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NASA NTRS2024

How Can Future Climate Change Affect the Corn Production System in Nebraska, USA?

The increasing temperatures and changing precipitation patterns have affected both crop yields and water requirements challenging agricultural systems in sustaining high crop yields and ensuring water and food security. In the present study, we evaluated the impact of climate change on the corn production under irrigated and rainfed cropping systems in Nebraska considering four climate zones until 2100 as a case study for the US Corn Belt. Additionally, suggests ways to reduce the impact of climate change on corn yield and water resources. Five global circulation model datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) interconnected to Shared Socioeconomic Pathways (SSPs), were selected. Three future periods: near (2020–2039), mid (2040–2069), and far future (2070–2100) under SSP226, SSP370, and SSP585 scenarios were used to predict the climate changes, crop cycle length, crop yield, and crop water requirement. Additionally, we input annual CO2 concentration scenarios for each SSP scenario to model changes from 2015 to 2100 using the CERES-Maize model. Temperatures are forecasted to rise, precipitation to decline, and crop cycle length to decrease, particularly for SSP370 (up to 49% yield decrease) and SSP585 (up to 67% yield decrease) by 2070–2100. Yields are expected to decline across all scenarios and periods for irrigated and rainfed fields due to the reduction in crop cycle length. Crop water demand is projected to increase, with rainfed systems facing heightened impact due to reduced precipitation. Addressing climate change in Nebraska requires actions like developing new corn varieties to longer the crop season, transitioning to efficient irrigation systems, improving irrigation management for water and food security, and reducing the risk of conflicts over its use. These results open possibilities for discussion about the actions that can be taken to cope with climate change and guarantee water and food security in the US Corn Belt.

CMIP6↗

NASA NTRSDate not supplied

Open Science Approach to Analyze Climate-Crop Relationships in the US Leveraging GES DISC and Galaxy Workflows

Understanding the intricate relationship between climate variability and agricultural production is crucial for ensuring food security. This study investigates the impact of climate parameters, such as temperature, precipitation, and soil moisture, on major US crop yields. Adopting an open science approach, the study analyzes the impact of climate on agricultural production in the United States. The Galaxy workflow engine serves as the primary tool for integrating climate data from the Goddard Earth Sciences Data and Information Services Center (GES DISC), retrieved via the Giovanni system, with yield statistics from the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS). Extensions for reading, preprocessing, and analyzing external data have been developed, enabling the creation of workflows within the Galaxy platform. The development of a reproducible workflow allows for the calculation of seasonal climate averages, which are then assessed for their correlation with crop yields. This methodology ensures the replicability of the research, promoting transparency and collaboration in the scientific community. Correlational and regression analyses have been applied to different sub-zones and crops. The findings from this research offer valuable insights into the relationship between climate parameters and crop yields. These insights contribute to a deeper understanding of climate-crop relationships, providing a solid foundation for informed decision-making in the agricultural sector. The high correlation values indicate a significant relationship between climate parameters and crop yields, underscoring the importance of considering climate factors in agricultural planning and policymaking. This research also exemplifies the power of open science in advancing our understanding of complex environmental and agricultural phenomena. By leveraging open data and services, it provides a robust and replicable framework for future studies in this critical field.

Open science↗

DOE OSTI2026

Data for Promoter Deletion in the Soybean Compact Mutant Leads to Overexpression of a Gene with Homology to the C20-Gibberellin 2-Oxidase Family

Height is a critical component of plant architecture, significantly affecting crop yield. The genetic basis of this trait in soybean remains unclear. In this study, we report the characterization of the Compact mutant of soybean, which has short internodes. The candidate gene was mapped to chromosome 17, and the interval containing the causative mutation was further delineated using biparental mapping. Whole-genome sequencing of the mutant revealed an 8.7 kb deletion in the promoter of the Glyma.17g145200 gene, which encodes a member of the class III gibberellin (GA) 2-oxidases. The mutation has a dominant effect, likely via increased expression of the GA 2-oxidase transcript observed in green tissue, as a result of the deletion in the promoter of Glyma.17g145200. We further demonstrate that levels of GA precursors are altered in the Compact mutant, supporting a role in GA metabolism, and that the mutant phenotype can be rescued with exogenous GA3. We also determined that overexpression of Glyma.17g145200 in Arabidopsis results in dwarfed plants. Thus, gain of promoter activity in the Compact mutant leads to a short internode phenotype in soybean through altered metabolism of gibberellin precursors. These results provide an example of how structural variation can control an important crop trait and a role for Glyma.17g145200 in soybean architecture, with potential implications for increasing crop yield.

