Browsing by Author "Ramsey, A. Ford"
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- Adoption Determinants and Impacts of Tuta absoluta Integrated Pest Management for Nepali Tomato FarmersKnaresboro, Lauren Marie (Virginia Tech, 2019-09-12)Tuta absoluta, a member of the moth family, causes devastating yield loss to tomato farmers around the world. Its recent migration into the tomato fields of Nepal puts tomato farmers at a high risk of yield loss. In response, chemical pesticide use by Nepali farmers is increasing. Integrated pest management (IPM) practices have been implemented in hopes of reducing the frequency of chemical pesticide use while controlling yield risks. This study examines the extent and determinants of Tuta absoluta IPM adoption and its effect on the frequency of pesticide use for Nepali tomato farmers. Primary data was collected from four-hundred and one households in four districts throughout Nepal. Two levels of IPM practices were assessed, simple and complex, based on the need for additional knowledge and tools. An instrumental variable probit analysis was used to analyze the determinants of IPM adoption. Household distance to nearest agricultural extension office was a significant factor decreasing the likelihood of the adoption of complex practices. Amount of land dedicated to tomato production, membership status of the primary decision maker, IPM training regarding Tuta absoluta practices and severity of Tuta absoluta were found to increase the likelihood of the adoption of complex practices. In order to analyze pesticide use, a simple linear regression was used. Primary decision maker's age, gender, and education level were significant determinates to decrease the amount of expenditures spent on chemical pesticides to control for Tuta absoluta. IPM adoption level, amount of land dedicated to tomato production and severity of Tuta absoluta damage were significant determinates to increase the amount of expenditures spent on chemical pesticides to control for Tuta absoluta.
- Agricultural Trade Performance and Potential: A Retrospective Panel Data Analysis of US Exports of Corn and SoybeansGrossen, Grace Elizabeth (Virginia Tech, 2019-08-22)There are a variety of international issues that disrupt the global trade market, an important one being national policies on the regulation of genetically modified organisms, or GMOs. Many crops have been genetically modified for reasons from herbicide resistance to correcting dietary shortfalls. This study evaluates the United States' exports of corn and soybeans from 1998 to 2016 to identify unusual shocks in trade values. In particular, this study quantifies how the importers' policy stance on the GMO issue impacts bilateral trade values. I estimate a gravity model with both ordinary least squares (OLS) and Poisson pseudo maximum likelihood (PPML) estimations. Residual analysis is used to assess the difference between actual trade and the trade levels predicted by the models. The results suggest that anti-GMO policies reduce trade values by an average of 11%. The largest difference between predictions and actual trade values is seen in corn exports to the European Union. Between 1998 and 2016, this forgone trade in corn was valued at $52.7 billion, which is $2.77 billion per year on average. This value is similar to the annual average value of U.S. exports of corn to Japan in the same period, $2.46 billion. The results have important implications for the agricultural industry. For developing nations, adoption of GMO crops could increase productivity and help alleviate poverty. Ultimately, the decision to adopt is up to the consumer, so the factors of consumer knowledge and opinions of GMOs are not to be ignored.
- Bayesian Hierarchical Models for Measuring Varietal Improvement in Tobacco Yield and QualityRamsey, A. Ford; Rejesus, Roderick M. (2021-11)We measure the economic impact of varietal improvement and technological change in flue-cured tobacco across quantity (e.g., yield) and quality dimensions under a voluntary quality constraint. Since 1961, flue-cured tobacco breeders in the United States have been subject to the Minimum Standards Program that sets limits on acceptable quality characteristics for commercial tobacco varieties. We implement a Bayesian hierarchical model to measure the contribution of breeding efforts to changes in tobacco yields and quality between 1954 and 2017. The Bayesian model addresses limited data for varieties in the trials and allows easy generation of the necessary parameters of economic interest.
- Cyberbiosecurity: A New Perspective on Protecting US Food and Agricultural SystemDuncan, Susan E.; Reinhard, Robert; Williams, Robert C.; Ramsey, A. Ford; Thomason, Wade E.; Lee, Kiho; Dudek, Nancy; Mostaghimi, Saied; Colbert, Edward; Murch, Randall Steven (Frontiers, 2019-03-29)Our national data and infrastructure security issues affecting the "bioeconomy" are evolving rapidly. Simultaneously, the conversation about cyber security of the U.S. food and agricultural system (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($ 6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse and they require advanced technologies and efficiencies that rely on computer technologies, big data, cloud-based data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bio economy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safetymanagement Hazard Analysis Critical Control Point systemconcept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). This analysis explores the relevant concepts of cyber biosecurity from food production to the end product user (such as the consumer) and considers the integration of diverse transportation, supplier, and retailer networks. We describe common challenges and unique barriers across these systems and recommend solutions to advance the role of cyber biosecurity in the food and agricultural sectors.
