Assessing soil fertility is crucial for developing effective soil management strategies that can enhance soil health, increase crop productivity, and promote sustainable agricultural practices. The current study was conducted to assess the soil fertility index and, to prepare a soil fertility zonation map using combine fuzzy and analytical hierarchy process (AHP) approaches in Udham Singh Nagar of Uttarakhand. Sixty GPSbased surface soil samples were collected (0-30 cm depth) from different locations, and analyzed chemical properties using a stratified multistage random sampling method and maps were prepared to identify their spatial distribution. The results show that the values of soil fertility index on the fuzzy scale (0-1) was varied from 0.04-0.62 and, therefore, the study area was classified as very low, low, and moderate soil fertility classes comprising 55.61%, 44.24% and 0.14%, respectively. AHP analysis revealed that the most important limiting factor for wheat production was available nitrogen, followed by phosphorous, potassium, organic carbon, pH and electrical conductivity. A correlation coefficient between wheat yield and soil fertility index was found to be as high as 0.86, and its validating the zonation of soil fertility classes. This study infers that combined fuzzy-AHP techniques may be used to compute soil fertility index and limiting factors of wheat production.

Frost, an important meteorological extreme event has discernible impacts on agricultural crops specifically in Indo-Gangetic plains. The information on time of occurrence of frost and its frequency can help in reducing the risks and avoidable losses due to frost. The study investigated the occurrence of frost indices viz., onset, cessation and frost-free duration at nine locations in six States in Gangetic Plains of India during November to March (151 Days) using daily minimum temperature from 1980 to 2015. The multiple risk levels (90%, 70% and 50%) were used to determine the occurrence of frost probability for all frost indices. Trends in the frost indices were analyzed using the non-parametric Mann-Kendall test. The results showed the onset of frost mostly in December and January, whereas cessation of frost was observed during January to March with variations in date of onset and cessation from one location to another. The mean value of frost duration varied between 42 days at Amritsar to 2 days at Delhi during past 36 years. Number of frost days showed negative trends reflecting reduction in duration of frost. The rate of decrease in frost duration ranges from 0.5 day to 1.5 days per decade depending on the location. Trends for onset, cessation and frost-free duration were inconsistent at different stations.

Multiple regression approach has been used to forecast the crop production widely. This study has been undertaken to evaluate the performance of stepwise and Lasso (Least absolute shrinkage and selection operator) regression technique in variable selection and development of wheat forecast model for crop yield using weather data and wheat yield for the period of 1984-2015, collected from IARI, New Delhi. Statistical parameters viz. R-2, RMSE, and MAPE were 0.81, 195.90 and 4.54 per cent respectively with stepwise regression and 0.95, 99.27, 2.7 percentage, respectively with Lasso regression. Forecast models were validated during 2013-14 and 2014-15. Prediction errors were -8.5 and 10.14 per cent with stepwise and 1.89 and 1.64 percent with the Lasso. This shows that performance of Lasso regression is better than stepwise regression to some extent.

Remote sensing-based Crop evapotranspiration can help and provide guidance for crop water management. This paper quantified crop water requirement and crop water stress for soybean according to vegetation cover and land surface changes in the semi-arid region using remote sensing data. In this regard, MODIS products of land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) were used to estimate crop coefficient (Kc) and crop water stress index (CWSI) for period of 2016-17. The FAO-Penman- Monteith (FPM) model was employed to compute reference evapotranspiration (ETo). Lysimeter crop data were collected IARI, New Delhi to evaluate performance of remote sensed crop evapotranspiration (ETc). The performance evaluation results viz. MBE, RMSE and MPE were found 0.44mm, 0.66mm and 25.15% respectively. A linear relation between lysimeter and remote sensed crop coefficient were established correlation coefficient as high as 0.86. Crop water requirement during crop growing period were 304.40mm and 349.50mm by remote sensing and lysimeter respectively. Crop was not suffering from water stress during initial to mid-stage, however, increasing trend was found at late-stage. It can be inferred that ETc and CWSI developed from remotely sensed indices are a useful tool for quantifying crop water consumption at regional and field scales.

Reliable forecast of crop production before the harvest is important for advance planning, formulation and implementation policies dealing with food procurement, its distribution, pricing structure, import and export decisions, and storage and marketing of the agricultural commodities. Weather plays a very important role in crop growth and development. Therefore, model based on weather variables can provide reliable forecast. Weather variables used can be employed for crop production forecast by making appropriate models. In this study, a statistical model is used for crop yield forecast at different growth stages of wheat crop. This model uses maximum and minimum temperature, rainfall, morning and evening relative humidity during crop growing period. The forecast model was developed using generated weather indices as regressors in model. In order to select significant weather variables affecting the yield of crop least absolute shrinkage and selection operator (LASSO) as well as stepwise regression methodology is applied. The result of lasso gives a better result as compared to stepwise regression. The R2 of lasso and stepwise regression are 0.84 and 0.85, respectively. The mean square error (MSE) and root mean square error (RMSE) of Lasso regression were better than stepwise regression, which leads to improvement of crop yield forecasting. It can be inferred that for the data under consideration, lasso works better than stepwise regression for variable selection.