Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India  
[PERSON]  
1 Geospatial Sciences and Big Data, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad 502324, India; [EMAIL_ADDRESS] (R.M. ); [EMAIL_ADDRESS] (P.K.B. ); [EMAIL_ADDRESS] (P.P. ); malianagaraj  
In Telangana, where rice is a dominant cultivated crop, the development of assured irrigation following the completion of irrigation projects and Mission Kakatiya [4, 5] resulted in a marked 29.9% to 50.3% increase in production from 2014-2015 to 2020-2021, with a subsequent 29.9% increase from 2019-2020 (19.3 million tons) to 2021-2021 (25.1 million tons) [6]. This cemented the state's position as a leader in the nation for paddy production. Meanwhile, Karimnagar is known as the Rice Bowl of Telangana due to its wide rice intensification area. The area under paddy in Karimnagar during 2019 and 2020 _Kharif_ (wet) and _rabi_ (dry) was 0.096 and 0.10 M ha, occupying more than 50% of the total cultivated area under Telangana (i.e., 1.96 and 2.13 M ha during 2020 and 2021) [7]. Annual rice crop yields differ considerably due to varied environmental circumstances. Hence, precise and timely crop yield statistics at national, international, and regional levels are becoming progressively essential to overcome food security worldwide [8]. This will ensure effective cropland management, policymaking, import and export decisions, insurance premium pricing, and long-term agricultural food production [9]. Recently many methods, including empirical formulae, remote sensing, and crop simulation modeling, have been applied to estimate crop yield at different scales. Remote sensing has proven extremely beneficial in monitoring agricultural crop growth as it accurately represents the agricultural sector with high revisit frequency and precision [10]. Several techniques based on machine learning have been employed to map crop extent [11, 12, 13]. For instance, Earth Resource Data Analysis System (ERDAS) has been used for supervised classification [14, 15]. Crop production varies geographically and temporally within an area, making it crucial to assess the pattern of grain yield spatially over the entire field to inform certain management strategies [16, 17]. Variations in the yield depend on many internal and external factors. Spatial maps depicting crop yield have been widely employed to elucidate factors contributing to yield variability within plots [18]. Yield loss can also be assessed based on other factors, such as drought and submergence, using remotely sensed data [19]. However, various vegetation indices should be employed as each provides a distinctive set of wave bands that can be associated with distinct crops and their growth phases [20]. Regional differences in crop extent, crop development phases, and leaf area index (LAI) are vital for spatial yield prediction [21]. Different crop simulation models have been used to analyze the potential effects of climate change (sensitivity analysis) on crop yields in varied regions of France, the United States, India, and other countries [22, 23]. The DSSAT (Decision Support System for Agro-Technology Transfer) crop model comprises the Crop Environment Resource Synthesis (CERES) model and Cropping System Model (CSM) and can be utilized for approximately 42 crops. Several studies have shown that CERES rice exhibits high agreement between predicted and observed yields under varied management practices in India [24, 25, 26]. CERES-maize and wheat have been used to study the effect of spatial precipitation variability and management practices on yield in the United States [27], India [28], and Canada [29]. For yield gap analysis, the DSSAT model is widely used [30, 31] under different scenarios. Though remote sensing and crop modeling methods have advantages, they are limited by the need for more accurate data throughout the crop season and the lack of available satellite data during cloudy days.