1,721,016 research outputs found
A set of software components for the simulation of plant airborne diseases
No full textModels to evaluate the impact of plant diseases on crop production under current and future climatic conditions are increasingly requested by different stakeholders. This paper presents four software components - InoculumPressure, DiseaseProgress, ImpactsOnPlants, AgromanagementDisease - which implement models to simulate the dynamics of generic polycyclic fungal epidemics and interactions with crop physiological processes. The software architecture adopted allows extending the components with alternate approaches to reproduce specific pathosystems or compare predictive capabilities. As proofs of concept, (i) the components are coupled with two crop simulators to reproduce wheat brown rust and rice blast epidemics and their impacts on leaf area and yield formation; (ii) spatially distributed sensitivity analyses are performed for rice in China and wheat in Europe to investigate model behaviour; (iii) a preliminary evaluation against observations of rice blast severity is performed in Northern Italy. The components are explicitly targeted to the modelling of crop-pathogen interactions to perform scenario analysis
Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration
No full textDespite modellers are paying increasing attention to analyse and manage the different sources of uncertainty affecting model predictions, the impact of the uncertainty in the observations used for calibration has been ignored. This study proposes a methodology for its quantification and provides an illustrative case study with data collected in two field experiments where rice was grown under flooded conditions in northern Italy in 2002 and 2004. Latin hypercube sampling was used to generate virtual series of observations from the mean and standard deviation of aboveground biomass values collected during the season in the two experiments. Each of the generated series was then used to calibrate the parameters maximum radiation use efficiency and optimum temperature for growth of the WARM model by means of the simplex optimization algorithm. The analysis of the distribution of key outputs (aboveground and panicle biomass at harvest) and of agreement metrics revealed that the impact of uncertainty in the observations used for calibration (explored here running calibration experiments for each of the generated series) can be large. The difference between maximum and minimum aboveground biomass at maturity was 2.79 t ha-1 and 3.78 t ha-1 for the datasets collected in 2004 and 2002, respectively. Corresponding values for panicle biomass were 0.97 t ha-1 and 2.36 t ha-1. In all cases, model outputs were normally distributed. Large differences were achieved also in the values of the agreement metrics, with RRMSE ranging from 13.64% to 36.22% and from 8.04% to 29.97% for the 2004 and 2002 datasets. The methodology proposed - although applicable to a variety of models and domains - deals only with the uncertainty due to random errors, which could derive, e.g. from non-representative sampling or from the repeatability of the method used to determine the variable of interest. Other sources of uncertainty, like those involved with systematic errors, need to be addressed in further studies. This study highlighted the need for conceptual and mathematical frameworks where the different sources of uncertainty affecting model predictions can be analysed in an integrated way
Quantifying plasticity in simulation models
No full textDifferent methodologies for evaluating aspects of model performance going beyond the pure agreement between measured and simulated data have been recently proposed. These indicators and criteria for the evaluation of, e.g., complexity and robustness can be used in conjunction with well-known metrics for the evaluation of model accuracy - such as root mean square error and modelling efficiency - to get a deeper knowledge about models structure and behaviour. The aim of this paper is to propose an indicator of model plasticity, defined as the aptitude of a model to change the sensitivity to its parameters while changing the conditions of application. Sensitivity was here analyzed using the Sobol' method for sensitivity analysis (SA). Concordance among parameters relevance (total order effect) estimated under different conditions allowed to quantify changes in the way models react to different environments. The concordance among the different SA results was related to the variability of a normalized agrometeorological indicator used to characterize the explored conditions. The plasticity indicator was tested using three different crop models (WARM, CropSyst, WOFOST; rice was simulated), 10 European locations, and 10 years for each location, for a total of 5,939,200 simulations and 300 SA experiments. Results indicated WOFOST as the most plastic, both within location, year, and using all the combinations location × year, whereas WARM showed to be the less plastic across the conditions explored. Previous studies carried out on the same models in northern Italy seem to suggest a direct relationship between model complexity and plasticity, whereas model accuracy seems to be unrelated to these features. This consideration underlines that, in case of availability of different models with a similar degree of accuracy, different choices should be performed for different modelling studies, characterized by different aims and conditions of application
Evaluating the suitability of a generic fungal infection model for pest risk assessment studies
