Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R²), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R² and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R² values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R² values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

Introduction: Climate change, nutritional security, land shrinkage, and an increasing human population are the most concerning factors in agriculture, which are further complicated by deteriorating soil health. Among several ways to address these issues, the most prominent and cost-effective means is to adopt an integrated farming system (IFS). Integrating farming systems with livestock enables a way to increase economic yield per unit area per unit of time for farmers in small and marginal categories. This system effectively utilizes the waste materials by recycling them via linking appropriate components, thereby minimizing the pollution caused to the environment. Further integrating livestock components with crops and the production of eggs, meat, and milk leads to nutritional security and stable farmer's income generation. So, there is a dire need to develop an eco-friendly, ecologically safe, and economically profitable IFS model.Methods: An experiment was conducted to develop a crop–livestock-based integrated farming system model for the benefit of irrigated upland farmers in the semi-arid tropics for increasing productivity, farm income, employment generation, and food and nutritional security through efficient utilization of resources in the farming system.Results and discussion: The IFS model has components, viz., crop (0.85 ha) + horticulture (0.10 ha) + 2 cattles along with 2 calves in dairy (50 m²) + 12 female goats and 1 male goat (50 m²) + 150 numbers of poultry birds (50 m²) + vermicompost (50 m²) + kitchen garden (0.02 ha) + boundary planting + supporting activities (0.01 ha) in a one-hectare area. The model recorded a higher total MEY (162.31 t), gross return (689,773), net return (317,765), and employment generation (475 mandays). Further negative emissions of −15,118 CO₂-e (kg) greenhouse gases were recorded under this model. The study conclusively reveals that integration of crop, horticulture, dairy, goat, poultry, vermicompost production, kitchen garden, and boundary planting models increases the net returns, B:C ratio, employment generation, nutritional security, and livelihoods of small and marginal farmers.

Agriculture is a significant contributor to greenhouse gas (GHG) emissions, with practices such as fertilizer application, soil tillage, and livestock management releasing carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4). To meet the demands of a growing population, it is essential to identify agricultural practices that boost food production while simultaneously reducing greenhouse gas emissions, contributing to climate change mitigation and adaptation. This study aimed to quantify GHG emissions from various farm typologies of marginal and smallholder households in the western regions of Tamil Nadu, India, using the Cool Farm Tool (CFT). Data was collected from 250 households in Coimbatore, Tiruppur, and Erode districts during 2022-2023 and farm typologies were identified through multivariate analysis, revealing four farm types: (i) cereal crop-dominated marginal farms, (ii) livestock-dominated marginal farms, (iii) cash crop-dominated marginal farms, and (iv) plantation crop-dominated small farms. The results show that cash crop and plantation crop dominated farms had the highest emissions, particularly from crop residue burning, fertilizer production and fertilizer application. In contrast, cereal crop-dominated farms recorded lower emissions. Livestock dominated farm type exhibited higher GHG emission from enteric fermentation and manure management due to higher number of livestock. The results of this study highlight the importance of developing farm-specific mitigation strategies to reduce emissions. The study also underscores the value of using tools such as CFT for comprehensive GHG quantification to ensure sustainable agricultural practices.

Brinjal (Solanum melongena) is one of the most tropical vegetable crops cultivated worldwide. Rhizosphere microbial dynamics play a crucial role in plant nutrition, providing valuable insights into soil fertility and sustainable agricultural practices. This study aims to identify sustainable nutrient management practices for brinjal, focusing on the rhizosphere microbiome by examining various nutrient management approaches, including integrated nutrient management (INM), inorganic fertilization, and organic fertilization. Root architectural analysis, LC-MS-based metabolite profiling, and shotgun metagenomics were employed to assess the various nutrient management-induced changes in metabolites and the microbial community. The result suggested that superior root features, including volume (16.3 cm³), surface area (399.48 cm²), and total root length (794.89 cm), were achieved under INM. Additionally, it encompassed the highest number and diversity of root metabolites, including both primary and secondary compounds. This can be the reason for INM maintaining a balance between the representation of bacteria (87.4%) and fungi (12.4%), with Actinomycota and Ascomycota being the dominant groups. Further diversity analyses revealed that INM soils supported the highest microbial richness and OTU abundance, while inorganic fertilization favored greater evenness of taxa but lower richness. Organic soils harbored unique, less abundant taxa, reflected in higher Fisher’s alpha values. The beta diversity analysis indicated distinct microbial community structures across different treatments. Therefore, INM is a sustainable solution for brinjal cultivation, since it improves crop performance, soil health, and microbial ecosystem services.

Background: Cotton, a popular fiber crop, is in high demand for textiles due to its natural properties. It plays a vital role in biological cycles and sustainable yield. A field experiment to compare natural, organic farming and integrated crop management to assess cotton fiber qualities was conducted in Tamil Nadu Agricultural University, Coimbatore, for two years (2021-22 and 2022-23) in Cotton cv. CO 17. Methods: The experiment had nine treatments, arranged in a Randomized Block Design and replicated thrice. A complete Zero Budget Natural Farming Practices were used, including Ghanajeevamirit as Basal, seed treatment with Beejamirit, Jeevamirit applied twice a month through irrigation, intercropping, mulching and Whapasa (Alternate Furrow irrigation). Organic and Integrated plots were also included. Result: The use of beejamirit, jeevamirit and ghanajeevamirit in natural farming led to substantial seed cotton yield of 1287 kg ha-1 in 2021-22 and 1364 kg ha-1 in 2022-23, comparable to organic plots of 1695 kg ha-1 in 2021-22 and 1768 kg ha-1 in 2022-23, while improving soil health, biodiversity and pest management. Quality characteristics did not significantly differ among the different farming practices.

Cotton, a valuable cash crop from the Gossypium genus, significantly contributes to the national economy. A primary challenge in cotton cultivation is the high labor demand for sowing. As labor shortages grow, sowing machinery has emerged as a viable alternative, decreasing labor expenses and operating duration. However, machine sowing requires wider inter-row spacing, and cotton's slow initial vegetative growth presents an opportunity to incorporate suitable intercrops. This technique optimizes resource utilization and offers potential supplementary income from intercropping in the event of primary cotton crop failure due to natural disasters. This study aims to identify suitable intercropping systems under mechanized sowing conditions to enhance yield, competitive indices, energy efficiency, and economic viability, supporting sustainable farmer incomes. The experiment employed a split-plot design with two main plots, five subplots, and three replications. Results showed that machine sowing was more profitable than manual sowing, reducing cultivation costs by 19.6% and increasing net returns by 22.7%, with a per-day return of 22.6%. Among intercropping systems, cotton + maize demonstrated superior performance, achieving significantly higher cotton-equivalent yield (22.2%), land equivalent ratio (32.0%), area-time equivalent ratio (21.0%), energy use efficiency (57.4%), energy productivity (63.5%), and net return (29.6%) compared to sole cotton. The study concluded that cotton + maize intercropping under mechanized sowing conditions improves yield, competitive indices, energy, and economic efficiency, enhancing overall farm productivity.