Fungicide efficacy on tar spot and yield of corn in the Midwestern United States

Tar spot of corn caused by Phyllachora maydis has recently led to significant yield losses in the eastern corn belt of the Midwestern United States. Foliar fungicides containing quinone outside inhibitors(QoI), demethylation inhibitors(DMI), and succinate dehydrogenase inhibitors(SDHI) are commonly used to manage foliar diseases in corn. To mitigate the losses from tar spot thirteen foliar fungicides containing single or multiple modes of action (MOA/FRAC groups) were applied at their recommended rates in a single application at the standard tassel/silk growth stage timing to evaluate their efficacy against tar spot in a total of eight field trials in Illinois, Indiana, Michigan, and Wisconsin during 2019 and 2020. The single MOA fungicides included either a QoI or DMI. The dual MOA fungicides included a DMI with either a QoI or SDHI, and fungicides containing three MOAs included a QoI, DMI, and SDHI. Tar spot severity estimated as the percentage of leaf area covered by P. maydis stroma of the non-treated control at dent growth stage ranged from 1.6 to 23.3% on the ear leaf. Averaged across eight field trials all foliar fungicide treatments reduced tar spot severity, but only prothioconazole+trifloxystrobin, mefentrifluconazole+pyraclostrobin+fluxapyroxad, and mefentrifluconazole+pyraclostrobin significantly increased yield over the non-treated control. When comparing fungicide treatments by the number of MOAs foliar fungicide products that had two or three MOAs decreased tar spot severity over not treating and products with one MOA. The fungicide group that contained all three MOAs significantly increased yield over not treating with a fungicide or using a single MOA.

Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a Rainfall-Runoff Model in the U.S. Corn Belt

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

A physiological signal derived from sun-induced chlorophyll fluorescence quantifies crop physiological response to environmental stresses in the U.S. Corn Belt

Sun-induced chlorophyll fluorescence (SIF) measurements have shown unique potential for quantifying plant physiological stress. However, recent investigations found canopy structure and radiation largely control SIF, and physiological relevance of SIF remains yet to be fully understood. This study aims to evaluate whether the SIF-derived physiological signal improves quantification of crop responses to environmental stresses, by analyzing data at three different spatial scales within the U.S. Corn Belt, i.e., experiment plot, field, and regional scales, where ground-based portable, stationary and space-borne hyperspectral sensing systems are used, respectively. We found that, when controlling for variations in incoming radiation and canopy structure, crop SIF signals can be decomposed into non-physiological (i.e., canopy structure and radiation, 60~82%) and physiological information (i.e., physiological SIF yield, ΦF, 17~31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether ΦF indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that ΦF responded to the proxy for physiological stress (partial correlation coefficient, rp=0.40, p<0.001) while non-physiological signals of SIF did not respond (p>0.1). The field-scale ΦF data showed water deficit stress from the comparison between irrigated and rainfed fields, and ΦF was positively correlated with canopy-scale stomatal conductance, a reliable indicator of plant physiological condition (correlation coefficient r=0.60 and 0.56 for an irrigated and rainfed sites, respectively). The regional-scale data showed ΦF was more strongly correlated spatially with air temperature and VPD (r=0.23 and 0.39) than SIF (r=0.11 and 0.34) for the U.S. Corn Belt. The lines of evidence suggested that ΦF reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of ΦF is spatially scalable. Utilizing ΦF for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.

Corn Belt as an Enterprise-Naming Custom in the United States

Enterprises, be they for-profit businesses or not-for-profit organizations, require names to differentiate themselves from other entities. Over a span of more than a hundred years entrepreneurs, corporate boards, and organizational founders have chosen to use Corn Belt or some spelling variant to identify their enterprises, perhaps believing that naming after this admired agricultural region will bless their enterprise with its longevity, productivity, and favorable image. This essay looks at the beginnings of Corn Belt as a vernacular term for an agricultural region, picks up the earliest uses of Corn Belt as an inspiration for enterprise names, tracks Corn Belt enterprises through time at one of the core locations of the naming practice, and presents the enterprises that in 2020 greeted the public with Corn Belt in their names.

Nitrous oxide emissions from agricultural soils challenge climate sustainability in the US Corn Belt

Agricultural landscapes are the largest source of anthropogenic nitrous oxide (N2O) emissions, but their specific sources and magnitudes remain contested. In the US Corn Belt, a globally important N2O source, in-field soil emissions were reportedly too small to account for N2O measured in the regional atmosphere, and disproportionately high N2O emissions from intermittent streams have been invoked to explain the discrepancy. We collected 3 y of high-frequency (4-h) measurements across a topographic gradient, including a very poorly drained (intermittently flooded) depression and adjacent upland soils. Mean annual N2O emissions from this corn–soybean rotation (7.8 kg of N2O–N ha−1⋅y−1) were similar to a previous regional top-down estimate, regardless of landscape position. Synthesizing other Corn Belt studies, we found mean emissions of 5.6 kg of N2O–N ha−1⋅y−1 from soils with similar drainage to our transect (moderately well-drained to very poorly drained), which collectively comprise 60% of corn–soybean-cultivated soils. In contrast, strictly well-drained soils averaged only 2.3 kg of N2O–N ha−1⋅y−1. Our results imply that in-field N2O emissions from soils with moderately to severely impaired drainage are similar to regional mean values and that N2O emissions from well-drained soils are not representative of the broader Corn Belt. On the basis of carbon dioxide equivalents, the warming effect of direct N2O emissions from our transect was twofold greater than optimistic soil carbon gains achievable from agricultural practice changes. Despite the recent focus on soil carbon sequestration, addressing N2O emissions from wet Corn Belt soils may have greater leverage in achieving climate sustainability.