About

Dr. Michael Langemeier is a faculty member at Purdue University within the Department of Agricultural Economics. His research focuses on farm policy, farmland markets, and agricultural economics, contributing to the understanding of land values, farmland market dynamics, and policy impacts on agriculture. He is involved in various research initiatives and presentations related to farm policy analysis, farmland valuation, and rural economic development, playing a key role in advancing knowledge in these areas through his academic and outreach activities.

Research topics

• Economics
• Biology
• Agronomy
• Computer Science
• Environmental science
• Chemistry
• Environmental economics
• Database
• Mathematics
• Agricultural science

Selected publications

• FALL 2025 FARM INCOME OUTLOOK FOR INDIANA

  2025-01-01

  reportOpen accessSenior author

  In September 2025, the U.S. Department of Agriculture (USDA)’s Economic Research Service (ERS) released state-level farm income estimates through calendar year 2024 and national farm income projections for calendar year 2025. The present report published by the Rural and Farm Finance Policy Analysis Center (RaFF) provides an updated outlook for Indiana farm income in calendar years 2025 and 2026. It intends to inform policymakers, industry analysts, and agricultural practitioners about the state agricultural sector’s expected profitability and its main drivers.

  PublisherOA PDFDOI

• Farm Resilience, Management Practices, and Producer Sentiment: Segmenting US Farms Using Machine Learning Algorithms

  Journal of Applied Farm Economics · 2025-01-24

  article

  Farm managers possess a broad spectrum of capabilities, ranging from tech-savvy, strategically focused managers to those grappling with operational challenges. Our study examines survey data from 403 US commercial producers in order to identify subsets of producers who differ in terms of resilience, management practices, producer sentiment, and other farm characteristics. Utilizing various supervised and unsupervised machine learning techniques, we uncover key farm characteristics that capture the most pronounced variations in survey responses. We then cluster the dataset using these key variables, maximizing separation between clusters and minimizing differences within clusters using Ward’s hierarchical clustering. Fisher’s exact tests are implemented to determine the statistical significance of differences in farm characteristics across the constructed clusters. Results confirm that resilience to strategic risk, managerial ability, producer sentiment, technology adoption, and demographics all vary significantly among commercial farms. In particular, we observe a trade-off among farms in regard to operators’ management abilities and their farms’ resilience. Farms with the highest resilience levels tend to show slightly lower managerial abilities. Conversely, farms with the strongest managerial abilities exhibit somewhat poorer farm resilience. The third group of farms, which encompasses 49% of our sample, displays the lowest levels of farm resilience, precision agriculture technology adoption, and managerial abilities and has the weakest growth expectations.

  PublisherDOI

• Examining the Relationship between Crop Net Returns, Risk, and Conservation Practices

  Agricultural sciences. · 2024-06-19

  book-chapterOpen access1st authorCorresponding

  This study aims to evaluate the effects of different tillage practices and cover crop options on crop net returns, downside risk, soil loss, and greenhouse gas (GHG) emissions in a corn-soybean rotation in central Illinois. The conceptual framework developed encompasses tradeoffs between net returns, downside risk, soil loss, and greenhouse gas emissions. The conservation system had the highest net return per acre. Crop net return differences were smaller between the conservation tillage and no-till systems than they were between the conservation tillage system with no cover crop and with a cover crop. The no-till and cover crop systems also exhibited more downside risk than the conservation system. However, utilizing the no-till system and the cover crop systems was an effective mechanism to reduce soil loss and greenhouse gas emissions.

