Evaluating genetic mechanisms and performance characteristics of alternative oilseed crops for on-farm biofuel production in Colorado

Includes bibliographical references.2015 Summer.Dryland winter wheat (Triticum aestivum) cropping systems dominate most of the agricultural landscape in Colorado’s semi-arid eastern plains. Since this area’s climate is characterized by frequent heat and drought, it is important to maximize water use efficiency to make agricultural lands as productive as possible. Adding a spring crop in rotation with winter wheat intensifies the rotation, increasing water use efficiency by up to 37%. Recent research has explored further intensifying this rotation by adding an oilseed crop into a wheat – spring crop – fallow rotation during the fallow period. Ideally, the oilseed crop acts as a cover crop for part of the season and leaves enough time at the end of the season to regenerate water in the soil profile before planting wheat in the fall. The oil from this crop can be used to produce on-farm biofuels, offsetting petroleum diesel costs without displacing high-value food crops. Additionally, the meal from this crop acts as a value-added byproduct by providing feed for livestock. Since traditional oilseeds such as soybean (Glycine max) and rapeseed (Brassica napus) do not perform well in Colorado, several alternative oilseeds have been tested to assess whether they can fill this niche. Camelina (Camelina sativa) has shown great potential, with high oil content and inherent resistance to many biotic and abiotic stressors. Other potential oilseeds include Brassica juncea and Brassica carinata, but both of these species have exhibited longer life cycles and lower yields than camelina. A major challenge to camelina production in Colorado is a susceptibility to heat stress during reproductive periods. Both short periods of intense heat stress and longer periods of mild heat stress can cause floral and seed abortion, resulting in reduced yield. In the current study, a quantitative trait locus (QTL) approach is used to identify heat and drought tolerance mechanisms and yield components, explore the extent of pleiotropy, epistasis, and linkage, and identify promising lines for study or production. Genetic resources for camelina are becoming more readily available and a newly developed genetic map with improved marker density was used for QTL discovery. Replicated field trials were performed during the 2014 growing season in Fort Collins and Greeley, Colorado, under differential irrigation treatments at each site to collect phenotypic data on a variety of traits. Sixteen new QTL were discovered from this data, along with nine QTL using data from Colorado trials of the same population in 2009 and 2010 performed by Enjalbert (2011). Seven QTL were discovered for yield, however, no QTL were found in more than two environments, indicating a lack of stable QTL for this trait. This was in contrast to results from Enjalbert (2011) where stable QTL for yield across environments were detected using the original, mainly AFLP generated, genetic map by Gehringer et al. (2006). This underscores the high amount of variation that can be caused by environment. QTL for other traits, such as plant height and days to flowering, were detected that were more robust, however, no QTL were detected with either data set that spanned more than three environments. Two loci were identified that affected multiple traits, supplying evidence of either pleiotropy or close linkage of genes. Several RIL performed well in multiple environments, indicating potential for production in Colorado, however, these lines were not in common with previous studies, so further trials will be needed to confirm consistently stable yields. In addition to the camelina QTL study, a two-year variety trial of Brassica carinata was performed in Fort Collins, CO during the 2013 and 2014 growing seasons under limited and full irrigation. Collaboration with the private Canadian oilseed company Agrisoma Biosciences spurred interest in reevaluating the potential of this alternative oilseed in Colorado cropping systems. Agrisoma Biosciences developed early flowering and early maturing germplasm that performs well in the Canadian prairie and is interested in testing their germplasm in new regions with potential for production. The company provided six lines for the trial, five experimental lines and one commercial check cultivar. Mean flowering time was over 13 days longer than previously tested African accessions that had been deemed too late flowering to be competitive in Colorado’s climate. Mean yields were low as well, at 669 kg ha⁻¹. The commercial check cultivar, A100, outperformed all of the experimental lines, with a mean yield of 1081 kg ha⁻¹ across environments. With a wide margin between the other lines and A100, this commercial cultivar was clearly more successful than any of the experimental lines. However, yields of this one cultivar were not sufficiently impressive to recommend on-farm testing of the crop

Plasticity, allelic diversity, and genetic architecture of industrial hemp (Cannabis sativa L.)

