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.

I directly estimate the acre-for-acre impact of crop insurance participation on Conservation Reserve Program (CRP) enrollment at the county level. The government may be sponsoring competing interests if subsidized insurance expands production at the expense of CRP. I employ an instrumental variables technique to correct for endogeneity in insurance decisions. Results suggest that an additional 1,000 acres insured reduces CRP enrollment by about three acres, though effect sizes vary by region. Local policy initiatives such as conservation compliance incentives could help offset local environmental consequences of converting land from CRP to insured production.

We use data from the Kansas Farm Management Association to estimate the impact of crop insurance liability and insurance indemnities on farm debt. Subsidized crop insurance may increase farms' financial risk through a mechanism known as “risk balancing.” Previous findings in support of risk balancing may suffer from bias due to unobservable farm characteristics and simultaneity in insurance and debt decisions. Employing a simultaneous equations model with farm fixed effects, we find no statistical relationship between crop insurance liability and debt, calling into question the risk balancing hypothesis in federal crop insurance. We show that large insurance indemnity payments reduce farms' reliance on short‐term debt, but leave the total debt level unchanged.

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.

The emergence of precision farming technologies has increased the amount and detail of farming data collected by producers. Data increases farm profitability by complementing digitally connected equipment and improving on‐farm decision making. The value generated by farm data may be capitalized into the underlying farmland asset, potentially raising sale and rental prices. However, the absence of clear property rights over farmland and farm operating practice data limits the ability to capture this value. We explore the issue of farm data through a property rights and transaction costs lens, and propose a conceptual framework of farm data valuation to identify conditions under which landowners and tenant‐operators can engage in mutually beneficial negotiation over farm data. We conclude that establishing property rights to farm data within the farmland lease can facilitate welfare‐improving exchange, allowing farm data records to be allocated to their highest valued use.