No full textPest risk assessment studies are aimed at evaluating if weather conditions are suitable for the potential entry and establishment of an organism in a new environment. For fungal plant pathogens, the crucial aspect to be explored is the fulfillment of the infection process, that constitutes the first phase of the development of an epidemic as mainly driven by temperature and leaf wetness duration. This is of particular interest for climate change studies, because the modified pattern of temperature and moisture regimes could completely alter the known distribution and severity of plant disease epidemics. Biophysical process-based models could effectively be used in such studies, because they allow, within their applicability range, estimating organisms responses to climatic drivers in environmental conditions not yet experienced. One of the prerequisite of their adoption in operational contexts is a sensitivity analysis assessment aimed at understanding their ability (i) to differentiate the responses according to different parameterizations and (ii) to be sensitive to the variability of the input data. In this study, a generic potential fungal infection model simulating four pathogens chosen to provide a wide range in temperature and moisture requirements was analyzed. The model was run under diverse climatic conditions. The sensitivity of the model significantly changed according to the pathogen tested, and the relevance of its parameters in explaining model output resulted strongly linked to the environmental conditions tested, indicating its to be used in pest risk assessment studies
Fungal infections of rice, wheat, and grape in Europe in 2030–2050
No full textAlthough models to predict climate impact on crop production have been used since the 1980s, spatial and temporal diffusion of plant diseases are poorly known. This lack of knowledge is due to few models of plant epidemics, high biophysical complexity, and difficulty to couple disease models to crop simulators. The first step is the evaluation of disease potential growth in response to climate drivers only. Here, we estimated the evolution of potential infection events of fungal pathogens of wheat, rice, and grape in Europe. A generic process-based infection model driven by air temperature and leaf wetness data was parameterized with the thermal and moisture requirements of the pathogens. The model was run on current climate as baseline, and on two time frames centered on 2030 and 2050. Our results show an overall increase in the number of infection events, with differences among the pathogens, and showing complex geographical patterns. For wheat, Puccinia recondita, or brown rust, is forecasted to increase +20-100 % its pressure on the crop. Puccinia striiformis, or yellow rust, will increase 5-20 % in the cold areas. Rice pathogens Pyricularia oryzae, or blast disease, and Bipolaris oryzae, or brown spot, will be favored all European rice districts, with the most critical situation in Northern Italy (+100 %). For grape, Plasmopara viticola, or downy mildew, will increase +5-20 % throughout Europe. Whereas Botrytis cinerea, or bunch rot, will have heterogeneous impacts ranging from -20 to +100 % infection events. Our findings represents the first attempt to provide extensive estimates on disease pressure on crops under climate change, providing information on possible future challenges European farmers will face with in the coming years
An integrated evaluation of thirteen modelling solutions for the generation of hourly values of air relative humidity
No full textThe availability of hourly air relative humidity (HARH) data is a key requirement for the estimation of epidemic dynamics of plant fungal pathogens, in particular for the simulation of both the germination of the spores and the infection process. Most of the existing epidemic forecasting models require these data as input directly or indirectly, in the latter case for the estimation of leaf wetness duration. In many cases, HARH must be generated because it is not available in historical series and when there is the need to simulate epidemics either on a wide scale or with different climate scenarios. Thirteen modelling solutions (MS) for the generation of this variable were evaluated, with different input requirements and alternative approaches, on a large dataset including several sites and years. A composite indicator was developed using fuzzy logic to compare and to evaluate the performances of the models. The indicator consists of four modules: Accuracy, Correlation, Pattern and Robustness. Results showed that when available, daily maximum and minimum air relative humidity data substantially improved the estimation of HARH. When such data are not available, the choice of the MS is crucial, given the difference in predicting skills obtained during the analysis, which allowed a clear detection of the best performing MS. This study represents the first step of the creation of a robust modelling chain coupling the MS for the generation of HARH and disease forecasting models, including the systematic validation of each step of the simulation
A proposal of an indicator for quantifying model robustness based on the relationship between variability of errors and of explored conditions
No full textThe evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (IR) were discussed according to the models' features and to the typology and number of processes simulated. IR increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between IR and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation
A model to simulate the dynamics of carbohydrate remobilization during rice grain filling
No full textThe remobilization of carbon reserves accumulated in stems during vegetative growth is known to significantly contribute to yield formation in many cereals, and to be modulated by water and nitrogen availability during grain filling. However, despite the extensive use of crop models to support irrigation and fertilization plans, current knowledge on carbohydrate remobilization is rarely formalized in the available simulation tools. This paper presents a model to simulate carbohydrate remobilization in rice, based on the balance between source (i.e., the carbon reserves in stems) and sink (i.e., the grains) strength and on the impact of water stress and nitrogen luxury consumption. The new approach was included in the WARM model and evaluated using data from published experiments where two cultivars were grown under two nitrogen fertilization levels and two irrigation strategies. Results highlighted the model effectiveness in reproducing the amount of remobilization under non stressed conditions (R2=0.99), as well as the impact of water and nitrogen availability (average R2=0.97) for Indica and Japonica rice cultivars. The proposed model can be easily plugged into available rice simulators to increase their adherence to the underlying system
ISIde : A rice modelling platform for in silico ideotyping
No full textEcophysiological models can be successfully used to analyze genotype by environment interactions, thus supporting breeders in identifying key traits for specific growing conditions. This is especially true for traits involved with resistance/tolerance to biotic and abiotic stressors, which occurrence can vary greatly both in time and space. However, no modelling tools are available to be used directly by breeders, and this is one of the reasons that prevents an effective integration of modelling activities within breeding programs. ISIde is a software platform specifically designed for district-specific rice ideotyping targeting (i) resistance/tolerance traits and (ii) breeders as final users. Platform usability is guaranteed by a highly intuitive user interface and by exposing to users only settings involved with genetic improvement. Other information needed to run simulations (i.e., data on soil, climate, management) is automatically provided by the platform once the study area, the variety to improve and the climate scenario are selected. Ideotypes indeed can be defined and tested under current and climate change scenario, thus supporting the definition of strategies for breeding in the medium-long term. Comparing the performance of current and improved genotype, the platform provides an evaluation of the yield benefits exclusively due to the genetic improvement introduced. An example of the application of the ISIde platform in terms of functionalities and results that can be achieved is reported by means of a case study concerning the improvement of tolerance to heat stress around flowering in the Oristanese rice district (Italy). The platform is currently available for the six Italian rice districts. However, the software architecture allows its extension to other growing areas – or to additional genotypes – via dedicated tools available at the application page
Comparison of sensitivity analysis techniques : a case study with the rice model WARM
No full textThe considerable complexity often included in biophysical models leads to the need of specifying a large number of parameters and inputs, which are available with various levels of uncertainty. Also, models may behave counter-intuitively, particularly when there are nonlinearities in multiple input-output relationships. Quantitative knowledge of the sensitivity of models to changes in their parameters is hence a prerequisite for operational use of models. This can be achieved using sensitivity analysis (SA) via methods which differ for specific characteristics, including computational resources required to perform the analysis. Running SA on biophysical models across several contexts requires flexible and computationally efficient SA approaches, which must be able to account also for possible interactions among parameters. A number of SA experiments were performed on a crop model for the simulation of rice growth (Water Accounting Rice Model, WARM) in Northern Italy. SAs were carried out using the Morris method, three regression-based methods (Latin hypercube sampling, random and Quasi-Random, LpTau), and two methods based on variance decomposition: Extended Fourier Amplitude Sensitivity Test (E-FAST) and Sobol', with the latter adopted as benchmark. Aboveground biomass at physiological maturity was selected as reference output to facilitate the comparison of alternative SA methods. Rankings of crop parameters (from the most to the least relevant) were generated according to sensitivity experiments using different SA methods and alternate parameterizations for each method, and calculating the top-down coefficient of concordance (TDCC) as measure of agreement between rankings. With few exceptions, significant TDCC values were obtained both for different parameterizations within each method and for the comparison of each method to the Sobol' one. The substantial stability observed in the rankings seem to indicate that, for a crop model of average complexity such as WARM, resource intensive SA methods could not be needed to identify most relevant parameters. In fact, the simplest among the SA methods used (i.e., Morris method) produced results comparable to those obtained by methods more computationally expensive
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