Search Results (657)

Northeast China is one of China’s major commodity grain bases and plays a strategic role in national food security. Against the background of rural population outflow and agricultural modernization, new agricultural business entities (NABEs), including family farms, farmers’ cooperatives, and agribusinesses, have become important actors in reshaping agricultural production organization. Based on registration data for 2014, 2018, and 2023, this study uses kernel density estimation (KDE), standard deviational ellipse (SDE) analysis, spatial autocorrelation analysis, ordinary least squares (OLS) regression, and multiscale geographically weighted regression (MGWR) to examine the spatiotemporal patterns and driving factors of NABEs in Northeast China. The results show that: (1) NABEs expanded rapidly from 2014 to 2023 and became increasingly concentrated in agriculturally advantageous plain areas. (2) Family farms showed the fastest expansion, farmers’ cooperatives had the widest spatial coverage, and agribusinesses were mainly concentrated around transport corridors and market nodes. (3) In terms of industrial structure, crop-production entities remained dominant, followed by animal husbandry entities, while forestry, fishery, and agricultural support service entities accounted for relatively small shares; however, their numbers continued to increase. (4) The OLS results showed that the reclamation rate and road network density had relatively stable associations with the spatial distribution of multiple entity types, whereas economic development, science and technology investment, and fiscal support showed differentiated relationships across entity types and regions. (5) The MGWR results further reveal spatial heterogeneity in the effects of driving factors. These findings provide empirical evidence for type-specific cultivation and differentiated policy support for NABEs in major grain-producing areas. Full article

Using a dataset covering January 1975 to March 2026 and six agricultural commodities, wheat, corn, soybeans, oats, sugar, and coffee, this paper explores the role of geopolitical risk (acts and threats) in shaping cross-market connectedness. It proposes a multilayer methodology based on the time-varying parameter vector autoregressive (TVP-VAR), the exponential GARCH with exogenous variables (EGARCH-X), and the wavelet quantile correlation (WQC) frameworks. This methodology captures cross-market volatility spillovers, assesses the effects of geopolitical risk and its components on the strength and instability of connectedness, and incorporates nonlinearity and asymmetry across investment horizons and market conditions. The results show a time-varying pattern in agricultural cross-market connectedness. Corn and soybeans transmit volatility shocks, while the other commodities are net receivers. These commodities have a central position in the connectivity network, whereas sugar and coffee are in the peripheral zone. The EGARCH-X results show that geopolitical acts and threats do not significantly alter the overall level of connectedness but intensify its volatility, suggesting that geopolitical tensions primarily influence stability rather than the intensity of connectedness. Economic policy uncertainty and oil price volatility have similar effects. In line with these results, the WQC analysis uncovers significant nonlinearity and state-dependent linkages, underscoring that the effect of geopolitical acts and threats becomes prominent over medium- and long-term horizons and during periods of market stress. These findings contribute to the literature by differentiating the effects of geopolitical incidents on agricultural market connectedness versus volatility. From an operational standpoint, these results imply that policymakers and market operators should enhance their risk-monitoring and hedging strategies during periods of high geopolitical stress, as such events can amplify instability across agricultural commodity markets. Full article

Transport infrastructure is widely viewed as a key lever for integrating lagging rural regions into broader economic systems. Western China, marked by vast territory, complex topography, and historically severe spatial market frictions, offers a particularly informative setting for examining this question within the sustainable rural development agenda. Exploiting the staggered rollout of China’s National Highway Expansion Program across 276 prefectures from 2003 to 2018, we combine high-frequency wholesale prices for 93 agricultural commodities, geocoded expressway network data, and the China Family Panel Studies. A staggered difference-in-differences design is supplemented by a time-varying minimum spanning tree instrument capturing network-efficiency considerations, alongside event-study and recently developed robust estimators for staggered treatments. Two-stage least squares estimates indicate that expressway connection raises the agricultural price integration index by 0.071, reduces within-prefecture price volatility by approximately 0.040 (about 13% of baseline), raises agricultural household income per capita by roughly 16%, and improves the household food-security index by 0.571 points. Event-study results show no pre-trends, with effects materializing over three to four years post-connection. Mechanism analysis highlights expanded market linkages, and the gains are stronger in nationally designated poverty counties and prefectures with rugged terrain. Partial-equilibrium welfare accounting implies annual gains of roughly USD 4.92 billion, and unconditional quantile regressions reveal a progressive distribution across farm incomes. These findings underscore the role of transport infrastructure in alleviating spatial frictions, integrating lagging regions, and advancing sustainable rural development while warranting careful attention to the environmental externalities of large-scale infrastructure. Full article

