A Fuzzy Approach to Linear Programming in Agriculture Land Allocation

Abstract

The agri-supply chain involves a number of highly uncertain factors, such as soil content, rainfall, humidity, and production forecasts, which may be addressed using fuzzy logic, which has proved to be helpful in managing uncertainty in a number of Concerns pertaining to agriculture. While establishing the best option for agriculture production planning, land constraints play a vital role. In agriculture, "land constraint" refers to limits or conditions placed on agricultural activities based on the quantity and quality of available land. This restriction may have a major effect on the agricultural sector's overall growth, sustainability, and productivity. The output function is the primary purpose of agricultural land; yet, as technology advances, the risk associated with land output is rising, particularly in places that produce a lot of grain and in urban suburbs. The proposed study suggest a fuzzy linear mathematical programming method (FLMP) aimed at the best distribution of arable land. FLMP is a more practical as well as adaptable way to find the optimal solution under uncertain circumstances like determining agricultural land distribution for different crops to optimize profit. The proposed model administrate the decision-making issues which are constructed with aspects of uncertainty and defined using fuzzy linear programming (FLP) models. For this purpose, A numerical example is also illustrated by using triangular fuzzy membership function. Making decisions by using this type of approximation can be practical and more profitable for land owners.