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 91.5% accuracy (.68 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 94% increase in 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 0.65% for maize and 0.35% 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.
In adapting US agriculture to the climate of the 21st century, a key unknown is whether cropping frequency may increase, helping to offset projected negative yield impacts in major production ...regions. Combining daily weather data and crop phenology models, we find that cultivated area in the US suited to dryland winter wheat-soybeans, the most common double crop (DC) system, increased by up to 28% from 1988 to 2012. Changes in the observed distribution of DC area over the same period agree well with this suitability increase, evidence consistent with climate change playing a role in recent DC expansion in phenologically constrained states. We then apply the model to projections of future climate under the RCP45 and RCP85 scenarios and estimate an additional 126-239% increase, respectively, in DC area. Sensitivity tests reveal that in most instances, increases in mean temperature are more important than delays in fall freeze in driving increased DC suitability. The results suggest that climate change will relieve phenological constraints on wheat-soy DC systems over much of the United States, though it should be recognized that impacts on corn and soybean yields in this region are expected to be negative and larger in magnitude than the 0.4-0.75% per decade benefits we estimate here for double cropping.
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.
Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. Spatially explicit crop-type information can be used to ...estimate crop areas for a variety of monitoring and decision-making applications such as crop insurance, land rental, supply-chain logistics, and financial market forecasting. However, there is no publically available spatially explicit in-season crop-type classification information for the U.S. Corn Belt (a landscape predominated by corn and soybean). Instead, researchers and decision-makers have to wait until four to six months after harvest to have such information from the previous year. The state-of-the-art research on crop-type classification has been shifted from relying on only spectral features of single static images to combining together spectral and time-series information. While Landsat data have a desirable spatial resolution for field-level crop-type classification, the ability to extract temporal phenology information based on Landsat data remains a challenge due to low temporal revisiting frequency and inevitable cloud contamination. To address this challenge and generate accurate, cost-effective, and in-season crop-type classification, this research uses the USDA's Common Land Units (CLUs) to aggregate spectral information for each field based on a time-series Landsat image data stack to largely overcome the cloud contamination issue while exploiting a machine learning model based on Deep Neural Network (DNN) and high-performance computing for intelligent and scalable computation of classification processes. Experiments were designed to evaluate what information is most useful for training the machine learning model for crop-type classification, and how various spatial and temporal factors affect the crop-type classification performance in order to derive timely crop type information. All experiments were conducted over Champaign County located in central Illinois, and a total of 1322 Landsat multi-temporal scenes including all the six optical spectral bands spanning from 2000 to 2015 were used. Computational experiments show the inclusion of temporal phenology information and evenly distributed spatial training samples in the study domain improves classification performance. The shortwave infrared bands show notably better performance than the widely used visible and near-infrared bands for classifying corn and soybean. In comparison with USDA's Crop Data Layer (CDL), this study found a relatively high Overall Accuracy (i.e. the number of the corrected classified fields divided by the number of the total fields) of 96% for classifying corn and soybean across all CLU fields in the Champaign County from 2000 to 2015. Furthermore, our approach achieved 95% Overall Accuracy by late July of the concurrent year for classifying corn and soybean. The findings suggest the methodology presented in this paper is promising for accurate, cost-effective, and in-season classification of field-level crop types, which may be scaled up to large geographic extents such as the U.S. Corn Belt.
•An in-season, field-level classification system for corn and soybean is presented.•Time series information of Landsat and the field boundary from CLU are used.•A case study has been demonstrated at a county in the US Corn Belt for 2000–2015.•Shortwave infrared bands provide most useful info for classifying corn and soybean.•95% classification accuracy can be achieved by late July of the concurrent year.
New advances in satellite data acquisition and processing offer promise for monitoring agricultural lands globally. Using these data to estimate crop yields for individual fields would benefit both ...crop management and scientific research, especially for areas where reliable ground-based estimates are not currently made. Here we introduce a generalized approach for mapping crop yields with satellite data and test its predictions for yields across more than 17,000 maize fields and 11,000 soybean fields spanning multiple states and years in the Midwestern United States. The method, termed SCYM (a scalable satellite-based crop yield mapper), uses crop model simulations to train statistical models for different combinations of possible image acquisition dates, and these are then applied to Landsat and gridded weather data within the Google Earth Engine platform, where the Landsat is composited to find the “best” dates of observations on a pixel-by-pixel basis. SCYM estimates successfully captured a significant fraction of maize yield variation in all state-years, with a range of 14–58% and an average of 35% for this particular study region and crop. Similar results were observed for soybean, with an average of 32% of yield variation captured. The multi-year yield estimates were also used to examine the temporal persistence of yield advantages for the top yielding fields in different counties, which is one measure of how important factors such as farmer skill are in explaining yield gaps. The strength of the SCYM approach lies in its ability to leverage physiological knowledge embedded in crop models to interpret satellite observations in a scalable way, as it can be readily applied to new crops, regions, and types and timing of remote sensing observations without the need for ground calibration.
