Impacts of the COVID-19 Economic Slowdown on Soybean Crop Yields in the United States

<jats:title>Abstract</jats:title>         <jats:p>It is without question that the COVID-19 pandemic has taken its toll on the U.S. economy. Stay-at-home orders led to reduced vehicular traffic and widespread declines in anthropogenic emissions, such as nitrogen oxides (NOx) and surface ozone (O3). This study is the first to explore the potential consequences of O3 changes resulting from the economic shutdown in the United States on soybean crop yields for 2020. The pandemic’s impact on surface O3 is quantified using the NOAA’s National Air Quality Forecasting Capability (NAQFC), which is based on the Community Multi-Scale Air Quality (CMAQ) model for May-July 2020. The “would-be”, 2020 level business-as-usual (BAU) emissions are compared to a simulation that uses representative COVID-19 (C19) emissions. For each emissions scenario, crop exposures are calculated using the AOT40 cumulative exposure index and then combined with county-level soybean production totals to determine regional yield losses. Exposure changes ranged between -0.8 - 1.25 ppmVhr-1. It was further shown that increased exposures (0.5 – 1.25 ppmVhr-1) in the Southeast counteracted decreased exposures (0.8 – 0.5 ppmVhr-1) in other regions. As a result, corresponding yield improvements counteracted yield losses around the Mississippi River Valley and allowed for minimal soybean production loss totaling $4.7 million over CONUS.</jats:p>

section_content: emissions changes. The impacts of COVID-19 related emission changes on soybean crop yields during the O 3 photochemical season in the U. S. is currently unknown. 

In this paper, we focus on the effects of the COVID-19 pandemic on air quality and agricultural production in the U. S. This study utilizes NOAA's National Air Quality Forecasting Capability (NAQFC), based on the Community Multiscale Air Quality Model (CMAQ), to predict the changes in O 3 concentration brought about by the pandemic 5. We then use the NAQFC predicted O 3 concentration changes and dose-response function relationships to quantify the pandemic-related changes on soybean crop yields for May-July (MJJ) 2020.

section_content: Changes in ground-level O 3. . The differences in hourly O 3 concentrations between a "business-as-usual" (BAU, i. e., the "would be" 2020 emissions without COVID-19 shutdowns) and actual COVID-19 (C19) scenarios are calculated and averaged for two-week periods for MJJ 2020 (Fig. 1 ; see Campbell et al. 5 for scenario details). From MJJ, there are notable increases in O 3, particularly in the Lower Midwest and Southeastern U. S. associated with NOx emissions increases among the rural NOx-limited regions (e. g., Midwest and Southeast). This relationship is not apparent for the major urban centers, which are VOC-limited during this time as suggested by the model 5. A clear example is the area surrounding Indianapolis (VOC-limited), which shows increased O 3 in May compared to the surrounding regions which show decreases in O 3. Outside of the urban center, the region is more NO x -limiting allowing decreases in NO x to drive a decrease in O 3. Regarding the spatial variability of O 3 changes, the widespread increases in the Southeast regions were demonstrated to be in qualitative agreement with the U. S. EPA AirNow network observations (https:// www. airnow. gov/) and simulated increases by NASA's GEOS Composition Forecasting (GEOS-CF) system (see Campbell et al. 5 for spatial variability). Increases can be attributed to rebounding emissions trends from MJJ when states began to lift restrictions 12. In addition, shifts to cleaner vehicle fleets under the BAU scenario may have contributed to fewer emissions compared to C19's shift to heavy-duty truck traffic during morning rush 5. In other words, trucks played a larger role in the C19 scenario than in the BAU scenario. It is suspected that heavy duty trucks resulted in increased emissions due to increases in online consumption which continued from the later stages of the pandemic into the photochemical O 3 season. Furthermore, under the BAU scenario, there was an expected 3-5% decrease in emissions due to the overturning of the vehicle fleet but COVID-19 interrupted this. It is important to note the uncertainty present due to background NO 2 and natural variability which could play a role here as well (see "Discussion"). The regions along the Mississippi River Valley (e. g., Lower Midwest and Southeast), which were characterized by O 3 increases, are of interest for this study since they occur throughout much of the soybean-producing region. 

Changes in crop exposures. In both the BAU and C19 cases, highest exposures (based on the AOT40 metric; see "Methods" section) were concentrated in the Southwest regions of the U. S. with maximums occurring over southern California. Exposure differences between BAU and C19 are summarized by region in Table 1. The change in exposures induced by C19 are depicted in Fig. 2. The Western region experienced the maximum changes due to the emission changes with exposures decreasing by as much as 1. 5 ppmVhr -1 in Central/Southern California and > 1. 5 ppmVhr -1 in Utah. However, these regions do not contain soybean crops so they can be disregarded. The focus is on exposure changes occurring in the Midwest and Southeast regions which exhibit variable regional characteristics. In the Southeast, exposures both increased and decreased depending on the region/ state. For example, in South Carolina and North Carolina exposures decreased (blue areas) by approximately 0. 8 ppmVhr -1. Meanwhile, in the areas around Arkansas, Tennessee, and Kentucky, exposures increased (red areas) between 0. 5 and 1. 25 ppmVhr -1. The Midwest exhibited mainly decreased exposures (~ 0. 75 ppmVhr -1 for Ohio, Indiana, Michigan and ~ 0. 5 ppmVhr -1 for South Dakota and North Dakota). It is important to note, under the C19 emissions changes, the soybean-producing regions of the United States experienced nearly equivalent increases and decreases in O 3 exposures (ranging between -0. 8 and 1. 25 ppmVhr -1 ) that were regionally dependent (see Fig. 2 ) and nearly characteristic of the O 3 changes determined for MJJ (see Fig. 1 ). 

Changes in yield loss. Differences in C19 and BAU soybean yield losses are presented in Table 1 by region. 

Yield loss changes are nearly reflective of the exposure changes (see Figs. 2 and 3 ). Under C19, yield losses were significantly heightened (20 K Bu-110 K Bu) in counties along the Mississippi River, with individual counties in Arkansas and Illinois experiencing up to 80 K Bu and 110 K Bu in yield losses, respectively. It is important to note maximum yield losses occurred in counties with the highest production totals. Throughout the rest of the Midwest, yield improvements (blue areas) of 10 K Bu-40 K Bu are evident which offset the increases. The total U. S. soybean production reduction (%) in Table 2. represents the fraction of total yield loss to the total annual production. As a result, overall soybean production under the BAU and C19 scenarios was reduced by approximately 5. 86% and 5. 84% for MJJ 2020, respectively, an approximate 4% improvement from production losses since 2005 11. These results are consistent with historical analyses, where production loss from O 3 in the U. S. for 1980-2011 is estimated to have ranged between 4 and 6% on average 13. In addition, they are consistent with Seltzer et al. 14 (AOT40 RYL from soybean equivalent to 4. 8% in 2015), Lobell et al. 15 (5% average total soybean yield losses over last two decades), Da et al. 16 In this work, it is important to note meteorological effects are not separated from Campbell et al. 5 and as a result, meteorology may be a strong controlling factor over COVID-19 emissions changes on O 3 concentration changes (and thus exposures) between 2019 and 2020. Isolating the meteorological vs. emissions impact on the COVID-19 related O 3 changes is beyond the scope of this study, however state-level natural variability factors indicate less natural variability for the months of March-June 2020 with a larger contribution from natural variability for the later summer months (e. g., July-September). Larger contributions in natural variability indicate there are strong meteorological drivers of the NO 2 precursor changes. This indicates O 3 concentrations may be less impacted by the COVID-19 emissions changes for this period which in part agrees with Goldberg et al. 18. 

There are minimal differences present between the BAU and C19 yield loss, given nonlinearity in the C19 O 3 concentration and exposure changes throughout the soybean-producing region for MJJ 2020. The regional decreases and increases in O 3 exposures and yield losses work to offset one other. As a result, over CONUS, yield loss was slightly improved, by 0. 02%, under the C19 scenario. The resulting economic effects of the changes in yield losses are summarized in Table 3, where soybean yield loss amounts were multiplied by the price in U. S. 13 (annual soybean losses of $2 billion for the 31-year period) and Da et al. 16 (average annual revenue losses of $1. 2 billion for 1980-2015). A closer examination reveals a slightly improved production loss, amounting to $6. 5 million, under C19 compared to the BAU emissions projections for 2020.
section_name: Results

