| 1. |
- Coucheney, Elsa
(författare)
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Accuracy, robustness and behavior of the STICS soil-crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France
- 2015
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Ingår i: Environmental Modelling and Software. - : Elsevier BV. - 1364-8152 .- 1873-6726. ; 64, s. 177–190-
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Tidskriftsartikel (refereegranskat)abstract
- Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved.
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| 2. |
- 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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| 3. |
- 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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| 4. |
- 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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| 5. |
- Coucheney, Elsa, et al.
(författare)
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Improving the sustainability of arable cropping systems by modifying root traits: A modelling study for winter wheat
- 2024
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Ingår i: European Journal of Soil Science. - 1351-0754 .- 1365-2389. ; 75
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Tidskriftsartikel (refereegranskat)abstract
- Modifying root systems by crop breeding has been attracting increasing attention as a potentially effective strategy to enhance the sustainability of agriculture by increasing soil organic matter (SOM) stocks and soil quality, whilst maintaining or even improving yields. We used the new soil-crop model USSF (Uppsala model of Soil Structure and Function) to investigate the potential of this management strategy using winter wheat as a model crop. USSF combines a simple (generic) crop growth model with physics-based descriptions of soil water flow, water uptake and transpiration by plants. It also includes a model of the interactions between soil structure dynamics and organic matter turnover that considers the effects of physical protection and microbial priming on the decomposition of SOM. The model was first calibrated against field data on soil water contents and both above-ground and root biomass of winter wheat measured during one growing season in a clay soil in Uppsala, Sweden using the GLUE method to identify five 'acceptable' parameter sets. We created four model crops (ideotypes) by modifying root-related parameters to mimic winter wheat phenotypes with improved root traits. Long-term (30-year) simulations of a conventionally tilled monoculture of winter wheat were then performed to evaluate the potential effects of cultivating these ideotypes on the soil water balance, soil organic matter stocks and grain yields. Our results showed that ideotypes with deeper root systems or root systems that are more effective for water uptake increased grain yields by 3% and SOM stocks in the soil profile by ca. 0.4%-0.5% in a 30-year perspective (as an average of the five parameter sets). An ideotype in which below-ground allocation of dry matter was increased at the expense of stem growth gave even larger increases in SOM stocks (ca. 1.4%). An ideotype combining all three modifications (deeper and more effective root systems and greater root production) showed even more promising results: compared with the baseline scenario, surface runoff decreased while yields were predicted to increase by ca. 7% and SOM stocks in the soil profile by ca. 2%, which is roughly equivalent to ca. 20% of the 4-per-mille target ().
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| 6. |
- 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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| 7. |
- Coucheney, Elsa, et al.
(författare)
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Long-term fertilization of a boreal Norway spruce forest increases the temperature sensitivity of soil organic carbon mineralization
- 2013
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Ingår i: Ecology and Evolution. - : Wiley. - 2045-7758. ; 3, s. 5177-5188
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Tidskriftsartikel (refereegranskat)abstract
- Boreal ecosystems store one-third of global soil organic carbon (SOC) and are particularly sensitive to climate warming and higher nutrient inputs. Thus, a better description of how forest managements such as nutrient fertilization impact soil carbon (C) and its temperature sensitivity is needed to better predict feedbacks between C cycling and climate. The temperature sensitivity of in situ soil C respiration was investigated in a boreal forest, which has received long-term nutrient fertilization (22 years), and compared with the temperature sensitivity of C mineralization measured in the laboratory. We found that the fertilization treatment increased both the response of soil in situ CO2 effluxes to a warming treatment and the temperature sensitivity of C mineralization measured in the laboratory (Q10). These results suggested that soil C may be more sensitive to an increase in temperature in long-term fertilized in comparison with nutrient poor boreal ecosystems. Furthermore, the fertilization treatment modified the SOC content and the microbial community composition, but we found no direct relationship between either SOC or microbial changes and the temperature sensitivity of C mineralization. However, the relation between the soil C:N ratio and the fungal/bacterial ratio was changed in the combined warmed and fertilized treatment compared with the other treatments, which suggest that strong interaction mechanisms may occur between nutrient input and warming in boreal soils. Further research is needed to unravel into more details in how far soil organic matter and microbial community composition changes are responsible for the change in the temperature sensitivity of soil C under increasing mineral N inputs. Such research would help to take into account the effect of fertilization managements on soil C storage in C cycling numerical models.
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| 8. |
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| 9. |
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| 10. |
- 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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