Doctor of Philosophy (PhD), Wright State University, 2023, Environmental Sciences PhD

Successful crop production relies on soils with balanced physical, chemical and biological properties. Demand for greater crop yields has led to the breakdown of soil properties through detrimental agricultural practices. To combat soil degradation, farmers employ field management practices including cover crop application, crop rotation strategies and organic soil amendment addition. These practices, used independently or in combination, can improve soil stability, increase soil nutrient content and functions of beneficial soil microbiota while increasing crop yield. Despite showing promise as an organic soil amendment, dredged sediments are still not well understood, due in part to the fresh or weathered conditions dredged sediments can be applied. Specifically, there is currently no research combining dredged sediments with cover crops, comparing different dredged sediments conditions in a single study or evaluating dredged sediment condition coupled with cropping strategies. To address these knowledge gaps, my dissertation evaluates changes in soil properties and crop responses when dredged sediments are coupled with these practices. I evaluated changes in dredged sediment property responses and corn production following winter rye cover crop application compared to a fallow season in a field experiment where I found cover crop application increased corn yields compared to a fallow season. These differences were driven by microbial-associated nutrient mineralization. Additionally, I quantified soil property and corn responses to different application ratios of fresh and weathered dredged sediments in a greenhouse experiment and determined applications of dredged sediments calculated based on the nutrient recovery ratio are not sufficient to provide benefits to agricultural soils. However, in 100% applications, weathered dredged sediments were more beneficial to corn growth than agricultural soils, while fresh dredged sediments proved detrimental to corn growth. (open full item for complete abstract)

Committee: Megan Rúa Ph.D. (Committee Chair); Silvia Newell Ph.D. (Committee Member); Louise Stevenson Ph.D. (Committee Member); Katie Hossler Ph.D. (Committee Member); Zheng Xu Ph.D. (Committee Member) Subjects: Environmental Science

Master of Science, The Ohio State University, 2023, Horticulture and Crop Science

When attempting to maximize soybean yield it is vital to analyze the interactions that occur between planting date and cover crop presence. Research has consistently shown that planting date has the greatest influence on soybean grain yield. However, studies testing for how early soybean can realistically be planted and impacts that may result from such a planting are non-existent. Therefore, the objectives of this study were to 1) determine the yield impact when growing soybean with or without a rye cover crop for three different planting dates and 2) to measure the survivability of soybean seedlings at each planting. A field experiment was conducted at two locations within Ohio in South Charleston and Wooster for the 2021 and 2022 growing seasons. A randomized complete block design was utilized consisting of four replications with nine treatments. The two factors examined were planting date and cover crop presence along with termination timing. Herbicide burndown of the cover crop was coordinated with the planting date of each soybean group. Plant population over time, soil temperature along with percent moisture over time, soybean yield, and rye biomass dry weight were evaluated. Plant populations were between 25% and 42% greater as planting was delayed into May and the best stands were achieved by forgoing a cover crop. However, the treatments with the greatest plant populations planted in May did not result in a yield advantage compared to the plantings in early or late April. When planting ultra-early prior to 15 April, a cover crop proved unnecessary and should be avoided. Planting ultra-early with a cover crop resulted in significant yield declines across both locations of between 24% and 59% compared to planting ultra-early without a cover crop.

Committee: Laura Lindsey (Advisor); Alex Lindsey (Committee Member); Marilia Chiavegato (Committee Member) Subjects: Agriculture; Agronomy

Master of Science, The Ohio State University, 2023, Environmental Science

A growing body of scientific literature has identified the potential that conservation agricultural practices can play in improving agroecosystem services, including sequestering carbon and lowering greenhouse gas (GHG) emissions while simultaneously making croplands more productive and resilient to the changing climate. To facilitate the adoption of conservation agricultural practices, farmers need incentives beyond yield. While agriculture companies have recently entered the carbon credit markets, there are a lot of uncertainties regarding the ability to measure and monitor site-specific carbon sequestration and GHG emissions, as well as the potential of agricultural practices to impact soil organic carbon (SOC), crop yield, and GHG emissions due to high degree of spatial and temporal variation that exist in agriculture production systems. Thus, the main objective of this research was to assess the extent to which row-crop production can sequester SOC and reduce GHG emissions while improving crop productivity under current and conservation agricultural practices. To meet the objective, a process-based biogeochemical Denitrification Decomposition (DNDC) model was developed at the HUC-12 subwatershed scale for the Maumee River Watershed (MRW). The model was calibrated, validated, and run from 2000 to 2020 to simulate SOC and GHG emissions under current practices. The model was then run under conservation agricultural practices, including no-tillage, reduced tillage, increased corn residue on field after harvest, and cover crops, and the effectiveness of these practices to improve SOC and corn and soybean yields were compared to the current agricultural practices. The average annual CO2 emissions ranging from 1,945 to 2,357 kg C/ha dominated the Southern and Northwest parts of the watershed while the pocket of areas with high average annual N2O emissions ranging from 3.31 to 11.63 kg N/ha was mostly located in the middle part, Northeast, and Northwest parts of the wa (open full item for complete abstract)

Committee: Sami Khanal (Advisor); Brent Sohngen (Committee Member); Bhavik Bakshi (Committee Member) Subjects: Agriculture; Environmental Science

Doctor of Philosophy, The Ohio State University, 2021, Environmental Science

Climate change is threatening food security as it is generally perceived to have negative impacts on agricultural production. Understanding this impact is central to adaptations to reduce potential yield loss. However, yield responses to changes in climate are complicated and have not been well understood. This project aims to characterize yield responses to the changing climate by utilizing modeling approaches, which in turn will help develop decision-supporting tools to inform policy or adaptation strategies. In this dissertation, we address several questions in modeling the impact of climate change on crop yield. First, in Chapter 2, we reviewed and synthesized current progress and findings from studies in the last 21 years using data-driven approaches. We found that previous studies generally agree that warming will negatively affect crop yields. For example, maize, wheat, soybean, and rice yield could be reduced by 7.5 ± 5.3%, 6.0 ± 3.3%, 6.8 ± 5.9%, and 1.2 ± 5.2% with 1 °C warming. Climate change could account for 37% of yield variability across the world. We also identified challenges and issues in previous studies, and thus developed a Bayesian model framework in Chapter 3 to overcome part of these challenges. The proposed Bayesian model framework was used in Chapter 4 to characterize spatial variations in yield responses to changes in climate variables with response curves. These response curves could help us identify what threats crop yield of a county is facing or will face and inform adaptation strategies to deal with these threats. If without adaptions, projected climate conditions of more than 36 climate models under four Coupled Model Intercomparison Project 5 (CMIP5) scenarios would benefit crops in some areas but could also cause severe yield loss in others. These yield changes are location- and scenario-specific. The Henry County in northern Ohio, for example, would have a yield increase of 1.2% and 0.7% under RCP 2.6 and 6.0 (both scenarios ar (open full item for complete abstract)

Committee: Kaiguang Zhao Dr. (Advisor); Gil Bohrer Dr. (Committee Member); Jay Martin Dr. (Committee Member); Yanlan Liu Dr. (Committee Member) Subjects: Agricultural Engineering; Agriculture; Climate Change; Ecology; Environmental Science

Doctor of Philosophy (PhD), Ohio University, 2017, Plant Biology (Arts and Sciences)

Throughout this dissertation I approach the long-term aim of improving photosynthesis through the lens of natural genetic variation for photosynthesis. To date few studies have directly asked how photosynthetic variation might inform or provide the genetic material required to enhance photosynthesis, despite the clear utility of this strategy for other types of agronomic improvement. Of the many traits underling variation in photosynthesis, mesophyll conductance – the diffusional flux of CO2 through the leaf interior – has potential to improve both photosynthesis and water use efficiency. I assess genetic variation for photosynthesis among ecotypes of the model plant Arabidopsis thaliana and cultivars of soybean (Glycine max). In both species, and across both controlled and field environments in soybean, I find heritable genetic variation for mesophyll conductance that is positively correlated to variation in photosynthetic rate, indicating that selection to enhance mesophyll conductance will increase photosynthesis. Genetic variation in mesophyll conductance though was largely unrelated to variance in water use efficiency as a result of phenotypic correlation between stomatal and mesophyll conductance. If trait variation is to prove useful for crop breeding, that trait must not have already been improved in the varieties currently used by farmers. In soybean, photosynthesis has improved slightly with breeding for yield across a historical set of cultivars. Mesophyll conductance is not responsible for this increase in photosynthesis; it remains unchanged after 75 years of selection for yield. Stomatal conductance is greater in modern varieties and I show that this increase scales from the leaf to the canopy. Greater canopy conductance in modern soybeans resulted in lower canopy temperatures and reduced leaf heat-stress. Few leaf-level photosynthetic traits were improved across this historical set of soybean cultivars. Given that I observed heritable genetic variatio (open full item for complete abstract)

Committee: David Rosenthal (Advisor); Ahmed Faik (Committee Member); Jared DeForest (Committee Member); Ryan Fogt (Committee Member) Subjects: Agronomy; Ecology; Physiology; Plant Biology

Master of Science, The Ohio State University, 2016, Food, Agricultural and Biological Engineering

