FACCE MACSUR Reports (Modelling European Agriculture with Climate Change for Food Security)
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Integrated modelling of agricultural adaptation and the value of precipitation information in a semi-arid Austrian region
No full textYield gaps of cereals across Europe
Full text linkThe increasing global demand for food requires a sustainable intensification of crop production in low-yielding areas. Actions to improve crop production in these regions call for accurate spatially explicit identification of yield gaps, i.e. the difference between potential or water-limited yield and actual yield. The Global Yield Gap Atlas (GYGA) project proposes a consistent bottom-up approach to estimate yield gaps. For each country, a climate zonation is overlaid with a crop area map. Within climate zones with important crop areas, weather stations are selected with at least 10 years of daily data. For each of the 3 dominant soil types within a 100 km zone around the weather stations, the potential and water-limited yields are simulated with the WOFOST crop model, using location-specific knowledge on crop systems. Data from variety trials or other experiments, approaching potential or water-limited yields, are used for validation and calibration of the model. Actual yields are taken from sub-national statistics. Yields and yield gaps are scaled up to climate zones and subsequently to countries. The average national simulated wheat yields under rainfed conditions varied from around 5 to 6 t/ha/year in the Mediterranean to nearly 12 t/ha/year on the British Isles and in the Low Countries. The average actual wheat yield varied from around 2 to 3 t/ha/year in the Mediterranean and some countries in East Europe to nearly 9 t/ha/year on the British Isles and in the Low Countries. The average relative yield gaps varied from around 10% to 30% in many countries in Northwest Europe to around 50% to 70% in some countries in the Mediterranean and East Europe. The paper will elaborate on results per climate zone and soil type, and will also include barley and maize. Furthermore we will relate yield gaps to nitrogen use
Model comparison and improvement: Links established with other consortia
Full text linkXC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls
Wheat grain yield and water use efficiency improved under climate change condition in semi-arid regions as predicted by APSIM crop model
No full textThe present study investigated the effect of climate change on crop productivity and water use efficiency at the regional scale. A general circulation model (HadCM3) was applied for two emission scenarios (A1B and A2) for three periods (2011-30, 2046-65 and 2080-2099) at nine locations in Fars province in central Iran. The APSIM crop model was used to simulate growth and development of wheat as well as water use efficiency under future climate scenarios. The results indicated that average temperature over the growing season increased from 12.15°C at baseline to 13.22°C in all future scenarios. The increase in CO2 concentration to 674 ppm in 2099 under A1B neutralized the negative effects of high temperature during the growing season and improved crop yield. Wheat grain yield increased from +10 to +41% over baseline for all future emission scenarios and periods at all study locations. The results indicate that, by the end of the century under the A2 emission scenario 10% to15% of Fars province will have a grain yield of more than 10 t ha-1 and about 65% will have a grain yield of 8 to 10 t ha-1. Averaged across locations, scenarios and periods, water use efficiency increased by 3.56 kg ha−1 mm−1 in the future scenarios over baseline. The improved water use efficiency under future climate change was largely the result of a significant increase in yield (from 6989.5 kg ha−1 at baseline to 8416.5 kg ha−1 in all future scenarios) and decreased evapotranspiration (from 506.8 mm at baseline to 478 mm in all future scenarios). A decrease in evapotranspiration as well as an increase in water use efficiency under future climate change could be beneficial for agricultural production systems, particularly under semi-arid conditions
Extending the BASGRA model for timothy grass with functions to simulate impacts of climate change and sward management on yield and nutritive value.
