About

Serkan Aglasan is an Assistant Professor in the Department of Agricultural and Resource Economics at the University of Arizona, Tucson, AZ. He holds a Ph.D. in Economics from North Carolina State University and an M.S. in Economics from the University of Illinois at Urbana-Champaign. His scholarly work broadly encompasses agricultural economics, climate change, risk analysis and management, crop insurance, applied econometrics, and price analysis. Over the past five years, his research has focused on climate change mitigation and adoption efforts, crop insurance and the Farm Bill, the economic assessment and impacts of transgenic crops such as Bt corn, agri-environmental policies, and spatial and vertical market integration in U.S. domestic and international commodity markets. Additionally, his work examines the effects of soil health management and farm management practices on climate resilience, yield, yield risk, agricultural land value, farm costs, sustainability, and environmental quality.

Research topics

• Economics
• Environmental science
• Agronomy
• Agricultural economics
• Biology
• Forestry
• Soil science
• Ecology
• Mathematics
• Natural resource economics
• Geography
• Horticulture
• Econometrics

Selected publications

• Lost ground, lost value: Investigating the relationship between soil erosion and agricultural land value

  American Journal of Agricultural Economics · 2026-01-30

  articleOpen accessSenior author

  Abstract This study examines the impact of soil erosion on agricultural land values in the United States (US) Midwest. Based on a novel county‐level panel data set with information on soil erosion levels and agricultural land values covering five census years (1997, 2002, 2007, 2012, and 2017), we separately investigate the direct effect of two types of soil erosion—water and wind erosion—on county‐level average agricultural land values. Linear panel fixed effects econometric models and a number of robustness checks are used to achieve the study objective. We find that increasing soil erosion levels have a statistically significant negative impact on agricultural land values at the county level. Our findings confirm that damages to the soil from water or wind erosion are capitalized into lower farmland values. The study provides new insights in terms of further understanding the economic damages due to soil erosion and the likely benefits from soil erosion control.

  PublisherOA PDFDOI

• Crop yield distribution modeling faces three key challenges: complex distributional structure, limited historical data at the county level, and the need to incorporate evolving climate conditions into distributional dynamics. We propose a Fixed-Effect Panel Neural Mixture (FEPNM) framework to address these challenges. FEPNM extends finite mixture models to a panel data setting, allowing information sharing across counties through fixed effects to mitigate short time-series limitations. We further generalize the mixture model into a Mixture-of-Experts (MoE) type specification by introducing a neural-network gating mechanism that flexibly maps climate variables and conservation practices to time-varying regime probabilities. This structure enables direct modeling of the probability of yield loss as a nonlinear function of climate exposure and management adoption. Simulations demonstrate that FEPNM substantially improves the precision of structural parameter estimates and average partial effects, particularly in short-T settings. In an empirical application to U.S. county-level corn yields, FEPNM outperforms conventional mixture and single-distribution specifications in both in-sample and out-of-sample likelihood. Our results provide structural evidence on how climate exposure and conservation practices jointly shape corn yield distributions. Heating Degree Days (HDD) significantly increase the probability of yield loss, while adoption of cover crops and no-tillage practices significantly reduces downside yield risk. These findings highlight the importance of incorporating nonlinear climate effects and management practices into distributional modeling for agricultural risk management and crop insurance design.

  AgEcon Search (University of Minnesota, USA) · 2026-01-01

  otherOpen accessSenior author

  Crop yield distribution modeling faces three key challenges: complex distributional structure, limited historical data at the county level, and the need to incorporate evolving climate conditions into distributional dynamics. We propose a Fixed-Effect Panel Neural Mixture (FEPNM) framework to address these challenges. FEPNM extends finite mixture models to a panel data setting, allowing information sharing across counties through fixed effects to mitigate short time-series limitations. We further generalize the mixture model into a Mixture-of-Experts (MoE) type specification by introducing a neural-network gating mechanism that flexibly maps climate variables and conservation practices to time-varying regime probabilities. This structure enables direct modeling of the probability of yield loss as a nonlinear function of climate exposure and management adoption. Simulations demonstrate that FEPNM substantially improves the precision of structural parameter estimates and average partial effects, particularly in short-T settings. In an empirical application to U.S. county-level corn yields, FEPNM outperforms conventional mixture and single-distribution specifications in both in-sample and out-of-sample likelihood. Our results provide structural evidence on how climate exposure and conservation practices jointly shape corn yield distributions. Heating Degree Days (HDD) significantly increase the probability of yield loss, while adoption of cover crops and no-tillage practices significantly reduces downside yield risk. These findings highlight the importance of incorporating nonlinear climate effects and management practices into distributional modeling for agricultural risk management and crop insurance design.

