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Dumont, B.; Basso, B.; Leemans, V.; Destain, J.P.; Bodson, B.; Destain, M.-F. |
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Yield variability linked to climate uncertainty and nitrogen fertilisation |
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2013 |
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9th ECPA - European Conference on Precision Agriculture, Lleida, Spain, 2013-07-07 to 2013-07-11 |
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MA @ admin @ |
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2411 |
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Zimmermann, A. |
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Title |
Yield trends and variability in the EU |
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2015 |
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TradeM |
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Reading (United Kingdom) |
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MACSUR Final Conference, 2015-04-08 to 2015-04-10, Reading |
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MA @ admin @ |
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2927 |
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Sinabell, F. |
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Yield potentials and yield gaps in soybean production in Austria – a biophysical and economic assessment |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-11 |
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context of analysis:• stakeholders. policy relevance: CC and protein crops• research problem:• how large is the yield gap and what can be done• data• approaches• findings• discussion and outlook yield gap analysis is a daunting task• what can be learned• economics matters: prices of crop and other crops• land expansion: more land becoming more marginal• management matters a lot but – not directly observable in data• significant knowledge gaps still there• way forward:• look at other crops• explore options to improve management |
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4829 |
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Schils, R. |
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Title |
Yield gaps of cereals across Europe |
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2017 |
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FACCE MACSUR Reports |
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10 |
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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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Rötter, R.P.; Höhn, J.K.; Palosuo, T.; Kassie, B.T.; Paff, K.; Tao, F.; Chen, Y.; Asseng, S.; et al. |
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Yield gap and variability analysis for different aro-technologies for maize and wheat (YGV study) |
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2015 |
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CropM; |
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Ithaca (U.S.A.) |
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2nd Global Food Security Conference, 2015-10-10- to 2015-10-15, Ithaca |
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MA @ admin @ |
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2770 |
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