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Article

Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method

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Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production (Inov4Agro), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
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Potsdam Institute for Climate Impact Research, PIK, 14473 Potsdam, Germany
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CoLAB VINES & WINES-National Collaborative Laboratory for the Portuguese Wine Sector, Associação para o Desenvolvimento da Viticultura Duriense (ADVID), Edifício Centro de Excelência da Vinha e do Vinho, Régia Douro Park, 5000-033 Vila Real, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Kyveryga
Agronomy 2021, 11(8), 1659; https://doi.org/10.3390/agronomy11081659
Received: 8 July 2021 / Revised: 12 August 2021 / Accepted: 18 August 2021 / Published: 20 August 2021
(This article belongs to the Special Issue Predictive Modeling to Aid Agronomic Decision Making)
Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation. View Full-Text
Keywords: grapevine modelling; Mediterranean climate; parameter estimation; parameter uncertainty; STICS; python platform grapevine modelling; Mediterranean climate; parameter estimation; parameter uncertainty; STICS; python platform
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MDPI and ACS Style

Yang, C.; Menz, C.; Fraga, H.; Reis, S.; Machado, N.; Malheiro, A.C.; Santos, J.A. Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method. Agronomy 2021, 11, 1659. https://doi.org/10.3390/agronomy11081659

AMA Style

Yang C, Menz C, Fraga H, Reis S, Machado N, Malheiro AC, Santos JA. Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method. Agronomy. 2021; 11(8):1659. https://doi.org/10.3390/agronomy11081659

Chicago/Turabian Style

Yang, Chenyao, Christoph Menz, Helder Fraga, Samuel Reis, Nelson Machado, Aureliano C. Malheiro, and João A. Santos. 2021. "Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method" Agronomy 11, no. 8: 1659. https://doi.org/10.3390/agronomy11081659

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