section_content: This study quantifies the impacts of changes in ground-level O 3 due to the COVID-19 (C19) pandemic on soybean crop yields for MJJ 2020. Compared to the "business-as-usual" (BAU) scenario, there were notable increases in O 3 exposures which occurred in the Mississippi River Valley and southeast U. S., which can be attributed to increased NOx concentrations throughout the southern U. S. in July. The soybean-producing regions of CONUS saw regionally dependent changes in O 3 exposures that were reflective of concentration changes shown for MJJ 2020. The Southeast and Midwest regions saw both increases and decreases in exposures (AOT40) that were equivalent in magnitude. Yield losses are reflective of these exposure changes with select counties in Arkansas and Illinois seeing increased yield losses up to 110 K Bu. Over CONUS, it is shown yield improvements counteracted by yield losses in the Mississippi River Valley regions allowed some improvement in production losses ($6. 5 million USD compared to 2019) to have occurred as a result of the O 3 concentration changes under C19. Overall, it was shown that 37% and 63% of the soybean-producing counties experienced yield loss increases and improvements, respectively. Yield improvements due to emission changes over CONUS represent 0. 02% of the total 2020 U. S. soybean production which amounted $46 billion (https:// www. nass. usda. gov/). Overall, while Campbell et al. 5 highlighted the nonlinearity of O 3 concentration changes due to the pandemic's economic slowdown, here, we further show that these nonlinear changes result in regionally dependent O 3 exposure changes (e. g., increases in the Southeast U. S. and widespread decreases elsewhere) throughout the soybean growing season. Such results draw attention to the shift back to normalcy following the initial onset of the COVID-19 pandemic. If shutdown orders had not been relaxed at the same time as shifts to cleaner vehicle fleets under the BAU scenario during the summer months throughout the soybean-producing region, reduced vehicular traffic emissions would have contributed to decreased O 3 exposures and improved yields. Throughout May, and the months that followed in our study, shutdowns were reduced throughout much of the U. S. resulting in the slightly changed chemistry we see for the summer months, despite reductions to overall vehicular traffic compared to 2019. 

It will be necessary to further study the impact of economic-related emissions changes on crop yields, perhaps on longer time scales and to distinguish the effects of regional meteorology on yield losses in the future. in part support this change. Qu et al. 6 approximate the impacts of natural/background effects when using NO 2 observations to infer NO x emissions changes during the pandemic. They found that the satellites show much weaker NO 2 responses in March-June and no decrease in July-August, consistent with a large background contribution to the NO 2 column in the U. S. This partly confounds the use of OMI in deriving the C19 NO x emissions changes and adds some inherent uncertainty to our work here. However, a detailed investigation into the natural variability is beyond the scope of Campbell et al. 5 or in this paper. Hence, the reader is further referred to Goldberg et al. 18 and Qu et al. 6 for detailed analyses of satellite observations and the natural variability impacts on NO 2 concentration changes during the COVID-19 lockdown.
section_name: Conclusion

section_content: Air quality model configuration. The NWS/NOAA National Air Quality Forecasting Capability (NAQFC) used in this work is a well-documented and evaluated air quality modeling system [19] [20] [21] [22] [23], and the experimental version used here is based on the offline-coupled North American Mesoscale Model Forecast System on the B-Grid (NMMB) 24, 25, which provides the driving weather data to the CMAQ model, version 5. 0. 2 26. The domain of the NAQFC covers the continental U. S. (CONUS) at a horizontal grid resolution of 12 × 12 km with 35 vertical levels. CMAQ simulates the formation, transport, and fate of a suite of atmospheric composition parameters. The NAQFC has provided real time air quality forecast guidance for over the past decade for different EPA-defined criteria pollutants including O 3 at a horizontal resolution of 12 × 12 km centered over CONUS. The full NAQFC model configurations and inputs are described in Campbell et al. 5. BASE (NEI2014v2) O 3 simulations compared against the U. S. EPA AirNow observations for April-September 2020 were indicative of acceptable (i. e., consistently fall within the criteria ranges for O 3 established by Emery et al. 29 ) model performance with little exceptions. For the detailed comparison, see Campbell et al. 5.
section_name: Methods

