| 1. |
- Bergjord Olsen, A. K., et al.
(författare)
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Estimating winter survival of winter wheat by simulations of plant frost tolerance
- 2018
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Ingår i: Journal of Agronomy and Crop Science. - : Wiley. - 0931-2250 .- 1439-037X. ; 204:1, s. 62-73
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Tidskriftsartikel (refereegranskat)abstract
- Based on soil temperature, snow depth and the grown cultivar's maximum attainable level of frost tolerance (LT50c), the FROSTOL model simulates development of frost tolerance (LT50) and winter damage, thereby enabling risk calculations for winter wheat survival. To explore the accuracy of this model, four winter wheat cultivars were sown in a field experiment in Uppsala, Sweden in 2013 and 2014. The LT50 was determined by tests of frost tolerance in November, and the cultivars’ LT50c was estimated. Further, recorded winter survival from 20 winter wheat field variety trials in Sweden and Norway was collected from two winter seasons with substantial winter damages. FROSTOL simulations were run for selected cultivars at each location. According to percentage of winter damage, the cultivar survival was classified as “survived,” “intermediate” or “killed.” Mean correspondence between recorded and simulated class of winter survival was 75% and 37% for the locations in Sweden and Norway, respectively. Stress factors that were not accounted for in FROSTOL might explain the poorer accuracy at the Norwegian locations. The accuracy was poorest for cultivars with intermediate LT50c levels. When low temperature was the main cause of damage, as at the Swedish locations, the model accuracy was satisfying.
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| 2. |
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| 3. |
- Collentine, Dennis, et al.
(författare)
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Consequences of future nutrient load scenarios on multiple benefits of agricultural production
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Nutrient load rates to the Baltic Sea need to be reduced. Agricultural land is regarded as the most significant contributor to the loads, and measures to reduce losses of nitrogen (N) and phosphorus (P) loads have been proposed, both for the near and far future. Agricultural production was to a large extent considered in these scenarios, whereas effects on other ecosystem services were not evaluated. The question to be answered by this report is whether the measures adopted to reduce N and P losses improve or impair multiple benefits of agriculture. The question is answered for a specific catchment (Svärtaån) located in Sweden, but the method is thoroughly described to provide a potential method to also evaluate other catchments. This work was performed as a part of the Baltic Compass project (2013). The answer to the main question of whether the measures adopted to reduce N and P losses from agricultural fields improved or impaired multiple benefits of agriculture, seems to be that they improved. Most of the BAP measures had a positive influence on most of the MB-categories, the clearest exception being liming in tile drains which only improved the cost effectiveness. Except for water protection, the biosecurity MB-category was positively influenced by the most measures (6 out of 9) and with soil quality the next highest (5 out of 9). It is less clear how the absolute values evaluated for the MB index, can be compared among MB-categories. Among the BAP-measures, structural liming was the most positive measure (summing up the indices of all MB-categories), followed by buffer-zones and spring cultivation the next, although the cost effectiveness of these latter measures was evaluated to decrease. In the “unavoidable” future (Future scenarios) GHG emissions strongly increased. The only measure that mitigated that effect was reduced N fertilisation, providing more arguments for applying reduced fertilisation than only to reduce leaching.
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| 4. |
- Coucheney, Elsa, et al.
(författare)
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Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops
- 2016
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Ingår i: Environmental Modelling and Software. - : Elsevier BV. - 1364-8152 .- 1873-6726. ; 80, s. 100-112
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Tidskriftsartikel (refereegranskat)abstract
- We compared the precision of simple random sampling (SimRS) and seven types of stratified sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modesty but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
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| 5. |
- Coucheney, Elsa, et al.
(författare)
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Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands
- 2017
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Ingår i: European Journal of Agronomy. - : Elsevier BV. - 1161-0301 .- 1873-7331. ; 88, s. 41-52
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Tidskriftsartikel (refereegranskat)abstract
- For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1-100 km grid cells show changes of 0.4-7.8% and 0.0-4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9-4.2 g C m-2d-1and 2.7-6.1 g C m-2d-1for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution.On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.
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| 6. |
- Coucheney, Elsa, et al.
(författare)
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Impact of spatial soil and climate input data aggregation on regional yield simulations
- 2016
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11
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Tidskriftsartikel (refereegranskat)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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| 7. |
- Coucheney, Elsa, et al.
(författare)
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Key functional soil types explain data aggregation effects on simulated yield, soil carbon, drainage and nitrogen leaching at a regional scale
- 2018
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Ingår i: Geoderma. - : Elsevier. - 0016-7061 .- 1872-6259. ; 318, s. 167-181
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Tidskriftsartikel (refereegranskat)abstract
- The effects of aggregating soil data (DAE) by areal majority of soil mapping units was explored for regional simulations with the soil-vegetation model CoupModel for a region in Germany (North Rhine-Westphalia). DAE were analysed for wheat yield, drainage, soil carbon mineralisation and nitrogen leaching below the root zone. DAE were higher for soil C mineralization and N leaching than for yield and drainage and were strongly related to the presence of specific soils within the study region. These soil types were associated to extreme simulated output variables compared to the mean variable in the region. The spatial aggregation of these key functional soils within sub-regions additionally influenced the DAE. A spatial analysis of their spatial pattern (i.e. their presence/absence, coverage and aggregation) can help in defining the appropriate grid resolution that would minimize the error caused by aggregating soil input data in regional simulations.
