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Gobin, A., Kersebaum, K., Eitzinger, J., Trnka, M., Hlavinka, P., Takáč, J., et al. (2017). Variability in the Water Footprint of Arable Crop Production across European Regions. Water, 9(2), 93.
Abstract: Crop growth and yield are affected by water use during the season: the green water footprint (WF) accounts for rain water, the blue WF for irrigation and the grey WF for diluting agri-chemicals. We calibrated crop yield for FAO’s water balance model “Aquacrop” at field level. We collected weather, soil and crop inputs for 45 locations for the period 1992–2012. Calibrated model runs were conducted for wheat, barley, grain maize, oilseed rape, potato and sugar beet. The WF of cereals could be up to 20 times larger than the WF of tuber and root crops; the largest share was attributed to the green WF. The green and blue WF compared favourably with global benchmark values (R² = 0.64–0.80; d = 0.91–0.95). The variability in the WF of arable crops across different regions in Europe is mainly due to variability in crop yield (c̅v̅ = 45%) and to a lesser extent to variability in crop water use (c̅v̅ = 21%). The WF variability between countries (c̅v̅ = 14%) is lower than the variability between seasons (c̅v̅ = 22%) and between crops (c̅v̅ = 46%). Though modelled yields increased up to 50% under sprinkler irrigation, the water footprint still increased between 1% and 25%. Confronted with drainage and runoff, the grey WF tended to overestimate the contribution of nitrogen to the surface and groundwater. The results showed that the water footprint provides a measurable indicator that may support European water governance.
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Hlavinka, P., Kersebaum, K. C., Dubrovský, M., Pohanková, E., Balek, J., Žalud, Z., et al. (2014). Water balance and yield estimates for field crop rotations present versus future conditions based on transient scenarios..
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Hlavinka, P., Kersebaum, K. C., Dubrovský, M., Fischer, M., Pohanková, E., Balek, J., et al. (2015). Water balance, drought stress and yields for rainfed field crop rotations under present and future conditions in the Czech Republic. Clim. Res., 65, 175–192.
Abstract: Continuous crop rotation modeling is a prospective trend that, compared to 1-crop or discrete year-by-year calculations, can provide more accurate results that are closer to real conditions. The goal of this study was to compare the water balance and yields estimated by the HERMES crop rotation model for present and future climatic conditions in the Czech Republic. Three locations were selected, representing important agricultural regions with different climatic conditions. Crop rotation (spring barley, silage maize, winter wheat, winter rape) was simulated from 1981-2080. The 1981-2010 period was covered by measured meteorological data, while 2011-2080 was represented by a transient synthetic weather series from the weather generator M& Rfi. The data were based on 5 circulation models, representing an ensemble of 18 CMIP3 global circulation models, to preserve much of the uncertainty of the original ensemble. Two types of crop management were compared, and the influences of soil quality, increasing atmospheric CO2 and adaptation measures (i. e. sowing date changes) were also considered. Results suggest that under a ‘dry’ scenario (such as GFCM21), C-3 crops in drier regions will be devastated for a significant number of seasons. Negative impacts are likely even on premium-quality soils regardless of flexible sowing dates and accounting for increasing CO2 concentrations. Moreover, in dry conditions, the use of crop rotations with catch crops may have negative impacts, exacerbating the soil water deficit for subsequent crops. This approach is a promising method for determining how various management strategies and crop rotations can affect yields as well as water, carbon and nitrogen cycling.
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Angulo, C., Gaiser, T., Rötter, R. P., Børgesen, C. D., Hlavinka, P., Trnka, M., et al. (2014). ‘Fingerprints’ of four crop models as affected by soil input data aggregation. European Journal of Agronomy, 61, 35–48.
Abstract: • Systematic analysis of the influence of spatial soil data resolution on simulated regional yields and total growing season evapotranspiration. • The responses of four crop models of different complexity are compared. • Differences between models are larger than the effect of the chosen spatial soil data resolution. • Low influence of soil data resolution due to: high precipitation amount, methods for calculating water retention and method of data aggregation. The spatial variability of soil properties is an important driver of yield variability at both field and regional scale. Thus, when using crop growth simulation models, the choice of spatial resolution of soil input data might be key in order to accurately reproduce observed yield variability. In this study we used four crop models (SIMPLACE<LINTUL-SLIM>, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo-morphologically) contrasting regions of the federal state of North-Rhine-Westphalia (Germany) for the period from 1995 to 2008. Three spatial resolutions of soil input data were taken into consideration, corresponding to the following map scales: 1:50 000, 1:300 000 and 1:1 000 000. The four crop models were run for water-limited production conditions and model results were evaluated in the form of frequency distributions, depicted by bean-plots. In both regions, soil data aggregation had very small influence on the shape and range of frequency distributions of simulated yield and simulated total growing season evapotranspiration for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation. No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation to evaluate model results on the basis of frequency distributions since these offer quick and better insight into the distribution of simulation results as compared to summary statistics only. Finally, our results support conclusions from other studies about the usefulness of considering a multi-model approach to quantify the uncertainty in simulated yields introduced by the crop growth simulation approach when exploring the effects of scaling for regional yield impact assessments.
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