Multi-resolution analysis of aggregated spatial data to simulate yield and irrigation water demand at regional scales Article Swipe
Jonathan J. Ojeda
,
EE Rezaei
,
Tomas Remenyi
,
Mathew Webb
,
Heidi Webber
,
Bahareh Kamali
,
Rebecca M. B. Harris
,
Jesslyn F. Brown
,
Darren Kidd
,
CL Mohammed
,
Stefan Siebert
,
Frank Ewert
,
Holger Meinke
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.13140/rg.2.2.16175.25766
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.13140/rg.2.2.16175.25766
Input data aggregation influences crop model estimates at the regional level (Eyshi Rezaei et al., 2015). Previous studies have focused on the impact of aggregating the climate data used to compute crop yields (Hoffmann et al., 2015; van Bussel et al., 2011; Zhao et al., 2015). Little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) model inputs. This study explores the implications of using coarse resolution input data on model outputs (irrigated and rainfed yield and irrigation water demand [IWD]) in Tasmania, Australia by (i) separately assessing the DAEc and DAEs of model input data and (ii) assessing the combined impact of DAEc and DAEs. We provide a framework to quantifying the input uncertainty introduced by using aggregated data to meet the objectives of modelling exercises.
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- Type
- article
- Language
- en
- OA Status
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- Related Works
- 20
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https://openalex.org/W3033528722Canonical identifier for this work in OpenAlex
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https://doi.org/10.13140/rg.2.2.16175.25766Digital Object Identifier
- Title
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Multi-resolution analysis of aggregated spatial data to simulate yield and irrigation water demand at regional scalesWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2020Year of publication
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2020-01-01Full publication date if available
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Jonathan J. Ojeda, EE Rezaei, Tomas Remenyi, Mathew Webb, Heidi Webber, Bahareh Kamali, Rebecca M. B. Harris, Jesslyn F. Brown, Darren Kidd, CL Mohammed, Stefan Siebert, Frank Ewert, Holger MeinkeList of authors in order
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- Concepts
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Irrigation, Environmental science, Yield (engineering), Crop simulation model, Data aggregator, Agricultural engineering, Crop, Mathematics, Econometrics, Computer science, Geography, Agronomy, Engineering, Forestry, Wireless sensor network, Materials science, Computer network, Metallurgy, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.van | 37 |
| abstract_inverted_index.(ii) | 102 |
| abstract_inverted_index.DAEc | 94, 108 |
| abstract_inverted_index.DAEs | 96 |
| abstract_inverted_index.This | 63 |
| abstract_inverted_index.Zhao | 42 |
| abstract_inverted_index.al., | 14, 35, 40, 44 |
| abstract_inverted_index.crop | 4, 31 |
| abstract_inverted_index.data | 1, 27, 52, 73, 100, 124 |
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| abstract_inverted_index.meet | 126 |
| abstract_inverted_index.soil | 59 |
| abstract_inverted_index.used | 28 |
| abstract_inverted_index.2011; | 41 |
| abstract_inverted_index.2015; | 36 |
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| abstract_inverted_index.model | 5, 61, 75, 98 |
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| abstract_inverted_index.using | 69, 122 |
| abstract_inverted_index.water | 83 |
| abstract_inverted_index.yield | 80 |
| abstract_inverted_index.(DAEc) | 57 |
| abstract_inverted_index.(DAEs) | 60 |
| abstract_inverted_index.(Eyshi | 11 |
| abstract_inverted_index.2015). | 15, 45 |
| abstract_inverted_index.Bussel | 38 |
| abstract_inverted_index.Little | 46 |
| abstract_inverted_index.Rezaei | 12 |
| abstract_inverted_index.[IWD]) | 85 |
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| abstract_inverted_index.demand | 84 |
| abstract_inverted_index.effect | 54 |
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| abstract_inverted_index.climate | 26, 56 |
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| abstract_inverted_index.outputs | 76 |
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| abstract_inverted_index.(Hoffmann | 33 |
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| abstract_inverted_index.Tasmania, | 87 |
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| abstract_inverted_index.resolution | 71 |
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| abstract_inverted_index.aggregating | 24 |
| abstract_inverted_index.aggregation | 2, 53 |
| abstract_inverted_index.quantifying | 116 |
| abstract_inverted_index.uncertainty | 119 |
| abstract_inverted_index.implications | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 13 |
| citation_normalized_percentile.value | 0.04892409 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |