1,721,139 research outputs found
Combining a weather generator and a standard sensitivity analysis method to quantify the relevance of weather variables on agrometeorological models outputs
No full textSensitivity analysis (SA) is increasingly used to explain models behaviour in response to inputs variation. Agrometeorologists are used to apply standard SA methods only on model parameters because of the difficulty of applying standard sampling techniques to derive series of weather data where each value cannot be sampled independently from those of the neighbouring days and from other variables in the same day. The impact of weather variability on a crop model was here analysed by coupling the Morris SA method to a weather generator. Spring barley in northern Italy was simulated and different outputs considered. Under the explored conditions, parameters involved with temperature generation resulted the most relevant in determining yield and maturity date. Radiation-related parameters were high-ranked for cumulated drainage and actual evapotranspiration. According to the author, this is the first time the sensitivity of a cropping system model to weather variables is quantified using standard SA techniques
Monte Carlo based sensitivity analysis of two crop simulators and considerations on model balance
No full textSensitivity analysis is crucial for building, understanding and using complex mathematical models for agroenvironmental applications. In this study, a variance-based sensitivity analysis was carried out for the first time on CropSyst and WOFOST, two of the crop models most diffused worldwide and different in their degree of mechanism. The scenario assumed for the simulations refers to paddy rice grown in Northern Italy. After screening parameters using the Morris method, the Sobol' approach was used for quantifying their influence on models output variability. Seven out of 34 parameters for WOFOST (mostly involved with CO2 assimilation and photosynthetates conversion into plant organs) and 3 out of 15 for CropSyst (biomass-transpiration coefficient, base temperature and light extinction coefficient) were responsible for about 90% of the total output variability. Assuming the homogeneity among the sensitivity indices of the parameters as an indicator of model balance, the Grubbs test for outliers detection was used to check if the relevance of a parameter (or a few of them) was significantly higher than the others. In the explored conditions (non-limiting for water and nutrients availability), CropSyst resulted unbalanced, being mostly driven by the biomass-transpiration coefficient, whereas no significant differences were identified among the relevance of the WOFOST parameters
1st European Meeting on WARM : a rice modelling experience : preface to the special issue
No full textCoSMo: a simple approach for reproducing plant community dynamics using a single instance of generic crop simulators
No full textGrassland productivity can be estimated using individual-centred models or via crop simulators parameterized to mimic average morphological and physiological features of the phytocoenosis as a whole. Although the latter is often considered an oversimplified solution, individual-centred models are characterized by a degree of complexity that often restricts their use to scientists specialized in pastures modelling or in crop-weed interaction. In this study, an intermediate solution is presented (CoSMo), based on two assumptions allowing the use of a single instance of a generic crop model to simulate phytocoenosis dynamics and productivity. The first is that community parameters can be derived at each time step from the relative presence of the different species and from parameter values determined for the species in monoculture. The second is that inter-specific competition and changes in species relative presence can be simulated as a function of species-specific responses to hierarchically arranged drivers (triggered and continuous) representing the suitability of the different species to the conditions explored at each time step. CoSMo was here analyzed by means of three simulation experiments, where changes in the relative presence of three species with different traits and the productivity of the community were simulated under current conditions and future climate projections. Results encourage further studies, given that the solution proposed is easy to implement and parameterize, and leaves users free to work with the generic crop simulator they are familiar with. These features make CoSMo suitable for being coupled - within integrated studies - to models developed for other domains by scientists not specialist in the ecophysiological aspects involved with inter-specific competition. However, this approach cannot be considered as an alternative to individual-centred models in case of in silico studies explicitly focusing on the relationships between inter-specific competition and species traits and phenotypic plasticity
Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy
No full textResearch on rice cropping systems carried out in Europe has to face the great variability of pedo-climatic conditions, and the linked abundance of cultivated varieties, characteristic of the high latitudes-temperate areas where rice is traditionally grown. Dynamic simulation models can provide an useful tool for system analysis needed to improve the knowledge, the agronomic management and crop monitoring. For calibrate and validate CropSyst (never used for rice), a process-based simulation model, for Indica-type and Japonica-type varieties, data obtained from five field experiments, carried out in Northern Italy between 1989 and 2002. were used. Plants were sampled during the life cycle from rice plots of five cv Loto, Cripto, Ariete, Drago, Thaibonnet and Sillaro, maintained at potential production, to determine some important crop variables and parameters such as aboveground biomass (AGB), leaf area index, specific leaf area, harvest index, the date of the main