William W. Wilson

Geopolitical disruptions in the Black Sea have increased maritime shipping risks, constrained port capacity, and accelerated the development of alternative Russian grain-export routes through the Baltic and Caspian Seas. This study examines how these changes affect maritime logistics, port competition, and global wheat trade. We develop a monthly stochastic minimum-cost network-flow model using Optimized Monte Carlo Simulation (OMCS) that incorporates uncertainty in export prices, ocean freight rates, export supply, import demand, and port capacity. The results show that logistical constraints are strongly seasonal: Russian and Ukrainian port capacities become binding during peak export periods, diverting or deferring shipments. Expansion of Russian Baltic capacity reallocates exports away from traditional Black Sea and Sea of Azov routes and displaces shipments from competing exporters, particularly the United States. Relaxation of Russia’s export quota similarly increases Russian exports and alters their seasonal distribution. The results demonstrate that geopolitical disruptions and infrastructure diversification affect not only individual ports but also competitive trade flows throughout the maritime network. More broadly, the analysis shows how seasonality, uncertainty, and capacity constraints interact to determine maritime network resilience and route substitution.

Whether sophisticated hedge-ratio models improve out-of-sample risk reduction remainsuncertain because performance depends on the strength of the cash–futures relationship. Weexamine how the value of dynamic hedging varies across cash–futures correlation regimes. Wecompare na¨ıve, ordinary least squares, constant conditional correlation GARCH, and dynamicconditional correlation GARCH hedge ratios using Minneapolis corn and soybean cash bidsand CBOT futures returns from July 2005 through May 2025. Hedge ratios are estimatedrecursively using information available at initiation and held fixed over nonoverlapping one-,three-, and eight-month holding periods. Performance is evaluated out of sample using hedgingeffectiveness, Value-at-Risk, Expected Shortfall, and block-bootstrap inference. DCC-GARCHproduces the largest gains at short and intermediate horizons, particularly for corn, increasinghedging effectiveness from 74.9% to 87.4% at one month and from 86.2% to 96.5% at threemonths relative to the OLS benchmark. DCC-GARCH yields statistically significant improve-ments over OLS in every low-correlation commodity–horizon comparison, whereas the incre-mental gains become economically small and often statistically insignificant as cash–futurescorrelation approaches unity. The findings show the value of model complexity is conditionalrather than universal. DCC-GARCH is most beneficial when weakening cash–futures depen-dence reduces the reliability of static hedge ratios, while simpler approaches remain competitivein highly integrated markets.

An emerging paradigm in public health focuses on enhancing nutrition in existing food staples to reduce chronic disease at the population scale, rather than relying on individuals to change their behavior. This paradigm leverages plant and animal breeding, production practices, and processing to enhance nutrition, whereby foods consumed by millions can be improved at low incremental cost. This article supports and operationalizes this paradigm, illustrating the potential to improve diets through a case study that increases the arabinoxylan fiber content of commodity wheat through classical plant breeding (a non-GMO technology). The approach described in this article proposes to link agricultural and food science with health system implementation to deliver equitable access, improved healthcare outcomes and cost savings, and improved community health. Based on published dose–response relationships, comparative risk modeling indicates that modest fiber increases achieved by the commodity wheat breeding led to reduced population-level risks of 1–3% for cardiovascular disease, 3–4.5% for type 2 diabetes, and 1–3.5% for colorectal cancer, translating into substantial healthcare cost savings when implemented at a national scale. This article outlines possible low-risk pathways for implementing these nutrition increases at the population scale through commodity supply chains and community-level nutrition improvement efforts and evaluates the ranges of potential population-level impacts.

Origin shippers of grains and oilseeds face market price risks and costs related to handling, storage, and transporting crops. A Monte Carlo optimization model, using Material Requirement Planning (MRP), was created to minimize the mean of these costs and the 5% value‐at‐risk (VaR). The primary decision variable was the number of shuttle trains to purchase in the rail market. Additional sensitivity and stress analyses were conducted to evaluate the optimal results. Key observations include the significance of primary and secondary railcar markets for risk management, as well as the importance of managing the seasonality of farmer deliveries through forward contracting. This study also highlights the utility of subject matter expert (SME) time series modeling in Monte Carlo simulation.

