Agricultural Economics Faculty Research and Publications

Permanent URI for this collectionhttps://hdl.handle.net/2097/982

Browse

Now showing 1 - 20 of 90

Open Access
Examining the relationship between vertical coordination strategies and technical efficiency: Evidence from the Brazilian ethanol industry
(2018-03-24) Sant'Anna, Ana Claudia; Bergtold, Jason S.; Shanoyan, Aleksan; Granco, Gabriel; Caldas, Marcellus M.
The sugarcane industry in Brazil, one of the world's leading producers of ethanol and sugar, is undergoing significant changes driven by geographic expansion and technological innovations. These changes are forcing sugarcane producers and processors, to re-evaluate their vertical coordination and growth strategies. This paper presents an empirical analysis of the relationship between the vertical coordination strategies at the production-processing interface of the Brazilian ethanol supply chain and the technical efficiency of the mills. It utilizes data envelopment analysis and a Tobit censored model in combination with unique data on 204 mills that account for around half of Brazil's sugar and ethanol production. Results indicate that vertical integration and the location of the mill have a statistically significant impact on efficiency. The findings show that the technical efficiency is not the main driver of vertical integration implying that such decisions are primarily motivated by strategic considerations. The mills are likely to forgo gains in technical efficiency in exchange for improving their strategic position through vertical integration. These findings shed light on the underlying motivation for the observed level of vertical integration that accompanies the expansion of the Brazilian sugarcane industry. [EconLit citations: L22, Q12, Q16].
Open Access
Examining farmers' willingness to grow and allocate land for oilseed crops for biofuel production
(2018-03-01) Embaye, Weldensie T.; Bergtold, Jason S.; Archer, David; Flora, Cornelia; Andrango, Graciela C.; Odening, Marting; Buysse, Jeroen
The purpose of this paper is to determine farmers' willingness to adopt and allocate land for growing non-food oilseeds as bio-energy crops across the western US. A mail survey was conducted in three regions of the western US from randomly selected wheat farmers. Data was analyzed using Heckman's two stage selection model to correct for selection bias. Under favorable contracts, the study found that 58% of sample farmers were willing to adopt oilseeds as bio-energy crops and initially contribute an average of 160 acres of land for production per farm. Concerning farmers' adoption decisions, factors such as experience growing oilseed crops, availability of a nearby crushing facility, use of no till, being a first adopter and having a college degree positively affected adoption, while risk behavior, farm experience and gender negatively affected adoption. With regard to the land allocation decision, factors such as farm income and gender positively affected land allocation decisions, whereas percentage of land rented on a crop share basis, profit ratio (wheat/canola) and livestock ownership negatively affected land allocation decisions.
Open Access
Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay
(2020-06-06) Ramsey, Steven M.; Bergtold, Jason S.
To satisfy the utility maximization hypothesis in binary choice modeling, logit and probit models must make a priori assumptions regarding the underlying functional form of a representative utility function. Such theoretical restrictions may leave the postulated estimable model statistically misspecified. This may lead to significant bias in substantive inferences, such as willingness-to-pay (or accept) measures, in environmental, natural resource and applied economic studies. Feed-forward back-propagation artificial neural networks (FFBANN) provide a potentially powerful semi-nonparametric method to avoid potential misspecifications and provide more valid inference. This paper shows that a single-hidden layer FFBANN can be interpreted as a logistic regression with a flexible index function and can be subsequently used for statistical inference purposes, such as estimation of marginal effects and willingness-to-pay measures. To the authors’ knowledge, the derivation and estimation of marginal effects and other substantive measures using neural networks are not available in the economics literature and is thus a novel contribution. An empirical application is conducted using FFBANNs to demonstrate estimation of marginal effects and willingness-to-pay in a contingent valuation and stated choice experimental framework. We find that FFBANNs can replicate results from binary choice models commonly used in the applied economics literature and can improve on substantive inferences derived from these models.
Open Access
Field-Level Land-Use Adaptation to Local Weather Trends
(2020-11-09) Ramsey, Steven M.; Bergtold, Jason S.; Heier Stamm, Jessica L.
