Corrigendum: Satellite detection of cover crops and their effects on crop yield in the Midwestern United States (2018 Environ. Res. Let. 13 064033)

The original raw dataset used to generate this work contained a number of duplicate entries—roughly 7% of the total farm fields. The substantive majority of these were from one large farm that had conducted their operations in a way that caused duplication as a side effect in our data generation process. Unfortunately, as the error was in the raw dataset, its correction required a re-run of the entire data pipeline, resulting in numerous small downstream changes. With respect to the most important numbers, the accuracy of the classifier went down slightly from 91.5% to 91.2% measured in absolute terms but increased from 0.68 to 0.74 measured by kappa. The trend in cover cropped acres grew slightly stronger, and the yield effects in maize and soybean moved from 0.65% to 0.71% and 0.35% to 0.29% respectively. None of the overall conclusions of the work have materially changed. Below, we provide all changes to the applicable sections of the original manuscript in bold underscore (or strikethrough) where applicable, in addition to modified versions of the corresponding figures and supplementary materials.


Abstract
The practice of planting winter cover crops has seen renewed interest as a solution to environmental issues with the modern maize-and soybean-dominated row crop production system of the US Midwest. We examine whether cover cropping patterns can be assessed at scale using publicly available satellite data, creating a classifier with 91.2% accuracy (0.74 kappa). We then use this classifier to examine spatial and temporal trends in cover crop occurrence on maize and soybean fields in the Midwest since 2008, finding that despite increased talk about and funding for cover crops as well as a more than doubling of cover crop acres planted from 2008-2016, increases in winter vegetation have been more modest. Finally, we combine cover cropping with satellite-predicted yields, finding that cover crops are associated with low relative maize and soybean production and poor soil quality, consistent with farmers adopting the practice on fields most in need of purported cover crop benefits. When controlling for invariant soil quality using a panel regression model, we find modest benefits of cover cropping, with average yield increases of 0.71% for maize and 0.29% for soybean. Given these slight impacts on yields, greater incentives or reduced costs of implementation are needed to increase adoption of this practice for the majority of maize and soybean acres in the US.

Data processing
Raw data on cover cropping status by field and year from 24 different farm operators and/or landowners or agencies across eight states were acquired for the purposes of the study. While cash crop production years from 2007-2018 were represented, 82% of the 2312 total field-years came from the penultimate three years of the interval.
K Once the final imagery was compiled, pixels for cover-cropped and non-cover-cropped field areas were sampled for each image. In order to avoid the potential for mixed pixels across classes, sampled pixels came only Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. from areas greater than 30 meters (one Landsat pixel) from a field boundary. Data from states bordering study area (e.g. Missouri and Pennsylvania) and 2018 were dropped at this step as well. 63 443 hectare-years of data (705 082 pixels) survived the buffer.
K Data from 2035 unique field-years survived all of these filters.

Classification
The classifier built for determining cover crop presence/ nonpresence had an out-of-sample accuracy of 91.2%. Given the high prevalence of non-cover cropped fields, a more appropriate measure of performance, Cohen's kappa, registered (0.74well above the middle of the interval of 'substantial agreement' given by [47]. This compares favorably to the EWG [3] work that reported an accuracy of 72% with no kappa released. Variable importance plots for the identically parameterized classifier built using the R programming language [41] ranked the number of GDDs from the beginning of the off-season to the image date of the maximum NDVI image as the most important variable based on the mean decrease in Gini coefficient criterion.