section_content: The emission changes caused by COVID-19 are derived from the difference from two scenarios: a "business-as-usual" (BAU) case and a COVID-19 (C19) case. In the BAU case, the emission data from the NEI 2014 version 2 (NEI2014v2) (i. e., the baseline emissions) are projected into the "would-be" 2020 level by using the mean rate of NO 2 trends observed from satellite and ground sensors for the period of 2014-2019, the year before the pandemic. In the C19 case, the observed NO 2 trends from 2014 to 2020, which are based on the vertical column density of NO 2 from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite, and the U. S. EPA Air Quality System ground network NO 2 observations, are used to represent the actual emission level under the pandemic conditions. For both cases, the NO 2 trend data are derived using the approach developed by Tong et al. 27, 28. Detailed data processing and quality control procedures are provided in Tong et al. 27. The emission data after adjustment are then used to drive the chemical transport model component of NAQFC, i. e., CMAQ, to calculate the near-surface O 3 levels under each scenario. The difference between the predicted O 3 concentrations in between BAU and C19 is attributed to the impact of the pandemic. Evaluation of the NAQFC surface O 3 concentrations using the NEI2014v2 (i. e., baseline emissions), BAU, and C19 scenarios demonstrated that the monthly MJJ 2020 model performance for surface O 3 was within statistical benchmark criteria defined by Emery et al. 29. Additionally, the BAU and C19 O 3 simulations displayed increased in correlation, R, Index of Agreement (IOA), and decreased Normalized Mean Error (NME) compared to the BASE case. (see Campbell et al. 5 for the full NAQFC statistical evaluation) Further details on the BAU and C19 emissions used in this work, as well as the state-level emission adjustment factors for scenarios of C19 and BAU in MJJ 2020 are found in Campbell et al. 5.
section_name: Emission changes caused by COVID-19.

section_content: To examine what effect the changes in the modeled NAQFC O 3 concentrations may have had on soybean crop yields, it is necessary to evaluate the degree to which crops are being exposed to O 3. Assessments of crop loss from O 3 exposures in the U. S. are based on dose-response function relationships. The soybean exposures are calculated using exposure indices that are related to those dose-response relationships from which yield losses are derived 11. For this study we utilize an index to quantify the accumulated O 3 exposure over a threshold of 40 ppb 8, AOT40, which is defined as:

(1)

The AOT40 index represents the sum of positive differences between the hourly mean O 3 concentration ( C O 3 ) and a threshold of 0. 04 ppm, multiplied by the 1-h averaging period (n), in a fixed growing season 30. The cutoff at 0. 04 ppm or 40 ppb is based largely on the anthropogenic component of the ozone exposure and does not imply a threshold for biological effects 31. In this study, we calculate AOT40 for 24-h periods in three consecutive months of the growing season under the BAU and C19 scenarios. AOT40 is calculated for MJJ. It is important to note, O 3 damage accumulates over the growing season 32. The earlier months of the growing season are used to examine the potential effects produced from the shutdown-related emission changes. Since crop production data in the U. S. is based on the county-level, the AOT40 indices are converted to a county-level average. O 3 concentrations of all related grid cells are averaged into a single county and weighted by area for consistency following the approach of Tong et al. 11. 

Calculation of crop yield loss. The dose-response function for AOT40 is based on a linear relationship for soybeans from Dingenen et al. 33. The relative yield loss (RYL), is calculated using this dose-response function and is defined as:

where the constant a = 0. 0113 is determined from Dingenen et al. 33 as a simple relationship between AOT40 and soybean crop yields. Soybean production amounts for 2020 (bushels of soybean per county) are obtained from the U. S. Department of Agriculture National Agricultural Statistics Service (https:// www. nass. usda. gov/ Data_ and_ Stati stics/) (see Table 4, for total production). A small amount of production data from combined counties is excluded since data from those individual counties could not be determined given individual farmer's privacy. The RYL values are then combined with the 2020 soybean production (approximately 4 billion bushels with the subtracted counties) to generate the actual yield loss in Bu.
section_name: Examination of soybean crop exposures.

section_name: Acknowledgements

section_content: This manuscript and research would not have been possible without the extensive support of my mentors Dr. Monika Kopacz and the late Dr. Ken Mooney, both of who offered me the funding and opportunity to research with Dr. Daniel Tong in addition to interning at NOAA's Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) Program. This study was also funded by the UMD/NOAA/CISESS: GMU Air Surface Exchange and Atmospheric Composition Research (NOAA Sponsor Number: 79785-Z7554202 Amend A; CISESS Sponsor Number: NA19NES4320002 ).
section_name: Acknowledgements

section_name: Data availability

section_content: All crop yield data used in this study are openly available from the United States Department of Agriculture's National Agriculture Statistics Service at https:// www. nass. usda. gov/ index. php. Dataset information for modeling inputs and observations are included in Campbell et al. 5.
section_name: Data availability

section_name: Author contributions

section_content: J. C., D. T., and P. C. C. designed the study. J. C. conducted the data analysis, wrote the main manuscript, and prepared Figs. 2, 3. S. M. prepared Fig. 1. All authors reviewed and approved the final manuscript.
section_name: Author contributions

section_content: The authors declare no competing interests.
section_name: Competing interests