Uncertainty analysis of the HORTSYST model applied to fertigated tomatoes cultivated in a hydroponic greenhouse system

Antonio Martínez-Ruiz,Prometeo Sánchez-García,Agustín Ruiz-García,Irineo L López-Cruz,Candido Mendoza-Pérez,Joel Pineda-Pineda

doi:10.5424/sjar/2021193-17842

Abstract

Aim of study: The objective was to perform an uncertainty analysis (UA) of the dynamic HORTSYST model applied to greenhouse grown hydroponic tomato crop. A frequentist method based on Monte Carlo simulation and the Generalized Likelihood Uncertainty Estimation (GLUE) procedure were used.Area of study: Two tomato cultivation experiments were carried out, during autumn-winter and spring-summer crop seasons, in a research greenhouse located at University of Chapingo, Chapingo, Mexico.Material and methods: The uncertainties of the HORTSYST model predictions PTI, LAI, DMP, ETc, Nup, Pup, Kup, Caup, and Mgup uptake, were calculated, by specifying the uncertainty of model parameters 10% and 20% around their nominal values. Uniform PDFs were specified for all model parameters and LHS sampling was applied. The Monte Carlo and the GLUE methods used 10,000 and 2,000 simulations, respectively. The frequentist method included the statistical measures: minimum, maximum, average values, CV, skewness, and kurtosis whilst GLUE used CI, RMSE, and scatter plots.Main results: As parameters were changed 10%, the CV, for all outputs, were lower than 15%. The smallest values were for LAI (10.75%) and DMP (11.14%) and the largest was for ETc (14.47%). For Caup (12.15%) and Pup (12.27%), the CV was lower than the one for Nup and Kup. Kurtosis and skewness values were close as expected for a normal distribution. According to GLUE, crop density was found to be the most relevant parameter given that it yielded the lowest RMSE value between the simulated and measured values.Research highlights: Acceptable fitting of HORTSYST was achieved since its predictions were inside 95% CI with the GLUE procedure.

Highlights

An important issue in greenhouse horticulture is optimization of water and nutrients, which can be tackled by using decision-support-systems (DSS) based on dynamic mathematical models such as VEGSYST (Gallardo et al, 2011)
Relative humidity was somewhat contrasting not because of its averaged values of 78.6% and 76.8%, but rather for its minimum values of 62.5% and 29.5% compared to its maximum values of 93.4% and 93.2%, respectively
Similar behavior was observed for larger uncertainty intervals, the range of uncertainty variation (CV values ranged from 22% to 30%)

Summary

Introduction

An important issue in greenhouse horticulture is optimization of water and nutrients, which can be tackled by using decision-support-systems (DSS) based on dynamic mathematical models such as VEGSYST (Gallardo et al, 2011). Uncertainty assessment (Walker et al, 2003) is a crucial stage in model development focused on quantifying the reliability of model predictions. Several methodologies and suitable tools for supporting uncertainty assessment have been developed and reported by Cooman & Schrevens (2006). There are few studies reporting the frequentist uncertainty analysis (Monte Carlo method) applied to greenhouse crop models, some of these are TOMGRO model applied to tomato (Cooman & Schrevens, 2006) and NICOLET model to lettuce (López-Cruz et al, 2012). Only few studies, such as one involving the SIMRIW model for paddy rice and another using the CSM-CROPGRO-cotton model for open field crops (Iizumi et al, 2009; Pathak et al, 2012) have studied the generalized likelihood uncertainty estimation (GLUE) method. To the best of our knowledge GLUE is the most reliable uncertainty analysis procedure developed until now

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