Mapping Conservation Management Practices and Outcomes in the Corn Belt Using the Operational Tillage Information System (OpTIS) and the Denitrification–Decomposition (DNDC) Model

Abstract

Identifying and quantifying conservation-practice adoption in U.S. cropland is key to accurately monitoring trends in soil health regionally and nationally and informing climate change mitigation efforts. We present the results of an automated system used across 645 counties in the United States Corn Belt from 2005 to 2018, mapped at field-scale and summarized for distribution at aggregated scales. Large-scale mapping by OpTIS (Operational Tillage Information System), a software tool that analyzes remotely sensed data of agricultural land, provides trends of conservation tillage (defined as >30% residue cover), cover cropping, and crop rotations, while modeling by DNDC (Denitrification–Decomposition), a process-based model of carbon and biogeochemistry in soil, provides estimates of the ecosystem outcomes associated with the changes in management practices mapped by OpTIS. Ground-truthing data acquired via OpTIS mobile, a roadside field-surveying app, were used for verification in 30 counties. OpTIS results for the Corn Belt show adoption of cover crops after planting corn and soy increased from 1% to 3% of the mapped area when comparing 2006 to 2018. Comparison of trends for conservation tillage use from 2006 to 2018 shows a slight decrease in conservation tillage adoption, from 46% to 44%. Results from DNDC show these soils sequestered soil organic carbon (SOC) at an area-weighted mean change in SOC (dSOC) rate of 161 kgC/ha/year. Comparatively, in a scenario modeled without the adoption of soil health management practices, the same soils would have lost SOC at an area-weighted rate of −65 kgC/ha/year. As many factors affect changes to SOC, including climate and initial SOC in soils, modeling counterfactual scenarios at the field scale demonstrates outcomes of current soil health management in comparison to regional management practices and best management practices, with respect to SOC sequestration. Regional trends in adoption rates of conservation agriculture and resulting soil health implications are of great use for a wide range of stakeholders. We demonstrate the capability of OpTIS remote sensing to deliver robust, large-scale, multi-sensor, ground-verified monitoring data of current and historical adoption of conservation practices, and of DNDC process-based modeling to provide assessments of the associated environmental outcomes across regions in U.S. cropland.

Recent rapid increase of cover crop adoption across the U.S. Midwest detected by fusing multi-source satellite data

“…Meanwhile, the changes in cover crop adoption of counties in the U.S. Midwest from our prediction are highly consistent with those reported in the USDA NASS Census of Agriculture in 2017 (Figure S5). In the past two decades, the average annual increase rates for cover crop adoption are around 0.20%/yr and similar to previous studies (Hagen et al, 2020). The rapid increase in cover crop adoption in recent years is highly related to the increase in state and federal investment for cover crop practices or programs during the same period with an R 2 of 0.91 (Wallander et al, 2021).…”

“…To further explore the benefits of temporally dynamic thresholds rather than the commonly used fixed threshold (Amy Logan & Robin McNeely, 2021;Hagen et al, 2020;Rundquist & Carlson, 2017), Figs. 2(D) and (E) illustrate the validation results of predicted cover crop adoption of each county in 2017.…”

Assessment of the Spatial and Temporal Patterns of Cover Crops Using Remote Sensing

“…This capability has recently been bolstered by the availability of a large volume of freely available data from frequently revisiting medium-resolution satellites such as Landsat (30 m) and Sentinel (10 m). Since green and healthy vegetations have higher reflectance in near-infrared (NIR) than other spectral regions, prior studies have leveraged visible and NIR (VIS NIR) satellite imagery to understand, classify and monitor winter cover crops at a landscape scale [13][14][15][16][17]. For instance, Hively et al [17] assessed cover crop areas in Chesapeake Bay watershed in southeastern Pennsylvania between 2010 and 2013 using one image per winter collected from Landsat and SPOT satellites.…”

“…Such single seasonal composites have limited use when it comes to understanding cover crop performance, including timing of their establishment and growth, which are often influenced by weather, management practices, and field conditions. Considering these limitations, Hagen et al [13] used a time series of NDVI data from November through July every year between 2005 to 2018 to estimate five major cover crop categories (i.e., winter kill, full cover, spring emergent, winter wheat, and not covered) in the midwestern United States. This study compared timing and intensity of cover crops' greenness with NDVI thresholds set at the HUC8 scale to determine cover crop categories.…”

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

“…The majority of recent mapping approaches provide rasterized soil indicators that are essential for accurate modelling of ecosystem processes, such as carbon exchange [18], specialization towards informed arable farming [19], and for long-term ecological monitoring [20]. Figure 3 illustrates the most important soil properties considered in this study.…”