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.

Climate-Smart Agricultural (CSA) technologies are essential in enhancing agricultural sustainability and resilience in the face of climate change. However, the successful adoption of these technologies remains a complex challenge influenced by various factors. This research aims to provide a systematic review of the factors affecting the adoption of CSA technologies and thoroughly examine academic research in this rapidly developing study area. Scientific mapping was conducted, analytical techniques were used to understand the annual scientific production countries with the highest citations and co-occurrence network analysis was also done using the key search terms of CSA adoption research. The literature on determinants of CSA adoption is synthesized in terms of theories, contexts, characteristics and methods (TCCM) framework to propose future research directions.Additionally, the study develops a conceptual framework incorporating the commonly mentioned antecedents, mediators, moderators and consequences in existing literature. Drawing upon an extensive review of peer-reviewed articles, this study categorizes factors into 3 main dimensions: Socio-Demographic and Socioeconomic factors, Farm and Land factors and Technological and Informational factors. This review of the research also reveals the impact of positive and negative aspects onCSA's adoption. In addition, this review summarizes the theoretical perspectives taken in this field of study. Furthermore, to develop this research domain, some interesting future research directions have been recommended by identifying understudied research areas.

Climate and climate change are critical factors influencing global ecosystems, agriculture and forestry. Climate refers to the long-term patterns of temperature, precipitation and atmospheric conditions, while climate change refers to significant alterations in these patterns over time. This review examines the impacts of climate change on natural systems, with particular focus on variations in temperature, precipitation and related climatic variables. The review integrates advanced statistical methodologies, including homogeneity tests, trend analysis and change-point detection, to analyze climate data and identify patterns of variability and shifts over time. Homogeneity tests are used to detect sudden changes or inhomogeneities caused by non-climatic variables (from station relocations or instrumental changes). Trend analysis is employed to detect long-term changes in climate variables such as temperature and precipitation, providing information on existing climate changes. Change-point detection tests help identify specific points at which significant shifts in climatic patterns occur, which may influence sudden transitions in ecosystems. Collectively, these tools enhance our ability to detect, interpret and respond to the evolving nature of climate change, thereby supporting timely and informed adaptation strategies in the fields of agriculture and forestry.

Ethnomedical practices of the tribal community in the Nilgiris district were documented and analyzed to assess their knowledge and perception of Indigenous Technical Knowledge (ITK) and to understand the constraints faced in utilizing the same. This study, in doing so, presents a discussion of the interface of socioeconomic variables with indigenous traditional knowledge to highlight the determinants that facilitate the retention and transmission of traditional medicine knowledge in these communities. The present study adopted an exploratory research design. Data were collected from six tribal communities through snowball sampling, with a total of 120 respondents in two blocks of the Nilgiris district. While analyzing the relationships, the socio-economic characteristics were correlated with the knowledge levels and constraints on traditional medicinal practices. Some of the other tools that were applied to these studies included rank-based quotients and multiple regression analysis. Findings indicate that a major section of the tribal population has a medium to low level of knowledge of ITK, with perception varying due to age, income, and information availability. Superstitions, extraction of scientific principles involved in allopathic science, and most of all, lack of interest shown by the younger generation are major factors inhibiting the spread of the practices. Diagnosing these challenges with specified community engagement strategies, education, and access to traditional medicine is thus the highway to sustaining this invaluable cultural heritage. The research suggests a holistic approach to conserve ITK and traditional medicine by marrying modern healthcare with traditional practices to maintain their relevance for posterity.

Optimization of parameters in food product processing is essential for achieving high-quality output and efficient production. While the use of fresh green bananas is limited, drying them into powder expands their applications significantly. This study focuses on optimizing the production of banana powder, which has numerous applications in the food industry. Key parameters such as steaming time, citric acid concentration, and drying time need to be optimized using Box-Behnken design of response surface methodology (RSM), as they directly impact the quality of the banana powder. The moisture content of the sample was measured using the gravimetric method, while water activity was assessed with a digital water activity meter. Amylose content was determined using a calorimeter, while starch content of the sample was measured using the anthrone method and color characteristics were evaluated with a spectrophotometer. The quadratic model was found to be the best fit for most of the responses (R² = 0.96–1), and the linear model was fit for starch content (R² = 0.73). Results obtained using Box-Behnken Design of response surface methodology reveals that steaming time of 10 minutes, citric acid concentration of 0.5% and a drying time of 7 hours are ideal conditions for enhance powder making. Under the optimized condition, the values of yield, moisture content, water activity, amylose, starch content and L* was predicted to be 19.99%, 5.73%, 0.40 aw, 23.43 g/100g, 86.72 g/100g and 50.82 of banana powder respectively, aligning with the experimental values using the desirability function.

Open and distance learning (ODL) plays a crucial role in agricultural education due to its potential to transform knowledge dissemination. It enhances agricultural productivity and addresses educational accessibility challenges. This study employs systematic literature review and bibliometric analysis to examine the evolution and impact of ODL in agricultural education. A total of 759 relevant studies were identified from the Web of Science database and analysed using VOS viewer and R Studio to map research trends and thematic clusters. The selection process was systematically documented following the guidelines outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The analysis highlights the growing scholarly interest in this field, with significant research output emerging between 2020 and 2024 and peaking at 90 articles in 2022. The bibliometric analysis reveals diverse authorship with 3789 contributors and a strong collaborative focus, as evidenced by a 36.76 % international co authorship rate. The thematic analysis underscores four major clusters focusing on the impact of COVID-19, training and technology integration, educational frameworks and curriculum design. Geographical contexts indicate varied implementation across regions, with countries such as the USA, the UK, Germany and Italy emerging as central nodes in research networks. Future research should explore low-tech solutions, assess long-term impacts, integrate emerging technologies and establish robust evaluation frameworks. The findings highlight ODL's vital role in creating an inclusive, knowledge driven agricultural sector aligning with global development goals.