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Trnka, M., Kersebaum, K., Christian,, & Olesen, J. E. (2015). Description of the compiled experimental data available in the MACSUR CropM database (Vol. 6).
Abstract: The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) have been collected through out the project together with data for additional analysis of abiotic factors influencing yields. A list of possible dataset was collated in the first year of project however very few of the existing datasets were found usable for the crop model simulation as they fell short of the requirements defined in the part 2.3. However database has been populated as planned with the results of the ongoing MACSUR studies and will serve in the same way for the MACSUR 2 duration. No Label
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Trnka, M. (2013). Guidelines on extending on-going experiments with additional measurements to support crop modelling – Field experimental protocol (Vol. 2).
Abstract: The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) are listed. A list of possible seasonal observations/measurements that could be carried out in existing experiments to increase their potential for crop modelling studies is also provided. The general methodology suitable to be used is outlined, but in all cases the selected method depends strongly on the experimental set-up and facilities/instruments at the disposal of the experimentalists. Such methodologies needs to be documented and preferably benchmarked against standard methods. No Label
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Schils, R. (2017). Yield gaps of cereals across Europe (Vol. 10).
Abstract: 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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Schils, R. (2015). Yield gap analysis of cereals in Europe supported by local knowledge (Vol. 5).
Abstract: The increasing demand for food requires a sustainable intensification of crop production in underperforming areas. Many global and local studies have addressed yield gaps, i.e. the difference between potential or water-limited yields and actual yields. Global studies generally rely on generic models combined with a grid-based approach. Although using a consistent method, it has been shown they are not suitable for local yield gap assessment. Local studies generally exploit knowledge of location-specific conditions and management, but are less comparable across locations due to different methods. To overcome these inconsistencies, the Global Yield Gap Atlas (GYGA, www.yieldgap.org) proposes a consistent bottom-up approach to estimate yield gaps. This paper outlines the implementation of GYGA for estimating yield gaps of cereals across Europe. For each country, climate zones are identified which represent the major growing areas. Within these climate zones, weather stations are selected with >=15 years of daily data. For dominant soil types within a buffer zone around the weather stations, the potential and water-limited yields are simulated with a crop model, using local knowledge on management. Actual yields are derived from sub-national statistics. Yield gaps are scaled up from buffer zones to climate zones and countries. We will present the first results for selected regions in Europe, and discuss methodological issues on location specific weather and upscaling from weather station buffer zones to climate zones and countries. Furthermore we will look ahead at the implementation of the yield gap cross cutting activity (XC9) in MACSUR-2. No Label
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Nieróbca, A. (2015). The regional trends in maize yield in Poland and its prediction according regional GLOBIOM –CAPRI baseline analysis for 2010, 2030 and 2050 (Vol. 5).
Abstract: The increase of maize production in Polish agriculture is considered as one of the indicators showing temporal climate change impact. The sowing area of grain maize in Poland increased from 152 thousand hectares in 2000 to 614 thousand hectares in 2013. In the same time, the area of maize production for forage in Poland has increased from 162 to 462 thousand hectares. There is observed increase of yield level but the regional differentiation of this trend is also visible. In the paper we discuss the temporal regional trends in maize yield in Poland connected to the limitation factors (soil, climate, fertilisation) and prognosis for further climate change impact using GLOBIOM-CAPRI regional simulations. The data for the analysis of regional trends for maize production level in 16th regions in Poland (NUTS2) were taken from National Statistical Offices Reports. The GLOBIOM-CAPRI regional simulations for baseline analysis 2010, 2030 and 2050 were obtained from MASCUR knowledge-hab evaluation exercises. As a limitation factors for of maize yield we considered the index for suitable soil and climate suitability index for climate developed in Poland and fertilisation. We have identified that observed positive trend in yield level at NUTS2 is correlated with the climate suitability index and level of NPK fertilisation, whilst there is no statistical relations with soil quality index. The GLOBIOM –CAPRI regional simulation for 2030 shows that the maize yield in Poland will further increase and it can be explained by realisation of existing trends. In simulations for the baseline 2050 year there is visible negative trend in yield level in some regions, where even in current climate there is high probability of deficit precipitation (eg. Wielkopolskia region). No Label
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