hide
Free keywords:
-
Abstract:
Crop yields are sensitive to climate variability. They also respond to inter-annual weather variability in essentially every phase of the vegetation period. Inter-annual dependencies are evident in oscillating wheat yield figures, yet have not been given the attention expected. How inter-annual wheat yield variability is affected by weather needs clarification, especially in the light of human induced climate change and increasingly intense and likely heat waves in Europe.
This study applies geostatistical methods and GIS (geographic information systems) to analyze spatial and temporal variability of wheat on three aggregation levels in Europe (EU-15, German states level, and county level in one German state). Daily homogenized weather data and annual statistical data on wheat yields were analyzed for these purposes.
Residuals are separated from long term yield trends with an extensively validated method and represent the base values for weather induced, short term inter-annual variations. They are correlated with selected climate variables in multiple regression models for qualitative and quantitative analyses of yields’ weather sensitivity.
The main focuses are
• to what extent inter-annual wheat yield variability can be explained by weather influences, and further, by selected meteorological parameters, and how sensitive they are to them. Heat wave years are given particular attention.
• an extensive analysis of effects of the 2003 heat wave in Europe on wheat and winter wheat yields.
• modeling wheat yields with multiple linear regression using yield anomalies as estimates for weather induced yield variations.
• the analysis of results with GIS and statistical methods, applied also to analyze spatial-temporal variability and to address transitional scaling effects between the aggregation levels in question.
The study produced the following results.
• Estimates of inter-annual yield variability through multiple linear regression of monthly climate variables are achieved with moderate to good explanations of variance, varying by the scale applied. R2 are comparable to similar studies performed in the past.
• Regions with linear trends in increasing yield figures are distinguished from ones with break points in the trend. Possible explanations are discussed.
• Absolute anomalies show an increasing trend, relative anomalies show a decreasing trend.
• Quantitative and qualitative analysis of wheat yield anomalies provide evidence for record yield collapses and identify "winners" and "losers" of the 2003 heat wave. Results can contribute to outlining future yield patterns in the light of a predicted increase of such heat wave events.
• Geostatistical and spatial analyses showed that marked homogeneous negative anomalies corresponded with the core extent of the stationary high pressure zone over Germany, France, and Austria. Countries north of the high pressure core recorded small negative to record high anomalies.
• In 8 selected heat wave years impact assessment of weather on crop yields showed that such events do not essentially lead to yield loss. This necessitates further studies of weather impact with high resolution data.
• Explorative results from simulating yield anomaly trends with climate scenario data indicate steady to markedly declining anomalies into the mid 21st century.
• Comparative studies of methods evaluating agronomic years illustrate how sensitive results are to the reference units selected.
Subsequent studies are recommended to incorporate
• high resolution climate data and phenological information of plant development phases into modeling wheat yield variability with inter-annual variations as well as applying wheat growth models
• more complex climate variables such as drought indices in using yield anomalies as estimates for weather induced variations to improve estimates and shed light on the effects of increasingly intense and likely heat waves.
