A comparative analysis of inter-state variations for three years i.e. 2019–20 to 2021–22 in agricultural development across India had been done by using secondary data from the major agricultural states for 28 key development indicators related to agriculture for Triennium Ending (TE) 2022. The composite indices of development based on the optimum combination of indicators related to agriculture had been worked out for four zones and the overall agricultural states of India. The results of Composite Index (CI) showed that Punjab (0.32), West Bengal (0.37), Gujarat (0.52), and Kerala (0.58) ranked highest in the north, east, west, and south zones, respectively. Overall, the state-wise CI ranged from 0.47 in Punjab to 0.83 in Odisha. The states were further ranked and categorised into high (H), high middle (HM), low middle (LM), and low (L) levels of development. Punjab (0.47), Haryana (0.51), Gujarat (0.59), and Madhya Pradesh (0.60) emerged as the most agriculturally developed states. Significant factors, namely gross irrigated area, mechanisation and technical adoption (tube wells), productivity [wheat (Triticum aestivum L.), maize (Zea mays L.), and vegetables], input usage (chemical fertiliser), and economic dimension of agriculture [sugarcane (Saccharum officinarum L.) returns] were identified among the development indicators. Enhancing these factors could improve the socio-economic conditions of Indian farmers. The study suggested that the low-developed states require improvements in various dimensions in most indicators to enhance the overall development of agriculture.

Understanding how agricultural energy use and cereal production choices—particularly between fine and coarse cereals—shape greenhouse gas (GHG) emissions is crucial for designing effective mitigation strategies in light of agriculture’s major contribution to national emissions and growing climate-induced productivity concerns. This study investigates the dynamic relationships between these factors in India using an Autoregressive Distributed Lag (ARDL) model on data spanning 1975–2019. Pre-analysis (Unit root, an ideal lag length, and co-integration testing) and post-analysis (serial correlation, heteroscedasticity, and recursive residuals) assumptions for ARDL model estimation were tested which came aligned with the research questions. The model robustness statistical diagnostic tests CUSUM (cumulative sum), CUSUMSQ (cumulative sum of squares), and variance decomposition testing were carried out and found to be satisfactory. The study aimed to provide comprehensive analysis of how different cereal types i.e. fine versus coarse cereals influence agricultural energy-emissions relationship and their long run effects on agricultural production-emission scenario of India. Our analysis reveals significant differences in the emissions impacts of different cereal types: while rice and wheat production contribute positively to emissions in the short run (0.06 % and 0.01 % respectively), coarse cereals demonstrate a substantial negative impact (−2.08 %) in the long run. The energy-emissions relationship shows increasing coupling over time, with elasticity rising from 0.02 % in the short run to 1.06 % in the long run. Variance decomposition analysis identifies rice production as the dominant contributor to emissions variability, accounting for 34.43 % of future fluctuations. These findings suggest that strategic crop diversification, particularly increased cultivation of coarse cereals, could significantly reduce agricultural emissions while maintaining food security. The study recommends a three-pronged approach i.e., investing in energy-efficient agricultural technologies, developing policy frameworks to incentivize coarse cereal adoption, and strengthening institutional mechanisms for technology transfer. These insights contribute to the development of targeted policies for sustainable agricultural energy transition in India.

Using monthly wholesale price data from January 2004 to September 2024 with climatic parameters, we built the autoregressive integrated moving average model and the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model to examine the impact of climatic parameters, specifically temperature and rainfall, on enhancing the accuracy of tomato price forecasting in Punjab. We find that minimum temperature and lagged rainfall significantly influence tomato prices and reflect their impact on production and supply dynamics. The SARIMAX [1,1,2],[0,0,3][12] model demonstrated the lowest root mean square error and mean absolute percentage error among all the models we tested, signifying superior performance. The study highlights the importance of integrating weather-based forecasting into agricultural planning to improve market efficiency and enhance farmer resilience. JEL Classification: C53, Q02, Q11, Q12

The study examined inter-state variations in educational development in India using secondary data from 2018-19 to 2020-21. Composite indices based on 26 indicators were evaluated for six zones and overall Indian states. Tamil Nadu, Goa, Kerala, Punjab, Maharashtra, and Sikkim emerged as highly developed states. The index ranged from 0.4599 (Tamil Nadu) to 0.9810 (Meghalaya), classifying states into high, high middle, low middle, and low development levels. Stepwise regression identified factors influencing educational development as number of schools, teacher availability, student pass rates, pupil-teacher ratio, gender parity, literacy gaps, and school infrastructure. Additionally, the study highlighted the declining share of education expenditure relative to GDP. Further, there is a need to increase public investments and improve infrastructure in underdeveloped states to reduce regional disparities.

