Temporary variation of the sensitivity indices of a crop model for tomato in greenhouse

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Antonio Martínez Ruiz Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias
Genaro Pérez Jiménez Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP)
Felipe Robert Flores de la Rosa Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP)
Miguel Servin Palestina Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP)
Abstract

Decision support systems (DSS) are tools that can be based on a crop model and are used to manage certain aspects in an agro-system such as: irrigation scheduling, climate control, fertilization, and yields. HORTSYST is a dynamic growth model, developed to be implemented in an expert system to manage irrigation and crop nutrition in intensive systems. It has 16 parameters, three state variables: dry matter production (DMP), photothermal index (PTI) and absorbed nitrogen (Nup), besides two output variables; Crop transpiration (ETc) and Leaf Area Index (LAI). The inputs of the model are: Global solar radiation (Rg), air temperature (Ta) and relative humidity (Hr). The simulations of HORTSYST model are based on estimation of a photothermal index. Which represents the development time of crop, which LAI is calculated. ETc is estimated with a mass and energy balance equation. DMP is determined with the radiation use efficiency (RUE) approach and Nup is calculated with dilution curve of nitrogen content in DMP. The objective of this work is to perform a global sensitivity analysis using the Sobol´s method, to know the importance of each parameter in the output variables at ten days after transplantation (DDT), during the vegetative stages (25 DDT), beginning of fruiting (40 DDT), harvest (80 DDT) and at the end (119 DDT) of the tomato crop cycle (Solanum lycopersicum L.), grown hydroponically in a greenhouse during spring-summer. It was found that the main and total effect indices, as a measure of the influence of the parameters, do not follow an established order as the crop cycle progresses. This temporal variation is a function of the stage of development and should be considered when conducting a sensitivity analysis.

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Author Biographies / See

Antonio Martínez Ruiz, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias

Investigador de tiempo completo

Genaro Pérez Jiménez, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP)

Investigador de tiempo completo

Cándido Mendoza Perez, Colegio de Postgraduados

Coordinador de área de reforestación

Felipe Robert Flores de la Rosa, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP)

Investigador de tiempo completo

Miguel Servin Palestina, Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP)

Investigador de tiempo completo

References

Brisson, Nadine et al. 1998. STICS: A Generic Model for the Simulation of Crops and Their Water and Nitrogen Balances. I. Theory and Parameterization Applied to Wheat and Corn. Agronomie 18(5–6):311–46.

Campolongo, F., & Saltelli, A. 1997. Sensitivity analysis of an environmental model: an application of different analysis methods. Reliability Engineering & System Safety, 57(1), 49-69.

Cooman, A. and E. Schrevens. 2006. A Monte Carlo Approach for Estimating the Uncertainty of Predictions with the Tomato Plant Growth Model, Tomgro. Biosystems Engineering 94(4):517–24.

Gallardo, M., R. B. Thompson, C. Giménez, F. M. Padilla, and C. O. Stöckle. 2014. Prototype Decision Support System Based on the VegSyst Simulation Model to Calculate Crop N and Water Requirements for Tomato under Plastic Cover. Irrigation Science 32(3):237–53.

Gallardo, M., Fernandez, M.D., Giménez, C., Padilla, F.M., and Thompson, R.B. 2016. Revised VegSyst model to calculate dry matter production, critical N uptake and ETc of several vegetable species grown in Mediterranean greenhouses. Agricultural Systems, 146, 30-43.

Heuvelink, E. 1999. Evaluation of a Dynamic Simulation Model for Tomato Crop Growth and Development. Annals of Botany 83:413–22.

Helton, J. C., F. J. Davis, and J. D. Johnson. 2005. A Comparison of Uncertainty and Sensitivity Analysis Results Obtained with Random and Latin Hypercube Sampling. Reliability Engineering & System Safety 89(3):305–30.

Janon, Alexandre, Thierry Klein, Agnès Lagnoux, Maëlle Nodet, and Clémentine Prieur. 2014. Asymptotic Normality and Efficiency of Two Sobol Index Estimators. ESAIM: Probability and Statistics 18(3):342–64.

López-Cruz, Irineo L., Raquel Salazar-Moreno, Abraham Rojano-Aguilar, and Agustín Ruiz-García. 2012. Análisis de Sensibilidad Global de Un Modelo de Lechugas (Lactuca Sativa L.) Cultivadas En Invernadero. Agrociencia 46(4):383–97.

