In this study, the Agro-ecological zones (AEZs) of Uttarakhand were delineated based on land use/land cover, slope, soil texture, temperature and length of growing period (LGP) by using remote sensing and GIS. The Decision Tree Classifier (DTC) algorithm technique was used for delineation of Agro-ecological zones (AEZs). The land use/ land cover map was used as base map and slope map of entire state (other than snow bound region) having five classes (0-5°, 5-15°, 15-30°, 30-50° and >50°) was overlaid on soil texture map having three soil classes (frigid soils, loamy soils and sandy soils). Thereafter, temperature map with three thermal regimes (<0°C, 0-15°C, and >15°C) and length of growing period with two distinct classes (<120 days and >120 days) were overlaid on existing map. The small classes having number of pixel <1000 and the regions having temperature <0°C and slope of >50° were removed from the analysis because agriculture is not possible over these regions. Thereafter, the entire state of Uttarakhand was divided into 38 agro-ecological zones (AEZs).

The current study "Calibration and validation of CERES-Wheat model for wheat varieties under Raipur district of Chhattisgarh" was carried out in the Department of Agrometerology, IGKV, Raipur, for which a field experiment was done at the college of Agriculture's farm field Raipur at latitude of 21.25’ N, longitudes 81.62’E and altitude 289.5 m above mean sea level. Along with 3 wheat variety (Kanchan, HD2967 and CG1013) and three growing environment D1 (26th Nov), D2 (06th Dec) and D3 (16th Dec). This paper aimed to Genetic co-efficient of three wheat varieties determined and calibration & validation the DSSAT model. Result of study revealed that closer estimation of Validation shows days to anthesis, days to maturity and grain yields % of error and Root mean square erro (RMSE) observed values were 2.8%, 2.8%, 4.2% of error and 1.41%,1.41%, 2.12% RMSE in D1, D2, D3 for Kanchan variety and HD2967 were observed 1.4%, 4.1%, 5.6% of error and 0.71%, 2.12%, 2.83% of RMSE respectively. CG1013 observed in 1.4%, 4.2%, 7.0% of error and 0.71%, 2.12%, 3.54% RMSE respectively. Days to maturity observed in 2.7%, 6.5%, 8.6% of error and 2.12%, 4.93%, 6.36% RMSE in D1, D2, D3 for Kanchan variety and HD2967 were observed 2.7%, 1.8%, 7.4% of error and 2.12%, 1.41%, 5.66% RMSE respectively. CG1013 observed values were 0.9%, 4.7%, 7.7% and RMSE observed were 0.71%, 3.54%, 5.66% respectively. Grain yield observed were 3.9%, 1.1%, 0.6% of error and 81.32%, 21.92%, 11.31% RMSE in D1, D2, D3 for Kanchan variety and HD2967 were observed 2.7%, -2.1, 0.8% and 47.38%, 31.82%, 12.02% of RMSE respectively. CG1013 observed in 0.8%, 4.1%, 0.5% and 17.68%, 84.85%, 9.90% RMSE respectively. The model has been successfully calibrated and validated for wheat growing in Raipur, Chhattisgarh environment and can now it can be taken for further applications in natural resources management and climate change impact studies. The model performance was evaluated using % of error and RMSE and it was observed that DASST CERES model was able to predict the growth parameters like days to anthsis, day to maturity and grain yield with reasonably good accuracy (error% less than 10).