Long-Term Agroecosystem Research Research Articles

The LTAR Cropland Common Experiment at Upper Mississippi River Basin-St. Paul.

The Soil and Water Management Research Unit of the USDA-Agricultural Research Service is located in St. Paul, MN, and conducts long-term research at the University of Minnesota Research and Outreach Center located at Rosemount, MN. As part of USDA's Long-Term Agroecosystem Research (LTAR) network, the croplands common experiment (CCE) at this location is focused on integration of a kura clover (Trifolium ambiguum M. Bieb.) living mulch (KCLM) system into the prevailing 2-year rotation of corn (Zea mays L.) and soybean (Glycine max L.) that is typical of the midwestern Corn Belt. The LTAR-CCE conducted at Rosemount, MN, aims to compare the long-term environmental and agronomic performance of KCLM while identifying challenges and developing management strategies for this alternative practice. The use of a living mulch for this region is advantageous because, once established, it does not require additional time for fall field operations typically associated with winter cover crops. Results from LTAR-CCE studies at this site show that KCLM results in a substantial increase in soil field-saturated hydraulic conductivity and decreases in leaching of nitrate-nitrogen (NO3 --N). Disadvantages of the KCLM system include potential for increased emissions of nitrous oxide (N2O) and reduced crop yields, particularly during drought. Also, the optimal approach for crop row establishment in the spring remains uncertain. Ongoing LTAR-CCE research with KCLM aims to better understand and quantify both benefits and risks across conditions of interannual weather variability and changing climate to develop guidance for suitable adoption and management of this alternative practice.

The LTAR Integrated Common Experiment at Upper Mississippi River Basin-Platteville.

Alternative agronomic practices are needed to address the various climatic, agronomic, edaphic, and water quality related challenges faced by the dairy farmers of the Driftless Area (DA) in the Upper Mississippi River Basin (UMRB). These practices should be innovative in nature, inclusive of regional stakeholders, and sustainable to meet the future food and climate related challenges of Wisconsin agriculture. Here, we outline our Integrated (grazing and cropland) Long-Term Agroecosystem Research Common Experiment at the University of Wisconsin-Platteville Pioneer Farm (UW-P PF) in the UMRB and describe our collaboration in this USDA network. In this field-scale experiment, we are comparing the conventional dairy production system common to this region (i.e., corn-on-corn [Zea mays L.] for 4 years, followed by alfalfa [Medicago sativa L.] for 3 years, with no cover crops) with two alternative dairy production systems-(1) soil health management with no-till, cover crops, and application of a novel manure-based nutrient-rich stable product, and (2) management intensive grazing-and rotational grazing on pastures established with diverse forage-legume mix. Meteorological, edaphic, hydrologic, and agronomic data are collected and analyzed at regular frequencies. Going forward, the experiment will continue as a form of stakeholder-driven adaptive research and receive evaluation on a regular basis to determine whether any changes are required to address the "real-world" challenges faced by the farmers in the Midwest.

The LTAR Grazing Land Common Experiment at the Great Basin.

The Long-Term Agroecosystem Research Network (LTAR), through its Common Experiment (CE) framework, contrasts prevailing and alternative agricultural practices for efficacy and sustainability within the indicator domains of environment, productivity, economics, and society. Invasive species, wildfire, and climate change are principal threats to Great Basin agroecosystems. Prescribed grazing may be an effective tool for restoring lands degraded by these disturbances. At the Great Basin (GB) LTAR site headquartered in Boise, ID, our contribution to the CE contrasts a prevailing (PRV), cattle grazing practice of fixed moderate stocking and duration with an alternative (ALT), prescribed grazing practice called high-intensity low-frequency (HILF) grazing where stocking and duration are tailored to suppress invasive annual grass competition with native or desirable plant species and thus promote recovery of rangelands degraded by annual grass invasion and recurrent wildfire. Preliminary results indicate cheatgrass density and fuel height have been reduced in ALT-treated paddocks compared to PRV paddocks. Since its inception in 2014, our GB CE has been a research co-production effort among ranchers, public land managers, and researchers. Future directions for this research will center on expanding the experiment to multiple study areas to better address the scope of the annual grass/wildfire problem. We expect this research will lead to effective and sustainable grazing practices for restoring >41 million hectares of degraded rangelands in the Great Basin and other areas of the western United States.

