The sustenance of marginal and small farm households is a pertinent question given that their number is on the rise in South-Asia. The study aims to assess their present socio-economic profile and the challenges faced in order to draw a roadmap of development for these underprivileged households. The study pertains to Indian Punjab and data from the Situation Assessment Survey of the 70th Round of NSSO are used. The concepts of economic surplus, occupational diversity, farm productivity and vertical and horizontal inequalities are used to achieve the objectives. This study reveals that marginal farm households faced food insecurity as they failed to meet consumption expenditure from the income earned. In fact, small farm households are left with an annual economic surplus of Rs 8890 per capita only, after meeting consumption needs. Income is unevenly distributed among farm households with a Gini coefficient of 0.48. The majority of the marginal and small farm households fall in lower-income quintiles and are occupationally more diversified than their larger counterparts. Horizontal inequalities are lower between the farm-size categories (0.14) than within farm-size categories (0.27). The Gini coefficient within each farm-size category is the highest among marginal farm households (0.50), followed by small farm households (0.45), highlighting their economic stress and tug-of-war survival. Further, the farm households belonging to socially lower castes falls only in the marginal farm-size category and represent the lowest income. Development must be sustainable and inclusive, hence, policies to develop marginal farmers’ centric farming systems and high value crops such as potato, cotton, sugarcane and oilseeds, providing high yielding livestock breeds, value addition through farmer-producer organizations, non-farm employment through MGNREGA, provision of institutional credit at subsidised rate of interest and quality health and education facilities in the public sector are recommended to uplift the affected households.

Agriculture sector is major sufferer of climate change both at a global level as well as at India level. Cereals account for about 92 % of India's total food grain output and climate change has a significant influence on the production of cereals. This study aimed to evaluate the long-term and short-term effects of climatic and non-climatic variables, specifically temperature, precipitation, cereal area, total cropped area, fertilizer consumption, and pesticide consumption, on cereal production in India. The study included annual time series data that covered the period from 1960 to 2018, covering a period of 58 years. Various econometric techniques were employed to examine these relationships. The validity of a long-term and short-term relationship among the relevant variables included in the study was validated by employing the Autoregressive Distributed Lag (ARDL) technique and the Johansen cointegration test. The ARDL model's estimation outcomes reveals that input factors such as cereal area became a key factor in rising cereal production, as evidenced by its positive coefficient. Similarly, fertilizer consumption and precipitation had positive effects on production in the long run whereas total cropped area and minimum temperature has little influence over the results of production both in short run as well as long run. Furthermore, the long-term findings were also supported using econometric tools like Canonical Cointegrating Regression (CCR) and Fully Modified Least Squares (FMOLS). These methods confirmed that variations in cereal production in India were significantly influenced by both climatic factors and agricultural inputs and factors. The study emphasizes the urgency for policymakers to prioritize proactive measures aimed at reducing the adverse impacts of climate change on cereal production in India. This necessitates a comprehensive strategy integrating sustainable practices, technological innovations, and robust policy frameworks to ensure resilient agricultural sectors and sustainable food production.

The study examines the performance of buffalo meat (HS-020230) trade globally and from India, assesses the export competitiveness of buffalo meat export, and identifies the determinants of export growth of buffalo meat from India. The study’s empirical finding reveals that India has approximately 54% of the world’s buffalo population and is one of the major buffalo meat suppliers to the international market. India’s proportion of worldwide buffalo meat exports has increased from 3.34% in 2000 to 11.23% in 2020. During TE 2020, India exported 3052.4 million USD worth of buffalo meat to the rest of the globe, and the single-largest reported destination for India’s buffalo exports was Vietnam, contributing 34% of India’s buffalo meat export. The study determines India’s competitiveness in the buffalo meat sector, and the results showed that China has RCA value greater than one, clearly indicating the strength of India in exporting meat to China. Buffalo population, total livestock population, and GDP of India have been identified as important determinants of the export of buffalo meat from India.

The present study examines the trade performance of millets, competitiveness and their impact on the country’s growth in the agriculture sector. Empirical findings revealed that Indian millet exports contributed significantly to the global basket from the year 2000–2020 and had a huge potential in the international market. During the year 2011–2020, India’s major exporting partners were neighbouring countries, i.e. Pakistan, Vietnam, Nepal and Saudi Arabia, wherein India exported more than 50% of the country’s millet export. The study looked at India’s comparative advantage of millets by country by using Revealed Comparative Advantage (RCA), and the results showed that Pakistan, Saudi Arabia, Vietnam, and Yemen were the major millet importing countries exhibiting RCA value greater than one, indicating that India had a significant advantage in exporting millets to these partner countries during the recent study period (2011–2020). Furthermore, gravity model is calibrated by using the variables like Gross Domestic Product (GDP), exchange rate and population of the respective countries. The results of the gravity model suggested that the country's GDP would grow with an increase in millets exports in India, but the country's exports would decrease with an increase in population.

Purpose: Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.Design/methodology/approach: This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.Findings: The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.Originality/value: The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

Uncertain price movement in staple food commodities puts agrarian economies at risk if not monitored and managed consistently. Hence, an attempt has been made to analyze the price behavior and integration across major wholesale and retail markets for rice and wheat in India. Monthly data (July 2000 to June 2022) on prices viz. wholesale and retail were sourced from the Food and Agriculture Organization and analyzed using growth rate, instability index, seasonal price index, Bai-Perron’s test for structural breaks, Johansen’s test on cointegration, Granger causality test, and impulse response function. Findings indicated strong evidence of price dynamics in the selected markets in terms of spatial and temporal variation, clear-cut seasonality linking to production, and price divergence between wholesale and retail markets. Johansen’s test indicated a strong cointegration between wholesale and retail prices after accounting for structural breaks, exhibiting unidirectional-, bidirectional- and no causality. Impulse response analysis revealed that the selected wheat and rice markets are efficient in terms of ‘price discovery’ which takes place initially in the wholesale market, and is then transmitted to the retail market. The study advocates decision-making information to the producers, traders, and consumers who are interested in taking advantage of the price movement. It is concluded that strengthening the market intelligence and reducing the distortion in markets will improve the existing overall performance.