Background: The per capita net availability of pulses in India has been increased from 15.5 kg per year in 2018-19 to 19.6 kg in 2021-22. Efforts made to bridge the gap between demand and supply of pulses in the country has resulted in reducing the gap to some extent in recent years and still country is depended on import to meet the growing demand of the pulses such as pigeon pea, lentils and peas. Few studies predicted the demand and supply of pulses as a whole in the country and no studies analyzed the pulse wise demand supply gap in India. Hence, the present study was proposed to predict the demand and supply of major pulses such as chickpea (Gram/Chana), pigeon pea (Tur/Arhar), black gram (urd bean), mung bean (Green gram) and lentil (Masur) in India for a period, 2024-2030. Methods: The present study has been used the crop data on area, production and yield of major pulses including chickpea (Gram/Chana), pigeon pea (Tur/Arhar), black gram (urd bean), mung bean (Green gram) and lentil (Masur) for a period of 29 years (1985-2024) collected from the Directorate of Economics and Statistics, Government of India, New Delhi. Household consumption expenditure data was collected from the National Sample Survey office for 2011-12 from the Government of India. The supply projection was estimated by using linear regression model and demand projections were done by using behaviouristic approach. Result: Decadal trend in area, production and yield of pulses from 1970-2010 showed mixed trends of increase and decrease, whereas in 2010-2020, chickpea, pigeon pea, mung bean and black gram showed positive trend in area, production and yield but lentil alone showed negative trend in production. Import dependency of the pulses in the total availability has reduced to 8.92 per cent in 2021-22 from 19.42 per cent in 2009-10. Import dependency of the pulses in the total availability has reduced to 8.92 per cent in 2021-22 from 19.42 per cent in 2009-10. The availability of pulses has grown at a rate of 3.73 per cent from 2009 to 2021. Chick Pea, Tur, Mung bean, Urd bean and Lentils together contributed on an average 88.7 per cent to the total pulses production and 83.73 per cent of pulses availability from 2013 to 2021. Production of these five pulses has increased to 23.04 million tonnes in 2020-21 from 17.3 million tonnes from 2013-14. Gram is the single pulse contributed more than 40 per cent of the pulse production of the country in the above period. Demand supply projection showed a net surplus of 0.81-3.89 mt in the case of pulses and 5.64 -7.63 mt in the case of gram and 0.89 to 1.91 mt in Urd and 0.68 to 1.57 mt in Mung bean during 2024-2030. But in the case of pigeon pea and lentil there may be a net deficit in the range of 3-2.42 and 1.13-1.04 mt, respectively, during 2024-2030. Efforts should be made to bridge the gap in demand and supply of pigeon pea and lentil by the way of bringing new areas under pulses and developing the technologies suitable for the clusters.

This research investigates price volatility and market integration within select small onion markets across Tamil Nadu, India. Using monthly wholesale price data from the Dindigul, Chennai, Coimbatore, and Idukki markets for the period 2014 to 2023, the study employs an array of econometric methods to analyze price dynamics and market interconnections. The ARCHGARCH model analysis revealed high price volatility persistence in selected small onion markets with the Coimbatore market displaying the highest levels of volatility. The Augmented Dickey-Fuller test established stationarity within the price series, while cointegration tests indicated a long-term equilibrium relationship across the markets analyzed in Tamil Nadu. Further examination through the Granger causality test revealed that price movements in the Coimbatore market had a causal impact on the Dindigul market. Additionally, the markets in Coimbatore, Dindigul, and Chennai were shown to exert influence over the Idukki market in Kerala. The Vector Error Correction Model (VECM) analysis demonstrated that in response to disequilibrium across the Coimbatore, Chennai, Dindigul, and Idukki markets, short -run price adjustments occurred at rates of 41%, 45%, 46%, and 48%, respectively, working towards restoring long-run equilibrium. In the Dindigul market, price adjustments with a one-month lag had a notable influence on current prices within Coimbatore, Chennai, and Dindigul. These findings underscore the interconnected nature of small onion markets within the region and shed light on price behavior patterns. Insights derived from this study could inform policy measures aimed at stabilizing prices and enhancing market efficiency in the small onion markets.

