Simulating the Impacts of Land-Use Land-Cover Changes on Cropland Carbon Fluxes in the Midwest of the United States

Understanding the major drivers of the cropland carbon fluxes is important for carbon management and greenhouse gas mitigation in agriculture. Past studies found that agricultural land-use and land-cover (LULC) changes, such as changes in cropland production technologies, tillage practices, and planted crop species, could have large impacts on carbon fluxes. However, the impacts remain highly uncertain at regional to global scales. Satellite remote sensing is commonly used to create products with geospatial information on LULC changes. This geospatial information can be integrated into biogeochemical models to simulate the spatial and temporal patterns of carbon fluxes. We used the General Ensemble Biogeochemical Modeling System (GEMS) to study LULC change impacts on cropland carbon fluxes in the Midwest USA. First we evaluated the impacts of LULC change on cropland net primary production (NPP) estimates. We found out the high spatial variability of cropland NPP across the study region was strongly related to the changes in crop species. Ignoring information about crop species distributions could introduce large biases into NPP estimates. We then investigated whether the characteristics of LULC change could impact the uncertainties of carbon flux estimates (i.e., NPP, net ecosystem production (NEP) and soil organic carbon (SOC)) using GEMS and two other models. The uncertainties of all three flux estimates were spatial autocorrelated. Land cover characteristics, such as cropland percentage, crop richness, and land cover diversity all showed statistically significant relationships with the uncertainties of NPP and NEP, but not with the uncertainties of SOC changes. The impacts of LULC change on SOC changes were further studied with historical LULC data from 1980 to 2012 using GEMS simulations. The results showed that cropland production increase over time from technology improvements had the largest impacts on cropland SOC change, followed by expansion of conservation tillage. This study advanced the scientific knowledge of cropland carbon fluxes and the impacts of various management practices over an agricultural area. The findings could help future carbon cycle studies to generate more accurate estimates on spatial and temporal changes of carbon fluxes

Factors Influencing Adoption and Adoption Intensity of Precision Agriculture Technologies in South Dakota

Precision agriculture can play an important role in preserving the environment and improving the economic conditions of agricultural producers. This thesis analyzes the determinants of adoption and adoption intensity of precision agriculture technologies in South Dakota. This analysis uses survey data collected from 199 farms distributed over 28 different counties in South Dakota, accounting for approximately 500,000 acres of tillable agricultural land, to (1) discover the factors impacting precision technology adoption; (2) compare and contrast several characteristics among adopters and non-adopters; and (3) develop probit, count, and negative binomial models to determine the significance of explanatory variables impacting precision technology adoption and adoption intensity. T-test results of the mean age of participants, Conservation Stewardship Program (CSP) enrollment, service center access, reliance on farm dealers for information, and computer usage for accounting purposes were statistically different between adopters and non-adopters of precision agriculture technologies. Probit model results indicate that age, spousal non-farm income, and service/repair access negatively influenced the decision to adopt, while the number of cropland acres, reliance on information from farm dealers, and use of computers for accounting activities positively impacted the decision to adopt. Results from the count model suggest that age, livestock owner status, spousal non-farm income, and service/repair access negatively influence the intensity of precision agriculture technologies adoption, while CSP enrollment, crop-land acreage, reliance on information from farm dealers, and using computers for accounting activities positively influenced the intensity of precision agriculture technologies adoption. Results of the negative binomial model indicate that only lack of access to service/repair facilities negatively affected the adoption intensity, and the adoption of different bundles of the six most popular precision technologies (auto-steer, variable rate systems, automatic section control/shut-offs, prescription field maps, yield monitors, and GPS guidance systems), while CSP enrollment, reliance on farm dealers as an information source, and using computers for accounting activities positively influenced precision technologies adoption intensity. The results of this study may help policy makers understand how agricultural producers perceive precision agriculture technologies in general, and the degree to which these technologies may be used to enhance productivity, profitability, and environmental quality. The result also provides useful insights on key determinants of the adoption of precision agriculture technologies. The results may further help farm dealers and repair service providers as they consider marketing precision agriculture technologies to agricultural producers. Precision agriculture technologies manufacturers and sellers can use these results to identify the demand of their product and services in the future