The intersection of agriculture and climate has been well researched for at least the last couple of decades. Largely, the motivation for previous research has been the potential impact on food security for the world's (growing) population. Many studies have predicted unfavorable yield scenarios for some geographic regions. As a result, another common research theme is farmer adaptation to a changing climate. Typically, these studies are concerned with what farmers could or should do to adapt to adverse outcomes. However, research examining whether farmers respond to weather patterns has largely been ignored. Answering this question can help provide more accurate food security analyses: if farmers do respond to changing patterns through cropping decisions, for instance, the global food supply outcome will be different than a world in which they do not respond. This article aims to provide insights into what and how farmers' cropping decisions respond to weather patterns. The study region is a set of eleven Kansas counties. The article provides an important step toward more credible estimates of global food supplies under changing climates and the methods themselves translate to other areas. Results suggest that land-use responses to changing weather patterns will vary across time and space.
Open Access
Marginal cost of carbon sequestration through forest afforestation of agricultural land in the southeastern United States
(2022-06-15) Obembe, Oladipo S.; Hendricks, Nathan P.
One tool to mitigate climate change is to sequester carbon through changes in land use. The purpose of this study is to analyze the cost-effectiveness of carbon sequestration through afforestation of cropland via the Conservation Reserve Program (CRP) in the United States. We use the correlated random effects (CRE) probit model to estimate the impact of an increase in the CRP rental payments on land use transitions between cropland and forest. Our estimates are used to simulate land use change and carbon sequestration supply curves over different time horizons. Increasing the CRP rent to reflect the social cost of carbon of $154/tonne of carbon increases annual carbon sequestered by 7.42 million tonnes, 23.58 million tonnes, and 34.96 million tonnes over 1, 5, and 10-year horizons.
Open Access
Annual bioenergy crops for biofuels production: Farmers' contractual preferences for producing sweet sorghum
(2017-01-15) Bergtold, Jason S.; Shanoyan, Aleksan; Fewell, Jason E.; Williams, Jeffery R.
Dedicated annual sorghum crops, such as sweet sorghum or energy sorghum, may provide an option for farmers to supply cellulosic feedstocks for biofuel production and help the industry meet government mandates. Kansas farmers are poised to be major producers of sweet sorghum for biofuels due to favorable agro-ecological conditions. The purpose of this paper is to assess Kansas farmers' willingness to grow sweet sorghum under contract as a feedstock for biofuel production. The paper examines farmers' willingness-to-pay for contract attributes and the impact of socio-economic factors on their willingness-to-pay for these attributes. A stated choice survey was administered to Kansas farmers to assess their willingness to grow sweet sorghum for biofuels under various contracting scenarios. Results show that farmers may be willing to grow biomass for bioenergy under contract, but may have varying preferences for the importance of contract attributes such as net returns, contract length, insurance availability, government incentives, and potential for biorefinery harvest options based on socio-economic characteristics of growers.
Open Access
Farmers’ Willingness to Produce Alternative Cellulosic Biofuel Feedstocks Under Contract in Kansas Using Stated Choice Experiments
(2014-09-01) Bergtold, Jason S.; Fewell, Jason; Williams, Jeffery R.
Many studies have assessed the technical feasibility of producing bioenergy crops on agricultural lands. However, while it is possible to produce large quantities of agricultural biomass for bioenergy from lignocellulosic feedstocks, very few of these studies have assessed farmers’ willingness to produce these crops under different contracting arrangements. The purpose of this paper is to examine farmers’ willingness to produce alternative cellulosic biofuel feedstocks under different contractual, market, and harvesting arrangements. This is accomplished by using enumerated field surveys in Kansas with stated choice experiments eliciting farmers’ willingness to produce corn stover, sweet sorghum, and switchgrass under different contractual conditions. Using a random utility framework to model the farmers’ decisions, the paper examines the contractual attributes that will most likely increase the likelihood of feedstock enterprise adoption. Results indicate that net returns above the next best alternative use of the land, contract length, cost share, financial incentives, insurance, and custom harvest options are all important contract attributes. Farmers’ willingness to adopt and their willingness-to-pay for alternative contract attributes vary by region and choice of feedstock.
Open Access
Willingness of Kansas farm managers to produce alternative cellulosic biofuel feedstocks: An analysis of adoption and initial acreage allocation
(2016-09-01) Lynes, Melissa K.; Bergtold, Jason S.; Williams, Jeffery R.; Fewell, Jason E.
