Search Results (172)

A life cycle assessment was conducted to compare the impacts of peas and lentils produced in Canada, France, and Russia, transported to market in Western Europe, to assess the systems-level sustainability implications of changing production and consumption profiles of internationally traded commodity pulse crops. For all but 1–2 impact categories, imported Canadian peas and lentils outperformed those imported from Russia, due to the lower yields, higher levels of tillage, higher field-level emissions, and higher distances of truck transportation for Russian pulses. French peas had higher impacts of production than Canadian peas, for all categories but land use, due to higher levels of fertilizer inputs, irrigation, field activities, and field-level emissions. However, for 7 out of 12 impact categories, the impacts of the transportation of Canadian peas to Western Europe outweighed the higher impacts of the production of French peas. This demonstrates potential sustainability benefits of Canadian pulses, with some trade-offs from the additional impacts of transportation to market, adding nuance to the discussion around the importance of “food miles” in agricultural sustainability. Compared to previous studies, this demonstrates the importance of multi-criteria and regionalized assessments. Full article

Industrial bio-inputs can improve commodity farming by replacing the use of agrochemicals. To assess the potential of agricultural bio-inputs to contribute to Brazil’s agro-industrial growth, we analyzed the market share held by domestic companies and the local market created by farmers who adopt bio-inputs. The results revealed that Brazilian companies accounted for 82.8% of the 221 companies with agricultural bio-inputs registered in Brazil by 2024. These domestic companies used technologies available to local investors and developed in collaboration with public innovation centers. Adoption levels among interviewed farmers ranged from 41.7% for biosolubilizers to 88.9% for bionematicides, revealing a large domestic market potential for bio-inputs in Brazil. We conclude that industrial agricultural bio-inputs represent an area of opportunity for Brazilian neo-industrialization based on local competitive advantages, low entry barriers, and domestic and foreign investments that can benefit from the local market for bio-inputs. Full article

The article aimed to verify the development of the productivity level of Polish agriculture after EU integration as a key determinant of agricultural income. The research in this area was concerted because the productivity of agriculture (its technical efficiency) is a specific measure of its social efficiency, as it determines the level of wealth and social welfare and, at the same time, it is a determinant of its competitiveness in the long term. At the same time, it should be noted that after integration, agricultural production in Poland was carried out under conditions of extensive restrictions resulting from the adopted principles of the common agricultural policy aimed at increasing environmental sustainability. Productivity was measured on individual farm data using the Data Envelopment Analysis (DEA) Slacks-Based Model. The results were then extrapolated to the broader collective of commodity farms in Poland and indirectly applied to the entire population. The obtained results allowed for the conclusion that, during the first years of membership, there was a systematic decrease in productivity, which was observed from 2004 to 2011. The average value of the productivity factor for the research sample decreased from 0.230 to 0.208, while for the population it decreased from 0.224 to 0.202. After then, there was a reversal in the direction of the development trend, and in the following years, an upward trend emerged. Thus, the phenomenon of convergence in agricultural productivity with other EU countries, as the main factor influencing the direction of its changes in Poland after accession to the European Union, was not confirmed. Also, in the post-integration period, there was no change in the expected directions of interaction between the main determinants of agricultural income. Indeed, the theoretically formulated and empirically verified relations between subsidies and price relations and productivity were confirmed. Using the world price index as an explanatory variable in the multiple regression model, it was possible to explain, to a large extent, the variability of the productivity of Polish agriculture. Hence the implication for policymakers is that, despite high levels of subsidisation, the market is the main determinant of productivity changes. The weak impact of the price ratio index in Poland (‘price scissors’) on productivity volatility indicates that the increase in production costs, including those related to environmental protection (sustainability), has been effectively offset. Full article

Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. Total glycoalkaloids (TGA) are nitrogen-containing secondary metabolites that are known to accumulate in the tuber as an effect of greening in-field or elsewhere in the supply chain. In this study, 210 Yukon Gold (YG) potatoes were exposed to a constant light source to green over a period of 14 days and sampled in 7-day intervals. The samples were scanned using a short-wave infrared (SWIR) hyperspectral imaging camera in the 900–2500 nm wavelength range. Once individually scanned, pixel-wise spectral data was extracted and averaged for each tuber and matched with its respective ground truth TGA values which were obtained using a High-Performance Liquid Chromatography (HPLC) system. Prediction models using the partial least squares regression technique were developed from the extracted hyperspectral data and reference TGA values. Wavelength selection techniques such as competitive adaptive re-weighted sampling (CARS) and backward elimination (BE) were deployed to reduce the number of contributing wavelengths for practical applications. The best model resulted in a correlation coefficient of cross-validation (R²_cv) of 0.72 with a root mean square error of cross-validation (RMSE_cv) of 51.50 ppm. Full article

The olive tree (Olea europaea L.) has been cultivated for millennia, with olive oil representing both a cornerstone of the Mediterranean diet and a major agricultural commodity. Its composition, rich in monounsaturated fatty acids, polyphenols, tocopherols and squalene, supports well-documented cardioprotective, antioxidant and anti-inflammatory benefits. Olive oil production generates substantial secondary streams, including pomace, leaves, pits and mill wastewater, which are rich in phenols, triterpenes and fibers. This review consolidates recent advances in their phytochemical characterization, innovative extraction technologies and health-promoting effects, while highlighting the economic and regulatory prospects for industrial adoption. Comparative analysis shows that olive leaves can produce up to 16,674.0–50,594.3 mg/kg total phenolics; oleuropein 4570.0–27,547.7 mg/kg, pomace retains 2.24 g GAE/100 g dried matrix (DM)total phenolics; oil 13.66% DM; protein 6.64% DM, and wastewater contains high concentration of phenolics content of olives. Innovative extraction techniques, such as ultrasound and microwave-assisted methods, allow for a recovery, while reducing solvent use and energy input. The analysis highlights opportunities for integrating these by-products into circular bioeconomy models, supporting the development of functional foods, nutraceutical applications and sustainable waste management. Future research should address techno-economic feasibility, regulatory harmonization and large-scale clinical validation to accelerate market translation. Full article

The objective of this research is to understand the relationship between the tone of information released in government harvest reports, in extreme market conditions (rising and falling), and the behavior of agricultural commodity prices. In the period between January/2017 and February/2023, an autoregressive model of moving averages was used with a generalized autoregressive conditional heteroscedasticity approach. The evidence allows us to infer that investors can, on some occasions, use this information to direct their portfolios in order to balance risk and return. However, the full impact of the tone is not reflected immediately, possibly requiring time to be absorbed. Depending on the informational weight, the commodity, and the market context, there may or may not be an impact. This divergent empirical evidence indicates that there is a complex relationship between tone reading and asset pricing. Full article

Food price volatility is a persistent challenge in Indonesia, where agriculture is central to food security and rural livelihoods. While price transmission has been studied, little is known about how volatility spreads sub-nationally in archipelagic economies with fragmented infrastructure. This study applies a Dynamic Conditional Correlation GARCH (DCC-GARCH) model to monthly rural producer price data from 2009 to 2022 for six commodities: rice, chicken, eggs, chili, cayenne, and shallots. Results show that Java functions as the core volatility transmitter, with long-run conditional correlations exceeding 0.92 in Sumatra, 0.91 in Kalimantan, and 0.90 in Papua, reflecting strong and persistent co-movements. Even in low-production regions such as Maluku, significant volatility linkages reveal structural dependence on Java. Volatility clustering is particularly intense for perishables like chili and shallots. The findings highlight the need for spatially differentiated stabilization policies, including upstream interventions in Java and cooperative-based storage systems in outer islands. This study is the first to apply a DCC-GARCH framework to rural producer price data in an archipelagic context, capturing volatility transmission across regions. Its novelty lies in linking these spillovers with regional market dependence, offering new empirical evidence and actionable insights for designing inclusive and geographically responsive food security strategies. Full article

