1,721,154 research outputs found
The simulation of soil water balance: comparison of CropSyst and MACRO model performances.
No full textAn 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
Technical approach for the measurement of surface runoff
No full textIn this paper we describe practical application, design and installation of an in-field runoff collector exploitable for monitoring nutrients, pesticides and sediments loadings in runoff, improved with a home made level reading system able to measure with high temporal resolution, the runoff rate variation.
This configuration simplifies and lower the cost of conventional instruments used for measuring runoff. A multislot divisor was used to reduce the volume of runoff and plastic tank were use to collect it. An electro-mechanic type, floating level transducer was proposed. The homemade level reading system is composed of three parts: floating level transducer, signal conditioning system and data storage. The total cost for entire system is approximately € 642
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
Comparison of WOFOST, CropSyst and WARM for simulating rice growth (Japonica type – short cycle varieties)
No full textQuantifying 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
Modelling nitrogen leaching from sewage sludge application to arable land in the Lombardy region (northern Italy)
No full textSewage sludge can be used as fertiliser, offering the possibility of safely recycling this waste product as a resource in agricultural applications. As the environmental concerns related to waste recycling in agricultural applications are well-known, restrictions on the use of sewage sludge have been implemented by the EU and local authorities. This work aimed to evaluate the nitrogen leaching associated with the application of sludge and the effectiveness of the temporal restrictions on its application implemented to safeguard the environment in the Lombardy region of northern Italy (120days in Nitrate Vulnerable Zones and 90days elsewhere) using the CropSyst model which was first validated. The effects of fertilisation using four different sludge types on N leaching were simulated at five sites under cultivation with maize and rice crops; six different timing schemes for sludge application were tested, three of which involved dates that were in agreement (AT) with the regulation, while the other three were not in agreement (NAT). We detected a significant effect of the sludge type and application timing, whereas the effect of their interaction was never significant. The mean annual leaching was 22 to 154kgNha-1. The higher the ammonium N content in the sludge was, the greater the potential for N leaching was found to be. For the maize crop, the distribution of sludge in the late fall period resulted in significantly greater N leaching (61kgNha-1) and led to lower yields (9t DMha-1) compared to late winter fertilisation (49kgNha-1; 10t DMha-1), whereas no differences in N leaching or yield were detected between AT and NAT, which was also observed for the rice crop. Therefore, the applied temporal constraints did not always appear to be advantageous for protecting the environment from leaching
Evaluation of mitigation strategies to reduce ammonia losses from slurry fertilisation on arable lands
No full textTo evaluate the best practices in reducing ammonia (NH3) losses from fertilised arable lands, six field trials were carried out in three different locations in northern Italy. NH3 emissions from cattle slurry were estimated considering the spreading techniques and the field incorporation procedures. The measurements were performed using long term exposure samplers associated to the determination of the atmospheric turbulence and the use of the backward Lagrangian stochastic (bLS) model WindTrax. The results obtained indicate that the NH3 emission process was exhausted in the first 24-48h after slurry spreading. The slurry incorporation technique was able to reduce the NH3 losses with respect to the surface spreading, where a contextual incorporation led to reductions up to 87%. However, the best abatement strategy for NH3 losses from slurry applications has proved to be the direct injection into the soil, with a reduction of about 95% with respect to the surface spreading. The results obtained highlight the strong dependence of the volatilisation phenomenon by soil and weather conditions
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