Insurance agents often provide crop hail damage estimates based on their personal experience and field samples, which are not always representative of the investigated field’s spatial variability. For these reasons, farmers insurance market ask for a reliable, objective, less labor-intensive method to determine losses. Integrating remote sensing modeling provides unique opportunity estimate dam...

The field experiment was conducted at the Farm of College Agricultural Engineering and Technology, Godhra, Gujarat during year 2018-19 2019-20 to simulate phenology yield rabi maize (Zea mays L.) in sandy loam soil central Gujarat. design split plot with 36 treatment combinations 3 replications including three irrigation regimes (1.0IW/CPE, 0.8IW/CPE 0.6IW/CPE), four mulch conditions (control, ...

Agroecosystem models provide valuable insights into agricultural management decisions and can serve as a useful tool to quantify the effects of practices under varying conditions where field experimentations are impractical. We evaluated newly integrated comprehensive model, DRAINMOD-DSSAT, for hydrology, nitrogen (N) dynamics, crop yield using eight years (1993–2000) measured data from no-till...

Sugarcane is one of the world's main carbohydrates sources. We analysed the APSIM-Sugar (AS) and DSSAT/CANEGRO (DC) models to determine their structural differences, and how these differences affect their predictions of crop growth and production. The AS model under predicted yield at the hotter sites, because the algorithm for computing the degree-days is based in only one upper cardinal tempe...

Accurate in-season crop phenology estimation (CPE) using remote sensing (RS)-based machine-learning methods is challenging because of limited ground-truth data. In this study, a biophysical model was used to guide neural network (NN)-based, CPE. Using the Decision Support System for Agrotechnology Transfer (DSSAT), we conducted uncalibrated simulations corn (Zea mays L.) across Iowa and Illinoi...