This paper examines the likelihood that farm managers would be willing to harvest crop residue, or grow a dedicated annual or perennial bioenergy crop. In addition, factors affecting how many initial acres adopters would be willing to plant of a dedicated annual or perennial bioenergy crop are assessed. The study finds several factors affect farm managers' decisions to harvest crop residue, or grow annual or perennial bioenergy crops, as well as their potential initial acreage allocation decisions. These factors lead to several policy implications that should be tailored to the specific type of cellulosic bioenergy crop.
Open Access
Farmers' willingness to contract switchgrass as a cellulosic bioenergy crop in Kansas
(2016-03-01) Fewell, Jason E.; Bergtold, Jason S.; Williams, Jeffery R.
Farmers' adoption of cellulosic biofuel feedstock enterprises plays an important role in the future of agriculture and the renewable fuels \industry. However, no set markets currently exist for bioenergy feedstocks outside of very localized geographic locations and farmers may be reluctant to produce the feedstocks without contracts that help mitigate uncertainty and risk. This study examines farmers' willingness to grow switchgrass under contract using a stated choice approach. Data were collected using an enumerated survey of Kansas farmers and analyzed using latent class logistic regression models. Farmers whose primary enterprise is livestock are less inclined to grow switchgrass. Shorter contracts, greater harvest flexibility, crop insurance, and cost-share assistance increase the likelihood that farmers will grow switchgrass for bioenergy production.
Open Access
Revisiting the statistical specification of near-multicollinearity in the logistic regression model
(2016-04-01) Atems, Bebonchu; Bergtold, Jason S.
This paper revisits the statistical specification of near-multicollinearity in the logistic regression model. We argue that the ceteris paribus clause, which assumes that the maximum likelihood estimator of β remains constant as the correlation ( ρ ) between the regressors increases, invoked under the traditional account of near-multicollinearity is rather misleading. We derive the parameters of the logistic regression model and show that they are functions of ρ , indicating that the ceteris paribus clause is unattainable. Monte Carlo simulations confirm these findings and further show that: coefficient estimates and related statistics fluctuate in a non-symmetric, non-monotonic way as | ρ |→1; that the impact of near-multicollinearity is centered on the estimates of β ; and that the impact on substantive inferences does not necessarily follow what the traditional account implies.
Open Access
The probabilistic reduction approach to specifying multinomial logistic regression models in health outcomes research
(2014-10-03) Bergtold, Jason S.; Onukwugha, Eberechukwu
The paper provides a novel application of the probabilistic reduction (PR) approach to the analysis of multi-categorical outcomes. The PR approach, which systematically takes account of heterogeneity and functional form concerns, can improve the specification of binary regression models. However, its utility for systematically enriching the specification of and inference from models of multi-categorical outcomes has not been examined, while multinomial logistic regression models are commonly used for inference and, increasingly, prediction. Following a theoretical derivation of the PR-based multinomial logistic model (MLM), we compare functional specification and marginal effects from a traditional specification and a PR-based specification in a model of post-stroke hospital discharge disposition and find that the traditional MLM is misspecified. Results suggest that the impact on the reliability of substantive inferences from a misspecified model may be significant, even when model fit statistics do not suggest a strong lack of fit compared with a properly specified model using the PR approach. We identify situations under which a PR-based MLM specification can be advantageous to the applied researcher.
Open Access
On the examination of the reliability of statistical software for estimating regression models with discrete dependent variables
(2018) Bergtold, Jason S.; Pokharel, Krishna P.; Featherstone, Allen M.; Mo, Lijia
The numerical reliability of statistical software packages was examined for logistic regression models, including SAS 9.4, MATLAB R2015b, R 3.3.1., Stata/IC 14, and LIMDEP 10. Thirty unique benchmark datasets were created by simulating alternative conditional binary choice processes examining rare events, near-multicollinearity, quasi-separation and nonlinear transformation of variables. Certified benchmark estimates for parameters and standard errors of associated datasets were obtained following standards set-out by the National Institute of Standards and Technology. The logarithm of relative error was used as a measure of accuracy for numerical reliability. The paper finds that choice of software package and procedure for estimating logistic regressions will impact accuracy and use of default settings in these packages may significantly reduce reliability of results in different situations.
