Semi Physical Approach for Sugarcane Yield Modelling with Remotely Sensed Inputs

Abstract

Sugarcane, being a cash crop, its yield forecasting is essential for making various agricultural decisions related to price, storage, export/import etc. Hence, the present study was carried out to estimate yield of sugarcane crop using remote sensing technology. This study developed an intermediate method based on the use of remote sensing and the physiological concepts such as the photo-synthetically active radiation (PAR) and the fraction of PAR absorbed by the crop (fAPAR) in Monteith’s radiation use efficiency equation). Net Primary Productivity (NPP) has been computed using the Monteith model and moisture stress has been applied to convert the potential NPP to actual NPP. Sugarcane yield was calculated using the NPP, harvest index and radiation use efficiency. Net Primary Productivity was assessed during the sugarcane crop season of 2015-16. Water stress scalar showed values ranged between 0.01 to 0.2 (Maximum stress) in Bahraich, Saharanpur, Meerut and Muzaffarnagar in the month of April and May as compare to the central and eastern part of Uttar Pradesh. APAR was found higher in western Uttar Pradesh ranged from 35.0 to 73.6 MJ/m2 which gradually decreased in the month of July which ranged between 25.0 - 75.2 MJ/m2. During crop season maximum yield was found between 50-60 t/ha for most of the western districts of Uttar Pradesh.