The LTAR Cropland Common Experiment at Central Mississippi River Basin.

The Central Mississippi River Basin (CMRB) Common Experiment, with its marginal soils and southern Corn Belt climate, is an ideal location for evaluating progress toward environmental, productivity, and climatic adaptation goals. Sustainable production with conventional row-crop systems is more challenging than in the upper Corn Belt, making evaluation and adoption of alternative farming practices crucial. This Common Experiment has a hydrologically restrictive layer causing reduced plant available water capacity in the root zone. The CMRB site joined the Long-Term Agroecosystem Research Network in 2011 with the Cropland Common Experiment established in 2015. The Common Experiment contrasts prevailing and alternative practices at plot and field scale. Improvement of the soil ecosystem is key, as it underpins other objectives, including reduced nutrient losses, increased soil water holding capacity, and yield stability.

The LTAR Grazing Land Common Experiment at Northern Plains.

The USDA Long-Term Agroecosystem Research (LTAR) network aims to enhance sustainable agricultural management practices through a coordinated, cross-site common experiment involving 18 locations across the United States. The objective of this paper is to provide an overview of the LTAR grazing lands common experiment at the Northern Plains (NP) site, where an experiment was initiated in 2019 to answer producers' and researchers' questions about whether the tactical application of fire or grazing can reduce the dominance of invasive Kentucky bluegrass in northern Great Plains ecosystems. As part of the LTAR common experiment, we contrast a prevailing practice (season-long grazing at moderate stocking rate) with four alternative practices at a half-hectare plot scale: (1) mob grazing by cattle, (2) multi-species grazing (mob grazing by cattle, with goats foraging at key times of the year), (3) prescribed fire, and (4) prescribed fire followed by cattle grazing. A stakeholder group is engaged in the co-production process to determine alternative practices and how to apply them. Every 5 years, the treatment with the best overall outcomes is applied at a field scale (15ha), resulting in a core treatment contrast of prevailing versus alternative grazing management systems. This experiment aims to develop alternative agroecological practices that optimize current and future economic and ecosystem benefits.

The LTAR Cropland Common Experiment at Eastern Corn Belt.

The Eastern Corn Belt (ECB) Long-Term Agroecosystem Research (LTAR) network site is one of the most agriculturally productive areas in the United States; however, nutrient and sediment losses from this region directly contribute to water quality impairment in both the Gulf of Mexico and Lake Erie. One of the largest challenges facing agricultural production in the ECB is water management, especially under a changing climate. Shifting precipitation patterns in combination with evolving infrastructure (i.e., tile drainage, irrigation) and management (i.e., crops planted; 4R nutrient management: source, timing, rate, and placement) in response to changing climate are likely to alter water quantity and quality and agricultural productivity. Through plot- and field-scale research, the ECB Common Experiment (CE) is focused on crop, soil, and water management to support sustainable agricultural intensification, with the goal of maximizing profitability, minimizing agriculture's environmental footprint, and enhancing ecosystem services. At both spatial scales, the CE aims to examine differences in water quantity and quality between the primary prevailing crop production system in the ECB (i.e., corn [Zea mays L.]-soybean [Glycine max L.] rotation, tillage prior to corn planting, free drainage, and fertility management consistent with tri-state recommendations) and an alternative system (e.g., adding small grains into the crop rotation, cover crops, limited phosphorus fertilizer, and drainage water management). Aligning producer and stakeholders needs with research objectives and long-term data collection, the ECB CE will tackle both ongoing and newly emerging research priorities and explore the effectiveness of conservation strategies to decrease nutrient and sediment losses from agricultural land.

The LTAR Cropland Common Experiment in the Upper Chesapeake Bay.

