The South Asian agricultural sector has experienced vigorous growth and structural transformation over the last few decades, albeit differently across the region. This study examines the crop diversification status and various determinants, such as socioeconomic (per capita gross domestic product, population, arable land, and cropland), soil/agronomic (root zone moisture), agricultural inputs (fertilizer and pesticide consumption), the productivity of food and non-food crops, international trade, and climate (maximum and minimum temperature and rainfall) factors. The share of cereals has decreased in most countries, but they continue to dominate South Asian agriculture. The area under high-value crops in India has increased significantly, replacing the area under cereal cultivation during the study period. Similar results were seen in the Maldives, where vegetables replaced oilseeds. The Hausman model test suggested a random-effects model for analyzing the determinants. All the determinants considered in the study explain 69 percent of the variation in the crop diversification index. The crop diversification in South Asia was influenced by per capita gross domestic product (G.D.P.), minimum temperature, pesticide consumption, food crop yield index, and non-food crop yield index during the study period. Cropland percentage and population, on the other hand, reduce crop diversification. The price factor contributed more than half to agricultural growth. It remained the primary source of growth in all South Asian countries, followed by yield, which is identified as the second most crucial factor. The contribution of crop diversification to agricultural growth has been declining over time.

This research illustrates the technical efficiency of the pan-India paddy cultivation status obtained through a stochastic frontier approach. The results suggest that the mean technical efficiency varies from 0.64 in Gujarat to 0.95 in Odisha. Inputs like human labor, mechanical labor, fertilizer, irrigation and insecticide were found to determine the yield in paddy cultivation across India (except for Chhattisgarh). Inefficiency in the paddy production in Punjab, Bihar, West Bengal, Andhra Pradesh, Tamil Nadu, Kerala, Assam, Gujarat and Odisha in 2016–2017 was caused by technical inefficiency due to poor input management, as suggested by the significant σ²U and σ²v values of the stochastic frontier model. In addition, most of the farm groups in the study operated in the high-efficiency group (80–90% technical efficiency). No specific pattern of input use can be visualized through descriptive measures to give any specific policy implication. Thus, machine learning algorithms based on the input parameters were tested on the data in order to predict the farmers’ efficiency class for individual states. The highest mean accuracy of 0.80 for the models of all of the states was achieved in random forest models. Among the various states of India, the best random forest prediction model based on accuracy was fitted to the input data of Bihar (0.91), followed by Uttar Pradesh (0.89), Andhra Pradesh (0.88), Assam (0.88) and West Bengal (0.86). Thus, the study provides a technique for the classification and prediction of a farmer’s efficiency group from the levels of input use in paddy cultivation for each state in the study. The study uses the DES input dataset to classify and predict the efficiency group of the farmer, as other machine learning models in agriculture have used mostly satellite, spectral imaging and soil property data to detect disease, weeds and crops.

Vegetable production in the low and mid hills is highly vulnerable to climatic vulnerability. The study evaluated the Agricultural Climatic Vulnerability Index (ACVI) for 51 blocks in the regions using the IPCC AR4 conceptual framework. The developmental blocks were categorized into three groups (Low, Moderate and Highly Vulnerable) to collect the primary data. A multistage stratified random sampling technique was employed, using a pre-tested questionnaire. The ACVI findings reveal that the Balh Valley is the most climate-vulnerable block, while Paonta Sahib is the least. Vulnerability is primarily driven by the temperature variations in the Kharif and Rabi seasons of exposure dimension. The farm income analysis shows a decline in crop feasibility from low to high-vulnerability groups. Maximum temperature significantly reduced net crop returns, except in the case of cauliflower. Rainfall negatively impacted the profitability of crops such as tomatoes, capsicum and peas. However, an increase in the minimum temperature significantly boosted vegetable crop profitability in vulnerable groups. A balanced use of fertilizer and pesticide application, crop diversification and increased irrigation coverage significantly mitigated climate change impacts across all vulnerability groups and improved crop profitability. Among the crops studied, tomato exhibited the highest carbon sequestration potential, followed by capsicum, pea, French beans and cauliflower. A significant variation was observed in the carbon sequestration level across vulnerability groups. Farmers in these regions have adopted various adaptation strategies, including crop diversification (76.11%), nutrient management (71.11 %), varietal changes (65.56 %), and water conservation (65.56 %). To enhance resilience, the study emphasizes the importance of improved technical knowledge, capacity building, adoption of better agronomic practices, increased financial support, and comprehensive stakeholder consultation within the agricultural and allied sectors.

