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

EIA-9983507帕克斯，布拉德利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：自动化政府决策的遗传粮食安全，作物改良和全球种质需求该奖项将支持规划工作提交一个完整的建议。 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）和其他几个起支持作用的机构，迫切需要优先考虑政府为作物改良而收购的目标种质（种子和砧木），并估计种质多样性在景观中的分布。 这些问题是复杂的和相互依赖的，因为种质多样性的分布必须推断（模拟）从生态地理指标，如温度，降水量和土壤类型，容纳有关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。 拟议的工作的目标是预测，根据生态因素，当目前的种质资源收集是不够的，他们应该如何增加新的材料，以及在哪里应该收集更多的种质。 这些问题的范围是全球性的，因为基因库，从种质可能被采样不限于国家或continents.An创新的方法，提出了推断种质多样性的广泛和不断扩大的国家资源的数字环境数据，应用多标准地理空间分层技术和地理信息系统（GIS）技术预测种质分布与环境因素。 这种方法将自动化以前不可比较的，不系统的和主观的程序，以创建能够自我监控的数字政府基因库，更适应不断变化的条件，并且效率更高。

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.