Abstract
Many factors interact to determine crop production. Cropping systems have evolved or been developed to achieve high yields, relying on practices that eliminate or minimize yield reducing factors. However, this is not entirely the case in many developing countries where subsistence farming is common. The soils in these countries are mainly coarse-textured, have low water holding capacity, and are low in fertility or fertility declines rapidly with time. Apart from poor soils, there is considerable annual variability in climate, and weeds, insects and diseases may damage the crop considerably. In such conditions, the gap between actual and potential yield is very large. These complexities make it difficult to use cropping system models, due not only to the many inputs needed for factors that may interact to reduce yield, but also to the uncertainty in measuring or estimating those inputs. To determine which input uncertainties (weather, crop or soil) dominate model output, we conducted a global sensitivity analysis using the DSSAT cropping system model in three contrasting production situations, varying in environments and management conditions from irrigated high nutrient inputs (Florida, USA) to rainfed crops with manure application (Damari, Niger) or with no nutrient inputs (Wa, Ghana). Sensitivities to uncertainties in cultivar parameters accounted for about 90% of yield variability under the intensive management system in Florida, whereas soil water and nutrient parameters dominated uncertainties in simulated yields in Niger and Ghana, respectively. Results showed that yield sensitivities to soil parameters dominated those for cultivar parameters in degraded soils and low input cropping systems. These results provide strong evidence that cropping system models can be used for studying crop performance under a wide range of conditions. But our results also show that the use of models under low-input, degraded soil conditions requires accurate determination of soil parameters for reliable yield predictions.
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References
Bationo A, Mokwunye U, Vlek PLG, Koala S, Shapiro BI (2003) Soil fertility management for sustainable land use in the West African Sudano-Sahelian zone. In: Gichuru MP et al (eds) Soil fertility management in Africa: a regional perspective. Academy Science Publisher & Tropical Soil Biology and Fertility, Nairobi, pp 253–292
Bennett JM, Mutti LSM, Rao PSC, Jones JW (1989) Interactive effects of nitrogen and water stresses on biomass accumulation, nitrogen uptake, and seed yield of maize. Field Crop Res 19:297–311
Boote KJ, Jones JW, Batchelor WD, Nafziger ED, Myers O (2003) Genetic coefficients in the CROPGRO-soybean model: links to field performance and genomics. Agron J 95:32–51
Casenave A, et Valentin C (1989) Les états de surface de la zone sahelienne; Influence sur l’infiltration. Les processus et les facteurs de réorganisarion superficielle. (ed) ORSTOM – Institut Français de Recherche Scientifique pour le Développement en Coopération. Collection Didactiques. Paris, pp 65–190
Dzotsi KA, Jones JW, Adiku SGK, Naab JB, Singh U, Porter CH, Gijsman AJ (2010) Modeling soil and plant phosphorus within DSSAT. Ecol Model 221:2839–2849
FAO (2001) Lecture notes on the major soils of the world. World Soil Res Rep. 289 pp
Fatondji D, Bationo A, Tabo R, Jones JW, Adamou A, Hassane O (2012) (this volume) Water use and yield of millet under the zai system: understanding the processes using simulation
Fatondji D, Martius C, Bielders C, Vlek P, Bationo A, Gérard B (2006) Effect of planting technique and amendment type on pearl millet yield, nutrient uptake, and water use on degraded land in Niger. Nutr Cycl Agroecosyst 76:203–217
Gijsman AJ, Hoogenboom G, Parton WJ, Kerridge PC (2002) Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter-residue module from CENTURY. Agron J 94:462–474
Godwin DC, Singh U (1998) Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Springer, Dordrecht, pp 55–78
Hammer GL, Muchow RC (1991) Climatic risk in crop production: models and management for the semi-arid tropics and subtropics. CAB International, Wallingford, pp 205–232
He J (2008) Best management practice development with the CERES-maize model for sweet corn production in North Florida. PhD dissertation, University of Florida, Gainesville, 329 pp
Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Batchelor WD, Hunt LA, Boote KJ, Singh U, Uryasev O, Bowen WT, Gijsman AJ, du Toit AS, White JW, Tsuji GY (2004) Decision support system for agrotechnology transfer version 4.0, [CDROM]. University of Hawaii, Honolulu
Hunt LA, Pararajasingham S, Jones JW, Hoogenboom G, Imamura DT, Ogoshi RM (1993) GENCALC: software to facilitate the use of crop models for analyzing field experiments. Agron J 85:1090–1094
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18(3–4):235–265
Matthews RB, Stephens W (2002) Crop-soil simulation models: applications in developing countries. CABI Publishing, Wallingford, 280 pp
Monod H, Naud C, Makowski D (2006) Uncertainty and sensitivity analysis for crop models. In: Wallach D, Makowski D, Jones JW (eds) Working with dynamic crop models: evaluation, analysis, parameterization, and applications. Elsevier, Amsterdam, pp 84–87
Naab JB (2005) Measuring and assessing soil carbon sequestration by agricultural systems in developing countries, 2004 annual report. Savanna Agricultural Research Institute, Wa
Naab JB, Koo J, Traore PCS, Adiku SGK, Jones JW, Boote KJ (2008) Carbon Enhancing Management Systems(CEMS): estimation of soil carbon sequestration potential in small-holder farming systems in Northern Ghana. Technical bulletin 2008–3, ABE Department, University of Florida, Gainesville, 11 pp
Porter CH, Jones JW, Adiku S, Gijsman AJ, Gargiulo O, Naab JB (2010) Modeling organic carbon and carbon-mediated soil processes in DSSAT v4.5. Oper Res Int J 10(3):247–278
R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. ISBN 3-900051-07-0
Ritchie JT (1998) Soil water balance and plant water stress. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Springer, Dordrecht, pp 41–54
Ritchie JT, Alagarswamy G (1989) Genetic coefficients for CERES models. In: Virmani SM, Tandon HLS, Alagarswamy G (eds) Modeling the growth and development of sorghum and pearl millet. ICRISAT research bulletin no. 12. ICRISAT, Patancheru, pp 27–34
Ritchie JT, Singh U, Godwin DC, Bowen WT (1998) Cereal growth, development and yield. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Springer, Dordrecht, pp 79–98
Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity analysis in practice: a guide to assessing scientific models. Wiley, Chichester
SimLab (2005) SimLab Ver. 2.2. Reference manual
Sobol IM (1993) Sensitivity estimates for non-linear mathematical models. Math Model Comp Exp 1(4):407–414
Soil Survey Staff (1998) Keys to soil taxonomy, 8th edn. USDA/NRCS, Washington, DC
Thornton PK, Wilkens PW (1998) Understanding options for agricultural production: systems approaches for sustainable agricultural development. Kluwer, Dordrecht, pp 329–345
Williams JR (1991) Runoff and water erosion. In: Hanks RJ, Ritchie JT (eds) Modeling plant and soil systems. Agronomy monograph #31, American Society of Agronomy, Madison
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Jones, J.W., Naab, J., Fatondji, D., Dzotsi, K., Adiku, S., He, J. (2012). Uncertainties in Simulating Crop Performance in Degraded Soils and Low Input Production Systems. In: Kihara, J., Fatondji, D., Jones, J., Hoogenboom, G., Tabo, R., Bationo, A. (eds) Improving Soil Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer (DSSAT). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2960-5_4
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DOI: https://doi.org/10.1007/978-94-007-2960-5_4
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