Assessment of Non-Linearity in Functional-Structural Plant Models

International audienceGlobal sensitivity analysis (SA) has known an increasing interest to assess the relative importance of parameters in ecological models [Cariboni et al., 2007] or crop models [Makowski et al., 2006]. Such methods have an important role to play in functional-structural plant growth modeling. The complexity of the underlying biological processes, especially the interaction between functioning and structure [Vos et al., 2009], usually makes parameterization a key step in modeling, and the analysis of model sensitivity to parameters provides useful information in this process. A side result of global SA is that it provides an indicator of the degree of non-linearity of the model by computing the level of interaction between parameters and how this interaction contributes to the variance of the output. Plants are known as complex systems with a strong level of interactions and compensations, and the aim of FSPMs is to describe and understand this complexity. As such, non-linearity is expected to play a key role in the study, since it reveals the interactions between parameters [Cariboni et al., 2007] [Saltelli, 2002]. The knowledge of the intrinsic non-linearity of the model and of its dynamic evolution throughout plant growth is very useful to study model behavior and properties, to underline the occurrence of particular biological phenomena or to improve the statistical analysis when confronting models to experimental data (e.g. statistical properties of estimators or numerical methods to compute the propagation of errors [Julier et al., 2000]). The objective of this paper is thus to explore the level of linearity of 3 FSPMs with different levels of complexity, and infer in each case what information can be drawn from this analysis. We first introduce the basic principles of Standard Regression Coefficients (SRC) method which is used for the analysis and gives a short overview of the different models addressed. We then analyze the results of the linearity study, particularly stressing on the emergence of non-linearity. We end by discussing the interest and potential extensions of this work

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58% of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and three-hour precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modellingPeer reviewe

Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters

Monitoring and modelling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity K-s and the Mualem-van Genuchten retention curve shape parameters n and alpha. Results further showed that different parameter optimization strategies (two-, three-, four- or six-parameter optimizations) did not affect the calculated water stress and water content as significantly as does the bottom boundary. In this case, a two-parameter scenario, where K-s was optimized for each layer under the condition of a constant groundwater depth at 135-140 cm, performed best. A larger yield reduction, and a larger number and longer duration of stress conditions occurred in the free drainage condition as compared to constant boundary conditions. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to 12-22 % irrigation water as compared to the current irrigation regime. This resulted in a yield increase of 4.5-6.5 %, simulated by the crop growth model

A measure to evaluate the sensitivity to genotype-by-environment interaction in 6 grapevine clones

Background and Aims: The development of an efficient clonal selection process requires 25 the study of genotype-by-environment (G×E) interaction. This work aims to evaluate the 26 variability of the G×E interaction among genotypes and to identify the less sensitive ones. 27 Methods and Results: The approach involves the fitting of mixed models to yield data taking 28 into account the correlation induced by the repeated measurements of the same plot over the 29 years. A measure for comparative evaluation of the G×E interaction among genotypes is 30 proposed (Interaction Sensitivity, IS), based on the variance of the values of the empirical best 31 linear unbiased predictors of G×E interaction effects across environments. In all cases studied 32 significant G×E interaction variability was found, and the proposed measure to rank the 33 sensitivity to G×E interaction varied widely among genotypes. 34 Conclusions: The existence of a common contribution shared by all observations made in the 35 same plot was detected, independently of the lag between years. The proposed measure to 36 rank the sensitivity to G×E interaction permitted identification of stable genotypes. 37 Significance of the Study: This work studied G×E interaction problem in the context of 38 grapevine and proposes a measure for the comparative evaluation of the G×E interaction 39 among genotypesinfo:eu-repo/semantics/acceptedVersio

Assessing long-term conservation of groundwater resources in the Ogallala Aquifer Region using hydro-agronomic modeling

