Climatically driven yield variability of major crops in Khakassia (South Siberia)
URI (for links/citations):https://link.springer.com/article/10.1007/s00484-017-1496-9
Elena A. Babushkina
Liliana V. Belokopytova
Dina F. Zhirnova
Santosh K. Shah
Tatiana V. Kostyakova
Хакасский технический институт — филиал СФУ
Научно-образовательная лаборатория "Дендроэкология и экологический мониторинг"
Journal Name:International Journal of Biometeorology
Journal Quartile in Scopus:Q1
Journal Quartile in Web of Science:Q2
Bibliographic Citation:Elena A. Babushkina. Climatically driven yield variability of major crops in Khakassia (South Siberia) [Текст] / Elena A. Babushkina, Liliana V. Belokopytova, Dina F. Zhirnova, Santosh K. Shah, Tatiana V. Kostyakova // International Journal of Biometeorology. — 2018. — Т. 62 (№ 6). — С. 939-948
We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: 1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; 2) the Central Zone, where crops yield depends mainly on temperatures; and 3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient and presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.