Integrating SAR Sentinel-1A and DSSAT CROPGRO Simulation Model for Peanut Yield Gap Analysis

Abstract

Crop yield data are critical for managing agricultural sustainability and assessing national food security. This study aims at increasing peanut productivity from its current levels by analyzing the yield gap (difference) of potential production between theoretical yield and actual farmers’ yields. The spatial yield gap of peanut for the Tiruvannamalai district of Tamil Nadu is examined in this investigation by integrating the products of microwave remote sensing (SAR Sentinel-1A) with the DSSAT CROPGRO Peanut simulation model. The CROPGRO (crop growth) Peanut model was calibrated and validated by conducting a field experiment at Oilseeds Research Station, Tindivanam during Rabi (spring) 2019 for predominant cultivars, i.e., TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing crop cutting experiments (CCEs) with the help of the Department of Agriculture Economics and Statistics in the respective monitoring villages. The regression analysis between the maximum recorded DSSAT leaf area index (LAI) at the peak flowering stage of peanut and the yield recorded by CCEs for the spatial yield estimation of peanut in the Tiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The DSSAT CROPGRO simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (from 1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in peanut for Tiruvannamalai district were assessed as 1890, 2324 and 2134 kg/ha, respectively. In order to reduce the production difference of peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

Keywords

• remote sensing
• synthetic aperture radar
• oilseed crops
• spatial variability
• precision agriculture