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Author |
Rusu, T.; Moraru, P.I. |
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Title  |
Impact of climate change on crop land and technological recommendations for the main crops in Transylvanian Plain, Romania |
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Journal Article |
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Year |
2015 |
Publication |
Romanian Agricultural Research |
Abbreviated Journal |
Romanian Agricultural Research |
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Volume |
32 |
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Pages |
103-111 |
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Keywords |
climate change monitoring; temperature regimes; soil moisture; adaptation technologies; transylvanian plain; agriculture; france; precipitation; circulation; adaptation; models |
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Abstract |
The Transylvanian Plain (TP) is an important agricultural production area of Romania that is included among the areas with the lowest potential of adapting to climate changes in Europe. Thermal and hydric regime monitoring is necessary to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data regarding ground temperature at 3 depths (10, 30, 50 cm), humidity at a depth of 10 cm, air temperature (at 1 m) and precipitation. For agricultural crops, the periods of drought and extreme temperatures require specific measures of adaptation to climate changes. During the growing season of crops in the spring (April – October) in the south-eastern, southern, and eastern escarpments, precipitation decreased by 43.8 mm, the air temperature increased by 0.37 degrees C, and the ground temperature increased by 1.91 degrees C at a depth of 10 cm, 2.22 degrees C at a depth of 20 cm and 2.43 degrees C at a depth of 30 cm compared with values recorded for the northern, north-western or western escarpments. Water requirements were ensured within an optimal time frame for 58.8-62.1% of the spring row crop growth period, with irrigation being necessary to guarantee the optimum production potential. The biologically active temperature recorded in the TP demonstrates the need to renew the division of the crop areas reported in the literature. |
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1222-4227 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4650 |
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Eyshi Rezaei, E.; Siebert, S.; Ewert, F. |
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Title |
Impact of data resolution on heat and drought stress simulated for winter wheat in Germany |
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Journal Article |
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Year |
2015 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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65 |
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69-82 |
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Keywords |
crop modeling; heat; drought; spatial resolution; wheat; high-temperature stress; climate-change; grain-yield; crop models; data aggregation; abiotic stress; short periods; variability; growth; duration |
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Heat and drought stress can reduce crop yields considerably which is increasingly assessed with crop models for larger areas. Applying these models originally developed for the field scale at large spatial extent typically implies the use of input data with coarse resolution. Little is known about the effect of data resolution on the simulated impact of extreme events like heat and drought on crops. Hence, in this study the effect of input and output data aggregation on simulated heat and drought stress and their impact on yield of winter wheat is systematically analyzed. The crop model SIMPLACE was applied for the period 1980-2011 across Germany at a resolution of 1 km x 1 km. Weather and soil input data and model output data were then aggregated to 10 km x 10 km, 25 km x 25 km, 50 km x 50 km and 100 km x 100 km resolution to analyze the aggregation effect on heat and drought stress and crop yield. We found that aggregation of model input and output data barely influenced the mean and median of heat and drought stress reduction factors and crop yields simulated across Germany. However, data aggregation resulted in less spatial variability of model results and a reduced severity of simulated stress events, particularly for regions with high heterogeneity in weather and soil conditions. Comparisons of simulations at coarse resolution with those at high resolution showed distinct patterns of positive and negative deviations which compensated each other so that aggregation effects for large regions were small for mean or median yields. Therefore, modelling at a resolution of 100 km x 100 km was sufficient to determine mean wheat yield as affected by heat and drought stress for Germany. Further research is required to clarify whether the results can be generalized across crop models differing in structure and detail. Attention should also be given to better understand the effect of data resolution on interactions between heat and drought impacts. (C) 2015 Elsevier B.V. All rights reserved. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4751 |
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Persson, T.; Kværnø, S.; Höglind, M. |
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Title |
Impact of soil type extrapolation on timothy grass yield under baseline and future climate conditions in southeastern Norway |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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71-86 |
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climate change scenarios; crop modelling; forage grass; lingra; soil properties; spatial variability; phleum pretense; poaceae; simulation-model; nutritive-value; systems simulation; catimo model; crop models; growth; nitrogen; scale; productivity; regrowth |