phonological stages. At the end of the calibration process to the parameters (the others were set to the default value, taken from the Literature or measured) optimum mean daily temperature for growth, specific leaf area (for Japonica varieties), stem/leaf partition coefficient (empirical), leaf duration, were assigned the following values: 28 and 27 degrees C respectively for Japonica and Indica varieties, 27 and 29.5 m(2) kg(-1) respectively for Japonica early and medium-late varieties, 4.5, 3, 1.5 for Japonica early, medium-late and Indica varieties, 700, 850, 950 degrees C-days for the three groups of varieties. The assessment of model performances has shown average RRMSEs of 20 and 22% at the end of calibration and for the validation process; the modelling efficiency is always positive and the coefficient of determination always very close to 1. General improvements will be achieved by the model by considering the thermal profile (strongly influenced by flooding water at mid latitudes) evolving in and over the canopy
Stazione meteorologica galleggiante
No full textL’invenzione riguarda una stazione meteorologica galleggiante per la misurazione delle variabili meteorologiche in bacini idrici poco profondi ed un originale misuratore/registratore del livello dell’acqua come accessorio per la stazione. L’intera struttura è in grado di galleggiare anche solo in 3 centimetri d’acqua senza perturbare l’ambiente. E’ costituita da una struttura in alluminio alla quale sono collegate piccole lastre di poliuretano per permetterne il galleggiamento. Alla struttura sono collegati diversi sensori ed una centralina per l’acquisizione dei dati. Un braccio mobile consente misurazioni esatte della temperatura in superficie nonostante eventuali oscillazioni (comunque ridottissime) della struttura in caso di perturbazioni all’ambiente. La modularità della struttura ne permette l’adattamento ad esigenze diversissime
Sensitivity analysis of a sensitivity analysis : We are likely overlooking the impact of distributional assumptions
No full textAlthough uncertainty in input factor distributions is known to affect sensitivity analysis (SA) results, a standard procedure to quantify its impact is not available. We addressed this problem by performing a SA (generating sample of parameter distributions) of a SA (generating samples of parameter values for each generated distribution) of the WARM rice model using the Sobol’ method. The sample of distributions was generated using distributions of jackknife statistics calculated on literature values. This allowed mimicking the differences in distributions that could derive from different selection of literature sources. Despite the very low plasticity of WARM, the ranks of the two most relevant parameters was overturned in 22% of the cases and, in general, differed from what achieved in earlier SAs performed on the same model under similar conditions. SA results were mainly affected by uncertainty in distribution of parameters involved in non-linear effects or interacting with others. The procedure identified parameters whose uncertainty in distribution can alter SA results, i.e., parameters whose distributions could need to be refined
A preliminary evaluation of the simulation model CropSyst for alfalfa
No full textThis work stems from the need to set-up appropriate simulation models for scenario analysis of intensive forage cropping systems in northern Italy, where alfalfa plays a major role. CropSyst is a deterministic, process-based, with daily time-step cropping systems simulation model. It can simulate crop growth and development, water and nitrogen balance for herbaceous annual and perennial crops. In this work, it was used to simulate aboveground biomass (AGB) accumulation and soil water content (SWC) for two alfalfa meadows seeded in 1996 and 1997 in Lodi, northern Italy (45°N latitude). The crop was parameterised with data from the literature, local experience and calibration with measured data from the first 2 years. Data from the third year were used for validation.
The cumulative yields of the 3-year periods were 38.2 and 36.9 t AGB ha−1, obtained with a total of 14 cuts. The set of crop parameters is consistent with values reported in the literature. For most of the cuts, the model simulates appropriately the growth of the crop: the relative root mean squared error (RRMSE) between observed and measured AGB ranged between 3 and 6% after calibration and between 3 and 5% after validation. RRMSE for SWC ranged between 13 and 21% after calibration and between 10 and 20% after validation. Even if some limitations are explicitly addressed, this crop parameter set can be already used for explorative scenario simulations in the study area. This work has demonstrated the robustness of the model for perennial forage crops simulations and has suggested some improvements of the model (automatic scheduling of cuts, role of crown reserves)
Biophysical models for cropping system simulation
No full textThe definition of mathematical models to estimate plants growth as a function of environmental variables has started many decades ago, for instance expressing the biomass growth of a plant as a function of the solar radiation intercepted (Warren Wilson 1967). Since then, crop models have evolved including sub-models to estimate plant development, and several other processes relevant to the simulation of the interaction plant-soil as affected by weather and agricultural management. Two main goals can be identified as drivers in plant model development: (1) studying the genotype × environment interaction, as a support tool to variety selection within a given species, or (2) studying production enterprises, hence comparing, from a biophysical point of view, yield, resource use, and externalities of agricultural production systems. Whether most of the models of the former group are specialized to a single crop, the latter includes multi-crop models to simulate crop sequences as in most production systems
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