Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur ($R_{cal}^2$ = 1, $R_{val}^2$= 1 and $RMS{E}_{cal}$ = 0.002, $RMS{E}_{val}$ = 0.003) and Surguja ($R_{cal}^2$ = 1, $R_{val}^2$= 0.99 and $RMS{E}_{cal}$ = 0.004, $RMS{E}_{val}$ = 0.214) districts as compared to the other models, whereas for Bastar, ELNET ($R_{cal}^2$ = 90, $R_{val}^2$= 0.48) and LASSO ($R_{cal}^2$ = 93, $R_{val}^2$= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R² = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.

Residue management has become a new challenge for Indian agriculture and agricultural growth, as well as environmental preservation. The rice-wheat cropping system (RWCS) is predominantly followed cropping system in the Indo-Gangetic plain (IGP), resulting in generating a large volume of agricultural residue. Annually, India produces 620 MT of crop residue, with rice and wheat accounting for 234 MT of the surplus and 30% of the total. Farmers are resorting to burning crop residue due to the short window between paddy harvest and seeding of rabi season crops, namely wheat, potato, and vegetables, for speedy field preparation. Burning of residues pollutes the environment, thus having adverse effects on human and animal health, as well as resulted in a loss of plant important elements. This problem is particularly prevalent in rice-wheat-dominant states such as Punjab, Haryana, Uttarakhand, and Uttar Pradesh. If we may use in situ management as residue retention after chopper and spreader, sowing wheat with Happy seeder/zero drill/special drill with full residue load, full residue, or full residue load incorporation with conventional tillage, burning is not the sole approach for residue management. In addition, off-farm residues generated are being utilized for animal feed and raw materials for industries. While there are regional variations in many mechanization drivers and needs, a wide range of mechanization components can be transported to new places to fit local conditions. This article focuses on innovations, methods, and tactics that are relevant to various mechanization systems in particular geographical areas. This article also stresses the need for a thorough analysis of the amount of residue generated, residue utilization using modern mechanical equipment, and their positive and negative effects on crop yield and yield attributes, weed diversity, soil physic-chemical, biological properties, beneficial, and harmful nematode populations in the IGP, which will aid researchers and policymakers in farming research priorities and policy for ensuring sustainability in RWCS.

Leaf tissue testing is a useful tool for monitoring nutrient requirements in northern highbush blueberry (Vaccinium corymbosum L.; abbreviated as “blueberry”) but may require adaptation to specific growing environments. The objective of this study was to evaluate macronutrient concentrations in early-, mid-, and late-season blueberry cultivars grown in two contrasting environments, specifically eastern and western Washington. Climate and soil conditions between these two regions differ tremendously with eastern Washington being more arid with naturally calcareous soils lower in soil organic matter. Sampling was conducted over a 3-year period in commercial fields. Leaf tissue nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg) and sulfur (S) concentrations were affected by year (Y), growing region (R), cultivar (C), and Day of Year (DOY) that the samples were collected with many interactions. Leaf nutrient concentrations were higher, on average, in western than eastern Washington except for Ca and Mg, indicating sufficiency levels should differ between these regions. Leaf macronutrients generally stabilized between DOY 212–243 (1–31 August), suggesting this period is optimal for tissue sampling. Findings from this study demonstrate the importance of considering regional effects and may be applicable for blueberry cultivated in similar pedo-climactic conditions around the world.