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Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction

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  • Makowski, David
  • Naud, Cédric
  • Jeuffroy, Marie-Hélène
  • Barbottin, Aude
  • Monod, Hervé

Abstract

Dynamic models are often used to predict the effects of farmers’ practices on crop yield, crop quality, and environment. These models usually include many parameters that must be estimated from experimental data before practical use. Parameter estimation is a difficult problem especially when some of the parameters vary across genotypes. These genetic parameters may be estimated from plant breeding experiments but this is very costly and requires a lot of experimental work. Moreover, some of the genetic parameters may account for only a very small part of the output variance and, so, do not deserve an accurate determination. This paper shows how methods of global sensitivity analysis can be used to evaluate the contributions of the genetic parameters to the variance of model prediction. Two methods are applied to a complex crop model for estimating the sensitivity indices associated to 13 genetic parameters. The results show that only five genetic parameters have a significant effect on crop yield and grain quality.

Suggested Citation

  • Makowski, David & Naud, Cédric & Jeuffroy, Marie-Hélène & Barbottin, Aude & Monod, Hervé, 2006. "Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1142-1147.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1142-1147
    DOI: 10.1016/j.ress.2005.11.015
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    Cited by:

    1. He, Jianqiang & Jones, James W. & Graham, Wendy D. & Dukes, Michael D., 2010. "Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method," Agricultural Systems, Elsevier, vol. 103(5), pages 256-264, June.
    2. Hao, Shirui & Ryu, Dongryeol & Western, Andrew W & Perry, Eileen & Bogena, Heye & Franssen, Harrie Jan Hendricks, 2024. "Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs," Ecological Modelling, Elsevier, vol. 487(C).
    3. Rahn, Eric & Vaast, Philippe & Läderach, Peter & van Asten, Piet & Jassogne, Laurence & Ghazoul, Jaboury, 2018. "Exploring adaptation strategies of coffee production to climate change using a process-based model," Ecological Modelling, Elsevier, vol. 371(C), pages 76-89.
    4. Wang, Bingqing & Li, Yongping & Huang, Guohe & Gao, Pangpang & Liu, Jing & Wen, Yizhuo, 2023. "Development of an integrated BLSVM-MFA method for analyzing renewable power-generation potential under climate change: A case study of Xiamen," Applied Energy, Elsevier, vol. 337(C).
    5. Zhao, Gang & Bryan, Brett A. & Song, Xiaodong, 2014. "Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters," Ecological Modelling, Elsevier, vol. 279(C), pages 1-11.
    6. Paleari, Livia & Movedi, Ermes & Zoli, Michele & Burato, Andrea & Cecconi, Irene & Errahouly, Jabir & Pecollo, Eleonora & Sorvillo, Carla & Confalonieri, Roberto, 2021. "Sensitivity analysis using Morris: Just screening or an effective ranking method?," Ecological Modelling, Elsevier, vol. 455(C).
    7. Daniel W. Gladish & Ross Darnell & Peter J. Thorburn & Bhakti Haldankar, 2019. "Emulated Multivariate Global Sensitivity Analysis for Complex Computer Models Applied to Agricultural Simulators," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(1), pages 130-153, March.
    8. Xenia Specka & Claas Nendel & Ralf Wieland, 2019. "Temporal Sensitivity Analysis of the MONICA Model: Application of Two Global Approaches to Analyze the Dynamics of Parameter Sensitivity," Agriculture, MDPI, vol. 9(2), pages 1-29, February.
    9. Lamboni, Matieyendou & Monod, Hervé & Makowski, David, 2011. "Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 450-459.
    10. Wu, Renye & Lawes, Roger & Oliver, Yvette & Fletcher, Andrew & Chen, Chao, 2019. "How well do we need to estimate plant-available water capacity to simulate water-limited yield potential?," Agricultural Water Management, Elsevier, vol. 212(C), pages 441-447.

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