This study provides a comprehensive examination of the inflation-hedging potential of commodity indices and futures for U.S. investors using monthly data spanning July 1959 to December 2025 for 27 individual commodities, and January 1947 to November 2025 for 13 commodity indices. We employ multiple complementary methodologies, including optimal hedge ratios with Newey–West standard errors, asymmetric hedging analysis, long-horizon regressions, rolling window stability tests, Granger causality analysis, out-of-sample validation, and Markov-switching vector error correction models (MS-VECM). Our results reveal substantial heterogeneity in hedging effectiveness across commodity sectors. Energy commodities, particularly gasoline and crude oil, demonstrate the strongest inflation-hedging properties with higher hedge ratios and hedging effectiveness. Industrial metals, represented by copper, also provide reliable hedging with stable performance across market conditions. In contrast, precious metals, including gold and silver, show weak contemporaneous hedging ability despite their traditional safe-haven reputation, though they may offer protection during specific market regimes. Agricultural commodities and livestock exhibit minimal or negative hedging effectiveness. The MS-VECM analysis confirms that hedging relationships are time-varying, with effectiveness differing significantly between stable and turbulent market regimes. These findings have important implications for portfolio construction and risk management strategies. Full article

Food loss and waste (FLW) represent a significant source of resource inefficiency in water-scarce economies. This study quantifies the water footprint (WF) of FLW in Saudi Arabia using product-level blue, green, and grey WF coefficients from the Water Footprint Network database. Our analysis covers 3.997 million tons of FLW across 19 commodities grouped into cereals, fruits, vegetables, and meat. Results indicate that FLW is associated with a total blue and green WF of 7.3 billion m³, of which 2.1 billion m³ is blue water directly associated with managed water resources. The blue WF is equivalent to approximately 20% of agricultural water withdrawals and 62% of domestic water demand. Despite constituting only 13% of total FLW by mass, meat products account for 53% of the total water footprint, driven by their exceptionally high water intensity (7474 m³/ton). The consumption stage dominates water losses, contributing 56% of the total blue and green WF. Based on alternative water supply cost benchmarks, the blue WF embedded in FLW corresponds to an indicative production-cost equivalent ranging from 1.03 to 6.5 billion SAR. A 25% reduction in FLW could save over 500 million m³ of blue water annually. These findings demonstrate that FLW reduction represents an important supporting strategy for water resource management and provides a quantitative basis for prioritizing intervention across food groups and supply-chain stages. Full article

Flowering period management is a critical component of orchard production, significantly influencing the accuracy and timeliness of agricultural decisions such as flower and fruit thinning, yield stabilization, improvement in fruit commodity value, and control of mold core disease. Aiming at the problems of traditional flowering period judgment relying on manual experience, strong subjectivity, low efficiency, and difficulty in large-scale implementation, this study proposes an accurate evaluation model for apple flowering period based on near–far view multimodal visual data. A dedicated near–far view combined vision acquisition system was built to synchronously obtain panoramic images of fruit tree canopies and high-definition close-up images of single flowers/clusters, constructing a multimodal dataset covering the canopy spatial structure and fine floral organ morphology. YOLOv5s and ResNet-50 were employed to extract macro flowering proportion features from far views and micro morphological features from near views, respectively. A feature fusion strategy was introduced to realize the deep fusion of macro–micro features, and finally, a multimodal flowering period classification model was constructed to accurately divide the apple flowering period into four stages: bud stage, initial bloom stage, full bloom stage and late bloom stage. The overall recognition accuracy of the model reached 95.7%. The accurate apple flowering period evaluation system built based on this model has realized the paradigm shift in flowering period judgment from “qualitative manual experience” to “accurate quantification by machine vision”, providing a scientific time window basis for core orchard operations such as pre-flower re-pruning, flowering pollination, fruit setting evaluation and fruit thinning and bagging, and effectively promoting the intelligent and operational development of orchard management. Full article