Climate change is posing a growing threat to all sectors, particularly agriculture, affecting millions of people who are directly or indirectly dependent on this sector. Climate-smart agriculture practices (CSAPs) acts as a catalyst to deal with this growing threat. Multinomial logit and multivalued treatment effects models were deployed to assess the key determinants affecting CSAP adoption and the impact thereof, respectively, from a field survey of 240 farmers selected through a multistage sampling technique during 2020-21. The major determinants for CSAP adoption are the age of the household head, operational landholding, and farmer training. Quantifying CSAP impacts, adoption of short-duration varieties (SDVs), and laser land leveling (LLL) increases paddy crop yield by 6 % (p

This study examined the dynamics of the black gram crop value chain in Punjab, focusing on farmers' willingness to participate. A sample of 60 mash growers from 16 villages in Pathankot and Gurdaspur districts was analyzed. The study identified major channels and value-added products, finding market Channel -I most preferred by growers. Six value chains involving processors were identified, with wholesalers receiving the highest share of marketable surplus. The most efficient channels were observed to be Channel V and VI, in which processors were involved. Among the agri-value chains (Channels-V and VI), the study also revealed the producer's share in the consumer rupee, with VCPB-II having the highest share at 69.18 per cent, followed by VCPB-I at 67.15 per cent, VCBF at 48.39 per cent, VCW at 29.03 per cent and VCN-II at 23.06 per cent. The study also explore d growers' perceptions and willingness to engage in value chains, revealing that 53 per cent were interested in government support. The policy options from the study included transport incentives, capacity building for higher production, and encouragement of processors to enhance black gram cultivation and benefit farmers, contributing to sustainable crop diversification in Punjab.

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.

It is inevitable to acknowledge the greenhouse gas emissions (GHGE)'s primary role in the planet's rising temperatures, which poses threat to ecosystem's sustainability. In India, a 18% of the total GHGE comes from agriculture. Agricultural systems, being complex, need highly efficient energy usage to ensure better yields, and hence, farmer income and food security. Within India, Punjab is the state with greatest agro-economic impact. Therefore, present study is an attempt to quantify the GHGE and energy use efficiency (EUE) in major crops (cotton, maize, paddy, wheat, and sugarcane) of Punjab based on 2019-2020 data. Results revealed that the direct energy and non-renewable energy contribution significantly exceeded the indirect energy and renewable energy, in all crops except sugarcane. Electricity and fertilizers were noted as key areas for energy sink for all crops studied. The specific energy based on economic yield was realized to be significantly higher in cotton (10.23 MJ Kg-1), followed by paddy (5.28 MJ Kg-1), and less than 5 MJ Kg-1 for other crops. High energy intensity indicates that there exists a better potential for further improvement in the energy productivity of cropping systems. In terms of total input-output energy, net energy gain and EUE, sugarcane was noted to be at the top, followed by paddy among other seasonal crops. Paddy was found to emit the highest CO2 eq. emissions (6718 kg CO2 eq. ha-1) of all crops and around 60% was contributed solely by methane (CH4) due to paddy cultivation in submerged water. Thus, optimizing fertilizer rates, precisely guided irrigation systems, adoption of resource conservation technologies (RCTs) i.e., DSR, Laser leveling, minimizing crop residue burning, and using them for energy supply are among best possible alternatives for improving EUE and reducing GHGE.

This study assessed the climate change impacts on productivity for major rabi and kharif in Punjab. We compiled data for 35 years (1986–2020) using 5 crops across Punjab to estimate the impact of climate change by using temperature and rainfall. Our results indicate that productivity decreases with an increase in average temperature in most of the crops. The adverse impact of climate change on agricultural production indicates food security threat to farming community. The findings of the study suggest to focuses on the climate-smart agriculture for effective solution to climate risks.