Marcelis, L.F.M., Elings, A., de Visser, P.H.B., and Heuvelink, E. 2009. Simulating growth and development of tomato crop. International Symposium on Tomato in the Tropics, 821, 101-110. https://doi.org/10.17660/ActaHortic.2009.821.10

Martínez-Ruiz, A., López-Cruz, I. L., Ruiz-García, A., & Ramírez-Arias, A. 2012. Calibración y validación de un modelo de transpiración para gestión de riegos de jitomate (Solanum lycopersicum L.) en invernadero. Revista mexicana de ciencias agrícolas, 3(SPE4), 757-766.

Martínez-Ruiz, A., López-Cruz, I. L., Ruiz-García, A., Pineda-Pineda, J., & Prado-Hernández, J. V. HortSyst: A dynamic model to predict growth, nitrogen uptake, and transpiration of greenhouse tomatoes. Chil. J. Agric. Res. 2019, 79(1), 89-102.

Martinez-Ruiz, A., Pineda-Pineda, J., Ruiz-García, A., Prado-Hernández, J.V., López-Cruz, I.L. and Mendoza-Pérez, C. 2020. The HORTSYST model extended to phosphorus uptake prediction for tomatoes in soilless culture. Acta Hortic. 1271, 301-306, DOI: 10.17660/ActaHortic.2020.1271.41

Monod, Hervé, Cédric Naud, and David Makowski. 2006. Uncertainty and Sensitivity Analysis for Crop Models. Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications 4:55–100.

Morris, M. D. 1991. “Factorial Sampling Plans for Preliminary Computational Experiments.” Technometrics 33(2):161–74.

Norton, John. 2015. An Introduction to Sensitivity Assessment of Simulation Models. Environmental Modelling and Software 69:166–74.

Pianosi, Francesca, Fanny Sarrazin, and Thorsten Wagener. 2015. A Matlab Toolbox for Global Sensitivity Analysis.

Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. and Tarantola, S. 2008. Global sensitivity analysis. The primer. John Wiley & Sons, Ltd. Chichester, England. 292 pp.https://doi.org/10.1002/9780470725184

Saltelli, a., T. H. Andres, and T. Homma. 1995. Sensitivity Analysis of Model Output. Performance of the Iterated Fractional Factorial Design Method. Computational Statistics and Data Analysis 20(4):387–407.

Saltelli, a, S. Tarantola, and K. P. S. Chan. 1999. A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output. Technometrics 41(1):39–56.

Saltelli, Andrea, Marco Ratto, Stefano Tarantola, and Francesca Campolongo. 2006. Sensitivity Analysis Practice: A Guide to Scientific Models.

Sobol, I. M. 1993. Sensitivity Analysis for Nonlinear Mathematical Models. Mathematical Modeling & Computational Experiment 1(4):407–14.

Stockle, C. O., M. Donatelli, and R. Nelson. 2003. CropSyst, a Cropping Systems Simulation Model. European Journal of Agronomy 18:289–307.

Vazquez-Cruz, M.A., Guzman-Cruz, R., Lopez-Cruz, I.L., Cornejo-Pérez, O., Torres-Pacheco, I., and Guevara-Gonzalez, R.G. 2014. Global sensitivity analysis by means of EFAST ans Sobol’ methods and calibration of reduced state-variable TOMGRO model using genetic algorithms. Computers and Electronics in Agriculture, 100, 1-12. https://doi.org/http://dx.doi.org/10.1016/j.compag.2013.10.006

Wang, Jing, Xin Li, Ling Lu, and Feng Fang. 2013. Parameter Sensitivity Analysis of Crop Growth Models Based on the Extended Fourier Amplitude Sensitivity Test Method. Environmental Modelling & Software 48(October):171–82.

Williams, J., C. Jones, J. Kiniry, and D. Spanel. 1989. The EPIC Crop Growth Model. Transactions of the ASAE USA 32:497–511.

Wu, Qiong-Li, Paul-Henry Cournède, and Amélie Mathieu. 2012. An Efficient Computational Method for Global Sensitivity Analysis and Its Application to Tree Growth Modelling. Reliability Engineering & System Safety 107:35–43.

Wu, Qiongli, Paul-henry Courne, and Paul-Henry Cournède. 2014. A Comprehensive Methodology of Global Sensitivity Analysis for Complex Mechanistic Models with an Application to Plant Growth. Ecological Complexity.

Zhang, X. Y., Trame, M. N., Lesko, L. J., & Schmidt, S. 2015. Sobol sensitivity analysis: a tool to guide the development and evaluation of systems pharmacology models. CPT: pharmacometrics & systems pharmacology, 4(2), 69-79.

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