Drought imprints on crops can reduce yield loss: Nature\u27s insights for food security

The Midwestern “Corn-Belt” in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid- to late-summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid- or late-season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field-grown crops and this response will protect crops against growth and yield losses from late-season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high-resolution daily weather data, satellite-derived phenological metrics, field yield data, and ecosystem-scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre-primed crop species may provide improved resilience to future climate change

Investigation of climate variability and climate change impacts on corn yield in the Eastern Corn Belt, USA

The increasing demand for both food and biofuels requires more corn production at global scale. However, current corn yield is not able to meet bio-ethanol demand without jeopardizing food security or intensifying and expanding corn cultivation. An alternative solution is to utilize cellulose and hemi-cellulose from perennial grasses to fulfill the increasing demand for biofuel energy. A watershed level scenario analysis is often applied to figure out a sustainable way to strike the balance between food and fuel demands, and maintain environment integrity. However, a solid modeling application requires a clear understanding of crop responses under various climate stresses. This is especially important for evaluating future climate impacts. Therefore, correct representation of corn growth and yield projection under various climate conditions (limited or oversupplied water) is essential for quantifying the relative benefits of alternative biofuel crops. The main objective of this study is to improve the evaluation of climate variability and climate change effects on corn growth based on plant-water interaction in the Midwestern US via a modeling approach. Traditional crop modeling methods with the Soil and Water Assessment Tool (SWAT) are improved from many points, including introducing stress parameters under limited or oversupplied water conditions, improving seasonal crop growth simulation from imagery-based LAI information, and integrating CO2 effects on crop growth and crop-water relations. The SWAT model’s ability to represent crop responses under various climate conditions are evaluated at both plot scale, where observed soil moisture data is available and watershed scale, where direct soil moisture evaluation is not feasible. My results indicate that soil moisture evaluation is important in constraining crop water availability and thus better simulates crop responses to climate variability. Over a long term period, drought stress (limited moisture) explains the majority of yield reduction across all return periods at regional scale. Aeration stress (oversupplied water) results in higher yield decline over smaller spatial areas. Future climate change introduces more variability in drought and aeration stress, resulting in yield reduction, which cannot be compensated by positive effects brought by CO2 enhancement on crop growth. Information conveyed from this study can also provide valuable suggestions to local stakeholders for developing better watershed management plans. It helps to accurately identify climate sensitive cropland inside a watershed, which could be potential places for more climate resilient plants, like biofuel crops. This is a sustainable strategy to maintain both food/fuel provision, and mitigate the negative impact of future climate change on cash crops

Modeling Nutrient Legacies and Time Lags in Agricultural Landscapes: A Midwestern Case Study