The paper explores the relationship between energy use, economic growth and emission rates for Asian giants such as China, India and Japan. As these countries are the regional economic powers and also the major global carbon emitters, it is inevitable to conduct a research study to find the possible relationship between energy use, emission and growth among these countries by the panel data analysis over the period from 1991 to 2020. Higher energy usage increases greenhouse gas (GHG) emissions in countries, with agricultural sectors, Foreign Direct Investment (FDI) and urban populations contributing significantly. Energy use influences economic growth and emission levels, positively affecting GHG emissions. Furthermore, current initiatives taken by these three Asian superpowers for net zero carbon emission and relevant suggestions are also highlighted for emission reduction without compromising the economic growth and sustainable use of energy resources.

Groundnut, predominantly cultivated as a rainfed crop, is highly susceptible to significant price volatility. This study aimed to investigate and enhance the performance of traditional models for forecasting the realized volatility of groundnut price returns across five Indian states (Tamil Nadu, Telangana, Karnataka, Maharashtra, and Gujarat) by evaluating traditional models and neural network-based frameworks. Using groundnut price returns data spanning fourteen years and six months (01 January 2010 to 30 June 2024), weekly realized volatility was computed. The predictive behaviour of Heterogeneous Autoregression (HAR)-based neural network frameworks was evaluated. Neural networks were assessed using time series cross- validation, and model metrics were employed to generate Model Confidence Sets (MCS). These sets were ranked based on model inclusion. The Extended Cochran-Armitage test was applied to identify and compare the best-performing models. Subsequently, model forecasts were tested and compared using the two-sided Diebold-Mariano test, and Model Confidence Sets were generated to evaluate predictive performance. For this unconventional weekly realized volatility forecast, the HAR (1,6,12) framework emerged as the most effective. Notably, the implementation of Convolutional Neural Network (CNNs) combined with RNNs, such as Conv1D-GRU and Conv1D-LSTM, demonstrated superior and consistent predictive performance across all states. Among standalone neural networks, GRU performed on par with CNN-based RNNs. These findings highlight the potential of CNN and GRU models as effective and accurate methods for forecasting agricultural price volatility.

Maize is one of the leading staple cereals in the world in terms of production and it is termed as a most versatile and multipurpose industrial and energy crop. Due to varied number of value-added products evolving in the recent years, maize has a major shift towards indirect consumption in Tamil Nadu. Therefore, the study is selected to analyse market efficiency of different marketing channels in the maize value chain in Tamil Nadu. The primary data on cultivation of maize is collected from 60 maize producers of Perambalur and Salem districts of Tamil Nadu. Value chain analysis of maize includes producers, traders, commission agents, processors (poultry feed units), wholesalers, retailers and the consumers (livestock farmers). Price spread and marketing efficiency of different marketing channels is calculated. Among the three existing marketing channels of maize in Tamil Nadu, Channel III is considered to be the best channel by the maize producers and it is confirmed by the Acharya’s approach which have higher marketing efficiency. The farmers share in consumer rupee in the marketing channel III was high compared to other marketing channels. The study is intended to help the maize farmers in adopting a better marketing channel where they can get better profit in maize cultivation by adopting less intervention of market intermediaries.

The study examines the interdependence between the returns of Nifty 50 Equity Index and Agricultural Commodities cotton, mentha oil, guar seed, jeera, turmeric and coriander over a period of time. By applying wavelet analysis, the interdependence of agricultural commodities and equity index during the COVID-19 pandemic, identified periods of both strong and weak correlations between these markets. It is critical to note that the reasons for the link between agricultural commodities and equity indexes during COVID-19 pandemic may vary according to market conditions, regional considerations and other contextual factors. According to this study, Nifty 50 has higher co-movements with MCX Cotton, MCX Mentha Oil, and NCDEX Turmeric and lower co-movements with NCDEX Guar seed, NCDEX Jeera and NCDEX Coriander. The low coherence intervals indicate the ability for commodities investments to diversify in the face of a pandemic such as Covid-19. The observed trends by commodity category revealed their potential utility in the development of cross-asset hedge strategies. Thus, combining commodities and stocks increases performance over a variety of investment horizons.. KEYWORDS :Commodities market, Equity market, Co-movements, Wavelet analysis, Covid-19.