  PublisherOA PDFDOI

• Farmer sentiment and farm service agency direct loan applications

  Agricultural Finance Review · 2024-05-14

  articleSenior author

  Purpose Farmer sentiment may be an important indicator for the agricultural sector, similar to the way that consumer sentiment is linked to the general economy. This study uses the Purdue University–CME Group Ag Economy Barometer to test the degree to which farmer sentiment is correlated with demand for United States Department of Agriculture Farm Service Agency (FSA) direct loan applications. Design/methodology/approach We estimate the dynamics between farmer sentiment and applications to FSA direct operating or farm ownership loans using monthly measures of farmer sentiment and loan applications from October 2015 to April 2023 and pairwise vector autoregression. Findings A negative relationship exists between farmer sentiment and FSA direct operating loan applications. In contrast, a positive relationship exists between farmer sentiment and FSA direct farm ownership loan applications. Together, the estimated nonzero relationships suggests that the Ag Economy Barometer may be a leading indicator for the Agricultural Economy and that FSA loan programs play a nuanced role in the agricultural credit market. Originality/value This study uses unique data sources to further the discussion on the link between farmer sentiment and real economic outcomes and the role of an important US Federal Government farmer lending program: FSA direct loans.

  PublisherDOI

• Change in farmer expectations from information surprises in the corn market

  American Journal of Agricultural Economics · 2024-07-03 · 2 citations

  articleOpen access

  Abstract Farmers make production decisions despite future output price uncertainty. As a result, farmers' expectation of future output price is an important determinant of investment and the supply of commodities. However, our understanding of the process by which farmers form their expectations is still limited. This study uses direct measures of farmers' financial condition expectations collected through the Purdue University–CME Group Ag Economy Barometer to measure the effect of surprise information on farmers' short‐ and long‐term expectations. The effect is identified using an event study framework previously used to examine the impact of market information on commodity futures markets. Using ordered logistic regressions and variation between professional and United States Department of Agriculture forecasts of corn ending stocks, we demonstrate that farmers' short‐term expectations of the financial condition of the broader agricultural economy is altered by surprise information. This study provides a novel step toward understanding the process by which farmers incorporate new information in their price expectations. For example, our findings suggest that farmers perceive short‐term corn market information surprises will affect the U.S. agricultural sector to a greater degree than their farm. Additionally, farmers do not perceive that short‐term corn market information surprises will carry long‐term implications.

  PublisherOA PDFDOI

• Economics of strip cropping with autonomous machines

  Agronomy Journal · 2024-02-09 · 9 citations

  articleOpen access

  Abstract Autonomous machines have the potential to maintain food production and agroecological farming resilience. However, autonomous complex mixed cropping is proving to be an engineering challenge because of differences in plant height and growth pattern. Strip cropping is technically the simplest mixed cropping system, but widespread use is constrained by higher labor requirements in conventional mechanized farms. Researchers have long hypothesized that autonomous machines (i.e., crop robots) might make strip cropping profitable, thereby allowing farmers to gain additional agroecological benefits. To examine this hypothesis, this study modeled ex‐ante scenarios for the Corn Belt of central Indiana, using the experience of the Hands Free Hectare‐Linear Programming (HFH‐LP) optimization model. Results show that per annum return to operator labor, management, and risk‐taking (ROLMRT) was $568/ha and $163/ha higher for the autonomous corn ( Zea mays L.) and soybean [ Glycine max (L.) Merr.] strip crop farm compared to the whole field sole crop and the conventional strip crop farms, respectively, that were operated by human drivers. The conventional strip cropping practice was found challenging as this cropping system required four times more temporary hired labor than autonomous strip cropping and three times more than whole field sole cropping. Even if autonomous machines need 100% human supervision, the ROLMRT was higher compared to whole field sole cropping. Profitable autonomous strip cropping could restore and improve in‐field biodiversity and ecosystem services through a sustainable techno‐economic and environmental approach that will address the demand for healthier food and promote environmental sustainability.

