Despite decades of study, current research on grazing management’s impacts on ecosystem health and its socioeconomic drivers remains too limited in scope and scale to enable adaptive, evidence-based decision making by producers. There is a pressing need for interdisciplinary research that collects ecosystem data at broader spatial and temporal scales while incorporating working farms and ranches. Such efforts are critical for informing grazing decisions and understanding grazinglands’ potential to deliver ecosystem services, including climate mitigation, water cycling, resilience, and rural livelihoods. The Metrics, Management, and Monitoring (3M) project addresses this need through a novel social-ecological framework that integrates biophysical, socioeconomic, and management data across U.S. grazinglands. The project combines controlled experiments at four intensively monitored “hubs” with data from 59 producer-managed farms and ranches. Its core objectives are to: (1) assess the social-ecological health of grazinglands across diverse ecoregions, (2) refine monitoring approaches to improve scalability and accuracy, and (3) integrate producer-led data to balance experimental rigor with real-world relevance. Over 50 scientists collaborate on 3M to evaluate how grazing strategies affect soil carbon, water dynamics, CO₂ fluxes, plant communities, productivity, social wellbeing, and producer economics. These insights support the development of ecosystem models and decision-support tools to help producers make evidence-based choices. Beyond data generation, 3M offers a scalable research model that bridges ecological and social sciences to support adaptive, informed grazing management. This integrated framework provides a transferable template for studying any working landscape where human and ecological systems are deeply interconnected.

The ability of livestock producers to adapt to climate change may vary by operation scale, with implications for consolidation in the beef cattle industry. This paper examines changes in the US beef cow sector across different herd size classes in response to climate extremes. We show that persistent exposure to drought and extreme heat shift beef cow inventories from larger to smaller herds, leading to a contraction in the beef cow herd. However, these conditions lead to a net increase in the number of beef cow operations. The overall effect is fewer animals distributed across more small-scale farms.

We examined the spatial movement behavior, growth rates, and enteric CH4 emissions of yearling beef cattle in response to spatial distribution management with virtual fencing (VF) in extensive shortgrass steppe pastures. Over the 110-d grazing season (mid-May to early September), 120 British-breed stocker steers (~12 months of age; mean body weight [BW] 382 kg ± 35) were grazed with VF management (active VF collars) or free-range (non-active VF collars) in two pairs of ~130 ha physically fenced rangeland pastures (i.e., VF-managed vs. control). One pair was associated with a diverse mosaic of soil types supporting alkalai sacaton (Sporobolus airoides [Torr.] Torr.), blue grama (Bouteloua gracilis [Willd. Ex Kunth] Lag. Ex Griffiths), and needle-and-thread (Hesperostipa comata [Trin. &Rupr.] Barkworth), while the other pasture-pair was associated with the Sandy Plains ecological site, primarily hosting western wheatgrass (Pascopyrum smithii [Rydb.] Á. Löve), needle-and-thread, and blue grama. Within each pair of pastures, one herd was rotated among sub-pastures using the VF system, which focused grazing on varying native plant communities over the growing season. In control pastures, steers had access to the entire pasture for the grazing season. Spatial distribution management with VF maintained steers within desired grazing areas occurred 94–99% of the time, even though five of the 60 VF-managed steers consistently made short daily excursions outside the VF boundary. In all four pastures, an automated head-chamber system (AHCS, i.e., GreenFeed) measured the enteric CH4 emissions of individual steers. Steers that met the criteria of a minimum of 15 AHCS visits in each of at least two VF rotation intervals were analyzed for spatial behavior, growth performance, and enteric CH4 emissions. Screening based on AHCS visitation requirements resulted in 15 steers (nine VF, six control) in the diverse mosaic pasture pair, and 39 (17 VF, 22 control) in the Sandy Plains pasture pair. VF management significantly reduced growth rates for all steers across both pasture pairs by an average of 9%, resulting in steers that were 7.3 kg lighter than unmanaged steers at the end of the grazing season. VF management effects on enteric CH4 emissions varied among rotation intervals and pasture type. In the diverse mosaic pair, VF management significantly reduced CH4 emissions during the first rotation interval, when VF steers were concentrated on the C3 grass-dominated plant community, but increased emissions in the second and third intervals when VF steers were concentrated on C4 grass-dominated areas. In the Sandy Plains pasture pair, where cattle were rotated between sub-pastures with and without palatable four-wing saltbush (Atriplex canescens [Pursh] Nutt.) shrubs, VF management reduced CH4 emissions in three of four rotations as well as over the full grazing season. CH4 emissions intensity increased with VF management in the diverse mosaic, but not in the Sandy Plains pastures. Overall, our findings show VF management (1) controlled animals spatially within sub-pastures, (2) did not improve growth performance but rather decreased it, (3) did not consistently reduce enteric CH4 emissions, and (4) tended to increase emissions per kg of product via lowering steer growth performance. While some have posited that VF is a potential tool to reduce enteric emissions, our findings suggest VF management is not a straightforward solution for mediating the relationships between forage resources, growth performance, and enteric CH4 emissions of stocker steers on extensive rangeland. Furthermore, our fusion of animal GPS tracking, growth rates and AHCS data indicated that differences in spatial behavior and weight gain were consistent between VF-managed and control steers irrespective of their AHCS-acclimation status, supporting the perspective that AHCS-based gas flux measurements are a valid means of estimating enteric emissions in extensive rangelands.