- Empirical Studies in Production Economics and International Agricultural Development IssuesVillacis, Alexis H. (Virginia Tech, 2020-07-16)This dissertation is composed of two manuscripts in Production Economics and two manuscripts in International Agricultural Development. The first two manuscripts focus on production economics, and both are an exploration of nitrogen use and its impact on continuous corn production and profitability in Colorado. The first manuscript titled "Switching Regression Stochastic Plateau Production Functions––A Comparison of Alternative Specifications" proposes an alternative approach for estimating crop yield response functions using a frequentist approach. The second manuscript titled "Profitability Effects of Different Tillage Systems in Continuous Corn Rotations" explores the interaction between different tillage systems and nitrogen fertilization in irrigated continuous corn production in northeastern Colorado. We find that strip tillage is better suited for continuous corn production under the agro-climatic conditions in northeastern Colorado. The third and fourth manuscripts focus on international agricultural development and analyze the role of factors that influence the agricultural development of small-holder farmers in Ecuador, namely, markets, food value chains, risk preferences, and risk perceptions. The third manuscript titled "Does the Use of Specialty Varieties and Post-Harvest Practices Benefit Farmers? Cocoa Value Chains in Ecuador" analyzes the impact of the use of specialty cocoa varieties on small-scale farmers' income. It finds that the use of specialty cocoa varieties has a low impact on small-scale cocoa producers' income, and that post-harvest practices may lead to substantial price responses irrespective of the type of cocoa grown. Finally, the fourth manuscript titled "Linking Risk Preferences and Risk Perceptions of Climate Change Using Prospect Theory" explores how farmers' risk preferences correlate with their perceptions of climate risk. It finds that farmers that behave in accordance to the assumptions of prospect theory are more likely to perceive greater risks from climate change, that is, they are more likely to perceive the risks associated with climate change as being more threatening at a personal level. Since risk perception is a necessary prerequisite for adaptation, the results presented in this manuscript, have important policy implications for process of adoption of new technologies aimed at mitigating effects of climate change.
- Estimating Economically Optimal Levels of Nitrogen Fertilizer in No-Tillage Continuous CornVillacis, Alexis H.; Ramsey, A. Ford; Delgado, Jorge A.; Alwang, Jeffrey R. (2020-11)Stochastic plateau production functions provide improved fertilizer recommendations based on multi-year agronomic experiments where weather and other stochastic variables change over time. This research assesses the profitability of no-tillage corn production in northeastern Colorado and determines economically optimal nitrogen fertilizer rates. It also proposes an alternative parameterization of the linear response stochastic plateau model which provides a robustness check against traditional parameterizations. Results show the current use of nitrogen fertilizer in the area exceeds estimated economically optimal levels. This suggests that a reduction in nitrogen use could increase expected profits and simultaneously reduce environmental costs.
- How Well Do Commodity Based ETFs Track Underlying Assets?Neff, Tyler Wesley (Virginia Tech, 2018-06-08)Exchange Traded Funds are growing in popularity and volume, however academic literature related to their performance is limited. This study analyzes how well the CORN, WEAT, SOYB, USO, and UGA commodity ETFs track their respective futures assets during the period of January 2012 to October 2017. Tracking error in this study is evaluated through 4 approaches to measure error, bias, systematic risk, and error magnitude. Additionally, a mispricing analysis is conducted as an alternative form of error measurement Results indicate that tracking error is small on average, however CORN shows average excess returns significantly smaller than zero. The CORN ETF is returning a smaller positive value compared to the asset basket when asset basket returns are greater than zero and a larger negative value compared to the asset basket when asset basket returns are less than zero. The CORN, WEAT, USO, and UGA ETFs are found to move less aggressively than the respective asset baskets they track. While errors were small on average, large tracking errors were present across ETFs. The size of errors were found to be impacted by large price moves, as well as seasonality on a monthly and yearly level. USDA reports impacted the size of errors for CORN, WEAT and SOYB while EIA reports had no impact on error size. The mispricing analysis concluded that CORN and SOYB trade at a discount to Net Asset Value on average while WEAT trades at a premium.