Open Access
A Primer on Marginal Effects—Part II: Health Services Research Applications
(2015) Onukwugha, Eberechukwu; Bergtold, Jason S.; Jain, Rahul
Marginal analysis evaluates changes in a regression function associated with a unit change in a relevant variable. The primary statistic of marginal analysis is the marginal effect (ME). The ME facilitates the examination of outcomes for defined patient profiles or individuals while measuring the change in original units (e.g., costs, probabilities). The ME has a long history in economics; however, it is not widely used in health services research despite its flexibility and ability to provide unique insights. This article, the second in a two-part series, discusses practical issues that arise in the estimation and interpretation of the ME for a variety of regression models often used in health services research. Part one provided an overview of prior studies discussing ME followed by derivation of ME formulas for various regression models relevant for health services research studies examining costs and utilization. The current article illustrates the calculation and interpretation of ME in practice and discusses practical issues that arise during the implementation, including: understanding differences between software packages in terms of functionality available for calculating the ME and its confidence interval, interpretation of average marginal effect versus marginal effect at the mean, and the difference between ME and relative effects (e.g., odds ratio). Programming code to calculate ME using SAS, STATA, LIMDEP, and MATLAB are also provided. The illustration, discussion, and application of ME in this two-part series support the conduct of future studies applying the concept of marginal analysis.
Open Access
A Primer on Marginal Effects—Part I: Theory and Formulae
(2015) Onukwugha, Eberechukwu; Bergtold, Jason S.; Jain, Rahul
Marginal analysis evaluates changes in an objective function associated with a unit change in a relevant variable. The primary statistic of marginal analysis is the marginal effect (ME). The ME facilitates the examination of outcomes for defined patient profiles while measuring the change in original units (e.g., costs, probabilities). The ME has a long history in economics; however, it is not widely used in health services research despite its flexibility and ability to provide unique insights. This paper, the first in a two-part series, introduces and illustrates the calculation of the ME for a variety of regression models often used in health services research. Part One includes a review of prior studies discussing MEs, followed by derivation of ME formulas for various regression models including linear, logistic, multinomial logit model (MLM), generalized linear model (GLM) for continuous data, GLM for count data, two-part model, sample selection (two-stage) model, and parametric survival model. Prior theoretical papers in health services research reported the derivation and interpretation of ME primarily for the linear and logistic models, with less emphasis on count models, survival models, MLM, two-part models, and sample selection models. These additional models are relevant for health services research studies examining costs and utilization. Part Two of the series will focus on the methods for estimating and interpreting the ME in applied research. The illustration, discussion, and application of ME in this two-part series support the conduct of future studies applying the marginal concept.
Unknown
Bernoulli Regression Models: Revisiting the Specification of Statistical Models with Binary Dependent Variables
(2010-01-01) Bergtold, Jason S.; Spanos, Aris; Onukwugha, Eberechukwu
The latent variable and generalized linear modelling approaches do not provide a systematic approach for modelling discrete choice observational data. Another alternative, the probabilistic reduction (PR) approach, provides a systematic way to specify such models that can yield reliable statistical and substantive inferences. The purpose of this paper is to re-examine the underlying probabilistic foundations of conditional statistical models with binary dependent variables using the PR approach. This leads to the development of the Bernoulli Regression Model, a family of statistical models, which includes the binary logistic regression model. The paper provides an explicit presentation of probabilistic model assumptions, guidance on model specification and estimation, and empirical application.
Unknown
Fields from Afar: Evidence of Heterogeneity in United States Corn Rotational Response from Remote Sensing Data
(2021-03-29) Pates, Nicholas J.; Hendricks, Nathan P.
We construct estimates of own- and cross-price corn rotation elasticities using a field-level dataset that accounts for over 83% of the US corn-producing area. We allow rotational response to vary by estimating separate models across 115 subsamples that we delineate using Major Land Resource Areas (MLRAs) and soil characteristics. The results show a high degree of rotational response heterogeneity. Across the country, we find that rotational response is elastic in some areas and near zero in others. After aggregating the results to the national level, we find that modeling rotational response without allowing for heterogeneity produces a short-run own-price elasticity of corn planting of around 0.50, which conforms to the latest estimates in the literature. When allowing heterogeneous price sensitivity, our preferred estimate of the rotation elasticity is 0.69. This is evidence that imposing a uniform rotation response could seriously bias aggregate elasticity estimates.