  PublisherOA PDFDOI

• Risk-adjusted farm returns and farm size

  Agricultural Finance Review · 2023-05-31 · 1 citations

  article

  Purpose The average U.S. farm size has risen dramatically over the last three decades. Motives for this trend are the subject of a large body of literature. This study incorporates farm size risk and return analysis into this research stream. In this paper, cross-sectional and temporal relations between farm size and returns are examined and characterized. Design/methodology/approach Relying on farm level panel data from Kansas Farm Management Association (KFMA) for 140 farms from 1996 to 2018, this article examines the relationship between farm size and returns and investigates whether farm size is related to risk. Two measures of farm returns are used: excess return on equity and risk-adjusted return on equity. Value of farm production and total farm acres are used as measures of farm size. Findings Findings suggest a significant and positive relationship between farm size and excess return on equity as well as farm size and risk-adjusted return on equity. However, this return premium associated with farm size is not associated with additional risk. Stated differently, farm size can be viewed as a farm characteristic that is associated with higher return without additional risk. Practical implications These findings provide further support for ongoing farm consolidation. Originality/value The results suggest the trend towards consolidation in production agriculture is likely to continue. Larger farms bear less risk.

  PublisherDOI

• Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices

  Agriculture · 2023 · 70 citations

  Senior authorCorresponding
  • Computer Science
  • Environmental economics
  • Computer Science

  The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review describes low adoption of Internet of Things (IoT)-based precision agriculture technologies, such as variable rate fertilizer (39%), variable rate pesticide (8%), variable rate irrigation (4%), cloud-based data analytics (21%) and telematics (10%) amongst Midwestern row crop producers. Barriers to the adoption of IoT-based precision agriculture technologies cited in the literature include cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing and data interoperability. Therefore, this study focused on exploring and understanding decision-making variables related to barriers through three focus group interview sessions conducted with eighteen (n = 18) subject matter experts (SME) in IoT- based precision agriculture practices. Dependency relationships described between cost, data latency, data scalability, power consumption, communication range, type of wireless communication and precision agriculture application is one of the main findings. The results might inform precision agriculture practitioners, producers and other stakeholders about variables related to technical and operational barriers for the adoption of IoT-based precision agriculture practices.

  PublisherOA PDFDOI

• Risk and return of heterogenous farmland locations and qualities

  The International Food and Agribusiness Management Review · 2022-04-07 · 3 citations

  articleOpen accessSenior author

  Using data on farmland values in Indiana and Iowa, this study examines the risk and return characteristics surrounding top, medium, and poor farmland qualities in different locations in these two states. We find that systematic risks of locations/qualities are very low (indistinguishable from 0). In terms of risk-adjusted return, our results show that Indiana farmland has more excess return and higher reward-to-risk ratios than Iowa. Also, adding the quality dimension to the geographic dimension in portfolio selection strategies improved the portfolio reward-to-risk ratio for Indiana but not for Iowa. Interestingly, we found that the average quality farmland has more weight in portfolios relative to top- and poor-quality farmland.

  PublisherOA PDFDOI

• Is Farmland a Common Risk Factor in Asset Pricing Models?

  Journal of Real Estate Portfolio Management · 2022-07-03 · 1 citations

  articleOpen accessSenior author

  Farmland represents the largest share of the U.S. agricultural balance sheet, accounting for nearly 80% of U.S. farm assets. Motivated by the well-documented real estate risk factor and the similarities between farmland and real estate investing, this paper examines whether farmland has a risk factor, like real estate, that is affecting asset returns. The proposed farmland risk factor is proxied by the National Council of Real Estate Investment Fiduciaries farmland property index (Farmland NCREIF). Relying on quarterly data from 1991-Q1 to 2016-Q2, we employed the Generalized Method of Moments (GMM) to provide empirical evidence that even though farmland exhibits diversification benefits, it fails to be a risk factor. Instead, market frictions and/or non-risk explanations might provide a more plausible description of farmland’s high risk-adjusted return.

  PublisherDOI

Frequent coauthors

• Allen M. Featherstone

  Kansas State University

  39 shared

• Ted C. Schroeder

  26 shared

• Michael Boehlje

  Purdue University West Lafayette

  19 shared

• Elizabeth A. Yeager

  16 shared

• James Mintert

  15 shared

• Yangxuan Liu

  Center for Excellence in Molecular Plant Sciences

  12 shared

• Ian M. Small

  University of Florida

  10 shared

• Laura Joseph

  Cornell University

  10 shared

Awards & honors

• James C. Snyder Memorial Lecture