Digital Government: Criteria-Based Inference from Geospatial Data: Automating Government Decision Making for Genetic Food Security, Crop Improvement, and Global Germplasm Needs

EIA-9983507 Parks，Bradley O.科罗拉多大学博尔德数字政府：基于标准的地理空间数据推理：自动化政府决策，用于遗传食品安全、作物改良和全球种质需求该奖项将支持提交完整提案的规划工作。政府合作伙伴主要包括国家海洋和大气管理局(国家地球物理数据中心)、美国农业部(农业研究服务处)和其他几个发挥支持作用的机构。迫切需要优先采购目标政府种质(种子和砧木)用于作物改良，并估计种质多样性在景观中的分布。这些问题是复杂和相互依存的，因为种质多样性的分布必须从温度、降水和土壤类型等生态地理指标中推断(建模)，容纳关于本地分布的轶事信息，而不知道这些元素之间的关系是如何控制的。拟议工作的目标是根据生态因素进行预测；当现有种质收集不足时，如何用新材料补充它们，以及在哪里收集更多的种质。这些问题的范围是全球性的，因为可以从其中采集种质的基因库并不局限于国家或大陆。提出了一种创新的方法，通过应用多标准地理空间分层技术和地理信息系统(GIS)技术来预测种质与环境因素的分布，从广泛且不断扩大的国家数字环境数据资源中推断种质多样性。这种方法将自动化以前不可比较的、非系统的和主观的程序，以创建能够自我监控、更适应不断变化的条件和更高效的数字政府基因库。

EIA-9983507Parks, Bradley O.University of Colorado BoulderDigital Government: Criteria-based Inference from Geospatial Data: Automating Government Decision Making for Genetic Food Security, Crop Improvement and Global Germplasm NeedsThis award will support a planning effort for submission of a full proposal. Government partners include primarily the National Oceanic and Atmospheric Administration (National Geophysical Data Center), the US Department of Agriculture (Agricultural Research Service, and several other agencies in supportive roles.There is immediate need to prioritize the target government germplasm (seeds and rootstocks) acquisitions for crop improvement and to estimate distribution of germplasm diversity in landscapes. These problems are complex and interdependent ones since the distribution of germplasm diversity must be inferred (modeled) from eco-geographical indicators such as temperature, precipitation, and soil type, accommodating anecdotal information about native distributions, without knowing how the relationships among these elements are controlled. The objectives of the proposed work are to predict, based on ecological factors; when current germplasm collections are inadequate, how they should be augmented with new material, and where additional germplasm should be collected. The scope of these problems is global since the gene pool from which germplasm may be sampled is not restricted to nations or continents.An innovative approach is proposed to infer germplasm diversity from the extensive and expanding national resource of digital environmental data by applying multiple-criteria geospatial stratification techniques and geographic information system (GIS) technology to predict germplasm distributions in relation to environmental factors. This approach will automate previously non-comparable, unsystematic, and subjective procedures to create digital government gene banks that are capable of becoming self-monitoring, more adaptable to changing conditions, and much more efficient.