Trends in cover cropping over time K
Running the classifier across imagery from the 2008-2016 crop production years resulted in the pattern shown in figure 1(c). Here, the increase in cover cropping is apparent, with the lowest amount of cover crop area in 2008 at approximately 2.3 million ha, or 4.3% of the total acres planted to maize and soybean. The highest number of cover crop acres was in 2016 with 5.0 million planted to cover crops, or roughly 8.8% of the maize and soybean area. The trend in cover crop plantings over this nine-year period is significant at p<0.01. Concern with multiple-year adoption of the practice led to a similar analysis for pixels in which at least two continuous years are cover cropped, showing a similar pattern with an increase from 0.51 to 1.09 million hectares in that category, or 1.0%-1.9% of cropped area (trend significant at p< 0.01). K Differences between cover cropped and non-covercropped fields Using a linear model with controls for weather and soil quality and a time trend, cover cropped areas were associated with 0.18 Mg ha −1 lower yields for maize and 0.05 Mg ha −1 lower yields for soybean overall relative to conventional fields in the eight states for maize and three for soybean where SCYM-generated yields were available. Results of similar models built for each individual year are shown in panel A of figure 3. For maize, the largest yield differences were seen for the crops harvested in 2013 and 2015 with yields relatively lower by as much as 0.35 Mg ha −1 . For soybean, cover crop yields were relatively lowest in 2014 and 2009 with 0.14 and 0.11 Mg ha −1 differences observed. The gap between cover cropped and conventional yields increased with year for maize (significant at p< 0.05), but not for soybean. Cover cropping was also associated with poorer quality soils, as seen in panel B. Pixels in the lowest decile for soil quality (as measured by the NCCPI) were 37% and 71% more likely than those at the median to be cover cropped for maize and soybean respectively. Pixels in the highest decile of soil quality were 26% and 46% less likely than the median maize and soybean pixel to be cover cropped. This is despite the fact that soil quality was not included in the classifier used to determine presence/ non-presence of cover crops. The pattern illustrated here even carries over into states with high general soil quality (e.g. Iowa).
Effects of cover cropping on yield by years cover cropped While NCCPI serves as the best currently available nationwide soil quality index, it cannot account for soil properties such as compaction or nutrient depletion which vary at a scale far below what is mapped in SSURGO [49]. The index also cannot account for farmer practices which are likely to vary at the field or subfield level. The results of the panel regression created to eliminate the effects of such latent variables are shown in figure 4. Overall, this model showed a 0.71% increase for maize yields and a 0.29% increase for soybean yields in areas that used cover crops for at least a single year.
The model used here also allowed for the examination of trends over space, though not over time. Panel A of figure 4 shows that on maize fields, cover crops were  most beneficial in Minnesota, Wisconsin and Michigan where benefits were 0.31, 0.08, and 0.08 Mg ha −1 respectively and least beneficial in Indiana and South Dakota with 0.03 and 0.19 Mg ha −1 decreases in yields with cover cropping. As shown in panel C, for soybean, benefits or losses were small regardless of state ranging from a loss of 0.01 Mg ha −1 in Indiana to a gain of 0.04 Mg ha −1 in Iowa.
Panels B and D of the figure show model outcomes comparing areas that had been cover cropped for at least three continuous years to areas not cover cropped. Here Michigan and Ohio show some of the largest benefits for maize, as illustrated in panel B; however, Indiana, Wisconsin, Iowa and Minnesota show yield decreases with long-term cover cropping (South Dakota was excluded here as less than 25 ha of maize area had followed the practice for three or more years in the dataset). For soybean, benefits increase with three years of cover cropping in Indiana, Illinois and Iowa with 0.03, 0.08 and 0.11 Mg ha −1 yield increases over effects seen with one year of cover cropping. In this figure in general, soybean yield effects are only shown in three states, reflecting the limited spatial extent of SCYM-generated yields for that crop.

K
The real test of the effects of cover cropping on yields is whether cover cropped years of the same exact areas have higher yields than non-cover cropped years, controlling for weather. Here, cover crops show modest benefits of 0.71% and 0.29% for maize and soybean.
These benefits are comparable to [52] which found a 1.3% increase in maize yields, however, they are lower than the 3.8% soybean yield benefit reported. These numbers, however, were from a self-reported survey of growers following the practice so respondents may be vulnerable to a choice supportive bias [53] of cover crop adopters.
Cover cropping for multiple years does not appear to add a clear direction to the effects of the practice for maize. Two of the four states that benefit most with one year of cover cropping are also in the top four that benefit the most with three years, and large losses from cover cropping become apparent in other areas, potentially due to imperfect controls. However, for soybean, longer periods of cover crop adoption did appear to increase benefits. Overall, the data one both one-and three-year yield effects on maize and soybean appear to indicate a lack of generalized economically large yield benefits to cover crops in the Midwest.