Using a nationally representative data-set this paper examines the sources of farmers’ income and their determinants. The study finds that middle income regions are more diversified relative to the poor and rich regions. Around 60% of the total income is contributed by agricultural and allied activities and remaining comes from farm and non-farm wage including salaries and non-farm businesses. Diversification is more prevalent in agro-climatically fragile regions characterised by greater risks to primary source of income. It is also found to be higher in low-income regions with relatively better infrastructure. Households in economically well-off regions tend to specialise; and those who diversify do it for income enhancement. In sum, income diversification is more associated with distressed factors. The paper suggests region-specific policies for sustenance of income generating activities for a smooth economic transformation.

Food demand is increasing and diversifying in times of climate change and population growth, which pose challenges to agriculture. In order to ensure the sustainability of modern agriculture, cutting-edge biotechnologies can be used to improve crop yields and nutritional quality. Genome editing is prospering dramatically and has an extraordinary potential to turn into the predominant innovation to deal with the drastically altered biological landscape. A variety of genome editing technologies are available, such as clustered regularly interspaced short palindromic repeats (CRISPR), nucleases targeted to specific sites, and oligonucleotides directed at specific sites are advantageous, and less tedious techniques embraced to mitigate the effects of climate change on agriculture. Furthermore, the availability of fully sequenced genomes of diverse crops, combined with advances in genome editing technologies (GETs) such as zinc finger nucleases (ZFNs), site-specific nucleases (SSNs), mega-nucleases, and transcription activator-like effector nucleases (TALENs), has given plant biologists the power to edit practically any desirable characteristic and supplied a magic stick for the crop's functional genomics. Plant researchers can target any gene of interest with first-generation GETs. This chapter will throw light on how cutting-edge genome editing technologies can aid in the identification and modification of novel genes associated with stress tolerance and also allow for the efficient targeted modification of practically all crops while also speeding up crop improvement operations. <!----> <!----> <!----> <!---->

Agriculture in Odisha is characterized by diverse production systems where rice, green gram and groundnut play critical roles in food, nutrition and income security. This study assessed farmers’ perceptions and attitudes towards these three crops to identify gaps and opportunities for improving production systems. A multistage random sampling design was employed to select 320 farmers across Baragarh (rice), Sonepur (green gram) and Jajpur (groundnut) districts. Data were collected through pretested interviews in Odia and analysed using a modified Likertscale and Chi-square tests to examine associations between perception and socioeconomic variables. Results revealed that ecological conditions, particularly soil and temperature, were generally perceived as favourable for all three crops, while rainfall variability was a recurring constraint. Input supply for fertilisers, improved seed varieties were largely satisfactory, but credit access, labour availability and irrigation emerged as major bottlenecks. Advisory services (training, demonstrations, timely guidance) and government policy support (procurement, incentives, crop insurance, remunerative pricing) were inadequate, limiting the adoption of improved practices. Socioeconomic factors such as age, education, income and market access significantly influenced perception levels. Overall, most farmers fell into the medium perception category, indicating scope for targeted interventions. The findings underscore the need to strengthen extension services, improve institutional credit and irrigation infrastructure and enhance policy implementation to bridge perception gaps. This study provides actionable insights for researchers and policymakers to enhance the productivity, resilience and sustainability of rice, pulse and oilseed systems in eastern India.