Dairy production is a key agricultural enterprise in the Upper Chesapeake Bay (UCB) basin, where phosphorous (P) and nitrogen (N) loading contribute to eutrophication. Import of forages and grains and application of mineral fertilizers contribute to nutrient imbalances in the basin. The UCB Long-Term Agroecosystem Research Cropland Common Experiment aims to evaluate diverse crop rotations that minimize the need for imported feed, maximize year-round living cover, and reduce nutrient losses. UCB's plot-scale experiment was established in 2018, incorporating an ongoing cropping system study that was established by the Pennsylvania State University in 2010. An alternative dairy cropping rotation (including silage and grain corn [Zea mayes L.], alfalfa [Medicago sativa L.]/orchardgrass [Dactylis glomerata L.] mix, winter rye silage [Secale cereale L.], and sorghum-sudangrass [Sorghum × drummondii (Steud.) Millsp. & Chas]) that employed manure injection, integrated pest management, and less frequent manure application was compared to a prevailing, conventionally managed silage corn-alfalfa rotation with higher manure application rates. A field-scale experiment was established in 2019 to monitor alternative production practices (manure injection and avoidance of neonicotinoid seed treatment) and prevailing practices in three fields on a commercial dairy farm. Findings suggest that crop rotation diversification, manure injection, and integrated pest management have the potential to increase the economic and environmental sustainability of dairy cropping systems, but long-term evaluation is needed for confirmation.

The LTAR Grazing Land Common Experiment at the Jornada Experimental Range: Old genetics, new precision technologies, and adaptive value chains.

Rangelands and the supply chains connected to them are central to the agrifood systems of the Southwestern United States. Local ranchers are simultaneously arid lands managers, herd managers, and marketing managers. To stay in business, they must adapt to unpredictable forage resources and markets while conserving soils and vegetation resources for the long term. As climate warming and drying exacerbate the complexity and difficulty of day-to-day production, producers and policymakers are seeking alternatives to "business as usual." The Long-Term Agroecosystem Research (LTAR)-Jornada team has developed a package of strategies to help producers adapt to the local and inter-regional challenges. The package includes heritage cattle, precision ranching systems, and adaptive value chains. Five ranches across the Southwest have adopted different combinations and are partnering with LTAR and each other to measure their benefits and drawbacks in real-world conditions. Opportunities for controlled experimentation differ among the ranches, so we use LTAR's indicator system to assess and compare results. Even as we invest in co-producing knowledge about these three strategies, we recognize that progressive aridification and urbanization of Southwestern rangelands create challenges for which a single "silver bullet" of agricultural innovation is unlikely to provide durable solutions. We are learning from our customers and stakeholders about ways to adjust the development of new options.

The LTAR Grazing Land Common Experiment at Archbold Biological Station-University of Florida.

The Archbold Biological Station-University of Florida (ABS-UF) Long-term Agroecosystem Research (LTAR) site lies in the heart of south-central Florida, representing subtropical humid grazing lands in North America and globally. Beef producers in this region face challenges due to climate variability, limited nutritive value of forages, poor soils, public concerns about water quality and greenhouse gas emissions, management trade-offs, economic uncertainty, and increasing urban encroachment. The ABS-UF Common Experiment, co-designed with stakeholders, will assess innovative management systems in comparison to prevailing management systems on key indicators of sustainability. Innovative management systems being tested are alternative fire (frequency and spatial extent) and grazing practices (stocking rate and system). The common experiment framework was implemented across a management intensity gradient spanning from native rangeland to cultivated pastures, including embedded wetlands. Issues that have arisen to date include difficulties in implementing prescribed fire and reduced productivity in cultivated pastures associated with innovative management, which led to an adjustment of the experimental treatment. A stakeholder advisory council will codesign future alternative treatments and guide experimental changes in this long-term experiment. Stakeholder engagement efforts revealed research priorities centered on financial strength, carbon (C) and greenhouse gas emissions, and water quality. Stakeholders are also interested in testing emerging technology such as the utility of virtual fencing. Results from ABS-UF provide a unique perspective from subtropical humid grazing lands for continental-scale cross-site synthesis on sustainable agroecosystems across LTAR.

The LTAR Cropland Common Experiment at the Texas Gulf.