2022 Spring.Includes bibliographical references.Groundwater is vital for domestic use, municipalities, agricultural irrigation, industrial processes, etc. Over the past century, excessive groundwater depletion has occurred globally and regionally, notably in arid and semi-arid regions, often due to providing irrigation water for crop cultivation. The High Plains Aquifer (HPA) is the largest freshwater aquifer in the United States and has experienced severe depletion in the past few decades due to excessive pumping for agricultural irrigation. There is a need to determine management strategies that conserve groundwater, thereby allowing irrigation for coming decades, while maintaining current levels of crop yield within the context of a changing climate. Numerical models can be useful tools in this effort. Hydrologic models can be used to assess current and future storage of groundwater and how this storage depends on system inputs and outputs, whereas agronomic models can be used to assess the impact of water availability on crop production. Linking these models to jointly assess groundwater storage and crop production can be helpful in exploring management practices that conserve groundwater and maintain crop yield under future possible climate conditions. The objectives of this dissertation are: i) to develop a linked modeling system between DSSAT, an agronomic model, and MODFLOW, a groundwater flow model to be used for evaluating long-term impacts of climate and management strategies on water use efficiency and farm profitability of agricultural systems while managing groundwater sustainably; ii) to use the DSSAT-MODFLOW modeling system in a global sensitivity analysis framework to determine the system factors (climate, soil, management, aquifer) that control crop yield and groundwater storage in a groundwater-stressed irrigated region, thereby pointing to possibilities of efficient management; and iii) to quantify the effect of groundwater conservation strategies and climate on crop yield and groundwater storage to identify irrigation and planting practices that will maintain adequate crop yield while minimizing groundwater depletion. These three objectives are applied to the hydro-agronomic system of Finney County, Kansas, which lies within the HPA. Major findings include: 1) climate-related parameters significantly affect crop yields, especially for maize and sorghum, and soybean and winter wheat yields are sensitive to a combination of cultivar genetic parameters, soil-related parameters, and climate-related parameters; 2) Climatic parameters account for 44%, 29%, 40%, and 36% variation in yield of maize, soybean, winter wheat, and sorghum; 3) Hydrogeologic parameters (aquifer hydraulic conductivity, aquifer specific yield, and riverbed conductance) have a relatively low influence on crop yields; 4) water table elevation, recharge, and irrigation pumping are considerably sensitive to soil- and climate-related parameters, while ET, river leakage, and groundwater/aquifer discharge are highly influenced by hydrogeological parameters (e.g., riverbed conductance, and specific yield); 5) the best management practice is the combination of implementing drip irrigation and planting quarter plots under both dry and wet future climate conditions. Other irrigation systems (sprinkler) and planting decisions (half-plots) can also be implemented without severe groundwater depletion. If crop yield is to be maintained in this region of the HPA, groundwater depletion can be minimized but not completely prevented. Results highlight the need for implementing new irrigation technologies, and likely changing crop type decisions (e.g., limiting corn cultivation) in coming decades in this region of the HPA. Results from this dissertation can be used in other groundwater-irrigated regions facing depletion of groundwater

Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change

Estimates of the response of crops to climate change rarely quantify the uncertainty inherent in the simulation of both climate and crops. We present a crop simulation ensemble for a location in India, perturbing the response of both crop and climate under both baseline (12 720 simulations) and doubled-CO2 (171 720 simulations) climates. Some simulations used parameter values representing genotypic adaptation to mean temperature change. Firstly, observed and simulated yields in the baseline climate were compared. Secondly, the response of yield to changes in mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. Thirdly, the relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes was examined. In simulations without genotypic adaptation, most of the uncertainty came from the climate model parameters. Comparison with the simulations with genotypic adaptation and with a previous study suggested that the relatively low crop parameter uncertainty derives from the observational constraints on the crop parameters used in this study. Fourthly, the simulations were used, together with an observed dataset and a simple analysis of crop cardinal temperatures and thermal time, to estimate the potential for adaptation using existing cultivars. The results suggest that the germplasm for complete adaptation of groundnut cultivation in western India to a doubled-CO2 environment may not exist. In conjunction with analyses of germplasm and local management practices, results such as this can identify the genetic resources needed to adapt to climate change

[i]In silico[/i] system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management

Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work

Towards Interpreting Multi-Objective Feature Associations

Understanding how multiple features are associated and contribute to a specific objective is as important as understanding how each feature contributes to a particular outcome. Interpretability of a single feature in a prediction may be handled in multiple ways; however, in a multi-objective prediction, it is difficult to obtain interpretability of a combination of feature values. To address this issue, we propose an objective specific feature interaction design using multi-labels to find the optimal combination of features in agricultural settings. One of the novel aspects of this design is the identification of a method that integrates feature explanations with global sensitivity analysis in order to ensure combinatorial optimization in multi-objective settings. We have demonstrated in our preliminary experiments that an approximate combination of feature values can be found to achieve the desired outcome using two agricultural datasets: one with pre-harvest poultry farm practices for multi-drug resistance presence, and one with post-harvest poultry farm practices for food-borne pathogens. In our combinatorial optimization approach, all three pathogens are taken into consideration simultaneously to account for the interaction between conditions that favor different types of pathogen growth. These results indicate that explanation-based approaches are capable of identifying combinations of features that reduce pathogen presence in fewer iterations than a baseline.Comment: The 18th Annual IEEE International Systems Conference 2024 (IEEE SYSCON 2024