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Interactions between soil properties and climate affect forage grass productivity. Dynamic models, simulating crop performance as a function of environmental conditions, are valid for a specific location with given soil and weather conditions. Extrapolations of local soil properties to larger regions can help assess the requirement for soil input in regional yield estimations. Using the LINGRA model, we simulated the regional yield level and variability of timothy, a forage grass, in Akershus and Ostfold counties, Norway. Soils were grouped according to physical similarities according to 4 sets of criteria. This resulted in 66, 15, 5 and 1 groups of soils. The properties of the soil with the largest area was extrapolated to the other soils within each group and input to the simulations. All analyses were conducted for 100 yr of generated weather representing the period 1961-1990, and climate projections for the period 2046-2065, the Intergovernmental Panel on Climate Change greenhouse gas emission scenario A1B, and 4 global climate models. The simulated regional seasonal timothy yields were 5-13% lower on average and had higher inter-annual variability for the least detailed soil extrapolation than for the other soil extrapolations, across climates. There were up to 20% spatial intra-regional differences in simulated yield between soil extrapolations. The results indicate that, for conditions similar to these studied here, a few representative profiles are sufficient for simulations of average regional seasonal timothy yield. More spatially detailed yield analyses would benefit from more detailed soil input. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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MA @ admin @ |
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4674 |
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Hoffmann, H.; Zhao, G.; Asseng, S.; Bindi, M.; Biernath, C.; Constantin, J.; Coucheney, E.; Dechow, R.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Heinlein, F.; Kassie, B.T.; Kersebaum, K.-C.; Klein, C.; Kuhnert, M.; Lewan, E.; Moriondo, M.; Nendel, C.; Priesack, E.; Raynal, H.; Roggero, P.P.; Rötter, R.P.; Siebert, S.; Specka, X.; Tao, F.; Teixeira, E.; Trombi, G.; Wallach, D.; Weihermüller, L.; Yeluripati, J.; Ewert, F. |
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Title |
Impact of spatial soil and climate input data aggregation on regional yield simulations |
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Journal Article |
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Year |
2016 |
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PLoS One |
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PLoS One |
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Volume |
11 |
Issue |
4 |
Pages |
e0151782 |
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Keywords |
systems simulation; nitrogen dynamics; winter-wheat; crop models; data resolution; scale; water; variability; calibration; weather |
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Abstract |
We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. |
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1932-6203 |
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CropM, ft_macsur |
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MA @ admin @ |
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4725 |
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Vosough Ahmadi, B.; Shrestha, S.; Thomson, S.G.; Barnes, A.P.; Stott, A.W. |
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Title |
Impacts of greening measures and flat rate regional payments of the Common Agricultural Policy on Scottish beef and sheep farms |
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Journal Article |
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Year |
2015 |
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Journal of Agricultural Science |
Abbreviated Journal |
J. Agric. Sci. |
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153 |
Issue |
04 |
Pages |
676-688 |
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CAP reform; models; level; water; Agriculture |
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The latest Common Agricultural Policy (CAP) reforms could bring substantial changes to Scottish farming communities. Two major components of this reform package, an introduction of environmental measures into the Pillar 1 payments and a move away from historical farm payments towards regionalized area payments, would have a significant effect on altering existing support structures for Scottish farmers, as it would for similar farm types elsewhere in Europe where historic payments are used. An optimizing farm-level model was developed to explore how Scottish beef and sheep farms might be affected by the greening and flat rate payments under the current CAP reforms. Nine different types of beef and sheep farms were identified and detailed biophysical and financial farm-level data for these farm types were used to parameterize the model. Results showed that the greening measures of the CAP did not have much impact on net margins of most of the beef and sheep farm businesses, except for ‘Beef Finisher’ farm types where the net margins decreased by 3%. However, all farm types were better off adopting the greening measures than not qualifying for the greening payments through non-compliance with the measures. The move to regionalized farm payments increased the negative financial impact of greening on most of the farms but it was still substantially lower than the financial sacrifice of not adopting greening measures. Results of maximizing farm net margin, under a hypothetical assumption of excluding farm payments, showed that in most of the mixed (sheep and cattle) and beef suckler cattle farms the optimum stock numbers predicted by the model were lower than actual figures on farm. When the regionalized support payments were allocated to each farm, the proportion of the mixed farms that would increase their stock numbers increased whereas this proportion decreased for beef suckler farms and no impact was predicted in sheep farms. Also under the regionalized support payments, improvements in profitability were found in mixed farms and sheep farms. Some of the specialized beef suckler farms also returned a profit when CAP support was added. |
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0021-8596 1469-5146 |
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MA @ admin @ |
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4654 |
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