RESEARCH PAPER
Impacts of regional climate change on barley yield and its geographical variation
in South Korea
1
Applied Plant Science, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea
2
Statistics, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea
3
Crop Production and Physiology Research, National Institute of Crop Science, 181 Hyeoksin-ro, Iseo-myeon, Wanju-Gun, Jeollabuk-do 55365, Republic of Korea
4
Department of Plant Science, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
Acceptance date: 2018-08-07
Publication date: 2019-02-14
Int. Agrophys. 2019, 33(1): 81–96
KEYWORDS
TOPICS
ABSTRACT
Geospatial variations in barley production due to climate change were evaluated for different geographic regions of South Korea over the next hundred years under the climate change scenarios of representative concentration pathways 4.5 and 8.5. We employed a geospatial crop simulation modelling strategy based on the CERES-barley model in the DSSAT crop model package version 4.6 to simulate grid-based geospatial variation in barley yield. An open field experiment and a temperature gradient field chamber experiment were performed to obtain model coefficients for South Korea and to assess the performance of CERES-barley under elevated temperature conditions. Projected barley yield data were further used to establish a new landscape classification system to provide agricultural policymakers with useful information on coping with climate change. Expected yields of four barley cultivars for the whole nation showed moderate increases under representative concentration pathways 4.5 and rapid increases under representative concentration pathways 8.5. More differences in yield were observed between different geospatial regions. Based on k-means clustering and the impact of climate change on barley yield, regional characteristics of the whole country could be classified into six categories. The geospatial crop simulation modelling could be extended to determine geospatial variations in staple crop productions due to other environmental scenarios of interest.
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