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Consumption of globally traded agricultural commodities like soy and palm oil is one of the primary causes of deforestation and biodiversity loss in some of the world’s most species-rich ecosystems. However, the complexity of global supply chains has confounded efforts to reduce impacts. Companies and governments with sustainability commitments struggle to understand their own sourcing patterns,while the activities of more unscrupulous actors are conveniently masked by the opacity of global trade. We combine state-of-the-art material flow, economic trade, and biodiversity impact models to produce an innovative approach for understanding the impacts of trade on biodiversity loss and the roles of remote markets and actors.We do this for the production of soy in the Brazilian Cerrado, home to more than 5% of the worlds species. Distinct sourcing patterns of consumer countries and trading companies result in substantially different impacts on endemic species. Connections between individual buyers and specific hot spots explain the disproportionate impacts of some actors on endemic species and individual threatened species, such as the particular impact of European Union consumers on the recent habitat losses for the iconic giant anteater (Myrmecophaga tridactyla). In making these linkages explicit, our approach enables commodity buyers and investors to target their efforts much more closely to improve the sustainability of their supply chains in their sourcing regions while also transforming our ability to monitor the impact of such commitments over time.

PurposeThe phenomenon known as financialization of commodities, arising from the speculation in commodity derivatives market, has raised serious concerns in the recent past. This has prompted distortion in agricultural commodity prices driving them away from rational levels of supply and demand shocks. In the backdrop of financialized commodities leading to increase in price of agricultural products and their interaction with equity markets, the authors examine the investment of institutional investors in impacting the agricultural returns. The paper aims to focus on the financial mechanism that drives extreme values and the mean of agricultural returns.Design/methodology/approachThe authors employ the Threshold AutoRegressive Quantile (TQAR) methodology to find evidence of linkages between the Indian agricultural and equity markets from January 2010 to May 2020 consistent with the rise in inflows of institutional investors in agricultural markets.FindingsThe results reveal that the investors impact the agricultural commodity markets strongly when the composite commodity index value (COMDEX) is low. Additionally, in the lower extreme quantiles (0.25) of agricultural returns, the integration between the equity index and agricultural returns is found to be highly significant compared to insignificant values in the higher quantiles (0.75 and 0.95) in both the regimes. The results suggest that low values of agricultural commodities are more closely linked to equity indices when composite commodity index value is low. This implies that, at the lower quantiles of COMDEX return (bad day), the investors move to the stock market. In that way, the commodity index returns are seen to be as a strong channel for the financialization of Indian agricultural commodities and suggesting potential involvement of investors during those regime.Research limitations/implicationsRegulators need to anticipate the price fluctuations in spot and futures markets. Investors in commodity markets need to strengthen risk awareness to carry out portfolio strategies.Practical implicationsFrom policy perspective, it is of pivotal importance to enhance the understanding of the financialization of agricultural products. The findings provide reference measures to stabilize the commodity markets, alleviate price distortions and carry out further evidence of price discovery and risk management in Indian commodity markets.Originality/valueTo the best of the authors’ knowledge, this study is the first to highlight the potential influence of financial markets on the financialization of agricultural commodities in an emerging economy like India.

This study measures the spillover effects of the Russia-Ukraine war and the COVID-19 pandemic on currency pairs as the Russian ruble, Czech koruna, Polish zloty, Hungarian forint, Swedish krona, Bulgarian lev, Danish krone, Romanian leu, Ukrainian hryvnia, and Turkish Lira. By employing the TVP-VAR model we investigate the dynamic connectedness among these currencies and key energy and agricultural commodities. The data series encompasses two consecutive non-economic shocks – the Ukraine war and the COVID-19 pandemic – and a preceding period of general stability during 2018 and 2019. The importance of geopolitical context in shaping currency dynamics was present in countries with heavy dependence on Russian gas. The findings indicate a limited direct impact of commodity price fluctuations on the value of these currencies. At the same time, geopolitical decisions primarily related to the Russian Ruble and energy dependencies significantly impacted their valuation. The study reveals the complexity of currency dynamics and the influence of geopolitical risks and global health crises on exchange rate volatility and commodity dependencies.

The aim of this research is to explore the volatility contagion among different agricultural commodity markets. For this purpose, this research make use of the copula-GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model for the daily spot prices of six major agriculture grain commodities including corn, wheat, soybeans, soya oil, cotton, and oat over the period from 2000 to 2019. Our results provide evidence that significant contagion effects and risk transmissions exist among different agricultural grain commodity markets, suggesting that potential speculation effects on one agricultural market could be contagious for another agricultural market and result an increase in volatility in agricultural product markets. Second, agricultural commodities appears to co-move symmetrically. We also find substantial extreme co-movements among agricultural commodity markets. This indicates that agricultural commodity markets tend to crash (boom) together during extreme events. Moreover, after the food crisis, contagion effects and risk transmissions among different agricultural commodity markets increased substantially. Fourth, we find that the strongest contagion effects and risk transmissions are between corn and soybeans, and the weakest contagion effects and risk transmissions are between soya oil cotton and between cotton and oat. Last, we document that the co-movement varies over time. Our findings hold important implications for modeling the co-movement by the copula-GARCH approach.

