Cargando…

Investigating the effects of inter-annual weather variation (1968–2016) on the functional response of cereal grain yield to applied nitrogen, using data from the Rothamsted Long-Term Experiments

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rotham...

Descripción completa

Detalles Bibliográficos
Autores principales: Addy, John W.G., Ellis, Richard H., Macdonald, Andy J., Semenov, Mikhail A., Mead, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Science Publishers B.V 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079297/
https://www.ncbi.nlm.nih.gov/pubmed/32308247
http://dx.doi.org/10.1016/j.agrformet.2019.107898
Descripción
Sumario:The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops.