Sensitivity analysis and parameterization of two agricultural models in cauliflower crops

  • Antonio Lidón Universitat Politècnica de València, Instituto Universitario de Ingeniería del Agua y del Medio Ambiente. Camí de Vera s/n, 46022 València
  • Damián Ginestar Universitat Politècnica de València, Instituto de Matemática Multidisciplinar. Camí de Vera sn, 46022 València
  • Sofía Carlos Universitat Politècnica de València, Dept. Ingeniería Química y Nuclear. Camí de Vera s/n, 46022 València
  • Carlos Sánchez de Oleo Universidad Autónoma de Santo Domingo, Escuela de Física, Laboratorio de Investigación en Matemática Aplicada. San Juan de la Manguana
  • Claudia Jaramillo Universitat Politècnica de València, Dept. Producción Vegetal. Camí de Vera sn, 46022 València
  • Carlos Ramos Instituto Valenciano de Investigaciones Agrarias. Apartado oficial 46113. Moncada (Valencia)
Keywords: soil water content, soil nitrogen, global sensitivity analysis, model calibration, Brassica oleracea


Aim of study: The development of a procedure to calibrate the LEACHM and EU-Rotate_N models for simulating water and nitrogen dynamics in cauliflower crops.

Area of study: Calibration was performed using experimental data obtained from measurements in a cauliflower crop sited in Valencia (Spain) region.

Material and methods: A procedure based on generalized sensitivity indices for time-dependent outputs was used to determine the most influencing model parameters, in order to reduce the number of parameters to be calibrated and to avoid overparameterization. The most influencing parameters were introduced in an optimization process that uses the experimental measurements of soil water and nitrate content to determine its optimal value and obtain calibrated models.

Main results: After this analysis, the most important hydraulic parameters found were the coefficients of Campbell’s equation for the LEACHM model and the soil water content at field capacity and drainage coefficient for the EU-Rotate_N model. For the N cycle, the most influencing parameters were those related with the nitrification, humus mineralization rate and residue decomposition for both models. Both calibrated models provided good simulation of soil water content with an error between 5-7%. However, larger errors in soil-nitrate content simulation were found, mainly in the period corresponding to the crop residues incorporation. The prediction of the calibrated models in a different plot gave error values of about 7-9% for soil water content, but for soil nitrate content errors computed were 34% and 58%.

Research highlights: After calibration, both models can be used to optimize the farmer water management and fertilization practices in horticultural crops, although in the N case further studies should be performed.


Download data is not yet available.


Allen RG, Pereira LS, Raes D, Smith M, 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrig Drain Paper nº 56, Roma.

Campbell G, 1974. A simple method for determining unsaturated conductivity from moisture retention data. Soil Sci 117: 311-314.

Cannavo P, Recous S, Parnaudeau V, Reau R, 2008. Modeling N dynamics to assess environmental impacts of cropped soils. Adv Agron 97: 131-174.

Childs SW, Hanks RJ, 1975. Model of soil salinity effects on crop growth. Soil Sci Soc Am Proc 39: 617-622.

Doltra J, Muñoz P, 2010. Simulation of nitrogen leaching from a fertigated crop rotation in a Mediterranean climate using the EU-Rotate N and Hydrus-2D models. Agric Water Manag 97 (2): 277-285.

Fang Q, Ma L, Yu Q, Malone RW, Saseendran SA, Ahuja LR, 2008. Modeling nitrogen and water management effects in a wheat-maize double-cropping system. J Environ Qual 37 (6): 2232-2242.

Greenwood DJ, Gastal F, Lemaire G, Draycott A, Millard P, Neeteson JJ, 1991. Growth rate and % N of field grown crops: Theory and experiments. Ann Bot 67: 181-190.

Hansen S, Jensen HE, Nielsen NE, Svendsen H, 1991. Simulation of nitrogen dynamics and biomass production in winter wheat using the Danish simulation model DAISY. Fert Res 27: 245-259.

Hutson JL, Cass A, 1987. A retentivity function for use in soil-water simulation models. J Soil Sci 38: 105-113.

Hutson JL, Wagenet RJ, 1991. Simulating nitrogen dynamics in soils using a deterministic model. Soil Use Manage 7: 74-78.

Jabro JD, Hutson JL, Jabro AD, 2011. Parameterizing LEACHM model for simulating water drainage fluxes and nitrate leaching losses. In: Methods of introducing system models into agricultural research; Ahuja LR & Ma L (eds.). pp: 95-115. Am Soc Agron, Crop Sci Soc Am, Wisconsin, USA.

Jaramillo C, 2016. Mineralización de la gallinaza y de los restos de cosecha en el suelo. Aplicación al cultivo de coliflor en la huerta de Valencia. Doctoral thesis, Univ. Politécnica, Valencia, Spain.

Johnsson H, Bergstrom L, Jansson PE, Paustian K, 1987. Simulated nitrogen dynamics and losses in a layered agricultural soil. Agric Ecosyst Environ 18 (4): 333-356.

Jung YW, Oh DS, Kim M, Park JW, 2010. Calibration of LEACHN model using LH-OAT sensitivity analysis. Nutr Cycl Agroecosys 87: 261-275.

Kersebaum KC, Hecker JM, Mirschel W, Wegehenkel M, 2007. Modelling water and nutrient dynamics in soilcrop systems: a comparison of simulation models applied on common data sets. In: Modelling water and nutrient dynamics in soil-crop systems; Kersebaum KC et al. (eds.). pp: 1-17. Springer, Dordrecht.

Lagarias JC, Reeds JA, Wright MH, Wright PE, 1998. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J Optimiz 9: 112-147.

Lamboni M, 2009. Multivariate global sensitivity analysis for dynamic crop models. Field Crop Res 113: 312-320.