Land-use change and agricultural intensification have increased food production but at the cost of polluting surface and groundwater. Best management practices implemented to improve water quality have met with limited success. Such lack of success is increasingly attributed to legacy nutrient stores in the subsurface that may act as sources after reduction of external inputs. These legacy stores have built up over decades of fertilizer application and contribute to time lags between the implementation of best management practices and water quality improvement. However, current water quality models lack a framework to capture these legacy effects and corresponding lag times. The overall goal of this thesis is to use a combination of data synthesis and modeling to quantify legacy stores and time lags in intensively managed agricultural landscapes in the Midwestern US. The specific goals are to (1) quantify legacy nitrogen accumulation using a mass balance approach from 1949 - 2012 (2) develop a SWAT model for the basin and demonstrate the value of using crop yield information to increase model robustness (3) modify the SWAT (Soil Water Assessment Tool) model to capture the effect of nitrogen (N) legacies on water quality under multiple land-management scenarios, and (4) use a field-scale carbon-nitrogen cycling model (CENTURY) to quantify the role of climate and soil type on legacy accumulation and water quality. For objectives 1 and 2, the analysis was performed in the Iowa Cedar Basin (ICB), a 32,660 km2 watershed in Eastern Iowa, while for objective 3, the focus has been on the South Fork Iowa River Watershed (SFIRW), a 502 km2 sub-watershed of the ICB, and for objective 4 the focus was at the field scale. For the first objective, a nitrogen mass balance analysis was performed across the ICB to understand whether legacy N was accumulating in this watershed and if so, the magnitude of accumulation. The magnitude of N inputs, outputs, and storage in the watershed was quantified over 64 years (1949 – 2012) using the Net Anthropogenic Nitrogen Inputs (NANI) framework. The primary inputs to the system were atmospheric N deposition (9.2 ± 0.35 kg/ha/yr), fertilizer N application (48 ± 2 kg/ha/yr) and biological N fixation (49 ± 3 kg/ha/yr) and while the primary outputs from the system was net food and feed that was estimated as 42 ± 4.5 kg/ha/yr. The Net Anthropogenic Nitrogen Input (NANI) to the system was estimated to be 64 ± 6 kg/ha/yr. Finally, an estimated denitrification rate constant of 12.7 kg/ha/yr was used to estimate the subsurface legacy nitrogen storage as 33.3 kg/ha/yr. This is a significant component of the overall mass budget and represents 48% of the NANI and 31% of the fertilizer added to the watershed every year. For the second objective, the effect of crop yield calibration in increasing the robustness of the hydrologic model was analyzed. Using a 32,660 km2 agricultural watershed in Iowa as a case study, a stepwise model refinement was performed to show how the consideration of additional data sources can increase model consistency. As a first step, a hydrologic model was developed using the Soil and Water Assessment Tool (SWAT) that provided excellent monthly streamflow statistics at eight stations within the watershed. However, comparing spatially distributed crop yield measurements with modeled results revealed a strong underestimation in model estimates (PBIAS Corn = 26%, PBIAS soybean = 61%). To address this, the model was refined by first adding crop yield as an additional calibration target and then changing the potential evapotranspiration estimation method -- this significantly improved model predictions of crop yield (PBIAS Corn = 3%, PBIAS soybean = 4%), while only slightly improving streamflow statistics. As a final step, for better representation of tile flow, the flow partitioning method was modified. The final model was also able to (i) better capture variations in nitrate loads at the catchment outlet with no calibration and (ii) reduce parameter uncertainty, model prediction uncertainty, and equifinality. The findings highlight that using additional data sources to improve hydrological consistency of distributed models increases their robustness and predictive ability. For the third objective, the SWAT model was modified to capture the effects of nitrogen (N) legacies on water quality under multiple land-management scenarios. My new SWAT-LAG model includes (1) a modified carbon-nitrogen cycling module to capture the dynamics of soil N accumulation, and (2) a groundwater travel time distribution module to capture a range of subsurface travel times. Using a 502 km2 SFIR watershed as a case study, it was estimated that, between 1950 and 2016, 25% of the total watershed N surplus (N Deposition + Fertilizer + Manure + N Fixation – Crop N uptake) had accumulated within the root zone, 14% had accumulated in groundwater, while 27% was lost as riverine output, and 34% was denitrified. In future scenarios, a 100% reduction in fertilizer application led to a 79% reduction in stream N load, but the SWAT-LAG results suggest that it would take 84 years to achieve this reduction, in contrast to the two years predicted in the original SWAT model. The framework proposed here constitutes a first step towards modifying a widely used modeling approach to assess the effects of legacy N on time required to achieve water quality goals. The above research highlighted significant uncertainty in the prediction of biogeochemical legacies -- to address this uncertainty in the last objective the field scale CENTURY model was used to quantify SON accumulation and depletion trends using climate and soil type gradients characteristic of the Mississippi River Basin. The model was validated using field-scale data, from field sites in north-central Illinois that had SON data over 140 years (1875-2014). The study revealed that across the climate gradient typical of the Mississippi River Basin, SON accumulation was greater in warmer areas due to greater crop yield with an increase in temperature. The accumulation was also higher in drier areas due to less N lost by leaching. Finally, the analysis revealed an interesting hysteretic pattern, where the same levels of SON in the 1930s contributed to a lower mineralization flux compared to current

