<p>Hemp (Cannabis sativa L.) is an annual crop used to produce a wide range of products including foods, beverages, nutritional supplements, fabrics, and textiles. Hemp has long been conflated with marijuana and has not been grown in the United States for decades. Due to recent legislation, the legal restrictions on growing hemp seem likely to be lifted. However, although interest is high, industrial hemp has not been grown in the U.S. for nearly 80 years and research on virtually all aspects of hemp production in the U.S. is in its infancy. We lack fundamental knowledge regarding cultivar performance, interactions with pests, particularly weeds, and nutrient requirements. Research is needed to address this knowledge gap and potential production issues as well as to determine the attitudes, perceptions and concerns of farmers regarding the potential adoption of this “new” crop. Importantly, research should be conducted before the crop becomes widely available so that farmers can make informed decisions and avoid costly mistakes. My dissertation consists of four chapters. In Chapter 1, I examine the literature for weed management in hemp production and identify research gaps. In Chapter 2, I investigate the complex legal framework that surrounds Cannabisand the resulting complications for hemp production. In Chapter 3, I present research conducted to determine the attitudes, perceptions, interests and concerns of organic farmers regarding the reintroduction and potential adoption of hemp was completed through survey research. Finally, in the fourth chapter, I present research conducted to characterize the growth and phenology of industrial hemp cultivars and identify cultivars suitable for growing conditions in the Midwest, and to determine the effect of delayed planting on the phenology and growth of seed and fiber hemp varieties in the Midwest.</p><p>Weed control and weed management in industrial hemp production is a surprisingly understudied field. Few peer-reviewed field studies on hemp exist on any subject and in particular, weed control and weed management is understudied. Specifically, only three studies designed to address a weed management issues exist in the literature dating back to 1900. Most commodity crops have extensive literature discussing weed management, and such an extensive gap in the hemp literature suggests that research needs to be conducted to determine the impacts of weeds on hemp production. Discrepancies among state laws and current federal drug legislation have created a convoluted, confusing, and impractical framework currently surrounds hemp production in the U.S. The building of pesticide regulation and product safety systems that are specific to the many end uses of Cannabis have yet to occur in the U.S. Interactions between producers, state and federal government, and third-party testing laboratories need to be facilitated to build regulation systems along with educational programs to train growers appropriate best management. Organic farmers are generally considered less risk adverse than the general farming population and often considered early adopters of technology. I surveyed organic farmers in seven Midwestern states and found that 98.5% of the respondents were generally open to new technologies, but that demographics variables explained little of the variation for respondents’ level of innovativeness as well as their openness to hemp.The respondents were generally open to hemp production (88.2% agreed with the statement that they were open to trying hemp production on their farm) and found that attributes of hemp production that conferred relative advantage and were compatible with existing systems were important. Delayed planting of hemp generally reduced the onset and duration of female flowering and the time to seed formation but the magnitude of these effects varied among cultivars. Seed, stalk, and total above ground dry weight yields varied across cultivar and planting date which may have been impacted by inconsistent stand densities stemming from heavy rainfall and wet soils. Results from this dissertation suggest that hemp is an understudied crop in the U.S., but that interest in its production among organic farmers exists. Field results support the importance of both planting date and cultivar for hemp phenology discussed in previous literature and so research needs to be conducted to explore best hemp production practices in the U.S.<br></p><p></p>

<p>Arbuscular mycorrhizal fungi (AMF) are best known for their potential to help plants acquire nutrients, especially phosphorous. These microbes improve soil health by promoting soil aggregation and carbon sequestration, and further benefit plants by helping them withstand biotic and abiotic stress. Currently, there are 200 recognized species of AMF within the phylum Glomeromycota. Recent studies indicate that individual AMF species differ in the benefits they provide, with some even acting as parasites. Moreover, AMF community composition can be altered by soil and crop management practices, but the effect of these changes on the benefits conferred by AMF are still not well understood. Consequently, the goal of this study was to determine how two widely used crop management systems can alter the composition of AMF species, and affect the potential for these communities to promote the productivity and drought tolerance. To accomplish this goal, we collected AMF inoculum from a long-term crop systems trial comparing organic and conventional management for use in greenhouse trials where we subjected plants to drought. We collected AMF inoculum during mid-summer when differences between the two management systems were likely cause larger effects on AMF communities, and again in autumn after harvest to see if differences in AMF communities would persist. We determined AMF species composition using next generation sequencing. Results of this study confirm that soil-building practices commonly used in organic farming systems can improve soil health and increase the productivity of food-grade soybeans. They also demonstrate that AMF communities in Indiana croplands are highly diverse, and some of these taxa can improve soybean growth and help plants tolerate water stress. Although the overall diversity of AMF communities did not differ between the organic and conventional management systems in mid-summer, individual AMF taxa did differ between the systems, which were likely responsible for the greater tolerance to water stress observed when plants were amended with inoculum from the organic system. AMF communities present during autumn were significantly different between the two crop management systems, but did not result in differences in drought tolerance of soybeans, indicating that the loss of key AMF taxa in the organic system from the first relative to the second experiment was likely responsible. Finally, plants grown using inoculum from both crop management systems in autumn had greater tolerance to water stress than plants that received a AMF commercial inoculum. This provides further evidence that individual AMF species vary in the benefits they provide, and that the presence of a diverse consortium of AMF species is needed to optimize plant health and productivity in agricultural systems. Agricultural producers should consider incorporating soil-building practices that are commonly used in organic farming systems such as planting winter cover crops, to improve the health of their soil and enhance the productivity of their crops. <b></b></p> <br>

