Recent Rapid Increase of Cover Crop Adoption Across the U.S. Midwest Detected by Fusing Multi‐Source Satellite Data

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of cover cropping variations and impacts of government policies remains very limited. We developed an accurate and cost‐effective approach utilizing satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties was stagnant from 2000 to 2011, but has significantly increased from 2011 to 2021. The adoption of 2021 is four times that of 2011, which was highly correlated to the funding for conservation programs. However, the percentage of cover crop adoption in the U.S. Midwest is still low (7.2%). Our work fills a critical gap in quantifying long‐term field‐level cover crop adoption at large regions and highlights the potential importance of incentive programs to promote sustainable agricultural practices.

[1]  F. Gao,et al.  Remote Sensing Evaluation of Winter Cover Crop Springtime Performance and the Impact of Delayed Termination , 2022, Agronomy Journal.

[2]  Philip A. Townsend,et al.  Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[3]  K. Guan,et al.  Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems , 2021, Field Crops Research.

[4]  Mahsa Khodaee,et al.  Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery , 2021, Remote. Sens..

[5]  D. W. Smith,et al.  Cover Crop Trends, Programs, and Practices in the United States , 2021 .

[6]  Qian Yu,et al.  The extent of soil loss across the US Corn Belt , 2021, Proceedings of the National Academy of Sciences.

[7]  KC Kushal,et al.  Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing , 2021, Remote. Sens..

[8]  Haixin Chen,et al.  Mapping Conservation Management Practices and Outcomes in the Corn Belt Using the Operational Tillage Information System (OpTIS) and the Denitrification–Decomposition (DNDC) Model , 2020, Land.

[9]  Kaiyu Guan,et al.  STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product , 2020, Remote. Sens..

[10]  D. Lobell,et al.  Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive , 2020, Scientific data.

[11]  Xinyan Fan,et al.  Winter cover crops in Dutch maize fields: Variability in quality and its drivers assessed from multi-temporal Sentinel-2 imagery , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Shuguang Liu,et al.  Multi-environmental impacts of biofuel production in the U.S. Corn Belt: A coupled hydro-biogeochemical modeling approach , 2020 .

[13]  J. Delgado,et al.  Potential use of cover crops for soil and water conservation, nutrient management, and climate change adaptation across the tropics , 2020 .

[14]  Seungyub Lee,et al.  Adoption of Cover Crops by U.S. Soybean Producers , 2019, Journal of Agricultural and Applied Economics.

[15]  David B. Lobell,et al.  How much will precision nitrogen management pay off? An evaluation based on simulating thousands of corn fields over the US Corn-Belt , 2019, Field Crops Research.

[16]  R. T. Maruthi,et al.  DIVA-GIS based insight into geographical distribution and diversity spectrum of Indian sunnhemp (Crotalaria juncea L.) accessions suitable for diversified applications , 2019, LEGUME RESEARCH - AN INTERNATIONAL JOURNAL.

[17]  Pete Smith,et al.  A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity , 2019, Global change biology.

[18]  J. Arbuckle,et al.  Conditional Causal Mediation Analysis of Factors Associated With Cover Crop Adoption in Iowa, USA , 2018, Water Resources Research.

[19]  Edgar G. Hertwich,et al.  The growing importance of scope 3 greenhouse gas emissions from industry , 2018, Environmental Research Letters.

[20]  J. L. Gabriel,et al.  Weed density and diversity in a long-term cover crop experiment background , 2018, Crop Protection.

[21]  Haishun Yang,et al.  Do shallow water tables contribute to high and stable maize yields in the US Corn Belt? , 2018, Global Food Security.

[22]  Jian Peng,et al.  STAIR: A generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product , 2018, Remote Sensing of Environment.

[23]  David B. Lobell,et al.  Satellite detection of cover crops and their effects on crop yield in the Midwestern United States , 2018, Environmental Research Letters.

[24]  F. Miguez,et al.  Cover crops use in Midwestern US agriculture: perceived benefits and net returns , 2018, Renewable Agriculture and Food Systems.

[25]  P. Sikkema,et al.  Suppression of Glyphosate-Resistant Canada Fleabane (Conyza canadensis) in Corn with Cover Crops Seeded after Wheat Harvest the Previous Year , 2018, Weed Technology.

[26]  Damien Arvor,et al.  Remote Sensing and Cropping Practices: A Review , 2018, Remote. Sens..

[27]  F. Miguez,et al.  Partial Budgets for Cover Crops in Midwest Row Crop Farming , 2018 .

[28]  J. Blesh.  Functional traits in cover crop mixtures: Biological nitrogen fixation and multifunctionality , 2018 .

[29]  D. Mortensen,et al.  The state of the cover crop nation and how to set realistic future goals for the popular conservation practice , 2017, Journal of Soil and Water Conservation.

[30]  Nataliia Kussul,et al.  Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model , 2017 .

[31]  J. Tyndall,et al.  The trouble with cover crops: Farmers’ experiences with overcoming barriers to adoption , 2017, Renewable Agriculture and Food Systems.

[32]  M. Quemada,et al.  Using cover crops to mitigate and adapt to climate change. A review , 2017, Agronomy for Sustainable Development.

[33]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[34]  Christopher Daly,et al.  Mapping Atmospheric Moisture Climatologies across the Conterminous United States , 2015, PloS one.

[35]  Shi Li,et al.  Deriving Vegetation Phenological Time and Trajectory Information Over Africa Using SEVIRI Daily LAI , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[37]  M. Anderson-Wilk.  The gap between cover crop knowledge and practice , 2008, Journal of Soil and Water Conservation.

[38]  Khaled H. Hamed.  Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis , 2008 .

[39]  R. Weil,et al.  Thinking across and beyond disciplines to make cover crops pay , 2007 .

[40]  William Lane Austin,et al.  The Census of Agriculture , 1930 .