Major Agricultural Commodities Research Articles

The Bitcoin‐agricultural commodities nexus: Fresh insight from COVID‐19 and 2022 Russia–Ukraine war

AbstractThis paper investigates the volatility connectedness and dynamic time–frequency relationship between Bitcoin (BTC) and 15 major agricultural commodity markets during the COVID‐19 and 2022 Russia–Ukraine war periods. We employ the TVP‐VAR‐based extended joint connectedness method, minimum connectedness investment portfolio, and wavelet coherence (WC) method. The results indicate that the sudden outbreaks of the two crises brought about increased volatility connectedness between BTC and agricultural commodity markets. Throughout the entire sample period, BTC remained a net transmitter of volatility. Moreover, in terms of the total connectedness index (TCI), the overall volatility correlation surged rapidly after the outbreak of COVID‐19 and the 2022 Russia–Ukraine war. The portfolio results demonstrated that BTC exhibited a low correlation with the agricultural commodity markets, suggesting diversification potential. Additionally, only Feeder Cattle served as an effective hedging asset for BTC throughout all periods. The WC analysis confirmed that during the COVID‐19 period and the 2022 Russia–Ukraine war, most of the linkages were primarily concentrated at medium‐ to long‐term frequencies. Our analysis will contribute to a deeper understanding of the interconnection between these markets, enabling market participants to consider risk mitigation measures and support portfolio diversification when formulating policies and regulations involving relevant markets in the future.

Various optimized machine learning techniques to predict agricultural commodity prices

AbstractRecent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

A Basic Guide to the Propagation and Manipulation of the Clubroot Pathogen, Plasmodiophora brassicae.

Clubroot caused by the obligate parasite Plasmodiophora brassicae is a devastating disease affecting the canola industry worldwide. The socio-economic impact of clubroot can be significant, particularly in regions where Brassica crops are a major agricultural commodity. The disease can cause significant crop losses, leading to reduced yield and income for farmers. Extensive studies have been conducted to understand the biology and genetics of the pathogens and develop more effective management strategies. However, the basic procedures used for pathogen storage and virulence analysis have not been assembled or discussed in detail. As a result, there are discrepancies among the different protocols used today. The aim of this article is to provide a comprehensive and easily accessible resource for researchers who are interested in replicating or building upon the methods used in the study of the clubroot pathogen. Here, we discuss in detail the methods used for P. brassicae spore isolation, inoculation, quantification, propagation, and molecular techniques such as DNA extraction and PCR. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Extraction of Plasmodiophora brassicae resting spores and propagation Support Protocol 1: Evans blue staining to identify resting spore viability Support Protocol 2: Storage of Plasmodiophora brassicae Basic Protocol 2: Generation of single spore isolates from P. brassicae field isolates Basic Protocol 3: Phenotyping of Plasmodiophora brassicae isolates Basic Protocol 4: Genomic DNA extraction from Plasmodiophora brassicae resting spores Basic Protocol 5: Molecular detection of Plasmodiophora brassicae.

Volatility and dependence in crude oil and agricultural commodity markets

ABSTRACT This paper studies the evolution, volatility dynamics, and nonlinear dynamics between crude oil prices and major agricultural commodity prices. Semiparametric GARCH-in-Mean copula models are applied to monthly crude oil, corn, soybean, palm oil, rice, sugar, and wheat prices from January 1990 to May 2023. We find that the Clayton copula is the best copula to describe the (bivariate) dependence structures between the crude oil and soybean, crude oil and palm oil, crude oil and sugar, and crude oil and wheat markets, suggesting the statistically significant lower tail dependence between crude oil and major agricultural commodity returns. Moreover, the tail dependence is strongest between crude oil and palm oil.

Effects of Government Price Policies on Major Agricultural Commodities in Andhra Pradesh, India

