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Author |
Nosalewicz, A. |
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The effect of combination of drought and heat stresses on plant transpiration and photosynthesis |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-41 |
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Expected increasing intensity and frequency of droughts with climate changes is often accompanied by increased air temperature resulting in decreased stability of crop yields. Owing to the complex nonlinear interactions between a plant and its environment, it is difficult to evaluate the effect of multi-stress on plant functioning.The main aim of presented research was to analyse spring wheat response to combination of two abiotic stresses: drought and heat.The growth chamber experiment with controlled environment was conducted on spring wheat growing in cylindrical soil columns. Four treatments were compared: control with optimum soil moisture and air temperature (C), heat wave (HW) – as C but with temperature elevated up to 34°C for four days at flowering, drought (D) with soil water content decreasing from initially optimum level to water deficit (pF> 3.4) at flowering, drought and heat wave (DHW) – the combination of two stresses .The results indicated different course of leaf transpiration and photosynthesis rates in analysed treatments in response to soil water content. HW treatment during period of increased temperature were characterised by significantly increased average transpiration as compared to all other treatments. However photosynthesis rate in this treatment were slightly lower than in control plants. Comparison of D and DHW treatments shows similarities in the trends of transpiration increase with increasing soil moisture with some offset to lower soil moisture in DHW resulting from higher evapotranspiration. Photosynthesis rate showed relatively large variation characterised by steeper increase with increasing soil water content in D as compared to DHW. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2156 |
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Nieróbca, A. |
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The regional trends in maize yield in Poland and its prediction according regional GLOBIOM –CAPRI baseline analysis for 2010, 2030 and 2050 |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-40 |
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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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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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2155 |
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Baranowski, P. |
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Multifractal analysis of meteorological time series to assess climate impact on chosen regions of Europe |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-4 |
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Over the last decades modelling of climate change through the analysis of empirical meteorological data has become of great interest. The standard approach gives satisfactory results only in the climatic zones with extreme dynamics of climate change, thus there is need to develop and apply more subtle methods such as fractal analysis and chaotic evolution analysis of the atmospheric system. The scaling analysis of meteorological time series is complicated because of the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the daily air temperature, wind velocity, relative air humidity, global radiation and precipitation through multifractal detrended fluctuation analysis on data from 31 years for stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response, indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent, by analysing the corresponding shuffled and surrogate time series. The results suggest that MFDFA is valuable for assessing the change of climate dynamics. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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2119 |
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Mittenzwei, K. |
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The importance of climate and policy uncertainty in Norwegian agriculture |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-39 |
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The paper addresses future climate and policy uncertainty for agricultural production and food security in Norway. The two crop simulation models, CSM-CERES-Wheat and, the LINGRA model, were used to determine the impact of climate change on grain yield of spring wheat, and harvest security and biomass yield of timothy, an important forage grass in Northern Europe, respectively. Harvestable yield distributions from the crop models were fed into a stochastic version of the economic sector model Jordmod. Distributions of the rates of agricultural subsidies rates were assessed based on past policy changes and prospective reforms. The model was used to assess the effects of both climate and policy uncertainty on agricultural production, land use, and national food security. Jordmod is comprised of a supply module in which stochastic profits for more than 300 regional farms are maximized and a deterministic market module which maximizes social welfare in the agricultural sector. Socio-economic scenarios were developed around the level of ambition of Norwegian agricultural policy makers. The model results were contrasted with the deterministic results based on average yield and payment rates. The innovation of this paper lays in assessing the combined effects of future climate and policy uncertainty for the agricultural sector in Norway. It also highlights the potential errors made by neglecting these types of uncertainty in economic modelling. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2154 |
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Author |
Martre, P. |
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Title |
Reducing uncertainty in prediction of wheat performance under climate change |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-38 |
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Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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Call Number |
MA @ admin @ |
Serial |
2153 |
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