Design and optimisation of a large-area process-based model for annual crops

By Andrew J. Challinor, Tim Wheeler, Peter Craufurd, J. Slingo, D. Grimes. 2004

294 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Soil Science

View Paper: https://www.semanticscholar.org/paper/9a6176847d77d49f618d18a1fa34ecf270e18f5d

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In Ecology, Evolution, Behavior and Systematics

In Global and Planetary Change

In Soil Science

Citations in the corpus

A Crop Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in Burkina Faso, West Africa (2014, methodology): “Genetic algorithms (GAs) were first introduced by Holland (1975). GAs are heuristic methods inspired by natural evolution.”
Crop modelling for integrated assessment of risk to food production from climate change (2015, background): “…et al., 2001; Tao et al., 2009b; Iizumi et al., 2011; R€otter et al., 2012a; Elliott et al., 2013; Hawkins et al., 2013b; Rosenzweig and Neofotis, 2013; Tao and Zhang, 2013a) and crop models for large area application were developed (Challinor et al., 2004; Bondeau et al., 2007; Tao et al., 2009a).”
The State of the Art in Modeling Waterlogging Impacts on Plants: What Do We Know and What Do We Need to Know (2020, background): “APSIM‐Soybean and APSIM‐Oryza had the highest accuracy across point‐based models, while GLAM‐WOFOST was most reliable of the regional models examined.”
Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability (2009, background): “While some efforts have been made to develop simplified regional scale models (e.g., Challinor et al., 2004; Potgieter et al. 2005), P. Reidsma (&) F. Ewert Department of Plant Sciences, Plant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands e-mail:…”
Is Cassava the Answer to African Climate Change Adaptation? (2012, background): “Ways of coupling regional models such as EcoCrop (Ramirez-Villegas et al., 2011) or large-area process-based models (Challinor et al., 2004; Tao et al., 2009) with detailed field-scale crop models need to be explored so that the representation of some processes can be improved.”
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies (2013, unknown): “Techniques used in agricultural models that operate on scales closer to global climate model resolutions and have regional and global foci (such as GLAM, Challinor et al., 2004; LPJmL, Bondeau et al., 2007; PEGASUS, Deryng et al., 2011; IMPACT, Nelson et al., 2009; GLOBIOM, Havlík et al., 2011) will”
Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles (2005, methodology): “Challinor et al. (2004) have developed a relatively simple, large-area, process-based model [the general largearea model for annual crops (GLAM)] which aims to combine the advantages of these two approaches.”
Brief history of agricultural systems modeling (2017, unknown): “…crop models for plant breeding and management purposes (e.g., see White and Hoogenboom, 1996, Hoogenboom and White, 2003; Hammer et al., 2006; Messina et al., 2006 Led to new methods for coupling crop simulation models to land surface schemes of numerical climate models (Challinor et al., 2004).”
Assessing uncertainty and complexity in regional-scale crop model simulations (2017, background): “In these areas, it has been shown that correlations between solar radiation and crop yield are negative, suggesting that higher solar radiation increases transpirative demand, thus enhancing water stress and lowering yields (Challinor et al., 2005; Ramirez-Villegas, 2014).”
Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. (2009, methodology): “Empirically determined parameters can be based entirely on processes (Challinor et al., 2004), or else a semi-empirical approach can be used, for example with a process-based plant water stress index being empirically related to yield (Potgeiter et al., 2005).”