Texas Gulf is one of the 18 regional sites that is part of the USDA-ARS Long-Term Agroecosystem Research (LTAR) Network and focuses on cropland and integrated grazing land research in Central Texas, addressing challenges posed by soil characteristics, climate variability, and urbanization. This paper provides brief site descriptions of the two Cropland Common Experiments being conducted in the Texas Gulf LTAR region, emphasizing conservation tillage practices and precision agriculture techniques. The plot-scale study is located in Temple, TX, at the USDA-ARS Grassland, Soil and Water Research Laboratory and examines conventional tillage, strip tillage, and no tillage practices. The field-scale study, located in Riesel, TX, at the USDA-ARS Riesel Watersheds, assesses the impact of no tillage, cover crops, fertility management, adaptive management, and precision conservation on crop yield, profitability, and environmental footprint. Key measurements include soil and plant analyses, greenhouse gas fluxes, runoff water quantity and quality, and field operations recorded with precision agriculture equipment. Despite challenges posed by urban encroachment, future research aims to incorporate new technologies, such as unmanned ground vehicles, to enhance sustainability and productivity of the agricultural landscape. These experiments provide valuable insights for stakeholders, contributing to the development of sustainable agricultural practices tailored to the unique challenges within the Texas Gulf LTAR region.

The LTAR Common Experiment at Upper Chesapeake Bay: Integrated.

Corn (Zea mays) crops harvested as grain in autumn do not provide opportunity for cover crop establishment, which may be remedied by interseeding cover crops into growing corn. Grazing cover crops after corn grain harvest could provide added revenues and increase nutrient cycling in the system while providing additional ecosystem services. However, tradeoffs between cash crop productivity and cover crop inclusion, and use as grazed forage, are not fully understood. This 4-year Long-Term Agroecosystem Research Integrated Common Experiment project evaluated the effect of interseeding cereal rye (Secale cereale) into corn for grazing after corn grain harvest on corn grain yield and late-season grazing. Cereal rye was interseeded into corn in early June. After corn grain harvest, six paddocks at each location were randomly allotted to grazed (GRAZ) or not grazed (NG). The GRAZ paddocks were grazed with beef cattle in late autumn and again in early spring if regrowth allowed. Paddocks were flown with an unmanned aerial system (UAS) to characterize spatial forage yield and quality. Cereal rye provided an additional 20-30 grazing days in the autumn for 24 beef cows on 4.8ha. Early spring growth shows potential to provide even greater forage yields than autumn, but growth is less dependable. Corn grain yields did not decrease except in 2019 (dry year) when yields were 40% lower. There were no significant differences in soil health indicators between GRAZ and NG paddocks. The UAS shows promise as a tool for monitoring forage yield and quality and optimizing grazing management.

The LTAR-integrated grazing land common experiment at the Texas Gulf.

Extreme weather and climate events have become more frequent and directly affect the ecological structure and function of integrated grazing lands. While the Great Plains have experienced a long history of regular disturbances from drought and floods, grazing, and fires, the increased frequency and magnitude of these disturbances can reduce ecological resilience, largely depending on management practices. Alternative strategies designed to adaptively manage grazing land resources based on the ecology of the system should increase the resistance and resilience to disturbances when compared to prevailing practices. Determining the ecologic and economic value of alternative strategies will require long-term evaluations across large spatial scales. The Long-Term Agroecosystem Research Network has been established to evaluate the differences between alternative and prevailing practices among 18 strategically located sites and across decadal time scales throughout the continental United States. A key integrated grazing land site within this network is the Texas Gulf located at the Riesel Watersheds in the Blackland Prairie of Central Texas. At this study site, the differences between alternative and prevailing grazing management strategies are now being evaluated. The alternative strategy was designed using a combination of knowledge of the site and species ecology with modern-day tools and technologies. Alternatively, the prevailing practice implements a conventional year-round continuous grazing system with heavy reliance on hay and supplemental protein during winter. Results will provide grazing land managers with economically viable adaptive management choices for increasing ecological resilience following extreme and frequent disturbance events.

The Long-Term Agroecosystem Research cropland common experiment at Northern Plains.

