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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, 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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Piontek, F., Müller, C., Pugh, T. A., Clark, D. B., Deryng, D., Elliott, J., et al. (2014). Multisectoral climate impact hotspots in a warming world. Proc. Natl. Acad. Sci. U. S. A., 111(9), 3233–3238.
Abstract: The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980-2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.
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Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., et al. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U. S. A., 111(9), 3268–3273.
Abstract: Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.
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Bertocchi, L., Vitali, A., Lacetera, N., Nardone, A., Varisco, G., & Bernabucci, U. (2014). Seasonal variations in the composition of Holstein cow’s milk and temperature-humidity index relationship. Animal, 8(4), 667–674.
Abstract: A retrospective study on seasonal variations in the characteristics of cow’s milk and temperature-humidity index (THI) relationship was conducted on bulk milk data collected from 2003 to 2009. The THI relationship study was carried out on 508 613 bulk milk data items recorded in 3328 dairy farms form the Lombardy region, Italy. Temperature and relative humidity data from 40 weather stations were used to calculate THI. Milk characteristics data referred to somatic cell count (SCC), total bacterial count (TBC), fat percentage (FA%) and protein percentage (PR%). Annual, seasonal and monthly variations in milk composition were evaluated on 656 064 data items recorded in 3727 dairy farms. The model highlighted a significant association between the year, season and month, and the parameters analysed (SCC, TBC, FA%, PR%). The summer season emerged as the most critical season. Of the summer months, July presented the most critical conditions for TBC, FA% and PR%, (52 054 ± 183 655, 3.73% ± 0.35% and 3.30% ± 0.15%, respectively), and August presented higher values of SCC (369 503 ± 228 377). Each milk record was linked to THI data calculated at the nearest weather station. The analysis demonstrated a positive correlation between THI and SCC and TBC, and indicated a significant change in the slope at 57.3 and 72.8 maximum THI, respectively. The model demonstrated a negative correlation between THI and FA% and PR% and provided breakpoints in the pattern at 50.2 and 65.2 maximum THI, respectively. The results of this study indicate the presence of critical climatic thresholds for bulk tank milk composition in dairy cows. Such indications could facilitate the adoption of heat management strategies, which may ensure the health and production of dairy cows and limit related economic losses.
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Eitzinger, J., Thaler, S., Schmid, E., Strauss, F., Ferrise, R., Moriondo, M., et al. (2013). Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. J. Agric. Sci., 151(6), 813–835.
Abstract: The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.
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