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| 8. |
- Coucheney, Elsa, et al.
(författare)
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The implication of input data aggregation on up-scaling soil organic carbon changes
- 2017
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Ingår i: Environmental Modelling and Software. - : Elsevier BV. - 1364-8152 .- 1873-6726. ; 96, s. 361-377
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Tidskriftsartikel (refereegranskat)abstract
- In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved.
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| 9. |
- De Toro, Alfredo, et al.
(författare)
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Effects of extreme weather on yield of major arable crops in Sweden
- 2015
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Yield data for a series of years on the main crops grown in Sweden were collected and summarised in order to identify years with extremely low yield, determine their frequency and risk level and relate these to weather data in order to identify weather events leading to large yield reductions. Annual yield data at county level for cereals, field beans, oilseed rape, potatoes and temporary grasses were taken from official statistics for the period 1965-2014. For the period 2005-2012, crop yield data on farm level were also available from official statistics. In addition, yield data for cereals and temporary grasses being studied in long-term experiments (more than 40 years) located in four different agro-ecological zones of Sweden were considered. Daily temperature and precipitation data for each of the 21 counties in Sweden during the period 1961-2012 were downloaded from the official Swedish weather data website. In general, yield reductions were higher in northern than in southern counties and higher for spring cereals than winter cereals. Oats, spring rape and potatoes were the crops with the highest yield variation at county level. The frequency of a 30% yield reduction at county level was very low or close to zero in those counties with widespread cereal production, but large reductions occurred in individual years and certain counties (e.g. -80% in Norrbotten county in 1987). Close agreement between annual area of non-harvested crops and a 30% yield reduction was observed for certain years, crops and counties. The northern counties had on average 4-11% non-harvested crop area, with Norrbotten county having the highest values. The non-harvested area of cereals in southern counties was on average 0-2%. The risk of severe crop losses on farm level was around 10%, although in a few cases the risk was 25%, depending on the county. More specifically, the overall risk among the counties for individual farms of obtaining 30% lower yield for winter wheat was 5-20%, for spring wheat 5-20%, for rye 5-10% and for spring barley 5-25%. The corresponding risk of obtaining 50% lower yield for oats was 5-20%. The yield data for individual farms showed large variations, even in years with ‘favourable’ weather conditions. In most years, yield on the lower 10th percentile of farms was less than half the average yield at county level. Winter wheat showed the lowest variation in southern counties and oats and spring rape the highest. Farm-level yield variations were also much higher in Norrbotten county than in southern counties. This large yield variation was confirmed by data from the long-term crop experiments, in which yield reductions exceeding 30% occurred in 5-18% of years (i.e. 2-8 years in the period 1965-2010). Most years with the lowest yield were associated with a prolonged dry period (<20 mm precipitation over 40 days) and/or a high level of precipitation during the harvesting period (>100 mm during August). However, attempts to correlate county average yields with indices based only on daily temperature and precipitation gave poor and inconsistent results. Similar results were obtained using yield data from the long-term experiments and indices based solely on precipitation. The large yield variations between individual farms, the heterogeneity of crop responses to Scandinavian weather conditions and the limitations of yield prediction models in terms of detailed input data and result accuracy indicate that yield reductions should be measured on farm level. Within the study period, precipitation during summer months appeared to increase over time, particularly in 25% of years in southern Sweden. If this situation persists, it will have conflicting effects on crop production, by reducing the risk of drought periods and increasing the risk of rainy harvesting periods.
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| 10. |
- Djurle, Annika, et al.
(författare)
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Complexity in climate-change impacts: an analytical framework for effects mediated by plant disease
- 2011
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Ingår i: Plant Pathology. - : Wiley. - 0032-0862 .- 1365-3059. ; 60, s. 15-30
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Forskningsöversikt (refereegranskat)abstract
- The impacts of climate change on ecosystem services are complex in the sense that effective prediction requires consideration of a wide range of factors. Useful analysis of climate-change impacts on crops and native plant systems will often require consideration of the wide array of other biota that interact with plants, including plant diseases, animal herbivores, and weeds. We present a framework for analysis of complexity in climate-change effects mediated by plant disease. This framework can support evaluation of the level of model complexity likely to be required for analysing climate-change impacts mediated by disease. Our analysis incorporates consideration of the following set of questions for a particular host, pathogen, host-pathogen combination, or geographic region. 1. Are multiple biological interactions important? 2. Are there environmental thresholds for population responses? 3. Are there indirect effects of global change factors on disease development? 4. Are spatial components of epidemic processes affected by climate? 5. Are there feedback loops for management? 6. Are networks for intervention technologies slower than epidemic networks? 7. Are there effects of plant disease on multiple ecosystem services? 8. Are there feedback loops from plant disease to climate change? Evaluation of these questions will help in gauging system complexity, as illustrated for fusarium head blight and potato late blight. In practice, it may be necessary to expand models to include more components, identify those components that are the most important, and synthesize such models to include the optimal level of complexity for planning and research prioritization.
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