Corn is pivotal in global agriculture, serving diverse purposes in the food, feed, and biofuel sectors. Despite its economic significance, corn price volatility, influenced by supply-demand dynamics, climate variations, and geopolitical tensions, poses challenges in decision-making processes. This necessitates accurate price forecasting of corn for producers and government alike to formulate effective policies that uphold stability and enhance efficiency within the corn market. Using long-term records of the monthly global price of corn spanning from January 2014 to December 2023, this study employs SARIMA modeling techniques to forecast the global price of corn. To find a solution, the auto.arima() function from the “forecast” package in R 4.3.2 for Windows was employed to identify both the structure of the series (stationary or not) and type (seasonal or not) and sets the model’s parameters, which takes into account the AIC, AICc or BIC values generated to determine the best fitting seasonal ARIMA model. Following the Box–Jenkins methodology, the best-fitting SARIMA (0,1,1) (0,0,1) [12] model was identified, supported by the lowest AIC value. The Ljung–Box Q–test further validated the model’s adequacy in capturing the data’s behavior, with a non-significant p-value of 0.7013. This analysis uncovered valuable insights into the fluctuations of corn prices, providing a comprehensive understanding of the interplay between economic factors and external influences. This study underscores the practical utility of SARIMA modeling for farmers and other relevant stakeholders in anticipating market fluctuations and devising adaptive strategies in response to evolving corn market dynamics.

Heirs’ property is a form of collective land ownership arising from intestate succession, often resulting in clouded titles, fractional ownership, and legal vulnerability. This tenure system is especially prevalent among African American landowners in the Southern United States and poses significant challenges for sustainable land use, agricultural development, forest management, and conservation. This paper presents an interdisciplinary review, research, and analysis encompassing legal studies, environmental policies, and rural social science to examine how heirs’ property status leads to diminished productivity, land underutilization, disinvestment, and involuntary land loss. Key issues include barriers to accessing USDA and NRCS programs, an inability to implement long-term land management plans, and an increased risk of partition sales and tax foreclosures. This review also examines demographic trends, regional concentration, and the broader socio-environmental impacts of insecure land tenure. Current policy responses, such as the Uniform Partition of Heirs Property Act (UPHPA), USDA land access provisions, and community-based legal interventions, are assessed for their effectiveness and limitations. The article concludes with policy and programmatic strategies to support title clearing, promote equitable land retention, and enhance participation in conservation and climate resilience initiatives. By highlighting the intersection of property law, racial equity, and environmental sustainability, this review contributes to a growing body of research aimed at securing land tenure for historically marginalized communities.

The global price of corn is critical in agricultural markets, food security, and trade policies, making accurate forecasting essential for policymakers and stakeholders. Traditional econometric forecasting models have been widely used but often fail to capture market volatility, nonlinear trends, and external shocks, necessitating advanced machine-learning techniques. This study explored forecasting models, including traditional and advanced machine learning and hybrid approaches, to determine the most effective method for predicting corn prices. Using monthly corn price data from 2014 to 2024, the study evaluated model performance based on different evaluation metrics. The results indicated that the Neural Network Auto Regressive (NNETAR) model outperformed traditional econometric models, effectively capturing complex temporal structures and adapting to sudden market fluctuations. Additionally, hybrid models integrating NNETAR and Seasonal and Trend decomposition using LOESS Model (STLM) demonstrated further improvements in forecasting accuracy, suggesting that combining statistical and machine learning methods enhances predictive reliability. These findings underscore the potential of machine learning in agricultural price forecasting, providing valuable insights for policymakers to implement market stabilization strategies, adjust trade policies, and enhance risk management in the U.S. corn sector.

Heirs’ property refers to land or housing inherited without formal legal documentation, such as a will or clear title. Heirs’ property poses numerous challenges, including legal uncertainty, increased risk of land loss, and restricted access to financial services, government programs, and disaster aid. These complications often prevent families from leveraging property for wealth building or development. To educate the community members about the risks and challenges of heirs’ property, the University of Maryland Eastern Shore (UMES) Extension conducted ten educational sessions on heirs’ property issues across Maryland. Post-event surveys from 78 respondents were analyzed to examine how socio-demographic characteristics relate to program participation, estate planning behavior, and exposure to heirs’ property risks. Respondents were 58% African American, 26% Asian, 9% White, and 4% Hispanic/Latino; 49% were female, and 42% held graduate or professional degrees. The bivariate binary logistic regression showed that higher educational attainment was positively associated with program participation (Odds Ratio = 3.5, p < 0.05). African American respondents had significantly lower odds of attending the program (OR = 0.20, p < 0.01), but higher odds of reporting heirs’ property challenges (Odds Ratio = 2.93, p < 0.10), land loss (OR = 2.93, p < 0.10), and lower likelihood of will-making (Odds Ratio = 0.37, p < 0.10). The findings suggest that education and race are the most influential factors of heirs’ property-related outcomes. These factors can guide more effective, targeted outreach and legal education strategies for heirs’ property in diverse communities.

This study provides a comprehensive economic assessment of supply, demand, and trade competitiveness in the U.S. eggplant (Solanum melongena L.) sector, using annual import and export data from 2000 to 2022. Although eggplant is a regionally important specialty crop in the United States, domestic production remains limited relative to rising consumer demand driven by demographic diversification and shifting dietary preferences. Using descriptive statistics, compound annual growth rates, correlation analysis, log-linear elasticity models, and multiple trade competitiveness indicators, this research examines long-term trends in trade quantity, value, and pricing; evaluates the responsiveness of trade flows to price changes; and analyzes the structural factors underlying persistent import dependence. Results show that both import and export quantities and values have increased steadily, with import volumes averaging nearly four times those of exports. Strong positive correlations among trade variables indicate a high degree of integration with global markets. Elasticity estimates reveal atypical positive price responsiveness in both imports and exports, indicating supply-driven price dynamics, limited substitutability, and quality-differentiated export behavior. Competitiveness indicators, particularly the consistently negative Trade Balance Index, high Import Dependency Ratio, and low Self-Sufficiency Ratio, confirm that the U.S. eggplant market is structurally reliant on foreign supply, despite the premium pricing of U.S. exports. The findings align with comparative advantage theory, market integration frameworks, and product differentiation models, showing that trade patterns are shaped by deep-rooted economic conditions rather than short-term fluctuations. This study highlights the need for strategies to enhance domestic production capacity, improve supply chain resilience, and strengthen competitive positioning in global fresh vegetable markets.