Title: Biophysical models for cropping system simulation
Authors: DONATELLI MarcelloCONFALONIERI Roberto
Publisher: Springer Science+Business Media B.V. 2011
Publication Year: 2011
JRC Publication N°: JRC64331
ISBN: 978-94-007-1901-9
URI: http://www.springer.com/economics/agricultural+economics/book/978-94-007-1901-9
http://publications.jrc.ec.europa.eu/repository/handle/JRC64331
DOI: 10.1007/978-94-007-1902-6_4
Type: Articles in books
Abstract: The definition of mathematical models to estimate plants growth as a function of environmental variables has started many decades ago, for instance expressing the biomass growth of a plant as a function of the solar radiation intercepted (Warren Wilson, 1967). Since then, crop models have evolved including sub-models to estimate plant development, and several other processes relevant to the simulation of the interaction plant-soil as affected by weather and agricultural management. Two main goals can be identified as drivers in plant model development: 1) studying the genotype x environment interaction, as a support tool to variety selection within a given species, or 2) studying production enterprises, hence comparing, from a biophysical point of view, yield, resource use, and externalities of agricultural production systems. Whether most of the models of the former group are specialized to a single crop, the latter includes multi-crop models to simulate crop sequences as in most production systems. The study of the interaction genotype x environment has been used via several types of modeling studies ultimately to assess either the potential or the actual performance of specific varieties, and to some extent providing breeders an estimate of value of some plants traits desirable for a given environment (e.g. Hammer et al., 2002). This has been further extended targeting the modeling of crop improvement using a genotype x environment x management framework, via relationships gene-trait-phenotype (Hammer et al., 2005; Messina et al. 2010). Several simulation tools allow estimating the impact of agricultural management on production activities in specific environments to be studied (e.g. Williams et al., 1983 and 1989; Brisson et al., 2003; Keating et al., 2003; Jones et al., 2003; Stockle et al., 2003; Van Ittersum et al., 2003). Because of their capability of simulating the dynamics of soil, water, and nutrients, in response to weather and agricultural management, such models allow exploring hypothesis of use of resources, and allow defining adaptation strategies to changing climate, to scenarios of resource availability, and to thresholds of externalities which may be set to limit the environmental impact of production systems. New developments in the technology to develop simulation systems have lead to modular software platforms OMS (David et al., 2002 ), TIME (Rhaman et al., 2003), APSIM (McCown et al., 1996), APES (Donatelli et al., 2010) to allow for fine granularity model comparison, to facilitate the transfer from research to operational tools, and for a easier extension if the system being simulated by including new processes. As an example, the list of type of models and outputs for the system APES is shown in Figure 1. The objective of this chapter is to describe biophysical models for simulating agricultural production in order to highlight their capabilities and the assumptions in the perspective of using them in modeling chains.
JRC Institute:Institute for Environment and Sustainability

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