems and operations
SELECTED PROBLEMS OF SOIL TILLAGE SYSTEMS AND OPERATIONS Editorial staff: Piotr F. Borowski Marek Klimkiewicz Małgorzata Powałka Faculty of Production Engineering Warsaw University of Life Sciences Warsaw 2010 Reviewers: Piotr F. Borowski Jerzy Buliński Marek Gaworski Marek Klimkiewicz Zbigniew Majewski Zygmunt Owsiak Małgorzata Powałka Janusz Wojdalski ISBN 978-83-928876-6-9 PRINT: WEMA 2 CONTENTS Chapter 1 The benefits of conservation tillage and its applications in the world Engin ÇAKIR 4 Chapter 2 Prediction of the effects of tillage in relation to soil organic matter content Ewa A. CZYŻ, Anthony R. DEXTER 11 Chapter 3 Determination of the soil stability in relation to ecological agriculture Ewa A. CZYŻ, Anthony R. DEXTER, Olga P. GAŢE 22 Chapter 4 Effect of applied simplifications in tillage systems on biological activity and POM content in soil Anna Maria GAJDA 35 Chapter 5 Data mining as a decision-support tool in sustainable agricultural production Jędrzej TRAJER, Jarosław STANKIEWICZ 47 Chapter 6 Site specific crop fertilization in Haute-Normandie: a tool to improve the profitability of cereal crop systems Jean Marc LLORENS, Olivier PERRIN, Odile BOURGAIN 57 Chapter 7 Ecological aspects of soil compaction by wheels of agricultural vehicles Jerzy BULIŃSKI 77 Chapter 8 Analysis of the working resistance of the working units used in simplified tillage Krzysztof PIECZARKA 90 Chapter 9 The influence of fertigation on agro-physical properties of arable plants Ján JOBBÁGY, Ján SIMONÍK, Marek KLIMKIEWICZ 99 Chapter 10 Ecological aspects of energetic plants harvesting Aleksander LISOWSKI, Adam STRUŻYK, Jacek KLONOWSKI, Tomasz NOWAKOWSKI, Jarosław CHLEBOWSKI, Michał SYPUŁA, Stanisław GACH 108 Chapter 11 Effect of season on carbon dioxide and ammonia production in broiler housing Štefan MIHINA, Monika KNÍŽATOVÁ, Jan BROUČEK 123 3 Chapter 1 THE BENEFITS OF CONSERVATION TILLAGE AND ITS APPLICATIONS IN THE WORLD Engin ÇAKIR INTRODUCTION To prevent soil degradation and to establish sustainable farming conservation tillage is necessary especially in the arid areas of the world. Beside this, increasing the cost of fuel in tillage operations are reducing the time are getting the main factor for changing the conventional farm techniques. Minimum tillage and direct seeding are some of the methods that farmers apply recently for a long-term erosion free farming with minimum fuel cost. Considering the negative effect of intensive farming in the field, direct seeding becomes more vital for farmers for establishing the nature of the soil and flora allowing natural plant growth with less plant protection problems. Conservation tillage was first introduced in the world in 1940, and first no-till applications were seen in 1960’s. Especially conservation tillage was progressed so quickly in last thirty years. No-till was applied in the United States of America 2,200,000 ha in 1973/74 and this increased to 4,800,000 ha and 19,400,000 ha in 1983 and 1997, respectively. If we look at the erosion damage in the world then, we can say that soil degradation is responsible for making from 2 million ha to 12 million ha or 0.3–0.8% of the world's arable land, unsuitable for agricultural production every year, with wind and water erosion accounting for 84% of the soil degradation [den Biggelaar et al., 2004a]. For this reason, good soil management to protect the soil against erosion in order to sustain long-term productivity is imperative for meeting the world's future needs for food and fibre production [den Biggelaar et al., 2004b]. In many studies, conservation tillage saves great deal of fuel and time. This is very important that most of the cost in agriculture is due to the fuel consumptions. Köller [2003] found 73% fuel energy save comparing the conventional method in direct seeding. According to the research findings, fuel consumptions of different tillage methods were found as 49,4 L ha-1 (100%), 31,2 L ha-1 (63,2%), 28,3 L ha-1 (57,3%), 25,2 L ha-1 (50,9%), 13,3 L ha-1 (27,08%) for plough, chisel, disk harrow, ridge-tillage and direct seeding respectively. 4 Conventional tillage methods not only cause soil loss but also wear outs the soil carbon in a long monoculture. In the depth of 0-20 cm, soil organic carbon lost increases with conventional method [Zinn et al, 2005]. Similar result was found from Mann [1986]. He found that organic carbon loss was 23% and which amounts 15 Mg ha-1. Conservation tillage establishes a good soil environment with a suitable soil flora. With a long run conservation tillage practices, soil regains its original natural strength which becomes less sensitive to the compaction due to the heavy load of machineries. The aim of this paper is to examine the conservation tillage practices and give some examples of applied conservation tillage studies in Turkey. BENEFITS OF CONSERVATION TILLAGE Traditional soil cultivation systems in the tropics and subtropics, with intensive soil tillage, will end in soil degradation and loss of crop productivity. This will result in poverty, exodus of farmers from rural areas, resulting in an increase of city slums and marginal populations, and finally in social conflicts. If we are to offer the farm family a chance to survive on the farm and if sustainable agriculture is to be achieved, than the paradigms of soil use and management must be changed and new farming practices must be implemented [Derpsch, 2006]. So we have to somehow convince the farmers about the benefits of conservation tillage by demonstrations and well extension services. It is vital that new techniques should be adapted to change the path of bad heading in soil degradation and erosion. Conservation tillage has mainly four important advantages: Less work, cost effective, erosion control and improvement in the quality of life. In fact the most reason anyone can find is to establish sustainable farming for a long term. Some of the benefits of the conservation tillage can be listed as: • Reduced labor requirements • Time savings • Fuel savings • Improved long- term productivity • Reduced machinery wear • Reduced soil erosion • Greater soil moisture retention • Improved water infiltration • Decreased soil compaction • Reduced release of carbon gases • Reduced air pollution Comparing the conventional methods, it is important that tillage is not necessary for crop production. If we can minimize or apply no-tillage along with good management, we can slowly form the natural environment for a good sustainable crop production. For example, instead Burying of plant residues with tillage implements, we leave them on the soil surface as mulch. Or we can replace the chemical pest control with biological pest control. We must definitely give up on monoculture and start using green manure and crop rotations. By using residue covered conservation tillage, we can reduce the wind and water erosion to near to zero. Giving up from the intensive soil tillage systems to minimized or no-till systems, we can create a good soil flora with more available soil 5 moisture, increased water infiltration into the soil and sequestered more carbon in the soil. CONSERVATION AGRICULTURAL PRACTICES IN THE WORLD Last 30 years or so, application of conservation tillage increased in the world very fast. Starting first with minimum tillage applications, conservation tillage is now reached to no-till or zero till agriculture in which tillage is not performed. No-till applications increased in the countries where at beginning it was applied hesitantly (Table 1-2). The leading countries in the world with the biggest area under no-tillage are the USA with 19.3 million hectares followed by Brazil with 11.2 million ha, Argentina with 7.3 million ha, Canada with about 4.1 million ha, Australia with 1 million ha and Paraguay with 790.000 ha of the technology being practiced by farmers. It is estimated that no-tillage is practiced on about 45 million hectares world wide. Table 1. Area under no-tillage in the seventies and eighties (Hectares) [Derpsch et al., 2006] Country 1973/ 74 1983/ 84 U.S.A 2,200,000 4,800,000 United Kingdom 200,000 275,000 France 50,000 50,000 Netherlands 2,000 5,000 Japan, Malaysia, Sri 200,000 250,000 Australia 100,000 400,000 New Zealand 75,000 75,000 Brazil 1,000 400,000 Table 2. Total area under no-tillage in different countries in 1996/ 97 (Hectares) [Derpsch at al, 2006] Country Area under no- tillage (ha) Argentina 4,400,000 Brazil 6,500,000 Canada 6,700,000 Mexico 490,000 Paraguay 500,000 Uruguay + Chile + Bolivia 500,000 U.S.A. 19,400,000 Australia 1,000,000 Others 460,000 Lafond et al. [2005] worked on the effects of three tillage methods; zero, minimum and conventional methods for three four-year crop sequences in grain yield over a 12-year period under Canadian growing conditions. Their results indicated that a one-year non-cereal break crop was enough to alleviate the negative effects of consecutive cereal crops on winter wheat. Results of this study also supported the large shifts towards in conservation tillage being observed in the Canadian prairies. Dam et al. [2005] studied the effect of different tillage practices and corn residues on soil bulk density, corn emergence rates and crop yields over an 11-year period for corn (Zea mays L.). They applied no-till, reduced tillage and conventional 6 tillage methods with corn residues and without residues. According to their findings, bulk density was 10% higher in no-till (1.37 Mg m−3) than in conventional tillage (1.23 Mg m−3), particularly at the 0–0.10 m depth. They found that No-till with residue (NTR) possibly had the slowest overall emergence due to the higher surface residue cover (8.5 Mg ha−1 in 1996) and higher bulk density (1.37 Mg m−3 over the 11 years). But, they observed that long-term mean dry matter corn yields were not affected by tillage and residue practices during the course of this study. Liebig et al. [2004] made a research in which they examined interactive effects of tillage, crop sequence, and cropping intensity on soil quality indicators for two longterm cropping system experiments in the northern Great Plains. They found that within the surface 7.5 cm, the continuous crop, no-till system possessed significantly more soil organic carbon (by 7.28 Mg ha-1), particulate organic matter (by 4.98 Mgha1), potentially mineralizable N (by 32.4 kg ha-1), and microbial biomass carbon (by 586 kg ha-1), as well as greater aggregate stability (by 33.4%) and faster infiltration rates (by 55.6 cm h-1) relative to the crop-fallow and conventional tillage system. From the results of long-term research made by Megyes et al. [2003], it was shown that crop production technologies based on reduced/conservation tillage methods may replace conventional tillage systems and are applicable under the environmental conditions of Hungary. Rajan and Khera conducted the field study to estimate the effect of tillage and different modes of mulch application on soil erosion losses. Treatments comprised minimum and conventional in the main plots and five modes of straw mulch applications; mulch spread over whole plot (Mw), mulch spread on lower one-third of plot (M1/3), mulch applied in strips (Ms), vertical mulching (Mv) and unmulched control (Mo). Mulch spread over whole plot reduced runoff by 33%. Runoff and soil loss were 5 and 40% higher under conventional tillage conditions. Straw mulching reduced maximum soil temperature and helped in conserving soil moisture. Minimum tillage coupled with mulch spread over whole plot was highly effective in reducing soil erosion losses, decreasing soil temperature and increasing moisture content by providing maximum surface cover. Anken and his friends examined how soil tillage systems influence soil quality especially under the conditions of moist cool conditions in Switzerland. They aimed to reduce the soil tillage and to check its effect on the yield. They found that reduced tillage increased earthworm populations, reduced Pseudocercosporella herpotrichoides infection in wheat (Triticum aestivum) and increased plant colonisation by arbuscular mycorrhizal fungi. They observed that yields for no-tillage and other ploughless cultivation techniques were on par with those obtained by ploughing. For direct-drilled maize (Zea mays), the yield was decreased 10% over the course of 14 years. The no-tillage method did not differ from the others in respect of bulk density, but it showed an increased preconsolidation stress and hence better trafficability. Direct seeding method in dry soil conditions with low stubble height had the minimum working time as 2.9 h ha-1 comparing other methods. Regarding silage corn yield, although conventional system provided 68.31 Mg ha-1 as the maximum yield while direct seeding had 67.58 Mg ha-1 yield, there was found no statistical differences between two methods. The lowest yield was found in direct seeding method applied in dry soil conditions with high stubble height as 47.08 Mg ha-1. The researchers also examined the change in organic matter in the soil and found the most increase 11.8% in direct seeding machine. According to the economic calculations of the methods, the cost of 422.63 USD ha-1 was the highest value found 7 in conventional tillage. The costs for direct seeding were 321.50 USD ha-1 and 305.33 USD ha-1 in the fields with high stubble height and low stubble height, respectively. Yalcin et al. [2003] studied the effect of direct seeding methods on second crop cotton in Soke region for 3 years. They applied conventional, minimum tillage and direct seeding methods and they examined the tillage effects on soil compaction, seedling emergency, plant growth and yield. Methods were applied in dry ad wet soil conditions. The results showed that conventional tillage method required 11.7 times more fuel comparing the direct seeding in second crop cotton. There are many studies in this field which many of them points out the benefits of application of conservation tillage especially no-till. We know very well from the experiences that, semi-arid and arid regions should strictly apply the conservation tillage. According to the resent studies, no-till should be practiced for a long term sustainable farm. Increasing the application of no-till in the world is a proof of erosion free conservation tillage. The reasons why conservation tillage methods especially direct seeding and no-till are not used by farmers widely can be listed as follows: • Not enough information available in this field, • They heard conservation tillage but they do not know how to do it, • Some of them tried earlier, but left this method and returned to the conventional tillage because received low yield, • There is not any government support, • Crop rotation is almost impossible due to low income of the farmers, • Direct seeding machines are not available in the market to try. CONCLUSIONS The advantages of conservation tillage methods such like low cost, time saving and sustainable farming will be very much convincing to spread the usage of these methods. The use of farming techniques providing high income with minimum damage to environment becomes the main objective of all the farmers in the world. Increase in the profit directly related with lowering the costs in the farm. The conservation tillage methods should be practiced not only for profitable farming, but also for preserving our natural resources and leaving to our next generation in good conditions. REFERENCES 1. Anken T., Weisskopf, P., Zihlmann, U., Forrer, H., Jansa, J., Perhacova K. 2004: Long-term tillage system effects under moist cool conditions in Switzerland. Soil & Tillage Research 78, s. 111-183. 2. Anonymous, 2007: Some Agricultural Statisticsi, 3. http://www.tuik.gov.tr/PreIstatistikTablo.do?istab_id=135 4. Anonymous, 1991: General Agricultural Census, Village Information Survey Results, T.C. Government Statistics Institutes DİE, Ankara, Turkey, 1991. 5. Anonymous, 1991: General Agricultural Census, 2004. T.C. Government Statistics Institutes DİE, Ankara, Turkey, 2004. 6. Anonymous 1998: TMMOB Chamber of Meteorological Engineers 1998 Year Agenda 8 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. Aykas, E., Onal, I. 1999: Effects of different tillage, seeding and weed control methods on plant growth and wheat yield. 7. International Congress on Mechanization and Energy in Agriculture , 26-27 May Adana- TURKEY Aykas, E., Yalçın, H., Çakır, E., Gülsoylu, E. 2005: Koruyucu Toprak İşleme Yöntemleri ve Doğrudan Ekim. E.Ü.Ziraat Fakültesi Dergisi, 42 (3), s. 195 - 207, ISSN:1018–8851, Bornova-İZMİR. Cakir E., H. Yalcin., E. Aykas., H. Akdemir., T. Ocel., H. Soya. 2005: Direct seeding applications on wheat in Odemis Region. Agricultural Machinery Science Magazine. ISSN 1306-0007. Vol. 1 No. 2 pp 109-114 İZMİR Çakır E., Aykas E., Yalçın H., 2003: Tillage Parameters and Economic Analysis of Direct Seeding, Minimum and Conventional Tillage In Wheat. ISTRO (International Soil Tillage Research Organization) Conference, Brisbane, Australia. Çakır E., Yalcin H., Aykas E., Gülsoylu E., Okur B., Delibacak S., Ongun A.R. 2006: The Effects of Conservation Tillage and Direct Seeding Applications on the Yield of Second Crop Mazie. ISTRO – 17th Conference of the International Soil Tillage Research Organization, August 28-Sept.3 2006 Kiel, GERMANY. Çakır E., Yalçın H., Aykas E., Akdemir H., Öcel T., Soya H. 2004: Ödemiş Koşullarında Buğday Tarımında Doğrudan Ekim Uygulamaları. Tarımsal Mekanizasyon 22. Ulusal Kongresi. 8-10 Eylül 2004, AYDIN, Bildiriler Kitabı. s. 17-18. Çakır E., Yalcin H., Aykas E., Gülsoylu E., Okur B., Delibacak S., Ongun A.R. 2006: Koruyucu Toprak İşleme ve Doğrudan Ekimin İkinci Ürün Mısır Verimine Etkileri: Birinci Yıl Sonuçları. Tarım Makinaları Bilimi Dergisi,. ISSN: 1306-0007 Cilt 2, Sayı 2, Sayfa: 139-146. 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Workshop on Conservation Tillage and Direct Seeding. İZMİR pp.122-142. Korucu T., Kirişçi V., Keskin S.G., 1999: Residue management for successful direct seeding. 7th International congress on agircultural mechanization and energy 26-27 May, 1999. Adana. Proceediings, p. 232-238. Kosutic S., Filipovic D., Gospodaric Z., 1999: energy Requirements in maize using conventional, conservation and no-tillage systems. 7th International congress on agricultural mechanization and energy 26-27 May, 1999. Adana. Proceediings, p. 1-6. Özpınar S., Işık A., 1999: Determination of the effects of the different tillage and planting methods on cotton yield in the southeast anatolia project area. 7th 9 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. International congress on agircultural mechanization and energy 26-27 May, 1999. Adana. Proceediings, p. 249-256. Liebig M.A., Tanaka D.L., Wienhold B.J., 2004. Tillage and cropping effects on soil quality indicators in the northern Great Plains. Soil & Tillage Research 78, 131-141. Mann L.K., 1986: Changes in soil carbon storage after cultivation. Soil Sci. 142, 279-288. Onal I., Aykas E.: 1999: Effects of different tillage, seeding and weed control methods on weed population in wheat production. 7. International Congress on Mechanization and Energy in Agriculture , 26-27 May Adana- TURKEY Özsert İ., Kara M.: 1987: Kuru tarım tahıl üretiminde değişik toprak işleme-ekim sistemleri ve enerji gereksinimleri. 3th International congress on agricultural mechanization and energy 26-29 October. Izmir. Proceediings, p. 238-245 Prechac-Garcia F., Ernst O., Siri-Prieto G., Terra J.A., 2004: Integrating no-till into crop-pasture rotations in Uruguay. Soil & Tillage Research 77, 1-13. Suzer S. 2003: A study on the possibilities of applying reduced tillage methods on Wheat. Workshop on Conservation Tillage and Direct Seeding. İZMİR pp.108121 Yalçın İ., Doğan T., 2000: Pamuk tarımında değişik toprak işleme yöntemlerinin toprak özellikleri, işgücü gereksinimleri ve verim parametrelerine etkileri üzerine bir araştırma. Tarımsal Mekanizasyon 19. Ulusal Kongresi 1 -2 Haziran 2000. Erzurum. Bildiri Kitabı sayfa 115-120. Yalçın H., Çakır E., Gülsoylu E., Keçecioğlu G., 2001: Tohum yatağı Hazırlamada Uygulanan Farklı Toprak İşleme Yöntemleri Üzerine Bir Araştırma. E.Ü.Ziraat Fakültesi Dergisi 38 (1), s. 71-78, Bornova-Izmir (Makale-Araştırma). Yalçın H., Çakır, E., Akdemir, H., Öcel, T., Soya, H., 2003: Doğrudan Ekim Ve Dipkazan Uygulamalarının İkinci Ürün Mısırda Verime Etkileri. Tarımsal Mekanizasyon 21. Ulusal Kongresi. 3-5 Eylül 2003, KONYA, Bildiriler Kitabı, S:167-171. Yalcin H., 1998: A study on investigation of the suitable tillage methods in second crop maize for silage. Ph.D.Thesis. Institute of Natural and Applied Science, Ege University, İzmir, p. 136. Yalcin H., E. Cakir H. Akdemir T. Öcel H. Soya. 2003: No-till planting of maize as a second crop and subsoil effect on yield. 21. National Congress on Mechanization and Energy in Agriculture. 3-5 September 2003, KONYA, Proceedings, pp.167-171. Yalcin H., Aykas E. 2003: Possibilities of direct drilling of second crop cotton ISTRO (International Soil Tillage Research Organization) Conference, Brisbane, Australia. Yalçın H, Çakır E. 2005: Tillage Parameters and Economic Analysis of Direct Seeding, Minimum and Conventional Tillage in Wheat. Journal of Agronomy 4 (4), ISSN 1812-5379, 329-332. Yalçın H., Çakır E. 2006: Tillage Effects and Energy Efficiencies of Subsoiling and Direct Seeding in Light Soil on Yield of Second Crop Corn for Silage in Western Turkey, Soil & Tillage Research, Volume 90, Page:250-255. Yalcin H., Aykas E., Çakır E. 2006: Reduced Tillage Systems on Second Crop Cotton in Aegean Region. ISTRO – 17th Conference of the International Soil Tillage Research Organization, August 28-Sept.3 2006 Kiel, GERMANY. Zinn Y.L., Lal R., Resck D.V.S. 2005: Changes in soil organic carbon stocks under agriculture in Brazil. Soil & Tillage Research 84, 28-40. 10 Chapter 2 PREDICTION OF THE EFFECTS OF TILLAGE IN RELATION TO SOIL ORGANIC MATTER CONTENT Ewa A. CZYŻ, Anthony R. DEXTER INTRODUCTION It is well known that organic matter is a “good thing” in soil and that it “improves” it. However, there is very limited information available about the effects of soil organic matter on soil/machine interactions. In this paper, we present predictions for the size distribution of clods and aggregates produced by tillage of soils having different contents of organic matter. The ideal seedbed has been described as having aggregates < 5 mm diameter [Braunack and Dexter, 1989; Russell, 1973]. On the other hand, clods (i.e. large aggregates) are considered to be a problem because they make the soil more heterogeneous and uneven. Here, we define clods as aggregates larger than 50 mm diameter. Here we use existing models to predict the soil break-up during tillage, and we examine the effects of soil organic matter content on the amount of small aggregates and clods produced during tillage. This is done in two steps: firstly, we use measured results for the soil water retention characteristics of a range of soils in an existing theory to obtain values of the index of soil physical quality, S* [Dexter, 2004]. We then apply these results in an existing empirical model for the soil crumbling that occurs during mouldboard ploughing. This gives us predictions of the size distributions of aggregates produced as a function of the content of organic matter. We note that all of the results used in this work were obtained using either undisturbed soil samples measured in the laboratory or real soil in real fields tilled with real mouldboard ploughs. MATERIALS AND METHODS Soils Soil samples were collected from fields in England, France and Poland that contained “old” experimental plots (more than 25 years old). The English soils were 11 collected from the Highfield experiment at Rothamsted Research Station at Harpenden (about 35 km north of the centre of London). The French soils were collected from two sites: Boigneville (about 60 km south of the centre of Paris at the Institut Technique des Céréales and Fourrages) and Feucherolles (about 30 km west of the centre of Paris). The Polish soils were from Grabów (about 20 km west from Pulawy). With the English soil, 5 treatments were measured: (a) permanent fallow which has been cultivated several times each year to kill weeds. (b) permanent arable which has been cropped annually with cereals since 1949. (c) rotation. This site has been maintained since 1949 in a 3-year grass-clover, 3 year cereal rotation. (d) reseeded grass. This plot was ploughed initially in 1948 and reseeded with grass shortly afterwards. (e) permanent grass. This is the original, unbroken grassland that used to cover the entire field. With the French soil, the treatments were started in 1970. Two treatments were measured: (f) tilled. This involves mouldboard ploughing annually to a depth of 25 cm. (g) direct drilled. In this treatment, the soil is disturbed only in the seed row to about 5 cm depth. In both treatments, the crop rotation is wheat – maize. With the Polish soil, an experiment on crop rotations and fertilization was sampled. The experiments were started in about 1975. Two treatments were measured: (h) low inputs of organic matter. The crop rotation is: potatoes, winter wheat, spring barley and maize. (i) high inputs of organic matter. The crop rotation is potatoes, winter wheat + mustard, spring barley + clover, and red clover + grass. Both treatments had conventional tillage with mouldboard ploughing. The particle size distributions of the soils were measured by the sedimentation (hydrometer) method. The contents of organic carbon were measured by wet oxidation. The water retention characteristics of the soils were measured using standard methods. These involved drying soil samples from saturation to a range of pore water suctions on sand tables with hanging water columns and in ceramic pressure plate extractors. The range of pore water suctions, h, applied was from 10 hPa to 15000 hPa. This range corresponds to pF1 to pF4.2 where pF is defined as the logarithm (base 10) of the pore water suction in hPa. For the French soils, samples were also equilibrated over saturated solutions of KCl, NaCl, NaBr, MgCl2 and LiCl to give values of pF = 5.34, 5.58, 5.85, 6.18 and 6.47, respectively. Tillage was done over a range of water contents in the field in Hungary and in Sweden. Large samples of tilled soil were analysed by sieving. The results were expressed as the proportions by mass of clods > 50 mm diameter for the Hungarian soils and proportions > 50, >10 and >5 mm diameter for the Swedish soils. Details of the field experiments have been published previously [Dexter and Birkás, 2004; Keller et al., 2007]. 12 Complexed and non-complexed organic matter Crop residues, when incorporated into soil, become decomposed by microbes to form the soil organic matter. This comprises humic acids and a range of other compounds. The soil organic matter (OM) interacts with the clay particles (< 2 µm) to form a complex that is relatively stable in water. It has been found that unit amount (by mass) of organic carbon (OC) forms a complex with m = 10 units of clay (Dexter et al., 2008). In this paper, we assume that OM = 1.724 OC. If the soil has a small content of OC (or OM), then all the OC will be complexed and some of clay will not be complexed. On the other hand, if the soil has a large content of OC, then all of the clay will be complexed but not all of the OC will be complexed. To distinguish between these possibilities, algorithms are necessary because continuous functions are not possible. The amount of complexed organic carbon, COC, can be calculated for each soil using the following algorithm: clay clay [ ] COC = IF OC < THEN OC ELSE m m (1) and the amount of non-complexed organic carbon, NCOC, using NCOC = IF [(C − COC ) > 0]THEN [C − COC ]ELSE [0] (2) It is possible to quantify the amounts of complexed and non-complexed clay in a similar way to the amounts of complexed and non-complexed carbon given by Eqs.(1 and 2), above. The amount of complexed clay, CC, is given by CC = IF [mOC < clay ]THEN [mOC ]ELSE [clay ] (3) and the amount of non-complexed clay, NCC, is given by NCC = IF [(clay − CC ) > 0]THEN [clay − CC ]ELSE [0] (4) The complex (COC + CC) is relatively stable in water, has a low density and is important in controlling several valuable soil physical properties [Dexter et al., 2008]. The NCOC is associated with soil hydrophobicity (or non-wetting behaviour) as found by de Jonge et al. [2009]. The NCC is associated with the soil content of readilydispersible clay, RDC (Dexter et al, 2008). RDC is associated with the instability of soil in water and the hard-setting phenomenon. Index of soil physical quality, S* The water retention data, measured as described above, in the range of 1.0 < pF < 4.2 were fitted to the Groenevelt and Grant [2004] equation: −k 0 w = k1 exp n ( pF0 ) 13 − exp − k 0 ( pF )n (5) in which k1, k0 and n are adjustable parameters. This equation has a sound basis in thermodynamic theory [Groenevelt and Bolt, 1972; Groenevelt and Grant, 2004] and is for systems in thermodynamic equilibrium. Additionally, for the French soils, the values of water content obtained over saturated salt solutions were included in the curve fitting. Eq. (5) states that the water content will be zero at pF0. The value of pF0 has been determined by the authors to be 6.6 [Dexter et al., 2010]. Eq. (5) gives the water content at saturation as k0 wsat = k1 exp ( pF0 )n (6) Eq. (5) has an inflection point (a point where the curvature = zero) at: nk pFi = 0 (n + 1) 1n (7) −k 0 − exp − (n + 1) wi = k1 exp ( pF0 )n n (8) The slope the curve of w plotted against pF has a slope at the inflection point given by: n + 1 dw − (n + 1) = −k1 exp * nk 0 d ( pF ) n nk 0 ((n +1) / n ) (9) The slope at the inflection point when w is plotted against ln(h) has been used as an index of soil physical quality, S. In previous work, S was calculated from fitted parameters of the van Genuchten [1980] water retention equation. In this present study, the slope is calculated from the fitted parameters of the Groenevelt and Grant [2004] equation, as in Eq. (9) above. This procedure gives slightly different values for the slope at the inflection point which we designate S*: −1 dw S* = * ln(10) d ( pF ) (10) In Eq. (10) the minus sign is added simply to make values of the index positive. There are only small difference in the values of S and S* calculated as described above. When we compared the values obtained previously for 8 soils, we obtained: S = 0.0029 + 0.954S*, r = 0.984, p < 0.0001 (±0.0030) (±0.070) (11) A fuller description of S and of the soil physical behaviour associated with different values of S is given in Dexter and Czyż [2007]. For the present purposes, it 14 is sufficient to know that values of S > 0.035 are associated with good soil physical properties whereas values of S < 0.035 are associated with poor soil physical properties. However, the changes in behaviour are progressive and there is no sudden change at S = 0.035. This value is empirical and is based on observations and experience of soil behaviour in the field. We have assumed that values of S and S* are equivalent and that the same categories of soil behaviour are associated with the same values of S*. Model for soil crumbling Tillage experiments were done in the field in Hungary [Dexter and Birkás, 2004] and in Sweden [Keller et al., 2007]. Five different soils were used in Hungary and four different soils in Sweden. In these experiments, tillage by mouldboard ploughing was done over a range of different water contents. The structure of the resulting tilled soil was analyzed by sieving to obtain the size distribution of the aggregates and clods produced. Also, the values of the index S were obtained for these soils. The results showed clearly that the water content given by Eq. (8) is the optimum water content for tillage. That is, tillage at this water content produces the minimum amount of clods (> 50 mm diameter) and the maximum amount of small aggregates. When tillage is done at the optimum water content, then the amounts of different sizes of aggregates produced may be estimated from Fig. 1. The results for clod production from Hungary and Sweden were almost identical even though the soils had very different genetic origins. 1.0 P > x mm 0.8 0.6 x=5 0.4 x = 10 0.2 0.0 0.00 x = 50 0.05 S* 0.10 0.15 Figure 1. Size distribution of aggregates produced by tillage as a function of S* [Keller et al., 2007] Here, we focus on the proportion, P(> x) of the tilled soil that is in the form of clods or aggregates (> x mm diameter) after tillage at the optimum soil water content. 15 RESULTS AND DISCUSSION Characterization of the experimental soils The compositions of the experimental soils are given in Table 1. The complexed and non-complexed components of the soil organic carbon and the clay as obtained using Eqs.(1-4) are given in Table 2. Table 1. Compositions of the experimental soils Site and treatment sand silt -1 (g kg ) (g kg-1) England, Rothamsted bare fallow 90 670 cont cereals 130 630 rotation 110 640 re-seeded grass 110 630 perm grass 110 670 France, Boigneville tilled 80 660 direct-drilled 78 686 Poland, Grabów low OM input 707 265 high OM input 722 255 clay (g kg-1) organic matter (g kg-1) 240 240 250 260 220 19.0 26.0 36.0 48.0 55.0 260 236 22.9 49.8 28 23 11.2 13.5 Table 2. Contents of organic carbon (OC), complexed organic carbon (COC), noncomplexed organic carbon (NCOC), complexed clay (CC) and non-complexed clay (NCC) Site and treatment OC COC NCOC CC NCC -1 -1 -1 -1 (g kg ) (g kg ) (g kg ) (g kg ) (g kg-1) England, Rothamsted bare fallow 11 11 0 110 130 cont cereals 15 15 0 150 90 rotation 21 21 0 210 40 re-seeded grass 28 26 2 260 0 perm grass 32 22 10 220 0 France, Boigneville tilled 13.3 13.3 0 133 127 direct-drilled 28.9 23.6 5.3 236 0 Poland, Grabów low OM input 6.5 2.8 3.7 28 0 high OM input 7.8 2.3 5.5 23 0 The parameters of the Groenevelt and Grant water retention equation, as obtained by non-linear curve fitting are given in Table 3. Calculated values of the index of soil physical quality, S*, are also presented in Table 3. The water retention curve over the whole range of water contents from saturation to complete dryness for Boigneville B soil is shown in Fig. 2, as an example. 