Abstract: This study deals with observed and simulated growth, development and yield of the fresh-market Thomas F1 tomato bush cultivar (Solanum lycopersicum L.) grown under open field conditions at farm scale in the Elbe lowland. The CROPGRO-Tomato model used in this study is part of the DSSAT V4.5 software. The model has been calibrated with growth analyses data from field experiments, agronomic evidence (GC UPRAVY software) and the most currently available data from the literature sources of cardinal temperatures for tomato phenology, fruit growth and photosynthesis (Tb – base temperature; Topt1 – the lowest temperature at which maximum rate is attained; Topt2 – the upper temperature at which maximum rate is sustained; Tmax – maximum temperature). The sampling plants were collected a once 14 days for analysis of basic physiological parameters: LAI (Leaf area index), LAR (Leaf Area Ratio), C (Crop Growth Rate), RGRw (Relative Growth Rate) and NAR (Net Assimilation Rate). Phenology observation was done weakly. Meteorological, soil and agro-technical parameters across the fields were monitored. The treatments were well-irrigated and well-fertilised, and therefore, no water or N stress was present.Parameters affecting leaf growth, dry biomass productions, and dry biomass of leaves, stem and generative organs from planting to harvest were calibrated against the observed data. Phenological development and growth processes such as leaf expansion and fruit growth depend on cardinal temperatures. Leaf area expansion depends on the new leaf mas produced and specific leaf area, which is influenced by light, temperature, root N uptake, and plant water status. Starting date for the simulation corresponds with transplanting date of the crop in the field, which was set at day 141. The simulation period ended at day 273, a reasonable estimate for the date when plants are stopped in practice. Initial input dry biomass at Mochov farm (Suchdol) was set to 2.25 (2.88), 1.71 (2.5) and 0.01 (0.78) grams for leaves, stem and generative organs, respectively. No Label

Abstract: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Abstract: Over the last decades modelling of climate change through the analysis of empirical meteorological data has become of great interest. The standard approach gives satisfactory results only in the climatic zones with extreme dynamics of climate change, thus there is need to develop and apply more subtle methods such as fractal analysis and chaotic evolution analysis of the atmospheric system. The scaling analysis of meteorological time series is complicated because of the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the daily air temperature, wind velocity, relative air humidity, global radiation and precipitation through multifractal detrended fluctuation analysis on data from 31 years for stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response, indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent, by analysing the corresponding shuffled and surrogate time series. The results suggest that MFDFA is valuable for assessing the change of climate dynamics. No Label