DOI: 10.1016/j.jag.2015.04.013
Scopus记录号: 2-s2.0-84943600559
论文题名: Impact of the spatial resolution of climatic data and soil physical properties on regional corn yield predictions using the STICS crop model
作者: Jégo G ; , Pattey E ; , Morteza Mesbah S ; , Liu J ; , Duchesne I
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 41 起始页码: 11
结束页码: 22
语种: 英语
英文关键词: Abundant rainfall
; Earth observation
; High pedodiversity
; Leaf area index
; Rainfed corn
; Yield prediction
Scopus关键词: climate effect
; crop yield
; leaf area index
; maize
; management practice
; modeling
; prediction
; rainfall
; rainfed agriculture
; soil property
; spatial resolution
; Zea mays
英文摘要: The assimilation of Earth observation (EO) data into crop models has proven to be an efficient way to improve yield prediction at a regional scale by estimating key unknown crop management practices. However, the efficiency of prediction depends on the uncertainty associated with the data provided to crop models, particularly climatic data and soil physical properties. In this study, the performance of the STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard) crop model for predicting corn yield after assimilation of leaf area index derived from EO data was evaluated under different scenarios. The scenarios were designed to examine the impact of using fine-resolution soil physical properties, as well as the impact of using climatic data from either one or four weather stations across the region of interest. The results indicate that when only one weather station was used, the average annual yield by producer was predicted well (absolute error <5%), but the spatial variability lacked accuracy (root mean square error = 1.3 t ha-1). The model root mean square error for yield prediction was highly correlated with the distance between the weather stations and the fields, for distances smaller than 10 km, and reached 0.5 t ha-1 for a 5-km distance when fine-resolution soil properties were used. When four weather stations were used, no significant improvement in model performance was observed. This was because of a marginal decrease (30%) in the average distance between fields and weather stations (from 10 to 7 km). However, the yield predictions were improved by approximately 15% with fine-resolution soil properties regardless of the number of weather stations used. The impact of the uncertainty associated with the EOderived soil textures and the impact of alterations in rainfall distribution were also evaluated. A variation of about 10% in any of the soil physical textures resulted in a change in dry yield of 0.4 t ha-1. Changes in rainfall distribution between two abundant rainfalls during the growing season led to a significant change in yield (0.5 t ha-1 on average). Our results highlight the importance of using fine-resolution gridded daily precipitation data to capture spatial variations of rainfall as well as using fine-resolution soil properties instead of coarse-resolution soil properties from the Canadian soil dataset, especially for regions with high pedodiversity. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79546
Appears in Collections: 气候变化事实与影响
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作者单位: Agriculture and Agri-Food Canada, Soils and Crops Research and Development Centre, 2560 Hochelaga Blvd., Quebec City, QC, Canada; Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, 960 Carling Ave., Ottawa, ON, Canada; La Financière agricole du Québec, 1400 De la Rive-Sud Blvd., St-Romuald, QC, Canada
Recommended Citation:
Jégo G,, Pattey E,, Morteza Mesbah S,et al. Impact of the spatial resolution of climatic data and soil physical properties on regional corn yield predictions using the STICS crop model[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,41