Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined

Abstract

The use of a crop model like STICS for appropriate management decision support requires a good knowledge of all the parameters of the model. Among them, the soil parameters are difficult to know at each point of interest and costly techniques may be used to measure them. It is therefore important to know which soil parameters need to be determined. It can be stated that those which affect significantly the output variable deserve an accurate determination while those which slightly affect the model output variable do not. This paper demonstrates how a global sensitivity analysis method based on variance decomposition can be applied on soil parameters in order to divide them in the two categories. The Extended FAST method applied to the crop model STICS and a set of 13 soil parameters first allows to calculate the part of variance explained by each soil parameter (giving global sensitivity indices of the soil parameters) and the coefficient of variation of the output variables (measuring the effect of the parameter uncertainty on each variable). These metrics are therefore used for deciding on the importance of the parameter value measurement. Different output variables (Leaf Area Index and chlorophyll content) are evaluated at different stages of interest while others (crop yield, grain protein content, soil mineral nitrogen) are evaluated at harvest. The analysis is applied on two different annual crops (wheat and sugar beet), two contrasted weather and two types of soil depth. When the uncertainty of the output generated by the soil parameters is large (coefficient of variation > 1/3), only the parameters having a significant global sensitivity indices (higher than 10%) are retained. The results show that the number of soil parameters which deserve an accurate determination can be significantly reduced by the use of this relevant method for appropriate management decision support

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

“…The Nash-Sutcliffe efficiency showed a better agreement between the two simulation scenarios, as all outputs related to the soil module recorded an NSE over 0.85. Wilmott's index of the agreement also confirmed the agreement of the results of the PTF-based simulation with the measurement-based simulation for all soil module Similarly, Varella et al [25] showed a higher sensitivity of field capacity when simulating the growth and yield of wheat and sugar beet using the STICS model. Chen et al [76] also showed a higher sensitivity of wheat yield to the available water capacity.…”

“…Many studies have been conducted to evaluate the sensitivity of plant parameters and the management practices of different crops under different models [24,[37][38][39][40][41][42][43]. Although the sensitivity assessment of soil is essential [25], limited attention has been paid to evaluating the sensitivity of soil parameters [42]. Hence, this study aims to evaluate the sensitivity of soil parameters to the growth and yield of rice using the APSIM-Oryza model in APSIM 7.10.…”

“…Therefore, ground-level observations may be successfully replaced with grid-based generated data. However, knowledge of soil parameters is crucial, as these parameters are largely responsible for the variability of process-based crop model outputs [24][25][26] and are often taken from nearby soil surveys or expert knowledge. However, due to its inherent variations, this information may need to be acquired at a finer scale for better estimations using process-based crop models [25].Crop modeling aims to describe and understand the relevant plant processes and their interactions with abiotic and biotic factors [27].…”

“…However, knowledge of soil parameters is crucial, as these parameters are largely responsible for the variability of process-based crop model outputs [24][25][26] and are often taken from nearby soil surveys or expert knowledge. However, due to its inherent variations, this information may need to be acquired at a finer scale for better estimations using process-based crop models [25].Crop modeling aims to describe and understand the relevant plant processes and their interactions with abiotic and biotic factors [27]. Agricultural Production Systems sIMulator (APSIM) [28], Decision Support Systems for Agrotechnology Transfer (DSSAT) [29], and Simulateur mulTIdisciplinaire pour les Cultures Standard (STICS) [30] are some of the most widely used dynamic crop models to analyze agricultural processes under different input parameters, such as abiotic and biotic factors, crop parameters, and management conditions.…”

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

“…In the context of sensitivity analysis, this method is called screening or factor fixing ( [11], [13]). [55] and [62] describe an application to the crop model STICS.…”