William Nganje

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.

Food safety is a major risk for agribusiness firms. According to the Centers for Disease Control and Prevention (CDC), approximately 5000 people die annually, and 36,000 people are hospitalized as a result of foodborne outbreaks in the United States. Globally, the death estimate is about 42,000 people per year. A single outbreak could cost a particular segment of the food industry hundreds of millions of dollars due to recalls and liability; these instances might amount to billions of dollars annually. Despite U.S. advancements and regulations, such as pathogen reduction/hazard analysis critical control points (PR/HACCP) in 1996 and the Food Modernization Act in 2010, to reduce food-safety risk, retail meat facilities continue to experience recalls and major outbreaks. We developed a stochastic-optimization framework and used stochastic-dominance methods to evaluate the effectiveness for three strategies that are used by retail meat facilities. Copula value-at-risk (CVaR) was utilized to predict the magnitude of the risk exposure associated with alternative, cost-effective risk-reduction strategies. The results showed that optimal retail-intervention strategies vary by meat and pathogen types, and that having a single Salmonella performance standard for PR/HACCP could be inefficient for reducing other pathogens and food-safety risks.

In this paper, we propose a general mathematical model for analyzing yield data. The data analyzed in this paper come from a characteristic corn field in the upper midwestern United States. We derive expressions for statistical moments from the underlying stochastic model. Consequently, we illustrate how a particular feature variable contributes to the statistical moments (and in effect, the characteristic function) of the target variable (i.e., yield). We also analyze the data with neural network techniques and provide two methods of data analysis. This mathematical model and neural network-based data analysis allow for better understanding of the variability within the data set, which is useful to farm managers attempting to make current and future decisions using the yield data. Lenders and risk management consultants may benefit from the insights of this mathematical model and neural network-based data analysis regarding yield expectations.

Limitations of the capital asset pricing model (CAPM) continue to present inconsistent empirical results despite CAPM’s firm mathematical foundations provided in numerous studies. In this article, the authors examine how estimation errors of the CAPM can be minimized using the cross-validation technique, a concept that is widely applied in machine learning (ML-CAPM). They apply their approach to test the assumption that the CAPM is a well-diversified portfolio model by using data from the S&P 500 and the Dow Jones Industrial Average. The results from the ML-CAPM validate both market indexes as well diversified, with statistically insignificant variation in unsystematic risks during and after the 2007 financial crisis. Furthermore, the ML-CAPM provides smaller root mean square error and mean absolute deviations compared to the traditional CAPM.