A review of ecosystem services from edge-of-field practices in tile-drained agricultural systems in the United States Corn Belt Region
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Environmental Sciences & Ecology↗
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A Multiyear Constraint on Ammonia Emissions and Deposition Within the US Corn Belt
Abstract The US Corn Belt is a global hotspot of atmospheric ammonia (NH 3 ), a gas known to adversely impact the environment and human health. We combine hourly tall tower (100 m) measurements and bi‐weekly, spatially distributed, ground‐based observations from the Ammonia Monitoring Network with the US National Emissions Inventory (NEI) and WRF‐Chem simulations to constrain NH 3 emissions from April to September 2017–2019. We show that: (1) NH 3 emissions peaked from May to July and were 1.6–1.7 times the annual NEI average; (2) average growing season NH 3 emissions from agricultural lands were remarkably similar across years (3.27–3.64 nmol m −2 s −1 ), yet showed substantial episodic variability driven by meteorology and land management; (3) dry deposition was 40% of gross emissions from agricultural lands and exceeded 100% of gross emissions in natural lands. Our findings provide an important benchmark for evaluating future NH 3 emissions and mitigation efforts.
Hu, Cheng↗
Heat Stress to Jeopardize Crop Production in the US Corn Belt Based on Downscaled CMIP5 Projections
CONTEXT Global food security faces increasing challenges from the changing climate. Changes of agricultural output from some of the most productive regions such as the US Corn Belt can largely affect the world's food market. Developing predictive understanding of the agricultural risk of climate change and potential mitigation strategies is critical for the global food security. OBJECTIVE The objective of this study is to assess the responses of maize and soybean yield to projected climate changes in the Corn Belt, identify the shifting environment stressors on crop yield, and tackle potential climate adaptation strategies. METHODS We drive a process-based model, the Decision Support System for Agrotechnology Transfer, with high-resolution statistically downscaled and bias-corrected historical and future climates from ten CMIP5 models in the MACA-2 database. RESULTS AND CONCLUSIONS The multi-model ensemble mean suggests a 12% decrease of maize yield by mid-century and 40% by late century, with a high degree of model consensus in the direction of changes; for individual models, the projected decrease of maize yield by late century ranges from <5% to over 80%, with the worst crop outcome corresponding to the most sensitive climate models. Soybean yield is projected to increase by midcentury with a high degree of model consensus, but such consensus is lost by late century as some projections shift to significant decreases. Crop yield in the Corn Belt is currently limited by water stress, but is projected to be increasingly limited by heat stress as well after the midcentury. The mounting heat stress will drive the most productive zone for maize to shift from central to northern part of the Corn Belt, but the projected increase in the northern states cannot fully compensate for the decrease in the south, causing the total production to decrease if agricultural practice stays the same. Earlier planting can alleviate only a small fraction of the heat-induced crop loss in a warmer climate. Climate change will (at least partially) offset the yield boost caused by agricultural technology and intensification. SIGNIFICANCE This study advances our predictive understanding of crop yield responses to climate change, and suggests that a multitude of strategies will be needed to address the climate change challenges for the U.S. agriculture.
Crop yield↗
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, Φ F , 17% ~ 31%), which confirms the contribution of physiological variation to SIF. We further evaluated whether Φ F indicated plant responses under high-temperature and high vapor pressure deficit (VPD) stresses. The plot-scale data showed that Φ F responded to the proxy for physiological stress (partial correlation coefficient, r p = 0.40, p < 0.001) while non-physiological signals of SIF did not respond ( p > 0.1). The field-scale Φ F data showed water deficit stress from the comparison between irrigated and rainfed fields, and Φ F 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 Φ F 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 Φ F reflects crop physiological responses to environmental stresses with greater sensitivity to stress factors than SIF, and the stress quantification capability of Φ F is spatially scalable. Utilizing Φ F for physiological investigations will contribute to improve our understanding of vegetation responses to high-temperature and high-VPD stresses.
54 ENVIRONMENTAL SCIENCES↗
Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach
The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N 2 O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys. The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N 2 O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $\$395$ M (uncertainty ranges from $\$114$ M to $\$1271$ M), including $\$334$ from social benefits (uncertainty ranges from $\$46$ M to $\$1076$ M), $\$100$ M from saving fertilizer (uncertainty ranges from $\$13$ M to $\$455$ M), and –$\$40$ M due to yield changes (uncertainty ranges from –$\$261$ M to $\$69$ M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.
54 ENVIRONMENTAL SCIENCES↗
Further adoption of conservation tillage can increase maize yields in the western US Corn Belt
Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.
54 ENVIRONMENTAL SCIENCES↗
Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020
Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.
54 ENVIRONMENTAL SCIENCES↗
Adaptation in U.S. Corn Belt Increases Resistance to Soil Carbon Loss With Climate Change
Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.
