ANALYSIS OF SUGARCANE ACREAGE AND YIELD ESTIMATES DERIVED FROM REMOTE SENSING DATA AND OTHER HYBRID APPROACHES UNDER FASAL PROJECT
Keywords: FASAL, Remote sensing, Sugarcane, Crop Acreage, Yield estimation, Accuracy Assessment
Abstract. Sugarcane (Saccharum spp.) is a most important cash crop of India. India has the largest area under sugarcane cultivation in the world and is the world’s second largest producer of sugarcane next to Brazil. Uttar Pradesh stands as the leading producer of sugarcane in India followed by Maharashtra. Under FASAL project, the crop acreage and yield forecasts at National/State/District level, is issued at two levels. This study was carried out to analyze under FASAL Project at Mahalanobis National Crop Forecast Centre, New Delhi during year 2018. Five-year state and district level area, yield and production estimates were observed and analyzed using different approaches in 6 major sugarcane producing states of India. The state and district-wise sugarcane area yield and production estimates of MNCFC were compared with the DES Data of the corresponding year. Two statistical parameters were computed namely, Root Mean Square Error RMSE (%) and correlation coefficient (R). The results when compared to the DES statistics revealed that the RMSE (%) for district level analysis were 23.4%, 30% and 18.35%, while the correlation coefficient (R) between the DES and FASAL estimates was computed 0.94, 0.95 and 0.60 for area, production and yield respectively. These results revealed that RS based technique can be effectively used for state level acreage, yield and production estimation. The national level statistics revealed that the correlation of FASAL sugarcane area and production is in good agreement with the DES estimates as compared to Yield estimates.