Coffee export price volatility is a relevant problem for producing countries because it affects commercial planning, contract negotiation, producers’ income stability, and the financial sustainability of the agro-export value chain. In economies that are highly dependent on primary commodities, abrupt fluctuations in international prices increase uncertainty and reduce the ability of economic agents to anticipate market behavior. In this context, the objective of this study was to forecast the behavior of the Peruvian coffee export price during 2025 by comparing econometric and time-series models. The research adopted a quantitative approach with a non-experimental, retrospective, and longitudinal design, using a monthly series for the 2010–2024 period. Seven specifications were estimated: linear model, quadratic model, Holt–Winters exponential smoothing, causal model, lagged model, ARIMA, and GARCH. The results showed that the GARCH (1,1) model achieved the best statistical performance, with the lowest Akaike Information Criterion, a Durbin–Watson statistic close to 2, an R² higher than that of the alternative models, and no residual autocorrelation. Likewise, the significance of the ARCH and GARCH components confirmed the existence of volatility clustering in the series. The projections for 2025 show a fluctuating trajectory, although with a tendency to stabilize around values close to 10 from March onward. It is concluded that the GARCH (1,1) model is the most appropriate specification for forecasting the Peruvian coffee export price, as it provides a useful tool for export planning, risk management, and decision-making in a context of high uncertainty in the coffee market. Full article

Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural investment analysis under uncertainty. The framework integrates predictive modeling, uncertainty representation, and multi-objective optimization within a unified computational architecture. The empirical analysis was conducted using agricultural, climate, and carbon-market datasets covering Europe, Asia, and Africa over the 2010–2025 period. Agricultural productivity indicators, commodity price variables, climate-risk parameters, and carbon-market data were integrated into the modeling process. LSTM models were employed to analyze temporal agricultural and climate-related dynamics, while Interval Type-2 fuzzy systems were used to represent ambiguity associated with environmental and market uncertainty. Genetic Algorithms were subsequently applied to optimize investment allocation under conflicting objectives related to profitability, sustainability, and risk. The findings suggest that the proposed hybrid framework may improve adaptive investment evaluation and optimization performance under uncertain climate conditions relative to standalone computational approaches within the scope of the analyzed datasets. The results further highlight the importance of integrating predictive analytics, uncertainty modeling, and sustainability-oriented optimization within agricultural decision-support systems. However, the framework should be interpreted as a climate-resilient decision-support architecture rather than a universally deterministic forecasting mechanism. Overall, the study contributes to the emerging literature on agricultural sustainability and climate-resilient investment by presenting a transparent and uncertainty-aware computational framework under evolving environmental and carbon-market conditions. Full article

Salinity is a critical agricultural threat that reduces the productivity of several crops. Tomato (Solanum lycopersicum) is the world’s second most significant horticultural commodity, which struggles due to salt concentrations in irrigation water, even in hydroponic systems. This research evaluated seed priming treatments (hydro-, halo-, bacterio-, and halo-bacteriopriming) at different phenological stages under two salinity conditions (0 and 16 mM NaCl) to improve crop production. After evaluating physiological variables and multivariate statistical analyses, this study’s main breakthroughs are: Priming treatments modified the physiological, nutritional, and productive metabolism of tomato plants. Bacterio- and halo-bacteriopriming using an endophytic and halophytic bacterial consortium reduced germination time, enhancing uniformity and synchronizing seedling emergence. Bacteriopriming enhanced N, P, Ca and Zn absorption in seedlings. In the vegetative and reproductive stages, bacteriopriming consistently increased concentrations of K, Mg, and Zn in leaves and fruits but depleted Na uptake. Improving the nutritional balance resulted in not only a higher concentration of chlorophyll but also an increase in the yield and beta-carotene concentration in tomato fruits. The results demonstrated that halo-bacteriopriming may be a biotechnological strategy for mitigating saline stress, optimizing tomato growth and nutraceutical quality, because it outperformed the plant response in all stages of development compared to the control and hydro- and haloprimed treatments. Full article

