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Identifying factors limiting legume biomass production in a heterogeneous on-farm environment

Published online by Cambridge University Press:  04 January 2012

S. DOUXCHAMPS ,
E. FROSSARD ,
N. UEHLINGER ,
I. RAO ,
R. VAN DER HOEK ,
M. MENA ,
A. SCHMIDT  and
A. OBERSON
S. DOUXCHAMPS*
Affiliation:
ETH Zurich, Institute of Agricultural Sciences, Eschikon 33, 8315 Lindau, Switzerland
E. FROSSARD
Affiliation:
ETH Zurich, Institute of Agricultural Sciences, Eschikon 33, 8315 Lindau, Switzerland
N. UEHLINGER
Affiliation:
ETH Zurich, Institute of Agricultural Sciences, Eschikon 33, 8315 Lindau, Switzerland
I. RAO
Affiliation:
Centro Internacional de Agricultura Tropical (CIAT), A.A. 6713, Cali, Colombia
R. VAN DER HOEK
Affiliation:
Centro Internacional de Agricultura Tropical (CIAT-Central America), Apartado Postal LM-172, Managua, Nicaragua
M. MENA
Affiliation:
Instituto Nicaragüense de Tecnología Agropecuaria (INTA), Managua, Nicaragua
A. SCHMIDT
Affiliation:
Centro Internacional de Agricultura Tropical (CIAT-Central America), Apartado Postal LM-172, Managua, Nicaragua
A. OBERSON
Affiliation:
ETH Zurich, Institute of Agricultural Sciences, Eschikon 33, 8315 Lindau, Switzerland
*
*To whom all correspondence should be addressed. Email: s.douxchamps@cgiar.org
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Summary

Multipurpose legumes provide a wide range of benefits to smallholder production systems in the tropics. The degree of system improvement after legume introduction depends largely on legume biomass production, which in turn depends on the legumes’ adaptation to environmental conditions. For Canavalia brasiliensis (canavalia), an herbaceous legume that has been recently introduced in the Nicaraguan hillsides, different approaches were tested to define the biophysical factors limiting biomass production on-farm, by combining information from topsoil chemical and physical properties, topography and soil profiles.

Canavalia was planted in rotation with maize during two successive years on 72 plots distributed over six farms and at contrasting landscape positions. Above-ground biomass production was similar for both years and varied from 448 to 5357 kg/ha, with an average of 2117 kg/ha. Topsoil properties, mainly mineral nitrogen (N; ranging 25–142 mg/kg), total N (Ntot; 415–2967 mg/kg), soil organic carbon (SOC; 3–38 g/kg) and pH (5·3–7·1), significantly affected canavalia biomass production but explained only 0·45 of the variation. Topography alone explained 0·32 of the variation in canavalia biomass production. According to soil profiles descriptions, the best production was obtained on profiles with a root aggregation index close to randomness, i.e. with no major obstacles for root growth. When information from topsoil properties, topography and soil profiles was combined through a stepwise multiple regression, the model explained 0·61 of the variation in canavalia biomass (P < 0·001) and included soil depth (0·5–1·70 m), slope position, amount of clay (19–696 kg/m2) and stones (7–727 kg/m2) in the whole profile, and SOC and N content in the topsoil. The linkages between topsoil properties, topography and soil profiles were further evaluated through a principal component analysis (PCA) to define the best landscape position for canavalia cultivation.

