Angulo, C., Rötter, R., Trnka, M., Pirttioja, N., Gaiser, T., Hlavinka, P., et al. (2013). Characteristic ‘fingerprints’ of crop model responses to weather input data at different spatial resolutions. European Journal of Agronomy, 49, 104–114.
Abstract: Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km x 10 km; 20 km x 20 km; 50 km x 50 km and 100 km x 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic ‘fingerprint’ of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Using one model (LINTUL-SLIM), we analysed how the aggregation strategy, i.e. aggregating model input versus model output data, influences the simulated yield frequency distribution. Results show that aggregating weather data has a smaller effect on the yield distribution than aggregating simulated yields which causes a deformation of the model fingerprint. We conclude that changes in the spatial resolution of weather input data introduce less uncertainty to the simulations than the use of different crop models but that more evaluation is required for other regions with a higher spatial heterogeneity in weather conditions, and for other input data related to soil and crop management to substantiate our findings. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies. (C) 2013 Elsevier B.V. All rights reserved.
|
|
Smoron, S. (2013). Dynamics of the mountain meadow yielding in period of 25 years after fertilization abandonment (Vol. 132).
|
|
Mansouri, M. (2013). Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods. In P. Masegosa, C. Villacorta, S. Cruz-Corona, M. Garcia-Cascales, J. Lamata, & A. Verdegay (Eds.), (pp. 112–137). Exploring Innovative and Successful Applications of Soft Computing. Hershey PA: IGI Global.
|
Rusu, T., Moraru, P. I., Bogdan, I., Pop, A., Coste, C., Marin, D. I., et al. (2013). Impacts of climate change on agricultural technology management in the Transylvanian Plain, Romania. Scientific Papers, Series A. Agronomy, Lvi, 113–118.
Abstract: The Transylvanian Plain, Romania is an important region for agronomic productivity. However, limited soils data and adoption of best management practices hinder land productivity. Soil temperatures of the Transylvanian Plain were evaluated using a set of twenty datalogging stations positioned throughout the plain. Each station stores electronic data of ground temperature on 3 different levels of depth (10, 30 and 50 cm), of soil humidity at a depth of 10 cm, of the air temperature at 1 meter and of precipitation. Monitoring the thermal and hydric regime of the area is essential in order to identify and implement sets of measures of adjustment to the impact of climatic changes. After analyzing the recorded data, thermic and hydric, in the Transylvanian Plain, we recommend as optimal sowing period, advancing those known in the literature, with 5 days for corn and soybeans, and maintaining the same optimum period for sunflower and sugar beet. Water requirements are provided in an optimum, of 58.8 to 62.1% for the spring weeding crops during the growing season, thus irrigation is necessary to ensure optimum production potential. The amount of biological active degrees registered in Transylvanian Plain shows the necessity to reconstruct crop zoning, known in the literature, for the analyzed crops: wheat, corn, soy, sunflower and sugar beet.
|
|
Graux, A. - I., Bellocchi, G., Lardy, R., & Soussana, J. - F. (2013). Ensemble modelling of climate change risks and opportunities for managed grasslands in France. Agricultural and Forest Meteorology, 170, 114–131.
|