This study was concerned with analysis of export competitiveness and quantification of impact of price policies in Andhra Pradesh on net social loss in production and consumption, and welfare gain of farmers and consumers and change in Government revenue with respect to major food grains and commercial crops. Policy Analysis Matrix and Partial Equilibrium Model in the Marshallian economic surplus framework are employed in this study. The findings from Policy Analysis Matrix revealed that, on the input side, the farmers are more subsidized for rice and maize, as Nominal Protection coefficient on Tradable Inputs are less than one across all the major importing countries (unlike for chickpea, cotton lint and chilies (dry)). However, from the output side (Nominal Protection coefficient on Tradable output), the farmers producing rice, maize and chilies (dry) are more protective compared to social prices. Considering both input and output policies together, the farmers are simultaneously protective (Effective Protection Coefficient) for rice and maize and hence, the overall transfer from society to farmers is positive (Subsidy Ratio to Producers). The findings from Partial Equilibrium Model showed that total net social loss was found to be positive implying protectionism favoured the farmers across all the selected commodities. Further, the net social loss in production turned out to be positive confirming the main postulate of this study—a price greater than the equilibrium price will reduce the quantity demanded. The protectionist policies further led to a positive effect of trade liberalization on the welfare in the State and an increase in foreign exchange earnings, except for chickpea (in view of its net-importer status). Unlike farmers, consumers in Andhra Pradesh suffered welfare loss due to higher domestic prices over border prices for rice, maize and chilies (dry), However, there is a considerable increase in Government revenue due to pricing policies formulated by the Government of Andhra Pradesh. The welfare gains to farmers are negative for chickpeas and cotton in view of higher domestic prices compared to border prices. So, the key point evident is that due to protectionism, the magnitude of net loss to consumers’ welfare compared to welfare gain of farmers is higher between Rs. 21916 lakh to Rs. 23768 during TE 2020-21 implying welfare transfers from consumers to farmers. Thus, the Government of Andhra Pradesh is spending an enormous amount of money to keep the protection policies going for the benefit of farmers. However, considering the net importer status of chickpeas, the Government should further escalate the subsidy both in production and marketing programs.

Investigating and forecasting the impact of crop production shocks on global commodity prices

In this study, we investigate and forecast the impact of crop production shocks on the global prices of three major international agricultural commodities: maize, soybean, and cocoa. We perform a thorough assessment of the forecasting performances of five econometric and machine learning models using 60 years of data. First, we train the models on production and price data to forecast the monthly price variations for each crop separately considering different time horizons. Next, we implement a cross-validation procedure to identify the models with the most accurate forecasting ability for each crop. After choosing the best forecaster, we identify the most influential producing areas using several local and global model-agnostic interpretation tools. Our findings indicate significant differences among commodities in terms of prediction accuracy, with cocoa exhibiting a higher level of prediction error compared to less volatile markets like maize and soybean. Our results reveal a significant influence of Northern America’s maize and soybean production on the global prices of these commodities. The effects of production on prices are asymmetrical: small decreases in US production lead to substantial price increases, while small increases in production do not systematically decrease prices. In contrast, cocoa price variations are influenced by production coming from several regions, not from a single one.

Changing Pattern of Prices and Arrivals of Agricultural Commodities Through e-NAM: A Case Study of Sirsa Apmc in Haryana

Abstract Subject and purpose of work The study examined the market profile and status of Electronic National Agriculture Market (e-NAM) in Sirsa Agricultural Produce Market Committee (APMC) The study also analysed the arrival and price pattern of major agricultural commodities before and after the implementation of e-NAM in the study area. Materials and methods For the present study, both primary and secondary data were used. The study was conducted between the years 2011-2012 to 2020-2021. The Compound Growth Rate (CGR) was computed for both the period before and after the implementation of e-NAM (2016-2017 to 2020-2021) to examine the growth of total arrivals, lots, traded quantity and value of commodities. Results The study shows incremental growth in the total number of arrived, quantity and traded value except for traded lots of commodities. The study observed that almost all required facilities for implementing e-NAM were available at the APMC yard. Conclusion The present study concluded that the overall impact of e-NAM has been positive on the arrivals and prices of major agricultural commodities in Sirsa APMC.

Asymmetric Effects of Cereal Crops on Agriculture Economic Growth: Case Study of Pakistan

Agricultural marketing includes all activities which add value to agricultural products as they move from areas of agricultural production to ultimate consumption points. An effective and efficient marketing system is required in order to ensure fair returns to stakeholders but unfortunately little attention was paid to this sector in Pakistan. This resulted in welfare risk to stakeholders as price variation and values are common phenomena in the country. Public and private sectors jointly run agricultural marketing system in the country, as public sector is responsible for framing and implementing rules and regulation whereas private sector operates the system. Agricultural markets are governed under the Agricultural Produce Markets Ordinance 1978 which needs to be updated Due to poor post-harvest management practices, almost 25-40% produce is wasted which caused considerable value losses. In this chapter, an effort has been made to describe theoretical foundation of agricultural marketing along with highlighting the marketing system of major and minor agricultural commodities in Pakistan and the role of various institutions and regulations. Marketing efficiency is often understood in terms of marketing margins, physical losses and state of market competition. These indicators are also discussed in this chapter. Further, a section is devoted to identify major agricultural marketing problems with their possible solutions

On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis

We use dynamic time warping, a non-parametric pattern recognition method, to study interlinkages between major energy and agricultural commodity prices. Cluster analysis is conducted to group commodity prices based on their behavioral likeness by maximizing the differences between groups while minimizing the differences within groups. Two clusters emerge: one comprises the prices of crude oil and six major agricultural commodities, whereas the other contains coal and natural gas prices. Regarding lead-lag associations, oil prices generally lag crop prices; however, there are periods during which the former lead the latter. Furthermore, the duration with which oil prices lead or lag crop prices changes frequently.

