| Records |
| Author |
van Middelkoop, J.C.; Kipling, R.P. |
| Title |
Modelling the impact of climate change on livestock productivity at the farm-scale: An inventory of LiveM outcomes |
Type |
Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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Volume  |
10 |
Issue |
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Pages |
L2.4-D |
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| Abstract |
The report presented here provides an inventory of reports and conference papers produced by the partners of the livestock and grassland modelling theme (LiveM) of the Modelling European Agriculture with Climate Change for Food Security (MACSUR) knowledge hub. The findings presented illustrate the diverse nature of the multidisciplinary LiveM research community, and provide a reference source for those seeking to identify and pull out farm-level modelling outputs from the work of MACSUR and its partners. The survey of farm-scale outputs from LiveM revealed the interdependent, dual role of a knowledge hub: to increase the capacity of modelling to meet stakeholder and societal needs under climate change, and to apply that increased capacity to provide new understanding and solutions at the policy and (the focus here) farm scale. While capacity building work across disciplines is time-consuming, difficult, and to a large extent invisible to stakeholders, such work is vital to ensuring that subsequent scientific outcomes reflect best practice, and integrated expertise. Long term, sustained funding of network-based capacity building activities is highlighted as essential to ensuring that the farm-scale modelling work highlighted here can continue to build on ongoing improvements in model quality, flexibility and stakeholder relevance. |
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LiveM |
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MA @ admin @ |
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4958 |
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Topp, K.; Eory, V.; Bannink, A.; Bartley, D.J.; Blanco-Penedo, I.; Cortignani, R.; Del Prado, A.; Dono, G.; Faverdin, P.; Graux, A.-I.; Hutchings, N.; Lauwers, L.; Özkan Gülzari, Ş.; Rolinski, S.; Ruiz Ramos, M.; Sandars, D.L.; Sándor, R.; Schoenhart, M.; Seddaiu, G.; van Middelkoop, J.; Weindl, I.; Kipling, R.P. |
| Title |
Modelling climate change adaptation in European agriculture: Definitions and Current Modelling |
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Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
10 |
Issue |
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Pages |
L2.3.2-D |
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Confidential content, in preparation for a peer-reviewed publication. |
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LiveM |
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MA @ admin @ |
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4959 |
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| Author |
Schils, R. |
| Title |
Yield gaps of cereals across Europe |
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Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
10 |
Issue |
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Pages |
Xc9.1-D1 |
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The 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. |
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XC, CropM |
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MA @ admin @ |
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4960 |
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| Author |
Schils, R. |
| Title |
Online maps of Yield Gaps of cereals across Europe |
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Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
10 |
Issue |
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Pages |
Xc9.1-D2 |
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The yield gap and water productivity analysis of key cereal crops in Europe is completed and results are available through www.yieldgap.org |
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XC, CropM |
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MA @ admin @ |
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4961 |
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| Author |
Leolini, L.; Moriondo, M.; Ferrise, R.; Bindi, M. |
| Title |
Relations between micrometeorological conditions and plant physiology |
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Report |
| Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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| Volume |
10 |
Issue |
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Pages |
XC1.1-D2 |
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The changing climate and environmental conditions play a key role on plant physiology. In this context, crop simulation models represent a useful tool for investigating the main plant processes and provide a reliable estimation of crop productivity and quality. However, the most common crop models showed many limitations, with particular concern on the effect of some meteorological variables on plant processes during sensitive stages of development. Improving models by implementing the effect of such variables on crop processes may help to improve the accuracy of models, thus their usefulness. Here we focus on the analysis of the effect of high and low temperatures during flowering in grapevine. To this, the fruit-set index, developed for taking into account for the effect of temperature on setting the number of berries per cluster and the fruit-set percentage, was applied in a preliminary explorative study to assess the impact of different conditions during flowering at European scales. The sensitivity of the index allowed to identify the differential impact of temperature around flowering in different environment and for different varieties. Once meteorological variables are available at field or sub-field scale, the index can be used to provide information about the spatial variability of crop growth, thus allowing to identify the most appropriate interventions to improve productivity. |
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XC |
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MA @ admin @ |
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4975 |
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