Risk and volatility for many commodities escalated sharply following the Russian invasion of Ukraine, creating numerous uncertainties for trading firms and importers. The purpose of this study is to analyze the bidding behavior in Egyptian wheat import tenders in the pre‐ and post‐invasion periods. Bids for each bidder in individual tenders were analyzed for the period from 2017 to mid‐2023. The results indicate that before invasion, there were lower standard errors, margins, and higher probabilities of bidding. These changed in the post‐invasion period and as a result the probability of winning, optimal bids and implied margins increased. Implications were identified for buyers, sellers and industry and policy analysis. First, bidding competition is important and strategically important. Second, for sellers, determining optimal bids is a strategic calculation and requires knowledge about competitors, competitor bids, and relationships over time, as well as a derivation to determine optimal bids. Third, buyers require a larger number of independent and symmetrically well‐informed bidders to fully exploit the virtues of bidding competition.

This paper aims to describe the evolution of firm and industry strategies and to analyze detailed transaction data on the structure of the international grain trading industry. Specifically, data are developed from vessel nominations, and these were used to derive measures of concentration and determine how these change across selected geographies and commodities, as well as to identify the clusters of firms in this industry. The results provide unique documentation on the structure and concentration of the world grain trading industry. The results indicate the levels of concentration are much less than traditionally suggested; there has been growth by many new entrants making up a viable competitive fringe; the industry seems to be comprised of three clusters of firms; and generally, competition in this industry would be characterized as being “fierce.”

Purpose: This study’s purpose is to analyze the effects of trade interventions and non-tariff impediments between the exporters (the United States and Brazil) and China for soybean trade.Design/methodology/approach: A spatial model is developed and solved using an optimized Monte Carlo simulation (OMCS) and is used to minimize the costs of supplying soybeans to China. The costs included the origin basis; transportation to ports, including trucks, railways and barges; demurrage; and ocean freight. The sum of these charges determines the delivered costs to China from each origin. Most variables are random and correlated. Time-series distributions are based on historical data. Production and exports are highly seasonal and important.Findings: Base-case flows are highly seasonal, are risky and reflect actual trade. Sensitivities illustrate the effects of mitigating the quality differentials and interpreting a term of the Phase One agreement that purchases would be made so long as the prices are competitive. The results are also used to illustrate the influence of diversifying from the United States as a supplier. Finally, the policy implications are discussed.Research limitations/implications: Removing the quality discounts for US soybeans raises the US market share by 9%. These results also illustrate that diversification of supply sources is important for the importing country. Indeed, if China were to pursue less diversification import costs and/or risks would escalate. Hence, these results suggest that diversification is an appealing element of an import strategy. The results suggest a large distribution of prices and costs, particularly in Brazil. On average, the United States is most likely to be competitive for only a few months of the year, and the results are highly seasonal.Practical implications: Competition in supplying soybean to China is extremely competitive and the underlying factors impacting spatial competition are risk, correlated and spatially dependent. In addition to these, there are quality differences, and there are trade policies and strategies that affect competition. The empirical model and results illustrate the intensity of competition in this market as well the impacts of these non-tariff barriers and trade strategies in this market.Social implications: Important policies have been taken and continue to be under review regarding competition and trade among these countries. These results illustrate the impacts of these policies on market shares and competition.Originality/value: This problem is important to the world soybean trading sector, and the methodology captures important seasonal and random variables that affect trade flows. The OMCS model is appropriate for this problem and has only been used minimally in the recent literature about commodity trade.

As is the case with many commodity processors, manufacturers of ethanol face considerable margin risk from a wide range of input and output prices and the interrelation of these risks with the technical operational efficiency of the plant. Buyers of ethanol desire greater budget certainty for future ethanol purchases. An approach to pricing that has found considerable use in the livestock, dairy, and milling industries is the use of component‐based formula pricing. This study constructs a Monte Carlo simulation model of a typical South Dakota ethanol plant that uses a formula contract to fix components of the ethanol price. The simulation results indicate that the contract has considerable margin risk management benefits for the ethanol producer. The buyer also achieves marginal risk management benefits but with greater budget certainty as the components are fixed. Additional observations of interest to the industry are drawn from the sensitivity results of the simulations. [EconLit Citations: Q13, G17].

Integrating trading and logistics is an important challenge in commodity trading. Trading and logistics are strategic decisions and are integral to most commodities including grain shipping by rail, in addition to other modes (barges, ocean shipping). There are substantial risks, such as the ordering and placement of rail cars. The other risk is having sufficient grain stocks to load rail cars. Alternatives for managing these risks include holding grain inventories and the strategic use of shipping options. The purpose of this study is to develop a model for determining an optimal grain inventory strategy for shippers. The real option methodology is used to value uncertainty in rail car velocity and determine optimal purchases and inventories. The results show the importance of integrating trading and logistics decisions and illustrate that inventories can be interpreted and valued as real options that are affected by uncertainties. Taken together, the real options indicate how much extra inventory shippers should maintain to mitigate risks and maximize profits.