The outbreak of corona virus has affected the financial market internationally in an unprecedented way. Due to the devastations that emerged in the international market, the Indian financial market proportionally reacted to the pandemic and further witnessed violent volatility. Considering the COVID-19 situation, this paper is an empirical investigation on the impact of COVID-19 on agricultural commodities, specifically on NCDEX platform. Using daily closing future prices of guar seed, jeera, turmeric, and Coriander on NCDEX, this study examines the impact of COVID-19 on the selected commodities over the period from 24th December 2019 to 24th June 2020, representing three months before and during the covid – 19 spread. This study has tried to compare the future prices in the pre-COVID-19 period and during the COVID-19 situation, by using GARCH Model. Findings reveal that the price of jeera, turmeric and coriander has encountered instability during the Corona pandemic period.

Aim: The main aim of the study is to estimate the degree of market integration among the selected major Copra markets in India. Place and Duration of Study: The study was carried out using the secondary data of average monthly prices of copra obtained from the AGMARKNET website from the period of 2014-2021 for Tamil Nadu and Karnataka. Copra price series data of Tamil Nadu (Anaimalai, Avalpoondurai, and Vellakoil markets) and Karnataka (Tiptur market) has been utilized for the study. Methodology: The time-series econometric tools including the Augmented Dickey-Fuller (ADF) test, Johansen cointegration test, and Granger causality test were used for analyzing the level of market integration. E-views software has been utilized to perform the entire analysis. Results: The unit root effects were applied to the price series for copra in the selected markets, and they were stationary at their first difference. The long-run equilibrium relationship among the copra markets revealed that the selected markets were integrated with each other. Tiptur market exhibited a bidirectional causational relationship with Anaimalai, Avalpoondurai, and Vellakoil markets. The Granger Causality test revealed the Tiptur market as the lead market, as it influenced the prices of Anaimalai, Avalpoondurai, and Vellakoil Copra markets. Conclusion: India's coconut market is prone to volatility and uncertainty due to frequent price fluctuations. Price changes typically result from altered market conditions brought on by seasonal and annual variations in output, in addition to competition from other edible oils. The government can put measures into place to lessen price fluctuations with the aid of integrated market information and the information would also benefit the Copra producers and other market players to select the most effective market.

Derivatives are innovative financial instruments in the 21st century to help the market participants in mitigating the risk. Commodity derivatives are not new to the world, but reentered with new face into the fray. In India, derivatives are introduced at first on index and followed by securities and commodities phase wise for the betterment of the markets and the price discovery. The present study explores the association and trend between the Spot and Futures Commodity Derivatives Market in India before and during the Covid – 19 pandemic. The study uses descriptive, trend analysis and correlation analysis. The analysis done by using the MCX four major agriculture and non-agriculture commodities such as Cotton, Mentha oil, Crude oil and Natural gas spot as well as future price. Indian commodity futures market can be used as hedging tool with financial instruments for diversifying the risk during crisis period.

In the recent past, agricultural exports, especially plantation crops, which are the backbone of India, have been subjected to many nontariff measures. Since the liberalisation of trade has led to the integration of global commodity markets, developing countries are significantly affected by these trade barriers, which indirectly hurt millions of plantation community. Traditionally, India is well known for its exports of beverages and stringent maximum residual limits, traceability issues, and food safety standards are complex issues surmounting trade in the plantation sector around the world. Hence, the present research study attempts to find the shock of nontariff measures on the prices of both export and domestic beverages and the hammering in returns to the Indian beverage industry by the partial equilibrium method. This model directly measures the simulation effect of nontariff measures by imposing NTM on tea and coffee sector. It is obvious that as the NTM percent increases from 10 percent to 25 percent on tea sector, the loss in export quantity was more from 22.24 million kg to 55.61 million kg and loss of revenue was from Rs. 2997 million to Rs. 7492 million for the corresponding NTMs. Likewise the loss in export quantity (62.85 million kg) and loss in revenue (Rs. 9412 million) were high in 25 per cent of NTM. The present study shows how to allow for market imperfections and trade facilitating effects of nontariff measures in the beverage sector.

India is one of the world's leading mango producers, generating more than half of the global supply. Mango exports from India in the year 2021-22 was 27,872.77 MT. Mangoes are accessible all year as fresh fruit and processed goods. However, in recent years India's export was failed to meet the international food safety requirements due to Sanitary and Phyto-sanitary problems. Hence, this study focussed on estimating the growth from 1991 to 2020 and the direction of mango exports from 2011 to 2020 from India. The time series data of mango area, production, productivity and export in quantity and value were collected from various publications like APEDA, Horticulture statistics at a glance. Over the entire period, production was significant at the one per cent level and the productivity of mangoes was increased. This study found that the export quantity and its value increased significantly and positively. But quantity was not equal in proportion to the growth rate of export in terms of value. The transition probability matrix revealed that UAE (United Arab Emirates) and Nepal were the stable markets for Indian mangoes, with trade retention of 59 and 22 per cent, respectively. This study suggested that developing norms for producing safe mangoes as knowledge advances would make it easier to grant, maintain and move forward with Good Agricultural Practices (GAPs).