Agricultural markets are increasingly exposed to global risks as climate change intensifies and macro-financial volatility becomes more prevalent. This study examines the dynamic interconnection between major agricultural commodities—soybeans, corn, wheat, rough rice, and sugar—and key uncertainty indicators, including climate policy uncertainty, global economic policy uncertainty, geopolitical risk, financial market volatility, oil price volatility, and the U.S. Dollar Index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, we assess both internal spillovers within the commodity system and external spillovers from macro-level uncertainties. On average, the external shock from the VIX to corn reaches 12.4%, and the spillover from RGEPU to wheat exceeds 10%, while internal links like corn to wheat remain below 8%. The results show that external uncertainty consistently dominates the connectedness structure, particularly during periods of geopolitical or financial stress, while internal interactions remain relatively subdued. Unexpectedly, recent global disruptions such as the COVID-19 pandemic and the Russia–Ukraine conflict do not exhibit strong or persistent effects on the connectedness patterns, likely due to model smoothing, stockpiling policies, and supply chain adaptations. These findings highlight the importance of strengthening international macro-financial and climate policy coordination to mitigate the propagation of external shocks. By distinguishing between internal and external connectedness under climate stress, this study contributes new insights into how systemic risks affect agri-food systems and offers a methodological framework for future risk monitoring. Full article

The South African exchange rate has been volatile in recent years affecting the competitiveness of commodities in the market. Consequently, South African agricultural exporters have faced lower profitability or entire losses. More South Africa is among the top agricultural exporters in Africa. Thus, the purpose of this study was to examine the effect of exchange rate volatility on agricultural exports in South Africa using the Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) model over the period extending from first quarter of 2013 to first quarter of 2024. The study finds that the exchange rate affects agricultural export negatively in South Africa. The findings display that the exchange rate is statistically significant in explaining agricultural exports in South Africa. In addition, this study finds interest rate affects agricultural exports negatively whereas investment and trade openness affect agricultural export positively in South Africa. This infers that agricultural exports in South Africa are explained by various economic factors. Therefore, this study proposes implementing currency stabilisation policies is a crucial strategy to reduce exchange rate volatility, thereby reducing the negative impact on agricultural exports in South Africa. The policymakers can use currency hedging as tool to lessen the negative impact associated with the exchange rate volatility. Full article

As a strategic material second only to grain, cotton serves both as a vital agricultural commodity and a key industrial crop. With the increasing frequency of global shocks and the deepening financialization of commodity markets, price linkages among major international cotton futures markets have strengthened. Consequently, in addition to fundamental supply and demand factors, cross-border price transmission has become a significant determinant of cotton pricing. This study employs daily closing prices of China’s cotton futures, cotton yarn futures, and U.S. cotton futures from 1 September 2017 to 31 March 2025 to examine the spillover effects among these three futures markets using time series models. The results reveal that U.S. cotton futures have dominated the Chinese cotton-related futures markets even prior to the onset of trade tensions, with strong domestic market comovements. However, both the U.S.-China trade war and the COVID-19 pandemic significantly weakened price co-movements while intensifying volatility spillovers. Although these external shocks enhanced the relative independence of China’s cotton yarn futures and modestly increased China’s pricing influence, U.S. cotton futures have consistently maintained their central role in price discovery. Full article

Futures and forward contracts together offer farmers of all sizes important tools for shifting and managing production risk. This risk shifting is particularly apparent in the U.S. grain complex, where the United States also has a significant export position. Because of this international reach, we argue that the futures and forward markets play a critical role in reducing world food insecurity and thus contribute to satisfying Sustainable Development Goal #2: Zero Hunger. We further argue that the presence of investors willing to take the opposite side of the farmers’ natural short hedge helps futures markets perform their key functions of price discovery and risk management. In addition to these roles, futures markets also enable farmers to finance their crops more efficiently over the production cycle, supporting operational stability. Finally, we highlight that agricultural markets in the United States are supported by significant regulation at the county, state, and federal levels. These farming regulations, coupled with federal oversight of agricultural futures markets, provide sufficient confidence that the goal of Zero Hunger is being pursued in an appropriate and effective manner, reinforcing the case for agricultural futures as a meaningful component of a broader sustainable investment strategy. Full article