Cropland agriculture in the northern Great Plains is challenged by variable weather, agricultural intensification, and competing use for energy development. Innovative cropland practices that address these challenges are needed to ensure regional agriculture can sustainably meet future food, fuel, and fiber demand. In response to this need, the Northern Plains Long-Term Agroecosystem Research Network site established a cropland experiment in 2019 that contrasts prevailing and alternative practices at plot and field scales over a proposed 30-year time frame. The experimental site is located on the Area IV Soil Conservation Districts Cooperative Research Farm near Mandan, ND. Cropping practices for the first 6 years of the experiment were developed with input from stakeholders and include a 3-year crop rotation of spring wheat (Triticum aestivum L.), corn (Zea mays L.), and soybean (Glycine max L.) with cover crops (alternative practice) and without (prevailing practice). The prevailing practice also involves the removal of crop residue, while a second alternative practice of perennial forages is included in the plot-scale experiment. Biophysical measurements are made at both spatial scales at frequencies aligned with approved methods for each agronomic and environmental metric. Findings from the first 6 years of the experiment will help identify tradeoffs associated with cover crop use and residue removal in dryland cropping systems. In the future, the experiment will adopt a knowledge co-production approach whereby researchers and stakeholders will work collaboratively to identify problems, implement research, and interpret results.

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

Uncertainty in phosphorus fluxes and budgets across the US long-term agroecosystem research network.

Phosphorus (P) budgets can be useful tools for understanding nutrient cycling and quantifying the effectiveness of nutrient management planning and policies; however, uncertainties in agricultural nutrient budgets are not often quantitatively assessed. The objective of this study was to evaluate uncertainty in P fluxes (fertilizer/manure application, atmospheric deposition, irrigation, crop removal, surface runoff, and leachate) and the propagation of these uncertainties to annual P budgets. Data from 56 cropping systems in the P-FLUX database, which spans diverse rotations and landscapes across the United States and Canada, were evaluated. Results showed that across cropping systems, average annual P budget was 22.4kg P ha-1 (range=-32.7 to 340.6kg P ha-1 ), with an average uncertainty of 13.1kg P ha-1 (range=1.0-87.1kg P ha-1 ). Fertilizer/manure application and crop removal were the largest P fluxes across cropping systems and, as a result, accounted for the largest fraction of uncertainty in annual budgets (61% and 37%, respectively). Remaining fluxes individually accounted for<2% of the budget uncertainty. Uncertainties were large enough that determining whether P was increasing, decreasing, or not changing was inconclusive in 39% of the budgets evaluated. Findings indicate that more careful and/or direct measurements of inputs, outputs, and stocks are needed. Recommendations for minimizing uncertainty in P budgets based on the results of the study were developed. Quantifying, communicating, and constraining uncertainty in budgets among production systems and multiple geographies is critical for engaging stakeholders, developing local and national strategies for P reduction, and informing policy.

Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method

Global, long-term, gap-free, high quality soil moisture products are extremely important for hydrological monitoring and climate change research. However, soil moisture products produced from satellite observations have data gaps due to the limited capabilities of satellite orbit/swath and retrieval algorithms, which limit the regional and global applications of soil moisture data in hydrology and agriculture studies. To solve this problem, we proposed a gap-filling method to reconstruct a global gap-free surface soil moisture product by applying the machine learning (Random Forest) algorithm on a pixel-by-pixel basis, taking into account the nonlinear relationship between surface soil moisture and the related surface environmental variables. The gap-filling method was applied to the NN-SM surface soil moisture product, which has a fraction of data gaps of around 50% globally on a multi-year average. A global daily gap-free surface soil moisture dataset from 2002 to 2020 was then generated. The reconstructed values of several sub-regions after manually eliminating the original values were cross-verified with the original data, and this clearly demonstrated the reliability of the reconstruction method with the correlation coefficient (R) ranging between 0.770 and 0.918, the Root Mean Square Error (RMSE) between 0.057 and 0.082 m3/m3, the unbiased Root Mean Square Error (ubRMSE) between 0.053 and 0.081 m3/m3, and Bias between −0.012 and 0.008 m3/m3. The accuracy of the reconstructed surface soil moisture dataset was evaluated using in situ observations of surface soil moisture at 12 sites from the International Soil Moisture Network (ISMN) and the Long-Term Agroecosystem Research (LTAR) network, and the results showed good accuracy in terms of R (0.610), RMSE (0.067 m3/m3), ubRMSE (0.045 m3/m3) and Bias (0.031 m3/m3). Overall, the reconstructed surface soil moisture dataset retained the characteristics of the NN-SM product, such as high accuracy and good spatiotemporal pattern. However, with the advantage of continuous spatiotemporal coverage, it is more suitable for further applications in the analysis of global surface soil moisture trends, land surface hydrological processes, and land-atmosphere energy and water exchanges, etc.

