Hostname: page-component-848d4c4894-cjp7w Total loading time: 0 Render date: 2024-07-02T11:24:41.070Z Has data issue: false hasContentIssue false
  • English
  • Français

ADDING VALUE TO FIELD-BASED AGRONOMIC RESEARCH THROUGH CLIMATE RISK ASSESSMENT: A CASE STUDY OF MAIZE PRODUCTION IN KITALE, KENYA

Published online by Cambridge University Press:  25 March 2011

P. N. DIXIT ,
P. J. M. COOPER ,
J. DIMES  and
K. P. RAO
P. N. DIXIT*
Affiliation:
International Crops Research Institute for the Semi Arid Tropics (ICRISAT), P.O. Box 39063, Nairobi 00623, Kenya
P. J. M. COOPER
Affiliation:
International Crops Research Institute for the Semi Arid Tropics (ICRISAT), P.O. Box 39063, Nairobi 00623, Kenya
J. DIMES
Affiliation:
International Crops Research Institute for the Semi Arid Tropics (ICRISAT), P.O. Box 776, Bulawayo, Zimbabwe
K. P. RAO
Affiliation:
International Crops Research Institute for the Semi Arid Tropics (ICRISAT), P.O. Box 39063, Nairobi 00623, Kenya
*
‡Corresponding author: p.dixit@cgiar.org
Get access Rights & Permissions [Opens in a new window]

Summary

In sub-Saharan Africa (SSA), rainfed agriculture is the dominant source of food production. Over the past 50 years much agronomic crop research has been undertaken, and the results of such work are used in formulating recommendations for farmers. However, since rainfall is highly variable across seasons the outcomes of such research will depend upon the rainfall characteristics of the seasons during which the work was undertaken. A major constraint that is faced by such research is the length of time for which studies could be continued, typically ranging between three and five years. This begs the question as to what extent the research was able to ‘sample’ the natural longer-term season-to-season rainfall variability. Without knowledge of the full implications of weather variability on the performance of innovations being recommended, farmers cannot be properly advised about the possible weather-induced risks that they may face over time. To overcome this constraint, crop growth simulation models such as the Agricultural Production Systems Simulator (APSIM) can be used as an integral part of field-based agronomic studies. When driven by long-term daily weather data (30+ years), such models can provide weather-induced risk estimates for a wide range of crop, soil and water management innovations for the major rainfed crops of SSA. Where access to long-term weather data is not possible, weather generators such as MarkSim can be used. This study demonstrates the value of such tools in climate risk analyses and assesses the value of the outputs in the context of a high potential maize production area in Kenya. MarkSim generated weather data is shown to provide a satisfactory approximation of recorded weather data at hand, and the output of 50 years of APSIM simulations demonstrate maize yield responses to plant population, weed control and nitrogen (N) fertilizer use that correspond well with results reported in the literature. Weather-induced risk is shown to have important effects on the rates of return ($ per $ invested) to N-fertilizer use which, across seasons and rates of N-application, ranged from 1.1 to 6.2. Similarly, rates of return to weed control and to planting at contrasting populations were also affected by seasonal variations in weather, but were always so high as to not constitute a risk for small-scale farmers. An analysis investigating the relative importance of temperature, radiation and water availability in contributing to weather-induced risk at different maize growth stages corresponded well with crop physiological studies reported in the literature.

