Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Altitude of the Potentiometric Surface in the Mississippi River Valley Alluvial Aquifer, Spring 2018

The Mississippi River Valley alluvial (MRVA) aquifer is an important surficial aquifer in the Mississippi Alluvial Plain (MAP) area. The aquifer is generally considered to be an unconfined aquifer (fig. 1; Clark and others, 2011), and withdrawals are primarily used for irrigation (Maupin and Barber, 2005). These groundwater withdrawals have resulted in substantial areas of water-level decline in parts of the aquifer. Concerns about water-level declines and the sustainability of the MRVA aquifer have prompted the U.S. Geological Survey (USGS), as part of the USGS Water Availability and Use Science Program and with assistance from other Federal, State, and local agencies, to undertake a regional water-availability study to assess the characteristics of the MRVA aquifer, including the potentiometric-surface altitude of the MRVA aquifer for spring 2018, and to provide information to water managers to inform their decisions about resource allocations and aquifer sustainability. The purpose of this report was to present a potentiometric-surface map for the MRVA aquifer using manually measured groundwater-altitude data and daily mean or maximum groundwater-altitude data from wells measured generally in spring 2018, which is after water levels have substantially recovered from pumping in the previous irrigation season and before pumping begins for the next irrigation season, and using the altitude of the top of the water surface in rivers in the area, hereinafter referred to as “surface-water altitude,” generally on April 10, 2018, from streamgages in the area. The term “potentiometric surface” is used in this report because it is applicable for maps of the groundwater-altitude surface in unconfined, semiconfined, and confined aquifers (Lohman, 1972). In this report, the maps of the MRVA aquifer’s groundwater surface are termed potentiometric-surface maps as opposed to water-table maps because, although the MRVA aquifer generally exhibits characteristics of unconfined conditions, where surface-water features may or may not be hydraulically connected, it also exhibits characteristics of confined or semiconfined conditions in some areas at least during part of the year. The location of these areas, where the aquifer is confined or semiconfined, is not well understood or defined (Arthur, 1994; Kleiss and others, 2000). Datasets used attache

Altitude of the Potentiometric Surface in the Mississippi River Valley Alluvial Aquifer, Spring 2018

The Mississippi River Valley alluvial (MRVA) aquifer is an important surficial aquifer in the Mississippi Alluvial Plain (MAP) area. The aquifer is generally considered to be an unconfined aquifer (fig. 1; Clark and others, 2011), and withdrawals are primarily used for irrigation (Maupin and Barber, 2005). These groundwater withdrawals have resulted in substantial areas of water-level decline in parts of the aquifer. Concerns about water-level declines and the sustainability of the MRVA aquifer have prompted the U.S. Geological Survey (USGS), as part of the USGS Water Availability and Use Science Program and with assistance from other Federal, State, and local agencies, to undertake a regional water-availability study to assess the characteristics of the MRVA aquifer, including the potentiometric-surface altitude of the MRVA aquifer for spring 2018, and to provide information to water managers to inform their decisions about resource allocations and aquifer sustainability. The purpose of this report was to present a potentiometric-surface map for the MRVA aquifer using manually measured groundwater-altitude data and daily mean or maximum groundwater-altitude data from wells measured generally in spring 2018, which is after water levels have substantially recovered from pumping in the previous irrigation season and before pumping begins for the next irrigation season, and using the altitude of the top of the water surface in rivers in the area, hereinafter referred to as “surface-water altitude,” generally on April 10, 2018, from streamgages in the area. The term “potentiometric surface” is used in this report because it is applicable for maps of the groundwater-altitude surface in unconfined, semiconfined, and confined aquifers (Lohman, 1972). In this report, the maps of the MRVA aquifer’s groundwater surface are termed potentiometric-surface maps as opposed to water-table maps because, although the MRVA aquifer generally exhibits characteristics of unconfined conditions, where surface-water features may or may not be hydraulically connected, it also exhibits characteristics of confined or semiconfined conditions in some areas at least during part of the year. The location of these areas, where the aquifer is confined or semiconfined, is not well understood or defined (Arthur, 1994; Kleiss and others, 2000). Datasets used attache

Surveillance of Anaplasma marginale in Arkansas Beef Cattle Herds

Anaplasmosis is an economically devastating disease in cattle that is caused by the rickettsial pathogen Anaplasma marginale. It is estimated that this parasitic bacterium causes over $300 million in expenses for the U.S. cattle industry annually. In Arkansas, the beef cattle industry is the fifth largest agricultural commodity in the state, thus necessitating a better understanding of this disease along with its prevalence. In this study, both polymerase chain reaction (PCR) and competitive enzyme-linked immunosorbent assay (ELISA) tests were used to determine the prevalence of A. marginale infection in Arkansas beef cattle on pasture in the six commonly known geographical regions within the state. Rates of regional seroprevalence and/or PCR prevalence ranged from 36.7% to 93.8% on samples obtained from 578 live beef cows that were two years of age or older. Overall, the highest percent prevalence was found along Crowley’s Ridge in the northeastern corner of the state. Regional percentages were applied to a state map identifying the geographical regions for distribution to county extension agents within the University of Arkansas System Division of Agriculture for educational purposes. Data from this study will also be used to determine which strains of A. marginale are present in Arkansas with the possibility of developing novel therapeutic interventions in the future

Developing a calibrated seepage meter to measure stream-aquifer interaction in the Mississippi delta

