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- 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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| 2. |
- Hudson, Lawrence N, et al.
(författare)
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The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project
- 2017
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Ingår i: Ecology and Evolution. - : John Wiley & Sons. - 2045-7758. ; 7:1, s. 145-188
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Tidskriftsartikel (refereegranskat)abstract
- The PREDICTS project-Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)-has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.
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| 3. |
- Hudson, Lawrence N., et al.
(författare)
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The PREDICTS database : a global database of how local terrestrial biodiversity responds to human impacts
- 2014
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Ingår i: Ecology and Evolution. - : Wiley. - 2045-7758. ; 4:24, s. 4701-4735
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Tidskriftsartikel (refereegranskat)abstract
- Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.
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| 4. |
- Høgevold, Nils M., et al.
(författare)
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A triple bottom line construct and reasons for implementing sustainable business practices in companies and their business networks
- 2015
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Ingår i: Corporate Governance. - Bingley : Emerald Group Publishing Limited. - 1472-0701 .- 1758-6054. ; 15:4, s. 427-443
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Tidskriftsartikel (refereegranskat)abstract
- Purpose – The purpose of this study is to test a Triple Bottom Line (TBL)-construct as well as to describe the TBL-reasons for implementing sustainable business practices in companies and their business networks. This study explores how linking these seemingly disparate pillars of sustainability may be facilitated through a TBL construct. The notion of sustainable business practices has been evolving and is increasingly understood to encompass considerations of economic viability, as well as environmental sustainability and social responsibility.Design/methodology/approach – The research is quantitative in nature, exploring and analysing how companies in different Norwegian industries implement and manage sustainable business practices based on TBL. The survey results are reported here.Findings – The relevance of TBL to different aspects of sustainable business practices is outlined. The study generally supports the view that a heightened propensity for sustainable business practices ensures that organisations are better equipped for meeting the challenge of integrating TBL in companies and their business networks.Research limitations/implications – The study tested a construct of TBL in the context of sustainable business practices. It may be incorporated in further research in relation to other constructs. Suggestions for further research are proposed.Practical implications – Useful for practitioners to get insights into TBL-reasons for implementing business-sustainable practices in companies and their business networks. It may also be valuable to assess the general status of business-sustainable practices in a company and their business networks.Originality/value – Linking two traditionally separate and encapsulated areas of research, namely, the area of business sustainable practices and the area of TBL. The current study has contributed to a TBL-construct in relation to other constructs in measurement and structural models. It has also contributed to provide insights of priority into the main reasons to implement the elements of TBL within companies and their business networks. © Emerald Group Publishing Limited.
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| 7. |
- Sosa, Carmen, et al.
(författare)
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Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables
- 2015
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Ingår i: Climate Research. - : Inter-Research Science Center. - 0936-577X .- 1616-1572. ; 65, s. 141-157
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Tidskriftsartikel (refereegranskat)abstract
- We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
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| 8. |
- Sosa, Carmen, et al.
(författare)
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Variability of effects of spatial climate data aggregation on regional yield simulation by crop models
- 2015
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Ingår i: Climate Research. - : Inter-Research Science Center. - 0936-577X .- 1616-1572. ; 65, s. 53-69
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Tidskriftsartikel (refereegranskat)abstract
- Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.
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