SEASONAL CROP YIELD FORECASTING-METHODS, ACCURACIES AND LIMITATIONS: A REVIEW

Abstract

Accurate crop yield forecasting helps the government to formulate sound policies related to import and exports, allocation of food grains and price setting. Similarly, the traders and industries can make decisions regarding business activities like wages, purchase of raw materials and working hours. Crop management practices can be standardized to get maximum yield to reduce the pre and post harvest losses of produce. The impact of climate change on the crops can also be known. This forecasting can be done using different techniques like statistical models and crop simulation models. The information of weather, plant characters, environment, remote sensors etc. can be used as input data for forecasting. It can be concluded that forecasting the crop yield near the harvest is more accurate, with r2= 0.7-0.8 being predominant than at the early stages (r2=0.5-0.6). Further improvement in the accuracy in forecasting is possible with the use of artificial intelligence and machine learning.