Mathematical models of stored-grain ecosystems for management of stored grains.

全球每年生产超过31亿吨谷物、油籽和豆类（统称为谷物），并在从田间到加工商或消费者的运输过程中储存在一个或多个地点。粮食是耐用品，在适当的控制条件下，可以长期储存（一年以上）。相反，不适当的储存条件会导致物理、化学和生物条件使粮食变质。在单个存储单元（例如，箱、袋、仓库或地堡）中，腐坏量可高达100%。因此，我的研究计划的长期愿景是减少储粮的定性和定量损失。我的研究学员和我将开发储藏粮食生态系统的三维（3-D）数学模型，并将使用全尺寸仓实验验证这些模型，以作为创新的储藏粮食管理工具。我的跨学科研究项目所采用的方法是建立热量、水分和二氧化碳（粮食腐败产物）传递的数学模型，以增强我们对重要的非生物因素（如温度、水分含量和二氧化碳浓度）和生物因素（如粮食、昆虫、螨虫、真菌和细菌）之间相互作用的理解。将进行试验研究，以确定：1)这些模型中需要作为系数的颗粒的性质；2)粮食长期贮藏期间昆虫种群在温度、水分和二氧化碳浓度下的变化；3)储粮温度、湿度和二氧化碳浓度梯度下昆虫运动参数。对世界各地不同类型的储存结构的大量生物和非生物因素进行这样的理解的实验研究将是成本高昂且耗时的。这些经过验证的数学模型被用作管理工具来预测昆虫和真菌破坏的位置。这些模型用于精确地确定粮食质量中必须取样以检测腐败的位置，以便在必要时采取早期预防措施。这些模型还用于确定传感器的位置和分辨率，测量二氧化碳或异味挥发物，以检测早期变质。这些模型还用于制定谷物保存指导方针，通过分析位于不同气候带的不同大小和类型的箱子的许多“假设”情景，并装满不同类型的谷物，以养活不断增长的世界人口。加拿大每年的粮食作物价值约为268亿美元，这项研究的结果将有助于加拿大保持其作为向国内和出口买家销售无昆虫、农药残留和真菌毒素的高质量粮食的声誉。

Globally, more than 3.1 billion tonnes of cereal grains, oil seeds and pulses (collectively referred to as grains) are produced annually, and stored at one or more points during their movement from the field to processors or to consumers. Grains are durable products, and at properly controlled conditions can be stored for a long time (more than one year). Conversely, improper storage conditions can result in physical, chemical, and biological conditions that spoil grain. The amount of spoilage can be up to 100% in a single unit of storage (e.g., a bin, bag, warehouse, or bunker). Therefore, the long-term vision of my research program is to reduce the qualitative and quantitative losses in stored grains. My research trainees and I will develop three-dimensional (3-D) mathematical models of stored-grain ecosystems and will validate these models using full-size bin experiments for use as innovative stored-grain management tools. The approach taken by my interdisciplinary research program has been to develop mathematical models of heat, moisture, and carbon dioxide (products of grain spoilage) transfer to enhance our understanding of the interactions among important abiotic factors (e.g., temperature, moisture content, and carbon dioxide concentration) and biotic factors (e.g., grain, insects, mites, fungi, and bacteria). Experimental studies will be conducted to determine: 1) the properties of grains required as coefficients in these models; 2) the changes in the insect populations at temperatures, moisture contents and carbon dioxide concentrations expected during long-term grain storage; and 3) insect movement parameters under gradients of temperature, moisture and carbon dioxide concentration occurring in stored grains. Experimental studies to develop such an understanding of a large number of biotic and abiotic factors for different types of storage structures around the world would be cost prohibitive and time consuming. These validated mathematical models are used as management tools to predict the locations of spoilage by insects and fungi. The models are used to pinpoint the locations in grain mass that must be sampled for detection of spoilage so that early preventive actions can be taken if necessary. The models are also used to determine the location and resolution of sensors, measuring carbon dioxide or off-odour volatiles, to detect incipient spoilage. These models are also used to develop guidelines for preserving grains to feed the increasing world population by analyzing many “what if” scenarios for different sizes and types of bins located in different climatic zones and filled with different types of grains. The value of Canada's annual grain crop is about $26.8 billion and the results of this research would help Canada to maintain its reputation as a marketer of high quality grain that is free of insects, pesticide residues, and mycotoxins to domestic and export buyers.