<p>This research focused on developing adaptable models and protocols for early warning systems for forecasting plant diseases and datasets. It compared the performance of deep learning models in predicting soybean rust disease outbreaks using three years of public epidemiological data and gridded weather data. The models selected were a dense network and a Long Short-Term Memory (LSTM) network. The objectives included evaluating the effectiveness of small citizen science datasets and gridded meteorological weather in sequential forecasting, assessing the ideal window size and important inputs, and exploring the generalizability of the model protocol and models to other diseases. The model protocol was developed using a soybean rust dataset. Both the dense and the LSTM networks produced accuracies of over 90% during optimization. When tested for forecasting, both networks could forecast with an accuracy of 85% or higher over various window sizes. Experiments on window size indicated a minimum input of 8 -11 days. Generalizability was demonstrated by applying the same protocol to a southern corn rust dataset, resulting in 87.8% accuracy. In addition, transfer learning and pre-trained models were tested. Direct transfer learning between disease was not successful, while pre training models resulted both positive and negative results. Preliminary results are reported for building generalizable disease models using epidemiological and weather data that researchers could apply to generate forecasts for new diseases and locations.</p>

Fruit set in cucurbit crops such as watermelon is entirely dependent upon pollinators, which makes them an important aspect of grower management. This reliance on pollinators means that growers must consider them when making pest management decisions, especially when using pesticides, which can have a negative impact on pollinators. Thus, pest management in watermelon production faces a potential trade-off between pests and pollinators. The ways in which growers manage this trade-off could have a large impact on the communities of both groups and the yield of the crop. <br>

<p>Cadmium (Cd) is a highly mobile and toxic heavy metal that negatively affects plants, soil biota, animals and humans, even in very low concentrations. Currently, Cd contamination of cocoa produced in Latin American countries is a significant problem, as concentrations can exceed acceptable levels set by the European Union (0.5 mg/kg), sometimes by more than 10 times allowable levels. In South America, <i>Theobroma cacao</i> is an essential component of the basic market basket. This crop contributes to the Latin-American trade balance, as these countries export cacao and chocolate-based products to major consumer countries such as the United States and Europe. Some soil amendments can alter the bioavailability and uptake of Cd into edible plant tissues, though cacao plants can accumulate Cd without displaying any visible symptoms of phytotoxicity, which makes it difficult to determine if potential remediation strategies are successful. Currently, the only effective way to quantify Cd accumulation in plant tissues is via destructive post-harvest practices that are time-consuming and expensive. New hyperspectral imaging (HSI) technologies developed for use in high-throughput plant phenotyping are powerful tools for monitoring environmental stress and predicting the nutritional status in plants. Consequently, the experiments described in this thesis were conducted to determine if HSI technologies could be adapted for monitoring plant stress caused by Cd, and estimating its concentration in vegetative plant tissues. Two leafy green crops were used in these experiments, basil (<i>Ocimum basilicum L.</i> var. Genovese) and kale (<i>Brassica oleracea L</i>. var. Lacinato), because they are fast growing, and therefore, could serve as indicator crops on cacao farms. In addition, we expected these two leafy green crops would differ in their morphological responses to Cd stress. Specifically, we predicted that stress responses would be visible in basil, but not kale, which is known to be a hyperaccumulator. The plants were subject to four levels of soil Cd (0, 5, 10 and 15 ppm), and half of the pots were amended with biochar at a rate of 3% (v/v), as this amendment has previously been demonstrated to improve plant health and reduce Cd uptake. The experiments were conducted at Purdue’s new Controlled Environment Phenotyping Center (CEPF). The plants were imaged weekly and manual measurements of plant growth and development were collected at the same times, and concentrations of Cd as well as many other elements were determined after harvest. Fourteen vegetation indices generated using HSI images collected from the side and top view of plants were evaluated for their potential to identify subtle signs of plant stress due soil Cd and the biochar amendment. In addition, three mathematical models were evaluated for their potential to estimate Cd concentrations in the plant biomass and determine if they exceed safe standards (0.28 mg/kg) set by the Food and Agriculture Organization (FAO) for leafy greens. Results of these studies confirm that like many plants, these leafy green crops can accumulate Cd levels that are well above safety thresholds for human health, but exhibit few visible symptoms of stress. The normalized difference vegetation index (NDVI) and the chlorophyll index at the red edge (CI_RE) were the best indices for detecting Cd stress in these crops, and the plant senescence and reflectance index (PSRI) and anthocyanin reflectance index (ARI) were the best at detecting subtle changes in plant physiology due to the biochar amendment. The heavy metal stress index (HMSSI), developed exclusively for detecting heavy metal stress, was only able to detect Cd stress in basil when images were taken from the top view. Results of the mathematical models indicated that principal components analysis (PCA) and partial least squares (PLS) models overfit despite efforts to transform the data, indicating that they are not capable of predicting Cd concentrations in these crops at these levels. However, the artificial neural networks (ANN) was able to predict whether leafy greens had levels of Cd that were above or below critical thresholds suggested by the FAO, indicating that HSI could be further developed to predict Cd concentrations in plant tissues. Further research conducted in the field and in the presence of other environmental stress factors are needed to confirm the utility of these tools, and determine whether they can be adapted to monitor Cd uptake in cacao plants.</p>

