On the use of statistical models to predict crop yield responses to climate change

▶ Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. ▶ Time-series models are better suited for predicting response to precipitation than temperature, whereas panel or cross-section models are better suited for tem...

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Veröffentlicht in:	Agricultural and forest meteorology Jg. 150; H. 11; S. 1443 - 1452
Hauptverfasser:	Lobell, David B., Burke, Marshall B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.10.2010 [Oxford]: Elsevier Science Ltd Elsevier
Schlagworte:	accuracy Africa Agricultural and forest climatology and meteorology. Irrigation. Drainage Agronomy. Soil science and plant productions air temperature Biological and medical sciences CERES-Maize Climate Climate change Computer simulation corn crop models Crops Fundamental and applied biological sciences. Psychology General agronomy. Plant production grain yield Maize Marksim Mathematical models meteorological parameters Panels Precipitation prediction spatial scale Statistical analysis statistical models Sub-Saharan Africa temporal variation time series analysis weather Zea mays Sub-Saharan Africa Africa Maize Marksim CERES-Maize Africa Biometeorology Monocotyledones Vegetals Zea mays Use Prediction C4-Type Forecast model Modeling Cereal crop Dynamical climatology Climate change Gramineae Statistical model Angiospermae Spermatophyta Yield Simulation model Cultivated plant
ISSN:	0168-1923, 1873-2240
Online-Zugang:	Volltext
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