Type
Research Article
Information
Experimental Agriculture , Volume 47 , Special Issue 2: Assessing and Addressing Climate-induced Risk in Sub-Saharan Rainfed Agriculture , April 2011 , pp. 317 - 338
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allan, A. Y. (1972). The influence of agronomic factors on maize yields in Western Kenya with special reference to time of plantings. PhD Thesis, University of East Africa, Uganda.Google Scholar
Bationo, A., Waswa, B., Kihara, J. and Kimetu, J. (eds.) (2007). Advances in Integrated Soil Fertility Management in sub-Saharan Africa: Challenges and Opportunities. Dordrecht: Springer.Google Scholar
Chikowo, R., Corbeels, M., Tittonell, P., Vanlauwe, B., Whitbread, A. and Giller, K. E. (2008). Aggregating field-scale knowledge into farm-scale models of African smallholder systems: Summary functions to simulate crop production using APSIM. Agricultural Systems 97: 151–166.CrossRefGoogle Scholar
Cooper, P. J. M. and Law, R. (1977). Soil temperature and its association with maize yields variations in the Highlands of Kenya. Journal of Agricultural Science, Cambridge 89: 355–363.CrossRefGoogle Scholar
Cooper, P. J. M. and Law, R. (1978a). Enhanced soil temperature during very early growth, and its association with maize development and yield in Highlands of Kenya. Journal of Agricultural Science, Cambridge 89: 569–577.CrossRefGoogle Scholar
Cooper, P. J. M. and Law, R. (1978b). Environmental and physiological studies of maize. Final Report-Part 3, Volume 1. Kenya Ministry of Agriculture and UK Ministry of Overseas Development, Maize Agronomy Research Project (R2536 and R2989).Google Scholar
Cooper, P. J. M. (1979). The association between altitude, environmental variables, maize growth and yield in Kenya. Journal of Agricultural Science, Cambridge: 93: 635–649.CrossRefGoogle Scholar
Cooper, P. J. M., Dimes, J., Rao, K. P. C., Shapiro, B., Shiferaw, B. and Twomlow, S. (2008). Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: An essential first step in adapting to future climate change? Agriculture, Ecosystems and Environment 126: 24–35.CrossRefGoogle Scholar
Dimes, J. P. (2005). Application of APSIM to evaluate crop improvement technologies for enhanced water use efficiency in Zimbabwe's SAT. In Management for Improved Water Use Efficiency in the Dry Areas of Africa and West Asia: Proceedings of a Workshop Organized by the Optimizing Soils Water Use (OSWU) Consortium. April 2002, Ankara, Turkey. 203–214.Google Scholar
FURP. (1987). Fertilizer Use Recommendations Project (Phase I). Final Report Annex III. Description of the First priority Sites in the Various Districts. Volume 10. Trans Nzoia District. Ministry of Agriculture and National Agricultural Laboratories Nairobi, June 1987.Google Scholar
Grenz, J. H., Manschadi, A. M., Voil, P., Meinke, H. and Sauerborn, J. (2006). Simulating crop-parasitic weed interactions using APSIM: model evaluation and application. European Journal of Agronomy 24: 257–226.CrossRefGoogle Scholar
Hansen, J. W., Mason, S. J., Sun, L. and Tall, A. (2011). Review of seasonal climate forecasting for agriculture in sub-Saharan Africa. Experimental Agriculture 47: 205–240.CrossRefGoogle Scholar
Hartkamp, A. D., White, J. W. and Hoogenboom, G. (2003). Comparison of three weather generators for crop modeling: a case study for subtropical environments Agricultural Systems 76: 539–560.CrossRefGoogle Scholar
Jama, B., Buresh, R. J. and Place, F. M. (1998). Sesbania tree fallows on phosphorus-deficient sites: maize yield and financial benefit. Agronony Journal 90: 717–726.CrossRefGoogle Scholar
Jones, M. J. (1987). Plant population, rainfall and sorghum production in Botswana. 1. Results of experiment station trials. Experimental Agriculture 23:335–347.CrossRefGoogle Scholar
Jones, P. G. and Thornton, P. K. (2000). MarkSim: Software to generate daily weather data for Latin America and Africa. Agronomy Journal 92: 445–453.CrossRefGoogle Scholar
Kihanda, F. M., Warren, G. P. and Micheni, A. N. (2007). Effects of manures application on crop yield and soil chemical properties in a long-term field trial in semi-arid Kenya. In Advances in Integrated Soil Fertility Management in sub-Saharan Africa: Challenges and Opportunities, 472–485 (EdsBationo, A., Waswa, B., Kihara, J., and Kimetu, J.). Dordrecht: Springer.Google Scholar
McCown, R. L., Hammer, G. L. and Hargreaves, J. N. G. (1996). APSIM: a novel software system for model development, model testing, and simulation in agricultural systems research. Agricultural Systems 50: 255–271.CrossRefGoogle Scholar
Robertson, M. J., Sakala, W., Benson, T. and Shamudzarira, Z. (2005). Simulating response of maize to previous velvet bean (Mucuna pruriens) crop and nitrogen fertilizer in Malawi. Field Crops Research 91: 91–105.CrossRefGoogle Scholar
Rosegrant, M. W., Cai, X. and Cline, S. A. (2002). The role of rain-fed agriculture in the future of global food production. IFPRI, Environment and Production Technology Division Discussion Paper No. 90.Google Scholar
Shamudzarira, Z. and Robertson, M. J. (2002). Simulating response of maize to nitrogen fertilizer in semi-arid Zimbabwe. Experimental Agriculture 38: 79–96.CrossRefGoogle Scholar
Smaling, E. M. A., Nandwa, S. M., Prestele, H., Roetter, R. and Muchena, F. N. (1992). Yield response of maize to fertilizers and manure under different agro-ecological conditions in Kenya. Agriculture, Ecosystems and Environment 41: 241–252.CrossRefGoogle Scholar
Washington, R., Harrison, M., Conway, D., Black, E., Challinor, A., Grimes, D., Jones, R., Morse, A., Kay, G. and Todd, M. (2006). African climate change: taking the shorter route. Bulletin of the American Meteorological Society 87: 1355–1366.CrossRefGoogle Scholar