Integrating seasonal climate prediction and agricultural models for insights into agricultural practice - PubMed (original) (raw)

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Integrating seasonal climate prediction and agricultural models for insights into agricultural practice

James W Hansen. Philos Trans R Soc Lond B Biol Sci. 2005.

Abstract

Interest in integrating crop simulation models with dynamic seasonal climate forecast models is expanding in response to a perceived opportunity to add value to seasonal climate forecasts for agriculture. Integrated modelling may help to address some obstacles to effective agricultural use of climate information. First, modelling can address the mismatch between farmers' needs and available operational forecasts. Probabilistic crop yield forecasts are directly relevant to farmers' livelihood decisions and, at a different scale, to early warning and market applications. Second, credible ex ante evidence of livelihood benefits, using integrated climate-crop-economic modelling in a value-of-information framework, may assist in the challenge of obtaining institutional, financial and political support; and inform targeting for greatest benefit. Third, integrated modelling can reduce the risk and learning time associated with adaptation and adoption, and related uncertainty on the part of advisors and advocates. It can provide insights to advisors, and enhance site-specific interpretation of recommendations when driven by spatial data. Model-based 'discussion support systems' contribute to learning and farmer-researcher dialogue. Integrated climate-crop modelling may play a genuine, but limited role in efforts to support climate risk management in agriculture, but only if they are used appropriately, with understanding of their capabilities and limitations, and with cautious evaluation of model predictions and of the insights that arises from model-based decision analysis.

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Figures

Figure 1

Figure 1

Correlation of observed versus predicted rainfall in the state of Ceará, northeast Brazil, as a function of scale of aggregation. Source: Gong et al. 2003.

Figure 2

Figure 2

(a) Predicted and observed October–December rainfall and (b) simulated maize yields, Katumani, Kenya. Maize yields are from APSIM run with 20 realizations of stochastic rainfall disaggregated from monthly hindcasts, 40 kg ha−1 applied N fertilizer, stand density of 3.5 m−2 (courtesy of K. P. C. Rao, ICRISAT Nairobi). Maize yields simulated with observed rainfall serve as the benchmark for comparison. Rainfall hindcasts are based on a linear transformation of ECHAM four simulations, forced with observed SST boundary conditions (Hansen & Indeje 2004).

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