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| rdf:type
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| Description
| - Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km x 10 km; 20 km x 20 km; 50 km x 50 km and 100 km x 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic 'fingerprint' of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies.
- Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km x 10 km; 20 km x 20 km; 50 km x 50 km and 100 km x 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic 'fingerprint' of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies. (en)
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| Title
| - Characteristic 'fingerprints' of crop model responses data at different spatial resolutions to weather input
- Characteristic 'fingerprints' of crop model responses data at different spatial resolutions to weather input (en)
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| skos:prefLabel
| - Characteristic 'fingerprints' of crop model responses data at different spatial resolutions to weather input
- Characteristic 'fingerprints' of crop model responses data at different spatial resolutions to weather input (en)
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| skos:notation
| - RIV/67179843:_____/13:00395764!RIV14-MSM-67179843
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| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| - I, P(EE2.3.20.0248), P(EE2.4.31.0056)
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/67179843:_____/13:00395764
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Crop model; Weather data resolution; Aggregation; Yield distribution (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
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| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...i/riv/nazevZdroje
| - European Journal of Agronomy
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...v/svazekPeriodika
| |
| http://linked.open...iv/tvurceVysledku
| - Hlavinka, Petr
- Rotter, R.
- Trnka, Miroslav
- Pirttioja, N. K.
- Ewert, F.
- Angulo, C.
- Gaiser, T.
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| http://linked.open...ain/vavai/riv/wos
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| issn
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| number of pages
| |
| http://bibframe.org/vocab/doi
| - 10.1016/j.eja.2013.04.003
|
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