In today's world, people are more aware of the product they consumed. Consumers are mainly relying on their health consciousness. However, people nowadays shift their consumption towards the traditional forms of sugar as they have better nutritional compounds compared to the refined forms of sugar. Here sample respondents are taken based on the traceability of value chains of alternate forms of sugar (Jaggery and Khandsari sugar, Coconut sugar, and Palm sugar). The study was limited to 150 sample respondents in Erode, Tiruppur, Namakkal, and Coimbatore districts of Tamil Nadu where these sugars are transferred with all actors involved in the value chain with the help of markets. Principal Component Analysis is used to analyze the major factor to influence the purchase of sugar. The approximate chi-square statistic (0.774) is also large (>0.50). These factors account for 66.43 percent of the variance in the data. The three components that have the Eigenvalue of 6.58, 2.17, and 1.20 showed the percentage of variance were 43.87, 14.50, and 8.05 respectively. Based on varimax rotation with Kaiser Normalisation, three factors have arrived. The major factors that influenced highly were Health and convenience factor (Issues in white sugar, taste, traditional sweetener, Health consciousness, and Quality), Branding (Price, service of the seller, texture, packaging, Colour, market cleaning, non-perishable and popularity) and the other factor influenced in a fewer way. The result of the study concluded that consumers were mainly oriented about their health conscious and shifting towards traditional based products. Promotional measures were taken to promote these kinds of sugar and aim to bring back the traditional forms of sugar with its innovative technology. Packaging may be improved with biodegradable bags.

The current study is aimed at using co-integration in assessing the level of market integration among selected cotton markets in India. Monthly cotton price data were collected for the period 2008-09 and 2016-17 from the AGMARKNET website. The advanced time series econometric tools like Augmented Dickey-Fuller (ADF) test, Johansen co-integration test and Granger Causality test were used to study market integration using E-Views software. The price series for cotton in selected markets were subjected to the consequences of unit root and were stationary at first difference. The long-run equilibrium relationship among the cotton markets indicated that these markets were integrated with each other. This implied that prices in Indian cotton markets tend together in response to changes in the demand and supply of cotton. Granger Causality test revealed that the Salem market was the lead cotton market because it influenced the prices of Kurnool and Warangal cotton markets.

Aim: The objective of the study is to evaluate the trade performance of the Indian cashew sector. This study uses Monke and Pearson's [1] Policy Analysis Matrix (PAM). Study Design and Methodology: Private and social prices are taken for studying PAM, while private prices of tradable and non-tradable inputs serve as domestic price and social price is the international price of Vietnam which is a major competitor to India in cashew trade. Results and Conclusion: Despite the distortions, the cashew nut industry is financially and commercially lucrative, according to this study (net margin, financial and economic profit are greater than zero). It is, however, inadequately protected at the producer level and vast scope for encouraging the export of cashew from India in future. This study suggests that Indian cashew sector was highly competitive in trade when it re-exports the processed cashew kernels to other countries. Since domestic cashew production and processing is labour intensive, appropriate technology for cashew processing is a must.

The present study aimed to assess water footprint in the production and export of rice in India. From recent few years, the water footprint conception in full swing to inward detection around the world. The amplified attention in the water footprint has impelled the trade of commodities between countries. Water footprint in the rice field is a sign of water use that exhibits direct and indirect water usage in the rice field. Rice is an important food crop in India. It accesses the flows of water virtually between countries/regions of the world to illustrate the dependency of countries/regions on water resources with other countries/regions under diverse feasible futures. Hence, it is gaining consequence to calculate the water foot print in production as well as export of rice. The Indian rice production and export of rice was calculated by using international trade and domestic production data. The study results indicated that the global footprint of rice production was 235774 Mm3 per ton which was 53 % of green water footprint, 41 % of blue water footprint and 6 % of grey water footprint for 2018-19. The virtual water flowed in trade was 24354 Mm3/year and the percolation was 16924 Mm3/year since rice is a more water consuming crop. The share of basmati and non-basmati trade accounted was 16 % and 42 %, respectively. Virtual water trade in rice can be minimized by exporting less water demand and high-value crops, proper water harvesting structures and other agronomic practices.

