AGRICULTURAL PRODUCTION IN PAPUA NEW GUINEA USING OPTIMIZATION AND STOCHASTIC MODELLING

Abstract

In Papua New Guinea, agriculture is one of the most important sectors, as it provides a livelihood of more than 80% of the rural population by contributing significantly to national income and food security. This paper applies optimization and stochastic modelling techniques to analyse agricultural production across selected provinces in PNG. A linear programming model is developed to determine the best way to allocate agricultural land among major crops such as coffee, cocoa, sweet potato, banana, taro, and coconut. A stochastic model is used to represent how crop yields can change due to uncertain factors such as weather, pests, or climate conditions by simulating these variations and assess production risk. Agricultural data were obtained from Food and Agriculture Organization Corporate Statistical Database (FAOSTAT), national agriculture reports and other published sources. The results demonstrate that using optimization to decide how land is allocated to crops can significantly improve agricultural productivity compared to traditional farming methods. The stochastic model highlights the importance of risk-aware decision-making, mainly under unpredictable climate conditions. The study concludes that combining optimization and stochastic modelling provides useful framework for improving agriculture planning and decision making. The findings may assist policy makers, agricultural planners and rural development agencies to promote sustainable agriculture production and efficient resources allocation.