This book provides a clear picture of the use of applied mathematics as a tool for improving the accuracy of agricultural research. For decades, statistics has been regarded as the fundamental tool of the scientific method. With new breakthroughs in computers and computer software, it has become feasible and necessary to improve the traditional approach in agricultural research by including additional mathematical modeling procedures. The difficulty with the use of mathematics for agricultural scientists is that most courses in applied mathematics have been designed for engineering students. This publication is written by a professional in animal science targeting professionals in the biological, namely agricultural and animal scientists and graduate students in agricultural and animal sciences. The only prerequisite for the reader to understand the topics of this book is an introduction to college algebra, calculus and statistics. This is a manual of procedures for the mathematical modeling of agricultural systems and for the design and analyses of experimental data and experimental tests. It is a step-by-step guide for mathematical modeling of agricultural systems, starting with the statement of the research problem and up to implementing the project and running system experiments.

Agriculture has experienced a dramatic change during the past decades. The change has been structural and technological. Structural changes can be seen in the size of current farms; not long ago, agricultural production was organized around small farms, whereas nowadays the agricultural landscape is dominated by large farms. Large farms have better means of applying new technologies, and therefore technological advances have been a driving force in changing the farming structure. New technologies continue to emerge, and their mastery and use in requires that farmers gather more information and make more complex technological choices. In particular, the advent of the Internet has opened vast opportunities for communication and business opportunities within the agricultural com- nity. But at the same time, it has created another class of complex issues that need to be addressed sooner rather than later. Farmers and agricultural researchers are faced with an overwhelming amount of information they need to analyze and synthesize to successfully manage all the facets of agricultural production. This daunting challenge requires new and complex approaches to farm management. A new type of agricultural management system requires active cooperation among multidisciplinary and multi-institutional teams and ref- ing of existing and creation of new analytical theories with potential use in agriculture. Therefore, new management agricultural systems must combine the newest achievements in many scientific domains such as agronomy, economics, mathematics, and computer science, to name a few.

Systems Analysis and Modeling in Food and Agriculture is a component of Encyclopedia of Food and Agricultural Sciences, Engineering and Technology Resources in the global Encyclopedia of Life Support Systems (EOLSS), which is an integrated compendium of twenty one Encyclopedias. Systems analysis and modeling is being used increasingly in understanding and solving problems in food and agriculture. The purpose of systems analysis is to support decisions by emphasizing the interactions of processes and components within a system. Frequently investigated systems level questions in agriculture and food are relevant to the 6 E's: Environment, Energy, Ecology, Economics, Education, and Efficiency. The theme on Systems Analysis and Modeling in Food and Agriculture with contributions from distinguished experts in the field provides information on key topics related to food and agricultural system. The coverage include an overview of food system; system level aspects related to energy, environment, and social/policy issues; knowledge bases and decision support; computer models for crops, food processing, water resources, and agricultural meteorology; collection and analysis methods for data from field experiments; use of models and information systems. This volume is aimed at the following a wide spectrum of audiences from the merely curious to those seeking in-depth knowledge: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs.

Why model? Agricultural system models enhance and extend field research...to synthesize and examine experiment data and advance our knowledge faster, to extend current research in time to predict best management systems, and to prepare for climate-change effects on agriculture. The relevance of such models depends on their implementation. Methods of Introducing System Models into Agricultural Research is the ultimate handbook for field scientists and other model users in the proper methods of model use. Readers will learn parameter estimation, calibration, validation, and extension of experimental results to other weather conditions, soils, and climates. The proper methods are the key to realizing the great potential benefits of modeling an agricultural system. Experts cover the major models, with the synthesis of knowledge that is the hallmark of the Advances in Agricultural Systems Modeling series.

Role of mathematical models in agriculture and agricultural research. Techniques: dynamic deterministic modelling. Techniques: mathematical programming. Testing and evaluation of models. Growth functions. Weather. Plant and crop processes. Crop responses and models. Plant diseases and pest. Animal processes. Animal products. Farm planning and control: I. Farm planning and control.

Role of mathematical models; Dynamic deterministic models; Mathematical programming; Basic biological processes; Growth functions; Simple dynamic growth models; Simple ecological models; Envinment and weather; Plant and crop processes; Crop models; Crop husbandry; Plant diseases and pests; Animal processes; Animal organs; Whole-animal models; Animal products; Animal husbandry; Animal diseases; Solutions exercises; Mathematical glossary.