16 Table 3. Parameters of the Groenevelt and Grant [2004] water retention equation (Eq.5) and values of the index of soil physical quality, S* Site and treatment k1 k0 n S* England, Rothamsted bare fallow cont cereals rotation re-seeded grass perm grass France, Boigneville tilled direct-drilled Poland, Grabów low OM input high OM input 2.5494 3.0205 0.8456 1.0783 1.0480 7.556 6.642 5.578 4.461 4.555 0.586 0.530 0.887 0.816 0.844 0.0203 0.0233 0.0279 0.0391 0.0396 0.6824 0.4377 6.838 11.920 1.021 2.049 0.0246 0.0474 0.1849 0.2108 12.418 11.894 2.588 2.777 0.0310 0.0406 Boigneville B -1 w (kg kg ) 0.3 0.2 0.1 0.0 1 2 3 pF 4 5 6 7 Figure 2. Water retention curve for the Boigneville B soil. The points shown as squares were measured using pressure plate equipment, and the points shown as triangles were measured by equilibration over saturated salt solutions. The smooth curve is the fit to the Groenevelt and Grant [2004] equation Predictions of the effects of soil organic matter content on the effects of tillage When we apply the values of S* given in Table 3 to the results in Fig. 1, we obtain the predictions given in Table 4. The gives estimates of the proportions of different size ranges of aggregates obtained by tilling the different soils at their optimum water contents. It can be seen that, when the content of organic matter is higher, then the amounts of clods produced is smaller and the amounts of small aggregates produced is greater. 17 Table 4. Values of S* and predicted proportions, P(<x) of the soil in aggregates or clods smaller than x mm diameter after tillage by a single pass of a mouldboard plough at the optimum water content Site and treatment S* P(<50) P(<10) P(<5) England, Rothamsted bare fallow cont cereals rotation re-seeded grass perm grass France, Boigneville tilled direct-drilled Poland, Grabów low OM input high OM input 0.0203 0.0233 0.0279 0.0391 0.0396 0.503 0.578 0.692 0.970 0.982 0.239 0.274 0.329 0.461 0.466 0.134 0.153 0.184 0.257 0.261 0.0246 0.0474 0.610 1.000 0.290 0.558 0.162 0.312 0.0310 0.0406 0.769 1.000 0.365 0.478 0.204 0.267 The lines in Fig. 1 that give the proportions of clods > x mm as a function of S* are given by P(> 50 mm) = 1.0 – 28.6S* (12) P(> 10 mm) = 1.0 – 11.8S* (13) P(> 5 mm) = 1.0 – 6.58S* (14) We note that the proportion of small aggregates (e.g. < x mm diameter) is given by P(< x mm) = 1 – P(>x mm). (15) P(<5 mm) = 6.58S*, for values of S* < 0.15 (16) For example, Some predictions from the above equations are given in Table 4. As discussed in the introduction. a good seedbed should be composed of aggregates < 5 mm diameter. The results in Table 4 show that such a seedbed cannot be produced by a single pass of a mouldboard plough even when the soil is in good physical condition (i.e. with a high value of S* ). This is consistent with experience and is the reason why secondary tillage (e.g. with harrows) is always performed. To study the effects of the amounts and types of non-complexed material, NCM, we have defined NCM = NCOC – NCC/10 (17) The value of 10 in Eq. (17) is the value of m in Eqs. (1-4). It is needed here to put NCOC and NCC onto effectively the same numerical scale because OC has 18 approximately 10 times the effect on soil physical properties as clay. The effect of NCM on S* is shown in Fig. 3. The regression equation is S* = 0.034 + 0.00096NCM, r = 0.88, (±0.002) (±0.00019) p < 0.002 (18) The value of S* at the intercept (when NCM = 0) is given by Eq. (18) as S* = 0.034 ± 0.002 which is not significantly different from the empirical value of S = 0.035 for the boundary between “good” and “poor” soil physical quality as presented by Dexter [2004] and Dexter and Czyż [2007]. At NCM = 0, all the clay and all the OM are complexed. 0.05 0.04 S* 0.03 0.02 -15 NCC/10 -10 -5 NCOC 0 NCM 5 10 Figure 3. Soil physical quality, S*, as a function of non-complexed material, NCM We have used Eqs. (12, 16, 17, 18) and the results in Table 2 to predict the proportions of clods (P>50 mm) and small aggregates (P<5 mm) produced by ploughing as a function of the soil content of non-complexed material, NCM, as defined in Eq. (17). These predictions are given in Fig. 4. The results for clods are consistent with the results in Fig. 1 and with the experimental results of Dexter and Birkás [2004] and Keller et al., [2007]. The results for small aggregates show that a single pass of a mouldboard plough produces < 30% of small aggregates even with soil in the best physical condition. 19 Proportion, P 0.5 NCOC NCC/10 0.4 0.3 0.2 P<5 mm 0.1 P>50 mm 0.0 -15 -10 -5 0 NCM 5 10 Figure 4. Predicted proportions of clods (> 50 mm diameter) and small aggregates (< 5 mm diameter) produced as a function of the soil content of non-complexed material, NCM We conclude that the new measure of non-complexed material, NCM, enables soils that are poor and rich in organic matter to be analysed on the same rational basis and physical scale which can then be used to predict the size distributions of clods and aggregates produced by tillage. ACKNOWLEDGEMENTS The authors would like to thank Dr. Guy Richard of INRA, France and Dr. D.S. Powlson of Rothamsted Research, England for their help. REFERENCES 1. Braunack, M.V. and Dexter, A.R. 1989: Soil aggregation in the seedbed: a review. II. Effect of aggregate sizes on plant growth. Soil and Tillage Res. 14, 281-298. 2. de Jonge, L.W., Moldrup, P. and Schjonning, P. 2009: Soil infrastructure, interfaces and translocation processes in inner space (“Soil-it-is”): towards a road map for the constraints and crossroads of soil architecture and biophysical processes. Hydrol. Earth Syst. Sci. 13, 1485-1502. 3. Dexter, A.R. 1988: Advances in characterization of soil structure. Soil and Tillage Res. 11, 199-238. 4. Dexter, A.R. and Birkás, M. 2004: Prediction of the soil structures produced by tillage. Soil and Tillage Res. 79 (2), 233-238. 5. Dexter, A.R. 2004: Soil physical quality. Geoderma 120, 201- 239. 6. Dexter, A.R. and Czyż, E.A. 2007: Applications of S-theory in the study of soil physical degradation and its consequences. Land Degradation and Development 18(4), 369-381. 20 7. 8. 9. 10. 11. 12. 13. Dexter, A.R., Richard, G., Arrouays, D., Czyż, E.A., Jolivet, C. and Duval, O. 2008: Complexed organic matter controls soil physical properties. Geoderma 144, 620-627. Dexter, A.R., Richard, G., Czyż, E.A. and Davy, J. 2010: Clay dispersion from soil as a function of antecedent water potential. Soil Sci. Soc. Am. J. (submitted). Groenevelt, P.H. and Bolt, G.H. 1972: Water retention in soil. Soil Sci. 113, 246253. Groenevelt P.H. and Grant, C.D. 2004: A new model for the soil water retention curve that solves the problem of residual water contents. Eur. J. Soil Sci. 55, 479-485. Keller, T., Arvidsson, J. and Dexter, A.R. 2007: Soil structures produced by tillage as affected by water content and the physical quality of soil. Soil and Tillage Res. 92 (1-2), 45-52. van Genuchten M.Th., 1980: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898. Russell, E.W. 1973: Soil Conditions and Plant Growth. 10th edition. Longman, Green and Co., London, 849pp. 21 Chapter 3 DETERMINATION OF SOIL STABILITY IN RELATION TO ECOLOGICAL AGRICULTURE Ewa A. CZYŻ, Anthony R. DEXTER, Olga P. GAŢE INTRODUCTION Ecological (organic) farming must protect the environment and at the same time produce good soil conditions for crop production. Structure is a complex property of the soil that is related to many agronomic and environmental processes [Díaz-Zorita et al., 2002] and has been the subject of study for several scientists [e.g. Dexter, 1988, 1997; Kay, 1990; Gaţe et al., 2004, 2005, 2006a, 2006b; Czyż and Dexter, 2009]. In ecological agriculture and horticultural production, the soil structure plays an important role for plant development, soil water balance and soil workability [Dexter, 1988; Watts et al., 1996b]. Moreover, the soil must have a structure that will persist in time. The ability of the soil structural units to resist changes after the application of a stress (i.e. dry or moist fragmentation, compaction, dispersion etc.) is known as soil structural stability [Dexter, 1988; Kay, 1990; Díaz-Zorita et al., 2002]. Dexter [1988] described two principal types of structural stability: (a) the ability of the soil to retain its structure under the action of water; and (b) the ability of drier soil to retain its structure under the action of external mechanical stresses. In the literature there is a wide range of methods used to assess soil structural stability which have been developed during the past six decades so that we are now in a position to give a quantitative, physically-based assessment of structural stability as influenced by the action of both water and mechanical stresses and over a range of structural size scales [Watts et al., 1996b]. However, recent studies have shown that it is the content of readily-dispersible clay which is the most appropriate measure of structural condition because clay dispersion is indicative of soil instability [Czyż et al., 2002; Dexter, 2002; Watts and Dexter, 1997; Watts et al., 1996a,b]. Clay particles together with other soil colloids constitute the basic binding agents of the soil micro-structure [Dexter, 1988, 2002], and therefore the amount of readily-dispersible clay in soils influences the stability of micro-aggregates and aggregates [Dexter, 2002]. Furthermore, the content of readily-dispersible clay in soil has been shown to affect a range of physical and mechanical properties [Kay and Dexter, 1992; Watts and Dexter, 1997; Watts et al., 1996a], and with complexed clay 22 and non-complexed clay [Dexter et al., 2008; Schjønning et al., 2009] and as a consequence Kay and Dexter [1990] suggested that the values of dispersible clay should be part of a minimum data set routinely collected for assessing the impact of new soil, crop and water management practices on soil properties. Dexter et al., 2008 showed that non-complexed clay is more easily dispersed in water than clay complexed with OM. The most important factor that affects the amount of dispersed clay is, undoubtedly, water. When soil comes into contact with water, the colloids within it swell causing it to become weaker and more susceptible to damage [Dexter, 1999]. During swelling, clay particles become more widely spaced and finally they can become dispersed and mobile both within the soil and the wider environment. The swelling ratio depends on several factors such as the type of cations adsorbed on the surfaces of clay particles and the quantity and quality of organic matter [Dexter, 1999, 2002]. Calcium ions adsorbed on the clay surfaces favour stability by keeping the clay flocculated whereas sodium ions encourage swelling and dispersion. The influence of organic matter on clay dispersion is manifested in two ways. The first is by acting as a ‘glue’ holding the particles together, and the second is by reducing the rate of wetting due to its hydrophobic characteristics [Dexter, 1999]. However, dispersion of clay depends also on the electrolyte concentration of the soil water [Dexter, 1988, 2002]. Clay dispersion can be prevented by maintaining the electrolyte concentrations above certain critical values that keep the clay flocculated [Raine and Loch, 2003]. This principle is used in the reclamation of saline soils by leaching. The amount of clay that is dispersed may also depend on soil moisture conditions prior to beginning dispersible clay measurements. Kay and Dexter [1990], in their study concerning the influence of antecedent water content on the dispersibility of clay on a red-brown earth from southern Australia, have found that spontaneously-dispersed clay and mechanically-dispersed clay increased with increasing antecedent water content (corresponding to potentials ranging from –10 to –0.3 kPa). The authors also hypothesized that, for the soils that they were using, the percentages of spontaneously-dispersed clay and mechanically-dispersed clay are controlled by the specific area of exposed aggregates surfaces and the dispersibility of clay per unit specific aggregate surface area. The authors have evaluated this hypothesis using aggregates of different sizes collected from two different management practices, namely wheat-fallow and continuous pasture rotations. It was found that the amount of dispersed clay per unit surface area of aggregates increased with increasing size of aggregates. In addition, the combined effect of increasing initial aggregate size and increasing antecedent water content was greatest on the least stable soil under a wheat-fallow crop rotation [Kay and Dexter, 1990]. The amount of clay that disperses may also vary with the amount of energy applied. Watts et al., [1996a] have quantified the mechanical energy inputs to soil during tillage in field experiments in order to assess the subsequent aggregate disruption by measuring the quantity of mechanically-dispersible clay. Their investigations have shown that increasing the tillage energy resulted in higher clay dispersion for a given soil water content. Also they reported that mechanical energy inputs had no significant effect on clay dispersibility when soil water content was less than the plastic limit. However, when the soil is wetter than the plastic limit, then the content of readily-dispersible clay increases with both water content and the input of specific mechanical energy [Dexter, 2002]. 23 Changes in dispersible clay due to changes in management practices have been suggested by Kay and Dexter [1990] to be manifested earliest in the weakest failure zones. Such zones have a distribution of strengths which is related to water content and processes which change pore characteristics and/or the cementation between structural units [Kay and Dexter, 1992]. Cementing materials may be of organic or inorganic form. Inorganic materials include particles of dispersed clay which become effective on drying by precipitation or flocculation at intergranular points at low water contents [Kay and Dexter, 1992]. Soils with high contents of readily-dispersible clay (RDC) in the presence of water may experience the collapse of their structure with the consequent loss of the inter-aggregate pores and soil homogenization [Czyż et al., 2002]. Problems associated with clay dispersion include: anaerobic soil that is unsuitable for plant root growth, reduced infiltration of water with associated risk of run-off, flooding and erosion [Dexter and Czyż, 2000; Czyż et al., 2002]. Also, such soils can form crusts at the surface or can hardset, both of which characteristics are associated with poor crop emergence and increased energy requirement for tillage [Dexter and Czyż, 2000; Czyż et al., 2002]. The aim of the research was determination of the content of readily-dispersible clay in soil samples collected from experimental fields at Huta, Kępa and Osiny which belong to Institute of Soil Science and Plant Cultivation - National Research Institute (IUNG-PIB) Puławy, and the investigation of the relationships between readilydispersible clay and other soil physical properties. MATERIAL AND METHOD Soil samples used in these measurements were collected from ecological farms at three different locations around Puławy (Huta, Kępa and Osiny) from the arable layer (0-20 cm) and sub-soil (20-35 cm). Spring wheat and spring barley was grown in traditional tillage with straw incorporation based on mouldboard ploughing (to 25 cm depth) and traditional soil management equipment. The samples were collected at different dates from field plots where spring cereals (spring wheat and spring barley) were grown. Samples collected for the measurement of readilydispersible clay were stored in sealed plastic bags and were kept at their prevailing water content until they were needed for analysis. Particle size distribution and organic matter measurements The particle size distributions of the soils studied were determined by Casagrande’s hydrometer method as modified by Prószyński [Lityński et al., 1976]. The organic matter content of the soils was measured by wet oxidation by the Tiurin method [Ostrowska et al., 1991]. Soil bulk density and water content by oven-drying Soil samples were collected from the field for determination of dry bulk density, (Mg m-3), and gravimetric water content (kg kg-1). The soil samples were collected from soil pits that were dug in the field in the different treatments. For the above, four replicates were collected from each of the following depth layers: 0-20 cm and 20-35 cm. The depth of sampling was selected to coincide with the depths of layers corresponding with the different tillage practices. Undisturbed soil cores were taken 24 by pushing stainless steel cylinders of 100 mL vertically into the soil using a hammer, and for each layer and location 4 replications were sampled. The cylinders were then closed with stainless steel end caps and were placed in polythene bags to prevent water loss. Dry bulk density and water content of the soils were measured in the laboratory by weighing the soil samples before and after drying at 105°C in an oven for 48 hours. The determinations were done immediately after returning from the field so that further water loss was avoided. The soil water content values were then calculated volumetrically and gravimetrically. The dry bulk density was calculated as the mass of dry soil per unit volume of moist soil. Readily-dispersible clay (RDC) The method used for determination of readily-dispersible clay, RDC, (NTU/(g L)-1), is that described by [Czyż et al., 2002] and Dexter and Czyż [2000], and is rather similar with that described by Kay and Dexter [1990], Watts and Dexter [1997] and Watts et al. [1996a,b], but was adapted for Polish sandy soils. The amount of readily-dispersible clay in water using a standard dilution and shaking procedure was measured using turbidimetry. For each soil sample 10 subsamples were used. About 5g of soil were weighed and placed in 150ml plastic bottles and then were shaken with 125ml distilled water in a standardized way (four complete rotations end-over-end). The bottles were then allowed to stand for 18 hours to allow the larger particles to sediment, leaving only dispersed colloids (in this case, mostly clay) in suspension. A 30ml sample of this suspension was extracted by pipette from the centre of each bottle and was transferred to a glass turbidity cell. Also, 10 sub-samples of the same soil sample were prepared for determination of the total clay content. In this case, about 18-20g of soil were weighed and placed in 750-100ml Berzelius glasses and were stirred with 500ml distilled water for 30min. Then 125ml from this suspension was poured into the plastic bottles and allowed to stand for 18 hours to allow the larger particles to sediment, leaving only colloids (clay) in suspension. A 30ml sample of this suspension was extracted by pipette from the centre of each bottle and was transferred to a glass turbidity cell. The turbidity was measured by light-scattering using a Hach 2100 AN ratio turbidimeter. (Fig. 1-3). Turbidity values are linearly proportional to the concentration of colloids (clay) in suspension [Dexter and Czyż, 2000]. The turbidimeter readings were expressed as NTU (Nephelometric Turbidity Units) and were normalized by dividing by the concentration of the original soil in the water to give NTU/(g L-1). The mass of soil was corrected to dry mass for this calculation. 0 90 detector Light path diagram Transmitted light detector Lamp Lens Sample cell Figure 1. Schematic diagram showing the principle of operation of the ratio turbidimeter 25 Figure 2. Model HACH 2100 AN Turbidimeter for determination of the content of readily-dispersible clay, RDC Figure 3. Photographs of bottles with soil suspensions (top) and measurement the content of readily-dispersible clay, RDC with HACH 2100 AN Turbidimeter (bottom) To determine the effects of air-drying of soil samples, some samples were airdried for comparison with samples that had been stored at their field water content. To test the reversibility of the effects of air-drying, some air-dried samples were 26 rewetted by saturating them and then storing them on a sand table at –40 hPa water suction for up to two weeks before measurement of content of readily-dispersible clay. RESULTS AND DISCUSSION Physical characterization of the soils used in these investigations is presented in Table 1. Table 1. Physical characteristics of the soils used in these investigations Location Layer* Sand Silt Clay FAO/USDA Organic (>50 µm) (50-2µm) (<2 µm) soil texture matter class (kg kg-1) (kg kg-1) (kg kg-1) (kg kg-1) Huta top-soil 0.65 0.32 0.03 sandy 0.0122 loam sub-soil 0.64 0.34 0.02 sandy 0.0017 loam Kępa top-soil 0.10 0.67 0.23 silt loam 0.0222 sub-soil 0.11 0.65 0.24 silt loam 0.0170 Osiny top-soil 0.70 0.26 0.04 sandy 0.0152 loam sub-soil 0.70 0.26 0.04 sandy 0.0122 loam Bulk density (Mg m-3) 1.574 1.683 1.438 1.614 1.633 1.709 */ Layer: top-soil (depth 0-20 cm), and sub-soil (depth 20-35 cm) The amount of readily-dispersible clay was measured on soil samples that had been stored at the prevailing water content. The mean values of the contents of readily-dispersible clay, as well as of total clay of the three soils are given in Table 2. For the soils investigated, the mean content of clay that is readily-dispersible was found to be 0,416 g/(100g soil). This value is slightly smaller than that found by Czyż et al., [2002] for 210 Polish soils (0,48 g/100g soil). In addition, there is a clear tendency of lower stability with increasing depth in the soil profile. This can be seen by the greater values of the content of readilydispersible clay of sub-soils. Also, it seems that this increase is greater in sandy soils (Huta and Osiny), where the amount of clay which is readily-dispersible in sub-soils is almost twice that in top-soils. These significant increases can be attributed to higher bulk densities and lower organic matter contents of the sub-soils, and probably because the sub-soils don’t dry as much as top-soils. Watts et al., [1996b] and Watts and Dexter [1997] have been also reported that the content of readily-dispersible clay is affected by compaction and organic matter respectively. According to the classification of sensitivity of Polish agricultural soils to destruction, as expressed by the content of readily-dispersible clay (g per 100g of soil), these three soils that were analized are classified as sensitive and highly sensitive to structural damage. 27 Table 2. Mean values of the contents of readily-dispersible clay, (RDC) and total clay, (TC) measured on soil samples collected on different dates Location Layer* RDC TC RDC (NTU/(g/L)) (NTU/(g/L)) (g/100g soil) Huta top-soil 0.515 5.948 0.260 sub-soil 1.736 7.911 0.439 Kępa top-soil 1.234 78.678 0.359 sub-soil 1.774 94.864 0.428 Osiny top-soil 1.863 19.505 0.366 sub-soil 1.125 14.889 0.647 Mean 1.375 36.966 0.416 s.d. 0.520 39.219 0.130 s.e. 0.164 12.402 0.041 */ Layer: top-soil (depth 0-20 cm), and sub-soil (depth 20-35 cm) Because the soil from Kępa had a significantly higher clay content than the two sandy soils from Huta and Osiny, the regression analysis of the results in the following discussion was done only for the latter two soils. A range of organic matter contents produced in soils by different management practices had a significant effect on the dispersibility of clay particles. The results which are plotted in Fig. 4 show a negative correlation between the amount of dispersible clay and the organic matter content of the soils. The regression line from Fig. 4 is given by the following equation: RDC = 1.408 – 0.766 · OM r2 = 0.62; p = 0.00031 (1) (±0.210) (±0.161) where RDC is the content of readily-dispersible clay (g per 100g of soil) and OM is the organic matter content (%). Soil hydrophobicity can be imparted by organic matter and this can reduce both the wetting rates and the subsequent soil disruption by slaking and dispersion [Dexter, 2002]. Lower values of the organic matter content have been found by Watts et al. [1996b] and Watts and Dexter [1997] to be associated with greater amounts of dispersed clay in Romanian soils and UK soils respectively. Organic matter not only stabilizes soil through reducing the content of RDC, but it also reduces the sensitivity of soil to destabilization by mechanical energy inputs [Watts et al., 1997]. A similar tendency of soil destabilisation (as measured by turbidimetry) due to lower contents of organic matter has been reported also by Dexter and Czyż [2000] in a Polish sandy soil. 28 readily-dispersible clay (RDC), g/(100g soil) 2,25 2,00 1,75 1,50 1,25 1,00 0,75 0,50 0,25 0,00 0,0 0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8 organic matter (OM), % Figure 4. Variation of the content of readily-dispersible clay as a function of organic matter for the sandy soils from Huta and Osiny Another aspect that we analized was the relationship between the amount of readily-dispersible clay and the total clay content of these soils, both being measured by using the same technique (turbidimetry). The mechanical energy inputs to soil during tillage can destabilize soil structure through increasing the content of readily-dispersible clay. If the soil disturbed by tillage contains more clay then there is an increase in the specific surface area of aggregate surfaces that will be exposed to further disruption. Moreover, these energy inputs by tillage can weaken the bonds between clay particles, especially when tillage is performed on soils that are wetter than the plastic limit [Dexter, 2002]. Fig. 5 shows the variation of the amount of readily-dispersible clay as a function of the total clay content, and the regression line is given by the equation: p < 0.0001 (2) RDC = - 0.868 + 0.142 · TC r2 = 0.84; (±0.334) (±0.017) where RDC is the content of readily-dispersible clay and TC is the total clay content, both expressed as NTU/(g/L). Usually, large values of the dispersibility means that the clay is loosely bound in the soil aggregates and that the soil is susceptible to dispersion and cementation during wetting and drying processes [Elmholt et al., 2000; Dexter, 2002]. Reorientation and hardening of the dispersed clay minerals can result in a dense and mechanically-strong soil [Kay and Dexter, 1992]. With these mechanisms, the result is the destruction of the existing soil structure, and as a consequence, this readilydispersible clay is expected to contribute to the agricultural problems which were described in the introduction. 29 readily-dispersible clay (RDC), NTU/(g/L) 3,5 3,0 2,5 2,0 1,5 1,0 0,5 5 10 15 20 25 30 total clay (TC), NTU/(g/L) Figure 5. Effect of total clay content on the amount of readily-dispersible clay in sandy soils from Huta and Osiny As we mentioned earlier, the soil water content is the most important factor affecting structural destabilisation as quantified by the amount of readily-dispersible clay. The values of readily-dispersible clay after the measurements were completed were plotted in Fig. 6, against their respective initial water contents and the analysis of the results showed that there is a consistent increase in readily-dispersed clay with increase in initial soil water content which can be described by: RDC = 0.063 + 3.006 · wi r2 = 0.54; p = 0.00178 (3) (±0.078) (±0.768) where RDC is the readily-dispersed clay (g per 100g of soil) and wi is the initial soil water content (kg per kg of soil). readily-dispersible clay (RDC), g/100g soil 0,55 0,50 0,45 0,40 0,35 0,30 0,25 0,20 0,06 0,07 0,08 0,09 0,10 0,11 0,12 0,13 0,14 -1 water content (wi), kg kg Figure 6. Effect of prevailing water content on the dispersibility of clay particles of sandy soils 30 The great sensitivity of readily-dispersible clay to water content is consistent with observations on an Australian red-brown earth [Kay and Dexter, 1990] and two soils in the UK [Watts et al., 1996a; Watts and Dexter, 1997]. High intensity tillage systems can increase the exposed surfaces of the aggregates to further disruption under the action of water. Watts et al., [1996a] showed that aggregates collected following tillage yield larger quantities of dispersible clay than those collected prior to tillage. Also they found that these differences appear to be larger the higher the water content at the time of tillage. An important aspect concerning the determination of readily-dispersible clay is related to the soil sample condition at the time of measurement. Gaţe [2004], Gate et al., [2006a, 2006b] found that the contents of readily-dispersible clay measured on air-dry soil samples were around 20 times smaller than when measurements were made on samples that had been stored at field water content. Fig. 7 illustrates this phenomenon, and the regression line is given by the following equation: RDCwi = 20.5 RDCdry – 1.9 r2 = 0.66; p = 0.008 (4) (±5.6) (±1.5) where RDCwi is the readily-dispersible clay measured at field water content, and RDCdry is the readily-dispersible clay measured on air-dried soil samples, both in units of NTU/(g L-1). RDCwi, NTU/(g/L) 10 8 6 4 2 0 0,0 0,1 0,2 0,3 RDCdry, NTU/(g/L) 0,4 0,5 Figure 7. Comparison of the values of readily-dispersible clay (RDC) obtained after different conditions of sample storage. Values on the x-axis were obtained with soil samples that had been air-dried whereas the values on the y-axis were obtained with samples that had been stored at field water content [Gate, 2004] If the soil is allowed to air dry, the cementing materials (e.g. dispersed clay) will precipitate or flocculate at intergranular points at low water contents [Kay and Dexter, 1992]. If this material deposition occurs near or at the ends of microcracks, then the micro-cracks will be stabilized and this will result in an increase of the tensile strength of the soil [Kay and Dexter, 1992]. Also, as soil dries, it is compressed by effective stresses due in part to the pore water pressure and in part to the surface tension in the water menisci. Such compression is non-reversible. Therefore, to determine the physical properties of soil as they occur in the field, samples must never be allowed dry to lower water contents than they have ever experienced in the field. 31 The irreversibility of the effects of drying is shown by the fact that re-wetting of air-dried soil for up to two weeks had no significant effect on the content of readilydispersible clay. Fig. 8 illustrates the effect of different tillage systems on soil physical quality [Czyż and Dexter, 2009]. The relationship between the index of soil physical quality, S, and the content of readily-dispersible clay (RCD) has been published earlier by Gaţe et al., [2004]. RDC has been found to be negatively-correlated with a measure of soil physical quality, S [Gaţe, 2006]. In other words, soil with a small content of RDC is usually of good physical quality (i.e. with high S) whereas soil with a large content of RDC is usually of poor physical quality (i.e. with low S). Soils with high S have been found to be more friable [Dexter, 2004], easier to till [Keller et al., 2007] are not hard-setting [Dexter, 2004], have lower penetration resistance [Dexter and Czyż, 2000] and have higher saturated hydraulic conductivity [Han, 2008]. Also Czyż and Dexter [2009] showed on samples collected from a long-term field experiment on a farm in Rogów with a silt loam soil, that reduced tillage and no-tillage reduced the amount of RDC especially in the top layer in comparison with traditional tillage. Reduced tillage and no-tillage also decreased the index of soil physical quality, S, after the first year in comparison with traditional tillage. However, after 4 years, the non-inversion plots (reduced tillage and no-tillage) showed some improvement in soil physical quality and soil stability in water. Reduced tillage decreased the quantity of readily-dispersible clay (RDC) and therefore increased soil stability, especially in the top layer 5-10 cm in both soils in comparison with traditional tillage [Czyż and Dexter, 2009; Dexter and Czyż, 2000; Gaţe et al., 2004]. 0,06 0,05 Values of S 0,04 0,03 0,02 0,01 0,00 2003 2004 2005 TT RT NT 2006 Years S values = 0.035 Figure 8. Effects different tillage systems (traditional, TT; reduced, RT; zero-tillage, NT) on soil physical quality values, S, at 5-10 cm depth in the first four years of use of different tillage systems [Czyż and Dexter, 2009] CONCLUSIONS The turbidimetric method for measurement of clay dispersion is easy and rapid. Because the amount of readily-dispersible clay has implications for a range of agricultural and environmental problems, it can be a useful method for describing of some important aspects of soil physical quality. 32 The amount of readily-dispersible clay (measured by turbidimetry) increases with decreasing soil organic matter. This rate of increase rises with increasing soil water content. The soils with greater total clay content have a greater specific surface area and this can result in more clay being dispersed by low energy inputs. Soil samples for measurement of RDC must never be allowed to dry to lower water contents than they have experienced in the field or the physical properties will be significantly and irreversibly changed. Several authors shown that the water-dispersible clay content is smaller when the soil OC content is larger [e.g. Watts and Dexter, 1997; Czyż et al., 2002]. Increasing amounts of readily-dispersible clay (RDC) have been shown to contribute to destabilization of soil structure. This dispersible clay is expected to have negative effects on other physical properties, such as soil aeration, water infiltration and soil strength. REFERENCES 1. Czyż, E. A., Dexter, A. R. and Terelak, H. 2002: Content of readily-dispersible clay in the arable layer of some Polish soils. In: Sustainable Land ManagementEnvironmental Protection. (Eds. Pagliai, M. and Jones R.). Advances in Geoecology, 35, s. 115-124. 2. Czyż, E.A. and Dexter, A.R. 2009: Soil physical properties as affected by traditional, reduced and no-tillage for winter wheat. Int. Agrophysics, 23(4), s. 319-326. 3. Dexter A.R. 2004: Soil physical quality: Parts I, II, III. Geoderma, 120, s. 201239. 4. Dexter, A.R. and Czyż, E. A. 2000: Effects of soil management on the dispersibility of clay in a sandy soil. Int. Agrophysics, 14, s. 269-272. 5. Dexter, A.R. and Czyż, E.A. 2007: Applications of S-theory in the study of soil physical degradation and its consequences. Land Degradation and Development, 18(4), s. 369-381. 6. Dexter, A.R. 1988: Advances in characterization of soil structure. Soil Till. Res., 11, s. 199-238. 7. Dexter, A.R. 1997: Physical properties of tilled soils. Soil Till. Res., 43,s. 41-63. 8. Dexter, A.R. 1999: Some thoughts on soil structure research. In: Proceedings of the Soil Structure/Carbon Workshop. (Eds. Reynolds, W. D., Drury, C.F. and Tan, C.S.), Leamington, Ontario, 23-25 August, pp. 11-23. 9. Dexter, A.R. 2002: Soil Structure: The Key to Soil Function. In: Sustainable Land Management-Environmental Protection. (Eds. Pagliai, M. and Jones, R.). Advances in Geoecology 35, s. 57-69. 10. Dexter, A.R., Richard, G., Arrouays, D., Czyż, E. A., Jolivet, C. and Duval, O. 2008: Complexed organic matter controls soil physical properties. Geoderma 144, s. 620-627. 11. Díaz-Zorita, M., Perfect, E. and Grove, J.H. 2002: Disruptive methods for assessing soil structure. Soil Till. Res., 64, s. 3-22. 12. Elmholt, S., Debosz, K., Munkholm, L.J. and Schjønning, P. 2000: Biotic and abiotic binding and bonding mechanisms in soils with long-term differences in management. Proceedings from Workshop on Soil Stresses, Quality and Care, Aas, Norway, 10-12 April. DIAS Report No. 38. The Danish Institute of Agricultural Sciences, s. 53-61. 13. Gaţe O.P., Czyż, E.A. and Dexter, A.R. 2004: Effects of readily-dispersible clay on soil quality and root growth. In: Plant Growth in Relation to Soil Physical 33 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. Conditions (Eds. J. Lipiec, R. Walczak, G. Jόzefaciuk), 48-56, Institute of Agrophysics, Lublin, Poland. Gaţe O.P., Czyż, E.A. and Dexter, A.R. 2005: Soil physical quality as affected by management practices. Mat. Conf. Eur. Geophysical Union, 25-30 April, Vienna, Austria. Gaţe O.P., Czyż, E.A. and Dexter, A.R. 2006a: Assessment and prediction of soil physical quality for Polish arable layers. Mat. Int. Conf. Soil protection strategy-needs approaches for policy support. 9-11 March, IUNG-PIB, Puławy, Poland. Gaţe O.P., Czyż, E.A. and Dexter A.R. 2006b: Soil physical quality, S, as a basis for relationships between key physical properties of arable soils. In: Soil management for Sustainability (Eds. Rainer Horn, Heiner Fleige, Stephan Peth, Xinhau Peng). Advances in GeoEcology, 38, s. 102-109, Catena Verlag: Reiskirchen (ISBN 3-923381-52-2). Han Han, Gimenez D. and Lilly, A. 2008: Textural averages of saturated hydraulic conductivity predicted from water retention data. Geoderma, 146, 121128. Kay, B.D. and Dexter, A.R., 1990: Influence of aggregate diameter, surface area and antecedent water content on the dispersibility of clay. Can. J. Soil Sci., 70, s. 655-671. Kay, B.D. and Dexter, A.R. 1992: The influence of dispersible clay and wetting/drying cycles on the tensile strength of a red-brown earth. Aust. J. Soil Res., 30, s. 297-310. Kay, B.D. 1990: Rates of Change of Soil Structure Under Different Cropping Systems. Advances in Soil Science (Ed. Stewart, B. A.), Springer-Verlag, 12, s. 1-52. Keller, T., Arvidsson J. and Dexter, A.R. 2007: Soil structures produced by tillage as affected by water content and the physical quality of soil. Soil Till. Res., 92(1-2), s. 45-52. Lityński, T. and Jurkowska, H. and Gorlach, E. 1976: Agricultural chemical analyses, soils and fertilizers. (in Polish). PWN Press, Warsaw, Poland, 330 pp. Ostrowska, A., Gawliński S. and Szczubiałka, Z. 1991: Methods of Analysis and Etimation of Soil and Plant Properties. (in Polish). Prot. Envir. Inst. IOŚ Press, Warsaw, Poland, 333 pp. Raine, S.R. and Loch, R.J. 2003: What is a sodic soil? Identification and management options for construction sites and disturbed lands. In: Roads, Structures and Soils in Rural Queensland. 29-30 July, Toowoomba. Queensland Department of Main Roads, Brisbane, 14 pp. Schjønning P., de Jonge L.W., Olsen, J.E. and Greve, M.H. 2009: Organic farming effects on clay dispersion in carbon-exhausted soils. ICROFS news, November/2009. Archived at http: //orgprints.org/16503 Watts, C.W. and Dexter, A.R. 1997: The influence of organic matter in reducing the destabilization of soil by simulated tillage. Soil Till. Res., 42, s. 253-275. Watts, C.W., Dexter, A.R. and Longstaff, D.J. 1996a: An assessment of the vulnerability of soil structure to destabilisation during tillage. Part II. Field trials. Soil Till. Res., 37, s. 175-190. Watts, C.W., Dexter, A.R., Dumitru, E. and Canarache, A. 1996b: Structural stability of two Romanian soils as influenced by management practices. Land Degradation & Development, 7, s. 217-238. 34 Chapter 4 EFFECT OF APPLIED SIMPLIFICATIONS IN TILLAGE SYSTEMS ON BIOLOGICAL ACTIVITY AND POM CONTENT IN SOIL Anna M. GAJDA INTRODUCTION Soil microbial biomass (SMB) and particulate organic matter (POM) are considered as sensitive indicators of the direction of soil quality changes caused by many environmental factors, among others applied farming systems. Because of their short turnover time (< 1 year) they respond rapidly to all conditions alter soil environment. It has been shown that microbial biomass carbon (MBC) comprises 1 to 5% of the total organic C [Anderson and Domsch, 1989; Sparling, 1992]. Also, it has been estimated that 90% of the CO2 evolved from soil comes from the activity of soil microorganisms. This illustrates the extremely important role of soil microorganisms that they play in soil environment [Doran, 1987; Smith and Paul, 1990]. Particulate organic matter (POM) and other size-defined fractions as density-flotation fractions of soil organic matter (SOM), collectively known as light fractions of SOM, have been shown to be dynamic and more sensitive to change than total SOM [Masto et al., 2006; Liebig at al., 2004; Gajda and Martyniuk, 2005]. For these reasons POM has been intensively studied as an indicator of changes in soil quality in response to various soil management systems, particularly soil tillage intensity and crop rotations [Liebig et al., 2004; Cambardella et al., 2001]. The aim of conducted research was to assess the effect of reduced tillage system on microbial biomass C and N content, number of nitrate-forming bacteria, dehydrogenase activity and particulate organic matter fraction as compared to the applied conventional tillage system. MATERIALS AND METHODS The studies were conducted on the long-term field experiment located on a private farm in Rogow (Lublin voivodeship) on a silt soil and at the IUNG-PIB Experimental Station in Grabow (Masovian voivodeship) on a sandy loam soil in the 35 years 2006-2008. The compositions of the soils were analyzed by standard methods. Reduced tillage RT Conventional tillage CT Photo 1. Field experiment at private farm in Rogow (Lublin voivodeship) on a silt soil. Conventional (CT) and reduced tillage (RT) systems Photo 2. Experimental Station of IUNG-PIB in Grabow (Masovian voivodeship) on a sandy loam soil. Conventional (CT) (read color) and reduced tillage (RT) (green color) systems At both experimental sites winter wheat was grown under two tillage systems: conventional tillage (CT) based on the moldboard plough and traditional soil tillage equipment and reduced tillage (RT) based on soil crushing-loosening equipment and rigid-tyne cultivator. Surface of experimental fields was mulched with chopped wheat straw under both tillage systems. The winter wheat was fertilized according to Polish agrotechnical recommendations. 36 Two tillage systems: traditional (conventional) tillage (CT) (surface covered with chopped wheat straw) based on mouldboard ploughing (to 25 cm depth) and traditional soil management equipment, and reduced tillage (RT) with surface mulching (chopped wheat straw) based on soil crushingloosening equipment and a rigid-tine cultivator (to 10 cm depth) Photo 3. Management equipment used on fields under CT and RT systems Soil samples were taken randomly during each vegetation season from 0-15; 15-30 cm depths. To determine soil water content samples were dried for 24 h at 105ºC. The samples were stored in plastic bags that were then stored in a cold room (4ºC) no longer than 10 days. These procedure ensured that the samples remained moist and had well preserved their biological activity. Photo 4. Collecting of soil samples at Rogow For analyses requirements, all samples were passed through a 2 mm sieve and stored fresh at 4ºC in a refrigerator. During two weeks all analyses required fresh 37 soil were completed. Analyses of microbiological properties of soil included: microbial biomass C content using the fumigation-incubation (F-I) method [Jenkinson and Powlson, 1976] with modifications given in Martyniuk et al. [2001], the rate of CO2 of evolution from soil using the titration method, microbial biomass N content using the fumigation-extraction (F-E) method [Jenkinson and Powlson, 1976] modified by Kenney and Nelson [1982], most probably number (MPN) of nitrate-forming bacteria according to McCrady [1980], soil dehydrogenase activity system [Casida et al., 1964], and content of particulate organic matter (POM). CT RT Photo 5. Estimation of activity of dehydrogenase system in soil Photo 6. Microorganisms isolated from soil under winter wheat For POM analysis representatives air dried (a. d.) soil samples were dispersed with 100 ml of 5-g L-1 sodium hexametaphosphate and shook for 18 h on reciprocal shaker. After shaking soil was passed through a 500 µm sieve and then through a 53 µm sieve. Particulate organic matter (POM - 0.053 mm fraction) quantities in the soils were determined using Cambardella and Elliott [1992] modified method in which POM was estimated by the Loss-On-Ignition procedure (LOI) described by Schulte [1988], Schulte and Hopkins [1996] and Gajda et al. [2001]. Once a year estimation of total N content (Dumas method – FP-528-LECO) and organic C content were performed in the certified Central Laboratory of Institute of Soil Science and Plant Cultivation, National Research Institute (IUNG-PIB) at Pulawy, Poland. All analyses were performed using fresh or air dried soil (according to the requirements of used methodology) in 3 replicates of each sample. Obtained results were statistically analyzed using ANOVA method. Differences at P<0.05 were 38 considered as signifinant. Details of the compositions of the studied soils are given in Table 1. Table 1. Some physical properties of the studied soils Site Soil Sand Silt g/100g g/100g Rogow Silt 14.0 81.0 sandy loam Grabow 68.0 28.0 Clay g/100g 5.0 4.0 C org. g/100g 1.24 1.23 These are originally different soils but with the same contents of organic carbon (Corg.). RESULTS AND DISCUSSION The highest content of soil microbial biomass C was estimated in RT soil taken from Rogow at the 0-15 cm depth – 430 µg CO2-C/g d. w. of soil (on average). Soil managed conventionally contained a smaller amount of microbial biomass C pool (about 80%) than the RT soil. Microbial biomass C contents were smaller at 15-30 cm depth than at 0-15 cm depth in both CT and RT soil by about 25% and 30%, respectively (significant at α = 0.05) as shown in Fig. 1A. There were no statisticallysignificant effect of tillage system and soil depth observed on microbial biomass C content in sandy loam soil ES in Grabow (Fig. 1B). Figure 1. Microbial biomass C content in soils under winter wheat grown in CT and RT tillage systems in Rogow (A) and Grabow (B) a, b, c – values marked with different letters are statistically significant at P >95.0% The evolution of CO2 from soil was influenced by both the tillage systems and soil depth. The largest amounts of CO2 evolved were measured with RT soil sampled at 0-15 cm depth in Rogow – (310 µg CO2-C/g d. w. of soil, on average). Evolution of CO2 at 15-30 cm depth was about 30% less than at 0-15 cm depth (significant at α = 0.05) in the RT system, but in the CT system the differences between soil depths were not statistically significant (Fig. 2A). Similar trends in CO2 evolution were observed in soil taken from experimental fields in Grabow, but the values obtained were less than 50% of those obtained with the Rogow soil (Fig. 2B). Also Balota et al. [2003], Doran and Parkin [1996], Marinari et al. 2006 and Masto et al. [2006] published a higher results of microbial biomass C content and the rate of CO2 evolution in soil under reduced as compared to the conventional tillage system. 39 Figure 2. The rate of CO2 evolution from soils under winter wheat grown in CT and RT tillage systems in Rogow (A) and Grabow (B). Explanations as in Fig. 1 An influence of tillage system on the microbial biomass N content in the studied soils was also observed. The greatest content of microbial biomass N was measured in RT soil in Rogow in both studied layers - 0-15 and 15-30 cm, 33.7 and 28.0 µg N/g d. w. of soil, respectively. In the CT soil the estimations of microbial biomass N were only about 20% (on average) of those measured with the RT soil. In the RT soil, an effect of soil depth (significant at α = 0.05) was observed. In the surface layer estimations of microbial biomass N were 20% greater than in the subsurface soil (Fig. 3A). The results with the Grabow soil showed similar trends to those obtained with the Rogow soil, but the values in the RT soil in Grabow were only about 20% of those obtained in Rogow (Fig. 3A, B). There were no statisticallysignificant effect of soil depth on the size of the microbial biomass N pools in the CT soil at both experimental sites (Fig. 3). Similar results has been published by Lalande et al. [2005], Calderon [2000] and Gajda and Martyniuk [2005]. B 40 b biomass N, ug N/g d. w. soil biomass N, ug N/g d. w. soil A 40 35 30 25 20 15 10 5 0 c a a CT 30 RT 20 10 a b a c 0 0-15 cm 0-15 cm 15-30 cm soil depth 15-30 cm soil depth Figure 3. Microbial biomass N content in soils under winter wheat grown in CT and RT tillage systems in Rogow (A) and Grabow (B). Explanations as in Fig. 1 At both experimental sites, tillage system and soil depth affected the number of nitrate-forming bacteria (significant at α = 0.05). Higher counts of nitrate-forming bacteria (about 1.5 times, on average) were found in the surface layer of conventionally-managed soil in Rogow and Grabow, as compared with the 15-30 cm layer, however in the RT soil the differences between analyzed soil depths were greater and counts of nitrate-forming bacteria in 0-15 cm layer were 2.5 times larger than in the 15-30 cm layer. In general, CT and RT soil taken in Rogow and Grabow contained similar numbers of nitrate-forming bacteria in both depths ranges. The results showed significantly greater nitrification strength of RT soil than soil managed 40 conventionally at both experimental sites (Fig. 4). Figure 4. Most probably number (MPN) of nitrate-forming bacteria (Nitrosomonas spp., Nitrobacter spp. and others) in soils under winter wheat grown in CT and RT in Rogow (A) and Grabow (B). Explanations as in Fig. 1 The activity of soil enzymes can be used as a sensitive indicator of changes in soil biological activity and fertility in response to various soil management practices [Doran, 1987; Bending et al., 2004; Marinari et al., 2006]. In our studies, the effect of soil tillage system on enzymatic activity of the soils was determined by assessing the activity of dehydrogenase systems, which provides correlative information on the biological activity and microbial populations in soil as well as indicates the rate of organic matter oxidation [Gajda et al., 2001; Martyniuk et al., 2001; Gajda and Martyniuk, 2005]. The activity of the dehydrogenase system in the studied soils was influenced by both the applied tillage systems and soil depth. The greatest activity of dehydrogenases was measured in RT soil in Rogow in the layer - 0-15 cm - 1517,6 mm3H2/100g d. w. of soil, and 2131,4 mm3H2/100g d. w. of soil, in 2006 and 2008, respectively. Similar trends were observed in the layer 15-30 cm. In the CT the measurements of dehydrogenases activity were only about 60% (on average) of those measured with the RT soil. In the RT soil, an effect of soil depth (significant at P<0.05) was observed, in both studied years. In the surface layer the measurements of dehydrogenase activity were about 25% (on average) greater than in the subsurface soil in both experimental years (Fig. 5A). The results with the Grabow soil showed similar trends to those obtained with the Rogow soil, but the values in the RT soil in Grabow were only about 35% of those obtained in Rogow (Fig. 5B). Similar results have been presented by other authors, e.g. Gajda et al. [2000]; Martyniuk et al. [2001]; Marinari et al. [2006]. 41 Figure 5. The dehydrogenase system activity in soils under winter wheat grown in CT and RT in Rogow (A) and Grabow (B). Explanations as in Fig. 1 According to Liang et al. [2002] the pool of labile organic matter fraction is several times greater than that of soil microbial biomass. He also reported the great importance of labile organic matter fraction in the turnover of organic matter in agricultural soils as a source of readily mineralizable substrate for soil microorganisms. In our studies, the quantity of POM was influenced by both the tillage system and soil depth. In the layer 0-15 cm the measured quantity of POM fraction in the Rogow soil was significantly higher (25% on average) in RT soil as compared to the POM content in CT soil. Similar trends in POM quantity were observed in the Grabow soil (Fig. 6). Figure 6. The average POM content in the 0-30 cm layer of Rogow and Grabow soils under winter wheat grown in CT and RT. Explanations as in Fig. 1 After three year studies the decrease of POM content in CT soil was noticed in both experimental sites. Content of POM in CT soil measured in 2008 was about 30% less, on average, than that measured in 2006. In soils under RT the increase of quantities of POM was significant at both experimental sites. In the Rogow soil 42 measured content of POM in 2006 was about 22% lower as compared to 2008. Obtained results in the Grabow soil showed similar trends to those in the Rogow soil, but the values of POM content in Grabow were about 30% (on average) lower than those obtained in Rogow (Fig. 7). Also, several authors reported the rapid depletion of the labile fraction of OM in soil as result of cultivation practices and the rapid increase when soil was not disturbed by tilling [Marriott and Wander, 2006]; Sleutel et al. [2006]; Wander and Yang [2000]. Figure 7. Changes in POM content in the 0-15 cm layer of soils under winter wheat grown in CT and RT systems in Rogow and Grabow. Explanations as in Fig. 1 At both experimental sites the quantity of POM especially in soils under RT was significantly lower in the 15-30 cm layer than that in surface soil (0-15 cm) by about 20-30%, on average. In general, there were no statistically-significant difference in the quantity of POM in studied depths of CT soils (Fig. 8). Similar results were obtained by Albrecht et al. [2000]. They also noticed that tillage created an even distribution of POM throughout the soil profile, but the decrease of tillage intensity increased the POM fraction, especially in the upper soil depths. Figure 8. The effect of soil depth on the quantity of POM in Rogow (A) and Grabow (B) soils under CT and RT systems 43 CONCLUSIONS 1) 2) 3) In general, at both experimental sites the size of microbial biomass C and N pools were significantly greater in soil under reduced tillage than in soil managed conventionally in both depths of soil profile: 0-15 and 15-30 cm. The rate of CO2 evolution, number of nitrate-forming bacteria and activity of dehydrogenase system reached the highest values mostly in soil under reduced tillage at both experimental sites. During three-year studies the decrease of POM content in soil under conventionally managed soil was noticed. In Rogow and Grabow soils under reduced tillage the significant increase of POM fraction content was observed. ACKNOWLEDGEMENTS The author would like to thanks to Polish Ministry of Education for the award of grants AGROGAS 17/E-184/SN-019/2006 and Project No. 6ZR7 2006C/06735. REFERENCES 1. Albrecht S. L., Fauci M. F., Skirvin K. W., Bezdicek D. F. 2000: Organic matter fractions from pacific northwest soils: depth and tillage effects. Columbia Basin Agricultural Research Annual Report Spec. Rpt. 1012, pp. 19-23, Oregon State Univ. in cooperation with USDA-ARS, Pendleton, OR 2. Anderson J.P.E., K.H. Domsch 1989: Ratios of microbial biomass carbon to total carbon in arable soils. Soil Biology and Biochemistry, 21, pp. 471-479 3. Balota E. L., A. Colozzi-Filho, D.S. Andrade D, R.P. Dick, 2003: Microbial biomass in soils under different tillage and crop rotation systems. Biol. Fertil. Soils, 38, pp. 15 4. Bending G.D., Turner M.K., Rayns F., Markx M.C., Wood M. 2004: Microbial and biochemical soil quality indicators and their potential for differentiating areas under contrasting agricultural management regimes. Soil Biol. Biochem., 36, 1785-1792 5. Calderon F. J., L.E. Jackson, K.M. Scow, D.E. Rolston, 2000: Microbial responses to stimulated tillage and in cultivated and uncultivated soils. Soil Biol. Biochem. 32, pp. 1547 6. Cambardella C. A., E.T. Elliott 1992: Particulate soil organic matter changes across a grassland cultivation sequence. Soil Sc. Soc. Am. J., 56, pp. 777 7. Cambardella C. A., A.M. Gajda, J.W. Doran, B.J. Wienhold, T.A. Kettler 2001: Estimation of Particulate and Total Organic Matter by Weight Loss-On-Ignition. In: R. Lal, J. M. Kimble, R. F. Follett, B. A. Stewart (Eds.), Assessement Methods for Soil Carbon, CRC Press LLC, Boca Raton, FL, pp. 349-359 8. Casida L.E., D.A. Klein Jr., T. Santoro 1964: Soil dehydrogenase activity. Soil Science, p. 98 9. Doran J.W. 1987: Microbial biomass and mineralizable nitrogen distribution in no-tilled and plowed soils. Biology and Fertility of Soils, 5, pp. 68-75 10. Doran J. W., T.B. Parkin, 1996: Quantitative indicators of soil quality: A minimum data set. In: Doran J. W., Jones A. J. (Eds.), Methods for Assessing Soil Quality. SSSA Special Publication 49, Madison, WI, pp. 25-37 11. Gajda A. M., J.W. Doran, T.A. Kettler, B.J. Wienhold, J.L. Pikul Jr., C.A. Cambardella 2001: Soil quality evaluations of alternative and conventional management systems in the Great Plains. In: R. Lal, J. M. Kimble, R. F. Follett, 44 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. Stewart B. A. (eds) Assessement Methods for Soil Carbon, Lewis Publishers, CRC Press LLC, Boca Raton, FL, USA, pp. 381-400 Gajda A.M., S. Martyniuk, 2005: Particulate organic matter and microbial biomass C contents in soils with different mechanical structure. Pam. Pul. 40, pp. 49 Gajda A., S. Martyniuk, A. Stachyra, B. Wróblewska, S. Zieba 2000: Relations between microbiological and biochemical properties of soil under different agrotechnical conditions and its productivity. Polish J. Soil Sci. 33, pp. 50 Jenkinson D.S., D.S. Powlson 1976: The effect of biocidal treatments on metabolism in soil V. A method for measuring microbial biomass. Soil Biology and Biochemistry, 8, pp. 209-213 Keeney D.R., D.W. Nelson 1982: Nitrogen: Inorganic forms. In: Page A.L. et. al. (eds.) Methods for analysis. Part 2. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI Lalande R., B. Gagnon, R.A. Chapman, G.M. Barnett 2005: Soil microbial populations, activity, and community structure in continuous corn or forage systems under organic or inorganic fertilization in eastern Canada. Canadian J. Soil Sci., Vol. 85, No. 1, pp. 27 Liang B. C., B.G. McConkey, J. Schoenau, D. Curtin, C.A. Campbell, A.P. Moulin, G.P. Lafond, S.A. Brandt, H. Wang 2002: Effect of tillage and crop rotations on the light fraction organic carbon and carbon mineralization in Chernozemic soils of Saskatchewan. Canadian J. of Soil Sc., 83, pp. 65-72 Liebig M. A., D.L. Tanaka, B.J. Wienhold 2004: Tillage and cropping effects on soil quality indicators in the northern Great Plains. Soil Till. Res., 78, pp. 131 Marinari S., R. Mancinelli, E. Campiglia, S. Grego 2006: Chemical and biological indicators of soil quality in organic and convewntional farming system in Central Italy. Ecological Indicators, 6, pp. 701-711 Marriott E. E., Wander M. 2006: Qualitative and quantitative differences in particulate organic matter fractions in organic and conventional farming system. Soil Biol. Biochem. 38, 1527 Martyniuk S., Gajda A., Kus J. 2001: Microbiological and biochemical properties of soil under cereals grown in the ecological, conventional and integrated systems. Acta Agrophysica, 52, pp. 185. Masto R. E., P.K. Chhonkar, D. Singh, A.K. Patra 2006: Changes in soil biological and biochemical characteristics in long-term field trial on a subtropical inceptisol. Soil Biol. Biochem. 38, pp. 1577 Schulte E. E. 1988: Recommended soil organic matter testes. In: Recommended Chemical Soil Test Procedures for the North Central Region. North Cen. Reg. Pub. No. 221, Bull. No. 499, North Dakota Ag. Exp. Stn., North Dakota State Univ., Fargo, ND, 58105, pp. 29-32 Schulte E. E., Hopkins B. G. 1996: Estimation of soil organic matter by weight loss-on-ignition. In: Soil Organic Matter: Analysis and Interpretation. SSSA Special Publication No. 46. Soil Science Society of America, Madison, WI, pp. 21-31 Sleutel S., De Neve S., Nemeth T., Toth T., Hofman G. 2006: Effect of manure and fertilizer application on the distribution of organic carbon in different soil fractions in long-term field experiment. Europ. J. Agronomy, 25, 280 Smith J.L., E.A. Paul. 1990: The significance of soil biomass estimations. In: Bollag J.M. and Stotsky G. (eds.), Soil Biochemistry, Marcel Dekker, New York, 6, pp. 357-396 45 27. Sparling G.P. 1992: Ratio of microbial biomass to soil organic carbon as a sensitive indicator of changes in soil organic matter. Australian Journal of Soil Research, 30, pp. 195-207 28. Voroney R.P., Paul E.A.1984: Determination of KC and KN in situ for calibration of the chloroform incubation method. Soil Biology and Biochemistry,16 pp. 9-14 29. Wander M. M., Yang X. 2000: Influence of tillage on the dynamics of loose- and occluded- particulate and humified organic matter fractions. Soil Biol. Biochem., 32, 1151 46 Chapter 5 DATA MINING AS A DECISION-SUPPORT TOOL IN SUSTAINABLE AGRICULTURAL PRODUCTION Jędrzej TRAJER, Jarosław STANKIEWICZ INTRODUCTION Sustainable agricultural production is a system of farming allowing for the development of a farm while maintaining ecological safety and taking the interrelations between the farm and the surroundings, i.e. environment and the infrastructure into consideration [Sawa 2008]. Such a system of farming can be implemented by means of ensuring soil fertility, appropriate selection of species and varieties of plants, fertilization and plant protection. It can be achieved by means of appropriate management, conscious use of self-regulation mechanisms in the ecosystems and applying methods and tools available due to scientific and technical progress. Farmers’ knowledge and skills are of primary importance in the farm development understood in this way, and their effectiveness may be improved by means of using appropriate methods supporting decision-making. Following sustainable farming principles requires a series of adjustments in the field of organization and technology, which are established on the basis of various criteria for the evaluation of agricultural production sustainability. It may apply to various aspects such as economic, social or ecological ones, which are analysed in relation to the intensity of production. The problem is complex and multi-dimensional. Therefore, advanced tools such as data mining should be used to analyse it. These methods allow for extracting knowledge from large datasets using statistics and artificial intelligence methods. The paper presents an example of using data mining for the analysis of black currant yield. Poland is one of the major soft fruit (especially black currant) producer. According to the data provided by the Central Statistical Office (GUS) [Statistical Yearbook, 2009] Poland’s share in the global production equals 40% and it is equal to 70% in the European Union. Approximately 24% of farmers grow and produce black currant, and the share of black currant plantations in the total area of orchards and berry fruit plantations equals 35%. Unfortunately, the structure of the production is fragmented and the size of most plantations does not exceed 1 ha (approximately 94%), and there are only a few thousand large and very large plantations. Such high 47 fragmentation leads to low specialization of production and results in treating black currant cultivation as less important and an additional source of income. However, there are also large, highly specialized farms in Poland, and their efficiency is much higher. Despite such a large share in the global production, Polish producers of black currant are still waiting for the stabilization of the market and strive with basic problems: unstable price and finding a market for their product. Yield is one of the major determinants in agricultural production, it determines additional profits as well as additional costs. Its value depends on numerous external factors, both humandependent and human-independent, e.g. on weather conditions. A thorough analysis of this quantity may allow for making fast and appropriate decisions that are essential for more efficient sustainable farming. RESEARCH METHODOLOGY Data mining makes use of advanced methods of analysis (statistics, artificial intelligence, databases), which make it possible to extract knowledge from large datasets using computers, which would otherwise be difficult [Berry and Linoff 2000]. Analyses are made by means of created models and formulated classification problems. The examples of such analyses are presented below. Black currant production analysis indicates that important factors which determine the yield include: variety – a very important factor as each variety is characterized by different yielding, disease resistance, flowering season/ maturation, which, in turn, is connected with another factor – minimum temperature during flowering season. If frost (even light frost) occurs during flowering season, which occurs quite often in this climate, as in the past few years, the yield may be considerably reduced as the majority of flowers die and fall. Another weather-related factors are total precipitation, especially in June, the month of the most intensive growth of fruit. However, the total precipitation itself does not provide sufficient information as single heavy rainfall is not desirable. Therefore, the number of rainy days was also taken into consideration. Sun exposure that has influence on the speed and uniformity of maturation was also taken into consideration in the model. Other significant factors, which to a greater extent depend on human, include: fertilization N, P, K as well as chemical protection against various kinds of diseases and insect pest. Neglecting these two factors may also seriously affect plants or lead to big loses that influence the yield not only in one particular year but also in successive years due to the fact that different kinds of insect pest may affect plants and damage them regularly. Another factor that has influence on the yield is plantation age, and the yield itself as the output data. Data for the analysis were obtained from horticulturists [Stankiewicz 2010], and weather data were obtained from the Institute of Meteorology and Water Management in Warsaw. Cluster Analysis Generalized EM method was used for the initial cluster analysis of data in order to identify homogeneous groups of cases, which may be analyzed in detail. Clustering using EM method [Stanisz 2007] is a modification of a statistic method of k-means method, involving the determination of a certain number k of homogeneous groups (clusters, concentrations), and the determination of the centre of each group as well as assigning a certain number of objects to them. It also involves calculating the probability, with which the objects match particular groups. In this method, the 48 distance between each object and the centre is calculated for each object, and the objects are assigned to individual groups depending on the shortest distance from the centre. After the objects are assigned for the first time, the centres are updated. It is done by calculating the means in each group. After that, the procedure is repeated, and the objects are updated; their distances from the centres are verified, and, if need arises, they are moved to other groups. The following steps are repeated until the shortest distances (differences) within each group are the shortest possible and the distances between the groups are the longest possible. Multilayer perceptron – MLP For the isolated groups it is possible to create models describing dependencies between input variables (predicators) and the output variable, which make it possible to analyse the influence of selected factors determining the yield. Artificial neural networks, MPL type [Osowski 1996] were used for that purpose. Figure 1 shows the network topology. Figure 1. Multilayer perceptron (MLP) topology The neuron on the right represents the output variable – yield. The neurons on the left represent the input neurons – quantity variables: (starting from the top) plantation age, fertilizers quantities (N – nitrogen, P- phosphor, K – potassium), the number of chemical treatments (herbicides, fungicides, and insecticides), minimum temperature in the flowering season, total precipitation, and number of days with precipitation in the period of fruit maturation and sun exposure, the latter one being of qualitative type (it represents three examined varieties). The number of neurons in the hidden layer is determined by means of testing various network topologies, decreasing the number of them, starting with 2n+1 until the learning quality does not worsen. Such maximum number of the neurons results from the Kolomogorov theorem [Osowski 1996], which says that a neural network can describe any nonlinearity using a multi-layer perceptron, in which the hidden layer has 2n+1 neurons (where n is the number of neurons in the input layer). Error backpropagation algorithm was used for training the network. This algorithm involves appropriate modification of weights so that the minimum error be obtained, here in the approximation task. In order to improve the quality of the created models the number of input variables was reduced, and those values which did not increase the error significantly were omitted. The models may be used to determine yields for different values of input variables and to determine the influence of changes in the 49 predicators’ values on the yield value (two- and three-dimensional charts may be created). Self-organizing map – SOM One of the most efficient tools supporting decision-making is a self-organizing map [Kohonen 1988] representing individual cases classified in the whole group in the graphical form. It makes it possible to assess them both individually and within a group with regard to the selected predicators as well as make clusters mapping similar cases according to other analysis criteria. Other tools do not provide such options. A map of features was created using Kohonen network, which has an input layer representing predicators selected for the analysis, as well as a radial output layer in the form of a topological map, fig. 2. Figure 2. Kohonen network topology The training process takes place without a teacher using the neighbourhood algorithm, in which similar cases are distributed closely on the topological map. Having finished training stage, individual cases are assigned to the localization on the topological map, and they may be assessed in relation to the predicators and according to the adopted criteria. New instances may also be analysed, which are interpreted based on the location in the previously identified area on the map. RESEARCH RESULTS Cluster analysis using EM method allowed to distinguish three homogeneous groups in 93 cases that were analysed, table 1. The symbols of the columns represent: NrP – number of case, P – probability with which a given case belongs to a given cluster, NrS – the number of cluster. There were so few cases in cluster number 2 that the cases it contained were assigned to cluster number 3 for the purpose of building neural models. The probabilities with which cases were assigned to clusters were so high that they confirmed the fact that the right number of clusters had been selected. Two groups were identified as a group with a black currant plantation, which is more than 8 years old (cluster 1), in the second one – younger black currant (cluster 3). 