The Vulnerability of Cuban Banana Production to Fusarium Wilt Caused by Tropical Race 4.

Bananas are major agricultural commodities in Cuba. One of the main constraints of banana production worldwide is Fusarium wilt of banana. Recent outbreaks in Colombia, Perú, and Venezuela have raised widespread concern in Latin America due to the potential devastating impact on the sustainability of banana production, food security, and livelihoods of millions of people in the region. Here, we phenotyped 18 important Cuban banana and plantain varieties with two Fusarium strains-Tropical Race 4 (TR4) and Race 1-under greenhouse conditions. These varieties represent 72.8% of the national banana acreage in Cuba and are also widely distributed in Latin America and the Caribbean region. A broad range of disease responses from resistant to very susceptible was observed against Race 1. On the contrary, not a single banana variety was resistant to TR4. These results underscore that TR4 potentially threatens nearly 56% of the contemporary Cuban banana production area, which is planted with susceptible and very susceptible varieties, and call for a preemptive evaluation of new varieties obtained in the national breeding program and the strengthening of quarantine measures to prevent the introduction of TR4 into the country.

The impact of oil price shocks on the agricultural commodity prices

AbstractIn this study, we empirically examine the effects of oil‐related shocks on the prices of nine agricultural commodities for the period 1974:01–2022:02. We decompose real oil prices into three independent parts: oil supply shocks; global demand shocks; demand shocks that are specific to the crude oil market applying a symmetric VAR model. Then, we assess the effects of these three shocks on the prices of nine major agricultural commodities individually. Based on our findings, we reveal that the responses of agricultural commodity prices to an oil price change depend significantly on the timing and the driver of the underlying oil price change.

Application of Market Information System for Major Agricultural Commodities in Haryana (India)

The study on “Application of Market Information System of Major Agricultural Commodities in Haryana” was carried out in Karnal, Ambala, Mahendergarh, Bhiwani and Sirsa districts of Haryana, selected purposively based on the highest area under the major agricultural commodities. Two blocks from each district i.e., from Karnal (Karnal and Assandh), Ambala (Naraingarh and Sahazadpur), Mahendergarh (Mahendergarh and Kanina), Bhiwani (Siwani and Tosham) and Sirsa (Dabwali and Baragudha) were selected purposively. From each district, four regulated markets were selected based on maximum arrival i.e., Basmati Rice from Karnal (Karnal, Gharaunda, Assandh and Taraori), maize from Ambala (Sahazadpur, Mullana, Naraingarh and Ambala city), mustard from Mahendergarh (Mahendergarh, Ateli, Kanina and Narnaul), gram from Bhiwani (Siwani, Charkhi Dadri, Tosham and Bhiwani) and cotton from Sirsa (Dabwali, Ellenabad, Sirsa and Kalanwali) under the major agricultural commodities. The total sample size consisted of 300 farmers, 200 traders and 20 market committees were surveyed. Out of the expectations of farmers on quality, prices in potential markets, price projections; only arrivals and prices were documented and disseminated with traditional/modern approach. Forecasting of prices showed that the prices of Basmati rice, maize, mustard, gram, and cotton in Karnal, Ambala, Mahendergarh, Bhiwani and Sirsa districts, respectively were moving in same trend as predicted.