- Impacts of COVID-19 and Price Transmission in US Meat MarketsRamsey, A. Ford; Goodwin, Barry K.; Hahn, William F.; Holt, Matthew T. (2021-05)Coronavirus 2019 (COVID-19) has caused ongoing disruptions to U.S. meat markets via demand and supply-side shocks. Abnormally high prices have been reported at retail outlets and meat packers have been accused of unfair business practices because of widening price spreads. Processing facilities have experienced COVID-19 outbreaks resulting in shutdowns. Using weekly data on wholesale and retail prices of beef, pork, and poultry, we characterize the time series behavior and dynamic linkages of U.S. meat prices before the COVID-19 pandemic. We model vertical price transmission using both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. With the estimated models, we then compare price movements under COVID-19 to model predictions. All three meat markets are well-integrated and we observe unexpected, large price movements in April and May of 2020. Early COVID-19 related shocks appear to be transitory with prices returning to expected levels at a pace consistent with the speed of transmission prior to the pandemic. This well-functioning market process suggests a degree of resilience in U.S. meat supply chains.
- Incorporating historical weather information in crop insurance ratingLiu, Yong; Ramsey, A. Ford (Wiley, 2022-06-16)Crop insurance programs rely on conditional predictive distributions of loss random variables (e.g., yield, revenue, loss costs, etc.) to determine probabilities and magnitudes of loss. The loss variables may be related to stochastic variables that are not known at the time the policy is priced. Such is the case for weather; weather is stochastic, realizations are not known when the crop insurance policy is sold, and there is often additional historical information on weather relative to the loss variable itself. We provide a Bayesian methodology for incorporating historical weather information in crop insurance rating. We apply the method in empirical applications to county-level U.S. corn yields and loss cost ratios in the Midwest. The historical weather-conditioned distributions differ from those based on shorter samples. In the yield distribution setting, additional temporal weather information leads to economic gains relative to other rating approaches; the magnitude of these gains increases with the amount of historical weather information included in the analysis.
- Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic ModelsSarkar, Sayantan; Ramsey, A. Ford; Cazenave, Alexandre-Brice; Balota, Maria (2021-06-18)Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a*, b*, u*, v*, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a*, u*, GA, GGA, and CSI were significantly (p <= 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.
- SmartFarm Innovation NetworkDuncan, Susan E.; Ramsey, A. Ford (2019-10-04)This poster represents a variety of projects funded, in part, by the Virginia Agricultural Experiment Station, Virginia Tech Institute for Creativity, Arts, and Technology, NIFA, USDA NIFA and other funding sources. Some of these researchers are affiliated with the Southwest Virginia Regional Node of the Commonwealth Cyber Initiative.
- Student-Managed Commodity Fund—A New Frontier in Experiential LearningIsengildina-Massa, Olga; Ramsey, A. Ford (Cambridge University Press, 2019-10-03)This study provides a road map for creating and operating a student-managed investment fund (SMIF) as an experiential learning opportunity in commodity market analysis. We describe the reasons for implementing a SMIF and the benefits it offers relative to traditional simulation approaches. We outline the necessary steps for starting a SMIF and explain its organizational structure. We discuss a SMIF’s operation and main activities, which include recruitment, training, trading, and interaction with the client and alumni. The implications of participating in a SMIF are reviewed within a cost-benefit framework.
- Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance ProgramRamsey, A. Ford; Goodwin, Barry K. (MDPI, 2019-04-16)The federal crop insurance program covered more than 110 billion dollars in total liability in 2018. The program consists of policies across a wide range of crops, plans, and locations. Weather and other latent variables induce dependence among components of the portfolio. Computing value-at-risk (VaR) is important because the Standard Reinsurance Agreement (SRA) allows for a portion of the risk to be transferred to the federal government. Further, the international reinsurance industry is extensively involved in risk sharing arrangements with U.S. crop insurers. VaR is an important measure of the risk of an insurance portfolio. In this context, VaR is typically expressed in terms of probable maximum loss (PML) or as a return period, whereby a loss of certain magnitude is expected to return within a given period of time. Determining bounds on VaR is complicated by the non-homogeneous nature of crop insurance portfolios. We consider several different scenarios for the marginal distributions of losses and provide sharp bounds on VaR using a rearrangement algorithm. Our results are related to alternative measures of portfolio risks based on multivariate distribution functions and alternative copula specifications.