50 Table 1. Cluster analysis results for the black currant yield NrP P NrS NrP P NrS NrP P NrS 3 32 1.0000 3 63 0.9996 3 1 0.9999 0.9988 3 0.9999 3 0.9999 3 2 33 64 3 34 0.9730 3 65 0.9999 1 3 0.9998 3 35 1.0000 2 66 0.9999 1 4 0.9997 3 36 0.9999 3 67 0.9999 1 5 0.9996 3 37 0.9998 3 68 0.9999 1 6 0.9997 2 38 0.9987 3 69 0.9999 1 7 0.9929 2 39 1.0000 2 70 0.9999 1 8 0.9601 3 40 1.0000 2 71 0.9988 1 9 0.9998 3 41 0.9996 3 72 1.0000 2 10 0.9979 3 42 0.9993 3 73 0.9999 1 11 0.9999 3 43 1.0000 3 74 1.0000 1 12 0.9999 3 44 1.0000 3 75 1.0000 1 13 0.9999 3 45 1.0000 3 76 1.0000 1 14 0.9990 3 46 1.0000 3 77 1.0000 1 15 0.9998 3 47 0.9999 3 78 1.0000 1 16 0.9997 3 48 1.0000 3 79 1.0000 1 17 0.9999 0.9984 3 0.9999 3 0.9999 1 18 49 80 3 50 0.9999 3 81 0.9999 1 19 0.9996 3 51 0.9999 3 82 0.9999 1 20 0.9998 3 52 0.9317 3 83 0.9999 1 21 0.9999 3 53 0.9998 3 84 0.9999 1 22 0.9998 2 54 0.9992 3 85 0.9999 1 23 0.9999 2 55 0.9995 3 86 0.9976 1 24 0.9659 3 56 0.9846 3 87 1.0000 2 25 0.9999 3 57 0.9999 3 88 1.0000 1 26 0.9994 3 58 0.9998 2 89 1.0000 1 27 1.0000 3 59 0.9997 3 90 1.0000 1 28 1.0000 3 60 0.9999 3 91 1.0000 1 29 1.0000 3 61 0.9999 3 92 0.9999 1 30 1.0000 0.9999 3 0.9996 3 1.0000 1 31 62 93 Neural forecast models were build for these groups. For cluster 3, with younger black currant, MLP9:16:1 model was obtained, in which input variable 3 (fertilization with phosphor) was omitted as it was considered insignificant. Sensitivity analysis shows the importance of other predicators, table 2. It is possible to observe that the variety which grows faster is an important factor for younger plantations. It was also shown in the sensitivity analysis, where the age of plantation was the second most important factor. In the first group, the program selected the number of days with precipitation as the most important factor, which reflects the reality as regular precipitation boost growth, which leads to higher yields. Similarly, for cluster 1 (plantation older than 8 years) MLP8:16:1 model was obtained, in which variables 1 and 8, representing age of plantation and the number of days during growing season respectively, were reduced. Sensitivity analysis (table 3) for this model shows a significance of the selection of black currant variety as well as appropriate fertilization. 51 Table 2. Sensitivity analysis of the black currant yield model for cluster 3 (plantation up to 8 years old) Table 3. Sensitivity analysis of black currant yield model for cluster 1 (plantation over 8 years old) Both models were positively verified on the basis of satisfactory values of regression statistics, table 4. Table 4. Regression statistics for neural models of black currant yield Training set Validation set Regression statistics up to 8 over 8 years up to 8 years over 8 years years old old old old Error ratio 0.557 0.399 0.533 0.299 Correlation 0.831 0.919 0.851 0.960 52 The values of regression statistics of the standard deviation ratio of the errors and Pearson correlation coefficient prove that the model for the plantation over 8 years old has slightly better quality. In order to improve the quality of both models, a bigger set of training cases should be used and appropriate predicates should be selected. The designed models allow for the assessment of the influence of values of each predicator on the value of the yield. Figures 3,4 and 5 show three dimensional graphs showing the influence of significant predicators for the model of young plantation. Figure 3. The influence of precipitation and fertilization on the yield of black currant, plantation up to 8 years old Figure 4. The influence of sun exposure and the number of treatments on the yield of black currant, plantation up to 8 years old 53 Figure 5. The influence of age plantation and the minimum temerature on the yield of black currant, plantation up to 8 years old Fig. 3 shows that fertilization in years with higher precipitation give better results. On the other hand, fig. 4 shows, that higher yield can be obtained when the number of treatments and sun exposure are higher. It is possible to notice that the a bigger number of treatments during increased sun exposure results in lower yield. It may be caused by damages during treatment. Fig. 5 shows relation between age plantation and minimum temperatute during vegetation. Topological map classifying cases according to selected predicators (here a Kohonen network was created for a plantation up to 8 years old, fig. 6.) may provide valuable information supporting decision-making concerning black currant production. Figure 6. Kohonen network topology for the classification of the yield from a plantation up to 8 years old The following predicators were used (from the top): fertilization with nitrogen and potassium, variety (one out of three), the number of protective treatments as well as the number of days with precipitation and sun exposure during maturation. 64 cases were used for the training purpose, which were properly distributed on a self- 54 organizing map, SOM, fig. 7, when the process of training was completed. This map has a size of 8x8 and shows distribution of 64 cases, in relation to selected predicators, and, additionally, according a new criterion, it shows areas (clusters) representing different yield values (from the smallest to the largest respectively, light colour to dark colour, white colour means that there is not a unique assignment). no 1 1 54,56 2 50 2 3 2,8 4 18,24 6 22 3 60,61, 64 4 59,62 33 41,42, 47 5 11,12 36 37 6 14 16 7 13 30 46 8 27,29 28,32 48 5 6 7 58 8 53 55 49,51, 52 38 57,63 34,40 1,7,9 15 35,39 3,4 10 19 4345 23 17,20, 21,25, 26,31 Figure 7. Self-organizing map SOM presenting black currant yield for plantation up to 8 years old The map is the basis of prediction for the new cases, yield value may be identified by reading their location on the map. CONCLUSIONS 1. 2. 3. Efficient sustainable agricultural production management requires a lot of experience and detailed knowledge, which is difficult or even impossible to obtain without sophisticated tools for decision support, such as data mining methods. The presented results of research on black currant yields using selected data mining methods confirmed that they have a potential as regards obtaining knowledge that would facilitate efficient decision-making process in sustainable agricultural production. The selection of the analysis method depends on the goal, and the results of research using the selected method may often be the basis for further research as the example of research on black currant yields confirms. In this example, cluster analysis allowed to distinguish homogeneous groups of cases, which could further be analysed in detail – using modelling and classification. 55 REFERENCES 1. Berry, M., J., A., Linoff, G. 2000: Mastering data mining. New York: Wiley. 2. Biuletyn Państwowej Służby Hydrologiczno-Meterologicznej, Rok 2002-2007. Miesięcznik Instytutu Meteorologii i Gospodarki Wodnej, Warszawa. 3. Kohonen T. 1988: Self organization and associative memory. Springer Verlag, New York. 4. Osowski S. 1996: Sieci neuronowe w ujęciu algorytmicznym. WNT, Warszawa. 5. Pluta S., Żurawicz E. 2006: Nowe Odmiany i Technologie Upraw Krzewów Jagodowych. Instytut Sadownictwa i Kwiaciarstwa, Skierniewice. 6. Rocznik statystyczny 2009. Główny Urząd Statystyczny, Warszawa. 7. Sawa J. 2008: Próba oceny zrównoważenia procesów produkcji rolniczej. Inżynieria Rolnicza. Nr 2 (100). 8. Stanisz A. 2007: Przystępny kurs statystyki. Tom 3. Analizy wielowymiarowe. StatSoft, Kraków. 9. Stankiewicz J. 2010: Analiza czynników determinujących plon czarnej porzeczki z wykorzystaniem data mining. Praca magisterska WIP SGGW, Warszawa. 56 Chapter 6 SITE SPECIFIC CROP FERTILIZATION IN HAUTE-NORMANDIE: A TOOL TO IMPROVE THE PROFITABILITY OF CEREAL CROP SYSTEMS Jean Marc LLORENS, Olivier PERRIN, Odile BOURGAIN INTRODUCTION Site specific crop management (SSCM) can potentially by used for numerous technical actions applied to crops: management of fertilizer, pesticides, seeding rates, or irrigation. Efficient use of agro-chemicals is beneficial for farmers as well as for the environment. However, in most cases, the agro-environmental interest conflicts with economic profitability [Atherton et al., 1999] or socio-economic resistance to innovation [Robert, 2002]. It appears even more important to have a macro view of the farm to better evaluate the economic efficiency of such techniques. Taking into account the cost of both the acquisition of information and the equipment used, profitability should be evaluated for aggregated crop requirements of the production systems. SSCM is more than a simple technology. It increases the relevance of decision making criteria of agricultural advisers [Swinton and Lowenberg-Deboer, 1998] as well as environmental considerations [Melakeberhan and Avendano, 2008]. Taking into account the current fluctuation of raw material costs and prices, the assessment of the economic profitability of new techniques such as precision agriculture is becoming more and more important. Previous studies in HauteNormandie have shown that SSCM has no negative effects on yields [Duval et al., 2007, Bécu et al., 2008]. Most of the time, the technical results obtained were partially evaluated from an economic point of view: limited to increased profit due to fertilizer reduction or gross profit margins. Moreover, these studies were carried out on specific crops, mainly wheat [Duval et al., 2007] or maize [Koch et al., 2004] and/or specific types of input, such as nitrogen [Dailey et al., 2006, De Vuyst and Halvorson, 2004 ; Koch et al., 2004 ; Link et al, 2006 ; Lobell, 2007 ]. However, very few studies have been undertaken to analyse global crop systems [Sartori et al., 2005] or to evaluate the profitability of SSCM [Haefele and Wopereis, 2005], due to the complexity of the work involved. 57 The methodology that we used in our region, allows different thresholds of profitability concerning investments of the SSCM equipment required in relation to soil heterogeneity levels to be assessed. We applied it to specific cropping systems in Haute-Normandie involving heterogeneous soil and variable agricultural surface areas. Furthermore, the methodology together with the specialised software has proved its efficiency to assist farmers, agricultural advisers and also regional policy decision-makers [Flemming and Adams, 1997, Link et al., 2006]. It has already been used in a context of watersheds subject to runoff and erosion to quantify anti erosive agricultural measures to be taken [Bourgain and Michaud, 2006]. Following this first successful experience it was decided to extend to the analysis of the profitability of PA under local pedoclimatic conditions. The three main aims of this study are: • To elaborate a methodology including farming systems based on field sampling and modelling. • To define relevant technique-economic indicators to try to estimate the profitability of precision agriculture for the total economic activities of the farm. • To apply this methodology to carry out simulations. Variables being soil heterogeneity and agricultural practices (with or without PA). MATERIAL AND METHOD Agricultural context: Typology of crop systems Our final scale of investigation is the farm system as an economic unit. One part of the economic and technical data is taken at the plot scale [Duval et al., 2007; Bécu et al., 2008] then it is aggregated at the level of the farm. Three crop systems (cereals, sugar beet-flax and potatoes) were selected (Table 1). Table 1. Typology of crop systems [Rosace, 2005] used in economic simulations with Olympe for precision agriculture in Haute-Normandie *Full Time Labour Unit Systems Cereals Main caracteristics Labour* Average area (ha) Farm Unit Type 1 95 (70-120) C1a 2 145 (120-180) C2 2 240 (180-350) C3 1 110 (80-140) BL1bis 2 200 (140-350) BL2bis 2 or more 150 (more than 90) PT more than 80% of arable land in cereals, oleo proteagineous + fallow 20 to 25 % of arable Sugar beet land in sugar beet – flax and flax Potatoes More than 10 ha of potatoes (25 ha average) 58 Each system is composed of the rotation of seven or eight different crops. In fact, these types of farms represent 40% of the farms in Haute-Normandie. They are defined as being without milk production and a limited surface area of permanent pasture (less than 15% of the cultivated surface area). The turnover for livestock is less than 20% of the total. The six cropping systems (Table 1) represented 87% of the arable farms in Haute-Normandie i.e. 2800 farms. This typology is the synthesis of six years of observations [Rosace, 2005]. Standard economic information taken from various sources was used to represent the farms in the region [Rosace, 2005, Agreste, 20082009, Teyssier, 2007, Chambre d’agriculture, 2008]. Pedoclimatic context: The trials were carried out in fields on commercial farms in Haute-Normandie, NW France. The soils of these fields are mainly silty over a layer of clay and flint. The principal heterogeneity is that of depth and the quantity of stone. In each field deep and shallow zones were determined by apparent soil electrical conductivity (ECa). The amount of stone can be up to 50% in some shallow soils, on the other hand it is almost absent in deep soils. The experimental plots were distributed in three geographic zones of increasing pluviometry from the south east to the North West of the region (between 550 and 700 mm/y). Characterization of soil heterogeneity The ECa was mapped in situ, using a mobile single-dipole Geonics EM38 unit with a mobile GPS-based system. This survey consisted of a grid of ECa measurements arranged in rows (2.8 measures per metre, each measure is the average of 13.9 readings). The rows were 12 metres apart, giving 1,667 readings par hectare. Each reading penetrates to a depth of approximately 1.35 m. In general, shallow soil readings were lower than 15 mS/m and deep soil readings were up to 35 mS/m. Modelling of crop response and the obtaining of technical optimums of nitrogen In our experimental procedure to modelling, there were nine N levels (9) and two replications for each level. The trials were carried out in 2005 and 2006. For each trial data, we have selected the best model by minimizing the root mean square error [Makowski et al., 1999]. The different models tested are as follows: 1) ADAS + Plateau [Min (B ; a – b N – c 0.99N)] ; B = maximum yield, N = N applied, a, b, c = parameters. (Laurent 2006, personal communication) 2) Linear + plateau [Min(c ; b + a N)] ; N = N applied, a, b, = parameters, c = maximum yield 3) Exponential (a - (a - b) e- c N ) ; N = N applied, a, b, c = parameters 4) Quadratic (a N² + b N + c), N = N applied, a, b, c = parameters 5) ADAS (a – b N – c 0.99N).N = N applied, a, b, c = parameters (Laurent 2006, personal communication) Methodology to determine the number of PK sampling The choice criteria of the plot on which the methodology is based are; the knowledge of the evolution of the regrouping of the land, the knowledge of the yield and conductivity heterogeneity and the knowledge of the crop successions. 59 We constituted a grid sampling which each sample was fifty meters apart from is neighbour (Figure 1) which corresponds to a frequency of 4 samples per hectare (4pha). This grid sampling, allowed us to test statistically 4 different methods of sampling for a frequency of 1 sample per hectare (1pha) and 2 different methods for a frequency of 2 samples per hectare (2pha) (Figure 1). Figure 1. Plot grid sampling (4 per hectare, 4pha). () 2 per hectare (2pha_V1) (+) 2 per hectare (2pha _V2) The soil samples were carried out after harvesting in our experimental agricultural systems. This means a period of time sufficiently distant from the field inputs to make sure this has no effect on our interpretation of the analytical results. The analyses were carried out on 500 g of soil, at each sampling location; a composite soil sample (18 subsamples of about 30 g) was systematically collected in a two circle pattern across the area, taken with a A3H Pro 15® ground auger. The nutrient soil rates were measured by the Olsen method for phosphorus [Olsen et Sommers, 1982] and by the exchangeable-K for potassium. Technical referential: The yields of wheat and oil seed rape (OSR) were taken and analyzed using the results of modeling of earlier agronomic trials [Duval et al., 2007 and Bécu et al., 2008]. For the other crops (Barley and peas), yields were estimated using expert advice. As fertilizer recommendation methodology for site specific crop management (SSCM) the COMIFER method was used for the calculation of P and K fertilizer recommendations [Llorens, 2001]. This consists of, first, multiplying the nutrient rate (kg of P2O5 or K2O / ton) contained in the plant by the yield (ton/ha). Then, to calculate a coefficient (C) which is the combination of four factors [Llorens, 2001]: the crop requirement, soil rate, recent history of fertilization and residue use from the previous crop. D (kg/ha) = B (kg/t) * Y (t/ha) * C Where D is the fertilizer amount, B the P or K content in plants, Y the crop yield and C the multiplying coefficient. 60 Recommendations for nitrogen fertilization concerning wheat and oil seed rape are mainly calculated by the balance-sheet methods used in France [Meynard et al., 1997]. Quantity of recommended nitrogen = crop requirement – soil reserve However, for oil seed rape soil reserve, a supplementary factor must be taken into account: that is the quantity of nitrogen absorbed by the plant at the end of winter, estimated by the weighing method. Calculation of the economic criteria The partial budget method The partial budget method is used in order to determine the economic interest of fertilization [Swinton et Lowenberg-DeBoer, 1998]. This methodology aims at determining if the adoption of precision agriculture has a positive effect on the profitability of the farming organisation (Cordonnier P. et al., 1970). The principle is simple: to determined the balance (∆) between gains and losses following the adoption of precision agriculture. LOSSES GAINS A : supplementary expenses C : supplementary products B : eliminated products D : eliminated expenses ∆ = GAINS – LOSSES = (C+D) – (A+B) If ∆ > 0, adoption of precision agriculture creates an increase in profit and is thus favourable to the farmer. If ∆ < 0, adoption of precision agriculture creates a decrease in profit and is thus unfavourable to the farmer The direct margin method The selected economic factors were production costs, specific fertilization materiel costs and direct profit margin (gross margin – specific fixed costs). In our case, Crop Product indicator was established according to the following definition: Crop Product (CP) = yield*sale price + direct crop subsidy. Variable costs and specific fixed costs for fertilization (VCSFCF) = inputs (fertilizers, seeds, pesticides) + traction equipment+ fertilizer spreader + localisation equipment (GPS, software, electronic box) + maintenance cost of the localisation equipment + soil analysis). Direct Profit Margin (DFM) = CP – VCSFCF Inputs, traction and soil analysis costs were calculated per hectare of crops. The other ones were distributed over the total crop land. Therefore, the later will be less significant as the surface area increases. Economic simulations Using Olympe (economic simulator software) we were able to work at different scales: field, crop system and farm (Figure 2). Thus we simulated the economic profitability of SSCM equipment in the most common crop systems in HauteNormandie. Olympe is a decision support software with various functionalities. It can be used as a database, an accounting calculator and a simulator. Different simulations can be done for a cropping system and be compared. In our study, they concerned: • soil heterogeneity of nitrogen, phosphorus and potassium, 61 • • yield potential of the fields, evaluated by the soil apparent electro conductivity (ECa) which represents in Haute-Normandie (France) soil water reserve [Duval et al., 2007] which is the main limiting factor, surface area variability of the cropping system (cereal production system in our study). Figure 2. “Olympe” an economic research decision making tool which is both a data base and a simulator [Le Bars et al., 2005] In order to simulate the SSCM equipment profitability, the calculation hypotheses were the following: Fertilizer amounts (N, P and K), SSCM fixed costs concerned site-specific or non site-specific (spreader, tractor, GPS and software, maintenance, and P and K soil analysis) and yield spatial variability (based on ECa). In order to illustrate our methodology in this paper, only cereal production systems results (Table 2) will be presented for 3 farm sizes (95 ha, 140 ha and 245 ha). Table 2. Average Crop distribution for crop systems in Haute Normandie (%) and average crop distribution for cereal production systems used for simulation (ha). SB = Sugar beet, OSR = oil seed rape, Pt = potatoes, WB = winter barley, Pa = pasture, F = fallow, P = Pea Crop distribution (ha) Cereal wheat SB flax Pt Pa OS P WB F R C1a (95 ha) 49 6 17 10 13 C2 (145 ha) 75 9 26 15 20 C3 (240 ha) 125 14 43 24 34 62 RESULTS Area evolution of crop management since 1947 The field area (21 ha) has changed considerably since 1947. At this time, three different zones should have been identified (Figure 3). In zone 1 (about 14 ha), the area was cropped in large plots of cereals. Zone 2 (about 3 ha) was partially wooded. In zone 3 (about 4 ha), the plots were essentially cropped with cereals, the areas are smaller than in zone 1 (Figure 3). From 1947 to 2003 the areas show a constant enlargement. In 2003, there was only one field, but we can still determine the three zones on the conductivity map (Figure 4). We have three aerial photographs (1947, 1960 and 2003) to monitor the area evolution of our field since 1947. Figure 3. Area evolution of our field since 1947 (21 ha in 2003) in Upper-Normandy (NW France). Three different zones in 1947: [1] large plots (cereals). [2] Wooded area. [3] Small plots (cereals) Heterogeneity of yields The results show that over the six years, with a N level calculated by the balance sheet method, the average yield on deep soil is always superior to shallow soils (Table 2). The loss in yield on shallow soils can reach 30% (from 9.07 to 5.9 t/ha for wheat in 2004). 63 Table 3. Aggregated average yields of wheat and oil seed rape observed on the trial plots for one rate of N calculated using the balance-sheet method for the period 2003 to 2008 year Average yields of wheat (t/ha) Average yields of OSR (t/ha) Shallow soil Deep soil Shallow soil Deep soil 2008 9.53 (± 0.06) 11.47 (± 0.6) 4.18 (±0.23) 5.77 (±1.5) 2007 7.23 (± 0.68) 8.83 (± 1.14) 3.40 (±0.41) 3.42 (±0.34) 2006 5.87 (± 0.1) 8.69 (± 0.0,7) 2.71 (±1.28) 3.7 (±0.98) 2005 7.76 (± 0.67) 9.09 (± 0.93) 4.05 (±0.73) 4.13 (±0.52) 2004 5.90 (± 0.79) 9.07 (± 1.17) 4.76 (±0.81) 5.13 (±0.81) 2003 7 (± 1.25) 8.98 (± 0.91) The amount of annual rainfall and its distribution during the year may cause differences of yield of wheat between the two types of soil ranging from one to three times greater on the deepest soils (1.33 t/ha in 2005 to 3.17 t/ha in 2004). Conductivity variability in relation to soil depth and yields in our pedoclimatic conditions In relation to the depth soil variations, the apparent electroconductivy (ECa) and the wheat yield map are very similar in our field (Figure 4). Figure 4. Conductivity (mS/m) and wheat yield map (t/ha) in our field Indeed, in 1947 and 1960 the areas where the soil was shallow (Zone 2) were wooded (Figure 3). The yield is the lowest in the shallow soil (Zone 2) because it is the less fertile area (Figure 4). In deep soil (zone 1) the yield can be twice as high as in zone 2 (7 to 12 t/ha) and in intermediate soils (zone 3) the yield is slightly higher than in zone 2. Modelling the yield as a function of the N application rate N To obtain the technical optimum, the first step was to model the yield as a function N application rate from the data obtained in 2005 and 2006 for wheat and 64 OSR. The N levels simulated by the models were 0, 50, 100, 150, 200, 250, 300 and 350 kg/ha of NN (Figure 5). The type of model used to best explain each variation in yield as a function of N application rate N varies according to the soil depth and the pluviometry of the year studied. The analysis of the curves shows that for each plot, no matter which year or crop, the maximum yield is obtained in the deep soil. The differences are more pronounced in 2006 than in 2005, particularly for wheat (Figure 5b). In fact, in 2006 the water deficit was more apparent which translates into lower maximum yields in 2006 than 2005. a) wheat 2005 8 6 4 . yield (t/ha) . 10 10 yield (t/ha) b) wheat 2006 8 4 2 2 0 100 200 300 Nitrogen level (kg/ha) 0 400 100 200 300 400 Nitrogen level (kg/ha) c) oil seed rape 2005 6 d) oil seed rape 2006 6 . 5 4 yield (t/ha) yield (t/ha) . 6 3 2 1 5 4 3 2 1 0 0 0 100 200 300 Nitrogen level (kg/ha) 0 400 100 200 300 400 Nitrogen level (kg/ha) Figure 5. Increase in yield of wheat and OSR in relation to different levels of N application on shallow soils (-----) and deep soils (____) in Haute-Normandie (France) in 2005 and 2006. Each symbol represents one field experiment Heterogeneity of P (P2O5) and K (K2O) soil rates The P and K soil rate maps interpolated by the Kriging method (Figure 6) are inversely related to wheat yield maps (Figure 4). When the yield is high the soil rate is low. The crop requirement reduced the soil rate during a period of time when the management was uniform. 65 Figure 6. P and K soil rate maps: P2O5 in mg/kg (a); K2O in g/kg (b). Geostatistic interpolation was performed by the Kriging method Minimum soil samples to describe the P and K soil rates heterogeneity We quantified the spatial structure of the P and K soil rates and we produced a prediction for each chosen soil sampling frequency. The most accurate map was produced with a frequency of four soil samples per hectare. This is consistent with the result shown in literature [Bruchou and Mary, 2007]. But this sample frequency is too expensive for the farmers. For the models produced from the one sampling data per hectare we demonstrated that there is no spatial dependence [Becu et al., 2008]. For the models produced from the two sampling data per hectare, the spatial variability of P and K soil rates is satisfactorily described, we created the interpolated maps which we used to calculate the fertiliser recommendation that will be applied by SSCM. Thus, two samples per hectare are sufficient to describe the soil rate variability in our experimental conditions [Becu et al., 2008]. Economic interest of precision agriculture from 2003 to 2008 We calculated an estimation of the evolution of the economic interest of precision agriculture from 2003 to 2008, a period marked by strong price fluctuation for agriculture inputs and outputs (Figure 7). The price references used come from "AGRESTE-la statistique agricole" (for use of price index and quantity in calculation of technical efficiency, Balk 1998). The price of inputs, related to the harvest of year N is an average of the monthly prices from 01/06/N-1 to 31/05/N. for this period, it was considered there was no modification of the technical procedures concerning the fertilizer application. 66 140 Input prices (€) 120 100 80 60 40 20 Ju ne 2 00 7 Ju ne 2 00 6 Ju ne 2 00 5 Ju ne 2 00 4 Ju ne 2 00 2 Ju ne 2 00 3 0 Ammonium Nitrate Super phosphate Potassium chlorate Fuel Figure 7. Price evolution of some principal agricultural inputs from 2003 to 2008 Expense in partial level in Partial Budget (€/ha) We calculated the profitability during a period from 2003 to 2008 based on fertilizer recommendation for SSCM on a oil seed rape and wheat crop rotation Oil seed rape –wheat-wheat. The gains connected to the use of SSCM are mainly due to a lower consumption of fertilizer. Indeed, on average, for the period 2003-2008, 50% of the gains were created by a reduction in supplies in N, 23% by a reduction of supplies of P and 27% by a reduction of supplies in K. However, the investment necessary for SSCM practice (SSCM equipment and information cost) involve economic losses which represent in the partial budget 41% of the total estimated gains. These observations vary according to the year under study. Comparative studies carried out for the period 2003-2008 demonstrate a constant increase in the balance of the partial budget (∆). This evolution represents the increase of the interest of the SSCM in preference to uniform fertilization (Figure 8). This increase has been linked to the rise in the price of agricultural inputs. 