| 1. |
- Deryng, Delphine, et al.
(författare)
-
Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity
- 2016
-
Ingår i: Nature Climate Change. - : Springer Science and Business Media LLC. - 1758-678X .- 1758-6798. ; 6:8, s. 786-790
-
Tidskriftsartikel (refereegranskat)abstract
- Rising atmospheric CO2 concentrations ([CO2 ]) are expected to enhance photosynthesis and reduce crop water use. However, there is high uncertainty about the global implications of these effects for future crop production and agricultural water requirements under climate change. Here we combine results from networks of field experiments and global crop models to present a spatially explicit global perspective on crop water productivity (CWP, the ratio of crop yield to evapotranspiration) for wheat, maize, rice and soybean under elevated [CO2 ] and associated climate change projected for a high-end greenhouse gas emissions scenario. We find CO2 effects increase global CWP by 10[0;47]%-27[7;37]% (median[interquartile range] across the model ensemble) by the 2080s depending on crop types, with particularly large increases in arid regions (by up to 48[25;56]% for rainfed wheat). If realized in the fields, the effects of elevated [CO2 ] could considerably mitigate global yield losses whilst reducing agricultural consumptive water use (4-17%). We identify regional disparities driven by differences in growing conditions across agro-ecosystems that could have implications for increasing food production without compromising water security. Finally, our results demonstrate the need to expand field experiments and encourage greater consistency in modelling the effects of rising [CO2 ] across crop and hydrological modelling communities.
|
|
| 2. |
- Folberth, Christian, et al.
(författare)
-
Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble
- 2019
-
Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:9
-
Tidskriftsartikel (refereegranskat)abstract
- Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.
|
|
| 3. |
- Frieler, Katja, et al.
(författare)
-
Understanding the weather signal in national crop-yield variability
- 2017
-
Ingår i: Earth's Future. - 2328-4277. ; 5:6, s. 605-616
-
Tidskriftsartikel (refereegranskat)abstract
- Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.
|
|
| 4. |
- Johannes, Schmidt, et al.
(författare)
-
Potential of biomass-fired combined heat and power plants considering the spatial distribution of biomass supply and heat demand
- 2010
-
Ingår i: International Journal of Energy Research. - : Wiley. - 0363-907X .- 1099-114X. ; 34:11, s. 970-985
-
Tidskriftsartikel (refereegranskat)abstract
- Combined heat and power (CHP) plants fired by forest wood can significantly contribute to attaining the target of increasingthe share of renewable energy production. However, the spatial distribution of biomass supply and of heat demand limits thepotentials of CHP production. This article assesses CHP potentials using a mixed integer programming model that optimizeslocations of bioenergy plants. Investment costs of district heating infrastructure are modeled as a function of heat demanddensities, which can differ substantially. Gasification of biomass in a combined cycle process is assumed as productiontechnology. Some model parameters have a broad range according to a literature review. Monte-Carlo simulations havetherefore been performed to account for model parameter uncertainty in our analysis. The model is applied to assess CHPpotentials in Austria. Optimal locations of plants are clustered around big cities in the east of the country. At current powerprices, biomass-based CHP production allows producing around 3% of the total energy demand in Austria. Yet, the heatutilization decreases when CHP production increases due to limited heat demand that is suitable for district heating.Production potentials are most sensitive to biomass costs and power prices.
|
|
| 5. |
- Leduc, Sylvain, et al.
