Dataset for multi-model comparison and ensemble simulations of canola growth and yield across global sites Article Swipe
Di He
,
Jing Wang
,
Julianne M. Lilley
,
Brendan Christy
,
Munir Hoffmann
,
Garry J. O’Leary
,
Jerry L. Hatfield
,
Luigi Ledda
,
Paola A. Deligios
,
Brian Grant
,
Qi Jing
,
Claas Nendel
,
Henning Kage
,
Budong Qian
,
Ehsan Eyshi Rezaei
,
Ward Smith
,
Wiebke Weymann
,
Frank Ewert
,
Enli Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.18174/odjar.v11i0.18569
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.18174/odjar.v11i0.18569
This paper describes the dataset that was used to test the reliability of eight crop models in simulating growth and yield of canola in response to sowing dates, nitrogen inputs and climate variability across five countries. The dataset includes four spring cultivars and three winter cultivars across six sites, which represents a diverse range of canola production areas around the world. Model calibration and validation were conducted in the framework of the Agricultural Model Intercomparison and Improvement Project for canola (AgMIP-Canola). Field experimental datasets include site characterization, soil profile characterization, initial soil conditions (soil water and mineral nitrogen contents), in-season and end-season crop measurements (phenology, LAI, biomass, and nitrogen content in leaves, stems and pods, some with seed oil content), and daily weather data. Simulation datasets include the simulation results generated by ten individual model frameworks (eight crop models, APSIM and DSSAT respectively by two groups) for the experimental periods, and scenario simulations using 30 years historical weather data (1981 – 2010) together with a full multi-factorial combinations of temperature (-3, 0, +3, +6, +9 oC), rainfall (-25%, -10%, 0, +10%, +25%), CO2 concentrations (360, 450, 540, 630, 720 ppm) and nitrogen input rates (0, +25%, +50%, +100%, +150%).
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- Type
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- https://odjar.org/article/download/18569/18276
- OA Status
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.18174/odjar.v11i0.18569Digital Object Identifier
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Dataset for multi-model comparison and ensemble simulations of canola growth and yield across global sitesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-10Full publication date if available
- Authors
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Di He, Jing Wang, Julianne M. Lilley, Brendan Christy, Munir Hoffmann, Garry J. O’Leary, Jerry L. Hatfield, Luigi Ledda, Paola A. Deligios, Brian Grant, Qi Jing, Claas Nendel, Henning Kage, Budong Qian, Ehsan Eyshi Rezaei, Ward Smith, Wiebke Weymann, Frank Ewert, Enli WangList of authors in order
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https://doi.org/10.18174/odjar.v11i0.18569Publisher landing page
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https://odjar.org/article/download/18569/18276Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://odjar.org/article/download/18569/18276Direct OA link when available
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Canola, Yield (engineering), Statistics, Econometrics, Environmental science, Mathematics, Agronomy, Biology, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Field | 81 |
| abstract_inverted_index.Model | 61, 73 |
| abstract_inverted_index.areas | 57 |
| abstract_inverted_index.daily | 121 |
| abstract_inverted_index.data. | 123 |
| abstract_inverted_index.eight | 13 |
| abstract_inverted_index.input | 192 |
| abstract_inverted_index.model | 134 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.pods, | 114 |
| abstract_inverted_index.range | 53 |
| abstract_inverted_index.rates | 193 |
| abstract_inverted_index.stems | 112 |
| abstract_inverted_index.three | 43 |
| abstract_inverted_index.using | 153 |
| abstract_inverted_index.water | 94 |
| abstract_inverted_index.which | 49 |
| abstract_inverted_index.years | 155 |
| abstract_inverted_index.yield | 20 |
| abstract_inverted_index.(-25%, | 177 |
| abstract_inverted_index.(eight | 136 |
| abstract_inverted_index.+100%, | 197 |
| abstract_inverted_index.+25%), | 181 |
| abstract_inverted_index.across | 33, 46 |
| abstract_inverted_index.around | 58 |
| abstract_inverted_index.canola | 22, 55, 79 |
| abstract_inverted_index.dates, | 27 |
| abstract_inverted_index.growth | 18 |
| abstract_inverted_index.inputs | 29 |
| abstract_inverted_index.models | 15 |
| abstract_inverted_index.sites, | 48 |
| abstract_inverted_index.sowing | 26 |
| abstract_inverted_index.spring | 40 |
| abstract_inverted_index.winter | 44 |
| abstract_inverted_index.world. | 60 |
| abstract_inverted_index.+150%). | 198 |
| abstract_inverted_index.Project | 77 |
| abstract_inverted_index.climate | 31 |
| abstract_inverted_index.content | 109 |
| abstract_inverted_index.dataset | 4, 37 |
| abstract_inverted_index.diverse | 52 |
| abstract_inverted_index.groups) | 145 |
| abstract_inverted_index.include | 84, 126 |
| abstract_inverted_index.initial | 90 |
| abstract_inverted_index.leaves, | 111 |
| abstract_inverted_index.mineral | 96 |
| abstract_inverted_index.models, | 138 |
| abstract_inverted_index.profile | 88 |
| abstract_inverted_index.results | 129 |
| abstract_inverted_index.weather | 122, 157 |
| abstract_inverted_index.biomass, | 106 |
| abstract_inverted_index.datasets | 83, 125 |
| abstract_inverted_index.includes | 38 |
| abstract_inverted_index.nitrogen | 28, 97, 108, 191 |
| abstract_inverted_index.periods, | 149 |
| abstract_inverted_index.rainfall | 176 |
| abstract_inverted_index.response | 24 |
| abstract_inverted_index.scenario | 151 |
| abstract_inverted_index.together | 162 |
| abstract_inverted_index.conducted | 66 |
| abstract_inverted_index.content), | 119 |
| abstract_inverted_index.cultivars | 41, 45 |
| abstract_inverted_index.describes | 2 |
| abstract_inverted_index.framework | 69 |
| abstract_inverted_index.generated | 130 |
| abstract_inverted_index.in-season | 99 |
| abstract_inverted_index.Simulation | 124 |
| abstract_inverted_index.conditions | 92 |
| abstract_inverted_index.contents), | 98 |
| abstract_inverted_index.countries. | 35 |
| abstract_inverted_index.end-season | 101 |
| abstract_inverted_index.frameworks | 135 |
| abstract_inverted_index.historical | 156 |
| abstract_inverted_index.individual | 133 |
| abstract_inverted_index.production | 56 |
| abstract_inverted_index.represents | 50 |
| abstract_inverted_index.simulating | 17 |
| abstract_inverted_index.simulation | 128 |
| abstract_inverted_index.validation | 64 |
| abstract_inverted_index.(phenology, | 104 |
| abstract_inverted_index.Improvement | 76 |
| abstract_inverted_index.calibration | 62 |
| abstract_inverted_index.reliability | 11 |
| abstract_inverted_index.simulations | 152 |
| abstract_inverted_index.temperature | 169 |
| abstract_inverted_index.variability | 32 |
| abstract_inverted_index.Agricultural | 72 |
| abstract_inverted_index.combinations | 167 |
| abstract_inverted_index.experimental | 82, 148 |
| abstract_inverted_index.measurements | 103 |
| abstract_inverted_index.respectively | 142 |
| abstract_inverted_index.concentrations | 183 |
| abstract_inverted_index.(AgMIP-Canola). | 80 |
| abstract_inverted_index.Intercomparison | 74 |
| abstract_inverted_index.multi-factorial | 166 |
| abstract_inverted_index.characterization, | 86, 89 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 19 |
| citation_normalized_percentile.value | 0.04857507 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |