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Zimmermann, A. (2015). Yield trends and variability in the EU.. Reading (United Kingdom).
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Rötter, R. P., Höhn, J. K., Palosuo, T., Kassie, B. T., Paff, K., Tao, F., et al. (2015). Yield gap and variability analysis for different aro-technologies for maize and wheat (YGV study).. Ithaca (U.S.A.).
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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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de Wit, A., Boogaard, H., van Diepen, K., van Kraalingen, D., Rötter, R., Supit, I., et al. (2015). WOFOST developer’s response to article by Stella et al., Environmental Modelling & Software 59 (2014): 44–58. Env. Model. Softw., 73, 57–59.
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Brzezinska, M. (2015). What is a stronger determinant of soil respiration: soil temperature or moisture (Vol. 5).
Abstract: Increased atmospheric concentrations of greenhouse gases have led to global warming and climatic changes. Both experimental and modelling studies are necessary to predict and to quantify gas exchange in agroecosystems. We studied the effect of the important environmental factors (soil moisture and temperature) on CO2 emission from agricultural soil (Orthic Luvisol developed from loess) under field and laboratory conditions. In the field experiment (winter wheat, permanent meadow or black fallow), the in situ CO2 efflux form the soil, soil moisture and temperature were measured from April to December 2013. The CO2 efflux was influenced by plant cover (F=7.96; p<0.001), and was related to both, soil temperature (p<0.001) and slightly less by soil moisture (p<0.01). In the second experiment, soil was collected from a depth of 0-10 cm, air-dried, and passed through an 2 mm sieve. Next, soil samples were rewetted to obtain soil moisture in a range from water saturation (pF 0) to plant wilting point (pF 4.2), and incubated at different temperatures (from 5oC to 30oC). Multifactor analysis of variance has shown that the soil respiration, as measured under controlled conditions, was much more affected by soil temperature (F=237.0; p<0.0001), than by soil moisture (F=4.99; p<0.01). No Label
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