280 260 ∆ 240 220 Uniform fertilizer expenses 200 180 160 SSCM Fertilizer expenses + SSCM specific expenses 140 120 100 2003 2004 2005 2006 2007 2008 Modulated f ertilisation Unmodulated f ertilisation Figure 8. Evolution of the partial budget balance between 2003 and 2008 67 Therefore, in the context of the increasing cost of agricultural inputs we demonstrated the rising interest in the use of precision agriculture [Llorens and Perrin, 2009], in particular towards the end of the study period where the interest rose 39% in one single year (18,6 €/ha in 2007 to 25,7 €/ha in 2008). Economic profitability simulations using the direct margin method To generate the data base (Figure 2), we have classified the soils into three levels of yield potential. For each yield level, we have defined three intrafield heterogeneity rates for each nutrient (N, P and K, [Table 4]). Table 4. Hypothesis concerning three levels of N, P and K heterogeneity of HauteNormandie’s soils (medium, minimum and maximum) in relation with three levels of crop yield potentials Soils of HauteLow potential Medium potential High potential Normandie 17% 58% 25% maximum Nitrogen 12% 68% 20% medium 8% 74% 18% minimum 30% 15% 55% maximum Phosphorus medium 13% 37% 50% 5% 70% 25% minimum 15% 20% 65% maximum Potassium medium 9% 41% 50% 0% 70% 30% minimum These rates are based on the results most commonly observed in HauteNormandie in a 6-year field investigation (70 farmers and over 9000 ha analysed) undertaken by the GRCETA27 [Becu, unpublished data]. The fertilizer amounts are calculated from these rates. Whatever the soil heterogeneity and the crop system surface area (CSSA), the direct profit margin (DPM) obtained through SSCM is higher than that of uniform application (Figure 9). The direct margin gains calculated using Olympe vary from 32 to 47 euros per ha (Figure 9). We can see that an increase of farm surface area and soil heterogeneity raises the profitability of site-specific farming compared to uniform farming. These results were obtained from experimental data [Duval et al., 2007, Bécu et al., 2008], expert advice and representative farm data (typical systems of Haute-Normandie). 68 Figure 9. Direct margin gains linked to the precision agriculture use in the cereals systems in Haute-Normandie (NW France) From an environmental point of view, SCCM allows a significant reduction in sprayed amounts for all heterogeneity levels. This difference increases according to the CSSA. For example, for nitrogen, sprayed amounts diminished by more than 1500 kg for a 95 ha CSSA, more than 2300 kg for a 145 ha CSSA and more than 3880 kg for a 240 ha CSSA. Even if fixed costs are higher in SSCM (€2075 as opposed to €470), whatever the farm surface area, the profits generated by the reduction in amounts of sprayed fertilizer and traction operation efficiency are higher than outlay. Indeed, the traction material is more efficient in terms of working time in SSCM than in UA which contributes to a higher profitability (Table 5). A larger surface area is covered each hour by SSCM material allowing farmers to double their application. 69 Table 5. Global direct margin (in euros) of the three cereal crop systems (95 ha [a], 145 ha [b], 240 ha [c]) in SSCM and UA related to different rates of silty soils heterogeneity in Haute-Normandie (France) cereals oil seed rape Crop product peas other TOTAL CROP PRODUCT fertilizer Variable seeds costs chemicals other Specific fixed costs TOTAL SPECIFIC COSTS DIRECT PROFIT MARGIN DIRECT PROFIT MARGIN GAIN cereals oil seed rape Crop product peas other TOTAL CROP PRODUCT fertilizer Variable seeds costs chemicals other Specific fixed costs TOTAL SPECIFIC COSTS DIRECT PROFIT MARGIN DIRECT PROFIT MARGIN GAIN Cereal crop system 95 ha (a) Precision Agriculture Uniform Minimum Medium Maximum Application Heterogeneity Heterogeneity Heterogeneity 75215 76620 76395 76395 13090 8500 9100 105905 16251 8387 14825 2166 470 13310 8670 9100 107700 13943 8387 14825 1642 2075 13200 8670 9100 107365 13244 8387 14825 1642 2075 13200 8500 9100 107195 13097 8387 14825 1642 2075 42099 40872 40173 40026 63806 66828 67192 67169 3022 3386 3363 Cereal crop system 145 ha (b) Precision Agriculture Uniform Minimum Medium Maximum Application Heterogeneity Heterogeneity Heterogeneity 115225 117080 117135 117135 20020 12750 13900 161895 24864 12783 22665 3314 470 20350 13090 13900 164420 21329 12783 22665 2511 2075 20240 12920 13900 164195 20373 12783 22665 2511 2075 20350 13090 13900 164475 20069 12783 22665 2511 2075 64096 61363 60407 60103 97799 103057 103788 104372 5258 5989 6573 70 cereals oil seed rape Crop product peas other TOTAL CROP PRODUCT fertilizer Variable seeds costs chemicals other Specific fixed costs TOTAL SPECIFIC COSTS DIRECT PROFIT MARGIN DIRECT PROFIT MARGIN GAIN Cereal crop system 240 ha (c) Precision Agriculture Uniform Minimum Medium Maximum Application Heterogeneity Heterogeneity Heterogeneity 192695 196235 195785 195505 33110 20400 23080 269285 41418 21140 37700 5521 470 33660 20740 23080 273715 35573 21140 37700 4179 2075 33550 20740 23080 273155 33889 21140 37700 4179 2075 33550 20740 23080 272875 33363 21140 37700 4179 2075 106249 100667 98983 98457 163036 173048 174172 174418 10012 11136 11382 DISCUSSION Influence of plot regrouping and agricultural practices on the heterogeneity of rates of P and K and the availability of these nutrients for crops. The regrouping of land and agricultural practices modify the methods of use of the cultivated areas. In our example (Figure 3), the cropping history of this plot since 1947, partially explains the three resulting soil rates zones. These zones create soil conditions which currently influence the availability of these nutrients on crops. Indeed, according to Marschner [1995] the conditions of nutrient uptake by plants are: the root interception (soil volume displaced by root volume), the nutrient diffusion in the soil solution towards the roots (nutrient transport along the concentration gradient) and the mass flow transport of bulk soil solution along the water potential gradient (driven by the transpiration). The effect of these three conditions is considerably reduced in zone 2 (Figure 3) where the soil is shallow, as the roots have problems in developing in this soil, the yield is less, therefore transpiration and consequently mass flow is reduced. The reduction of these two parameters lowers the nutrient uptake and induces an accumulation of P and K in this zone, more noticeable for P which is less mobile in soil [Fardeau et al., 1991, Lewis and McGechan, 2002, Mollier et al., 2008] than is K [Holmqvist et al., 2003]. This element can be leached in this zone. If, on this area, homogeneous fertilization is applied, the calculation of P and K fertilizer amount is based on the lowest soil test found in zone 1 which is the most representative of the plot (the largest area, Figure 3). These low rates of P and K would imply fertilizing advice beyond that required for zone 2. Thus, the nutrient mass balance is extremely positive because the amount added by the fertilizer is higher 71 than that removed by the plant during harvesting, thus, little by little, the rate of this zone will increase creating considerable heterogeneity in this part of the field. This is more important for P than K due to difference in mobility previously mentioned. Minimum samplings per hectare to be carried out to describe heterogeneity applicable for precision agriculture In this study, the variability of P and K rates can be considered as permanent variables in soil [Bruchou and Mary, 2007] which is relevant to the dynamic of these nutrient in the soil [Lewis and McGechan, 2002; Holmqvist et al., 2003]. A study based on 8 soil parameters and 10 plots (Piraux, 2005) and a far wider grid sampling than ours (10 samples par hectare) has shown that in a same field the variability and the spatial structure of the pedological parameters of interest can vary considerably from one plot to another: some seem generally more variable and better structured than other. In our case, models of variograms for sampling frequency of two samples per hectare allowed us to demonstrate mathematically that we have sufficiently shown the heterogeneity of P and K in relation to the precision of the site specific crop management material available. Two analyses per hectare is an operational sampling for SSCM in our pedoclimatic conditions. Assessment of the environmental profitability The assessment of farm input use and efficiency are on the agenda in many countries with intensive agricultural production, as a response to the increased interest in the environmental performance of different farming systems. Input–output accounting systems can facilitate farmers and advisors to improve the environmental performance of both crop and livestock production [Halberg et al., 2005]. In this survey, thanks to the underlying Kriging method we could adjust SSCM maps allowing the reduction of the excess or potential lack of P and K [Becu et al., 2008]. In zone 2 (Figure 3) where there is an excess of P, the environmental risk of eutrophication is low because there is no river close to this field. But if it were not the case, the use of SSCM would be an appropriate technique for reducing P environmental impact. To complete this physicochemical approach it would be interesting to test the influence of SSCM on soil microbiology: reducing the lacks and the excess of P and K within the field could considerably change the structure of the microbial population and thus the nutrient mobility towards the root [Deneux-Mustin et al., 2003]. Uniform management implies surplus which could lead to P accumulation in soils, making them long-term diffuse sources of P loss to water [Hooda et al. 2001]. Soil available P has been considered as an important indicator in both evaluating the soil P supply capacity and determining the P fertilization rate and P loss risk from runoff [Liu et al., 2007]. Economic interest of precision agriculture in fertilisation management In a context where world agricultural supply prices rise significantly, SSCM can provide an efficient solution. However, the drop in petrol prices of the second semester 2008 could result in a decrease in the price of fertilizer in 2009; so far not observed. Whatever the situation the products (yield*price) are higher in PA than in uniform application (UA). Fertilizer utilization Costs is systematically lower when PA is practised [Bourgain et Llorens, 2009]. 72 The net gains in terms of direct profit margins vary between 32 and 47 €/ha. These variations, favourable to the site specific management of inputs, are created through an optimisation of yields in addition to more efficient management costs. Other technical criteria such as the composition of fertilizers could have been taken into account. In UA farmer can use compound fertilizers (binary [PK] or ternary [NPK]). In this case, they can reduce the number of tractor operations (by 2 PK fertilizer and by 3 for NPK) which limit mechanisation costs. However, in the study region farmers are still advised to use simple fertilizers because it is easier to adapt the dose to the plant needs (Bourgain and Llorens, 2009). Indeed, the proportions in PK rates in the soil can be quite different from those of commercial compound fertilizer. In this paper we have chosen to simulate the real use of fertilizer by farmers: the objective being to generate decision-making criteria which can be applied by agricultural advisers. The results of our simulation show that the interest of SSCM for cereal production systems is higher when the farm surface area is larger [Bourgain and Llorens, 2009] which confirms our previous partial results [Duval, et al., 2007 and Bécu et al., 2008]. This is due to the distribution of the fixed costs on a larger surface area. The logic of these results is established but our methodology and simulator provide precise estimations. This could provide a convincing argument for these application techniques [Jochinke et al., 2007] which reconcile economy and environment and allow their assessment whichever production system chosen. Within the perspectives of this research we plan to study these results, in terms of optimal profitability using a mathematic model based on the production function. Another follow up study could draw up an environmental criteria simulation by using Olympe in terms of the reduction of the impact of fertilizers in particular concerning water resources (especially for nitrates and phosphates). Concerning the agricultural policy-makers, the optimisation of fertiliser use thanks to the modulation of inputs can create an optimisation of protein production and thus an increase in profits for producers wishing to improve the protein level [Bongiovanni et al 2007]. From the industrial point of view, development of such models could have an effect on the choice of new material to be developed. For policy-makers, the focus is mainly on the environmental aspects associated with the economic viability of this sector in order to provide efficient management of water resources [Rejesus and Hornbaker, 1999]. CONCLUSION The methodology used supported by the simulator models and experimental trials has shown the economic interest of PA in the pedoclimatic context of HauteNormandie. Thus applying PA reduces input costs and increases profit margins without affecting yields. This is particularly relevant at a time of increasing input prices and global food insecurity. In follow up studies we will simulate all systems and practices under different economic situations. We will also simulate environmental externalities such as potential pollution from excess application of nitrogen or phosphorus, and thus enabling us to offer a wider range of decision-making criteria to agricultural advisors and policy makers. 73 Acknowledgements This work could not have been carried out without the experimental field trials undertaken by two French farmer groups (GRCETA de l’Evreucin and DEFISOL 27). We should, first of all, like to thank the advisors of GRCETA Vincent Debandt and David Mahieu for their comments and their expert agronomic advice. We should also like to thank Maxime Bécu and Charles Duval from Défisol27 for their technical data concerning soil description and crop systems which was essential for this research. Finally, a special mention for Hazel Favre and Bill Edmonds for helping us to find the right words to create this document in English. BIBLIOGRAPHY 1. AGRESTE, 2008 et 2009: bulletins de conjoncture de août 2008 et janvier 2009, 2. 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Sartori, L., Basso, B., Bertocco, M., Oliviero, G. 2005: Energy Use and Economic Evaluation of a Three Year Crop Rotation for Conservation and Organic Farming in NE Italy. Biosystems Engineering 91: (2) 245–256. 41. Swinton SM, Lowenberg-DeBoer J. 1998: Evaluating the profitability of sitespecific farming, Journal of precision agriculture 11 (4), 439-446. 76 Chapter 7 ECOLOGICAL ASPECTS OF SOIL COMPACTION BY WHEELS OF AGRICULTURAL VEHICLES Jerzy BULIŃSKI INTRODUCTION Problem of excessive soil compaction concerns all agricultural sectors dealing with field production, including typical agricultural crops, vegetables, horticultural production or fruit culture. It can be observed also in human activities in forestry, therefore, in every place where the root system of cultivated plants, bushes or trees can not properly develop or function as a result of soil stress caused by the pressure of wheels of implements or tractors, or where it is mechanically damaged. However, the most noticeable effects of soil compaction can be found in agricultural field production, connected with human activity and application of technical means of production. Cause of this situation is well known and has been described in references for many years. It is believed that soil compaction occurs when the stress values created by wheel of vehicles running over the field exceed a natural soil strength [Van den Akker 1994]. Majority of published works concerning compaction [Botta et al. 2004, Alakukku et al. 2003, Güclü et al. 2002, Way et al. 2005] point out that the main factors leading to excessive compaction of agricultural soils are changes in technologies of mechanized field operations. These changes result from economic limitations which force application of high-capacity outfits driven by powerful tractors [Abu-Hamdeh 2003]. According to Alakukku et al. [2003] the sale of brand new tractors of power above 44 kW increased in Germany more than twice over 6 years. Similar trend can be found in Poland where during last 9 years the number of tractors used in agriculture increased by over 20%, while average tractor power increased by over 25% [GUS 2009]. There is a strict connection between weight and power of tractors in Podlaskie province [Marczuk 2006] and an increase in tractor power requirement for driving machines usually results in the increased field pressure loading. 77 Figure 1. Dependence between weight and power of tractors in farms of Podlaskie province [Marczuk 2006] Trend towards application of heavier machines and tractors can be found in entire Europe. The weight of sugar beet combine harvesters produced currently can reach during work at full load even 60 Mg with axle pressure over 10 Mg [Nosalewicz 2005]. Importance of the problem was emphasized in 1980 when there were started serious international studies on the impact of high axle pressure vehicle wheels, and International Working Group for Soil Compaction by Vehicles of High Axle Loads (within ISTRO) was set up. Initially, this team involved scientists from Denmark, Finland, Norway, Sweden, Netherlands, Canada and USA, and afterwards scientists of 35 European scientific institutes and universities were involved (including Poland. Hitherto investigations on soil compaction [Soane et al. 1995, Güclü et al. 2002, Halvorson et al. 2003, Meyer et al. 1996] point out clearly that the created wheel pressures lead to very unfavourable changes in physical, chemical and biological properties, both in topsoil and subsoil layers; they influence intensity of mineral and organic substances, thus, the soil fertility and potential yielding ability. It is also known that restoration of the initial state of highly compacted deeper layers of the soil profile is very difficult. ASSESSMENT OF SOIL COMPACTION Soil density and soil compaction are the commonly used indices [Ronai and Shmulevich 1995; USDA-NRCS, 1996; Lipiec 2002]. Various measuring devices are used in field investigations to determine these parameters. Unification of applied methods is important with respect to reliability of obtained results of investigations, correctness of conclusions and possibility of comparisons between results achieved in various scientific institutions. Under Polish conditions, procedures of soil properties measurements can be found in PN-88/B-04481 Standard. However, in many cases there are used the methods and devices which make difficult interpretation or comparison of results. Samples in investigations on soil density are usually taken by open pit method and by horizontal driving in the separate cylinders into investigated layer (Fig. 2a) – often differing in volume or construction (e.g. edge for cutting the sample). This measurement can be executed with the use of Eijkelkamp probe vertically driven in soil and equipped with a set of cylinders, placed one above another in the probe’s pipe (Fig. 2b). Different measurement methods can lead to different findings (Fig. 2c). 78 b/ a/ c/ Figure 2. Effect of measurement method for soil density on obtained values [Powałka and Buliński 2003] Soil compaction is usually measured with the use of cone penetrometers driven in soil manually or mechanically using electric or hydraulic drive. The ASAE Standard [1993] commonly used in ground investigations recommends application of probes with a cone end of apex angle 30º, driven in soil at speed 30 mm/s. The Standard allows for application of two types of cones – smaller one of diameter 12.83 mm and cross section at the base 130 mm2 (0.505 in.) and bigger one of diameter 20.27 mm and cross section 323 mm2 (0.798 in.). Differentiation of the cone size is determined by measurement conditions: bigger cone for light soils, smaller cone for heavy soils. Other cone dimensions can also be found in investigation methodology. In soil bin investigations carried out by Buliński and Majewski [1998] the soil compaction was measured with the use of two cone probes: St1 – cone diameter - 79 11.40 mm, apex angle – 600, and St2 – cone diameter -20.27 mm, apex angle – 300 . Three speeds of probe driving in soil amounted to 0.6 - 1,2 - 1.8 m min-1 and five soil compaction levels (expressed by soil volumetric density) were equal to 1.65,-1.67,1.69,-1.77,-1.81 g cm-3. The cone dimensions St2 and highest speed of driving in soil corresponded to ASAE S313.2 Standard in the range of measurement execution and equipment used. Statistical analysis on the results obtained under same conditions of measurements proved significance of differences In soil compaction measured with the use of both probes. Comparison between probe indications is presented in Fig. 3; it can be seen how important is observing the standard recommendations and determination of precise measurement conditions. 900 St [kPa] 2 800 700 600 500 400 300 200 100 0 St1 [kPa] 0 200 400 600 800 1000 1200 1400 Figure 3. Values of soil compaction measured with probes St1 i St2 [Buliński and Majewski 1998] In field investigations one can find penetrometers acting dynamically (impact probes) which determine the soil compaction on the basis of weight’s impact number (descending on the probe’s shank from a defined height), needed to drive the cone to a given depth, e.g. 0.15 m. This measurement method is quite different and its results can not be related to the results obtained by application of static probes. EFFECT OF COMPACTION ON CHANGES IN SOIL PHYSICAL PROPERTIES One of the factors which change considerably the physical properties of soil in wheel track zone is multiple running of outfit wheels over the same track. Horn et al. [2003] found that as a result of this running the soil is deformed both horizontally and down its profile (Fig. 4). 80 Figure 4. Effect of multiple running of tractor on soil deformation [Horn et al. 2003] As it is evident from investigations the soil density gradually increased in entire profile of arable layer up to 10th run. According to researchers the soil compaction can be initially connected with decreasing space between the soil aggregates and separate aggregates are durable. As stress increases further, the layer’s homogeneity in the profile along with damage of its structure and changes in configuration of particles. Under pressure of wheels the part of pores elongated in the layer under wheel track reduce their dimensions, loose continuity and change configuration from vertical to parallel in relation to the field surface [Pagliai 1987, Słowińska-Jurkiewicz and Domżał 1991]; this makes difficult a vertical penetration of plant roots through the layers created. In the compacted layers there were found [Larson et al. 1994, Lowery and Schuler 1994] the limited gas exchange, decreased water permeability and its availability for plants, deterioration of draining system functioning, worse development of root system and poorer uniformity of plant growth. Increase in soil density is accompanied by a decrease in biggest pores [Buliński, Niemczyk 2007], which are taken mainly by the roots of cultivated plants, enable soil aeration and adjust its hydraulic conductivity. Kuht et al. [2003], investigating changes in nitrogen content in plants during germination and shooting phases in variably compacted soils, found no significant differences in the content of this component in plants growing under higher moisture content. An increase in soil compaction above 1.58 g cm-3 led to a clear decrease in nitrogen content in the plants’ dry mass during germination. It has been pointed out since many years [Trzecki 1969, Lipiec 2002] that maximal ability for vertical overcoming soil resistance by plant roots does not exceed 3 MPa, while at compaction bigger than 2 MPa the root development is limited [Copas et al. 2009]. Differences in percentage distribution of roots in particular layers of not compacted and compacted soil profile (Fig. 5) can differ substantially [Lipiec et al. 2003]. 81 Figure 5. Dependence between root distribution of spring barley and soil density in arable layer [Lipiec i inni 2003] In the case of root plants such as sugar beet or carrot these difficulties can lead to deterioration of their technological value. Investigations of Powałka [2005] carried out with application of a camera recording image of field surface in the zone of wheel tracking proved that the front tractor wheel moving over freshly ploughed field creates a soil wedge (Fig. 6a), and the soil is pushed aside creating in a superficial layer the linear fractures along planes of biggest stresses (Fig. 6b). According to the author, this phenomenon concerns mainly the narrow front wheels of light tractors, when side shifting of soil includes almost entire strap aside the track of width 300 mm. When tyres of front tractor wheels are wide, width of a side zone of superficial deformations does not exceed 100 mm. Referring these results to agricultural practice one can find that passage of tractor over the freshly ploughed field leaves behind the zone of changed soil properties in a strap 09-10 m wide along the track axis. Compacting effect of agricultural vehicle wheels is not limited to superficial layers of field. Figure 6. Passage of wheel on loosened soil layer: a/ - longitudinal section of field profile, b/ - soil fractures and deformations along the length and breadth of passage [Powałka 2005] The substantially compacted zones, reported in many investigations, reached beneath the range of annual tillage operations, especially if performed at substantial soil moisture content. Passages of 6-row sugar beet combine harvester of total mass 82 35 t resulted in strong soil compaction and decreased water conductivity in a layer 0.3-0.5 m by almost 90% [Arvidsson 2001], while under conditions of bigger moisture content the changes in soil compaction were found even at 0.7 m, and an increased compaction (penetration resistance) was found on light (sand) and heavy (clay) soils. It is believed that soil resistance to stress decreases with an increase in water content [Horn, Lebert 1994]; it should be noted that every soil has its specific moisture content, when pressing causes the biggest compaction. This moisture ranges 14% for clayish sands and sandy clays to 26% for dusts and heavy clays [Buliński 1998] which means that soil reaction to pressures often can vary, even during one day when it rains [Sommer and Brunotte, 2003]. In determination of soil susceptibility to compaction PD (Packing Density) index is proposed, which includes soil volumetric density, structure, organic matter and clay particle contents. The index is calculated with equation: PD = Db + 0.009×C [Van Den Akker and Hoogland 2009], where PD - packing density in g.cm-3, Db – volumetric density in g cm-3, and C - clay content (weight %). There are three ranges of PD parameter: low < 1.4, medium 1.4 – 1.75, high > 1.75. The higher PD parameter value, the lower soil susceptibility to compaction. Since soi compaction results from the wheel impacts, Prinker and Grečenko [2009] proposed numerical index CC (Compaction Capacity) for determination of the risk of harmful soil compaction by a loaded wheel. An external effect of soil compaction is a rut created within the wheel passage; his leads to a transverse irregularity of field surface. Depending on soil type, its moisture content during passage and wheel pressure, the rut depth can vary substantially. It is evident from the Author’s own investigations that a single passage of medium power tractor (Ursus 4512) over the ploughed clayish sand of moisture content 10% created the ruts of depth 12 cm, and after eight passages the rut bottom was situated 16 cm beneath field (Fig. 7). Figure 7. Cross section of field surface within tractor passage: Y0 – shape of field surface prior to passage, Y1 – after one passage, Y8 – after eight passages of tractor Source: author’s investigations The soil uplift marked on diagram aside the track, referred to field level prior to passages (marked with broken line (Y0)), confirms the previously cited investigations [Powałka 2005]. Index of transverse field irregularity after 8 passages increased over 2.5-fold in relation to a state priori to passages; it is also an important factor 83 influencing operational conditions of the machines and operation quality of their working units. Changes in operational conditions of agricultural implements and machines on compacted soils are also connected to an increase in working resistance leading to increased costs of operations, bigger fuel consumption and bigger frictional wear of machines’ elements. It is evident from investigations [Buliński 1998] that substantial increase in ploughing resistance occurred particularly in root crop cultivation technologies, as a result of soil compaction by heavy combine harvesters and trailers. After potato harvest the mean soil compaction increased by over 70%, dry soil density by over 100% when compared with the period prior to harvest; this resulted in an increase in ploughing resistance by about 25%. EFFECT OF COMPACTION ON CROP YIELD An important and measurable effect of excessive soil compaction is a decrease in crop yield connected with deterioration of their growth and development conditions. Figures reported in numerous references vary substantially depending on crop type, soil conditions, climate and especially moisture content. Ablas et al. [1994] reported that soil compaction caused a 38% yield decrease of maize for silage on light soils at low precipitations, Jorajuria et al. [1997] found a 74% yield decrease of grass within the passage track and 18% decrease in adjacent areas, Botta et al. [2004] reported a 9 - 38% soybean yield decrease depending on passage intensity, while according to Radford et al. [2000] the soil compaction decreased wheat germination by over 20%. As a result of soil compaction the potato yield decreased by 21-35%, sugar beet yield by 8.7% , wheat by12.9 – 13.6% (depending on date of compacting), maize by 7.5% [Soil compaction 2001]. It can be seen that connected reference data are very different, but they underline importance of the soil type. It was also found that under various soil conditions the same loading of field with machine passages can change the water infiltration intensity, soil fauna size, while the cereal grain yield and straw yield on clay loam soil can be decreased by several per cents; however, on silt loam soil it is not valid [Blanco-Canquia and Lal 2008]. Besides, application of mounted machines instead of trailed ones for fertilizing and plant protection enabled to decrease a mean soil compaction by 9% and dry soil density by 10%, due to a machine mass transfer on wider rear wheels of the tractor and due to decrease in total wheel pressures [Buliński 1998]. [Gemtos et al. 1999] maintain that evaluating the effect of soil compaction on crop yield one should consider soil chemical properties and also statistical criteria of changes: significance, range, differentiation and stability and also economic aspects. STABILITY OF SOIL COMPACTION EFFECTS Unfavourable effects of excessive soil compaction by vehicle wheels can be found also in subsequent years. Hakansson [2007] reported that passages of outfits of axle loading 10 t resulted in an increase of soil density at depth 50 cm and it was visible after 8 years. According to Grogan [2003] passages of machines of axle loading 20 Mg lead to a deep soil compaction and it can be found over 11 years and even longer [Alakukku 2000], although these changes are most visible during 2-3 years after compacting and a decrease in crop yield can occur in the next year and amount to 30%. Under favourable soil and climatic conditions the compaction effects may not be obvious, but can occur in moisture shortage periods or after large 84 precipitation deteriorating the air-water balance; it can limit the range of root penetration and possibility for nutrients uptake and also deteriorate the root metabolism. METHODS FOR REDUCING VEHICLES’ WHEELS SOIL COMPACTION BY AGRICULTURAL Suggestions on reducing soil compaction by agricultural vehicles’ wheels reported most often in references can be generalized as the following principles. Reducing field area compacted by wheels – this need results from the fact that in typical field operation technologies such as cereal or potato cultivation over half of field area at harvest time is covered by tracks of passages [Buliński 1998]. In some places the wheel tracks overlap more than 20 times resulting in deep and substantial changes in soil structure. In places of double overlapping the aeration porosity at depth up to 150 mm can approach 15%, which is considered as limit value from the viewpoint of proper conditions for plant development [Powałka 2005]. Special frame devices reported in references [Raper 2005] astriding field straps of width equal to several meters and moving along constant paths with mounted implements and machines are still mainly within scientific investigations due to specific construction and limitations. Traffic paths set up during sowing do not consider the outfit passages executed prior to sowing, i.e when impact of wheels on a wet and loosened soil can lead to substantial and unfavourable changes in soil properties. Some investigations [Buliński 1998, Marczuk 2006] point out that problem can be solved by application of pre-sowing paths set up at secondary tillage operations with the use of satellite aiding systems of positioning accuracy equal to several centimeters. In that system the area free of compaction after cereal harvest amounted to almost 64%. Great importance can also have execution of ploughing with „on land” system and application of multifunctional machine sets performing several operations during one passage; however, in small farms it can be difficult with respect to required equipment. Reducing axle load of vehicles should concern mainly the heavy harvesting machines like potato combine harvesters of total weight 50 Mg and single axle load of 10 Mg. With respect to severe effects of strong soil compaction there is recommended in Sweden to limit axle load of agricultural vehicles to 6 Mg [Danfors, 1974]. Activities connected with optimization of outfit mass distribution can be also rational, since no critical states are created. The former Sowiet Union GOST Standards determined values of pressures with respect to admissible stress values at depth 0.5 m depending on soil type and moisture content at passage [Rusanov 1994]. The International Working Group (by ISTRO) dealing with problem of soil compacting by vehicles of big axle loads assumed a load level of 50 KN as „high”. Reducing tyre inflation pressure can be achieved by application of special lowpressure wide-profile tyres working in tandem or twin system, as well as by reducing inflation pressure in typical tyres. Although it is rarely used in practice, it enables to increase a contact area of tyre with ground by 3 - 19% [Powałka 2005]. Similarly, an increase in tyre width enabled to decrease specific pressure values by almost 20%, and soil compaction and density within the rut by about 10% [Buliński 1998]. Some manufacturers give various inflation pressure values for transport and field operations. Another solution often cited in references and used to decrease the traction system pressure on soil and soil compaction is a tracklayer mechanism. New materials and modern production technologies enable to obtain a quality of tracks 85 comparable to tyres, with better traction parameters which are significant during field operations. As a result, the offer of heavy tractors and combines manufacturers more and more often includes the products equipped with tracklayer traction systems. Reducing operations on wet soils is difficult, since spring field operations are mostly executed under such conditions. On wet soil the range of wheel compaction is bigger. It is recommended to delay operations by several days, if possible. Equally important is modification of plant cultivation systems by reduction of ploughing and application of shallow soil loosing operations, if possible. SUMMARY The mentioned above methods aimed at reducing harmful compaction do not exhaust all possibilities. This activity calls for a system approach with consideration to farmer’s needs and requirements of environment connected to plant protection. 