With the rising cost of inputs and the shrinking profit margins in agriculture, farmers are looking to manage at the plant level to increase crop yields. As the physical size of agricultural field machinery continues to grow, many agriculture professionals recognize the negative effects of increasing gross vehicle weights on soil structure, health and productivity. The persistent trend of increased machinery size and gross weights thus exacerbating soil compaction which reduces crop yields and impacts profitability. This manuscript focuses on assessing the adverse impact of high axle loads on field productivity for corn production. Historically, many studies were performed using axle loads ranging from 10 T to 20 T. Few, if any, studies were conducted at axle loads in excess of 20 T. A better understanding of higher axle loads is needed in view of the trend of increasing equipment size where axle loads now approach 50 T. Development of a compaction model combined with data tools will allow users to process remote sensed imagery and CANbus data to better visualize and estimate the yield-related effects of compaction. The overarching goal of the envisioned tool is to provide farm managers and decision makers with actionable information as they assess the ever-expanding number of equipment options available in the marketplace. By coupling remote sensed imagery, yield monitor data, CANbus data and field trial results, the envisioned tool aids producers in making informed decisions specific to their equipment complements and soils via information extraction and synthesis from the ever-expanding quantity of data being collected on their farms. This manuscript details a series on investigations undertaken to better understand the potential effects of each pass of machinery over a field. These investigations were designed to: 1) develop an empirical model framework to predict the magnitude of compaction events and the resulting yield penalty based on axle (open full item for complete abstract)

Committee: Scott Shearer Dr. (Advisor); John Fulton Dr. (Committee Member) Subjects: Agricultural Engineering

Master of Science, The Ohio State University, 2012, Horticulture and Crop Science

Several winegrapes grown in cool climates, including Vitis vinifera Cabernet Franc and Vitis sp. Chambourcin, benefit from crop reduction. The practice promotes timely fruit maturation and can improve fruit quality. Balanced pruning and cluster thinning are the cultural practices used to attain the desired crop load. However, crop reduction by cluster thinning is labor intensive, costly and typically not mechanized for winegrapes. In this study, the practice of early season leaf removal by hand and with a mechanized leaf remover is proposed as an alternate tool to reduce crop level, thereby optimizing crop load and fruit quality. The objectives were to determine the effects of the timing of leaf removal (pre-bloom, bloom, or fruitset) on yield components, crop load, fruit quality, and cold hardiness in Chambourcin and to 2) determine the effects of manual versus mechanical leaf removal at different phenological stages on yield components, growth, crop load, and fruit quality in Cabernet Franc. In Chambourcin, leaf removal at pre-bloom in 2010 and bloom in 2010 and 2011 reduced yield as compared to defoliation at fruitset and control (no removal). Early season leaf removal reduced crop load (Ravaz index) in both years. Defoliation at bloom increased bud lignification in both years and reduced bud injury in fall of 2010. Defoliation at pre-bloom reduced bud injury in winter in one of two years. In Cabernet Franc, manual leaf removal at pre-bloom and mechanical leaf removal at bloom reduced yield. Leaf removal had no negative effects on pH, titratable acidity, soluble solids, or total phenolics in either cultivar in either year. Early season leaf removal can be used to control yield without negatively impacting growth, cluster compactness, disease incidence, fruit composition, or lignification. It is concluded that early season leaf removal is a viable alternative to cluster thinning as a method of yield reduction in Chambourcin. Early season mechanical leaf removal is (open full item for complete abstract)

Committee: Imed Dami Dr (Advisor); Douglas Doohan Dr (Committee Member); Joseph Scheerens Dr (Committee Member); Michael Ellis Dr (Committee Member) Subjects: Agriculture; Horticulture

PHD, Kent State University, 2011, College of Arts and Sciences / Department of Geography

Corn and soybean yield maps derived from yield monitors can be applied for precision agricultural practices by using them to develop or help develop management zones (field areas managed homogeneously). Applying variable fertilizer rates to zones based on need has been shown to increase profits, in part, due to less fertilizer being used than with uniform application. This can have environmental benefits by resulting in less run-off or leaching of fertilizer into the hydrologic system. Many corn and soybean farmers do not have yield monitors to produce yield maps. To help resolve this problem, this research focuses on predicting corn and soybean yield at the field scale. Corn and soybean yield monitor data were acquired and cleaned by different methods to develop better data to base predictions on. Correlations between different Landsat-derived values and corn or soy yield at different growth stages were made. Artificial neural networks (ANN) models based on four independent variables were developed to predict yield and results were compared to multiple linear regression (MLR). Yield cleaning methods that included median neighborhood statistics processing produced better data. Landsat correlations with soybean yield were most reliably high when solely using band 4 during much of the reproductive stage (R²=0.63) while corn yield was better predicted during later vegetative stages. Many different indices proved useful to predict corn, with soil-adjusted vegetation indices having the highest correlations (R² ranging from 0.60 to 0.62). Overall, it was shown that Landsat can predict yield better and, hence, sense crop condition better at distinctly different times of the season for corn and soybeans. ANN predicted yield slightly better than MLR, having an R² value 0.03 higher and increased the R² value with the Landsat crop condition variable by 0.115. Additionally, a Landsat-based county corn yield prediction model that included imagery from the end of July to the latt (open full item for complete abstract)

Committee: Mandy Munro-Stasiuk (Advisor); Scott Sheridan (Committee Member); Emariana Taylor (Committee Member); Joseph Ortiz (Committee Member); Murali Shanker (Committee Member) Subjects: Agriculture; Geographic Information Science; Remote Sensing