Efficacy of Different Herbicides on Weed Flora of Berseem (\u3cem\u3eTrifolium alexandrium L.\u3c/em\u3e)

Berseem is one of the prominent winter legume fodder crops. It has 20-24% crude protein and 70% digestible dry matter. Common weeds found in berseem are Cichorium intybus, Cornopus didimus, Spergula arvensis, Chenopodium album, Rumex dentatus and some grass family weeds. Weeds compete with main crop for essential plant nutrients, light, moisture and space. They not only deteriorate fodder quality but also decrease fodder and seed yield. Weed infestation reduces normally 25-35% green fodder and seed yield. It is the major challenge to control the berseem weeds for enhancement of productivity and quality of fodder and seed yield. Hence the present investigation is undertaken to study the efficacy of some herbicides for berseem weed management

Understanding Soil Carbon and Phosphorus Dynamics under Grass-Legume Intercropping in a Semi-Arid Region

An integrated forage-legume cropping system has immense potential to address the issue of land degradability. It provides a critical understanding of the capacity of diversified species mixes vs. monocultures to boost forage production and the dynamics of soil organic carbon (SOC) and phosphorus (P). In this study, we assessed the performance of Napier Bajra Hybrid (NBH) (Pennisetum glaucum × P. purpureum) + cowpea (Vigna unguiculata) and tri-specific hybrid (TSH) (P. glaucum × P. purpureum × P. squamulatum) + cluster bean (Cyamopsis tetragonoloba) as compared to monocultures of NBH and TSH. The legume equivalent yield of NBH + cowpea and TSH + cluster bean intercropping systems were found −31% and −23% higher than monoculture systems. The SOC increased by −5% in the NBH + cowpea system as compared to NBH monoculture. The carbon mineralization rates under NBH + cowpea and TSH + cluster bean were −32% and −38% lower than the NBH and TSH monoculture cropping systems, respectively. It was found that the legume intensification with the forage significantly improved the soil’s P status. The research suggested that coalescing diverse crops (e.g., grass and legume) poses enormous potential for sustaining soil health and productivity in semi-arid regions of India. This study advances the research on characterizing the crucial factors of grass-legume-based cropping systems and helps in assessing the impact of these factors on long-term sustainability

Response of Organic and Inorganic Nutrient Sources on Growth, Productivity and Nutrient Content of Wheat

A study was conducted at college farm of Swami Keshwanand Rajasthan Agricultural University, Bikaner, Rajasthan, India to evaluate the influence of integrated application of different nutrient sources on growth, yield and nutrient content of wheat (Triticum aestivum L). Experiment was conducted in a randomized block design with three replications. Under different nutrient sources, i.e. control, 50 to 100% RDF, FYM 5 t ha-1, biofertilizers and their combined application were done. Findings exhibits that the application of 75 % RDF+5 t FYM ha-1+Azotobacter+PSB in wheat, significantly enhanced all growth (dry matter, chlorophyll content, total tillers, CGR, RGR and others) & yield attributes (Effective tillers, test weight and others), grain yield (4.12 t ha-1) and as quality, nutrient (N, P, K) content and protein content of wheat over rest of treatments, but it remained statistically at par with 100 % RDF+5 t FYM ha-1+ Azotobacter+PSB (grain yield 4.18 t ha-1). Thus, it is concluded that for better nutrient management, an integration of organic, inorganic and biofertilizers sould be done. With application of 75 % RDF+5 t FYM ha-1+Azotobacter+PSB, there is 25% saving of nutrients as compared to 100 % RDF+5 t FYM ha-1+ Azotobacter+PSB

Evaluation of machine learning models for prediction of daily reference evapotranspiration in semi-arid India

Reference evapotranspiration (ET0) is controlled by climatic factors; hence, its estimation provides an idea about the atmospheric demand of water. Machine learning techniques like elastic net (ELNET), K-nearest neighbours (KNN), multivariate adaptive regression splines (MARS), partial least squares regression (PSLR), random forest (RF), support vector regression (SVR), XGBoost and cubist were employed to predict daily reference evapotranspiration based on daily weather parameters of twenty years. Penman-Monteith method was used as the reference method for ET0 estimation. All models performed well during calibration showing higher coefficient of determination (R2) which ranged from 0.97 (for PLSR) to 1 (for cubist models). Mean absolute error during calibration ranges from 0.027 mm-1 d-1 for cubist to 0.607 mm-1 d-1 for ELNET. Cubist model (R2= 1, MAE = 0.017 mm d-1, RMSE = 0.027 mm d-1) outper formed other models during the calibration. During validation, the coefficient of determination (R2) for the machine learning models varied from 0.819 to 1, RMSE varied from 0.06 to 0.60 mm d-1 and MAE varied from 0.031 to 0.38 mm d-1. Based on statistical parameters, best performance was observed for cubist model (R2 = 1, RMSE = 0.06 mm d-1, MAE = 0.031 mm d-1) among the studied machine learning models for the prediction of reference evapotranspiration. Hence, the cubist model may be used to estimate daily reference evapotranspiration for the studied region