Genomic signature of adaptive divergence despite strong nonadaptive forces on Edaphic Islands: A case study of primulina juliae

Both genetic drift and divergent selection are expected to be strong evolutionary forces driving population differentiation on edaphic habitat islands. However, the relative contribution of genetic drift and divergent selection to population divergence has rarely been tested simultaneously. In this study, restriction-site associated DNA-based population genomic analyses were applied to assess the relative importance of drift and divergent selection on population divergence of Primulina juliae, an edaphic specialist fromsouthern China. All populations were found with low standing genetic variation, small effective population size (NE), and signatures of bottlenecks. Populations with the lowest genetic variation were most genetically differentiated from other populations and the extent of genetic drift increased with geographic distance fromother populations. Together with evidence of isolation by distance, these results support neutral drift as a critical evolutionary driver.Nonetheless, redundancy analysis revealed that genomic variation is significantly associated with both edaphic habitats and climatic factors independently of spatial effects. Moreover, more genomic variationwas explained by environmental factors than by geographic variables, suggesting that local adaptationmight have played an important role in driving population divergence. Finally, outlier tests and environment association analyses identified 31 singlenucleotide polymorphisms as candidates for adaptive divergence. Among these candidates, 26 single-nucleotide polymorphisms occur in/near genes that potentially play a role in adaptation to edaphic specialization. This study has important implications that improve our understanding of the joint roles of genetic drift and adaptation in generating population divergence and diversity of edaphic specialists

Developing Metrics for Novel Value-Added Products: The Case of Hemp in Vermont

Vermont Farm to Plate 2020 identifies hemp as one of ten emergent agricultural products critical for Vermont’s future and has made recommendations for investments in hemp research, education, feasibility, and innovation programs. These investments are essential to develop niche food, feed, fiber, and industrial products, professionals, and markets that go “beyond CBD” (VFP, 2020). This project develops indicators for an important, value added budding crop in Vermont: hemp. For the purposes of this white paper, indicators are “a way to measure, indicate or point to with more or less exactness,” or “something used to show the condition of a system” (Feenstra et al., 2005). We focus on the example of hemp to illustrate how emerging value-added crops contribute to sustainable food systems. We use a set of design principles to ensure the applicability of developed indicators for decision making. This framework, its processes, and measures, are transferable to any nascent crop for evaluating economic, environmental, and social sustainability. Our objectives are to: Develop a set of indicators to measure the economic, social, and environmental inputs of hemp in Vermont. Identify techniques and data sources for mining hemp metrics. Evaluate the hierarchical levels of mined data and transecting indicators to inform growing discussions of metric integration and forecasting agricultural food sustainability. Our approach is grounded in the FAO food systems model, Doughnut Economics, which uses the UN Sustainable Development Goals as a foundation to describe “social floors” and planetary boundaries, and the concepts of seven community capitals: political, cultural, human, social, financial, built, and environmental. Our work plan included a two-day virtual workshop with required reading prior to the event, and involved both University researchers and stakeholders representing production, industry, finance, government, and NGOs. We describe 35 metrics to assess the sustainability of hemp in the Vermont economy, environment and community going forward (Figure 2 and Table 1). We also make several recommendations to move the collection of food system metrics forward. General recommendations include: Farmer surveys to specifically address challenges facing farmers growing a novel crop. In hemp this is particularly important, as the crop attracts many who are new to farming, and no one has been able to legally grow it at field scale in the US for the past several generations. Community/consumer surveys to collect data on community needs and impacts of novel value added crops (hemp) Collection and curation of spatial data tied to appropriate metrics Allocation of ARS funds for at least one data professional with skills across data types and methods, including individual, spatial, community level, etc. Funds to build out nutrient mass balance and soil carbon stock models for different soil types and production approaches The use of a grower-friendly tracking App with incentives (GoCrop) Development of a dashboard to easily visualize direction and degree of movement toward a desired state We make more specific recommendations in the Appendix where each metric is described in detail

