Spatio-temporal trends of mean air temperature during 1961–2009 and impacts on crop (maize) yields in the most important agricultural region of Romania

Abstract

Climate change analysis is essential, considering the numerous economic and ecological implications of this critical global environmental issue. This paper analyzes the spatial and temporal trends of mean air temperature in Romania's most important agricultural area, the south and south-eastern region, between 1961 and 2009. In this respect, multiannual (the entire period) and multidecadal ) trends were analyzed using the Mann-Kendall test and Sen's slope method at 23 weather stations, annually, seasonally and for the growing season of the region's main agricultural crops (maize and wheat). Multiannually, the results showed statistically significant temperature increases, on all temporal scales (maximum rate of 0.06°C/year recorded in summer, equivalent to a net temperature rise of 2.82°C), except for the autumn season (cooling without statistical significance). Multidecadally, the 1961-1990 period is marked by a general cooling, especially in autumn (maximum values of -0.07°C/year or over 2°C net cooling). In the 1971-2000 and 1981-2009 periods, a general warming was observed (maximum in summer for both multidecades, when positive rates peaked at 0.09°C/year, or 2.5-3°C net warming), but the warming of the last three decades is the most prominent in terms of spatial average magnitude and trend significance. Upon analysis of the impact of climate warming on agricultural yields (maize) through linear regression, in the 1991-2000 decade, considered as case study, it was found that in 32 % of the total analyzed area there are evident relationships between the two variables (p value \0.05). In this case, a dependency of 33-50 % (40 %, on average) of maize to climate was found, and a sensitivity (loss) ranging between 0.9 and 1.5 t/ha/year (1.2 t/ha/year, on average) for a 1°C temperature rise. At the same time, significant losses (of up to 1.7 t/ha/year) of maize for a 1°C temperature rise were identified in 51 % of the area, but with little p value significance (between 0.05 and 0.1). It is however necessary to analyse the agro-climatic results cautiously, considering that only one decade of climate-agriculture relationship was studied. The results can be useful first and foremost for mitigating the climate change impact on agricultural systems, by prioritizing future adaptation strategies enforced by policy makers.

The impact of global warming on lake surface water temperature in Poland - the application of empirical-statistical downscaling, 1971-2100

“…Although there are numerous processes impacting LSWT, a rise in near-surface air temperature is considered a key factor behind the imbalance in heat fluxes (Livingstone and Padisak, 2007;Piccolroaz and Toffolon, 2013). The influence of a recently observed increase in near-surface air temperature is clearly seen in terms of different components of the natural environment, and not surprisingly, it is also clearly visible in both global and local water cycles (IPCC, 2014;Bazrafshan, 2017;Prăvălie et al, 2017). Enhanced lakeair temperature gradients (Desai et al, 2009) due to increasing instability of planetary (atmospheric) boundary layer (Pucik et al, 2017) are likely to result in enhanced heat loss, gas fluxes, and evaporation from lakes (Woolway et al, 2017a), that may also have further consequences e.g.…”

Hotspots for warm and dry summers in eastern Europe, with a focus on Romania

“…There are the north and north-western part of the country and the Dobrogea region. Dobrogea region is a region which has been subjected to a significant increase in the mean air temperature and a significant decrease in the summer precipitation (Chelcea et al, 2015;Prăvălie et al, 2017). Overall, there is a significant increase, of ~0.2HWs/decade in June, over most parts of the country, except some small regions in the north-eastern part (Figure 4a).…”

SPATIO‐TEMPORAL VARIATIONS OF SEVEN WEATHER VARIABLES IN IRAN: APPLICATION OF CRU TS AND GPCC DATA SETS†

“…This confidence level has been used in numerous studies of agro-climatic trends (e.g. Ageena et al, 2014;Appiotti et al, 2014;Fathian et al, 2014;Moore and Lobell, 2015;Zheng et al, 2015;Prăvălie et al, 2017Prăvălie et al, , 2019b and was chosen to avoid discarding grid cells where trends were detected with slightly lower confidence and therefore produce continuous, clear spatial patterns. In order to make PET and PRE values comparable, PET (in mm day À1 ) was converted to total PET by multiplying the raw values by the number of days in the corresponding calendar months.…”