The three data sets generated and used in the present study were found to be complementary. The profile description demonstrated that studies documenting heterogeneity in soil fertility should also consider deeper soil layers, especially for deep-rooted plants such as canavalia. The combination of chemical and physical soil properties with soil profile and topographic properties resulted in a holistic understanding of soil fertility heterogeneity and shows that a landscape perspective must be considered when assessing the expected benefits from multipurpose legumes in hillside environments.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 150 , Issue 6 , December 2012 , pp. 675 - 690
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Agbenin, J. O. & Tiessen, H. (1995). Soil properties and their variations on two contiguous hillslopes in Northeast Brazil. Catena 24, 147–161.CrossRefGoogle Scholar
Alvarenga, R. C., da Costa, L. M., Moura Filho, W. & Regazzi, A. J. (1995). Potential of some green manure cover crops for conservation and recuperation of tropical soils. Pesquisa Agropecuaria Brasileira 30, 175–185.Google Scholar
Anderson, J. M. & Ingram, J. S. I. (1993). Tropical Soil Biology and Fertility. A Handbook of Methods. Wallingford, UK: CAB International.Google Scholar
Anderson, M. J. (2004). CAP: a FORTRAN Computer Program for Canonical Analysis of Principal Coordinates. Auckland, New Zealand: Department of Statistics, University of Auckland.Google Scholar
Baddeley, A. & Turner, R. (2005). Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 12, 1–42.CrossRefGoogle Scholar
Boddey, R. M., de Moraes Sá, J. C., Alves, B. J. R. & Urquiaga, S. (1997). The contribution of biological nitrogen fixation for sustainable agricultural systems in the tropics. Soil Biology and Biochemistry 29, 787–799.Google Scholar
Bourget, S. J. & Kemp, J. G. (1957). Wet sieving apparatus for stability analysis of soil aggregates. Canadian Journal of Soil Science 37, 60–61.CrossRefGoogle Scholar
Bouyoucos, G. J. (1962). Hydrometer method improved for making particle size analyses of soils. Agronomy Journal 54, 464–465.Google Scholar
Brady, N. C. & Weil, R. R. (2007). The Nature and Properties of Soils. 14th edn. Upper Saddle River, NJ: Pearson Education Addison Wesley.Google Scholar
Burle, M. L., Lathwell, D. J., Suhet, A. R., Bouldin, D. R., Bowen, W. T. & Resck, D. V. S. (1999). Legume survival during the dry season and its effect on the succeeding maize yield in acid savannah tropical soils. Tropical Agriculture 76, 217–221.Google Scholar
Butler, J., Goetz, H. & Richardson, J. L. (1986). Vegetation and soil–landscape relationships in the North-Dakota Badlands. American Midland Naturalist 116, 378–386.CrossRefGoogle Scholar
Cantarella, H., van Raij, B. & Quaggio, J. A. (1998). Soil and plant analyses for lime and fertilizer recommendations in Brazil. Communications in Soil Science and Plant Analysis 29, 1691–1706.CrossRefGoogle Scholar
Chambers, J. M., Freeny, A. E. & Heiberger, R. M. (1992). Analysis of variance; designed experiments. In Statistical Models in S (Eds Chambers, J. M. & Hastie, T. J.), pp. 145–193. Pacific Grove, CA: Wadsworth & Brooks/Cole.Google Scholar
Cherr, C. M., Scholberg, J. M. S. & McSorley, R. (2006). Green manure approaches to crop production: A synthesis. Agronomy Journal 98, 302–319.CrossRefGoogle Scholar
CIAT (2008). Summary Annual Report 2008. SBA3: Improved Multipurpose Forages for the Developing World. Cali, Colombia: CIAT.Google Scholar
Daellenbach, G. C., Kerridge, P. C., Wolfe, M. S., Frossard, E. & Finckh, M. R. (2005). Plant productivity in cassava-based mixed cropping systems in Colombian hillside farms. Agriculture, Ecosystems and Environment 105, 595–614.Google Scholar
de Costa, W. & Sangakkara, U. R. (2006). Agronomic regeneration of soil fertility in tropical Asian smallholder uplands for sustainable food production. Journal of Agricultural Science, Cambridge 144, 111–133.Google Scholar
Dharmakeerthi, R. S., Kay, B. D. & Beauchamp, E. G. (2005). Factors contributing to changes in plant available nitrogen across a variable landscape. Soil Science Society of America Journal 69, 453–462.CrossRefGoogle Scholar
Douxchamps, S., Humbert, F. L., van der Hoek, R., Mena, M., Bernasconi, S. M., Schmidt, A., Rao, I., Frossard, E. & Oberson, A. (2010). Nitrogen balances in farmers fields under alternative uses of a cover crop legume: a case study from Nicaragua. Nutrient Cycling in Agroecosystems 88, 447–462.CrossRefGoogle Scholar
