Overview

Small-holder peanut farmers are under increasing pressure to increase peanut production, in the face of increasing constraints of limited natural resources and increasing degradation of their natural Soil resources. A systems analysis approach could be very helpful to effectively address some of these issues, based on analyzing different components of the peanut production system, identifying potential limiting factors within this production system, and integrating various research outcomes into computer simulation models. The computer models can then be used in a decision support and delivery mode, to recommend alternative management practices that will improve production efficiency, while at the same time maintaining effective use of the natural resources.

Weather uncertainty affects peanut yields (too little food and income). During hot dry years, peanut yields will be reduced and the risk of aflatoxin will increase. A systems modeling approach could be helpful to evaluate peanut breeding, crop management, and governmental policy aspects to improve response to weather risks, both over the long-term of historical weather (as in cultivar improvement) or over present, immediate food shortage (to set governmental policy to meet shortfall). Such a systems modeling approach could be used to maximize the efficient use of natural resources used in peanut production.

Low yields of peanut (Arachis hypogaea L.) are common in developing countries. A systems approach, using peanut crop growth simulation, can be used to evaluate potential yields as determined by weather conditions and soil water holding traits. In field situations, the actual yields may be much lower, but armed with simulated potential yield information, researchers could then focus on non-weather induced limitations related to management, genotype, soil, and pests. Our experience with crop model analyses relative to peanut experiments conducted in India have indicated a substantial “yield gap” not associated with climatic limitations. Discovering “yield gaps” in this way can lead to experiments to evaluate yield losses caused by biotic stresses or management.