Biomass Analytics↗

DOE OSTI2023

Promoter deletion in the soybean Compact mutant leads to overexpression of a gene with homology to the C 20 -gibberellin 2-oxidase family

Height is a critical component of plant architecture, significantly affecting crop yield. The genetic basis of this trait in soybean remains unclear. In this study, we report the characterization of the Compact mutant of soybean, which has short internodes. The candidate gene was mapped to chromosome 17, and the interval containing the causative mutation was further delineated using biparental mapping. Whole-genome sequencing of the mutant revealed an 8.7 kb deletion in the promoter of the Glyma.17g145200 gene, which encodes a member of the class III gibberellin (GA) 2-oxidases. The mutation has a dominant effect, likely via increased expression of the GA 2-oxidase transcript observed in green tissue, as a result of the deletion in the promoter of Glyma.17g145200. We further demonstrate that levels of GA precursors are altered in the Compact mutant, supporting a role in GA metabolism, and that the mutant phenotype can be rescued with exogenous GA3. We also determined that overexpression of Glyma.17g145200 in Arabidopsis results in dwarfed plants. Thus, gain of promoter activity in the Compact mutant leads to a short internode phenotype in soybean through altered metabolism of gibberellin precursors. Furthermore, these results provide an example of how structural variation can control an important crop trait and a role for Glyma.17g145200 in soybean architecture, with potential implications for increasing crop yield.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2016

Climate Analogues Suggest Limited Potential for Intensification of Production on Current Croplands Under Climate Change

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand.

land-use patterns↗

NASA NTRS1996

Development of a Land Use Mapping and Monitoring Protocol for the High Plains Region: A Multitemporal Remote Sensing Application

The purpose of this project was to develop a practical protocol that employs multitemporal remotely sensed imagery, integrated with environmental parameters to model and monitor agricultural and natural resources in the High Plains Region of the United States. The value of this project would be extended throughout the region via workshops targeted at carefully selected audiences and designed to transfer remote sensing technology and the methods and applications developed. Implementation of such a protocol using remotely sensed satellite imagery is critical for addressing many issues of regional importance, including: (1) Prediction of rural land use/land cover (LULC) categories within a region; (2) Use of rural LULC maps for successive years to monitor change; (3) Crop types derived from LULC maps as important inputs to water consumption models; (4) Early prediction of crop yields; (5) Multi-date maps of crop types to monitor patterns related to crop change; (6) Knowledge of crop types to monitor condition and improve prediction of crop yield; (7) More precise models of crop types and conditions to improve agricultural economic forecasts; (8;) Prediction of biomass for estimating vegetation production, soil protection from erosion forces, nonpoint source pollution, wildlife habitat quality and other related factors; (9) Crop type and condition information to more accurately predict production of biogeochemicals such as CO2, CH4, and other greenhouse gases that are inputs to global climate models; (10) Provide information regarding limiting factors (i.e., economic constraints of pumping, fertilizing, etc.) used in conjunction with other factors, such as changes in climate for predicting changes in rural LULC; (11) Accurate prediction of rural LULC used to assess the effectiveness of government programs such as the U.S. Soil Conservation Service (SCS) Conservation Reserve Program; and (12) Prediction of water demand based on rural LULC that can be related to rates of draw-down of underground water supplies.

Price, Kevin P.↗

DOE OSTI2022

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗

DOE OSTI2021

Heat stress on agricultural workers exacerbates crop impacts of climate change

Abstract The direct impacts of climate change on crop yields and human health are individually well-studied, but the interaction between the two have received little attention. Here we analyze the consequences of global warming for agricultural workers and the crops they cultivate using a global economic model (GTAP) with explicit treatment of the physiological impacts of heat stress on humans’ ability to work. Based on two metrics of heat stress and two labor functions, combined with a meta-analysis of crop yields, we provide an analysis of climate, impacts both on agricultural labor force, as well as on staple crop yields, thereby accounting for the interacting effect of climate change on both land and labor. Here we analyze the two sets of impacts on staple crops, while also expanding the labor impacts to highlight the potential importance on non-staple crops. We find, worldwide, labor and yield impacts within staple grains are equally important at +3 ∘ C warming, relative to the 1986–2005 baseline. Furthermore, the widely overlooked labor impacts are dominant in two of the most vulnerable regions: sub-Saharan Africa and Southeast Asia. In those regions, heat stress with 3 ∘ C global warming could reduce labor capacity in agriculture by 30%–50%, increasing food prices and requiring much higher levels of employment in the farm sector. The global welfare loss at this level of warming could reach $136 billion, with crop prices rising by 5%, relative to baseline.

de Lima, Cicero Z. (ORCID:0000000297005435)↗

NASA NTRS2011

Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attach s, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Historically, these analog years are visually identified; however, the qualitative nature of this method sometimes precludes the definitive identification of the best analog year. Thus, one goal of this study is to derive a more rigorous, statistical approach for identifying analog years, based on a modified coefficient of determination, termed the analog index (AI). A second goal is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data).