Invasive wild pigs can impose significant economic costs on crop and livestock farms. Many factors influence the incidence and intensity of these losses, making efforts to reduce or eradicate these populations complex. While farm and ranch operators may perceive wild pigs as agricultural pests, other landowners often see them as wild game with recreational value. This study investigates the relationship between landowner practices that attract wild pigs and the likelihood of pig presence and damage on farm and ranch operations. It considers the farmers' own actions that attract wildlife, neighboring landowner actions, the heterogeneity of the surrounding landscape, and county-level factors. The findings show a significant and positive associations between neighbors' actions and the probability of wild pig presence and financial losses from wild pig damage. Additionally, increasingly heterogeneous landscapes may further exacerbate this challenge. This research indicates that the choices made by adjacent property owners can undermine the effectiveness of public and private efforts to manage wild pig populations. Conversely, the impacts of wild pig management likely extend beyond specific management areas. Holistic eradication or population control programs should consider these externalities to adequately and efficiently address their impacts.

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)

Crop insurance is delivered to farmers and ranchers through a partnership among the Federal Crop Insurance Corporation (FCIC), part of the United States Department of Agriculture, and the crop insurance industry. The FCIC offers financial incentives, through reinsurance and subsidies, to private insurance companies for insurance contracts sold in accordance with the Standard Reinsurance Agreement (SRA). Crop insurance agents play an important role in the delivery of the federal crop insurance program, acting as intermediaries between farmers and crop insurance companies. Little is understood about the supply of crop insurance agents and the role of government policy in the provision of agent services, particularly after the 2010 SRA. We model the equilibrium supply of crop insurance agents to derive testable hypotheses about the factors that influence agent concentration across space. We evaluate our model using spatial econometric techniques and a novel dataset of crop insurance agent locations by county. Generally, forces that raise agent compensation, including the degree of competition among insurance companies, are shown to increase the local supply of agents. Results vary by government-defined reinsurance regions. Notably, historical average premium rates, which both reflect actuarial risk and influence farmer insurance demand, are negatively related to agent competition in the low-risk Group 1 states, which contributes over 40% of insured liabilities. These factors produce spatial spillovers, suggesting the presence of agglomeration effects in the market for agent services. Proposed changes to the SRA should consider impacts on the regional distribution and local supply of agents.

Purpose Climate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology. Design/methodology/approach Using farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias. Findings The authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status. Originality/value Understanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.

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.

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.

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.

Digital agriculture, with the incorporation of Internet-of-Things (IoT)-based technologies, presents the ability to control a system at multiple levels (individual, local, regional, and global) and generates tools that allow for improved decision making and higher productivity. Recent advances in IoT hardware, e.g., networks of heterogeneous embedded devices, and software, e.g., lightweight computer vision algorithms and cloud optimization solutions, make it possible to efficiently process data from diverse sources in a connected (smart) farm. By interconnecting these IoT devices, often across large geographical distances, it is possible to collect data at different time scales, including in near real-time (i.e., with delays of only a few tens of seconds). This data can then be used for actionable insights, e.g., precise applications of soil supplements and reduced environmental footprint. Through LATTICE, we present an integrated vision for IoT solutions, data processing, and actionable analytics for digital agriculture. We couple this with discussion of economics and policy considerations that will underlie adoption of such IoT and ML technologies. Our paper starts off with the types of datasets in typical field operations, followed by the lifecycle for the data and storage, cloud and edge analytics, and fast information-retrieval solutions. We discuss what algorithms are proving to be most impactful in this space, e.g., approximate data analytics and on-device/in-network processing. We conclude by discussing analytics for alternative agriculture for generation of biofuels and policy challenges in the implementation of digital agriculture in the wild.