The Mississippi Alluvial Plain (MAP) is a premier region for irrigated agriculture in the United States producing approximately 9 billion dollars in annual revenues. The region receives around 138 cm of precipitation annually; however irrigation is necessary to maximize crop yields as most of the precipitation does not occur during the growing season. There are 8 million irrigated acres within the Mississippi Alluvial Plain. The source of most of the irrigated water is the surficial aquifer in the Mississippi Embayment the Mississippi River Valley Alluvial Aquifer (MRVAA) and due to the reliance on irrigation for maximum crop yields recent potentiometric surface maps of the MRVAA show 1-1.5 ft/yr declines in groundwater levels. The US Geological Survey and US Department of Agriculture have produced models of the MRVAA to address groundwater sustainability issues. A large source of uncertainty within the Mississippi Embayment Regional Aquifer System (MERAS) and MAP project models is the contribution to groundwater from surface streams. Geophysical data estimating streambed sediment texture has been collected on numerous reaches within the MAP but physical measurements are still desirable to constrain modeling efforts. Seepage meters are a potential tool for physical measurements of streambed seepage. Like all instruments seepage meters must be calibrated to validate field measurements. Due to space limitations within the USDA National Sedimentation Laboratory a tank with sufficient surface area to test a full-scale seepage meter was not feasible. Therefore a scale-model seepage meter and seepage flux tank were constructed. Any consistent bias if present in measured seepage rates through the seepage flux tank could be accounted for by applying a correction coefficient. From the seepage flux tank data a 95% confidence interval was calculated for the linear regression trendline through the data. The 1:1 line lies within the 95% confidence interval indicating there is no need for a correction factor and any bias in measurements is due to installation of the instrument and not to system configuration. A field demonstration of the seepage meter was conducted within Goodwin Creek in Panola County Mississippi yielding results consistent with estimates of seepage calculated using creek parameters and measured discharges

GIS Modeling of the Prominent Geohazards in Arkansas

The State of Arkansas is prone to numerous geohazards. This thesis is a twofold study of prominent geohazards in Arkansas: the first fold includes a novel triggerless approach for mass wasting susceptibility modeling applied to the Boston Mountains in NW Arkansas, and the second fold is a GIS-based regression modeling of the extreme weather patterns at the state level. Each study fold is presented in this thesis as a separate chapter embracing a published peer-reviewed paper. In the first paper, I have used the analytical hierarchy process to assign preliminary statistical weights to the most cogent variables influencing mass wasting in the central Boston Mountains. These most significant variables are then incorporated in Fuzzy modeling of mass wasting susceptibility within the 1200 km2 study area. For comparison and accuracy assessment, a second model has been established using a conventional weighted overlay (WO) approach. Results indicate that the developed novel approach is superior, with approximately 83% accuracy, to the traditional WO approach that has a marginal success of about 28% accuracy. Road related mass wasting events recorded by the Arkansas Department of Transportation have been used to validate both models. In the second paper, I have conducted a systematically gridded analysis of severe weather events, including tornadoes, derechos, and hail, during 1955-2015. The study examines and statistically determines the most significant explanatory variables contributing to the spatial patterns of severe weather events between 1955 and 2015, consequently it identifies severity indices for the entire state. These weather-related hazards and their associated risk will always abide; therefore, the best defense is employ geospatial technologies to plan for hazard mitigation. The mass wasting model developed in this study contributes pivotal information for identifying zones of high risk along roadways in NW Arkansas, which definitely can be adapted to avoid disastrous road failures. In addition, the weather-related severity indices determined at the state level can profoundly benefit state and federal agencies focused on increasing the availability of public and private storm shelters in previously under-represented zones of high risk. This undoubtedly will save lives from unavoidable catastrophic events across the entire state

Watershed prioritization to reduce nutrient export: A framework for the State of Arkansas based on ambient water quality monitoring data

The annual formation of the Northern Gulf of Mexico hypoxic zone is driven by nutrient loading from the Mississippi-Atchafalaya River Basin (MARB). Member States of The Mississippi River/Gulf of Mexico Hypoxia Task have developed statewide strategies to identify priorities and opportunities for nutrient export reduction in the MARB. In 2014, the State of Arkansas joined the Task Force and initiated an Arkansas Nutrient Reduction Strategy (ANRS), which currently prioritizes ten Hydrologic Unit Code 8 (HUC-8) watersheds (ANRD, 2014). These priority watersheds were not selected based on measured in-stream nutrient concentrations or trends, which impedes quantitative assessment, goal setting, and linking investments to nutrient reduction progress. The ANRS is currently under revision to address these concerns, and the goal of this project was to develop a prioritization framework for the State of Arkansas based on robust statistical analysis of extensive, statewide ambient water quality monitoring data sets

The General Ensemble Biogeochemical Modeling System (GEMS) and its Applications to Agricultural Systems in the United States

The General Ensemble Biogeochemical Modeling System (GEMS) (Liu, 2009; Liu et al., 2004c) was developed to integrate well-established ecosystem biogeochemical models with various spatial databases for the simulations of biogeochemical cycles over large areas. Figure 18.1 shows the overall structure of the GEMS. Some of the key components are described below. General Ensemble Biogeochemical Modeling System (GEMS) 310 Multiple Underlying Biogeochemical Models 310 Monte Carlo Simulations 311 Model Inputs: Management Practices and Others 311 Model Outputs 311 Data Assimilation 311 Simulation of Agricultural Practices: EDCM as an Example 312 Net Primary Production (NPP) and Improvements in Crop Genetics and Agronomics 312 Soil Carbon Dynamics 312 Impacts of Soil Erosion and Deposition 313 CH4 and N2O Fluxes 313 Study Areas and Modeling Design 314 Study Areas 314 Nebraska Eddy Flux Tower Sites 314 Regional Applications: Mississippi Valley and Prairie Potholes 315 Modeling Design 315 Results 316 Impacts of Management Practices on SOC at Site Scale 316 Quantification of Regional Carbon Stocks and GHG Fluxes 317 Prairie Pothole Region 317 Mississippi Valley 319 Discussion 32