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Przegląd Techniki Rolniczej i Leśnej 10/2003, str 14-16 Powałka M. 2005: Wpływ nacisków kół ciągników rolniczych na zagęszczenie gleby w warstwie ornej. Dysertacja WIP. SGGW Warszawa 2005. s.84. Prikner P., Grečenko A. 2009: Compaction capacity rating of off-road tires. Járművekés Mobilgépek,II. Évf. No.1. 259-268 Radford B. J., Bridge B. J., Davis R. J., McGarry D., Pillai U. P., Rickman J. F., Walsh P. A., Yule D. F. 2000: Changes in the properties of a Vertisol and responses of wheat after compaction with harvester traffic. Soil & Tillage Research, Vol. 54, Issues 3-4, April 2000, 155-170. Raper R.L. 2005: Agricultural traffic impacts on soil. Journal of Terramechanics 42: 259–280 Ronai D., Shmulevich I. 1995: Comparative analysis of some soil compaction measurement techniques. International Agrophysics, Vol. 9, nr. 3, s.227-223. Rusanov, V.A. 1994: USSR standards for agricultural mobile machinery: permissible influences on soils and methods to estimate contact pressure and stress at a depth of 0.5 m. Soil & Tillage Research. 29, 249–252 Słowińska-Jurkiewicz A.,Domżał H. 1991: The structure of the cultivated horizon of soil compacted by the wheels of agricultural tractors. Soil & Tillage Research., Vol 19, No 2-3, 215-226. Soane B.D., van Ouwerkerk C. 1995: Implications of soil compaction in crop production for the quality of the environment. Soil & Tillage Research., Vol.35, nr 1-2, s. 5-22, Soil Compaction: Causes, Effects, and Control. University of Minnesota. Paper FO-03115. 2001 Sommer C., Brunotte J. 2003: Lösungsansätze zum Problembereich Bodenschadverdichtung in der Pflazenproduktion. Landnutzung und Landentwicklung. Vol. 44, nr 5, s. 220-228. Trzecki S. 1969: Próba wyznaczenia granicznych oporów gleby I zawartości powietrza dla początkowego wzrostu korzeni roślin uprawnych (badania wazonowe). Postępy Nauk Rolniczych. 6. 71 – 78. USDA-NRCS 1996: Soil Quality Resource Concerns: Compaction. USDANRCS Soil Quality Inst. Ames. IA http:// www.statlab.iastate.edu/survey/SQ1/sqihome.shtml 88 45. 46. 47. 48. Van den Akker, J.J.H, Hoogland T. 2009: Comparison of Risk Assessment Methods to Determine the Subsoil Compaction Risk of Agricultural Soils in The Netherlands ISTRO 18th Triennial Conference Proceedings, June 15-19, Izmir-TURKEY T4 - 034 – 1-9 Van den Akker, J.J.H. 2009: Prevention of subsoil compaction by tuning the wheel load to the bearing capacity of the subsoil. Proceedings of the 13th International Conference of ISTRO Aalborg, Denmark, 537-542. Way T.R,. Erbach D.C., Bailey A.C, Burt E.C,. Johnson C.E. 2005: Soil displacement beneath an agricultural tractor drive tire. Journal of Terramechanics 42 35–46. PN-88/B-04481 – Grunty budowlane. Badania Laboratoryjne 89 Chapter 8 ANALYSIS OF THE WORKING RESISTANCE OF THE WORKING UNITS USED IN SIMPLIFIED TILLAGE Krzysztof PIECZARKA INTRODUCTION Success in the modern agricultural market can be achieved only by enterprises that are able to offer good products at a competitive, low price [Michałek 1997]. One important element of a financial strategy leading to lower prices is the reduction of operating costs for agricultural equipment, and the reduction of losses associated with their use. This reduction in operating costs is an urgent challenge for the whole of the Polish agriculture industry, and also considering the issue of environmental degradation, primarily to the soil and air. Modern agriculture is characterized by extensive human interference in plant genetics, broad introduction of chemicalization, and maximum reduction in manual labour in cultivation by mechanization and automation. More efficient modern technologies allow the connection of working units to tractors with greater and greater widths, power and weight. These types of tractor and working units are characterized by higher yields, but on the other hand may damage soil structure [Bekker 1977, Błaszkiewicz 1995, Buliński 2000, Dawidowski 2000]. In these conditions of a global crisis, continuing increases in the prices of the means of production (e.g. fertilizers, liquid fuels, etc.) and the constant level of purchase prices of products are decreasing the profitability of agricultural production. Financial solvency of farms can be maintained through a reduction in production costs, for example through the elimination of various tillage operations, and consequently lower energy consumption. This simplification in tillage has been discussed for a dozen years or so. Test have been performed to examine the assumptions that the simplified method is competitive when compared to traditional methods of tillage, and that it will gradually displace them and result in the introduction of new alternative solutions. The results of these studies indicate that in addition to a reduction in the time of a tractor's work compared with traditional methods, a decrease in energy consumption and labourconsumption can also be obtained [Elster 1991]. The simplified method may also lead to a reduction in emissions and better use of the tractor's engine, in compliance with the recommended agronomic time 90 limits, which is important especially in large scale farms. Simplified tillage is gaining more and more supporters because of the minimal input of energy. Traditional tillage is the most energy-intensive; it can be responsible for up to 40% of the total energy consumption. A large number of tractor runs (5-15) causes the compaction of up to 200% of the cultivation area. The high energy consumption compared to simplified tillage was for example shown by Heyland et al. [1997] and Roszkowski [1980]. Ploughing by only 0.01 m deeper increased fuel consumption by about 0.5 dm3/ha [Gonet 1991]. Several experiments confirm that the introduction of simplifications does not reduce the yield of maize and oats. Simplified tillage also has several significant drawbacks. These include the adverse accumulation of phosphorus and potassium compounds, and decreased respiration capacity and pH of the soil [Pabin et al. 2000]. Additionally, some researchers observed no differences in yield between the traditional and simplified methods, for example Biskupski et al. [2000] who used a rigid tine cultivator as the basic tool for tillage for spring barley and peas on medium and heavy soils. Simplified methods even resulted in lower yields for winter triticale. The observed advantage of the simplified method was lower fuel consumption during tillage for spring barley and broad bean. Simplified cultivation technology is only possible with the use of specialized machines which are offered in a varied range and often with very different construction solutions. The aim of this paper is to examine their operating resistance, not previously discussed in specialist literature. AIM OF THE STUDY The aim of this study was to analyze the operating resistance of selected working units used in simplified tillage. The study was conducted with a rigid tine cultivator, a disc harrow and a seed drill for direct sowing. The operating depths and speeds of the units were measured to determine their effect on resistance. MATERIAL The study was carried out with three units used in simplified tillage technologies. The first unit was a rigid tine cultivator used for no-till loosening of the soil immediately after harvest or after the initial cultivation, performing post-harvest or pre-sowing cultivation. The analyzed cultivator is manufactured by the Czech company Farmet. Technical and operational parameters of the cultivator are presented in Table 1. Table 1. Technical specifications of the Farmet cultivator Parameters Value Unit Number of tines 7 [pcs] operating width 3 [m] operating depth 0.08-0.15 [m] minimum power requirement 100/136 [kW/KM] surface efficiency 2.4-3.6 [ha*h-1] operating speed 2.2-3.3 [m*s-1] weight 1140 [kg] 91 Another unit analyzed was the disc harrow DC Classic 4500 by Czech company Strom. Technical and operational parameters of the unit are summarized in Table 2. This unit is equipped with discs on a central frame and operating sections arranged in the shape of the letter "X". The unit has a rear bar which prevents the formation of ruts in the field. Operating angles are controlled mechanically with pins. Lateral, vertical and angular adjustment of dispersion zones are available. Its side discs are smaller in diameter, 0.56 m at the first section and 0.61 m at the second section. Solid construction, large weight of the unit, and the weight on individual discs provides excellent soil penetration even at low moisture levels and in soils for which plowing is not recommended. Table 2. Technical specifications of the disc harrow Dowlands Classic DC 4500 manufactured by Strom Parameters Value Unit operating width 4.5 [m] operating depth 0-0.15 [m] Max. weight per disc 121 [kg] Number of discs 40 [pcs] Total weight 4460 [kg] Power requirement 100/136 [kW/KM] The cutting of the stubble and the depth of soil loosening can easily be adjusted by setting the angle of the operating sections, in accordance to soil conditions. The third examined machine is a versatile tilling-sowing Ultima unit manufactured by Köckerling. The unit can be used on the arable layer, mulch sowing or direct sowing. The operating elements of the unit are spring tine hoes with a high load rating (up to 200 kg), capable of band-sowing and with hydraulically adjustable operating depth and shock absorption. The unit is also equipped with an STS shaft with narrow self-cleaning rings that fill with soil. Depth of sowing is regulated by individual wheels, leading each hoe independently, ensuring a good fit to the irregularities of surfaces. The unit also has spring levellers before the STS shaft, and a harrow as the last element of the unit. Technical and operational parameters of the unit are summarized in Table 3 Table 3. Technical specifications of the ULTIMA unit by Köckerling Parameters Value Unit operating width 4 [m] weight of sowing submin. 200 [kg] units number of sowing sub23 [pcs] units minimum power 125/170 [kW/KM] requirement STS shaft ø 0.53 [m] weight 5500 [kg] harrow ø 0.013 [m] 92 METHODS Field studies were conducted in August 2009 in Chwałów, Lower Silesia, Wrocław County, Mietków commune, on a private farm. Measurements were made on the stubble after the harvest of winter oilseed rape. The tests were performed on soil classified as a very good rye complex, valuation class IIIa. Granulometric composition of the soil was determined in accordance with PN-R-04032; soil was classified as light clay. Soil moisture (12.5%) was determined in accordance with standard ISO11461:2001. Physical and mechanical properties of the soil were described as maximum shear stress and soil compactness. Maximum shear stress was determined by an Eijkelkamp field shear vane borer. Measurements were made at the following soil profiles: 0.03-0.07m, 0.08-0.12m, and 0.13-0.17m. Soil compaction was measured using a conical penetrator with electronic registration of penetration resistance and depth. The cone used had a base area 0.0001 m2 and 60° side angle. The rate of penetration was 0.03 m*s-1 in the range of depth 0-0.2m. The simultaneous recording of penetration resistance and depth was performed by a CompactDAQ station manufactured by National Instruments, connected to a computer recording analog signals at 200 Hz sampling rate on each channel. Measurements of the working resistance of the examined units were performed using two tractors: basic (CASE MX170) and auxiliary (Zetor 16145). Tractors were coupled using a specially designed tow bar, in which an electronic force transducer was mounted (Tecsis, measurement range 0-100 kN). Diagram with measurements is shown in Figure 1. Figure 1. Method of measuring the resistance of an operating unit: 1- tested unit, 2auxiliary tractor, 3-tow bar with a force transducer, 4 - basic tractor Analog signals from the force transducer were recorded in a fashion similar to the measurements of soil compactness. During the test, three speeds were selected: 1.7, 2.2 and 2.8 m*s-1, and three values of operating depth: 0.05, 0.1 and 0.15 m. The only exception was the ULTIMA unit, for which research was carried out only for 93 a depth of 0.05m. The rolling resistance of the Zetor 16145 tractor was also measured and subtracted from the obtained results. RESULTS AND ANALYSIS The compaction of soil describes the binding of individual soil particles with one another, which results in soil resistance when loosened. This parameter also directly affects the resistance of operating machines. Figure 4 shows the relative compaction of the experimental plot. The diagram presented in Figure 2 shows that the compaction of the 0-0.04 m layer is fixed at around 0.06 MPa. at the depth range of 0.04-0.14m compaction increased proportionally with depth from 0.61 MPa to 2.51 MPa. The measured values of compaction therefore suggest that increasing the operating depth will manifest in increased operating resistance to the analyzed units. Table 4 summarizes values of maximum shear stress for established soil profiles. soil compactness [MPa] 3 2,5 2 1,5 1 0,5 0,18-0,2 0,16-0,18 0,14-0,16 0,12-0,14 0,1-0,12 0,08-0,1 0,06-0,08 0,04-0,06 0,2-0,04 0-0,02 0 depth of penetration [m] Figure 2. The values of relative compaction for each soil depth range Table 4. Summary of maximum shear stress 0.03-0.07 m 0.08-0.12 m maximum shear stress 19.5 41.5 [kPa] 0.13-0.17 m 43.5 The data presented in Table 4 shows that stress measured at 0.08-0.12m and 0.13-0.17m is twice that at 0.03-0.07m. Figure 3 shows resistance of the Farmet cultivator at the studied speeds and depths, indicating that increasing the depth of work for established speeds always increases the operating resistance of this unit. The largest increase in resistance values, by 5280 N (61%), was observed for a depth increase from 0.05 to 0.1m at a speed of 1.7 m/s. The smallest increase in resistance, by 2370 N (15%), occurred for an increase in depth from 0.1 to 0.15 m at a speed of 2.2 m/s. An increase in the operating speed of the unit also caused an increase in resistance at the established 94 operating depths. In this respect, the largest increase in resistance, by 2040 N (23%), was observed for the speed increase from 1.7 to 2.2 m/s and at a depth of 0.05m. The smallest increase in the value of resistance was 30 N (0.2%), which occurred for the increase in speed from 2.2 to 2.8 m/s at a depth of 0.1 m. Figure 4 presents the resistance on the DC 4500 disc harrow at the established speeds and depths. working resistance [N] 25000 20000 15000 0,05 m 0,1 m 0,15 m 10000 5000 0 1,7 2,2 2,8 speed of the unit [m/s] Figure 3. Draft resistance on the Farmet cultivator at the established speeds and depths 30000 working resistance [N] 25000 20000 0,05 m 0,1 m 15000 0,15 m 10000 5000 0 1,7 2,2 2,8 speed of the unit [m/s] Figure 4. Resistance on the disc harrow DC 4500 at the established speeds and depths The diagram shown in Figure 4 indicates that increasing the operating depth at the established speeds always increases the resistance of the operating unit. The largest increase in resistance, by 6060 N (42%), was measured at the increase of depth from 0.1 to 0.15m at a speed of 1.7 m/s. The smallest increase in resistance, by 3650 N (26%), occurred for the increase in depth from 0.05 to 0.1 m at a speed of 2.2 m/s. An increase in speed of the unit also caused an increase in resistance at the established operating depths. The greatest increase in resistance, by 1670 N (14%), was measured for the speed increase from 1.7 to 2.2 m/s at a depth of 0.05m. The 95 smallest increase in the value of resistance, by 420 N (3%), was observed for the increase in speed from 2.2 to 2.8 m/s at a depth of 0.05m. Figure 5 shows resistance on the ULTIMA unit at the established speeds at 0.05m. working resistance [N] 25000 20000 15000 10000 5000 0 1,7 2,2 2,8 speed of the unit [m/s] Figure 5. Resistance of the ULTIMA unit at the established speeds at 0.05m The diagram shown in Figure 5 shows that at a depth of 0.05m, resistance at the established speeds did not differ significantly, which was confirmed by a statistical test. Increasing speed from 1.7 to 2.2 m/s increased resistance by 1050 N (6%). Due to the fact that the analyzed units had various operating widths, the measured resistance values were divided by the width of the operating machines and then compared with one another. Figure 6 shows the resistance values per meter of the analyzed units, at an operating depth of 0.05 m. 6000 opór roboczy [N] 5000 4000 3000 2000 1000 0 1,7 2,2 2,8 prędkość agregatu [m/s] kultywator Farmet brona talerzowa agregat Ultima Figure 6. Resistance of the analyzed units per meter of operating width and 0.05 m depth 96 The diagram in Figure 6 shows that at the established speeds, the highest resistance per meter of operating width was measured for the Ultima unit and the smallest for the disc harrow. At 1.7 m/s, differences in the resistance between these units was 1910N (71% increase). For 2.2 and 2.8 m/s, the difference was about 1700 N (55% increase). However, given that the Ultima unit can perform cultivation of soil and sowing in one drive, the related energy saving is probably significant. Comparing the resistance per meter between the Farmet cultivator and the disc harrow at 1.7 m/s, the difference was 8%. For 2.2 and 2.8 m/s, the resistance difference was about 17%. Thus, the use of the Fermet cultivator entails higher energy expenditure than the disc harrow. In order to determine the effect of independent variables on the dependent variables, the obtained results were subjected to multivariate analysis of variance. Calculations were performed using Statistica 8.0. Results of the analysis are summarized in Table 5. Table 5. Results of multivariate analysis of variance type of unit operating speed resistance 0.37 0.0493* operating depth 0.0002* * has a significant effect at α=0.05 The analysis shows that the operating resistance is significantly influenced by the speed and depth of work, at a significance level α = 0.05. The type of unit had no effect on the operating resistance. CONCLUSIONS 1. 2. 3. 4. For the analyzed units, increasing operating speed and depth of work increased the working resistance, as confirmed by statistical analysis. An increase in the speed of the Farmet unit from 1.7 to 2.2 m/s, at 0.05m, caused a significant increase in resistance (by 23%). An increase in speed from 2.2 to 2.8 m/s had no significant effect on resistance. In the case of the disc harrow, the greatest difference (by 14%) was measured for the speed increase from 1.7 to 2.2 m/s at a depth of 0.05 m. In contrast, the Ultima showed no significant differences in relation to differences in resistance for operating speed. An increase in operating depth of the Farmet unit from 0.05 to 0.1m, at a speed of 1.7m/s, caused the greatest increase in its resistance by 60%. For the disc harrow, the greatest increase in resistance (increase by 41%) was observed for an increase in depth from 0.1 to 0.15m at a speed of 1.7 m/s. Resistance per meter of operating width, at a depth of 0.05 m, was the highest for Ultima and the smallest for the disc harrow. REFERENCES 1. Bekker M.G. 1977: Evaluation of tires in road locomotion. Zesz. Probl. Post. Nauk Roln. 183, 125-131. 2. Biskupski A., Kaus A., Włodek S., Pabin J. 2000: Wpływ uproszczeń uprawy roli na plonowanie roślin, czasochłonność uprawy i zużycie paliwa. Inżynieria Rolnicza 6, 85-90. 97 3. Błaszkiewicz Z. 1995: ,Badania wpływu rolniczych opon prowadzących na zagęszczenie i właściwości wodno-powietrzne gleby lekkiej. Probl. Inż. Roln. 4, 6, 5-13. 4. Buliński J. 2000: Wpływ prędkości ruchu i rodzaju agregatu ciągnikowego na zagęszczanie gleby kołami. Inżynieria Rolnicza 6, 111-117. 5. Dawidowski J.B., 2000. Zagęszczanie gleby kołami pojazdów rolniczych. Inżynieria Rolnicza 6, 23-30. 6. Elster M. 1991: Conservation of soil and water by using a new tillage system for row crops. Crovet crop for clean water. Soil and Water Conservation Society, Jackson, Tennessee. 7. Gonet Z. 1991: Metoda i niektóre wyniki badań energochłonności systemów uprawy roli. Fragmenta Agronomica 2, 7-18. 8. Heyland K., Solansky S., 1997. Berichte über Landwirtschaft. Neue Folge 195, s. 15-30. 9. Michałek R. 1997: Inżynieria rolnicza w procesie transformacji polskiego rolnictwa do Unii Europejskiej. Inżynieria Rolnicza 1, 13-20. 10. Pabin J., Włodek S., Biskupski A., Runowska-Hryńczuk B., Kaus A., 2000. Ocena właściwości fizycznych gleby i plonowania roślin przy stosowaniu uproszczeń uprawowych. Inżynieria Rolnicza 6, 213-219. 11. Roszkowski A. 1980: Ocena efektywności energetycznej i ekonomicznej produkcji kukurydzy i jęczmienia. IBMER, Warszawa. 98 Chapter 9 THE INFLUENCE OF FERTIGATION ON AGRO-PHYSICAL PROPERTIES OF ARABLE PLANTS Ján JOBBÁGY, Ján SIMONÍK, Marek KLIMKIEWICZ INTRODUCTION Fertigation was compared to broadcast application of solid NPK fertilizer with cabbage, carrot and onion. In the broadcast application, P and K were given as a single application in spring and N was split according to the existing recommendations. Growth and nutrient uptake were monitored by monthly samplings. In 1999, natural rainfall was low, and irrigation was applied according to the tensiometer measurements. Cabbage yields averaged to over 90 t ha-1 in both years. Carrot yielded according to the samplings close to 90 t ha-1. Onion yielded 4050 t ha-1 [Salo et all, 2002]. In the years of 2004-2006 research on influence of surface and subsurface drip irrigation on yield and quality of carrot grown on ridges and on the flat ground was investigated. Nitrogen fertilizers (100 kg.ha-1) were applied in two rates. The first rate was preplant applied; the second was used by fertigation. Surface and subsurface drip irrigation significantly increased the yield of carrot. Drip irrigation at both cultivation methods increased pH and decreased the salt concentration of the soil [Dyśko, Kaniszewski, 2007]. The optimal nutrition of plants suppose the supply of root zone with nutrients especially during their intensive uptake because the deficit of nutrients or their temporal deficiency, may cause decreasing of expected crop. Therefore, the technologies of fertilization begin ever more asserting with focus to consistent feeding of nutrients to root zone of arable crops, during vegetation. Its efficiency increase with it, which show with stabilization and crops increasing. The possibility of water using as a carry medium of nutrients, so called fertigation is the ideal solution of this problem (AGREF, 2007). The fertigation and feeding of nutrients and fertilizers by irrigation water utilizing the basis of micro-irrigations, especially of drop irrigation. It is precise distribution of irrigation water; it is in precise amount and time, directly to localized root zone. Distribute also the dissolved nutrients and fertilizers at the same degree of uniformity distribution of irrigation water [Hríbik, 2009]. 99 Effects are noteworthy: • highest degree of utilization reflect in economical and ecological results, • better communication between need and prompt accessibility of nutrients, obtained with possibilities of divided multiple application of nutrients, • multiple synergy effect of current irrigation and supplies of nutrients to amount and quality of production [Hríbik, 2009]. The aim of chapter is to research the effect of fertigation to properties of selected plant. MATERIAL AND METHODS The irrigation with fertigation using was applied on allocated field. The grown plant was carrot. Part of field was without fertigation application. The basic fertilization - 500 kg.ha-1 fertilizer NPK was used in the whole field. The irrigation rate 25 mm was apply on the field, with parallel application of fluid saltpetre calcic, amount 40 to 45 kg.ha-1. The same irrigation rate was applied on the second field without fertilization. The carrot is cultivating on long planar fields, whereby steep land is very slow or zero. The grown species of carrot was MAESTRO (Nederland) with recommended length of vegetation term 120 days. The cultivation works were not realized during vegetation, done was one chemical sprinkling with preparation AFALON, ration 2 dm3.ha-1. The carrot is vegetable with middle requests for soil water. Its moisture need during vegetation is 520-620 mm. Its successful growing request to fill up the natural rainfalls with irrigation water in range 120-180 mm, at average year, in our condition. The belt irrigator CIPA INTERNATIONAL VILLA MINOZZO ITALIA, model 600GX completed with fertilize equipment was used (Fig. 1, 2). Figure 1. Mobile irrigator VILLA MINOZZO 600GX ITALY The fertilize equipment APH use the power water, which by distributing valve, work cylinders and force arm start hydraulic cylinders. The membrane pump sucks and injects fertilizer solution to the pipes of irrigation equipment. The amount of transported fertilizer is adjustable in range 1 to 400 dm3.h-1. The equability of fertilizer distribution depends on uniformity of irrigation water distribution on side of irrigator. 100 Conic Ku = φh − φk , l (1) where: φ h – diameter of root head, m, φk – diameter of root end, m, l – root length, m Coefficient of convergence (λ1) is expressed with ratio of diameters for head ( φ h ) and bottom part ( φk ) of root. Coefficient of slenderness (λ) is expressed with ratio of length ( l ) and with head diameter ( φ h ) of root. Figure 2. APH Fertilization device RESULTS AND DISCUSSION For determination of advantages and disadvantages of fertigation there was needed to take samples from row length 1.42 m conform for area 1 m2, from parcel with fertilization and than thereafter of etalon. The following operations were performed: • weighing of all roots, kg, • separation of toppings, • weighing of particular roots, kg, • determination of length of particular roots, m, • determination of head and bottom diameters of roots, m. The 102 pieces of carrots were gathered on point of supply (with fertilization). The length of roots was ranged 71-271 mm with value of variation coefficient 23.20%. The average value of roots length was 169.06 mm. The measured values were ranged 10 to 241.30 g, for evaluation of single roots weight. The variation coefficient value was 60.10% for median 78.51 g. Determination of conic was got out from length and diameters measuring. the values ranged froom 0.021 to 0.135. The average value was 0.069 with value of variation coefficient 32.19%. The average length varied in dependence on: kinds, penetrometric resistance of soil, soil structure, nutrient uptake during vegetation, locality of growing and the method of growing, and using of irrigation or without its using –using of fertigation or without its using. Every individual needs for its growth not only nutrients, water, sunlight but also sufficient place for supple growth. 101 25 120,00% 100,00% Frequency, pc 20 80,00% 15 60,00% 10 40,00% 5 20,00% 0 ,00% 90,80 110,61 130,41 150,21 170,01 189,82 209,62 229,42 249,23 269,03 288,83 Další Roots length, mm Početnosť Freq. Kumul. % Cumul. % Frequency, pc Figure 3. Root lengths – with fertilizing 35 120,00% 30 100,00% 25 80,00% 20 60,00% 15 40,00% 10 20,00% 5 0 ,00% 32,90 55,80 78,71 101,61 124,51 147,41 170,31 193,22 216,12 239,02 261,92 Roots weight, g Další Početnosť Freq. Kumul. % Cumul. % Frequency, pc Figure 4 Root weights – with fertilizing 30 120,00% 25 100,00% 20 80,00% 15 60,00% 10 40,00% 5 20,00% 0 ,00% 0,03 0,04 0,05 0,07 0,08 0,09 0,10 0,11 0,12 Conical, m m Figure 5. Conical – without fertilizing 102 0,13 0,14 Další Freq. Početnosť Cumul. Kumul. % % Table 1. Basic statistics of measurements parameter (with fertilizing) Parameter Length, mm Weight, g Conical, Mean 169,06 78,51 0,069 Median 171,00 69,60 0,066 Modus 178,00 26,20 0,051 Standard deviation 39,23 47,18 0,022 Minimum 71,00 10,00 0,021 Maximum 271,00 241,30 0,135 Number of samples 102 102 102 Coefficient of 23,20 60,10 32,19 variability The most important indicator of production is biological crop. The carrots samples were separated of toppings and after than were weighted. It results from weight measuring that for assigned measuring was biological crop of carrots 80.08 t.ha-1. WITHOUT FERTIGATION Gathered was from 1 m2 73 pc of carrots by sampling on researched parcel without ferigation. The odds are that from results of pieces number, that the number decreased about 29 pc. The measured lengths were ranged from 40 to 256 mm. The average value was 174.74 mm with value of variation coefficient 26.47%. For used fertigation was its value lower. The results of single roots weight measuring were ranged by evaluation of crop at interval from 5.5 to 350.2 g. The average value was 97.69 g with variation coefficient value 65.21%. The value of variation coefficient returns to be higher. 120,00% 25 100,00% Frequency, pc 20 80,00% 15 60,00% 10 40,00% 5 20,00% 0,00% 0 65,28 90,56 115,84 141,12 166,40 191,69 216,97 242,25 Root length, mm 267,53 Další Početnosť Freq. Kumul. Cumul. %% Figure 6. Root lengths – without fertilizing Also crop results from measuring of the roots weight, which was 71.31 t.ha-1. The average value of conic was 0.075 with value of variation coefficient 49.76%. Its values ranged at interval 0.007 to 0.227. We will find by comparison of results with 103 results of measuring at fertigation that the average value of conic was lower for fertigation. From measured values of the root crop growth results that the average biological crop varies in dependence on kinds, localities in which is grown and the soil conditions. Results from summary of growths characteristic that biological crop is for ours grower conditions good. In the year 2000 was in Kováčovce the average biological crop of carrots Cartágo 29.6 t.ha-1 (min. 24.6 and max. 41.8 t.ha-1), in the year 2001 on the fields PD Bušince was the average biological crop 43 t.ha-1 (min.38 and max.55 t.ha-1). The soil on the SELEKT Bučany [2001] was processed after ploughing with the reploughing only to knolls at autumn and than at spring before seeding was regenerated shape of knolls with the reploughing. The average biological crop of carrot was 64.8 t.ha-1 (min. 38 and max. 82 t.ha-1) [Poničan and kol., 2004]. In this chapter, we were deal with determination of coefficients of the slenderness (λ) and the convergence (λ1) of the toots, too. The coefficient of slenderness (λ) presents the relative relation of the average value of the roots length (ℓ) to the root head diameter ( φh ). It is the relative value which higher value presents the slimmer shape of root and on the contrary. The coefficient of convergence (λ1) presents the relative relation of the root head diameter ( φh ) to its bottom root diameter ( φk ). It is the relative value, which value near to one present the cylindrical shape of root and for values higher as one are conical shapes of roots. The measured and calculated values were introduced in Tables 4 and 5. 120,00% 25 100,00% Frequency, pc 30 20 80,00% 15 60,00% 10 40,00% 5 20,00% 0 0,00% 45,84 86,19 126,53 166,88 207,22 247,56 287,91 328,25 Root weights, g Figure 7. Root weights – without fertilizing 104 368,60 Další Freq. Početnosť Cumul. Kumul.% % 40 120,00% Frequency, pc 35 100,00% 30 80,00% 25 20 60,00% 15 40,00% 10 20,00% 5 0 ,00% 0,03 0,06 0,08 0,11 0,14 0,16 0,19 0,21 0,24 Další Početnosť Freq. Conical, - Kumul.