Crop–climate relationships of cereals in Greece and the impacts of recent climate trends

Abstract

Notwithstanding technological developments, agricultural production is still affected by uncontrollable factors, such weather and climate. Within this context, the present study aims at exploring the relative influence of growing season climate on the yields of major cereals (hard and soft wheat, maize, and barley) on a regional scale in Greece. To this end, crop–climate relationships and the impacts of climate trends over the period 1978–2005 were explored using linear regression and change point analysis (CPA). Climate data used include maximum (Tx) and minimum temperature (Tn), diurnal temperature range (Tr), precipitation (Prec), and solar radiation (Rad). Temperature effects were the most substantial. Yields reduced by 1.8–7.1 %/°C with increasing Tx and by 1.4–6.1 %/°C with decreasing Tr. The warming trends of Tn caused bilateral yield effects (from −3.7 to 8.4 %/°C). The fewer significantly increasing Rad and decreasing Prec anomalies were associated with larger yield decreases (within the range of 2.2 % MJ/m2/day (for maize) to 4.9 % MJ/m2/day (for hard wheat)) and smaller yield increases (from 0.04 to 1.4 %/mm per decade), respectively. Wheat and barley—the most vulnerable cereals—were most affected by the trends of extreme temperatures and least by Tr. On the contrary, solar radiation has proven to be the least affecting climate variable on all cereals. Despite the similarity in the direction of crop responses with both analyses, yield changes were much more substantial in the case of CPA analysis. In conclusion, regional climate change has affected Greek cereal productivity, in a few, but important for cereal production, regions. The results of this study are expected to be valuable in anticipating the effects of weather/climate on other warm regions worldwide, where the upper temperature limit for some cereals and further changes in climate may push them past suitability for their cultivation.

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Correspondence to Theodoros Mavromatis.

Appendices

Appendices

Appendix 1. Regression vs. Mann–Kendall trend analysis

The Shapiro–Wilk test (Shapiro and Wilk 1965) and the Lilliefors test (a modification of the Kolmogorov–Smirnov test) (Lilliefors 1967) have been used to test the first assumption (normality of residuals), while the independency of residuals was checked with the Durbin–Watson statistic (Durbin and Watson 1950). When both assumptions proved valid, a two-tailed Student’s t test, having a 95 % level of significance, was applied on the slope (b) of the regression line in order to examine any possible trend; the least-squares fitting process was used to fit the line in this case. When either of the two assumptions (normality, serial correlation) was violated, a nonparametric test, the Mann–Kendall trend test, as implemented by Helsel et al. (2006), was applied to estimate the slope and intercept of the trend. The Mann–Kendall trend test was selected because it runs equally well, or even better, whenever the actual situation conflicts with the abovementioned assumptions—even to a small extent (Hirsch et al. 1991).

The frequency in the violation of the assumptions linear regression requires (i.e., normality and serial correlation of residuals) in the crop–climate relationships, when statistically significant growing season climate trends were identified (26 cases), was considered first. The assumption of error independence was violated in four cases only (i.e., 15.4 %). On the other hand, the normality assumption was violated much more frequently: in 16 out of 26 cases (i.e., 61.5 %). In nine cases (37.5 %), both assumptions held; the slopes in these cases were estimated using only regression analysis, because the parametric approach is more powerful than the Mann–Kendall trend analysis and slope estimation, as it presents lower error variance (Hirsch et al. 1991). In four more cases with either of the two assumptions violated, the nonparametric trend analysis identified no trends, contrary to regression analysis. The slopes of linear trends estimated with linear regression and Mann–Kendall trend analyses for the remaining 13 cases of statistically significant growing season climate trends only are illustrated in Fig. 7. Despite the small overestimation and underestimation of negative and positive slopes by the Mann–Kendall trend analysis, respectively, both approaches presented a high agreement (as evidenced by the high R 2 and the low RMSE) on trend slopes, regardless of the violation in assumptions. For this reason, regression analysis will be used from this point onwards and will be compared with the CPA approach, taking also into consideration its advantageous properties.

Appendix 2

Fig. 7
figure7

The slopes of linear trends estimated with linear regression and Mann–Kendall analyses for the statistically significant growing season climate trends only (13 cases)

Fig. 8
figure8

Linear trends of the frequencies of days with Tx > 25 °C and Tx > 30 °C in the growing seasons of hard wheat in Thessaly (upper graph) and maize in C. Greece (lower graph), respectively

Fig. 9
figure9

The correlations between the climate parameters during growing season for wheat (hard (HW) and soft (SW)) in C. Macedonia and for barley (BR) in Thrace. Only the moderate or higher (r ≥ 0.5) correlations (Hinkle et al. 1994) are shown

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Mavromatis, T. Crop–climate relationships of cereals in Greece and the impacts of recent climate trends. Theor Appl Climatol 120, 417–432 (2015). https://doi.org/10.1007/s00704-014-1179-y

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Keywords

  • Diurnal Temperature Range
  • Yield Change
  • Crop Response
  • Soft Wheat
  • Change Point Analysis