•A new approach to mapping crop yields is presented.•Estimates are made within Google's Earth Engine, allowing broad scale application.•Field-level estimates are tested against over 29,000 ground-based records.
Core Ideas
Analysis of 748,374 yield records showed a 4.3% yield penalty for continuous corn.
Corn yield penalties were more severe in areas with low moisture and low yields.
Continuous soybean ...showed a 10.3% yield penalty, worse in low‐yielding years.
Corn yield penalties grew with up to 3 yr of continuous cropping, but not more.
Soybean penalties increased monotonically with number of years continuously cropped.
The effects of crop rotations on yields have historically been assessed with field trials, but new datasets offer an opportunity to evaluate these effects using data from commercial farmers’ fields. Here we develop a unique dataset of 748,374 joint observations of field‐level yields, crop histories, and soil and weather conditions across the U.S. Midwest to empirically evaluate crop rotations. For rainfed fields, we found an average continuous corn (Zea mays L.) yield penalty (CCYP) of 4.3% and continuous soybean Glycine max (L.) Merr. yield penalty (CSYP) of 10.3% during the 2007 to 2012 growing seasons. The CCYP is greater in locations with low moisture, while the CSYP shows the opposite pattern. Relatedly, irrigation decreases the CCYP but not the CSYP. Both penalties increased with the number of years a field had been continuously cropped, and while the CCYP leveled off after 3 yr in corn, the CSYP showed significant increases out to the (very rare) 5‐yr continuous soybean sequence. An analysis of weather, soil, and planting date interactions with the CCYP and CSYP suggests that timely planting, favorable soil‐climate, and warm early and late‐season minimum temperatures correlate with reductions in the CCYP, while dry conditions and less favorable soil‐climate correlate with reductions in the CSYP. The results of this study not only help refine estimates of rotation effects in commercial fields, but also shed light on the relationships between rotation effects and other factors, thereby offering insight into potential causal mechanisms.
Zusammenfassung
Die Popularität von Investitionen in Gold über Exchange Traded Commodities (ETC) hat u. a. durch die steigendende Goldnachfrage aufgrund der Covid-19-Pandemie zugenommen. Der ...vorliegende Beitrag quantifiziert anhand von statistischen Kennzahlen und Verfahren die Replikationsgüte von ausgewählten Gold-ETC in Relation zum Gold-Kassakurs. Es wird deutlich, dass es den entsprechenden Wertpapieren nicht durchwegs gelingt, die Goldentwicklung nachzubilden. Außerdem steht die Relevanz der Steuergesetzgebung bei Privatanlegern in Deutschland im Fokus. Hierbei werden anhand eines Difference-in-Differences-Ansatzes mögliche Effekte eines steuerlichen Gesetzesvorhabens auf die Renditen sowie das Investorenverhalten bewertet. Die Grundlage bildet eine ursprünglich geplante Gesetzesänderung für die Besteuerung von Einkünften aus ETC eines Privatanlegers im Zuge des Jahressteuergesetzes 2020.
Die Popularität von Investitionen in Gold über Exchange Traded Commodities (ETC) hat u. a. durch die steigendende Goldnachfrage aufgrund der Covid-19-Pandemie zugenommen. Der vorliegende Beitrag ...quantifiziert anhand von statistischen Kennzahlen und Verfahren die Replikationsgüte von ausgewählten Gold-ETC in Relation zum Gold-Kassakurs. Es wird deutlich, dass es den entsprechenden Wertpapieren nicht durchwegs gelingt, die Goldentwicklung nachzubilden. Außerdem steht die Relevanz der Steuergesetzgebung bei Privatanlegern in Deutschland im Fokus. Hierbei werden anhand eines Difference-in-Differences-Ansatzes mögliche Effekte eines steuerlichen Gesetzesvorhabens auf die Renditen sowie das Investorenverhalten bewertet. Die Grundlage bildet eine ursprünglich geplante Gesetzesänderung für die Besteuerung von Einkünften aus ETC eines Privatanlegers im Zuge des Jahressteuergesetzes 2020.
