Optimizing Multiple Cropping Systems: A Systems Approach

Abstract

ABSTRACT THE objective of this study was to develop a framework for optimizing multiple cropping systems by selecting cropping sequences and their management practices as affected by weather and cropping history. Several alternative formulations of multiple cropping problems were studied with regard to their practicality for solutions. A deterministric activity network model that combined simulation and optimization techniques was developed to study this problem. Irrigation strategies of various crops were used to demonstrate the ability of the model to determine crop sequences as well as within-season management practices. Application of the model to other types of management decisions (i.e. pesticide application) was also discussed. Simulation models were needed to predict crop yield responses to management practices and to simulate the state of the field after each crop. In this case, the state of the field was the remaining soil water. A crop phenology model was used to predict the duration of each crop, depending on when it was planted and subsequent weather conditions. A crop yield response model estimated the yield of the crop based on weather and irrigation management. A soil water balance model predicted available soil water during each season and also provided the values for available water during the time when a new crop was being selected. These models were combined to simulate the decision network. Then, the K Longest Path algorithm was applied to optimize cropping sequences. The results showed that combined network optimization-simulation was an effective way to study multiple cropping systems. With appropriate simulation models, this approach can be used to study multiple cropping under various management conditions.