Selecting crop models for decision making in wheat insurance

Castañeda-Vera, Alba; Leffelaar, P.A.; Álvaro‐Fuentes, Jorge; Cantero‐Martínez, Carlos; Mínguez, M. Inés

doi:10.1016/j.eja.2015.04.008

Cited by 34 publications

(18 citation statements)

References 34 publications

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“…Recently, uncertainty from model structure has been of key concern (Asseng et al., , ; Bassu et al., ; Castañeda‐Vera, Leffelaar, Álvaro‐Fuentes, Cantero‐Martínez, & Mínguez, ; Li et al., ; Martre et al., ; Palosuo et al., ; Pirttioja et al., ; Rötter, Carter et al., , ; Vanuytrecht & Thorburn, ; Wang et al., ). Major international efforts, such as the Modelling European Agriculture with Climate Change for Food Security (MACSUR) project (Ewert et al., ) and the Agricultural Model Inter‐comparison and Improvement Project (AgMIP) (Rosenzweig et al., ), have made great progress in quantifying and reducing uncertainty from model structure in simulating the response of crop yields to climate (e.g.…”

Section: Introductionmentioning

confidence: 99%

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Tao

Rötter

Palosuo

et al. 2018

Global Change Biology

176

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Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

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Section: Introductionmentioning

confidence: 99%

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Tao

Rötter

Palosuo

et al. 2018

Global Change Biology

176

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“…Bannayan, Crout, & Hoogenboom, 2003;Zhao et al, 2011) and preferred for the simulation of winter wheat growth at field scale in semi-arid conditions in Spain after a detailed comparison (Castañeda-Vera et al, 2015). (Table 2) and following the same approach as in Castañeda-Vera et al, (2015).…”

Section: Attainable Rainfed Yieldsmentioning

confidence: 99%

“…Castañeda-Vera, A., Leffelaar, P.A., Álvaro-Fuentes, J., Cantero-Martínez, C., Mínguez, M.I., 2015. Selecting crop models for decision making in wheat insurance. European Journal of Agronomy.…”

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confidence: 99%

Agricultural insurance policies in Spain and the EU: an analysis of existent and new risk management tools focusing on indirect risk assessment, and asymmetric information

Vera¹

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“…CERES-Wheat has been widely applied at the regional scale (e.g. Bannayan et al, 2003;Zhao et al, 2011) and preferred for the simulation of winter wheat growth at field scale in semi-arid conditions in Spain after a detailed comparison (Castañeda-Vera et al, 2015).…”

Section: Attainable Rainfed Yieldsmentioning

confidence: 99%

“…]), following Castañeda-Vera et al (2015). The experimental sites are widely spread throughout the grain production areas and are representative of its climate and soil variability.…”

Section: Attainable Rainfed Yieldsmentioning

confidence: 99%

Crop insurance demand in wheat production: focusing on yield gaps and asymmetric information

Castañeda-Vera¹,

Saá-Requejo

Mínguez³

et al. 2018

Span. j. agric. res.

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Analysis of yield gaps were conducted in the context of crop insurance and used to build an indicator of asymmetric information. The possible influence of asymmetric information in the decision of Spanish wheat producers to contract insurance was additionally evaluated. The analysis includes simulated yield using a validated crop model, CERES-Wheat previously selected among others, whose suitability to estimate actual risk when no historical data are available was assessed. Results suggest that the accuracy in setting the insured yield is decisive in farmers' willingness to contract crop insurance under the wider coverage. Historical insurance data, when available, provide a more robust technical basis to evaluate and calibrate insurance parameters than simulated data, using crop models. Nevertheless, the use of crop models might be useful in designing new insurance packages when no historical data is available or to evaluate scenarios of expected changes. In that case, it is suggested that yield gaps be estimated and considered when using simulated attainable yields.Additional keywords: risk management; rainfed wheat; crop insurance penetration rate; crop models; Spain. Abbreviations used: AIC (Akaike information criterion); AsymB (asymmetric information indicator for the insurance option Basic); AsymE (asymmetric information indicator for the insurance option Extended); AsymEc (asymmetric information indicator for the insurance option Extended including the complementary insurance); CyL (Castilla y León); RMSE (root mean square error); SiAR (Sistema de Información Agroclimática para el Regadío); Ya (actual yield); Yexp (expected rainfed yield ); YinsB (average insured yield in option Basic); YinsE (average insured yield in option Extended before sowing); YinsEc (average insured yield in option Extended including the complementary insurance ); YinsZ (zonal maximum insurable yield); Ythresh (average insured yield in insurance option Extended including the complementary insurance after subtracting a 30% deductible).Authors' contributions: Conception or design; acquisition, analysis, or interpretation of data; drafting of the manuscript: ACV. Statistical analysis: AS. Coordinated the research project: MIM. Critical revision of the manuscript for important intellectual content, supervising the work: AG.Citation: Castañeda-Vera, A.; Saa-Requejo, A.; Mínguez, I.; Garrido, A. (2017). Crop insurance demand in wheat production: focusing on yield gaps and asymmetric information. Spanish Journal of Agricultural Research, Volume 15, Issue 4, e0119. https://doi.

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Selecting crop models for decision making in wheat insurance

Cited by 34 publications

References 34 publications

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Agricultural insurance policies in Spain and the EU: an analysis of existent and new risk management tools focusing on indirect risk assessment, and asymmetric information

Crop insurance demand in wheat production: focusing on yield gaps and asymmetric information

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