Environmental modeling to support agriculture and water management decision making

环境模型提供了一系列可用于农业决策的产出，如作物发育阶段、作物产量、作物质量、蒸散率和作物病原体风险的估计。 此外，这些模型还可以估计控制防洪、灌溉、径流和减少侵蚀风险系统运作的水管理人员感兴趣的参数。 该研究计划的长期目标是通过与潜在用户组织的合作研究，开发和测试具有农业和水资源管理实用性的模型，以确定他们的需求并以促进最终采用模型进行业务使用的方式测试模型性能。 来自作物冠层内的环境数据可以为作物条件模型提供改进的输入。 这将用作物病害发生率来检验。 将确定冠层小气候与疾病发病率之间的关系。 然后，将开发模型，从标准天气数据中使用额外的信息，如冠层厚度，得出冠层小气候。 这两个步骤的结合将提供一种基于标准天气的菌核病茎腐病风险评估方法。 现场监测系统将构成小面积土壤水分建模系统的基础，该系统可以处理一系列土壤类型和植被覆盖。 跨区域土壤水分建模的问题之一是土壤持水特性的广泛性。NSERC发现补助金将为博士学位提供部分支持。学生研究所需的缩放技术，以模拟土壤剖面水含量在整个地区使用数据从离散点监测气象站。 作为研究计划这一阶段的进一步措施，将进一步开发土壤水分建模系统，以利用遥感图像，主要是对农业景观中的植被生物量进行参数化。

Environmental models provide a range of output that can be used for agricultural decision-making such as estimates of crop development stage, crop yield, crop quality, evapotranspiration rates and crop pathogen risk. In addition, the models can estimate parameters of interest to water managers who control the operation of systems for flood control, irrigation, runoff and erosion risk mitigation. The long-term objective of the research program is to develop and test models with utility for agriculture and water management through collaborative research with potential user organizations to both identify their needs and to test model performance in a manner that fosters eventual model adoption for operational use. Environmental data from within crop canopies may provide improved input for crop condition models. This will be tested using crop disease incidence. The relationships between canopy microclimate and disease incidence will be determined. Then models will be developed to derive canopy microclimate from standard weather data using additional information such as canopy thickness. The combination of these two steps will provide a method for standard weather-based assessment of sclerotinia stem rot risk. An in situ monitoring system will form the basis for a small area soil moisture modeling system that can handle a range of soil types and vegetation cover. One of the issues for modeling soil moisture across areas is the wide range of soil water-holding characteristics. The NSERC Discovery Grant will provide partial support for a Ph.D. student to investigate the scaling techniques required to model soil profile water content across an area using data from discrete points monitored by weather stations. As a further step in this phase of the research program, the soil moisture modeling system will be further developed to take advantage of remote sensing images, mainly to parameterize vegetation biomass across agricultural landscapes.