(författare)
-
Methanol production by gasification using a geographically explicit model
- 2009
-
Ingår i: Biomass and Bioenergy. - : Elsevier BV. - 0961-9534 .- 1873-2909. ; 33:5, s. 745-751
-
Tidskriftsartikel (refereegranskat)abstract
- Methanol mixed with 15% gasoline appears to be a viable alternative energy source for the transportation sector. Produced from gasification of certified wood coming from well-managed forests, its production could be considered as sustainable and the well-to-wheel emissions can be reduced significantly. The physical flows of the entire bio-energy chain consisting of harvesting, biomass transportation, methanol production by gasification, methanol transportation, and methanol distribution to the consumers are assessed and costs are estimated for each part of the chain. A transportation model has been constructed to estimate the logistic demands of biomass supply to the processing plant and to the supply of gas station. The analysis was carried out on a case study for the geography of Baden-Württemberg, Germany. It has been found that a typical optimal size for methanol production of some 130,000 m3, supplies about 100 gas stations, and the biomass supply requires on average 22,000 ha of short-rotational poplar, with an average transportation distance of biomass of some 50 km to the methanol processing plant. The methanol production costs appear to be most sensitive with respect to methanol plant efficiency, wood cost, and operating hours of the plant. In an area where biomass is spread heterogeneously, apart from the demand, the geographical position of the plant would appear to have a major impact on the final biofuel cost.
|
|
| 6. |
- Leduc, Sylvain, et al.
(författare)
-
Optimal location for a biomass based methanol production plant : case study in Northern Sweden
- 2007
-
Ingår i: From Research to Market Deployment. - Florence : ETA - Renewable Energies. - 3936338213 - 9783936338218
-
Konferensbidrag (refereegranskat)abstract
- Methanol appears to be a new alternative fuel in the transport sector. Methanol can be produced through gasification of lignocellulosic biomass, which makes it a renewable fuel, and its utilization has therefore an impact on greenhouse gas emissions. The county of Norrbotten in northern Sweden has the characteristic to have great amount of woody biomass, and a sparsely inhabited area. Transportation distances of both biomass and methanol would then have a great impact on the final cost of methanol depending on where the methanol plant is located. This county was therefore studied as a case study with a twenty year perspective in order to validate an optimization model. The optimal locations of three different sizes of methanol plants were studied for four demographic scenarios. From this study it appears that methanol plants of 100 MWbiomass and 200 MWbiomass would be set up closer to the demand area than a 400 MWbiomass that would optimally be set up more inlands close to the available biomass.
|
|
| 7. |
- Leduc, Sylvain, et al.
(författare)
-
Optimal location of wood gasification plants for methanol production with heat recovery
- 2008
-
Ingår i: International Journal of Energy Research. - : Hindawi Limited. - 0363-907X .- 1099-114X. ; 32:12, s. 1080-1091
-
Tidskriftsartikel (refereegranskat)abstract
- Second generation biofuels from wood gasification are thought to become competitive in the face of effective climate and energy security policies. Cost competitiveness crucially depends on the optimization of the entire supply chain-field-wheel involving optimal location, scaling and logistics. In this study, a linear mixed integer programming model has been developed to determine the optimal geographic locations and sizes of methanol plants and gas stations in Austria. Optimal locations and sizes are found by the minimization of costs with respect to biomass and methanol production and transport, investments for the production plants and the gas stations. Hence, the model covers competition in all levels of a biofuel production chain including supply of biomass, biofuel and heat, and demand for bio- and fossil fuels.The results show that Austria could be self-sufficient in the production of methanol for biofuels like M5, M10 or M20, using up to 8% of the arable land share. The plants are optimally located close to the potential supply of biomass (i.e. poplar) in Eastern Austria, and produce methanol around 0.4 is an element of(-1). Moreover, heat production could lower the methanol cost by 12%.
|
|
| 8. |
|
|
| 9. |
- Milham, Michael P., et al.
(författare)
-
An Open Resource for Non-human Primate Imaging
- 2018
-
Ingår i: Neuron. - : Elsevier BV. - 0896-6273 .- 1097-4199. ; 100:1, s. 61-74
-
Tidskriftsartikel (refereegranskat)abstract
- Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.
|
|
| 10. |
- Müller, Christoph, et al.
(författare)
-
Global gridded crop model evaluation : Benchmarking, skills, deficiencies and implications
- 2017
-
Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 10:4, s. 1403-1422
-
Tidskriftsartikel (refereegranskat)abstract
- Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
|
|