Maternal and child under-nutrition resulting in childhood stunting remains prevalent in east Africa, leading to increased disease risk, limiting cognitive development, and impeding human capital accumulation that constrains individuals, communities, and nations from reaching their full potential. In a western Kenyan population with a high prevalence of childhood stunting, frequency of milk consumption has been shown to increase monthly height gain in children, indicating the potential to improve health through livestock productivity. However, calving rates remain low, constraining the availability of milk to the household. Here we model average herd-level calving intervals and its relation to milk yield and nutrition in the context of an agricultural household production model, applying a dynamic panel econometric approach to household level data. We provide evidence that targeted on-farm specialization leads to significantly higher calving rates and shorter calving intervals, which in turn predictably increase milk production. Importantly, we show that the positive link between calving and household milk nutrition is present across households that primarily consume milk produced on-farm (“producer-consumers”) and those that predominantly purchase milk (“milk buyers”), indicating that efforts to improve herd fertility in western Kenya could improve food security on a community scale.

We examine how competition among crop insurance agents affects coverage choice in the federal crop insurance program. Agents may influence producers’ insurance decisions to maximize their total compensation. We develop a theoretical model of producer–agent interaction to examine how loss potential, agent compensation mechanisms, and market competition affect the coverage level selected. Using crop insurance unit-level datasets from five states, we find evidence that agent market concentration and agents’ market share matter in the insurance coverage decisions of producers but that the economic significance of the influence is relatively small. Agent influence over coverage level, premium, and liability choice is generally positive but inconsistent across states, which may be attributable to differences in loss risk and agent compensation mechanisms.

Digital agriculture has the promise to transform agricultural throughput. It can do this by applying data science and engineering for mapping input factors to crop throughput, while bounding the available resources. In addition, as the data volumes and varieties increase with the increase in sensor deployment in agricultural fields, data engineering techniques will also be instrumental in collection of distributed data as well as distributed processing of the data. These have to be done such that the latency requirements of the end users and applications are satisfied. Understanding how farm technology and big data can improve farm productivity can significantly increase the world's food production by 2050 in the face of constrained arable land and with the water levels receding. While much has been written about digital agriculture's potential, little is known about the economic costs and benefits of these emergent systems. In particular, the on-farm decision making processes, both in terms of adoption and optimal implementation, have not been adequately addressed. For example, if some algorithm needs data from multiple data owners to be pooled together, that raises the question of data ownership. This paper is the first one to bring together the important questions that will guide the end-to-end pipeline for the evolution of a new generation of digital agricultural solutions, driving the next revolution in agriculture and sustainability under one umbrella.

We examine how competition among crop insurance agents affects coverage choice in the federal crop insurance program. Agents may influence producers' insurance decisions to maximize their total compensation. We develop a theoretical model of producer-agent interaction to examine how loss potential, agent compensation mechanisms, and market competition affect the coverage level selected. Using crop insurance unit-level datasets from five states, we find evidence that agent market concentration and agents' market share matter in the insurance coverage decisions of producers but that the economic significance of the influence is relatively small. Agent influence over coverage level, premium, and liability choice is generally positive but inconsistent across states, which may be attributable to differences in loss risk and agent compensation mechanisms.

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.

Agritourism comprises heterogeneous firms that cannot be easily categorized. We apply principal components analysis to data in Washington state to uncover latent relationships among agritourism activities. We find most agritourism firms cluster in four groups, labelled: seasonal activities, event providers, farm and ranch tourism providers,and direct sales. A sorting mechanism assigns most firms to these groups. Using ordered logit regressions, we find that clustering and sorting reveal differences in how groups perceive barriers to success. We confirm that most firms are concerned about regulations and zoning laws. However, we find nuances in the concerns over insurance availability, and need for financial assistance and business knowledge