Mycotoxins are compounds produced by the secondary metabolism of certain fungi. These compounds contaminate foods worldwide and pose a severe threat to the health of humans and animals. They also cause huge economic losses. A plethora of methodologies, encompassing agricultural, biological, chemical, and physical approaches, have been devised to curtail the presence of mycotoxins in food commodities. Among the physical processes, cold plasma (CP) has emerged as a useful technique for controlling the presence of toxigenic fungi in foods and for degrading the mycotoxins occurring in them without significantly affecting the quality and organoleptic properties of the treated commodities. The present review endeavors to demonstrate the efficacy of CP as a method of eradicating or reducing both the toxigenic mycobiota and the mycotoxins present in the most contaminated foods, including nuts, dried fruits, and cereal grains. The mechanisms of toxin degradation proposed by the different researchers are also examined and compared. Furthermore, the impact of the CP effect on the quality, sensorial characteristics, and toxicological properties of the treated food is thoroughly examined. Full article

Soybeans are a strategic commodity in global agricultural trade, and disruptions to their pricing system have direct implications for food security and trade patterns. This paper examines how major external shocks, particularly the Sino–U.S. trade wars, reshaped the dynamic connectedness and risk transmission structure of the global soybean price system. Using daily data from 2015–2025 for five key benchmarks, Chicago Board of Trade (CBOT) soybean futures, Dalian Commodity Exchange (DCE) No. 1 soybean futures, and cost-and-freight (CNF) prices for U.S. Gulf, Brazil, and Argentina shipments to China, we apply the time-varying parameter vector autoregression Diebold–Yilmaz connectedness model (TVP-VAR-DY) and the time-varying parameter vector autoregression Baruník–Křehlík frequency connectedness model (TVP-VAR-BK) models to quantify time-varying spillovers across short-, medium-, and long-run horizons. The results indicate that the global soybean market is highly integrated, while systemic risk transmission is predominantly short-run and declines sharply at longer horizons. CBOT futures remain the principal source of spillovers, especially in the short term, yet their net influence weakens noticeably after the 2018 trade-friction episode and declines further following the 2025 episode, particularly with respect to South American CNF benchmarks. Frequency-specific evidence suggests that trade-policy escalations are increasingly priced as structural shocks, strengthening medium- and long-horizon connectedness, while DCE’s outward spillovers rise markedly around 2025, consistent with the emergence of a more regionalized pricing architecture centered on Chinese demand. Within South America, Brazil leads short-run price formation, whereas longer-horizon dynamics are more exposed to Argentine policy risk spillovers. These findings provide new evidence on supply-chain reconfiguration and benchmark rebalancing in global soybean pricing and offer policy implications for strengthening China’s pricing capacity and enhancing multi-horizon supply-chain risk management. Full article

This paper investigates the dynamic linkages and volatility transmission among global food prices, biofuel commodity prices, and crude oil prices, with a focus on the profound disruptions caused by the COVID-19 pandemic. While interdependencies between energy and agricultural markets are well-studied, the specific role of biofuels as a transmission channel and the exacerbating effects of the crisis remain underexplored, especially through a robust multivariate volatility framework. Utilizing A VECM-CCC-GARCH models, this study captures both mean and conditional variance dynamics, allowing for the examination of asymmetric news impacts and volatility spillovers. The analysis employs a comprehensive dataset including the FAO Food Price Index, key biofuel, ethanol, biodiesel, and crude oil prices (Brent and WTI), alongside proxies for the pandemic’s severity. The research hypothesizes that the COVID-19 pandemic significantly amplified the volatility and strengthened the price transmission channels. We expect to find increased co-movement and volatility spillovers, reflecting reduced demand for transport fuels, agricultural supply chain disruptions, and shifting biofuel production incentives. The TARCH component will discern if negative news (e.g., sharp drops in oil demand) had a disproportionately larger impact on volatility than positive news. By providing a nuanced understanding of these complex interdependencies, this study offers valuable insights for policymakers addressing food security, energy transition strategies, and macroeconomic stability in the post-pandemic world, particularly concerning the strategic role of biofuels. Full article