Soil Organic Carbon (SOC)↗
Explicit Consideration of Plant Xylem Hydraulic Transport Improves the Simulation of Crop Response to Atmospheric Dryness in the U.S. Corn Belt
Abstract Atmospheric dryness (i.e., high vapor pressure deficit, VPD), together with soil moisture stress, limits plant photosynthesis and threatens ecosystem functioning. Regions where rainfall and soil moisture are relatively sufficient, such as the rainfed part of the U.S. Corn Belt, are especially prone to high VPD stress. With globally projected rising VPD under climate change, it is crucial to understand, simulate, and manage its negative impacts on agricultural ecosystems. However, most existing models simulating crop response to VPD are highly empirical and insufficient in capturing plant response to high VPD, and improved modeling approaches are urgently required. In this study, by leveraging recent advances in plant hydraulic theory, we demonstrate that the VPD constraints in the widely used coupled photosynthesis‐stomatal conductance models alone are inadequate to fully capture VPD stress effects. Incorporating plant xylem hydraulic transport significantly improves the simulation of transpiration under high VPD, even when soil moisture is sufficient. Our results indicate that the limited water transport capability from the plant root to the leaf stoma could be a major mechanism of plant response to high VPD stress. We then introduce a Demand‐side Hydraulic Limitation Factor (DHLF) that simplifies the xylem and the leaf segments of the plant hydraulic model to only one parameter yet captures the effect of plant hydraulic transport on transpiration response to high VPD with similar accuracy. We expect the improved understanding and modeling of crop response to high VPD to help contribute to better management and adaptation of agricultural systems in a changing climate.
Environmental Sciences & Ecology↗
Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt
As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.
09 BIOMASS FUELS↗
Soil carbon sequestration potential and the identification of hotspots in the Eastern Corn Belt of the United States
Soil carbon sequestration is a significant CO 2 mitigation strategy but precise assessments of sequestration require spatially explicit modeling of potential changes in soil organic carbon (SOC) in response to soil, climate, land condition and management interactions. We assessed the SOC sequestration potential of the Eastern Corn Belt (ECB) in the USA (Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, and West Virginia) in response to the adoption of conservation farming practices and land use change (pasture and forestation) using the SOCRATES model. Input data was provided through an intersection of the STATSGO, National Land Cover Database and a PRISM climate surface. At the end of the 20th century, the 15.3 Mha of cropped soils in the ECB contained 632 Tg C, an estimated reduction of 52% since the introduction of agriculture in the mid-1800s. Complete adoption of no-tillage practices on Prime Cropland would potentially recover 147 Tg SOC over 20 years, whereas a continuation of conventional tillage would produce a loss of 35 Tg SOC. Sequestration hotspots (> 500 Gg increase in SOC) under no-tillage cover 2.3 Mha providing 28 Tg C over 20 years. The conversion of Marginal (non-Prime) agricultural lands to Forests would yield an additional 13 Tg C in SOC and 381 Tg C in aboveground biomass. The rehabilitation of Minelands to Forest would yield an additional 4 Tg C in SOC and 42 Tg C in biomass. Here, opportunities to sequester C in the ECB via tillage and reforestation are substantial and should be incorporated into regional and national climate change mitigation strategies.
59 BASIC BIOLOGICAL SCIENCES↗
Modeling the Sources and Transport Processes During Extreme Ammonia Episodes in the U.S. Corn Belt
Abstract Atmospheric ammonia (NH 3 ) is the primary form of reactive nitrogen (N r ) and a precursor of ammonium (NH 4 + ) aerosols. Ammonia has been linked to adverse impacts on human health, the loss of ecosystem biodiversity, and plays a key role in aerosol radiative forcing. The midwestern United States is the major NH 3 source in North America because of dense livestock operations and the high use of synthetic nitrogen fertilizers. Here, we combine tall‐tower (100 m) observations in Minnesota and Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) modeling to investigate high and low NH 3 emission episodes within the U.S. Corn Belt to improve our understanding of the distribution of emission sources and transport processes. We examined observations and performed model simulations for cases in February through November of 2017 and 2018. The results showed the following: (1) Peak emissions in November 2017 were enhanced by above‐normal air temperatures, implying a Q 10 (i.e., the change in NH 3 emissions for a temperature increase of 10°C) of 2.5 for emissions. (2) The intensive livestock emissions rom northern Iowa, approximately 400 km away from the tall tower, accounted for 17.6% of the abundance in tall‐tower NH 3 mixing ratios. (3) Ammonia mixing ratios in the innermost domain 3 frequently (i.e., 336 hr, 48% of November 2017) exceeded 5.3 ppb, an important air quality health standard. (4) In November 2017, simulated NH 3 net ecosystem exchange (the difference between NH 3 emissions and dry deposition) accounted for 60–65% of gross NH 3 emissions for agricultural areas and was 2.8–3.1 times the emissions of forested areas. (5) We estimated a mean annual NH 3 net ecosystem exchange of 1.60 ± 0.06 nmol · m −2 · s −1 for agricultural lands and −0.07 ± 0.02 nmol · m −2 · s −1 for forested lands. These results imply that future warmer fall temperatures will enhance agricultural NH 3 emissions, increase the frequency of dangerous NH 3 episodes, and enhance dry NH 3 deposition in adjacent forested lands.
