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

在全球范围内，每年生产超过31亿吨的谷物、油料种子和豆类（统称为谷物），并在从田间运输到加工商或消费者的过程中储存在一个或多个地点。谷物是耐用产品，在适当的控制条件下可以储存很长时间（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，使谷物变质。在单个存储单元中，腐败的量可以高达100%（例如，垃圾箱、袋子、仓库或掩体）。因此，我研究计划的长期愿景是减少储存谷物的定性和定量损失。我的研究学员和我将开发三维（3-D）数学模型的储粮生态系统，并将验证这些模型使用全尺寸箱实验作为创新的储粮管理工具。我的跨学科研究计划所采取的方法是建立热量，水分和二氧化碳（谷物腐败的产物）转移的数学模型，以提高我们对重要的非生物因素（例如，温度、水分含量和二氧化碳浓度）和生物因素（例如，谷物、昆虫、螨类、真菌和细菌）。将进行实验研究以确定：1）这些模型中所需的系数谷物的性质; 2）在长期谷物储存期间预期的温度、水分含量和二氧化碳浓度下昆虫种群的变化;以及3）在储存谷物中发生的温度、水分和二氧化碳浓度梯度下的昆虫运动参数。为了解世界各地不同类型的储存结构的大量生物和非生物因素而进行的实验研究将是成本高昂和耗时的。这些经过验证的数学模型被用作管理工具，以预测昆虫和真菌腐败的位置。该模型用于精确定位谷物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，以检测早期腐败。这些模型还被用于制定保存谷物的指导方针，以养活不断增长的世界人口，方法是分析位于不同气候带、装满不同类型谷物的不同大小和类型的仓的许多“假设”情景。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高品质谷物销售商的声誉，这些谷物不含昆虫，农药残留和真菌毒素，可供国内和出口买家使用。

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.