  PublisherDOI

• Soil Characteristics and Crop Insurance Losses

  Journal of Agricultural and Applied Economics · 2026-02-18

  articleOpen accessSenior author

  Abstract We consider the relationship between soil characteristics and crop insurance losses. Note that crop insurance losses are typically considered to be a reliable measure of overall yield risk. Our results indicate that several soil characteristics linked to erosion are related to overall loss ratios in the federal crop insurance program. If premium rates adequately account for the risks associated with soil characteristics, there should be no relationship between loss ratios and soil characteristics. Thus, our results indicate that gains in the accuracy of insurance premium rates may be achievable from a greater focus on soil characteristics. We also consider the relationship of specific hazards with soil characteristics and find that different soil factors have varied relationships with specific causes of loss in the federal crop insurance program.

  PublisherOA PDFDOI

• Rate Revisions and Risk Transfer Incentives in Agricultural Insurance

  AgEcon Search (University of Minnesota, USA) · 2026-01-01

  articleOpen access

  Public-Private Partnerships (PPPs) are a common approach for offering agricultural insurance, but their stability can be threatened by asymmetric information, which allows private insurers to strategically transfer risk to the public sector. This study quantifies the economic value of routine premium rate updates in mitigating the ability of private insurers to extract economic rents via underwriting gains within the U.S. Federal Crop Insurance Program (FCIP). Using a counterfactual simulation on program data from 2001-2024, we model potential underwriting gains for private insurers by counterfactually simulating underwriting gains in a scenario where premium rating updates are delayed. Results indicate that forgoing a single annual rate update would allow private insurers to capture an additional $1.3 billion in underwriting gains on average, equivalent to 17% of total premiums.

  PublisherDOI

• Do Cover Crops Reduce Downside Production Risk?

  Agricultural Economics · 2025-11-16

  article1st authorCorresponding

  ABSTRACT This study examines whether cover crop adoption reduces downside production risk. A crop insurance loss measure is used as the main measure of downside production risk. To achieve the study objective, we utilize a unique county‐level panel data set with information on cover crop adoption rate, crop insurance production losses, and weather variables. The data covers the main corn and soybean production regions in the Midwestern United States for the period 2005–2018. We employ linear fixed effects econometric models and a number of robustness checks in the empirical analysis (i.e., implementing different estimation procedures and a variety of empirical specifications). The different estimation methods employed leverage the panel nature of the data to address various specification and endogeneity issues. Our estimation results suggest that counties with higher cover crop adoption tend to have lower crop insurance losses and lower downside production risk. This finding supports the idea that the soil health benefits from cover crop use translate to a reduced likelihood of production losses.

  PublisherDOI

• Economic and Policy Drivers of Climate-Smart Soil Health Practices in the United States

  Annual Review of Resource Economics · 2025-05-22 · 3 citations

  articleOpen access

  Climate-smart soil health practices, such as cover crops and no-till, are considered to be key elements for climate change adaptation and mitigation in agriculture. We examine the empirical literature that provides data-driven evidence on the impact of cover crops and no-till on several economic variables of interest, such as yields, inputs, profits, risk, erosion, and water quality. In general, existing studies provide mixed evidence on the impacts of cover crops and no-till on these outcomes. Moreover, we discuss the empirical literature exploring the impact of economic and policy-related variables on adopting cover crops and no-till. We find that a number of studies examine the effects of federal payment programs on cover crop and no-till adoption, but more work is still needed to examine the effects of payments from state-level programs and carbon markets. We also identify promising research topics in the economics of climate-smart soil health practices.

  PublisherDOI

• The effect of crop insurance on agricultural loan delinquencies

  Journal of the Agricultural and Applied Economics Association · 2024-03-01 · 4 citations

  articleOpen access

  Abstract This study addresses how participation in the Federal crop insurance program influences agricultural loan delinquencies. To achieve this objective, we use 1994–2015 county‐level panel data for corn production in the Midwestern United States (US). Traditional linear fixed effect (FE) models, instrumental variable‐based FE estimation, and several robustness checks are used in the empirical analysis. Estimation results suggest that counties with higher levels of crop insurance participation tend to have statistically lower rates of agricultural loan delinquency. This is evidence that the US crop insurance program helps reduce financial stress and facilitates the continued viability of the agricultural credit system.