Climate Services for Agriculture: Tools for Informing Decisions Relating to Climate Change and Climate Variability in the Wine Industry

Background and Aims. Australia’s changing climate is already impacting the agriculture sector and will continue to do so in the future. To help respond to these impacts, the Climate Services for Agriculture (CSA) platform presents readily accessible climate data, including future climate projections, relevant to specific agricultural commodities. This wine industry example aims to demonstrate the functionality and utility of the CSA for national use across a broad range of commodities. Methods and Results. The platform includes commodity-relevant climate indices designed in consultation with experts to ensure that they are as salient to producers as possible; the wine-grape specific indices include measures of growing season temperature, rainfall, extreme heat, and frost. Here, we describe the research behind the wine-grape specific indices and present sample outputs from the CSA platform for a site within a selected winegrowing region. We note the CSA platform has been developed through an extensive and continuing user engagement initiative, ensuring it meets the needs of the agriculture community as they grapple with how to make decisions based on longer term climate projections. Conclusions. Provision of past, seasonal outlook, and future climate information for Australia and for a range of important agricultural commodities can help improve on-farm planning and decision-making to respond to climate risks. The wine industry provides a leading example of how to use these data for decision-making, noting ongoing adjustments will be needed. Significance of the Study. The CSA platform brings together historical climate data, seasonal climate outlooks, and future climate projections to assist agricultural producers to better manage climate variability and climate change. It aims to nationalise this information for all major agricultural commodities in Australia. We use wine production as a demonstration case here.

Dynamic high-frequency dependence structure of Chinese agricultural commodity futures based on the semi-parametric copula

This paper investigates the dynamic high-frequency dependence structure of Chinese four major agricultural commodity futures by utilizing a semi-parametric copula-based multivariate model with 5-minute high-frequency trading data. The empirical results show that the daily dependence between the agricultural commodity futures is time-varying and slightly asymmetric, and that this dependence and its asymmetry are more pronounced during the world food crisis (2007–2008) and the global financial crisis (2008–2011). Furthermore, the intraday dependence structure exhibits a lopsided inverted U-shaped pattern with relatively lower dependence level around the opening and closing time, and a peak around the mid-trading day.

Regional Analysis of Nitrogen Flow within the Chesapeake Bay Watershed Food Production Chain Inclusive of Trade.

In the Chesapeake Bay Watershed, excess nitrogen has contributed to poor water quality, leading to nitrogen mitigation efforts to restore and protect the watershed. The food production system is a top contributor to this nitrogen pollution. While the food trade plays a vital role in distancing the environmental impacts of nitrogen use from the consumer, previous work on nitrogen pollution and management in the Bay is yet to carefully consider the effect of embedded nitrogen found in products (nitrogen mass within the product) imported and exported throughout the Bay. Our work advances understanding across this area by creating a mass flow model of nitrogen embedded in the food production chain throughout the Chesapeake Bay Watershed that separates phases of the production and consumption processes for crops, live animals, and animal products and considers commodity trade at each phase by combining aspects of both nitrogen footprint and nitrogen budget models. Also, by tracking nitrogen embedded in products imported and exported in these processes, we distinguished between direct nitrogen pollution and nitrogen pollution externalities (displaced N pollution from other regions) from outside of the Bay. We developed the model for the watershed and all its counties for major agricultural commodities and food products for 4 years 2002, 2007, 2012, and 2017 with a specific focus on 2012. Using the developed model, we determined the spatiotemporal drivers of nitrogen loss to the environment from the food chain within the watershed. Recent literature leveraging mass balance approaches has suggested that previous long-term declines in nitrogen surplus and improvements in nutrient use efficiency have stagnated or begun to reverse. Our results suggest that within the Chesapeake Bay, increased corn and wheat acreage and steadily increasing livestock/poultry production may have led to the stagnation in decreasing N loss trends from agricultural production observed over the past two decades. We also show that at the watershed scale, trade has reduced the food chain nitrogen loss by about 40 million metric tons. This model has the potential to quantify the effect of various decision scenarios, including trade, dietary choices, production patterns, and agricultural practices, on the food production chain nitrogen loss at multiple scales. In addition, the model's ability to distinguish between nitrogen loss from local and nonlocal (due to trade) sources makes it a potential tool to optimize regional domestic production and trade to meet local watershed's needs while minimizing the resulting nitrogen loss.

Information rigidities in USDA crop production forecasts

AbstractUSDA invests significant public resources into developing its crop projection reports. These publications inform decisions across the supply chain. Several previous studies find that revisions to the department's production and yield forecasts for major agricultural commodities are positively correlated and conclude that they deviate from what would be observed under rational expectations, possibly due to smoothing on the part of forecasters. Yet correlated revisions may also be explained by information rigidities that cause forecasts to be infrequently or only partially updated. We apply a recently developed test to these USDA revisions for corn, soybeans, and wheat, and find no significant evidence that the forecasts are smoothed strategically. Rather, we show that information rigidities are the more likely culprit, due to production and yield information that is either too costly to obtain or too noisy. Our results demonstrate that data challenges are the main source of inefficiency in USDA projections, and that the department can improve the efficiency of its forecasts by making investments that improve its access to crop data, perhaps through crop‐monitoring satellite and remote sensing technology.