Multi-Criteria Evaluation Model for Classifying Marginal Cropland in Nebraska Using Historical Crop Yield and Biophysical Characteristics

Marginal cropland is suboptimal due to historically low and variable productivity and limiting biophysical characteristics. To support future agricultural management and policy decisions in Nebraska, U.S.A, it is important to understand where cropland is marginal for its two most economically important crops: corn (Zea mays) and soybean (Glycine max). As corn and soybean are frequently planted in a crop rotation, it is important to consider if there is a relationship with cropland marginality. Based on the current literature, there exists a need for a flexible yet robust methodology for identifying marginal land at different scales, which takes advantage of high spatial and temporal resolution data and can be applied by researchers and outreach professionals alike. This research seeks to individually identify where cropland is marginal for corn and soybean as well as classify the extent of marginality that exists. This research also seeks to classify cropland as being part of a long-term corn-soybean crop and see if marginality differs between this cropland and the remainder of cropland. Two crop-specific multi-criteria evaluations (MCE), consisting of crop production, climate, and soil criteria, was performed using Google Earth Engine to identify and classify marginal cropland. Criteria were individually thresholded before addition to the MCEs. Cropland that was classified as part of a long-term corn-soybean crop rotation was identified by factoring in the balance of corn and soybean occurrence on long established cropland. Most cropland in Nebraska has at least some marginality for corn while most has no marginality for soybean. Marginality classification is spatially distributed with increasing marginality from the northeast to the southwest. Cropland under a long-term crop rotation shows much less marginality compared to non-rotation cropland. This study improves upon previous attempts to identify marginal cropland in Nebraska by increasing spatial and temporal resolution, providing a programmatic and replicable methodology, and confining the classification to existing cropland. The implications of these findings are useful for policy makers and agricultural extension efforts in Nebraska to identify opportunities for conservation, solar energy capture, and biofuel production on cultivated land. Advisor: Yi Q

The Dynamics of Supply: U.S. Corn and Soybeans in the Biofuel Era

We estimate U.S. corn and soybean supply responses by exploiting the large exogenous price variations associated with implementation of the Renewable Fuel Standard. We focus on recent years and on the twelve Midwestern states, and estimate a system of dynamic equations that is consistent with the role of crop rotation. Corn and soybean acreages respond more in the short run than in the long run. Cross-price elasticities of acreage responses are negative and fairly large in absolute value such that, when corn and soybean prices move together, the response of total acreage allocated to these two crops is extremely inelastic

Synergistic integration of optical and microwave satellite data for crop yield estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regressionand its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment. Results show that (1) the proposed EVI-VOD lag metric correlates well with crop yield and outperforms common single-sensor metrics for crop yield estimation; (2) the statistical (machine learning) models working directly with the time series largely improve results compared to previously reported estimations; (3) the combined exploitation of information from the optical and microwave data leads to improved predictions over the use of single sensor approaches with coefficient of determination R 2 ≥ 0.76; (4) when models are used for within-season forecasting with limited time information, crop yield prediction is feasible up to four months before harvest (models reach a plateau in accuracy); and (5) the robustness of the approach is confirmed in a multi-year setting, reaching similar performances than when using single-year data. In conclusion, results confirm the value of using both EVI and VOD at the same time, and the advantage of using automatic machine learning models for crop yield/production estimation