<p>Cereal rye (<i>Secale cereale</i>, L., CR) is the most commonly utilized cover crop species within the United States. Yet, the total land area planted to CR on an annual basis remains relatively low despite its numerous proven environmental benefits. The relatively low rates of CR adoption could be due to a dearth of knowledge surrounding certain agronomic and economic components of CR adoption. Currently, there exists knowledge gaps within the scientific literature regarding CR performance, N cycling, and associated economic risk. <a>Thus, to address the above-mentioned knowledge gaps, three individual studies were developed to: i) investigate the fate of scavenged CR nitrogen (N) amongst soil N pools, ii) assess the suitability of visible-spectrum vegetation indices (VIs) to predict CR biomass and nutrient accumulation (BiNA), and iii) characterize the economic risk of CR adoption at a regional scale over time.</a></p> <p>In the first study, ¹⁵N, a stable isotope of N, was used in an aerobic incubation to track the fate of CR root and shoot N among the soil microbial biomass, inorganic, and organic N pools, as well as explore CR N bioavailability over a simulated corn growing season. In this study, the C:N ratio of the shoot residues was 16:1 and the roots was 31:1 and differences in residue quality affected the dynamics of CR N release from each residue type. On average, 14% of whole plant CR N was recovered in the soil inorganic N pool at the final sample date. Correspondingly, at the final sampling date 53%, 33%, and less than 1% of whole plant CR N was recovered as soil organic N, undecomposed residue, and as microbial biomass N, respectively. Most CR N remained unavailable to plants during the first cash crop growing season subsequent to termination. This knowledge could support the advancement of N fertilizer management strategies for cropping systems containing cereal rye.</p> <p>In the second study, a commercially available unmanned aerial vehicle (UAV) outfitted with a standard RGB sensor was used to collect aerial imagery of growing CR from which visible-spectrum VIs were computed. Computed VIs were then coupled with weather and geographic data using linear multiple regression to produce prediction models for CR biomass, carbon (C), N, phosphorus (P), potassium (K), and sulfur (S). Five visible-spectrum VIs (Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Green (ExG)) were evaluated and the results determined that MGRVI was the best predictor for CR biomass, C, K, and S and that RGBVI was the best predictor for CR N and P. Furthermore, the final prediction models for the VIs selected as the best predictors developed in this study performed satisfactorily in the prediction of CR biomass, C, N, P, K, and S producing adjusted R² values of 0.79, 0.79, 0.75, 0.81, 0.81, and 0.78, respectively. The results of this study have the potential to aid producers in making informed decisions regarding CR and fertility management. </p> <p>In the final study, agronomic data for corn and soybean cropping systems with and without CR was collected from six states (Illinois, Indiana, Iowa, Minnesota, Missouri, and Wisconsin) and used within a Monte-Carlo stochastic simulation to characterize the economic risk of adopting CR at a regional scale over time. The results of this study indicate that average net returns to CR are always negative regardless of CR tenure primarily due to added costs and increased variability in cash crop grain yields associated with CR adoption. Further, the results demonstrate that the additional risk assumed by adopting CR is not adequately compensated for with current CR adoption incentive programs and that the risk premium necessary can be 1.7 to 15 times greater than existing incentive payments. Knowledge gained from this study could be used to reimagine current incentive programs to further promote adoption of CR.</p>