An optimal estimation technique for increasing the accuracy of crop forecasts by combining remotely sensed and conventional forecast results.

Abstract

An optimal estimation (OE) technique has been used to increase the accuracy of crop acreage and yield estimates by combining results from remotely sensed (RS) data and conventional models. For crop acreage estimation the OE increased the accuracy of wheat acreage estimation when the first forecasts of the Directorate of Economics and Statistics (DES) were combined with state level RS estimates over the states of Haryana and Punjab in India. To increase the accuracy of wheat yield forecasts an autoregressive (AR) model was developed. Results of AR model were optimally combined with RS-based estimates for Hisar and Karnal districts in Haryana, India. The OE results for a total of eight forecasts had a lower mean absolute per cent deviation than the forecasts using RS and AR approaches. The power of OE was further demonstrated by combining weather-based wheat yield model results for the state of Punjab (India) with first order AR model results, suggesting an increase in accuracy of forecasts by optimally combining results from two or more algorithms.