No full textGrass-based dairy and meat production constitute the economic backbone of agriculture in Northern Europe including Scandinavia. Timothy (Phleum pratense L.) is one the most important forage grasses in Sandinavia as well as in high latitude regions in North America and Japan. Grassland productivity is expected to be affected by climate change. Process-based models for weather dependent grass growth can assist farmers and plant breeders in adapting to climate change by simulating different options. These models can also be used to investigate different management options such as the prediction of the optimal harvest time for use in tactical planning at farm level under prevailing conditions. The BASGRA model was originally developed to investigate the interaction between the weather, soil and cutting regime on forage dry-matter yield. Recently, BASGRA was extended with functions for simulating nutritive value including crude protein, NDF fibres and fibre digestibility. The aim of this presentation is to give a brief overview of the new version of BASGRA, and to show an example of application of the model to multi-year simulation of timothy growth, yield and nutritive value at two sites in Norway under current and projected future climate conditions, including different fertilizer levels and cutting regimes. Information about the impact of climate change and management on sward nutritional value from such simulations is of particular importance to understand the interaction between these factors and livestock production, and thus to design livestock production systems for future climates
Crop residue management as a strategy of adaptation and mitigation to climate change
No full textThis paper reports the first results of a research developed in the context of the three-years (2013-16) research project "IC-FAR - Linking long term observatories with crop system modelling for better understanding of climate change impact and adaptation strategies for Italian cropping systems" (www.icfar.it).The goals are : i) to parameterize crop models considering two Long Term Agro-Ecosystem experiments (LTAE) located in experimental farms of Foggia (FG) and Papiano, Perugia (PG), in Southern and Central Italy, respectively and ii) to evaluate the crop residue (CR) management as a strategy of adaptation and/or mitigation to climate change forecasted for the reference areas of the LTEs in study. Climate scenarios were generated by setting up a statistical model using predictors from ERA40 reanalysis and seasonal indices of temperature and precipitation from E-OBS gridded data for the period 1958-2010. The statistical downscaling model was applied to CMCC-CM predictors to obtain climate scenarios at local scale over the period 1971-2000 and 2021-2050 (RCP4.5 and RCP8.5 emission scenarios)
Integrated assessment of farm level adaptation in Flevoland, the Netherlands: what did we learn from multiple methods and model chains
Full text linkClimate change impact assessment requires farming systems analysis and integrated assessment. However, multiple models can be used to assess changes in drivers. In addition, farms are complex systems and many assumptions need to be made regarding objectives and constraints. Here, we evaluate the impact of different models and assumptions on impacts of climate change on arable agriculture in Flevoland, the Netherlands. We performed three studies. Firstly, we used the crop model WOFOST, market model CAPRI, and bio-economic farm model FSSIM. Secondly, we used the crop model SIMPLACE, an adapted version of CAPRI, and a different set up of FSSIM. Thirdly, we used the crop model WOFOST, estimates of impacts of extreme events by the Agro Climate Calender, and the bio-economic farm model FarmDesign. In general, climate change is projected to have positive impacts. The first two studies however showed that impacts of technology and price changes are larger. But while changes in gross margins are more influenced by results from crop and market models, changes in farm plans are more influenced by assumptions regarding resources and constraints. Assumptions regarding the available land for rent largely influence results. The third study showed that when policy constraints are neglected, impacts on gross margin are more positive. Positive impacts of average climate change may however be counterbalanced by negative impacts of extreme events, but adaptation measures are available. When considering soil quality as important objective, adaptation at farm level will be different: instead of more potato or sugar beet, farms will grow more wheat. We conclude that climate change impacts depend on assumptions, but when making this transparent, it can inform adaptation
The vulnerability and risk assessment of agricultural crops in the conditions of expected climate change in the Republic of Armenia.
No full textWhat are the risks of food price changes? A time series analysis.
No full textIt is a widely held belief (IPCC) that climate change brings more risks to the world. With regard to food production, market prices might be expected to be more volatile. So far, the evidence of this is meager. With novel methods I show that the price volatility of wheat indeed has increased the last sixty five years. It cannot be proved, however, that the additional volatility is due to climate change. Alternatively, the cause might be market regime changes that arose with the oil embargo of 1973-74. Sixty five years of observations seems far too short to assess the long term relationship between climate and food price volatilities. Regardless of cause, commodity price changes has skewed distributions with higher probability for a certain price increase than for an equally sized decrease. This implies that the incentives for storage is stronger for users than for suppliers