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.

PurposeFarmer 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/approachWe 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.FindingsA 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/valueThis 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.

Precision farming data enhance agricultural productivity by informing site- specific resource management. These benefits may be capitalized into the underlying value of the farmland, raising rental rates. This article uses a stated preference choice experiment to estimate farmers' willingness to pay for farm data in farmland rental markets. Farmers are willing to pay a small premium to acquire data accrued by previous operators, depending on the field type and quality information provided by the landowner and farmers' use of precision agriculture technology. We find evidence that farm data confer both a "management value" and a "signaling value" to prospective tenants. (JEL Q15, Q16)

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.

We explore the relationship between precision agriculture (PA) technology adoption and technical efficiency using the 2016 USDA Agricultural Resource Management Survey (ARMS). Efficiency gains from PA are likely cumulative, that is, the true impact of precision farming depends on the integration of complementary tools. To examine the efficiency benefits of different PA bundles, we perform a two-step analysis. First, we use cluster analysis to identify distinct producer groups based on patterns in PA technology adoption. These producer groups map naturally onto the classic technology adoption curve (laggards, late majority, early majority, innovators). Second, we use stochastic frontier analysis (SFA) and stochastic meta-frontier analysis (SMFA) to estimate differences in technical efficiency between PA adoption groups. We find that farms with advanced PA technology bundles are significantly more technically efficient than non-adopters. Differences in technical efficiency are not found to be driven by heterogeneous production technologies, but rather inefficiencies in input usage at the farm level. Our results have strong implications for farm consolidation in US agriculture.

We explore the relationship between precision agriculture (PA) technology adoption and technical efficiency using the 2016 USDA Agricultural Resource Management Survey (ARMS). Efficiency gains from PA are likely cumulative, that is, the true impact of precision farming depends on the integration of complementary tools. To examine the efficiency benefits of different PA bundles, we perform a two-step analysis. First, we use cluster analysis to identify distinct producer groups based on patterns in PA technology adoption. These producer groups map naturally onto the classic technology adoption curve (laggards, late majority, early majority, innovators). Second, we use stochastic frontier analysis (SFA) and stochastic meta-frontier analysis (SMFA) to estimate differences in technical efficiency between PA adoption groups. We find that farms with advanced PA technology bundles are significantly more technically efficient than non-adopters. Differences in technical efficiency are not found to be driven by heterogeneous production technologies, but rather inefficiencies in input usage at the farm level. Our results have strong implications for farm consolidation in US agriculture.

Enthusiasm regarding the “digital agriculture” revolution is widespread, yet objective research on how commercial farms actually use data and data services remains limited. The purpose of this research is to better understand the current positioning of U.S. commercial corn and soybean farms within the farm data lifecycle, including the collection, use, and impact of farm data. Using survey data from a sample of 800 commercial-scale U.S. corn and soybean farms, the factors associated with progression within the farm data lifecycle are examined. Results indicate that the majority of commercial U.S. corn and soybean farms collect data, indicate that the data they collect influences their decisions, and perceive positive yield benefits as a result of their data-informed decisions. However, farms vary in intensity of their data usage. Investments in data management and analysis resources are associated with progression within the farm data lifecycle. These investments comprise software products that manage and analyze data, including creating GPS maps, layering different data sources, and generating recommendations. Investments in human capital, either in on-farm employees with designated data responsibilities or in trusted off-farm service providers, are also associated with progression within the farm data lifecycle. Farms that have not yet invested in these types of data management and data analysis resources may be forfeiting the potential benefits associated with using their farm’s data to improve on-farm decision making.

AbstractThe objective of this research was to evaluate producers’ perspectives of four key precision agriculture technologies (variable rate fertilizer application, precision soil sampling, guidance and autosteer, and yield monitoring) in terms of the benefits they provide to their farms (increased yield, reduced production costs, and increased convenience) using a best-worst scaling choice experiment. Results indicate that farmers’ perceptions of the benefits derived from various precision agriculture technologies are heterogeneous. To better understand farmers’ adoption decisions, or lack thereof, it is important to first understand their perceptions of the benefits precision agriculture technologies provide.

The Purdue Center for Commercial Agriculture Crop Basis Tool is an open-access web-based tool that provides members of the grain industry with access to weekly historical and contemporaneous corn and soybean basis data for local market regions in the eastern Corn Belt. Previously unavailable to most producers in the region, the information the Crop Basis Tool provides has the potential to greatly improve producers' marketing risk management decisions through improved basis forecasts. In addition, there are a myriad of opportunities for Extension personnel to incorporate the Crop Basis Tool in their marketing risk management education and outlook programming.

This paper re-evaluates practical methods of forecasting corn and soybean basis in the eastern Corn Belt. The accuracy of forecast methods differs over the course of the crop-marketing year. At harvest, historical moving average forecasts perform best. Post-harvest forecasts may be improved at short forecast horizons (<8-12 weeks ahead) by combining historical moving averages and recent basis levels. Results suggest that using 3-to-5-year moving average forecasts for corn basis and a 2- or 5-year moving average for soybean basis from harvest through April. The accuracy of these corn and soybean basis forecasts decreases markedly during the summer months.

Cattle and calves on feed for the slaughter market in the United States for feedlots with capacity of 1,000 or more head totaled 11.5 million head on July 1, 2019. The inventory was 2 percent above July 1, 2018. This is the highest July 1 inventory since the series began in 1996. The inventory included 7.01 million steers and steer calves, down 2 percent from the previous year. This group accounted for 61 percent of the total inventory. Heifers and heifer calves accounted for 4.47 million head, up 8 percent from 2018.

Precision farming utilizes information technology to add exactness to the quantity, quality, timing and location in the application and utilization of inputs in agricultural production. Though having tremendous potential, after two decades of work pertaining to precision agriculture, our abilities to capitalize on this technology have fallen far short of expectations. This manuscript frames the discussion concerning the challenges and opportunities of precision farming identifying critical knowledge gaps that must be addressed before precision agriculture technologies will be more widely embraced. A review of the literature provides the foundation for this discussion, helps identify knowledge gaps and outline some of the progressions in technologies needed for the industry to better capitalize on the potential advantages offered by precision agriculture.

In a seminal Harvard Business Review article published in 1979, Michael Porter identified five competitive forces that shape industry competition, all of which feed into rivalry among existing competitors. Porter’s framework has most often been applied to industries producing and marketing differentiated products. Conversely, U.S. livestock and milk production still mostly resembles a commodity market with largely undifferentiated products marketed to a processing sector that, based on the Herfindahl-Herschman Index (HHI)-an industry concentration measure based on market shares that is easily computed and compared across industries- and Department of Justice and Federal Trade Commission definitions, is unconcentrated to approaching moderately concentrated. Despite this distinction, we find that several aspects of Porter’s forces are important in analyzing the U.S. livestock sector. The remainder of this article reviews the key aspects of Porters five forces as they relate to the competitive structure of the U.S. livestock sector, plus two additional dynamic forces which we believe are also important; technology and drivers of change. (This abstract was borrowed from another version of this item.)