Douxchamps, S., Mena, M., van der Hoek, R., Benavidez, A. & Schmidt, A. (2011). Canavalia brasiliensis. Forraje que restituye la salud del suelo y mejora la nutrición del ganado. Managua, Nicaragua/Lindau, Switzerland: INTA/ CIAT/ETH. Available online at http://www.ciat.cgiar.org/ourprograms/Agrobiodiversity/forages/Pages/Publications.aspx (verified 10 October 2011).Google Scholar
Ebanyat, P., de Ridder, N., de Jager, A., Delve, R. J., Bekunda, M. A. & Giller, K. E. (2010). Impacts of heterogeneity in soil fertility on legume-finger millet productivity, farmers' targeting and economic benefits. Nutrient Cycling in Agroecosystems 87, 209–231.Google Scholar
Gandah, M., Brouwer, J., Hiernaux, P. & Van Duivenbooden, N. (2003). Fertility management and landscape position: farmers' use of nutrient sources in western Niger and possible improvements. Nutrient Cycling in Agroecosystems 67, 55–66.CrossRefGoogle Scholar
Giller, K. E. (2001). Nitrogen Fixation in Tropical Cropping Systems. Wallingford, Oxon, UK: CABI.CrossRefGoogle Scholar
Guretzky, J. A., Moore, K. J., Knapp, A. D. & Brummer, E. C. (2004). Emergence and survival of legumes seeded into pastures varying in landscape position. Crop Science 44, 227–233.CrossRefGoogle Scholar
Haydock, K. P. & Shaw, N. H. (1975). The comparative yield method for estimating dry matter yield of pasture. Australian Journal of Experimental Agriculture and Animal Husbandry 15, 663–670.Google Scholar
INETER (2009). Banco de Datos Meteorológicos, 2007–2008. Managua, Nicaragua: Instituto Nicaragüense de Estudios Territoriales, Direccion de Meterología.Google Scholar
Iqbal, J., Read, J. J., Thomasson, A. J. & Jenkins, J. N. (2005). Relationships between soil-landscape and dryland cotton lint yield. Soil Science Society of America Journal 69, 872–882.Google Scholar
Kravchenko, A. N. & Bullock, D. G. (2002). Spatial variability of soybean quality data as a function of field topography: I. Spatial data analysis. Crop Science 42, 804–815.Google Scholar
Kravchenko, A. N., Bullock, D. G. & Boast, C. W. (2000). Joint multifractal analysis of crop yield and terrain slope. Agronomy Journal 92, 1279–1290.Google Scholar
Krom, M. D. (1980). Spectrophotometric determination of ammonia – a study of a modified Berthelot reaction using salicylate and dichloroisocyanurate. Analyst 105, 305–316.Google Scholar
Kuntze, H., Niemann, J., Roeschmann, G. & Schwerdtfeger, G. (1981). Bodenkunde. Stuttgart: Ulmer.Google Scholar
Mackean, S. (1993). Manual de Analisis de Suelos y Plantas. Cali, Colombia: Centro Internacional de Agricultura Tropical (CIAT).Google Scholar
Maraux, F., Lafolie, F. & Bruckler, L. (1998). Comparison between mechanistic and functional models for estimating soil water balance: deterministic and stochastic approaches. Agricultural Water Management 38, 1–20.Google Scholar
Mardia, K. V., Kent, J. T. & Bibby, J. M. (1979). Multivariate Analysis. London: Academic Press.Google Scholar
Nelson, D. W. & Sommers, L. E. (1982). Total carbon, organic carbon and organic matter. In Methods of Soil Analysis. Part 2: Chemical and Microbiological Properties (Eds Page, A. L., Miller, R. H. & Keeney, D. R.), pp. 539–580. Madison, WI: American Society of Agronomy.Google Scholar
Ojiem, J. O., Vanlauwe, B., de Ridder, N. & Giller, K. E. (2007). Niche-based assessment of contributions of legumes to the nitrogen economy of Western Kenya smallholder farms. Plant and Soil 292, 119–135.CrossRefGoogle Scholar
Olsen, S. R. & Sommers, L. E. (1982). Phosphorus. In Methods of Soil Analysis. Part 2: Chemical & Microbiological Properties (Eds Page, A. L., Miller, R. H. & Keeney, D. R.), pp. 403–430. Madison, WI: American Society of Agronomy.Google Scholar
Oswald, A., de Haan, S., Sanchez, J. & Ccanto, R. (2009). The complexity of simple tillage systems. Journal of Agricultural Science, Cambridge 147, 399–410.CrossRefGoogle Scholar
Oyama, M. & Takehara, H. (1967). Revised Standard Soil Color Charts. Tokyo, Japan: Research Council of Agriculture, Forestry and Fisheries.Google Scholar
Pansak, W., Hilger, T. H., Dercon, G., Kongkaew, T. & Cadisch, G. (2008). Changes in the relationship between soil erosion and N loss pathways after establishing soil conservation systems in uplands of Northeast Thailand. Agriculture Ecosystems and Environment 128, 167–176.Google Scholar
Peel, M. C., Finlayson, B. L. & McMahon, T. A. (2007). Updated world map of the Koppen-Geiger climate classification. Hydrology and Earth System Sciences 11, 1633–1644.Google Scholar