Zusammenfassung
Das deutsche Steuerrecht bietet für private Immobilieninvestoren zwei grundsätzliche Optionen der Vermögenszuordnung: Die Investition kann dem Privatvermögen zugeordnet oder über eine ...(im Privatvermögen gehaltene) vermögensverwaltende Personen- oder Kapitalgesellschaft organisiert werden. Mit den Alternativen sind jeweils unterschiedliche steuerliche Konsequenzen verbunden. Bei der Zuordnung zum Privatvermögen kann der Steuerpflichtige nach Ablauf einer Haltefrist von einer nicht steuerbaren Veräußerung profitieren. Der Vorteil einer vermögensverwaltenden Immobiliengesellschaft liegt dagegen in einer begünstigten Besteuerung der laufenden Einkünfte, da hier unter bestimmten Voraussetzungen eine vollständige Freistellung von der Gewerbesteuer beantragt werden kann. Der vorliegende Beitrag analysiert anhand einer modelltheoretischen Untersuchung die Vorteilhaftigkeit der Investitionsformen im Hinblick auf die ertragsteuerliche Gesamtbelastung. Hierzu werden die beiden Investitionsformen in Abhängigkeit von unterschiedlichen Parametern (Investitionszeitraum, Finanzierungsform, Wertsteigerung, Mietrendite etc.) mit Hilfe der Kapitalwertmethode gegenübergestellt. Der Aufsatz dient so als Entscheidungshilfe bei privaten Immobilieninvestitionen.
As our species moves deeper into an era in which we have an increasing influence over the climate and health of our planet, it is important to examine the likely effects of our activities as well as ...the tools we can use to adapt to coming changes. Occupying more land area than any other human activity and employing biological systems vulnerable to extreme heat, agriculture is chief amongst vital industries impacted by a changing climate. Previous work has focused on those impacts, finding potentially drastic effects for countries like the United States, the world's largest producer of maize and soybean, whose major production regions are fortuitously positioned near a climate optimum for those key crops. This dissertation examines various specific practices that could be deployed to build resilience and prevent the degradation of the U.S. agricultural system under potential 21st century climate regimes. Double cropping, crop rotation, cover cropping, and irrigation all have their place as potential adaptations. This work uses mechanistic and statistical models as well as newly available datasets and data processing methodologies to explore the expansion of suitability, the spatial variability, the yield effects, and the temporal trends in adoption of these practices respectively.Chapter 1 runs mechanistic phenological models for winter wheat and soybean under recent and future climate scenarios, finding that even small increases in expected temperature and growing season length can lead to large increases in double crop suitability. These changes in suitability have already been occurring over the last few decades and appear poised to accelerate along with our changing climate. While the increase the area suitable for this cropping practice is large, especially later in this century, the implied increase in agricultural production that accompanies it is substantially smaller than potential yield losses.Building on the first chapter but exploring inter-yearly crop rotation patterns versus intra-yearly patterns, Chapter 2 uses a large dataset of field-level yields to examine the yield penalties seen in continuous maize and soybean fields. Yield loss from continuous cropping found in the model was broadly consistent with findings from field trials.Additionally, the spatial breadth and temporal depth of the dataset enabled us to find that areas with large negative yield anomalies see worse yield penalties for continuous cropping, as do soybean crops grown in areas or years with low early season vapor pressure deficit and maize crops grown in areas or years with low early or late minimum temperatures.Chapter 3 examines another promising crop configuration with potential to serve as a climate adaptation; cover crops. In it, we build a cover crop classifier based on remotely sensed data and cross the classifier's output with already existing soil quality as well as maize and soybean yield maps. The raw classifier output shows that, as intended, cover crops are more likely to be found on poorer soils in the Midwest. Contrary to other sources, however, yield benefits for adopters of the practice are quite mild, even after a number of years following the practice. Combining this conclusion with the currently high cost of cover crop adoption, continued expansion of government funding for cover cropping appears necessary to propagate the practice.Chapter 4 uses methods built in Chapter 3, but with a different aim in mind—mapping irrigation and its adoption in two key states in the western U.S. maize-soybean belt. Here we find that irrigation has indeed been on the increase over the last decade and a half in Nebraska, though no definitive trends were seen in Iowa. The increase in Nebraska does not appear to be driven by changes in the difference between irrigated and dryland yields, and irrigation adoption was more likely to be undertaken on higher quality land from 2003-2017 versus earlier in the practice's history.
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