Non-linear regression models for time to flowering in wild chickpea combine genetic and climatic factors

Background: Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. Results: We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R 2=0.97. Conclusions: We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited

Dynamical climatic model for time to flowering in Vigna radiata

Background: Phenology data collected recently for about 300 accessions of Vigna radiata (mungbean) is an invaluable resource for investigation of impacts of climatic factors on plant development. Results: We developed a new mathematical model that describes the dynamic control of time to flowering by daily values of maximal and minimal temperature, precipitation, day length and solar radiation. We obtained model parameters by adaptation to the available experimental data. The models were validated by cross-validation and used to demonstrate that the phenology of adaptive traits, like flowering time, is strongly predicted not only by local environmental factors but also by plant geographic origin and genotype. Conclusions: Of local environmental factors maximal temperature appeared to be the most critical factor determining how faithfully the model describes the data. The models were applied to forecast time to flowering of accessions grown in Taiwan in future years 2020-2030

The Farm-Community Nexus: Metrics for Social, Economic, and Environmental Sustainability of Agritourism and Direct Farm Sales in Vermont

Viable working landscapes, vibrant communities, and healthy ecosystems are the building blocks of sustainable food systems. Small and medium farms are connective tissue, creating a system that is greater than the sum of its parts by linking consumers to producers and promoting environmental stewardship. Our approach considers sustainability through connections between farms, their communities, and visitors within an agritourism framework, including on-farm experiences, direct sales of agricultural products, and farmer-consumer interactions at markets. The goal is to contribute to the understanding, operationalization, and integration of metrics built on the ideals that viable, sustainable, and resilient food systems must support social, economic and environmental goals. The approach presented in this white paper: 1. Applied a sustainability framework to identify metrics relevant for social, economic, and environmental dimensions across farm, household, community, and statewide scales. 2. Identified existing data sets and current data gaps. 3. Identified linkages and impacts between social, economic and environmental dimensions of sustainability across scales and different frameworks. 4. Considered sustainability applied to direct sales and agritourism, with particular emphasis on the social floor required to promote individual, farmer, and community well-being, while protecting the environment by respecting our planetary boundaries. We categorized priority metrics under primary sustainability dimensions: Environmental – Open Space, Farm Products, Stewardship, and the Vermont Brand Economic – Economic Impacts, Consumer Spending, Farm Profitability, Farm Labor, and Farmland Social – Cultural Ecosystem Services, Labor Opportunities and Conditions, Social and Informational Infrastructure, Sense of Community, Demographic and Cultural Diversity, Good Governance, and Health, Safety, and Wellbeing Based on our assessment of existing and needed metrics summarized in this white paper, key recommendations to the UVM-ARS Center include: 1. Catalyze and synergize efforts and resources in Vermont to holistically address sustainability. 2. Explore and identify ways the Vermont brand—an important component of the state’s social, ecological and economic identity and culture—supports sustainability. 3. Focus on informational and data needs that are central to understanding and ensuring sustainability in Vermont, including longitudinal producer and consumer surveys. 4. Support a deep convergence of social and natural sciences in addressing sustainability. The goal is to provide an essential foundation for future research that will place the UVM-ARS Center for Food Systems Research at the forefront of this critical transdisciplinary area

Seeds of Resilience: Learning from COVID-19 to Strengthen Seed Systems in Vermont