Open Access
Input Use Decisions with Greater Information on Crop Conditions: Implications for Insurance Moral Hazard and the Environment
(2019-08-20) Yu, Jisang; Hendricks, Nathan P.
Emerging precision agriculture technologies allow farms to make input decisions with greater information on crop conditions. This greater information occurs by providing improved predictions of crop yields using remote sensing and crop simulation models and by allowing farms to apply inputs within the growing season when some crop conditions are already realized. We use a stylized model with uncertainty in yield and price to examine how greater information on crop conditions (i.e., a “forecast”) affects input use for insured and uninsured farms. We show that moral hazard decreases—farms apply more inputs—as the forecast accuracy improves when the forecast indicates good yields, and vice versa when the forecast indicates bad yields. In the long run, moral hazard decreases in response to an improvement in forecast accuracy. Even though moral hazard decreases in the long run, indemnity payments are likely to increase in the long run—driven by the increase in moral hazard when the forecast indicates bad crop conditions. We use the results of our model to discuss the potential impact of different technologies and types of inputs on the federal crop insurance program and the environment.
Open Access
Lessons from local governance and collective action efforts to manage irrigation withdrawals in Kansas
(2021-03-31) Perez-Quesada, Gabriela; Hendricks, Nathan P.
This study evaluates four groundwater management plans to increase the understanding of how local governance and collective action can be effectively implemented to manage irrigation withdrawals in Kansas. The results of our analysis demonstrate five key lessons that highlight the challenges of collective action efforts to manage common-pool resources in a developed country setting. First, the four management plans generally follow Ostrom’s design principles for collective action. However, there are important areas—particularly boundaries and allocations definition—where the management plans could be improved to better align with Ostrom’s design principles. Second, a majority of farmers agree that action is needed to reduce the rate of aquifer depletion but management plans have not substantially reduced water use. Third, management plans that allow for voluntary participation have not received more support than those that require mandatory compliance, perhaps due to the classic free-rider problem. Fourth, there is no clear evidence that heterogeneous benefits from management explain support within a management area. Fifth, groundwater users generally perceive that they have an acceptable level of information. Our analysis highlights the significant challenges facing successful collective action efforts to manage water in the USA. and that the efforts are most likely to be successful when they are small-scale, mandatory, and involve users in the formation process.
Open Access
Additionality from Payments for Environmental Services with Technology Diffusion
(2019-08-12) Pates, Nicholas J.; Hendricks, Nathan P.
Because payments for environmental services (PES) often subsidize practices that offer latent private benefits, there are concerns that PES programs may provide little additional environmental benefits. Previous literature has framed the problem of non-additionality as an adverse selection problem. We develop a model where moral hazard can also arise because some agents delay adoption due to the incentive of potentially receiving a payment in the future. Moral hazard arises when agents have expectations of potential future subsidies, the technology naturally diffuses without a policy, and a subsidy is only available if the agent has not previously adopted the technology. We develop a conceptual model to illustrate the moral hazard incentive and conduct numerical simulations to understand the impact of policy parameters on aggregate outcomes. Numerical simulations illustrate that moral hazard creates a non-monotonic relationship between policy parameters—such as the subsidy and budget levels—and the net change in adoption induced by the program because some agents delay adoption. We also find that the cost-effectiveness of the policy is smaller when the policy is introduced during periods of rapid technology adoption.
Open Access
The Opportunity Cost of the Conservation Reserve Program: A Kansas Land Example
(2020-02-14) Taylor, Mykel R.; Hendricks, Nathan P.; Sampson, Gabriel S.; Garr, Dillon
The effects of the Conservation Reserve Program (CRP) on farmland values is investigated using a set of parcel-level data for land sales in Kansas over the period 1998 to 2014. The sales data are used to estimate a hedonic model of land values that allows for the opportunity cost of CRP enrollment to vary across space and time. Factors impacting the opportunity costs include the relative productivity of land, returns to farming, and the time remaining under the CRP contracts. We find that the discount associated with having land under CRP contract averages 7%.