Advances in Meteorology

Research Article

A Methodical Approach to Design and Valuation of Weather Derivatives in Agriculture

Table 7

The most significant correlation coefficients between barley yield per hectare and the average characteristics of weather in regions of the CR (1970–2009).
RegionAir temperature Rainfall Drought index
Středočeský (6, ) (4, ) (3, )
(5-6, ) (3, ) 
(5–7, ) (12, , ) 
(4–6, )  
(6-7, )  
Jihočeský (6, ) (6, )
(6-7, ) 
(5-6, ) 
(5–7, ) 
Vysočina (6, ) (3, ) (3, )
(6-7, ) (1–3, ) (6, )
(5–7, )  (4–6, )
(4–7, )  (5-6, )
(5-6, )  
Královéhradecký (6, ) (1–8, ) (3, )
(5-6, ) (1–7, ) (1–3, )
(5–7, ) (1–3, ) (2-3, )
(6-7, ) (3–8, ) (1–4, )
(4–7, ) (7-8, ) (6, )
Jihomoravský (6, ) (4–6, ) (4–6, )
(5-6, ) (4-5, ) (5-6, )
(4–6, ) (3, ) (4-5, )
(4–7, )  (6, )
(5–7, )   (5, )
Olomoucký (6, ) (3, ) (6, )
(5-6, ) (1–3, ) (5-6, )
(5–7, ) (1–4, )0.33 (3, )
(4–6, )  
(6-7, )  
The data in front of round brackets are correlation coefficients. The figures in brackets denote critical months for yield formation. The values test the two-tailed statistical significance of the correlation coefficient. The term “ ” indicates no statistically significant correlation (Pearson, Spearman) at significance level 0.05. We put a maximum of 5 most statistically significant correlation coefficients.
Source: authors.