%% Cumul. Figure 8. Conical – without fertilizing Table 3. Basic statistics of measurements parameter (without fertilizing) Parameter Length, mm Weight, g Conical, - (4) Mean 174,74 97,68 0,075 Median 176,00 85,30 0,067 Modus 176,00 103,40 #N/A Standard deviation 46,25 63,70 0,037 Minimum 40 5,50 0,007 Maximum 256 350,20 0,227 Number of sample 73 73 73 Coefficient of 26,47 65,21 49,76 variability From obtained results it is possible to state that for fertigation using was the value of slenderness coefficient higher – that is slimness shape of root. The values for evaluation of slenderness coefficient vary few, is possible to state that for fertilization approach to cylindrical shape of root shape. This shape is advantageous from view point of roots manipulation by cleaning, sorting and packing. Table 4. Root slimness Irrigation methods Irrigation with fertilizing Irrigation without fertilizing Measuring data, mm Average of root length Average of root head Average of root length Average of root head 105 169,06 28,03 174,4 30,22 Slenderness coefficient (λ) 6,03 5,77 Table 5. Root convergence Irrigation methods Irrigation with fertilizing Irrigation without fertilizing Measuring data, mm Average of root head Average of root end Average of root head Average of root end 28,03 16,59 30,22 17,65 Slenderness coefficient (λ1) 1,69 1,71 The slimmest shape of the root had a carrot growned on the fields in Bušince, year 2001 (kind Sugarsnax). The roots of this kind have the slenderness coefficient (λ) 8.67. The roots of carrot (kind Bolero) growned in Bučany (year 2001) have at least slender shape, which value of slenderness λ = 5.03 [Poničan a kol., 2004]. From view point of the root shape (cylindrical) approach species Bolero grown on the fields in Bučany (year 2001), where the value of root shaping λ=1.25 [Poničan a kol., 2004]. From view point of evaluation of agro-physical properties of roots definite with slenderness and convergence of the root results that upon this properties influence especially the species properties and also method of over seeding preparation of soil [Poničan a kol., 2004]. Salo et. all [2002] researched the efficiency of fertigation applied on field for growing of cabbage, carrot and onion. CONCLUSION The aim of attempt was to determinate the advantages of fertigation. The grown agricultural plant was carrot – species Maestro. It results that by fertigation is increasing of crop. The increase of crop was 8.77 t.ha-1 (80.08 – 71.31). It is visible that for fertigation using the input costing increasing (fertilizes price, fertilizing equipment, installation etc.). The results of carrot crop out of fertigation using ranged 50 - 60 t.ha-1. It is to consider by every farmer if sufficed the accruals of crops by growing with fertigation to covering of increased costs. The average crops accompanied by company Paulis – Gardening services are without using of fertigation 53 t.ha-1 and with using fertigation 81 t.ha-1. The increase of crop is 28 t.ha-1. REFERENCES 1. AGREF KOMÁRNO, 2007: Rukopis prednášky. 2. Dyśko J. – Kaniszewski S. 2007: Effect of drip irrigation, N-fertigation and cultivation methods on the yield and quality of carrot. In: Vegetable Crops Research Bulletin, Poland, 2007 vol. 67, 25-33, DOI: 10.2478/v10032-0070027-3 3. Hríbik, J. 2009: Synergické účinky zavlažovania špeciálnych plodín. Dostupné na intrnete: http://www.zahradaweb.cz/projekt/clanek.asp?pid=2&cid=2014 4. Poničan, J. – Jech, J. – Angelovič, M. – Žitňák, M. 2004: Vplyv predsejbovej prípravy pôdy na jej vlastnosti ovplyvňujúce pestovanie koreňovej zeleniny v podmienkach precízneho poľnohospodárstva. In: Acta technologica agriculturae, č.1 , ročník 7, 2004, s.7-10, ISSN 1335-2555 106 5. 6. Poničan, J. – Jech, J. – Angelovič, M. – Žitňák, M. 2004: Vplyv vlastností pôdy na agrofyzikálne vlastnosti koreňovej zeleniny. In: Acta technologica agriculturae, č.4 , ročník 7, 2004, s.94-98, ISSN 1335-2555 Salo, T., Suojala, T. And Kallela, M. 2002: The effect of fertigation on yield and nutriet uptake of cabbage, carrot and onion. Acta Hort. (ISHS) 571:235-241, http://www.actahort.org/books/571/571_28.htm 107 Chapter 10 ECOLOGICAL ASPECTS OF ENERGETIC PLANTS HARVESTING Aleksander LISOWSKI, Adam STRUŻYK, Jacek KLONOWSKI, Tomasz NOWAKOWSKI, Jarosław CHLEBOWSKI, Michał SYPUŁA, Stanisław GACH INTRODUCTION Poland’s obligations to reduce air pollution can be filled up by using the appropriate amount of biomass to decrease emission of greenhouse gasses. It will be possible at considerable increase in the area of long-term plantations of energetic plants. Intensive agricultural production technologies could contribute to environmental degradation. Therefore, great attention should be directed to any environmentally friendly measures, including those related to reasonable cost, new techniques and harvesting of these plants. Harvest technologies of the plants for energy purposes have passed through different stages of the evolutionary development. In the initial period harvesting of energetic plants applied the techniques used in agriculture or forestry, depending on plant species. Successively with acquired experience working units or the complete machines were modified, and a mature stage of development of this market segment offered specialized machines with modern solutions of working and mobile systems. The current market offers the machines that do not provide further opportunities to develop and improve techniques and technology of energetic plants harvesting. Although the potential use of modern harvest technology is high, in practice, there are still used very different techniques of mowing and processing of biomass. It concerns especially Polish conditions, where the cultivation of energetic plants is very dispersed and areas of single plantations are extremely diversified and range from few ares up to few hundred hectares. Depending on the accepted harvest technology there are used different machines, which can be equipped with units with installed mechanisms, of specific functional properties, adapted for the plant species and the harvest cycle. TECHNIQUES OF HARVESTING ENERGETIC PLANTS Analysing the environmental risks associated with plants harvest technologies for energy purpose to reduce the negative impact on the environment it is possible to 108 allocate technical risks involved with applying tools and agricultural machines and technological hazards associated with the use of particular harvesting methods. Choice of plant cutting elements is a critical decision in the phase of the machines design for the harvest of energetic plants. It is being taken back especially to bushes and trees. While cutting the willow shoots, a working unit has the task of the simultaneous cutting of few shoots of different diameters. All shoots, growing from the snags, strive for vertical settings, also the ones which are offshoots. Cutting these offshoots requires the greater energy than vertical shoots, since they have the greater horizontal cutting surface. Diversifying cross sections of shoots among rows of plants and along rows makes harvest difficult. It is easier to cut smaller shoots, but it is possible to scroll them by moving parts of the cutting mechanism. It would apply the element taking stems down right above the cutting plain. In order to reduce the negative effect for growing again of new shoots they should be cut in the first year after seating, at about 50 mm level above the soil surface [Szczukowski et al., 2006], and in consecutive 100-150 mm. The result is a better separation of shoots, minimized damage to snag, reduced propagation of diseases and improved yields [Stuart, 1994; Wilkinson et al., 2007]. Cutting mechanisms, which are installed on machines to harvest of energetic plants can be divided into unsupported and supported ones. In units cutting shrubs and trees there can be used disc saws, chain saws, disc knife, plates and flail knives, which is one of the unsupported units. Support cutting is used in latter units and rotational knives are common in adapters of tractor forage harvesters to harvest maize for silage. Choice of harvest technology of energetic plants depends on many factors. Whole stalks can be used on the short, niche market and at schools where they should be always in the excellent condition without breakdown barks. Whole plants can also be harvested in two stages. This allows the drive units to use less power than a single-stage during the harvest [Lechasseur and Savoie, 2005]. As suggested by Hilton et al. [2005] smaller machines should be more useful in technologies using natural drying. The harvest of plants in the grounded form is being recommended on large plantations and where there is a well-developed market, with the possibility of the biomass use for co-burning in the industrial coal combustion [Gera et al., 2002]. The plant material ground down in the forage harvester working unit can constitute the raw material for further processing in the production of briquettes or pellets. It is necessary to grind down further the chips or chaff, obtained from the forage harvester [Bitra et al., 2008]. The particles are too large to produce durable briquettes or pellets. The harvest of energetic plants takes place in late autumn or winter, during the dormant vegetation. Unfortunately, during this period in most regions there remains bad weather or high soil moisture in autumn or early spring, making it marshy, or is too much snow, which not only covers the surface fields, but they among clumps of plants or on stems and leaves of grass. This is not the only characteristic of our Central European area. Even worse conditions occur in Scandinavian countries, Great Britain or Canada [Spinelli, 2001]. Using caterpillar suspension systems or wide tires is necessary in such conditions. Depending on the applied technology and the cutting mechanism, the snow can also be taken by biomass and find its way to the cutting unit, and further with chips to the load-carrier of transport means [Pellerin et al., 1999; Boyd et al., 2000]. Snow mixed with chips increases their moisture, and consequently leads to more rapid digestion of biomass and its loss [Boyd et al., 109 2000]. The date of harvest and its periodicity depends on plant species and the raw material harvested. The selection of the harvest technology of energetic plants depends on their species. Quick-growing shrubs and trees, which can include shrubby willow, poplar and Acacia Robinia, it is possible to harvest one or two stages in the annual cycles or more in the multi [Lisowski, 2006; Szczukowski et al., 2006; Faber et al., 2009]. Shrubs such as Rosa multiflora and Acacia Robinia and perennials (Virginia mallow, Jerusalem artichoke) and multiannual (Miscanthus, switchgrass, spartina prairie, Cymbopogon Gerard, reed canary grass) it is possible also to harvest one or two stages, but only in a one-year cycle (Table 1). The one-stage harvest consists of simultaneous cutting plants and breakingup, mostly using a forage harvester, both tractor driven and self-propelled. The logical consequence is therefore that it is also harvest of plants in the form of chips (willow, poplar, shrubs) or chaff (grasses, perennials). A harvest in two-stage comprises two independent phases, which are performed by separate machines. In the first phase the plants are cut, and in the other they are broken-up. Highlighting these phases due to wrong forms of raw material or product that we receive. Between these phases may occur several operations, depending on the plant species and forms of a cut material. In the case of short rotation crops, in two-stages dominates a harvest as a whole plants and after drying of material, its grinding in stationary conditions. Grasses and perennials, after cutting, are collected by balers or pick-up trailers. Generally, the harvest of grasses and perennials plants can be done by machines commonly used to harvest green fodder, hay or straw. Willow in Polish climatic conditions is best harvested from mid-November to mid-March, but weather conditions do not always allow for termination of harvest in this period. At that time the plant material moisture content is around 45-55%, depending on the variety, region and weather conditions. In good weather and soil conditions and well fertilization, some good variety of annual short rotation plants are achieving yields 20 tDM·ha-1. Typically, the yield of these plants is between 812 tDM·ha-1. Harvest of willows in subsequent years can be done through various techniques. The appropriate cut-off already a mature shoot from snag is a very important element of its viability. Shoots should be cut at the height of 5-10 cm above the soil to have visible two and ultimately one dormant bud. Cutoff must be done in a typically sharp tool such as scissors and high-speed saw. Work of scissors secateurs is very inefficient and this technique can be used in particularly small plantations harvested in the one-year cycle [Dubas and Tomczyk, 2005]. With very spreading shoots an additional person is sometimes needed, apart from operator, to incline and receive of cut stems. On the basis of tests Kwaśniewski et al. [2006, 2008] found that when cutting with a chain saw, better performance (0,024 ha·h-1) is achieved by employment of two helpers, than one (0,016 ha·h-1). The work performance achieved under these conditions was significantly lower than in mechanized harvesting (0,7 ha·h-1), but the study of authors suggests that the time predisposed to the harvesting of plants is quite long. However, in respect to the risks associated with this technique of harvesting and for safety reasons it cannot be recommended. In addition, when planning a harvest one should take into account the possibility of adverse weather conditions, such as the autumn and spring sloppy, residual snow cover, which may affect the performance of manual harvesting. Real disposable time to harvest willow and other energetic plants can be much shorter than the 110 theoretically predisposed to mid-October to mid-March. With rising labor costs harvesting by hand will be displaced by mechanical harvesting. Application of forage harvesters or special machines, provides performance of 3-4 ha/day [Dreszer et al., 2003]. Table 1. Harvest technologies for energetic crops and machinery used Method of harvest Short rotation crops (poplar, Grasses (Miscanthus, willow) and shrubby (Rose switchgrass, spartina prairie, multiflora, Acacia Robinia) reed canary grass, Cymbopogon Gerard) and perennials (Virginia mallow, Jerusalem artichoke) Cycle: Mounted, pulled or selfMounted, pulled or selfone-year propelled forage harvesters propelled forage harvesters with adapter for maize harvest, with a adapter to maize non-row dependent head harvest, row or non-row Onepreferably (Kemper, Krone). dependent head. stage Cycle: Tractor or self-propelled forage harvesters with special 2-5-year adapter. Transport Tractor volume trailers or trucks Cutting; one Secateurs, combustion Rotary mowers (with the year cycle strimmers, tractor mowers with conditioner for grasses), circular saws, special cutting tractors driven or selfmachines and pilling, bundling propelled. or pressing. Cutting; 2-5- As for the one-year cycle of year cycle willow, plus combustion saw. Cutting; 5Poplar: forest machinery, harvesters group (≤ Ø15 cm) Two- 30-year harvesters (> Ø15 cm). stages cycle Bailing / Round balers, large-size balers, pick-up trailers. harvest Loading and Manual, front or grab loaders, Front or grab loaders for bales. unloading forwarders or skidders. Transport Tractor trailer or cars, trailers Tractor trailers or trucks, selfforest. loading trailers. Wood chippers, chippers Straw cutters, knife grinders. Shredding device. Source: authors’ elaboration Among the energetic plants willow ligneous can be harvested by using the most diverse technologies. One-year willow can be harvested using standard machines, which are used to harvest corn. If the harvest takes place in 1 -, 2 - or 3year cycles willows can be harvested in the form of whole stems or broken-up by forage harvesters equipped with a special adapter. With longer harvest rotation cycles, plant shoots are thicker and have more uniform quality wood with less content and thin bark of young plant parts. Harvest of plants with larger diameter shoots, however, requires the use of machines more durable and energy units more powerful. In the case of harvesting the whole plants there are also needed transport 111 means with longer load-carriers and more schedule a larger width of returns on plantation fields. According to Scandinavian experience harvest of willows in 3 to 5year-old cycles is most justified economically [Mola-Yudego and Pelkonen, 2008], and poplar and 5-7 years (even 20-30) [Molas, 2008]. In the third year willow shoots reach the height of 6-8 m and a diameter of 80-100 mm. Willow tree with a diameter greater than 100 mm should be harvested using techniques used in forestry. In the three-year harvest cycle, yield can be very high and ranges 60-120 t·ha-1 of wet material (with an average moisture content around 46%). One-year willow shoots can also be harvested wholly in bulk or in bundles or bales, using a special round balers equipped with the cutting disc and the beater breaking-up units [Lavoie et al., 2008] or only beater cut-breaking-up unit [Schroeder et al., 2008]. With a harvest of one-stage-related disadvantages: a very high initial cost and the resulting high cost of the purchase price depreciation, need to start and run plantations in accordance with the requirements of combine harvesting, the need to establish large integrated plantations, which may reduce the transport costs of combine harvester on long-distance, chipping out the possibility of wet weight of the storage environment, hence the extracted biomass must be burned immediately or subjected to drying, which considerably increases the price per unit of energy obtained [Pasyniuk, 2007]. The use of heavy machinery poses a significant threat to soil compacting, which contributes to the destruction of the structure and subsequent soil erosion. Needs to be tackled by using tracked systems or driving low-pressure tires. Past experience and the specifics of Polish agriculture indicate that established and future plantations are scattered and range in size from a few to 20 hectares. This implies the implementation of the machines, which will be available to owners of small plantations, allowing for timely and proper carrying out of all quality agrotechnical operations, while a relatively low utilization rate does not affect significantly the costs of sustainable production of solid biofuels. Conclusions from the studies carried out so far are varied and it is difficult to clearly identify and to recommend the use of a specific harvest technology. Similarly, the harvest of maize and other plants recommended for different technologies vary depending on plantation size, the existing machines in the farm, holding of another production, possession of transport means and other technical facilities. In general, the one-stage technology is recommended for large plantations, and two-stage for the small ones. The experience of countries that grow energetic willow (Australia, Brazil, Denmark, Finland, Ireland, Canada, Germany, Norway, Sweden, USA, United Kingdom, Italy) are varied and recommendations with respect to harvesting technologies of energetic plants depend on many factors, which should be considered in Polish climatic conditions. In harvesting of grass and perennial plants there can be proposed the same techniques as for green and low- and high-stems plants (standing or strawy materials from swaths) (Table 1). Cutting units of harvesting machines should be set to the lowest cutting height, in order to avoid losses, but not too low, to avoid picking-up soil or polluting strawy material. Some machines of Claas and Deutz-Fahr companies are specially adapted to cutting plants and their bindings. To increase the economic effects the plant cutting, bailing and logistics still must be developed. Loss of material during the harvesting of plants may reach even 50-60% (reed canary grass), due to low specific density, especially when the material is broken-up into chaff. Due to low specific density the transport of material of certain plants is economically justified only 112 on a small distance, less than 80 km. Rectangular bales are more adventageous than the round ones and more suitable for long-distance transport. WILLOW HARVESTING MACHINES Among the willow harvesting machines there are two basic types of solutions: cutting biomass with breaking-up into chips or harvesting the whole plants (Fig. 1). One-stage harvest may be performed using the modified machines which had been originally used for harvesting other plants, such as maize or sugar cane. Modification focuses on the adapter for cutting plants. Harvest of whole shoots in the annual cycles is performed with the use of machines such as reaper, bundler and a specially designed machine connected to a tractor. In the 90s of twentieth century, when cultivation of short rotation crops was started on a commercial basis, there was no suitable machines for the harvest. After the first attempts to use the existing machines, which were designed to harvest other plants, the work began on the modification of machines or prototypes that meet the user requirements. Scandinavian companies have been pioneers and also Denmark, the Netherlands, Great Britain, Italy, Germany, Australia and Canada. In 1993, the Swedish company Salix Maskiner AB, part of Salixphere made two prototypes of machines. One of them was a pulled machine - Bundler model (Fig. 1e), equipped with a chain saw and binding units. During cutting off the bundle by circular saw the tractor-machine aggregate has to stop, thereby reducing performance of harvest. The second machine was a chopper of Bender 1 mounted on the linkage of the tractor, equipped with a reversible system (Fig. 1d). The machine underwent further modifications, and currently available version is Bender 6WG. A characteristic feature of the machines offered by this company is cutting of shoots by a chain saw, carried out on four rollers. Application of chain saw reduces weight of machines and simplifies the transmission system. Positive feature of the chain saw is also the fact that during the harvest of willows in winter conditions with covering snow, the amount of its inclusion in the harvested biomass, compared to other cut mechanisms, is minimal. Critical remarks on the Bender machine presented the U.S. experience [Abrahamson et al., 2008]. At first independently Nordnic Biomass company from Denmark, and then in cooperation with the Border Biofuels Ltd company from Great Britain produced few generations of machines for harvesting willow in the whole stems form. At first they were simple machines attached to the tractor. For the second and third generation self-propelled machines were qualified, respectively HE ALL - Rounder and Mantis. With the newest machine of fourth generation, offered by Nordic Biomass, is attached, Stemster TR model from 2006. Both the Mantis self-propelled machine, and attached Stemster TR are characterized by a high performance from similar under the technical consideration and functional Empire 2000, designed by Segerslätt. 113 a b c d e f g h i Figure. 1. Willow harvesting machines: a - Claas Jaguar harvester with HS2 adapter, b - New Holland harvester with CRL adapter, c - Krone Big X forage harvester with HTM Woodcut 1500 adapter, d - Salix Maskiner mounted Bender 6WG forage harvester with a chain saw, e - Bundler's prototype, f - New Holland prototype cutbaling machine, g - KWE-7 prototype pulled mower of Metaltech Ltd (PIMR project), h, i - prototype semi-mounted harvester's SIPMA SA (an original concept, the implementation of the project PBZ-MNiSW-1/3/2006) Source: Elaborated on the basis of [Spinelli, 1999; Lechasseur and Savoie, 2005; Lavoie et al., 2007; Pasyniuk, 2007; Lisowski, 2009] In the international scale the most field self-propelled forage harvester became commonly used with adapters for the harvest of willow and the modernised Austoft 7700 combine harvester for sugar cane harvesting. The German Claas company offered the first HS1 adapter with two circular saws in Claas Jaguar 695 forage harvester. The newest HS2 adapter from 2006 works with the Claas Jaguar 900 forage harvester (Fig. 1a). In 1998, the company Coppice Resources Ltd (CRL) from the UK, in cooperation with Claas, developed a slightly different adapter (Fig. 1b). The latest versions of the CRL adapters are adapted for forage harvesters of Claas, Case New Holland, John Deere, Krone companies, and are more widespread than the HS2. The HS2 adapter is equipped with two circular saws with teeth cutting from sintered carbides. The material is broken-up by a standard drum unit of Jaguar forage harvester cutting into chips of the required length up to 60 mm. The maximum diameter of willow stalk, which can be cut and broken-up is 70 mm. Depending on conditions, the type of field, and the maximum diameter of stalks the peak performance of the machine amounts to 70 t·h-1. With the HS2 it is possible to harvest two twin rows of plants between which should be kept a distance 0.75 m, and between next pairs of rows - 1.5 m. These requirements sometimes make the direct 114 application of the Jaguar combine harvester with HS2 adapter impossible in harvest of willow; therefore the plantation was put on excluding restrictions resulting from the construction of the machine meant for the more late harvest of plants. Works on adapting forage harvesters or mowers to the harvest of willow were carried out also by other companies. In 2006 Krone equipped the mower BIG X V8 with the Woodcut 750 adapter with two circular saws of diameter of 750 mm each, and in 2007 Woodcut 1500 (Fig.1c) with a circular saw of diameter 1500 mm. Another machine used in willow harvest is Austoft 7700, which in the basic version had been designed to harvest the sugar cane. Its modification was to use circular saws of diameter 600 mm, instead of knife disc with 5 knives and removing the unit cutting green tops of the sugar cane and redesign of the discharge spout. It is self-propelled machine weighing about 12.5 t, equipped with a caterpillar suspension system, which reduces soil compaction. The performance of harvester Austoft 7700 in willow harvest is similar to Claas Jaguar [Pellerin et al., 1999]. In other studies, Spinelli and Kofman [1996] found some problems with obstructing cut material, uneven height of cutting, damage snag and poor quality of wood chips, which related mainly to the great unevenness of the distribution of large particle size. Two-stage harvest with the possibility of natural drying can be carried out using a special cut-baling machine, which cuts down, breaks-up and rolls material into a cylindrical bale [Lavoie et al., 2007]. Modified baler (Fig. 1f) perhaps to be also available in the technology of the harvest much cheaper than breaking-up by forage harvester, therefore can be more available on small farms or in common use on a smaller scale. The machine can be used to harvest willow, annual or perennial plants and grasses [Feler et al., 1999]. In 2007 in the cut-baling machine there was introduced another modification, consisted of dismantling four circular saws and installing a new cutting unit with a beater rotor [Schroeder et al., 2008]. Experience shows that in case of the willow harvest with beater unit the speed of shooting of stems can be smaller [Stuart, 1994]. Other models of machines, specially designed to willow harvest, or modified often by users themselves, either finished functioning on tests of the prototype or were used in the limited range, without their dissemination. Similar activities can be observed on the Polish market of willow harvesting machines. There is therefore a characteristic, initial phase of development of these machines. Apart from previously mentioned manual tools, to willow harvest there are used adapted machines, designed by the user's own structures and simple machines made by small companies. These machines are cheap and characterized by a very simple construction, but do not meet even minimal safety requirements and, therefore, cannot be recommended. The professionally fabricated prototypes of willow harvesting machines can now include three models. In 2008 PIMR workers in Poznan developed and constructed a machine for mowing and automatic tying of stems using the wicker basket and KWE-7 pulled mower (Fig. 1g), working in the same way as the model of Roland's Stemster Nordic Biomass. In 2009, on the basis of concept of workers of the Department of Agricultural and Forest Machinery of WULS in Warsaw, the SIPMA SA company developed technical documentation, and made a semi-mounted harvester prototype (Fig. 1h, i). KWE-7 energetic willow mower, made by the Mechanical Unit of Metaltech Ltd in Mirosławiec, is designed to cut the whole stems of energetic willow and cut off stems laying on the load-carrier of the machine. To cut stems of plants at a height of 15 cm above the ground there are used two circular saws. Cut stalks are picked up 115 by two V-shaped conveyors, which also perform the role of distributors. Stalks assembled in the centre area of the distributor are transported by the next pair of driving conveyor to the load-carrier, equipped with the special system of chain conveyors. Chain conveyors, moving perpendicularly to the direction of machine, gather the cut stalks on the left side of load-carrier. After filling the load-carrier, its contents can be discharged anywhere on the field or substituted trailer. It is being reached after the get out of plumb of left load-carrier side by hydraulic cylinders and running the chain conveyor with drivers of triangular shape. The machine weighing 8430 kg has a robust design, supported by a tandem system, but requires a tractor with power at least 105 kW. Working units of the machines are controlled by hydraulic system independently. Functioning mechanisms and working units are controlled by an electronic control and steering is held from the cab of the tractor operator. The maximum diameter of harvested willow stems can amount to 70 mm and the height to 7 m. The length of the load-carrier is 4.5 m, capacity 18 m3 and the loading of about 7 t. Tractor forage harvester (Fig. 1h, i), constructed under the project PBZMNiSW-1/3/2006 was designed after a detailed analysis of specially constructed machines to harvest of energetic plants or modified machines, which primary purpose was the harvesting of high-stems plants. The conclusions of this analysis and the assumptions adopted by the authors [Nowakowski et al., 2008] were the basis for designing of our own construction of the machine, to which was applied for a patent no P 385 536 [Lisowski et al., 2008]. To transport position the machine is assembled behind the tractor to reduce the width and to meet requirements of traffic regulations. In this position the machine is mounted on the linkage of the tractor (Fig. 1i). In the working position the part of the machine is resting on the outer ground wheel, and the second wheel is raised to avoid out rigidity of system. On the part of the frame attached to a tractor there are put: the tank with hydraulic oil, bevel gear transmission transmitting the drive from the PTO of tractor to the drum with knives and the hydraulic pump powering, through the electro hydraulic distributor, the electronic motors and hydraulic cylinders. In the second part of the frame, supported on wheels, there is a drum cutting unit. Cut stalks are directed by discharge spout to the load-carrier of transport means. The throw direction of material is set through turning of the discharge spout by hydraulic motor, and its range - by proper lowering of the deflector installed on the end of the discharge spout. The fed cutting material is compacted and held while cutting by feedcompacted toothed rollers. The force of rollers is adjusted by changing preload springs. To the body of the rolls unit there are pivotally suspended exchangeable cut adapters. The adapters are unweighted by screw springs of pre-regulated tension. The height of cutting plants is adjusted continuously by the two plunger cylinders which are symmetrically arranged under the unit. The cutting adapter with driving and pick-up-feeding units is designed in two versions (Fig. 2). In one of the solutions in the feeding unit there were applied rotational fingers drivers (Fig. 2a), and the second - vertical worm rollers (Fig. 2b). In both cases, the drive circular saws and rolls with driver fingers or rolling of worms hydraulic motors were applied. Shafts of circular saws are powered by high-speed hydraulic motors with a maximum speed around 4000 rpm, what at 0.5 m diameter of the disc allows for cutting of the peripheral speed of 100 m·s-1. Such value of the speed results from the need of obtaining a smooth cut surface and reduces the risk of tearing bast under bark, which may contribute to the putrefactive processes of snags. Therefore, in the willow harvesting machines the peripheral speed of circular saws 116 are applied 63-118 m·s-1 [Lechasseur and Savoie 2005; Lisowski, 2009]. Rolls with the driver elements are driven by the low speed hydraulic motor by a gear reducing the speed to 38 rpm. At the external diameter of drivers 0.5 m, the ends of fingers are reaching the peripheral speed of 1 m·s-1. The ratio of peripheral speed to take material to the cutting plants is very large and is 1:100. These parameters can be obtained from the peripheral speed at nominal speeds of hydraulic motors. Since the speed tractor-machine