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Elsgaard, L., Børgesen, C. D., Olesen, J. E., Siebert, S., Ewert, F., Peltonen-Sainio, P., et al. (2012). Shifts in comparative advantages for maize, oat and wheat cropping under climate change in Europe. Food Addit. Contam. Part A, 29(10), 1514–1526.
Abstract: Climate change is anticipated to affect European agriculture, including the risk of emerging or re-emerging feed and food hazards. Indirectly, climate change may influence such hazards (e.g. the occurrence of mycotoxins) due to geographic shifts in the distribution of major cereal cropping systems and the consequences this may have for crop rotations. This paper analyses the impact of climate on cropping shares of maize, oat and wheat on a 50-km square grid across Europe (45-65°N) and provides model-based estimates of the changes in cropping shares in response to changes in temperature and precipitation as projected for the time period around 2040 by two regional climate models (RCM) with a moderate and a strong climate change signal, respectively. The projected cropping shares are based on the output from the two RCMs and on algorithms derived for the relation between meteorological data and observed cropping shares of maize, oat and wheat. The observed cropping shares show a south-to-north gradient, where maize had its maximum at 45-55°N, oat had its maximum at 55-65°N, and wheat was more evenly distributed along the latitudes in Europe. Under the projected climate changes, there was a general increase in maize cropping shares, whereas for oat no areas showed distinct increases. For wheat, the projected changes indicated a tendency towards higher cropping shares in the northern parts and lower cropping shares in the southern parts of the study area. The present modelling approach represents a simplification of factors determining the distribution of cereal crops, and also some uncertainties in the data basis were apparent. A promising way of future model improvement could be through a systematic analysis and inclusion of other variables, such as key soil properties and socio-economic conditions, influencing the comparative advantages of specific crops.
Keywords: Agriculture/*economics/trends; Animals; Avena/chemistry/economics/*growth & development/microbiology; *Climate Change/economics; Crops, Agricultural/chemistry/economics/*growth & development/microbiology; Europe; *Food Safety; Forecasting/methods; Fungi/growth & development/metabolism; Humans; Models, Biological; Models, Economic; Mycotoxins/analysis/biosynthesis; Soil Pollutants/adverse effects/analysis; Spatio-Temporal Analysis; Triticum/chemistry/economics/*growth & development/microbiology; Uncertainty; Weather; Zea mays/chemistry/economics/*growth & development/microbiology
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Himanen, S. J., Ketoja, E., Hakala, K., Rötter, R. P., Salo, T., & Kahiluoto, H. (2013). Cultivar diversity has great potential to increase yield for feed barley. Agron. Sust. Developm., 33(3), 519–530.
Abstract: This study shows an average yield increase of 415–1,338 kg ha−1 per unit increase of the Shannon diversity index for feed barley cultivar use. There is a global quest to increase food production sustainably. Therefore, judicious farmer choices such as selection of crop cultivars are increasingly important. Cultivar diversity is limited and, as a consequence, corresponding crop yields are highly impacted by local weather variations and global climate change. Actually, there is little knowledge on the relationships between yields of regional crops and cultivar diversity, that is evenness and richness in cultivar use. Here, we hypothesized that higher cultivar diversity is related to higher regional yield. We also assumed that the diversity-yield relationship depends on weather during the growing season. Our data were based on farm yield surveys of feed and malting barley, Hordeum vulgare L.; spring wheat, Triticum aestivum L.; and spring turnip rape, Brassica rapa L. ssp. oleifera, from 1998 to 2009, representing about 4,500–5,500 farms annually. We modeled the relationships between regional yields and Shannon diversity indices in high-yielding (south-west) and low-yielding (central-east) regions of Finland using linear mixed models. Our results show that an increase of Shannon diversity index increases yield of feed barley. Feed barley had also the greatest cultivar diversity. In contrast, an average yield decrease of 1,052 kg ha−1 per unit increase in Shannon index was found for spring rape in 2006 and 2008. Our findings show that cultivar diversification has potential to raise mean regional yield of feed barley. Increasing cultivar diversity thus offers a novel, sustainability-favoring means to promote higher yields.
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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.
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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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Rötter, R. P., Höhn, J. G., & Fronzek, S. (2012). Projections of climate change impacts on crop production: A global and a Nordic perspective. Acta Agriculturae Scandinavica, Section A – Animal Science, 62(4), 166–180.
Abstract: Global climate is changing and food production is very sensitive to weather and climate variations. Global assessments of climate change impacts on food production have been made since the early 1990s, initially with little attention to the uncertainties involved. Although there has been abundant analysis of uncertainties in future greenhouse gas emissions and their impacts on the climate system, uncertainties related to the way climate change projections are scaled down as appropriate for different analyses and in modelling crop responses to climate change, have been neglected. This review paper mainly addresses uncertainties in crop impact modelling and possibilities to reduce them. We specifically aim to (i) show ranges of projected climate change-induced impacts on crop yields, (ii) give recommendations on use of emission scenarios, climate models, regionalization and ensemble crop model simulations for different purposes and (iii) discuss improvements and a few known unknowns’ affecting crop impact projections.
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