India-China Agricultural Trade Based on Virtual Water: A Heckscher- Ohlin Approach

Aim: This paper provides the economics of Virtual Water (VW) exporting to other countries through major agricultural commodities from India. Virtual Water is interconnected with food sustainability and it is the amount of hidden water transferred to other countries through trade. India produces and exports high water-consuming products but contains only 2.56 per cent of total water available in our world. By analysing VW, the total VW export from and import to India and the comparative advantage in producing the commodity in India can be obtained.
 Methodology: VW for major crops is estimated by dividing the total water required or applied for the specified crop by the total yield of the crop. In this paper, we computed the virtual water trade for the major crops in India and analysed the comparative advantage for India in producing the crop. The data required for the analysis are collected from various secondary sources like the Directorate of Economics and Statistics (DES, GoI), Indian Agricultural Statistical Research Institute (IASRI), EXIM Bank, and FAO Aqua Stat.
 Results: In the years 2018-19 and 2017-18, India exported 34515 MCM and 41080 MCM of VW through rice followed by 420 MCM and 622 MCM of VW through Wheat, 276 MCM and 184 MCM of VW through Maize. When comparing the production of rice and groundnut in China and India in water requirement aspect, India has the comparative advantage in the production of groundnut and china has the comparative advantage in the production of rice and also shows the same in the yield aspect.
 Conclusion: With the growing water scarcity in India, we should shift the focus from the high-water requirement crop to the lower crop. In the end, we sort out the water scarcity problem and can attain sustainability.

Export performance and direction of trade of major Agricultural commodities exported from India with special reference to Nepal

Export performance and direction of trade of major Agricultural commodities exported from India with special reference to Nepal

A STUDY ON ARRIVALS AND PRICES OF MAJOR AGRICULTURAL COMMODITIES AT LARGEST APMC IN VIDARBHA REGION

The present Research Article, "A Study on Arrivals and Prices of Major Agricultural Commodities at Largest APMC in Vidarbha Region" therefore, is a small attempt in reviewing the performance of the Regulated market by Analysing the trends in Arrivals and Prices and Marketing Channel of Major Commodities at the micro-level. This study, thus by way of evaluates the performance of market regulation through various dimensions. Chhatrapati Shivaji Market Yard, Hinganghat is on the forefront in business activities and on developmental aspects. The Hinganghat APMC has been ideally outlined on an area of 10.03 ha. The market provides all essential amenities to farmers and trader’s viz., auction platform, shopcum-godown, weighbridge, city bus service terminus. The market has spacious auction platforms, sale halls, parking space. It has built commercial shops. The Hinganghat APMC has 2 sub-markets with all amenities. The APMC is famous for Cotton and Soybean marketing. The main sources of income were market and license fees and the expenditure items were salary and miscellaneous. Market committee follows sale of produce by open auction method only which is prescribed in the Maharashtra Agricultural Produce Marketing (Regulation) Act, 1963. The declining trend was observed in number of market functionaries over the study period. It is noticed that although trend of arrivals of some commodities viz., Cotton, Soybean and Gram was increasing but the prices of all the selected commodities showed the significant increasing trend during the period of 10 years i.e., 2012-13 to 2021-22. Further, it is observed that prices were more dependable upon lagged year prices in case of all commodities.

A Longitudinal Study of Brazilian Food Production Dynamics

Brazil is among the world’s leading food producers and exporters. The opportunity arose to examine and analyse dynamics of spatiotemporal of major agricultural crop and animal commodities within Brazil. An investigation was carried out on shifts in distribution density across Brazil’s different agricultural production regions between 1990 and 2015. This focused mainly on the midwest region, Brazil’s main agricultural frontier in the 21st century. This process enabled an analysis of the potential areas for maintenance and expansion of food supply chains and confirmed an increase in agricultural production in country’s central region. Geographical transformations were noted in the midwest region’s interior and its frontier with the Amazon biome. Over the study period, geographical midpoints of some key agricultural commodities (e.g., soybean, maize) and beef cattle production shifted towards the midwest’s interior, whereas milk, poultry, and pork production shifted southward. A vital issue in discussing contemporary rural areas, agricultural food production is tied to the quality of life, food supply, distribution, and consumption, as well as social, economic, and spatial inequalities. The development of science and technology applied to agriculture has implications regarding production growth and innovation targeted toward guaranteeing sustainable long-term production.