unit can be changed depending on field conditions, and therefore use a hydraulic motor, electro-controlled distributor, will refine the optimum speed, so that peripheral speed is at least greater than the speed of motion of the tractor. The surplus of the speed of 10-30%, determined by kinematic factor, should provide the proper picking up cut stems of plants. a b Figure 2. Cutting adapters with: a) divers fingers, b) spiral rolls Source: authors' elaboration Application of tilting element, hydraulically controlled, allows for the plants cutting by circular saws when plants are bent. The accumulated energy in the inclined stem will be used after its felling to direct part of the butt-end stem to the next feed-compacted rolls unit. Other working units of the machines are similar to those found in forage harvesters to harvest of low- and high-stalks plants to the silage. Assuming that the machine will be equipped with row independent unit, with adjustable spacing dividers, it can cut single rows spaced 0,7-0,8 m [Nowakowski et al., 2008] or a twin rows spaced 0.75 m. The ground speed of tractor-machine aggregate will depend on field conditions, but does not exceed the recommended maximum 8 km·h-1 [Lechasseur and Savoie, 2005]. Cutting plant height of 100 mm on the surface corresponds to the scope of existing solutions (50-100 mm) [Szczukowski et al., 2006]. The basic machine is equipped with a breaking-up drum unit, allowing for cutting the stalks of diameter 70 mm into chips of dimension 2060 mm (according to the standard PN-91/D-95009). Preliminary examination of the machine led to determination of total power input from the tractor PTO through the machine at idle, which amounted to 19.14 kW. The greatest requirement for power have circular saws (9.93 kW) and hydraulic pump (4.48 kW), which represents 76% of total capacity idle. Power consumption of each of the other units (active dividers, driver fingers, rolls unit, breaking-up drum) ranged from 1.03 to 1.35 kW. The analysis of knowledge about the technology and machines used to harvest of energetic plants that are used in various solutions and launched in the 90s twentieth century boom in energetic plants in the Scandinavian countries, extend to Poland. Moreover, despite of many studies and tests there are still unresolved questions about the economic and ecological profitability of the cultivation of 117 energetic plants and there is no indication of optimum conditions for biomass cultivation, harvesting and processing. Frequently cited examples are the results of natural experiments, and there is lack of special model studies. As an example of a model approach to analyze a harvest of energetic plants can serve the experience of Sokhansanj and Fenton [2006]. To calculate the cost of harvesting and transportation of biomass, they applied a mathematical model IBSAL - Integrated Biomass Supply Analysis and Logistics developed by Sokhansanj [2006]. The IBSAL model contains various algorithms, including harvesting, processing, milling, storage and transport. Model input data include: information about local weather, the net average yield of biomass, timing of harvest, losses of dry matter, plant moisture content during harvest, operating parameters, equipment, machinery costs in $·h-1. For this reason this research aimed at development of a mathematical model and an algorithm for calculation and simulation of complex research; the analysis that will allow for selection of the best harvest technologies for energetic plants was formulated. SIMULATION MODEL OF THE PLANTS HARVESTED FOR ENERGY PURPOSES The selection of machines found in plant production technologies should be guided by specific requirements, which include, among the others, issues concerning compacting of soil, resistance of machines and slippage occurred. Accomplishment of the requirements specified above will allow for performing the determined work with less fuel consumption, and lower emission of exhaust gas into the atmosphere. Choice of technology outside the one- and two-stage harvest is also often conditioned with machines possessed in the household, in order to reduce investment. It also follows from other requirements for the machines to harvest of plants with one, two and three years. Proposed simulation model takes into account both current and new technologies of the harvest, namely: • harvest of broken-up plants with tractor forage harvester - Z1 technology, • harvest of broken-up plants with propelled forage harvester - Z1a technology, • harvest of plants with cut-baling machine - Z2 technology, • harvest of whole stems with cutting machine - Z3 technology, • harvest of whole stems with cut-piling machine - Z3a technology, • harvest in bundles (packages) with cut-packaging machine - Z4 technology. In each of these harvest technologies, recognized as basic, there are options that include various solutions of the receipt, the transport and breaking-up of plants. In order to facilitate the comparison of methods there was assumed that in every technology material is broken-up as final product (chaff or chips). A mathematical algorithm that enables to perform simulation of the expenditures incurred in technologies of the willow harvest was developed. For calculations, the following preliminary assumptions were adopted: the plantation area of 10 ha, the yield of annual plants of 25 t·ha-1, plants moisture content during harvest 53%, the transport distance of 1.0 kilometers, the time of working shift 8 h, the time of the harvest dispositional to 30 days. Initial assumptions can be changed depending on the plantation. It was also assumed that in considered technologies the harvesting machines (basic) would be single. Depending on the parameters of these plantations the sets of machines will be selected, which ensure the continuous process of harvest. Subsequently accepted sets of machines used in harvesting technology. Working parameters, the prices of machines and tractors were described with relations. 118 Calculations carried out allowed for getting the efficiency of units. For determining the criterion indicators the methodological studies were used [Muzalewski, 2006; Muzalewski and Olszewski, 2000], as well as information material [Gromadzki, 2009]. The specific costs of biomass producing for energy purposes can be characterized by many factors, among others the fuel and labour consumption. Fuel consumption related to 1 t of dry matter, abstracted in these technologies is different. The lowest fuel consumption was found in the Z1a technology, and the largest in the Z3, which was respectively 7.24 and 12.68 kg·t-1DM. Fuel consumption in other technologies is 26.3-40.1% lower than fuel consumption in Z3 technology. The fuel consumption of a tractor in the aggregate to the harvest in relation to consumption in the whole technology was 13.6-39%. Breaking-up of plants after harvest increases fuel consumption by 24.4-41.5%. Also drying of broken-up plants in Z1 and Z1a technologies increases the fuel consumption by 29.2-34.1%. A similar trend is shown for labour consumption in individual technologies as for fuel consumption. The smallest expenditure of labour working 0.61 rbh·t-1DM was found in Z1a technology because of the smaller number of operations, the greatest one in Z3 technology (6.2 rbh·t-1DM) because of operations carried out manually, which caused decreasing of efficiency. The labour consumption in other technologies is about 72.4-81.8% less. The labour consumption of plants harvesting in relation to the total labour consumption of technologies represented 9.5-21.7% and was dependent on the efficiency of harvesting machines. In all technologies the transport amounts to 8.4-30.1% and handling - 11.5-70.6% of expenditure of labour. The high labour consumption in Z3 technology results from the low efficiency of manually handling work. Breaking-up of stalks after drying increases the labour consumption by 11.5-22.1% and drying of chaff in Z1 and Z1a technologies increases it by 42.170.5%. In the structure of chips cost productions the Z1 technology is characterized by the smallest specific costs (119.77 zl·t-1DM or 6.45 zl·GJ-1), and the largest - Z1a (366.12 zl·t-1DM, 19.73 zl·GJ-1). The costs of other technologies amount to 11,9615.22 zl·GJ-1 and are about 22.9-39.4% lower than in Z1a technology. The high costs of harvest (81.2 and 71.2%) in Z1a and Z3a technologies are caused by the use of expensive machines, in this case on too small areas. In Z3 technology the harvest amounts to 8.9% of total costs, and handling operations up to 50.5% due to the low productivity of manual work. Breaking-up of stalks after drying increases the cost up to 8.6-22.4% and drying of chaff in Z1 and Z1a technologies up to 6.6-13.2%. SUMMARY Technologies of direct harvest of willow in chips, with the application of existing machines on appropriately large plantations, are cheaper than a two-stage techniques in the form of whole stems [Styles et al., 2008]. The harvest technologies of whole stems are more flexible, due to fewer constraints of harvest conditions and can benefit [Kofman, 2005], which result from lower costs, the possibility of natural drying, letting for acquiring better fuel [Harders, 2002]. In Great Britain, the plants stored in such conditions, within seven months reduced the relative moisture content of 53-55% to 19% [Hilton, 2000]. The disadvantages of this willow harvesting method can also include a larger number of rides of transport means, the need to use special trailers for the transport of long stalks (up to 8 m) and the loader to load them, providing larger width for returns and surface storage of large volumes of raw 119 material. Essentially in this harvest technology is to perform more operations and is more laborious than the single-stage harvest technology. Based on previous research results [Lechasseur and Savoie, 2005] it can be concluded that the use of machines to harvest the whole stems of willow, can significantly reduce the specific fuel consumption per ton of fresh plants. It was found that during the operation of Empire 2000, Frobbesta and Hyd-Mech machines harvesting the willow in whole stems form the specific fuel consumption was 0.501.00 l·t-1, and machines of Claas Jaguar 695, Bender and Austoft 7700, which simultaneously cut down and breaking-up stalks - 0.95-1.20 l·t-1. Smaller values of this index for machines harvesting whole plants result from the fact that during this treatment plants are only cut and transported as a whole to the load-carrier area of the machine or the trailer. After the storage period the breaking-up of stalks on chips are done. Therefore, direct comparison between the requirement for power or other energy indicators of harvesting machines that perform different functions is a simplification. 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Biomass and Bioenergy, 31(7), 469-474. 122 Chapter 11 EFFECT OF SEASON ON CARBON DIOXIDE AND AMMONIA PRODUCTION IN BROILER HOUSING Štefan MIHINA, Monika KNÍŽATOVÁ, Jan BROUČEK INTRODUCTION Modern poultry housing is designed and constructed to reduce heat loss and improve energy efficiency, however when coupled with reduced ventilation, can result in elevated levels of carbon dioxide, ammonia and other air contaminants, which may adversely affect the health and productivity of flocks [Olanrewaju et al., 2008]. On the other hand Lendelová and Botto [2009] documented that pre-warming of incoming air could decrease this negative influence of reduced ventilation. Air quality is one of the most difficult aspects of broiler management to grasp. This is probably because it is not possible to “see”. It is difficult to visually determine the volume and direction of fresh air being brought into a broiler house. During cold weather the primary function of the ventilation system is to eliminate ammonia and moisture from the broiler house. Carr et al. [1990] relate higher concentrations of NH3 in winter months with reduced ventilation rates to conserve as much heat as possible. Many producers underestimate the detrimental effect of ammonia. Ammonia levels of just 25 ppm have been found to depress growth and increase feed conversion in broilers [Miles et al., 2004]. The housing environment, including factors like carbon dioxide levels and oxygen levels, is known to influence the incidence of ascites (broiler pulmonary hypertension syndrome) in broiler chickens. The problem arises from very high metabolic rate of rapidly growing broiler strains. Subsequently, in less well ventilated poultry house as well as at higher altitudes, oxygen becomes a limiting factor as far as their health, welfare and performance are concerned [Movassagh Ghazani et al., 2008]. The quality of the inhouse environment is highly dependent upon litter quality, but the quality of litter is seldom given sufficient emphasis. In most instances, excess ammonia in broiler house is due to wet litter and insufficient ventilation. Litter moisture may affect the conversion rate of uric acid to ammonium nitrogen [Liu et al., 2006]. The transformation of organic material in litter is also accompanied by the release of carbon in the form of CO2, methane (CH4) and other organic gases [Nicks et al., 2003]. However, the main sources of CO2 within a poultry house include fuel 123 combustion, bird respiration and ambient air content (typically 300-500 ppm) [Olanrewaju et al., 2008]. Both, the gas furnaces and the broilers generate CO2 and consume O2. Oxygen consumed is equal to the volumetric CO2 produced by the birds and is assumed double the carbon dioxide produced by open-flame natural gas furnaces [McGovern et al, 2001]. This means, the combustion of one molecule of fuel (CH4) generates one molecule of CO2 and consumes two molecules of O2. Management of the broiler house environment is a study in conflict between the need for temperature and humidity control for optimal bird performance, feed conversion, and energy conservation. Ventilation is used to remove noxious gases including ammonia, and carbon dioxide as well as dust and moisture. With the advent of modern nipple-type drinkers, most broiler operations are experiencing less difficulty with interior moisture but greather concentrations of dust, NH3 and CO2. Recent research suggests that current recommendations for minimum ventilation should be made based on minimum accceptable CO2 and NH3 concentrations, rather than moisture [Xin et al., 1996]. The objective of this study was to explain the potencial sources of CO2 and to examine the effect of some litter variables on NH3 volatilisation as well as to calculate emission rates of both problematic gases occuring at crirical levels in broiler facilities. MATERIAL AND METHODS A common broiler rearing facility was monitored during 6 consecutive fattening periods specified below (Table 1). The begining and ending dates are provided for each flock to document the time of year when each flock was kept. Table 1. Monitoring schedule Fattening Season period 1 2 3 4 5 6 summer / autumn autumn autumn / winter spring / summer summer summer / autumn II Date Duration (days) 30.07. - 07.09 23.09. - 01.11 18.11. - 27.12 02.05. - 10.06 16.06. - 25.07 10.08. - 18.09 40 40 40 40 40 40 Average number of chickens 23,929 24,310 24,502 24,287 23,908 24,016 Housing description Approximately 25,000 chicks yielding a stocking density of 18 – 22 birds per sq. m. were placed in a concrete-flored commercial broiler facility with a housing area of 1 128 m2 (94 x 12 m) and/or interior volume of 4,455 cubic meters (0.178m3/head). The housing area was heated to nominal temperature of 31-33° C by two gas furnaces (Table 2). Ambient temperature was reduced as the birds progressed in age by approx. 2° C each week to ensure their comfort. The house was mechanically ventilated with combined tunnel and cross two-sided ventilation. Six ceiling axial fans, with maximum capacity of 12,000 m3/h of each, and four frontal fans with maximum capacity of 35,000 m3/h assured air exchange in chicken house. Fresh air inlets were placed on both side walls of the hall. A breeding area was equipped with 4 nipple drinker lines and 3 tube-style pan feeder lines that were filled automatically. Birds (Ross 308) were fed ad libitum to a 124 final market weight of around 2 kilograms. Each one of flock was kept for 40 days of fattening period. New straw was used for each subsequent fattening period, littered to a depth of 5 to 10 cm (approx. 1.6 kg/m2). The age of litter corresponded with age of birds. No additional litter material or amendments were added to the litter at any time throughout the study. Table 2. Gas furnaces characteristic Model Power output (kW) Natural gas consumption (m3.h-1) Ventilation rate (m3.h-1) Heating distance (m) Weight (kg) GP 70 70 7.5 5,000 50 36 GP 120 120 12.5 7,000 50 64 Sampling and calculation Carbon dioxide and ammonia concentrations were measured by infrared analyzer (1312 Photoacoustic Multi-gas Monitor). A self-contained pump draws air samples into the analyzer via sample tubes from five measuring points. Air samples were taken from air stream at two ceiling fans, two frontal fans and from outdoor environment. The sampling points were placed approx. 180 cm over the floor. Monitoring task operated continuously with one hour sampling interval. At the same points, air temperature was measured by thermocouple probe. Two thermocouple probes were placed also into litter (30 mm deep), in the front part and opposite end of house. Emission factors were determined using the average concentration near the house exhaust fans reduced by outdoor concentrations of monitored gases and multiplied by the volume of air that has passed through the building. The ventilation rate of exhausted air was based on current ventilation capacity (%) and known rate of air flow at 100 % efficiency (212,000 m3/h). Statistical evaluation A statistical analysis system (SAS ver. 9.1) and descriptive statistic were used for a research data processing. Spearman correlation was calculated for the evaluation of relationships between gases production and observed variables of indoor environment. The differences were declared significant when their probability levels were below 0.05. Tukey HSD comparison method was chosen from multiple comparison procedures performed by analytical software Statistix 9.0 to test significant differences between means. RESULTS AND DISCUSSION It is generally recommended that CO2 concentrations should be kept below 3,000 ppm in broiler housing environment [Council Directive 2007/43/EC, 2007]. The critical values of CO2 were reached in all observed periods and CO2 level was sometimes even tree times higher than it is allowed. Particularly during the first and the fourth quarter of periods chickens were exposed to very high levels of carbon 125 dioxide (Table 3). However, it is important to point out, that the concentrations were not measured at the level of chickens' heads. CO2 accumulation can occur when additional CO2 is produced by direct heating systems (where the exhaust gases remain inside the broiler house) and when the ventilation rate is operated at extremely low level [EC, 2000].There are two main sources of CO2 in general. The first one is supposed to be heaters, however, as the birds approached marked age, the CO2 source is primarily from the birds respiration [McGovern et al., 2001]. A speculation can be accomplished if is assumed, that a typically natural gas consists of 97.6 % methane, 1.5 % ethane, propane, butane, 0.1 % CO2, and 0.8 % nitrogen. The gas furnaces used for heating in chicken house (Table 2) had natural gas consumption of 7.5 and 12.5 m3/h and the power output of 70 and 120 kW. If we burn completely 20 m3 of natural gas and assuming that it is pure methane (CH4), it follows this equation: CH4 + 2O2 → CO2 + 2H2O Since 1 mole of a gas occupies 22.4 liters at STP (standard temperature and pressure, i.e., temperature of 0° C and pressure of 101.325 kPa), 20,000 liters of CH4 contains 20,000/22.4 = 892.86 moles of CH4. Since for each mole of methane we get one mole of carbon dioxide (see equation above) and one mole of CO2 has a mass of approx. 44 g, so 892.86 mole of CO2 has a mass of approx. 892.86 x 44 or 39.3 kg of CO2. That is, the complete combustion of 20 m3/h at STP of natural gas results in the production of about 39.3 kg/h of CO2. However, both heating units were not running continuously. Thus we can not say this amount of CO2 was produced every hour. Table 3. CO2 and NH3 concentration range in individual quarters of fattening periods Days of fattening period 1. t o 10. x CO2 (ppm) summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II NH3 (ppm) summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II 4,123 1 1. to 2 0. mi n ma x (n = 960) 939 x 2 1. t o 3 0. mi n ma x (n = 960) 9,427 2,323 x mi n ma x (n = 960) 993 5,272 2,459 5,767 1,395 10,067 4,394 1,172 7,141 3,694 3 1. t o 4 0. x mi n ma x (n = 960) 1. t o 4 0. x mi n ma x (n = 3840) 986 5,050 2,651 1,092 5,161 2,889 959 7,643 3,273 1,069 5,693 4,282 939 9,427 959 10,067 6,242 1,129 6,108 4,035 8,889 4,940 2,261 7,419 5,759 2,686 9,157 5,425 1,698 8,471 5,592 1,129 9,157 9,828 3,449 1,224 6,296 2,922 1,324 6,732 2,889 1,564 6,112 3,842 977 9,828 887 10,489 2,209 994 4,363 2,783 1,283 5,418 2,856 1,461 5,554 2,971 887 10,489 977 3,960 1,043 10,236 2,630 1,289 4,913 3,091 1,601 6,313 2,943 1,515 5,375 3,156 1,043 10,236 (n = 960) (n = 960) (n = 960) (n = 960) (n = 3840) 2.8 0.7 5.1 2,2 0.8 6.1 4,2 0.8 13.3 4.7 1.1 13.3 3.5 0.7 13.3 4.0 1.6 6.6 3,8 1.2 7.6 4,8 1.4 12.3 5.8 2.0 11.6 4.6 1.2 12.3 2.3 1.1 4.2 2,6 1.1 10.5 10,2 4.0 26.0 13.7 4.6 29.1 7.2 1.1 29.1 1.8 0.4 4.1 1,0 0.1 2.6 3,4 0.6 14.0 4.2 1.4 14.0 2.6 0.1 14.0 1.2 0.0 11.1 0,5 0.0 2.1 3,7 0.4 14.1 5.0 1.9 14.0 2.6 0.0 14.1 1.2 0.1 3.4 1.7 0.2 6.2 5.8 1.6 19.6 4.7 2.2 11.1 3.3 0.1 19.6 Carbon dioxide levels in the broiler house atmosphere tend to increase over time with bird growth and respiration [Miles et al., 2006]. Corresponding to intensive heating at the begining, CO2 concentration decreased from placement to midfattening and then increased slightly towards the end of periods (Figure 1). It means, one source of CO2 (gas burning) was replaced with other one (birds respiration). This 126 effect is not very evident, since more intensive ventilation (Table 4) entered this process and CO2 was diluted in fresh air from outdoor environment. CO2 concentration (ppm) 8000 7000 6000 5000 4000 3000 2000 1000 0 0 5 10 15 20 25 30 35 40 Time (day) summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II Figure 1. Course of carbon dioxide concentrations during individual fattening periods Table 4. Parameters of indoor environment during individual fattening periods Days of fattening period 1. to 10. x 3 Ventilation rate (m /h) summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II Air temperature (°C) summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II Litter temperature (°C) summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II sd (n = 240) 11. to 20. x sd (n = 240) 21. to 30. 31. to 40. x x sd (n = 240) 39,079 27,921 73,361 49,373 114,772 49,642 28,284 7,503 34,159 4,173 46,349 10,328 25,581 5,029 32,330 2,525 35,060 2,227 22,711 7,580 39,061 14,191 126,458 58,806 27,322 19,426 68,105 44,233 98,819 55,572 30,873 13,656 66,877 41,550 124,594 64,793 (n = 960) 29.2 29.7 32.5 29.3 29.2 28.6 1.6 1.8 3.9 2.2 3.8 2.3 (n = 480) 26.7 30.7 25.2 26.9 26.2 28.7 0.49 1.52 1.26 1.76 2.09 1.04 (n = 960) 26.5 26.1 24.7 23.5 25.2 24.9 2.0 2.1 1.6 2.3 2.1 1.7 (n = 480) 27.9 27.1 28.1 27.1 27.7 28.1 0.89 1.96 1.04 1.76 1.42 1.78 (n = 960) 25.1 21.3 21.7 24.3 23.7 24.4 1.8 1.6 1.4 2.7 1.8 2.7 (n = 480) 31.1 25.3 30.5 30.3 30.0 30.8 1.56 1.58 0.85 2.12 1.76 2.12 sd (n = 240) 130,725 90,524 39,406 102,387 128,764 145,785 58,893 18,899 2,287 41,257 47,680 55,652 (n = 960) 24.7 23.7 19.7 22.8 23.8 23.3 1.8 2.5 1.4 2.3 1.8 2.5 (n = 480) 34.4 29.0 33.3 32.9 33.5 32.2 0.97 0.89 0.72 1.05 1.35 1.01 sd = standard deviation The amount of CO2 produced by respiration of chickens can be explained in a similar manner as a fuel combustion mentioned before. The amount of CO2 produced 127 by birds is proportional to the heat production of the animal (1 liter CO2 for every 24.6 kJ of total heat produced). This corresponds approximately to 1.5 l/h/kg liveweight [EC, 2000]. The production of carbon dioxide in the experiment carried out by Para et al. [2003] decreased with increasing weight of broilers from the mean weight of 0.25 kg/head always up to the final weight of 1.5-2.0 kg/head; the initial value reaching 1.85 l/h/kg and the final one 1.23 l/h/kg, resp. Since 1.5 liter of CO2 corresponds to 0.06696 moles (1 mole CO2 = 22.4 liters at STP) and one mole of CO2 has a mass of 44 g, then a chicken exhales of approx. 0.06696 x 44 or 2.946 g CO2 /h/kg. At market age of 2 kg and the capacity of 25,000 chickens kept in this broiler house, 50,000 x 2.946 g or 147.3 kg of CO2 is emitted per hour as a consequence of birds respiration. If we take into consideration the first day of just hatched chicks with liveweight of 40 g, 2.9 kg of CO2/h is produced by their respiraton. However, it is also important to point out, that the breathing frequency changes by age markedly. Values of NH3 were recorded in concentration range between 0.0 and 29.1 ppm. Ammonia concentration had rising tendency in all periods (Figure 2). Correlation with bird (litter) age was very highly significant (P<0.001) (Table 5). Vučemilo et al. [2007] associate the increasing of air concentration of ammonia with the increase in animal age and air humidity. He reported almost septuple higher level of NH3 concentration between the first and the fifth week of age (litter – mixture of wooden sawdust and shavings). In our measurement we found approximately triple increase in ammonia concentration between the first and last quarter of fattening period (Table 3). Since the temperature of litter was quite stable during the whole year (Table 4) and the tested differences between fattening periods were also not significant (Table 6), lower NH3 concentrations during summer periods must have been caused by other factors (intensive ventilation, drier litter, and crust on its surface). Moreover, we did not notice marked differences in temperatures of air when comparing the summer and winter period as well (Table 6). NH3 concentration (ppm) 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 0 5 summer/autumn I 10 autumn 15 20 25 Time (day) autumn/winter spring/summer 30 summer Figure 2. Course of changes in ammonia concentrations 128 35 40 summer/autumn II Table 5. Correlations between studied variables and production of gases Variables CO2 conc. summer/autumn I autumn autumn/winter spring/summer summer summer/autumn II NH3 conc. summer/autumn I autumn autumn/winter spring/summer summer summer/autumn II Air temperature Litter temperature Chicken (litter) age Ventilation rate 0.23227 0.68612 +++ 0.23450 0.64880 +++ 0.19248 -0.02198 - 0.26454 0.41463 ++ -0.24237 -0.51859 +++ 0.02576 0.18901 - -0.35066 + -0.84578 +++ -0.19743 + -0.76916 +++ 0.00563 0.06354 - -0.51503 +++ -0.84712 +++ -0.37745 + -0.70775 +++ -0.14816 -0.04149 - -0.65235 +++ -0.44897 ++ -0.80878 +++ -0.09191 -0.28434 -0.71000 +++ 0.69925 +++ 0.00281 0.82104 +++ 0.75779 +++ 0.74765 +++ 0.71508 +++ 0.64015 +++ 0.72289 +++ 0.92368 +++ 0.62293 +++ 0.70269 +++ 0.78864 +++ 0.49261 ++ 0.69803 +++ 0.75421 +++ 0.60674 +++ 0.68581 +++ 0.74108 +++ + P<0.05; ++ P<0.01; +++ P<0.001; - non-significant The comparison of concentration and emission rate according to the fattening period (season) is given in Table 6. The highest NH3 concentrations were recorded in fattening period 3 (autumn/winter) and 2 (autumn), and the lowest in period 4 (spring/summer). The differences were significant (P<0.001 and P<0.05 respectively). CO2 concentrations reached also the highest values in period 3 (autumn/winter). The lowest ones were recorded in period 1 (summer/autumn I) (P<0.001). In case of NH3 and CO2 emissions, the seasonal differences were not significant. Liang et al. [2003] reported higher emission rates in summer than in winter because of highest ventilation capacity, even thought the lower concentrations. A number of authors [Coufal et al., 2006; Redwine et al., 2002] published similar seasonal changes in emissions. The ventilation rate showed a rise in all periods. The ventilation system operated at the highest capacity during period 6 (summer/autumn II) and at the most reduced capacity during period 3 (autumn/winter) to maintain the suitable indoor temperature. The difference was significant (P<0.001) (Table 6). Ammonia concentration and amount of air exhausted through the ventilation system were in positive correlation (P<0.001) unlike carbon dioxide (Table 5). It means, that in spite of rising ventilation rate towards the end of the fattening period, the ammonia concentration did not decrease but it had even slightly increasing tendency. 129 Table 6. A seasonal comparison of observed variables Fattening period NH3 conc. (ppm) x sd Significance (n = 3840) 1 3.5 1.8 2 4.6 1.2 3 7.2 5.3 4 2.6 1.8 5 2.6 2.3 6 3.3 2.5 NH3 emission (kg/h) Fattening period CO2 conc. (ppm) F = 17.17 P = 0.0000 3:2,1,6,5,4 +++ 2:5,4 + sd 2889 1430 2 4282 1456 3 5592 979 4 3842 1761 5 2971 1535 6 3156 1311 0.16 0.14 1 225.6 119.1 2 0.13 0.10 2 258.4 110.0 3 0.16 0.14 3 280.5 66.2 4 0.13 0.16 4 288.0 199.7 5 0.15 0.17 5 233.4 151.5 6 Litter temperature (°C) 0.20 0.18 6 Air temperature (°C) 291.8 186.3 (n = 1920) 30.0 3.2 1 26.4 2.5 2 28.0 2.6 2 25.2 3.7 3 29.3 3.2 3 24.7 5.4 4 29.3 3.0 4 25.0 3.5 5 29.3 3.2 5 25.5 3.4 6 29.9 2.3 6 25.3 3.1 Ventilation rate (m /h) 3 F = 2.48 P = 0.0325 (n = 3840) 1 F = 2.69 P = 0.0219 F = 37.89 P = 0.0000 3:2,4,6,5,1 +++ 2:6,5,1 +++ 4:1 +++ 5:4 ++ 4:6 + (n = 3840) 1 F = 1.13 P = 0.3454 Significance (n = 3840) 1 CO2 emission (kg/h) (n = 3840) x F = 1.32 P = 0.2582 (n = 960) 1 89,484 59,724 2 49,828 27,001 3 33,094 5,970 4 72,654 56,594 5 80,752 57,714 6 92,032 66,165 F = 14.76 P = 0.0000 6:2,3 +++ 1:2,3 +++ 5,4:3 +++ 5:2 + + P<0.05; ++ P<0.01; +++ P<0.001; sd = standard deviation From a seasonal point of view, CO2 emissions reached the highest values (280 t) in flock “summer/autumn II“ (Table 7). This was attributed to increasing ventilation rate of the building (P<0.001). Relatively high emission rates were also determined in flocks “autumn/winter“ (269 t) and “spring/summer“ (276 t). This was significantly affected not only by ventilation rate, but also by increasing CO2 concentrations (P<0.01, P<0.001). There was also a moderate correlation between ventilation rate and CO2 concentration - the higher the ventilation rate, the lower the concentration of CO2. Moreover, there was also no statistically significant correlation between CO2 concentration and CO2 emission in three flocks (Table 5). The NH3 emission factor ranged from 5.17 g/head to 7.81 g/head per period. Gates et al. [2008] reported almost 3 times higher ammonia emission (17.4 g/head for one period in fattening to life weight 2.1 kg housed on sawdust litter). Lacey et al. [2002] emphasize that different values of emission factors published by American and European authors are caused by different climatic conditions and differences in average live weight of animals. He reports emission factor 19.8 g NH3/head for 49 days fattening cycle (average life weight of chickens 1.03 kg). 130 Table 7. Summaries of CO2 and NH3 emission data (kg/h) Days of fattening period CO2 summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II NH3 summer / autumn I autumn autumn / winter spring / summer summer summer / autumn II 1. to 10. 11. to 20. 21. to 30. 31. to 40. 1. to 40. Total emission (kg) EF 1) (kg/head) EF 2) (kg/head) 73.11 155.75 240.21 247.02 198.11 120.29 131.96 137.91 212.57 238.70 169.18 130.72 177.71 270.09 232.31 311.21 445.82 284.22 398.38 338.60 348.55 325.07 338.93 398.31 459.27 216,563 248,075 269,280 276,497 224,050 280,156 9.05 10.20 10.99 11.39 9.37 11.67 9.46 14.02 58.81 72.51 154.8 6.47 . 10-3 14.35 16.64 29.56 65.03 125.58 5.17 . 10-3 7.72 12.14 54.31 82.33 156.5 6.39 . 10-3 5.89 5.04 62.64 53.58 127.15 5.24 . 10-3 4.38 4.91 51.18 82.94 143.41 6.00 . 10-3 4.121 15.15 82.49 85.78 187.54 7.81 . 10-3 0.043 1) Partial emission factor calculated from average number of chickens in individual periods, represents emission of CO2 or NH3 per head and 40 days of fattening period 2) Annual emission factor, i.e. emission converted into seven fattening periods in one productive year CONCLUSION From calculations performed in this study it can be concluded, that a natural gas burning process is responsible for a substantial part of CO2 emissions during first days of periods, and later, the respiration of animals takes dominance. The major part of CO2 seemed to have it´s origin in a bird respiration with assumed production of approx. 147 kg of CO2/h. The heaters could be theoretically a possible source of approx. 39 kg each hour, if they would work continuously. This evaluation of CO2 emission sources could be complete if also CO2 releasing from litter decomposition would be taken ito the consideration. The temperature of litter and the temperture of air related with chicken age and ventilation rate were main parameters observed during this experiment, to assess their influence on carbon dioxide and ammonia production. There was a great influence of chicken age on CO2 emissions (P<0,001), but ventilation rate could markedly affect this correlation.The amount of ammonia emissions was increasing by chicken (litter) age (P<0,001) probably as a consequence of both, increasing NH3 concentration and ventilation rate (P<0,001). 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