Teng, William↗

DOE OSTI2020

Predicting Field Efficiency of Round-Baling Operations in High-Yielding Biomass Crops

Model simulations for bioenergy harvest planning need to utilize equipment-capacity relationships for equipment operating under the high-yield conditions typical of a biomass crop. These performance assumptions have a direct bearing on the estimates of machine capacity, the number of machines required, and, therefore, the cost to fulfill the biorefinery plant demands for a given harvest window. Typically, two major issues in these models have been poorly understood: the available time required to complete the harvest operation (often called probability of workdays) and the capacity of the harvest equipment as impacted by yield. Simulations use annual yield estimates, which incorporate weather events, to demonstrate year-to-year effects. Some simulations also incorporate potential yield increases from genetically modified energy crops. There are limited field performance data for most current forage equipment used for harvesting high-yield biomass crops. Analysis shows that the impact of wrap/eject time for round balers resulted in a 50% reduction in achieved throughput capacity (Mg/h). After the maximum throughput is reached, the cost of the round bale operation (3.23 USD/Mg) is double that of the large-square baler (1.63 USD/Mg). The round baler achieved throughput capacity is 50% less (32.7 Mg/h compared to 71.0 Mg/h) than the large-square baler.

09 BIOMASS FUELS↗

NASA NTRS2012

Integrating NASA Satellite Data Into USDA World Agricultural Outlook Board Decision Making Environment To Improve Agricultural Estimates

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. The goal of the current project is to improve WAOB estimates by integrating NASA satellite precipitation and soil moisture observations into WAOB's decision making environment. Precipitation (Level 3 gridded) is from the TRMM Multi-satellite Precipitation Analysis (TMPA). Soil moisture (Level 2 swath and Level 3 gridded) is generated by the Land Parameter Retrieval Model (LPRM) and operationally produced by the NASA Goddard Earth Sciences Data and Information Services Center (GBS DISC). A root zone soil moisture (RZSM) product is also generated, via assimilation of the Level 3 LPRM data by a land surface model (part of a related project). Data services to be available for these products include GeoTIFF, GDS (GrADS Data Server), WMS (Web Map Service), WCS (Web Coverage Service), and NASA Giovanni. Project benchmarking is based on retrospective analyses of WAOB analog year comparisons. The latter are between a given year and historical years with similar weather patterns and estimated crop yields. An analog index (AI) was developed to introduce a more rigorous, statistical approach for identifying analog years. Results thus far show that crop yield estimates derived from TMPA precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include LPRM surface soil moisture data and model-assimilated RZSM.

Teng, William↗

DOE OSTI2020

Are scenario projections overly optimistic about future yield progress?

Historical increases in agricultural production were achieved predominantly by large increases in agricultural productivity. Intensification of crop and livestock production also plays a key role in future projections of agricultural land use. Here, we assess and discuss projections of crop yields by global agricultural land-use and integrated assessment models. To evaluate these crop yield projections, we compare them to empirical data on attainable yields by employing a linear and plateauing continuation of observed attainable yield trends. While keeping in mind the uncertainties of attainable yields projections and future climate change impacts, we find that, on average for all cereals on the global level, global projected yields by 2050 remain below the attainable yields. Additionally, this is also true for future pathways with high technological progress and mitigation efforts, indicating that projected yield increases are not overly optimistic, even under systemic transformations. Furthermore, on a regional scale, we find that for developing regions, specifically for sub-Saharan Africa, projected yields stay well below attainable yields, indicating that the large yield gaps which could be closed through improved crop management, may also persist in the future. In OECD countries, in contrast, current yields are already close to attainable yields, and the projections approach or, for some models, even exceed attainable yields by 2050. This observation parallels research suggesting that future progress in attainable yields in developed regions will mainly have to be achieved through new crop varieties or genetic improvements. The models included in this study vary widely in their implementation of yield progress, which are often split into endogenous (crop management) improvements and exogenous (technological) trends. More detail and transparency are needed in these important elements of global yields and land use projections, and this paper discusses possibilities of better aligning agronomic understanding of yield gaps and yield potentials with modelling approaches.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Implications of Climate Mitigation for Future Agricultural Production