Peters, M., Franco, L. H., Schmidt, A. & Hincapié, B. (2002). Especies forrajeras multipropósito: opciones para productores de Centroamérica. CIAT publication no. 333. Cali, Colombia: Centro Internacional de Agricultura Tropical (CIAT).Google Scholar
Pinheiro, J. C. & Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. Berlin: Springer.CrossRefGoogle Scholar
R Development Core Team (2007). R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Richards, L. A. & Weaver, L. R. (1944). Moisture retention by some irrigated soils as related to soil-moisture tension. Journal of Agricultural Research 69, 215–235.Google Scholar
Rockström, J. & de Rouw, A. (1997). Water, nutrients and slope position in on-farm pearl millet cultivation in the Sahel. Plant and Soil 195, 311–327.CrossRefGoogle Scholar
Rockström, J., Barron, J., Brouwer, J., Galle, S. & de Rouw, A. (1999). On-farm spatial and temporal variability of soil and water in pearl millet cultivation. Soil Science Society of America Journal 63, 1308–1319.Google Scholar
Ruhe, R. V. & Walker, P. H. (1968). Hillslope models and soil formations. I. Open systems. In Transactions of the 9th International Congress of Soil Science vol. 4 (Eds International Society of Soil Science), pp. 551–560. Adelaide: International Society of Soil Science.Google Scholar
Said, A. N. & Tolera, A. (1993). The supplementary value of forage legume hays in sheep feeding: feed intake, nitrogen retention and body weight change. Livestock Production Science 33, 229–237.Google Scholar
Salinas, J. G. & Garcia, R. (1985). Métodos quimicos para el analisis de suelos ácidos y plantas forrajeras. Cali, Colombia: Centro Internacional de Agricultura Tropical (CIAT).Google Scholar
Schmidt, A., Peters, M. & Schultze-Kraft, R. (2001). Desmodium heterocarpon (L.) DC. subsp ovalifolium (Prain) Ohashi. Rome: FAO. Available online at http://www.fao.org/ag/AGP/agpc/doc/Gbase/DATA/Pf000038.htm (verified 11 October 2011).Google Scholar
Stone, J. R., Gilliam, J. W., Cassel, D. K., Daniels, R. B., Nelson, L. A. & Kleiss, H. J. (1985). Effect of erosion and landscape position on the productivity of Piedmont soils. Soil Science Society of America Journal 49, 987–991.Google Scholar
Tardieu, F. (1988). Analysis of the spatial variability of maize root density 1. Effect of wheel compaction on the spatial arrangement of roots. Plant and Soil 107, 259–266.Google Scholar
Thelemann, R., Johnson, G., Sheaffer, C., Banerjee, S., Cai, H. W. & Wyse, D. (2010). The effect of landscape position on biomass crop yield. Agronomy Journal 102, 513–522.Google Scholar
Tiessen, H. & Moir, J. O. (1993). Characterisation of available P by sequential extraction. In Soil Sampling and Methods of Analysis (Ed. Carter, M. R.), pp. 75–86. Boca Raton, FL: CRC Press Inc.Google Scholar
Tittonell, P., Vanlauwe, B., Leffelaar, P. A., Rowe, E. C. & Giller, K. E. (2005). Exploring diversity in soil fertility management of smallholder farms in western Kenya – I. Heterogeneity at region and farm scale. Agriculture, Ecosystems and Environment 110, 149–165.Google Scholar
Unkovich, M. J., Baldock, J. & Peoples, M. B. (2010). Prospects and problems of simple linear models for estimating symbiotic N-2 fixation by crop and pasture legumes. Plant and Soil 329, 75–89.Google Scholar
Vanlauwe, B., Bationo, A., Chianu, J., Giller, K. E., Merckx, R., Mokwunye, U., Ohiokpehai, O., Pypers, P., Tabo, R., Shepherd, K. D., Smaling, E. M. A., Woomer, P. L. & Sanginga, N. (2010). Integrated soil fertility management operational definition and consequences for implementation and dissemination. Outlook on Agriculture 39, 17–24.Google Scholar
Velasquez, E., Lavelle, P. & Andrade, M. (2007). GISQ, a multifunctional indicator of soil quality. Soil Biology and Biochemistry 39, 3066–3080.CrossRefGoogle Scholar
Venables, W. N. & Ripley, B. D. (2002). Modern Applied Statistics with S. 4th edn. Berlin: Springer.Google Scholar
Wezel, A. (2006). Variation of soil and site parameters on extensively and intensively grazed hillslopes in semiarid Cuba. Geoderma 134, 152–159.Google Scholar
Yoder, R. E. (1936). A direct method of aggregate analysis of soil and study of the physical nature of erosion losses. Journal of the American Society of Agronomy 28, 337–351.Google Scholar
Zingore, S., Murwira, H. K., Delve, R. J. & Giller, K. E. (2007). Influence of nutrient management strategies on variability of soil fertility, crop yields and nutrient balances on smallholder farms in Zimbabwe. Agriculture, Ecosystems and Environment 119, 112–126.Google Scholar
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