  PublisherOA PDFDOI

• Understanding the yield impacts of alternative cover crop families and mixtures: Evidence from side‐by‐side plot‐level panel data

  Agrosystems Geosciences & Environment · 2024-12-01 · 3 citations

  articleOpen access

  Abstract The short‐run effects of cover crop use on cash crop yields (e.g., corn [ Zea mays L.] and soybeans [ Glycine max (L.) Merr.]) have been a topic of debate given that evidence from previous literature has generally been mixed on this issue. Past studies suggest that the observed yield effect varies (i.e., negative, positive, or insignificant), often depending on the applied cover crop species used, weather conditions, and farm management practices implemented (among others). In this study, we examine the short‐run (i.e., 1 year) yield impact of four different cover crop families—grasses ( Poaceae ), broadleaves ( Brassicaceae ), legumes ( Fabaceae ), and others—both as single‐family groups and as mixtures. Data from side‐by‐side on‐farm experimental plots in six Eastern US states were collected from 2017 to 2019 in order to achieve the objective of the study. Statistical analysis of this multi‐year plot‐level data suggests that the majority of the cover crop families and mixtures investigated in this study do not have a statistically significant short‐run effect on subsequent corn yields. In some cases, cover crop treatment even resulted in short‐run yield losses (i.e., a yield penalty). These results imply that cash crop yield benefits from cover crop adoption are likely not going to be observed with just 1 year of use. This lack of immediate economic benefit may explain the relatively low cover crop adoption rate currently observed in the United States and the need for upfront cost‐share subsidy payments to encourage further uptake of this practice.

  PublisherOA PDFDOI

• Medium-term economic impacts of cover crop adoption in Maryland

  Soil Security · 2024-09-28 · 6 citations

  articleOpen access

  Cover cropping has the potential to generate private economic benefits to farm operations as well as larger-scale environmental benefits to the broader community. However, cover crop adoption remains limited in the United States (US) (i.e., 4.7 % in 2022 (USDA-NASS, 2024)), primarily due to uncertainty in economic outcomes, with several studies showing potentially negative net returns from cover crop use in the short-term (1–3 years). This study investigates the medium-term (5–7 years) economic impact of cover crop adoption using plot-level data from field experiments in the state of Maryland. The empirical analysis employs ordinary least squares (OLS) statistical models and partial budgeting techniques to achieve the study objective. Our results show that cover crops do not have a statistically significant effect on crop yield, fertilizer costs, or pesticide costs, but we find that cover crop use statistically increases field operation and seed expenses. As a result, the private net return from cover crop adoption in the medium term is generally negative based on the Maryland field trial data used in the analysis. Specifically, the average net return per acre with cover crops is approximately $60-$90 lower for corn and around $60 lower for soybeans compared to fields without cover crops. This empirical finding suggests that public support through incentive payments may help further incentivize cover crop adoption in the US, which can then provide environmental and ecosystem services that are of benefit to the general public.

  PublisherDOI

• The Impact of Conservation Tillage Intensities on Mean Yields and Yield Risk

  Soil Security · 2024-01-03 · 2 citations

  articleOpen access

  Understanding the productivity and production risk effects of conservation tillage practices are important so that growers can make better decisions about tillage systems appropriate for their farm operations. This study investigates the mean yield and yield risk effects of conservation tillage practices with varying levels of tillage intensity and timing. Long-term field trial data for corn (Zea mays, L.) and soybeans (Glycine max, L. Merr.) in the North Carolina Piedmont, together with moment-based regression models, were used to achieve the objective of the study. Our empirical analysis suggests that conservation tillage treatments (with lower tillage intensities and higher residue levels) consistently have higher mean yields than conventional tillage practices in the sandy loam soils of the North Carolina Piedmont. However, we find that conservation tillage practices with lower intensities (and higher residue levels) do not generally have a consistent statistically significant risk reducing effect based on the higher-order moments of the yield distribution (e.g., variance, skewness, and kurtosis). This indicates that conservation tillage does not consistently result in statistically lower production risk relative to conventional tillage methods.

  PublisherDOI

Frequent coauthors

• Roderick M. Rejesus

  North Carolina State University

  28 shared

• Barry K. Goodwin

  North Carolina State University

  7 shared

• Erin D. Lane

  5 shared

• David Y. Hollinger

  5 shared

• Stephen Hagen

  5 shared

• A.R. Cooray

  4 shared

• Zheng Li

  4 shared

• Lynn G. Knight

  4 shared

Awards & honors

• Award for Best Graduate Student Paper: Applied Risk Analysis…