Contract farming, wherein a processor contracts out the production of an agricultural commodity to a grower, is the first step toward more vertically coordinated — and thus more modern — agricultural value chains. As such, in principle contract farming is a necessary condition for the structural transformation of developing economies to occur. Yet contract farming is far from monolithic, and the institution takes on a variety of forms. In this article, we describe how the institution of contract farming varies in cross-sectional data covering 1,200 households across six regions of Madagascar, half of which are growers in contract farming agreements covering a dozen different crops. In this setting, participation in contract farming has been associated with increases in income, improvements in food security, and reductions in income variability. Given those presumed effects in this setting of participation in contract farming, we then look at the correlates in our data of participation in contract farming. as well as one’s willingness to pay to participate in contract farming as a grower in an attempt to better target policies aimed at encouraging participation in contract farming.

Fluctuations in agricultural commodity prices affect the supply and demand of agricultural commodities and have a significant impact on consumers. Accurate prediction of agricultural commodity prices would facilitate the reduction of risk caused by price fluctuations. This paper proposes a model called the dual input attention long short-term memory (DIA-LSTM) for the efficient prediction of agricultural commodity prices. DIA-LSTM is trained using various variables that affect the price of agricultural commodities, such as meteorological data, and trading volume data, and can identify the feature correlation and temporal relationships of multivariate time series input data. Further, whereas conventional models predominantly focus on the static main production area (which is selected for each agricultural commodity beforehand based on statistical data), DIA-LSTM utilizes the dynamic main production area (which is selected based on the production of agricultural commodities in each region). To evaluate DIA-LSTM, it was applied to the monthly price prediction of cabbage and radish in the South Korean market. Using meteorological information for the dynamic main production area, it achieved 2.8% to 5.5% lower mean absolute percentage error (MAPE) than that of the conventional model that uses meteorological information for the static main production area. Furthermore, it achieved 1.41% to 4.26% lower MAPE than that of benchmark models. Thus, it provides a new idea for agricultural commodity price forecasting and has the potential to stabilize the supply and demand of agricultural products.

Global development initiatives frequently promote agricultural commodity chain projects to improve livelihoods. In Morocco, development projects, including the Plan Maroc Vert (PMV), have promoted apple production in rural regions of the country. In order to access domestic markets, these new apple producers often use pesticides to meet market standards. Through situated ethnographic inquiry and commodity chain analysis, using a combination of surveys (n = 120) and interviews (n = 84) with apple wholesalers, government officials, along with farmers, this paper works to critique the PMV’s development approach that implicitly values commodification. By exploring interconnected processes of commodification, I link subsidized apple saplings and cold storage infrastructure to the dependence on pesticide usage, which has become a part of daily village life. This has important implications for community health and riparian ecosystems. Alternatively, I propose how we can imagine different development trajectories that decommodify livelihoods by focusing on local knowledge creation and diversification strategies.

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China’s crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security.

This article analyzes whether there are asymmetric relations between real interest rates and agricultural commodity prices using quarterly data of US interest rates and agricultural commodity prices over the period of 1983q1–2014q4. While the literature has identified statistically significant negative relations between real interest rates and agricultural commodity prices, this article extends the analysis by testing for threshold effects using Hansen’s (J Econom 93(2):345–368. 10.1016/S0304-4076(99)00025-1, 1999) fixed-effect panel threshold model and testing procedure. The empirical results indicate that real interest rates and agricultural commodity prices follow a U-shaped relationship, with − 1.45 being the turning point from negative to positive effects. Specifically, if real interest rates below the threshold of − 1.45 are increased by 1%, agricultural commodity prices will decrease by 8.1%, and if real interest rates are equal or above − 1.45 and are increased by 1%, agricultural commodity prices will increase by 3.4%. As the literature suggests an inverse proportional relation between real interest rates and agricultural commodity prices, a theoretical explanation for this phenomenon has yet to be found but is probably related to assumptions about market participants’ expectations and risk behavior.

In this study, we investigate appropriate machine learning methods for predicting agricultural commodity prices. Since environmental factors including weather affect price fluctuations of agricultural commodities, we constructed a multivariate time series dataset combining wholesale prices of four agricultural commodities in South Korea, six weather variables, and week numbers. We adopted two prominent prediction methods based on recurrent neural networks (RNN) and graph neural networks (GNN): one is the stacked long short-term memory, and the other consists of two GNN-based methods, the spectral temporal graph neural network (StemGNN) and the temporal graph convolutional network. Also, we utilized a univariate prediction model as a control to evaluate the effectiveness of the multivariate approach for predicting agricultural commodity prices. In this investigation, we applied five different smoothing time window lengths to evaluate the effect of mitigating short-term fluctuations on the predictive performance of the models. The experimental results showed that the mitigation of short-term fluctuations had a greater impact on improving the performance of multivariate prediction models compared to the univariate prediction model. Among the multivariate prediction models, the GNN-based network outperformed the RNN-based network. In view of the trained model, we analyzed the main weather variables affecting agricultural commodity prices by utilizing the adjacency weight matrices in the self-attention mechanism of StemGNN.