Lamboni M, Monod H, Makowki D, 2011. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Reliab Eng Syst Safe 96: 450-459.

Lidón A, Lado L, Berbegall F, Ramos C, 2011. Influencia de la calibración de los parámetros hidráulicos del modelo EU-Rotate_N en el balance de N en un cultivo de col china. Actas Hort 61: 45-51.

Lidón A, Ramos C, Ginestar D, Contreras W, 2013. Assessment of LEACHN and a simple compartmental model to simulate nitrogen dynamics in citrus orchards. Agric Water Manag 121: 42-53.

Mahmood B, Tillman R, 2015. Sensitivity of LEACHN model predictions to changes in Nitrogen transformation rate constants and bulk density. T ASABE 58: 353-366.

Makowski D, Hillier J, Wallach B, Andrieu B, Jeuffroy MH, 2006. Parameter estimation for crop models. In: Working with dynamic crop models; Wallach D et al. (eds.). pp: 101-149. Elsevier, Amsterdam.

Monod H, Naud C, Makowski D, 2006. Uncertainty and sensitivity analysis for crop models. In: Working with dynamic crop models; Wallach D et al. (eds.). pp: 55-99. Elsevier, Amsterdam.

Nendel C, Venezia A, Piro F, Ren T, Lillywhite RD, Rahn CR, 2013. The performance of the EU-Rotate_N model in predicting the growth and nitrogen uptake of rotations of field vegetable crops in a Mediterranean environment. J Agric Sci 151: 538-555.

Nimah MN, Hanks RJ, 1973. Model for estimating soil water, plant, and atmospheric interrelations: I. Description and sensitivity. Soil Sci Soc Am J 37(4): 522-527.

Rahn C, Zhang K, Lillywhite R, Ramos C, Doltra J, De Paz JM, Riley H, Fink M, Nendel C, Thorup K, Pedersen A, Piro F, Venezia A, Firth C, Schmutz U, Rayns F, Strohmeyer K, 2010. EU-Rotate_N - a decision support system - to predict environmental and economic consequences of the management of nitrogen fertiliser in crop rotations. Eur J Hortic Sci 75: 20-32.

Ramos C, 2014. Integración de medidas de suelo, planta y modelos de simulación para el manejo eficiente del nitrógeno en los cultivos hortícolas. Project RTA2011-00136-C04-01, Final Report, INIA, Madrid, Spain.

Ritchie JT, 1998. Soil water balance and plant water stress. In: Understanding options for agricultural production; Tsuji GY et al. (eds.). pp: 41-54. Kluwer Acad Press, Dordrecht.

Ritter A, Muñoz-Carpena R, 2013. Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480: 33-45.

Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S, 2008. Global sensitivity analysis: the primer. John Wiley & Sons Ltd, Chichester, England. 312 pp.

Sánchez de Oleo C, 2016. Estimación de parámetros en modelos de transporte de agua y nitrógeno en el suelo. Doctoral thesis, Univ. Politécnica, Valencia, Spain.

Saxton KE, Rawls WJ, Romberger JS, Papendick RI, 1986. Estimating generalized soil-water characteristics from texture. Soil Sci Soc Am J 50 (4): 1031-1036.

Schmied B, Abbaspour K, Schulin R, 2000. Inverse estimation of parameters in a nitrogen model using field data. Soil Sci Soc Am J 64: 533-542.

Sogbedji JM, Van Es HM, Melkonian J, Schindelbeck R, 2006. Evaluation of the PNM model for simulating drain flow nitrate-N concentration under manure-fertilized maize. Plant Soil 282: 343-360.

Søgaard HT, Sommer SG, Hutchings NJ, Huijsmans JFM, Bussink DW, Nicholson F, 2002. Ammonia volatilization from field-applied animal slurry: the ALFAM model. Atmos Environ 36: 3309-3319.

Soto F, Gallardo M, Giménez C, Peña-Fleitas T, Thompson RB, 2014. Simulation of tomato growth, water and N dynamics using the EU-Rotate_N model in Mediterranean greenhouses with drip irrigation and fertigation. Agric Water Manag 132: 46-59.

Stella T, Frasso N, Negrini G, Bregaglio S, Cappelli G, Acutis M, Confalonieri R, 2014. Model simplification and development via reuse, sensitivity analysis and composition: a case study in crop modelling. Environ Modell Softw 59: 44-58.

Suarez-Rey EM, Romero-Gámez M, Giménez C, Thompson RB, Gallardo M, 2016. Use of EU-Rotate_N and CropSyst models to predict yield, growth and water and N dynamics of fertigated leafy vegetables in a Mediterranean climate and to determine N fertilizer requirements. Agric Syst 149: 150-164.

Suárez-Rey EM, Gallardo M, Romero-Gámez M, Giménez C, Rueda FJ, 2019. Sensitivity and uncertainty analysis in agro-hydrological modelling of drip fertigated lettuce crops under Mediterranean conditions. Comput Electron Agr 162: 630-650.

Sun Y, Hu K, Fan Z, Wei Y, Lin S, Wang J, 2013. Simulating the fate of nitrogen and optimizing water and nitrogen management of greenhouse tomato in North China using the EU-Rotate_N model. Agric Water Manag 128: 72-84.

Wagenet RJ, Hutson JL, 1989. LEACHM: Leaching Estimation and Chemistry Model: A process based model of water and solute movement, transformations, plant uptake and chemical reactions in the unsaturated zone, vers. 2. Water Resour Inst, Cornell Univ, Ithaca, NY.

How to Cite
LidónA., GinestarD., CarlosS., Sánchez de OleoC., JaramilloC., & RamosC. (2020). Sensitivity analysis and parameterization of two agricultural models in cauliflower crops. Spanish Journal of Agricultural Research, 17(4), e1106.
Soil science