Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications
Mathematical models are being used more and more widely to study complex dynamic systems (global weather, ecological systems, hydrological systems, nuclear reactors etc. including the specific subject of this book, crop-soil systems). The models are important aids in understanding, predicting and managing these systems. Such models are complex and imperfect. One fundamental research direction is to seek a better understanding of how these systems function, and to propose mathematical expressions embodying that understanding. However, this is not sufficient. It is also essential to have tools (often mathematical and statistical methods) to aid in developing, improving and using the models built from those equations. The book is specifically concerned with the application of methods to crop models, but much of the material is also applicable to dynamic system models in other fields. The goal of this book is to fill that gap.
* State-of-the-art methods explained simply and illustrated specifically for crop models* Parameter estimation – applying statistical methods to the complex case of crop models, including Bayesian methods * Includes model evaluation, understanding and estimating prediction error* Offers a unique data assimilation by using the Kalman filter and beyond
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Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization ...
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Agricultural Systems Agronomy algorithm applied approach average AZODYN bias biomass calculated Chapter climate criterion crop management crop models CROPGRO cropping system CROPSYST cultivars dataset decision rules defined denitrification density dry matter Dynamic Crop Models effects Ensemble Kalman filter environments equations estimated parameter evaluation example explanatory variables field Figure function gene genotypes genotypic parameters harvest initial seed bank input factors input variables interactions irrigation kiwifruit large number leaf area index least squares linear Makowski management strategy matrix mean squared error measurements methods model error model output model parameters model predictions MODERATO MSEP nitrogen observed obtained optimization output variables parameter estimation parameter values pedotransfer functions problem random variable regression remote sensing sample scenarios seed bank sensitivity analysis soil water sowing date soybean statistical STICS Table target distribution temperature traits uncertainty variance varieties vector Wallach winter wheat yield