Recognizing the crop and region-specific irreversible effects of climate change on agriculture is unavoidable. The Southeastern United States region (SE-US) contributes significantly to the United States (US) economy through its diverse agricultural productivity. Climatically, this region is more vulnerable than the rest of the country. This study was designed to quantify the effect of changing climate, i.e., daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation, on oats (Avena sativa L.) and sorghum (Sorghum bicolor L. Moench) in SE-US. The panel data approach with a fixed effects model was applied by creating a production function on a panel dataset (1980-2020) of climate and yield variables. The required diagnostic tests were used to statistically confirm that the dataset was free of multi-collinearity, unit root (non-stationarity), and auto-correlation issues. The results revealed asymmetric warming (Tmin increase > Tmax increase) over the region. Tmax and Tmin significantly increased during the oats growing season (OGS) and sorghum growing season (SGS). Precipitation increased during OGS and decreased during SGS. The growing season average values of Tmax, Tmin, and Tavg (daily average temperature) have shifted by 1.08 degrees C (0.027 degrees C/year), 1.32 degrees C (0.033 degrees C/ year), and 1.20 degrees C (0.030 degrees C/year) in OGS and by 0.92 degrees C (0.023 degrees C/year), 1.32 degrees C (0.033 degrees C/year), and 1.12 degrees C (0.028 degrees C/year) in SGS. However, precipitation had shifted by 23.2 mm (0.58 mm/year) in OGS and shifted (decreased) by-5.2 mm (-0.13 mm/year) in SGS. Precipitation had a non-significant effect on oats and sorghum yields. With every 1 degrees C increase in Tmin and Tmax, oats yield was reduced by (-5%) and (-4%), respectively, whereas sorghum yield was increased by (+13%) and decreased by (-7%), respectively. Taken together, a 1 degrees C net rise in overall temperature reduced oats yield (-9%) while increased sorghum yield (+6%).

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.

The study uses the fixed effect panel model to examine the impact of the Covid-19 pandemic on market arrivals and wholesale prices of potato in Punjab. We found that the wholesale prices of potato increased over different phases of the lockdown due to decrease in supply. However, after a sharp increase in potato prices in April, there was a decline due to the easing of restrictions during subsequent phases of the lockdown. In addition, during the lockdown, the arrivals were negatively affected due to the restrictions on inter and intra-state movements. However, improving market infrastructure and strengthening linkages with the market intermediaries may increase markets' resilience to such disruptions.

The COVID-19 pandemic has adversely affected dairy farmers with the demand shrinking due to income losses of the consumers, disruptions in the supply chains reduc- ing supply, raising costs and increasing wastage. The present study examined such disruptions in Punjab, India, with the primary survey covering dairy farmers, intermediaries, consumers and other stakeholders in the dairy industry. The results reveal a significant fall in farmgate milk prices, disruption in transporting milk within the supply chains, labour shortages, rise in pro- duction costs and lack of operating capital. The demand for milk and milk products declined sharply during the pandemic. To dispose of the excess milk supply, dairy farmers turned to localized value chains catering directly to consumer households. Approximately half of the far- mers lost almost one-third of their income from processed milk products like ghee and butter. The dairy farmers agreed to strengthen the dairy value chains through better integration of the stakeholders. The inability of the farmers to quickly shift to digital platforms for sales of milk and milk products during the pandemic calls for special capacity-building efforts.

Punjab Agriculture is trapped in the complex nexus of groundwater depletion and food insecurity. The policymakers are concerned about reducing groundwater extraction at any cost for irrigation without jeopardizing food security. In this regard, the Government of Punjab introduced the “Punjab Preservation of Subsoil Water Act, 2009”. The present paper examines the impact of the “Preservation of Sub Soil Water Act, 2009” on pre- and post-water levels in Punjab using the difference-in-difference (DiD) approach. The state has witnessed a severe fall of 0.50 m per year and 0.43 m per year for the post-monsoon and pre-monsoon season, respectively. Only 2.62 per cent of wells were in the range of 20–40 m depth in the state in 1996, which increased to 42 per cent and 67 per cent in 2018 for the pre-monsoon period, and post monsoon period respectively, depicting an increase of 25 times. The groundwater depth in high rice-growing(treated) districts declined by 1.53 and 1.39 m than the low rice-growing (control) districts in the pre-monsoon and post-monsoon periods respectively post the enactment of PPSW Act, 2009. A groundwater governance framework is urgently needed to manage the existing and future challenges connected with the groundwater resource.