Seeds are central to crop-based production systems, yet in the United States seeds have been largely overlooked in both research and local and regional food systems initiatives. This report seeks to address the gap in seed-related research by assessing current strengths and vulnerabilities of Vermont’s seed systems. In particular, the findings presented in this report illuminate how seed systems can maintain function in the face of external shocks such as the COVID-19 pandemic, and how we can apply the lessons learned toward building resilience for an uncertain future due to factors such as climate change. Despite the turmoil caused by the pandemic, the last several years have provided a unique opportunity to identify strategies to strengthen Vermont’s seed systems. The data presented and discussed in this report build on existing research and showcase a myriad of seed-related efforts in Vermont. We use an interdisciplinary approach to study the supply and demand for seed among farmers and gardeners in Vermont during the COVID-19 pandemic. To contextualize our report, we begin with a brief summary of findings taken from the 2020 and 2021 Vermonter Polls regarding seed system trends. Data from these surveys, conducted by the UVM Center for Rural Studies, were collected in February-March 2020 (before and during the onset of the pandemic in Vermont) and February- March 2021 (nearly a year into the pandemic). We then present the findings from two online surveys of commercial farmers (n=73) and non-commercial seed growers (n=75) in Vermont, which include a specific focus on five commonly grown crops in Vermont: garlic, tomatoes, squash, lettuce, and potatoes. In the future, this work can guide collaborative and participatory responses to seed system vulnerabilities exposed by the COVID-19 crisis

Resilient Soils for Resilient Farms: An Integrative Approach to Assess, Promote and Value Soil Health for Small- and Medium-Size Farms

Our team was a collaborative group of academic, extension and doctoral student researchers who met internally and conducted an IRB-approved survey that engaged with myriad stakeholders. The result was a clear trifecta on relative timing of soil health initiatives: 1) Stakeholders (91%) embrace soil health and believe soil health should be the top priority for UVM research and outreach. 2) Existing policy demands farmers assess elements of soil health every two years for nutrient management plans. 3) Only a subset of desired metrics is available at commercial laboratories, most soil analyses are sent out of state to Maine or New York, and most data are privately held instead of deposited into public databases. Together, these three findings indicate that soil health be a central focus of UVM\u27s ARS program. Yet, due to attrition, there are no longer any UVM faculty dedicated to updating the 30-year-old soil recommendations upon which regulations rely. There is opportunity for university-government-community partnerships and expanded employment opportunities in Vermont if collaborative resources were assigned to soil health

Simulation Model for Time to Flowering with Climatic and Genetic Inputs for Wild Chickpea

Accurate prediction of flowering time helps breeders to develop new varieties that can achieve maximal efficiency in a changing climate. A methodology was developed for the construction of a simulation model for flowering time in which a function for daily progression of the plant from one to the next phenological phase is obtained in analytic form by stochastic minimization. The resulting model demonstrated high accuracy on the recently assembled data set of wild chickpeas. The inclusion of genotype-by-climatic factors interactions accounted to 77% of accuracy in terms of root mean square error. It was found that the impact of minimal temperature is positively correlated with the longitude at primary collection sites, while the impact of day length is negatively correlated. It was interpreted as adaptation of accessions from highlands to lower temperatures and those from lower elevation river valleys to shorter days. We used bootstrap resampling to construct an ensemble of models, taking into account the influence of genotype-by-climatic factors interactions and applied it to forecast the time to flowering for the years 2021–2099, using generated daily weather in Turkey, and for different climate change scenarios. Although there are common trends in the forecasts, some genotypes and SNP groups have distinct trajectories

Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of the plant from planting to flowering are obtained in analytic form and depend on daily values of climatic factors and genetic information. The resulting pool of models demonstrated high accuracy on the dataset. Day length, solar radiation and temperature had a large impact on the model accuracy, while the impact of precipitation was comparatively small and the impact of maximal temperature has the maximal variation. The model pool was used to investigate the behavior of accessions from the dataset in case of temperature increase by 0.05–6.00°. The time to flowering changed differently for different accessions. The Pearson correlation coefficient between the SNP value and the change in time to flowering revealed weak but significant association of SNP7 with behavior of the accessions in warming climate conditions. The same SNP was found to have a considerable influence on model prediction with a permutation test. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time

Modeling of Flowering Time in <i>Vigna radiata</i> with Artificial Image Objects, Convolutional Neural Network and Random Forest

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time