In this study, we investigate the return propagation mechanism, hedging effectiveness, and portfolio performance across several common agricultural commodities, crude oil, and S&P 500 index, ranging from July 2000 to June 2024 by using a time-varying parameter vector autoregression (TVP-VAR) connectedness approach and three common multiple assets portfolio optimization strategies. The empirical results show that, the total connectedness peaked during the 2008 global financial crisis, followed by the European debt crisis and the COVID-19 pandemic, while it remained relatively lower at the onset of the Russia-Ukraine conflict. In the transmission mechanism, commodities and S&P 500 index exhibit distinct and dynamic characteristics as transmitters or receivers. Portfolio analysis reveals that, with exception of the COVID-19 pandemic, all three dynamic portfolios outperform the S&P 500 benchmark across major global crises. Additionally, the minimum correlation and minimum connectedness strategies are superior than transitional minimum variance method in most scenarios. Our findings have implications for policymakers in preventing systemic risk, for investors in managing portfolio risk, and for farmers and agribusiness enterprises in enhancing economic benefits. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

According to the FAO, wheat, corn, and rice are staple crops that support global food security, providing 50% of the world’s dietary energy. The ability to predict accurately these key food crop agricultural commodity prices is important in stabilizing markets, supporting policymaking, and informing stakeholders’ decisions. To this aim, machine learning (ML), ensemble learning (EL), deep learning (DL), and time series methods (TS) have been increasingly used for forecasting due to the rapid development of computational power and data availability. This study presents a systematic literature review (SLR) of peer-reviewed original research articles focused on forecasting the prices of wheat, corn, and rice using machine learning (ML), deep learning (DL), ensemble learning (EL), and time series techniques. The results of the study help uncover suitable forecasting methods, such as hybrid deep learning models that consistently outperform traditional methods, and they identify important limitations in model interpretability and the use of region-specific datasets, highlighting the need for explainable and generalizable forecasting solutions. This systematic review adheres to the PRISMA 2020 reporting guidelines. Full article

The prediction of agricultural commodity futures returns is crucial for understanding global economic trends, alleviating inflationary pressures, and optimizing investment portfolios. However, current research that uses full-sample decomposition to predict agricultural futures returns suffers from data leakage, and the resulting forecast bias leads to overly optimistic outcomes. Additionally, previous studies have lacked a comprehensive consideration of key economic variables that influence agricultural prices. To address these issues, this study proposes the “Rolling VMD-LASSO-Mixed Ensemble” forecasting framework and compares its performance with “Rolling VMD” against univariate models, “Rolling VMD-LASSO” against “Rolling VMD”, and “Rolling VMD-LASSO-Mixed Ensemble” against “Rolling VMD-LASSO”. Empirical results show that, on average, “Rolling VMD” improved MSE, MAE, Theil U, ARV, and DA by 3.05%, 1.09%, 1.52%, 2.96%, and 11.11%, respectively, compared to univariate models. “Rolling VMD-LASSO” improved these five indicators by 2.11%, 1.15%, 1.09%, 2.13%, and 1.00% over “Rolling VMD”. The decision tree-based “Rolling VMD-LASSO-Mixed Ensemble” outperformed “Rolling VMD-LASSO” by 1.98%, 0.96%, 1.28%, 2.55%, and 4.18% in the five metrics. Furthermore, the daily average return, maximum drawdown, Sharpe ratio, Sortino ratio, and Calmar ratio based on prediction results also show that “Rolling VMD” outperforms univariate forecasting, “Rolling VMD-LASSO” outperforms “Rolling VMD”, and “Rolling VMD-LASSO-Mixed Ensemble” outperforms “Rolling VMD-LASSO”. This study provides a more accurate and robust forecasting framework for the global agricultural futures market, offering significant practical value for investor risk management and policymakers in stabilizing prices. Full article