Amid the ongoing transformation of global climate governance, climate policy uncertainty has emerged as an increasingly important factor influencing both energy and agricultural commodity markets, with direct implications for energy and food security. Using monthly data from 2008 to 2025, this study applies the TVP-VAR-DY and TVP-VAR-BK frameworks, together with complex network analysis, to investigate spillover dynamics among climate policy uncertainty, energy, and agricultural markets from both time-varying and frequency-based perspectives. The results show that spillover effects evolve substantially over time and become more pronounced during periods of major external shocks, particularly under the influence of short-run factors. Notably, the transmission effect of climate policy uncertainty is stronger for bioenergy-related agricultural commodities, especially soybeans and corn. While the agricultural market exhibits strong internal connectedness, cross-market risk transmission is heterogeneous across commodities, with corn remaining a relatively stable net transmitter of risk. By contrast, crude oil generally acts as a net receiver, whereas climate policy uncertainty behaves as a net receiver in the short run but gradually shifts into a net transmitter over the medium and long term, suggesting a lagged transmission pattern. Robustness checks based on alternative lag lengths, forecast horizons, and CPU proxies confirm that the main connectedness structure is stable and not driven by specific parameter choices. These findings provide useful evidence for policymakers seeking to improve the stability and transparency of climate policy and mitigate cross-market risk, while also offering practical guidance for investors in portfolio allocation and hedging against policy-induced volatility. Full article

Indigenous communities in northern Canada experience severe household food insecurity rates ranging from 21.8% to 70%. However, the relationship between national-level economic and environmental indicators and Indigenous Community Well-being (ICWB) remains inadequately understood. This study examines national-level correlates of ICWB from 1991 to 2021, analyzing relationships between ICWB scores and agricultural production volumes (canola, corn, wheat, soybeans), their commodity prices, and greenhouse gas (GHG) emissions, with a particular focus on the role of traditional food systems. The study uses data from the Government of Canada, Statistics Canada, and Environment Canada, supplemented by secondary literature on Indigenous traditional food systems. Three documented mechanisms provide a framework for interpreting how national indicators may affect northern communities: commodity price transmission through integrated markets, federal policy responses calibrated to national economic data, and supply chain dependencies linking southern production to northern availability. Correlation analysis reveals significant positive associations between ICWB and production volumes of canola, corn, and soybeans, as well as the prices of wheat, corn, canola, and soybeans. Regression analysis that accounts for temporal trends reveals that soybean and canola prices are negatively associated with ICWB, indicating that increasing prices may reduce community well-being, potentially reflecting increased economic pressure or reduced affordability. GHG emissions correlate positively with ICWB, likely reflecting confounding by economic development rather than direct environmental benefits. These national-level correlates have potential implications for northern Indigenous food security and well-being through recognized transmission mechanisms. The paradoxical positive correlation between rising commodity prices and ICWB is consistent with an adaptive response: as market food costs increase, communities may strengthen traditional food harvesting and local production, though higher equipment and resource prices may constrain these efforts, making food sovereignty enhancement a complex challenge. Findings suggest that northern communities participate in national economic systems through price, policy, and supply chain pathways, but may yet retain adaptive capacity through traditional food systems if persistent multi-stage supports are provided. Policy implications include indexing northern food subsidies to commodity price volatility, prioritizing funding for Indigenous-led food sovereignty initiatives that integrate traditional knowledge with modern techniques, and investing in infrastructure to reduce supply chain vulnerabilities. Future research should examine community-specific responses to national economic patterns and identify local factors that strengthen nature-led traditional food systems in northern Indigenous contexts. Full article