Short survey Scaling-up crop models for climate variability applications $

By James Hansen, James W. Jones. 2000

310 citations recorded by the source corpus.

Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/392c281f685610765097b0bcec9b9b56e7375c91

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In Ecology

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In Environmental Engineering

Citations in the corpus

Adaptation of agricultural and food systems to climate change: an economic and policy perspective. (2010, background): “More detailed crop simulation models also exhibit such thresholds (Hansen and Jones 2000; Antle and Stoorvogel 2006; Challinor et al. 2009).”
Adaptation to climate variability and change in the US Great Plains:: A multi-scale analysis of Ricardian climate sensitivities (2001, background): “This approach is in step with recent calls to move away from reductionism and toward a more holistic understanding of the relations between agricultural system components and the system as a whole (Turner and Meyer, 1991; Turner et al., 1995; Easterling, 1997; Hansen and Jones, 2000).”
A regional comparison of the effects of climate change on agricultural crops in Europe (2012, background): “…of changes in crop productivity Process-based crop models provide the means to derive information of crop responses to climate and management when experimental data is not available (Lobell and Burke 2010; Iglesias et al. 2000; Porter and Semenov 2005; Steduto et al. 2009; Hansen and Jones 2000).”
Stochastic disaggregation of monthly rainfall data for crop simulation studies (2005, background): “Spatial averaging or interpolation of daily weather data among stations tends to distort day-to-day variability (Osborn and Hulme, 1997), biasing simulated crop response (Hansen and Jones, 2000).”
Assessing the vulnerability of food crop systems in Africa to climate change (2007, background): “Climate models typically operate on spatial scales much larger than those of crop models ( Hansen and Jones 2000; Challinor et al. 2003; Baron et al. 2005).”
Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles (2005, background): “The spatial scale on which simulations are carried out is one source of uncertainty: when a single set of crop model parameters is used to represent crop growth over a large area, subgrid heterogeneity may result in poor agreement between simulated and measured yields (e.g. Hansen and Jones, 2000).”
The DSSAT cropping system model (2003, background): “…Precision agriculture Han et al. (1995), Sadler et al. (2000), Paz et al. (1998, 1999), Irmak et al. (2001), Paz et al. (2001a, Climate variability Hansen and Jones (2000), Jones et al. (2000), Mearns et al. (1996) Yield forecasting Carbone (1993), Carbone et al. (1996), Chipanshi et al. (1997,…”
Vulnerability and adaptation of European farmers: a multi-level analysis of yield and income responses to climate variability (2009, background): “It is widely recognized that site-specific crop models are developed for field scale application and that they cannot directly be used at higher aggregation levels (e.g., Passioura 1996; Marshall et al. 1997; Landau et al. 1998; Hansen and Jones 2000; Tubiello and Ewert 2002).”
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies (2013, unknown): “Aggregation of ﬁeld-scale crop model outputs to a regional or larger-scale economic model generally follows one of several approaches (e.g., Hansen and Jones, 2000; Ewert et al., 2011).”
Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. (2009, background): “As well as dealing with climate model error, postprocessing can deal with the disparity in spatial scale between climate and crop models (see, for example, Hansen and Jones, 2000; Challinor et al., 2003, 2004).”
Adaptation and evaluation of the CROPGRO-soybean model to predict regional yield and production ☆ (2002, background): “A number of studies (Kunkel and Hollinger, 1991; Moen et al., 1994; Haskett et al., 1995; Supit, 1997; Rosenthal et al., 1998; Hansen and Jones, 2000 ) have shown the necessity of model calibration for estimating large area yields to account for aggregation errors, lack of specificity in crop manag”
Adaptation, Spatial Heterogeneity, and the Vulnerability of Agricultural Systems to Climate Change and CO2 Fertilization: An Integrated Assessment Approach (2004, background): “Also, by conducting the analysis at the level of the economic decision unit, our integrated assessment approach avoids the problem of aggregation bias that is known to occur with spatially aggregated data (Hansen and Jones, 2000; Antle and Stoorvogel, 2001).”