Crop Selection and Land Allocation under Uncertainty: Multi-Product Multi-Objective Optimization

Abstract

The agriculture sector plays an essential role in a country’s development. This is a primary source through which the human’s basic needs are fulfilled. Getting the maximum output of agricultural land is a challenge for professionals. By utilizing agricultural land resources effectively, the revenue is increased. This research aims to develop a mathematical model for sustainable production considering economic, environmental, and social factors under uncertainty. Addressing crop rotation in different areas is more critical to acquiring the fundamental goals and objectives by optimizing crop selection and land allocation optimally. Optimal results are computed by a mathematical programming model using MATLAB that deals with multi-objectives for multi products under uncertain circumstances and ensure the high utilization of resources, fulfills national food security, provides sustainable production techniques, and creates job opportunities with human health safety. The Augmented Epsilon Constraint Method with lexicographic optimization has dealt with conflicting multi-objectives (including Profit, Job opportunities, and yield). The proposed model was validated with primary data from the farmers in the Peshawar district of Pakistan. The land is classified based on land types such as rainfed, irrigated, etc. This study compares the single cropping pattern with intercropping, considering the three objectives. Three crops, ladyfinger, tomato, and round gourd were selected along with their intercropping combinations. The result shows that intercropping of ladyfinger with a round gourd gives a high profit and yield, whereas the intercropping of tomato with ladyfinger creates more jobs. Labor is the leading crop-farming resource, contributing 53% to the total cost. The proposed approach of crop selection and land allocation with intercropping shows an increase in total revenue, job opportunities, and yield compared to the traditional approach that has been practiced for the last decade.