2019 Summer.Includes bibliographical references.The first time in United States history that hemp was legally distinguished from high-THC Cannabis (marijuana) was in 2014 when the Farm Bill was passed. Although the two crops had been distinguished by their usage for thousands of years, their monospecific nature led to both psychoactive and non-psychoactive forms being legislated in tandem from the time that Americans began regulating Cannabis cultivation and usage. A simple statement in the 2014 Farm Bill distinguished hemp as Cannabis sativa L. with a tetrahydrocannabinol (THC) content of 0.3% or less. A second sentence enabled research into the crop and production within pilot programs in states where it is legal. This minor change in legality, followed by subsequent relaxation of laws surrounding hemp in the 2018 Farm Bill, has allowed a burgeoning hemp industry to form in the United States and enabled the return of a relict crop. Due to the long period of prohibition, hemp did not undergo the same type of crops research as other staple American crops. Consequently, little is known about the genetic mechanisms that control many of the key traits in hemp production. Understanding basic information about how traits are affected by environmental factors is highly important when regulation of the crop is based on a stringent and arbitrarily set threshold for chemical content. In 2016, we performed field trials of a diverse set of industrial hemp cultivars in multiple growing environments and assessed a wide range of traits. Expression of some traits, like days to maturity and THC content, were strongly influenced by genotype. Other traits, such as grain yield and plant height, exhibited large proportions of variance due to environmental factors and genotype-by-environment interactions. There were also varying ranges of plasticity exhibited between cultivars, underscoring the importance of selecting the right cultivar for target production environments. This highlights the importance of thoroughly characterizing genotype-by-environment interactions when breeding locally adapted hemp cultivars. Understanding genetic control of important traits and their range of plasticity enables the development of locally adapted cultivars for a wide range of end uses. Another aspect of Cannabis that is understudied is the genetic basis for differentiating hemp and high-THC Cannabis. Since the legal distinction is based on a strict threshold placed on a quantitative trait and not any known geographic or biological reproductive barriers, it is unclear whether or not there is genetic evidence to support the distinction or if the two groups are simply divergent phenotypes. A joint-site frequency and FST analysis show that individuals of the two groups mainly share common polymorphisms, with a small number of loci where differentiation occurs. These loci serve as the basis for distinguishing the two groups, but more study is needed to determine if alleles in these regions were driven to fixation via genetic drift and selection on unrelated traits, or if there is an evolutionary basis for the observed differences. When heterozygosity was assessed in these samples, the hemp group had higher overall heterozygosity levels, but the high-THC Cannabis group had more outliers which lead to a wider distribution with more extreme minimum and maximum values. Although it is clear that there are genetic differences distinguishing the two groups, extensive human vectoring and admixture between the groups, both historically and currently, makes it difficult to differentiate causes for the differences. A lack of centralized germplasm makes large-scale genomic studies of the species difficult, but, as more samples are surveyed over time, a more detailed picture of the genomic variation will emerge. These types of studies will be able to provide a more nuanced picture of the evolutionary history and current state of allelic variation within the species. In addition to plasticity and allelic diversity, genetic architecture of traits has also largely been ignored until recently. The first QTL study in Cannabis was performed in 2015 and was limited by legal restraints. Since understanding how economically relevant traits function is important to breeding improved hemp cultivars, we developed a genetic mapping population that captured variation for a wide range of traits. Utilizing whole-genome sequencing and phenotype data from a replicated field trial, we were able to detect 121 QTL associated with 38 agronomic and biochemical traits. Some traits, like days to maturity, had single loci of large effect accounting for the majority of trait variance, while other traits, like α-Pinene production, exhibited more complex polygenic architecture with epistatic interactions. Colocalization of QTL and significant trait correlations showed that there were positive relationships within both agronomic and biochemical trait groups. Although this study was limited by assessment of the population in a single environment, detecting these putative QTL serves as a substantial step forward in characterizing many relevant production traits

Feature Learning for Multispectral Satellite Imagery Classification Using Neural Architecture Search

Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such problems. However, building deep learning models to process the data requires expert knowledge of machine learning. We introduce DELTA, a software toolkit to bridge this technical gap and make deep learning easily accessible to earth scientists. Visual feature engineering is a critical part of the machine learning lifecycle, and hence is a key area that will be automated by DELTA. Hand-engineered features can perform well, but require a cross functional team with expertise in both machine learning and the specific problem domain, which is costly in both researcher time and labor. The problem is more acute with multispectral satellite imagery, which requires considerable computational resources to process. In order to automate the feature learning process, a neural architecture search samples the space of asymmetric and symmetric autoencoders using evolutionary algorithms. Since denoising autoencoders have been shown to perform well for feature learning, the autoencoders are trained on various levels of noise and the features generated by the best performing autoencoders evaluated according to their performance on image classification tasks. The resulting features are demonstrated to be effective for Landsat-8 flood mapping, as well as benchmark datasets CIFAR10 and SVHN

Improvement of Information Retrieval Systems

Recent advances in modular archetypes and knowledge-based archetypes are never at odds with extreme programming. In this paper, authors disconfirm the development of active networks, demonstrates the typical importance of machine learning. We use collaborative archetypes to demonstrate that operating systems can be made constant-time, linear-time, and relational

Free-electron lasers : echoes of photons past

High-harmonic generation is an established method to significantly upshift laser photon energies. Now, researchers at the SLAC National Accelerator Laboratory have used echo concepts to generate coherent high-harmonic output from an electron-beam light source

Systematically identifying relevant research: Case study on child protection social workers’ resilience

Context: The development of a consolidated knowledge base for social work requires rigorous approaches to identifying relevant research. Method: The quality of 10 databases and a web search engine were appraised by systematically searching for research articles on resilience and burnout in child protection social workers. Results: Applied Social Sciences Index and Abstracts, Social Services Abstracts and Social Sciences Citation Index (SSCI) had greatest sensitivity, each retrieving more than double than any other database. PsycINFO and Cumulative Index to Nursing and Allied Health (CINAHL) had highest precision. Google Scholar had modest sensitivity and good precision in relation to the first 100 items. SSCI, Google Scholar, Medline, and CINAHL retrieved the highest number of hits not retrieved by any other database. Conclusion: A range of databases is required for even modestly comprehensive searching. Advanced database searching methods are being developed but the profession requires greater standardization of terminology to assist in information retrieval. </jats:p

Agricultural Awareness Days: Integrating Agricultural Partnerships and STEM Education

In the United States there is a need to educate young children in science, technology, and agriculture. Through collaboration with many agricultural groups, the Southern Piedmont Agricultural Research and Education Center has set up a program that works with 3rd grade students and teachers to reinforce the science that has been taught in the classroom in a hands-on environment. This program has grown in size and scope over the years that it has been in place, but the partnerships that come together from Extension, Virginia Tech, USDA, and many others is what makes this program such a success

Industry and Extension Partnership to Enhance STEM and Agricultural Education

STEM education has become essential in the United States, and agriculture allows for a great opportunity to teach STEM education in a fun, hands-on manner. The Virginia Southern Piedmont Agriculture Research and Extension Center (SPAREC), in partnership with King Arthur Flour, has created a program that reinforces what is taught in the classroom while also adding in new lessons in citizenship. This program has been very successful and serves as an excellent model for future partnerships between industry and Extension