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1 Gardner, A. S.; Gaston, K. J.; Maclean, I. M. D. 2021. Combining qualitative and quantitative methodology to assess prospects for novel crops in a warming climate. Agricultural Systems, 190:103083. [doi: https://doi.org/10.1016/j.agsy.2021.103083]
Crops ; Cultivation ; Assessment ; Climate change ; Temperature ; Farmland ; Farmers ; Borago officinalis ; Soybeans ; Hemp ; Sea kale ; Sweet potatoes ; Microclimate ; Models / England / Cornwall / Isles of Scilly
(Location: IWMI HQ Call no: e-copy only Record No: H050369)
https://www.sciencedirect.com/science/article/pii/S0308521X21000366/pdfft?md5=76ecc004f7310db623d676ce82532ce1&pid=1-s2.0-S0308521X21000366-main.pdf
https://vlibrary.iwmi.org/pdf/H050369.pdf
https://vlibrary.iwmi.org/pdf/H050369.pdf
(2.76 MB) (2.76 MB)
Context: Climate change will alter the global distribution of climatically suitable space for many species, including agricultural crops. In some locations, warmer temperatures may offer opportunities to grow novel, high value crops, but non-climatic factors also inform agricultural decision-making. These non-climatic factors can be difficult to quantify and incorporate into suitability assessments, particularly for uncertain futures.
Objective: To demonstrate how qualitative and quantitative techniques can be combined to assess crop suitability with consideration for climatic and non-climatic factors.
Methods: We carried out a horizon scanning exercise that used Delphi methodology to identify possible novel crops for a region in south-west England. We show how the results of the expert panel assessment could be combined with a crop suitability model that only considered climate to identify the best crops to grow in the region.
Results and conclusions: Whilst improving climate and crop models will enhance the ability to identify environmental constraints to growing novel crops, we propose horizon scanning as a useful tool to understand constraints on crop suitability that are beyond the parameterisation of these models and that may affect agricultural decisions.
Significance: A similar combination of qualitative and quantitative approaches to assessing crop suitability could be used to identify potential novel crops in other regions and to support more holistic assessments of crop suitability in a changing world.
Objective: To demonstrate how qualitative and quantitative techniques can be combined to assess crop suitability with consideration for climatic and non-climatic factors.
Methods: We carried out a horizon scanning exercise that used Delphi methodology to identify possible novel crops for a region in south-west England. We show how the results of the expert panel assessment could be combined with a crop suitability model that only considered climate to identify the best crops to grow in the region.
Results and conclusions: Whilst improving climate and crop models will enhance the ability to identify environmental constraints to growing novel crops, we propose horizon scanning as a useful tool to understand constraints on crop suitability that are beyond the parameterisation of these models and that may affect agricultural decisions.
Significance: A similar combination of qualitative and quantitative approaches to assessing crop suitability could be used to identify potential novel crops in other regions and to support more holistic assessments of crop suitability in a changing world.
2 Wilkinson, R.; Mleczko, M. M.; Brewin, R. J. W.; Gaston, K. J.; Mueller, M.; Shutler, J. D.; Yan, X.; Anderson, K. 2024. Environmental impacts of earth observation data in the constellation and cloud computing era. Science of The Total Environment, 909:168584. (Online first) [doi: https://doi.org/10.1016/j.scitotenv.2023.168584]
(Location: IWMI HQ Call no: e-copy only Record No: H052382)
https://www.sciencedirect.com/science/article/pii/S0048969723072121/pdfft?md5=1d91787dcc10d76821d2ba12ec331dd4&pid=1-s2.0-S0048969723072121-main.pdf
https://vlibrary.iwmi.org/pdf/H052382.pdf
https://vlibrary.iwmi.org/pdf/H052382.pdf
(1.76 MB) (1.76 MB)
Numbers of Earth Observation (EO) satellites have increased exponentially over the past decade reaching the current population of 1193 (January 2023). Consequently, EO data volumes have mushroomed and data storage and processing have migrated to the cloud. Whilst attention has been given to the launch and in-orbit environmental impacts of satellites, EO data environmental footprints have been overlooked. These issues require urgent attention given data centre water and energy consumption, high carbon emissions for computer component manufacture, and difficulty of recycling computer components. Doing so is essential if the environmental good of EO is to withstand scrutiny. We provide the first assessment of the EO data life-cycle and estimate that the current size of the global EO data collection is ~807 PB, increasing by ~100 PB/year. Storage of this data volume generates annual CO2 equivalent emissions of 4101 t. Major state-funded EO providers use 57 of their own data centres globally, and a further 178 private cloud services, with considerable duplication of datasets across repositories. We explore scenarios for the environmental cost of performing EO functions on the cloud compared to desktop machines. A simple band arithmetic function applied to a Landsat 9 scene using Google Earth Engine (GEE) generated CO2 equivalent (e) emissions of 0.042–0.69 g CO2e (locally) and 0.13–0.45 g CO2e (European data centre; values multiply by nine for Australian data centre). Computation-based emissions scale rapidly for more intense processes and when testing code. When using cloud services such as GEE, users have no choice about the data centre used and we push for EO providers to be more transparent about the location-specific impacts of EO work, and to provide tools for measuring the environmental cost of cloud computation. The EO community as a whole needs to critically consider the broad suite of EO data life-cycle impacts.
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