Agricultural Systems Modeling and SimulationRobert M. Peart, W. David Shoup Offers a treatment of modern applications of modelling and simulation in crop, livestock, forage/livestock systems, and field operations. The book discusses methodologies from linear programming and neutral networks, to expert or decision support systems, as well as featuring models, such as SOYGRO, CROPGRO and GOSSYM/COMAX. It includes coverage on evaporation and evapotranspiration, the theory of simulation based on biological processes, and deficit irrigation scheduling. |
Contents
Simulation of Biological Processes | xi |
Using Mathematics as a ProblemSolving Tool | xlii |
An Energy Example | lxiii |
Modeling Processes and Operations with Linear Programming | lxxvi |
Expert Systems for SelfAdjusting Process Simulation | 14 |
Evaporation Models | 46 |
GOSSYMCOMAX | 72 |
Dynamical Systems Models and Their Application to Optimizing Grazing Management | 79 |
Modeling and Simulation in Applied Livestock Production Science | 253 |
The PlantAnimal Interface in Models of Grazing Systems | 1968 |
Field Machinery Selection Using Simulation and Optimization | 2000 |
An ObjectOriented Approach | 2016 |
Fundamentals of Neural Networks | 2037 |
ObjectOriented Programming for Decision Systems | 2058 |
CROPGRO Model | 2073 |
Other editions - View all
Agricultural Systems Modeling and Simulation Robert M Peart,W. David Shoup No preview available - 2019 |
Common terms and phrases
Agric Agricultural analysis animal application ASAE behavior biological biomass calculated canopy carbohydrate cell wall COMAX components constraints cost cotton crop model CROPGRO cultivar daily described determine digestion dry matter dynamic effects energy equations estimated evaporation example expert system factors farm feed FIGURE forage function Gainesville GOSSYM grazing systems growth rate heat herbage implement increase input intake rate irrigation irrigation scheduling J. W. Jones leaf area leaf area index limited linear programming lysimeter machinery mathematical maximum neural network nitrogen nodes nutrient object operations optimal output parameters partial field phase plane photosynthesis physiological age PNUTGRO potential predict problem processes production PROLOG protein relationships root rumen scenarios scheduling seed selection senescence sensor simulation models soil water solution soybean soybean rust spatial specific strategy surface sward temperature tissue tractor University of Florida values variables vegetative weight yield