Aims: The research study aims to study the decadal growth in agricultural trade of top five agricultural commodities between India and the European Union and also the quantification of Non-Tariff Measures (NTM) of select agricultural commodities to give trade policy suggestions to the concerned commodity sectors Study Design and Methodology: A decadal growth in top five agricultural commodities were studied for 28 European Union Countries (EU-28) and India using Compounded Annual Growth Rate and NTM of three subsections of WTO were quantified using inventory-based approaches; coverage ratio and frequency index. Results and Conclusion: In terms of quantity exported, positive annual growth rate of 0.24, 0.48 and 0.76 per cent in marine products, coffee and castor oil is noticed. A negative growth of 0.67 and 2.6 per cent in spices and tobacco unmanufactured is witnessed during the study period. Export value recorded the positive annual growth rate of 2.96, per cent in marine products and spices and a negative growth of 1.7,0.16 and 2.8 per cent in coffee, castor oil and tobacco unmanufactured respectively. Export value per unit showed annual positive growth of 2.7,2.2 and 0.94 per cent in marine products, coffee and spices and negative growth of 0.91 and 0.22 per cent in castor oil and tobacco unmanufactured respectively. Both Sanitary and Phyto Sanitary (SPS) and Technical Barriers to Trade (TBT) Measures had a greater impact in the agricultural export form India to EU-28 during 2010-11 to 2019-20.

Due to reduced crop area under pulses and ever-increasing domestic demand, India imports every year large chunks of a variety of pulses. The time series econometric analyses was used to estimate market integration, price transmission, and price volatility happening in the major domestic markets for one of the imported pulses namely green gram. The time-series data pertained to prices of a green gram for the major markets are sourced from 2006 to 2018 on a monthly frequency. Further, the vertical integration among the production and consumption markets were also studied using these analyses. In order to remove the non-stationarity element in the price series, the ADF test was used, which were non-stationary at levels but became stationary at the first difference. The price volatility prevailing in the green gram markets of India was estimated by GARCH (1,1) model revealed that all the markets were exhibiting consistent variability in market prices. Among these markets, there existed bi-directional causation which was confirmed by the Granger Causality test. The presence of three co-integration vectors for these markets proved the existence of a long-run equilibrium for green gram prices. Tamil Nadu market came under short-run equilibrium whereas remaining markets had long-run equilibrium estimated by VECM. Retail prices of Madurai market had bi-directional price influence with Tamil Nadu market. Hence it is suggested that better price discovery and timely market intelligence would be necessary to manage the price shocks occurring in the green gram markets in India.

The present study aimed to analyze the potato market integration in India, specifically how the Tamil Nadu market behaves with respect to the behavior of other potato markets across India. Major potato markets, such as Madhya Pradesh, Uttar Pradesh and Gujarat, which have a majority share in the total supply potato to Tamil Nadu were selected for market integration analysis. Since price data for Tamil Nadu market was non-stationary and other market prices were stationary in level form, Autoregressive Distributed Lag Model (ARDL) was used to estimate cointegration (long run equilibrium) among these markets. Month wise potato price data from January 2005 to September 2016 were collected from different sources and used for analysis. Results revealed that long run equilibrium existed among the potato markets in Tamil Nadu, Madhya Pradesh, Uttar Pradesh and Gujarat but the speed of adjustment of equilibrium level is very less in the long run. Change in the potato price of Gujarat market was the key determinant of shocks in the potato market of Tamil Nadu.

This paper examined the relationship between turmeric futures price traded in National Commodity and Derivatives Exchange (NCDEX), Mumbai and spot price prevailed in Erode market over a period of eight years (2004 -2012). It was found in the study that futures price of turmeric led the spot market in price discovery. The result showed the presence of unidirectional causality from futures price to spot price. In turmeric this implied that futures market discovered prices for turmeric and spot market prices were influenced by the futures market price. The results of Vector Error Correction model indicated that when the co-integration series was in disequilibrium in the short run, it was the spot price that makes greater adjustment in order to reestablish the equilibrium. This study also proved the occurrence of price transmission from futures market to spot prices of turmeric. The result of the study showed commodity futures market with respect to turmeric are efficient, since they played a fair role in price discovery.