Hu, Cheng↗
BESS-STAIR: a framework to estimate daily, 30m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt
Abstract. With increasing crop water demands and drought threats, mapping andmonitoring of cropland evapotranspiration (ET) at high spatial and temporalresolutions become increasingly critical for water management andsustainability. However, estimating ET from satellites for precise waterresource management is still challenging due to the limitations in bothexisting ET models and satellite input data. Specifically, the process of ETis complex and difficult to model, and existing satellite remote-sensing datacould not fulfill high resolutions in both space and time. To address theabove two issues, this study presents a new high spatiotemporal resolution ETmapping framework, i.e., BESS-STAIR, which integrates a satellite-drivenwater–carbon–energy coupled biophysical model, BESS (Breathing Earth SystemSimulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation). In this framework, STAIR provides daily 30'mmultispectral surface reflectance by fusing Landsat and MODIS satellite datato derive a fine-resolution leaf area index and visible/near-infrared albedo,all of which, along with coarse-resolution meteorological and CO 2 data, are used to drive BESS to estimate gap-free 30 m resolution daily ET.We applied BESS-STAIR from 2000 through 2017 in six areas across the US CornBelt and validated BESS-STAIR ET estimations using flux-tower measurementsover 12 sites (85 site years). Results showed that BESS-STAIR daily ETachieved an overall R2=0.75, with root mean square error RMSE=0.93 mm d -1 and relative error RE =27.9 % when benchmarkedwith the flux measurements. In addition, BESS-STAIR ET estimations capturedthe spatial patterns, seasonal cycles, and interannual dynamics well indifferent sub-regions. The high performance of the BESS-STAIR frameworkprimarily resulted from (1) the implementation of coupled constraints onwater, carbon, and energy in BESS, (2) high-quality daily 30 m data from theSTAIR fusion algorithm, and (3) BESS's applicability under all-skyconditions. BESS-STAIR is calibration-free and has great potentials to be areliable tool for water resource management and precision agricultureapplications for the US Corn Belt and even worldwide given the globalcoverage of its input data.
54 ENVIRONMENTAL SCIENCES↗
Redefining droughts for the U.S. Corn Belt: The dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of Maize and Soybean
Not Available
Kimm, Hyungsuk (ORCID:0000000181890874)↗
Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data
Not Available
Kimm, Hyungsuk (ORCID:0000000181890874)↗
The Polarimetric Sensitivity of SMAP-Reflectometry Signals to Crop Growth in the U.S. Corn Belt
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Nereida Rodriguez-Alvarez↗
Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a Rainfall-Runoff Model in the U.S. Corn Belt
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Navid Jadidoleslam↗
Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?
Droughts represent one of the most severe abiotic stress factors that could result in great crop yield loss. Numerous vegetation indices have been proposed for monitoring the vegetation condition under stress and assessing drought impacts on yield loss. However, the understanding and comparison between traditional vegetation indices (VIs) and the newly emerging satellite Sun-Induced Chlorophyll Fluorescence (SIF) for monitoring vegetation condition is still limited especially under drought stress and at multiple spatial scales. In this study, the potential of satellite observation SIF for monitoring corn response to drought was investigated based on the 2012 drought in the US Corn Belt. The standardized precipitation evapotranspiration index (SPEI) was used here to quantify drought. We found that all SPEI were above –1, except for July (–1.27), August (–1.39) and September (–1.14) in 2012, indicating the severity of this drought. We examined the relationship between satellite measurements of SIF, SIF yield , VIs (e.g., NDVI and EVI) and SPEI. Results indicated that SIF yield was sensitive to drought and SIF captured the stress more accurately both at the regional and state scales for the US Corn Belt. Quantitatively, SIF yield had a high correlation with SPEI (r = 0.987, p < 0.05) over the entire Corn Belt, and it indicated losses in response to drought approximately one month earlier than SIF/NDVI/EVI. Furthermore, our results demonstrated that SIF could be trusted as an effective indicator to study the relationship between GPP (R 2 ≥ 0.8664, p < 0.01) under drought conditions across the Corn Belt. Finally, this study highlighted the advantage of using satellite SIF observations to monitor the drought stress on crop growth especially GPP at regional scale.
54 ENVIRONMENTAL SCIENCES↗