Within-field yield stability and gross margin variations across corn fields and implications for precision conservation

Soil spatial variability is a primary contributor to within-field yield variation across farms. Spatio-temporal yield stability and variability can be assessed through multi-year yield monitor data and geostatistical techniques. Our objective was to delineate yield stability zones using multi-year yield data coupled with gross margins to plan precision conservation prescriptions. This study employed corn yield measurements from 2018, 2019, 2020 and farm economics data to compile yield stability and gross margin maps for nine Texas Blackland Prairie corn fields, and identified nonprofitable areas in each field that may be unsuitable for crop production. Yield stability zones were delineated using mean and coefficient of variation of multi-year yield maps (Zone A: high yield, stable; Zone B: high yield, unstable; Zone C: low yield, unstable; and Zone D: low yield, stable). Approximately 57% of the area in the fields was classified as unstable and, nearly 29% of the area yielded consistently below the field mean (Zone D). Gross margin for stability zones ranged from − $693 to $775/ha. Stability zones A and B generally had positive gross margins, whereas zones C and D had negative margins. Based on yield and gross margin assessment, yield stability zone D could be removed from row crop production. As a part of the Long-Term Agroecosystem Research Network Common Cropland Experiment, Zone D was removed from production (fields Y-8 and Y-13) or received reduced inputs (field SW-16 and W-13). Further study is needed to verify the farm-level economic benefits to producers and to evaluate the environmental benefits of precision conservation.

Intensification differentially affects the delivery of multiple ecosystem services in subtropical and temperate grasslands

Intensification, the process of intensifying land management to enhance agricultural goods, results in “intensive” pastures that are planted with productive grasses and fertilized. These intensive pastures provide essential ecosystem services, including forage production for livestock. Understanding the synergies and tradeoffs of pasture intensification on the delivery of services across climatic regions is crucial to shape policies and incentives for better management of natural resources. Here, we investigated how grassland intensification affects key components of provisioning (forage productivity and quality), supporting (plant diversity) and regulating services (CO2 and CH4 fluxes) by comparing these services between intensive versus extensive pastures in subtropical and temperate pastures in the USDA Long-term Agroecosystem Research (LTAR) Network sites in Florida and Oklahoma, USA over multiple years. Our results suggest that grassland intensification led to a decrease in measured supporting and regulating services, but increased forage productivity in temperate pastures and forage digestibility in subtropical pastures. Intensification decreased the net CO2 sink of subtropical pastures while it did not affect the sink capacity of temperate pastures; and it also increased environmental CH4 emissions from subtropical pastures and reduced CH4 uptake in temperate pastures. Intensification enhanced the global warming potential associated with C fluxes of pastures in both ecoregions. Our study demonstrates that comparisons of agroecosystems in contrasting ecoregions can reveal important drivers of ecosystem services and general or region-specific opportunities and solutions to maintaining agricultural production and reducing environmental footprints. Further LTAR network-scale comparisons of multiple ecosystem services across croplands and grazinglands intensively vs extensively managed are warranted to inform the sustainable intensification of agriculture within US and beyond. Our results highlight that achieving both food security and environmental stewardship will involve the conservation of less intensively managed pastures while adopting sustainable strategies in intensively managed pastures.

Infusing ‘long-term’ into social science rangelands research

• Social science rangelands research has advanced substantively in the last few decades as a multidisciplinary endeavor, and notably through increased capacity to integrate with ecologically centered approaches. • The diversity of social science-related contributions to rangelands research continues to expand with both breadth and depth of approaches, perspectives, and backgrounds of participating scholars. • The USDA Long-term Agroecosystem Research (LTAR) Network advances a unique long-term and large-scale effort to incorporate social science research into a long-term “common experiment” across multiple sites within varied rangelands contexts of the United States.