Climate change is projected to negatively impact biophysical agricultural productivity in much of the world. Actions taken to reduce greenhouse gas emissions and mitigate future climate changes, are thus of central importance for agricultural production. Climate impacts are, however, not unidirectional; some crops in some regions (primarily higher latitudes) are projected to benefit, particularly if increased atmospheric carbon dioxide is assumed to strongly increase crop productivity at large spatial and temporal scales. Climate mitigation measures that are implemented by reducing atmospheric carbon dioxide concentrations lead to reductions both in the strength of climate change and in the benefits of carbon dioxide fertilization. Consequently, analysis of the effects of climate mitigation on agricultural productivity must address not only regions for which mitigation is likely to reduce or even reverse climate damages. There are also regions that are likely to see increased crop yields due to climate change, which may lose these added potentials under mitigation action. Comparing data from the most comprehensive archive of crop yield projections publicly available, we find that climate mitigation leads to overall benefits from avoided damages at the global scale and especially in many regions that are already at risk of food insecurity today. Ignoring controversial carbon dioxide fertilization effects on crop productivity, we find that for the median projection aggressive mitigation could eliminate approximately 81% of the negative impacts of climate change on biophysical agricultural productivity globally by the end of the century. In this case, the benefits of mitigation typically extend well into temperate regions, but vary by crop and underlying climate model projections. Should large benefits to crop yields from carbon dioxide fertilization be realized, the effects of mitigation become much more mixed, though still positive globally and beneficial in many food insecure countries.

Mueller, Christoph↗

DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗

NASA NTRS1982

Yield model development project implementation plan

Tasks remaining to be completed are summarized for the following major project elements: (1) evaluation of crop yield models; (2) crop yield model research and development; (3) data acquisition processing, and storage; (4) related yield research: defining spectral and/or remote sensing data requirements; developing input for driving and testing crop growth/yield models; real time testing of wheat plant process models) and (5) project management and support.

Ambroziak, R. A.↗

NASA NTRS2021

Future Climate Change Significantly Alters Interannual Wheat Yield Variability Over Half of Harvested Areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Climate change↗

DOE OSTI2022

Prairie Strips and Lower Land Use Intensity Increase Biodiversity and Ecosystem Services

Agricultural landscapes can be managed to protect biodiversity and maintain ecosystem services. One approach to achieve this is to restore native perennial vegetation within croplands. Where rowcrops have displaced prairie, as in the US Midwest, restoration of native perennial vegetation can align with crops in so called “prairie strips.” We tested the effect of prairie strips in addition to other management practices on a variety of taxa and on a suite of ecosystem services. To do so, we worked within a 33-year-old experiment that included treatments that varied methods of agricultural management across a gradient of land use intensity. In the two lowest intensity crop management treatments, we introduced prairie strips that occupied 5% of crop area. We addressed three questions: (1) What are the effects of newly established prairie strips on the spillover of biodiversity and ecosystem services into cropland? (2) How does time since prairie strip establishment affect biodiversity and ecosystem services? (3) What are the tradeoffs and synergies among biodiversity conservation, non-provisioning ecosystem services, and provisioning ecosystem services (crop yield) across a land use intensity gradient (which includes prairie strips)? Within prairie strip treatments, where sampling effort occurred within and at increasing distance from strips, dung beetle abundance, spider abundance and richness, active carbon, decomposition, and pollination decreased with distance from prairie strips, and this effect increased between the first and second year. Across the entire land use intensity gradient, treatments with prairie strips and reduced chemical inputs had higher butterfly abundance, spider abundance, and pollination services. In addition, soil organic carbon, butterfly richness, and spider richness increased with a decrease in land use intensity. Crop yield in one treatment with prairie strips was equal to that of the highest intensity management, even while including the area taken out of production. We found no effects of strips on ant biodiversity and greenhouse gas emissions (N 2 O and CH 4 ). Our results show that, even in early establishment, prairie strips and lower land use intensity can contribute to the conservation of biodiversity and ecosystem services without a disproportionate loss of crop yield.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

kemmerl4/LTER_prairie_strip_2019-2020: Data for: Prairie strips and lower land use intensity increase biodiversity and ecosystem services

54 ENVIRONMENTAL SCIENCES↗