Simulated impacts of rainfall extremes on yield responses of various barley varieties in a temperate region
Chang Yong Yoon 1  
,   Sojung Kim 2  
,   Kyu Nam An 1  
,   Sumin Kim 3  
More details
Hide details
Crop Research Division, Jeollanamdo Agricultural Research and Extension Services, Naju-si, Jeollanam-do 58123, Korea
Department of Industrial and Systems Engineering, Dongguk University-Seoul, Seoul 04620, Korea
Department of Environmental Horticulture and Landscape Architecture, College of Life Science and Biotechnology, Dankook University, Cheonan-si, Chungnam 31116, Korea
Sumin Kim   

Department of Environmental Horticulture & Landscape Architecture, College of Life Science & Biotechnology, Dankook University, Korea (South)
Final revision date: 2021-03-05
Acceptance date: 2021-03-08
Publication date: 2021-04-15
Int. Agrophys. 2021, 35(2): 119–129
As population rises, more people need to be fed. With increasing income, the potential exists for increases in the demand for cereals (i.e., barley). Since barley has a high level of tolerance to environmental stressors, this crop has been recommended as a potential crop for food security in marginal environments. In this study, a crop growth Agricultural Land Management Alternatives with Numerical Assessment Criteria model, was parameterized and used to simulate the yields of two barley types grown in a temperate environment at a latitude of 35°N. In order to apply this crop model to barley, 19 years of field data were used to model calibration and validation. As a result, the ALMANAC model accurately simulated yields for both barley types. The validated model was used to predict yields under three diverse seasonal rainfall scenarios associated with different patterns of the Central Pacific El Niño influence. According to the simulation results, excessively high seasonal rainfall decreased barley yields. Crop price and annual revenue of the two barley types were also evaluated using a non-linear regression model. For the malt type, the food price was higher with a higher rainfall, while naked barley had a higher revenue under the conditions of a lower rainfall.
Baik B.K., Newman C.W., and Newman R.K., 2011. In Barley: Production, Improvement, and Uses. (Ed. S.E. Ullrich). WileyBlackwell, Oxford, UK.
Baik B.K. and Ullrich S.E., 2008. Barley for food: characteristics, improvement, and renewed interest. J. Cereal Sci., 48(2), 233-242.
Barboza G., Gavinelli L., Pede V., Mazzucchelli A., and Di Gregorio A., 2020. A contribution to the empirics of food price behavior: the case of rice price dynamics in Italy. BFJ 2020,
Behrman K.D., Keitt T.H., and Kiniry J.R., 2014. Modeling differential growth in switchgrass cultivars across the Central and Southern Great Plains. BioEnergy Res., 7, 1165–1173.
Cammarano D., Ceccarelli S., Grando S., Romagosa I., Benbelkacem A., Akar T., Al-Yassin A., Pecchioni N., Francia E., and Ronga D., 2019. The impact of climate change on barley yield in the Mediterranean basin. Eur. J. Agron., 106, 1-11.
Carter C.A., 1994. The economics of a single North American barley market: A reply. CJAE, 42, 413-419.
Choi J.S., Kim H., Jung M.H., Hong S., and Song J., 2010. Consumption of barley β‐glucan ameliorates fatty liver and insulin resistance in mice fed a high‐fat diet. Mol. Nutr. Food Res., 54(7), 1004-1013.
Debaeke P., Caussanel J.P., Kiniry J.R., Kafiz B., and Mondragon G., 1997. Modelling crop:weed interactions in wheat with ALMANAC. Weed Res., 37, 325-341.
Druille M., Williams A.S., Torrecillas M., Kim S., Meki N., and Kiniry J.R., 2020. Modeling climate warning impacts on grain and forage sorghum yields in Argentina. Agronomy, 10, 964.
Eissa M.A. and Al Refai H., 2019. Modelling the symmetric and asymmetric relationships between oil prices and those of corn, barley, and rapeseed oil. Resour. Policy, 64, 101511.
FAO, Food and Agriculture Organization of United Nations, 2020. FAOSTAT. FAO Rome, Italy. Available online: (Accessed on October 10th 2020).
Grassini P., van Bussel L.G.J., Wart J.V., Wolf J., Classens L., Yang H., Boogaard H., de Groot H., van Ittersum M.K., and Cassman K.G., 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res., 177, 49-63.
Khush G.S., 1987. Rice breeding: past, present and future. J. Genetics, 66, 195-216.
Kim J.S., Son C.Y., Moon Y.I., and Lee J.H., 2017. Seasonal rainfall variability in Korea within the context of different evolution patterns of the central Pacific El Nino. J. Water Clim. Change, 8, 412-422.
Kim S., Kim S., Cho J., Park S., Jarrín Perez F. X., and Kiniry J.R., 2020. Simulated biomass, climate change impacts, and nitrogen management to achieve switchgrass biofuel production at diverse sites in US. Agronomy, 10(4), 503.
Kim S., Kiniry J.R., Williams A.S., Meki N., Gaston L., Brakie M., Shadow A., Fritschi F.B., and Wu Y., 2017. Adaptation of C4 bioenergy crop species to various environments within the southern great plains of USA. Sustainability, 9, 89.
Kiniry J.R., Major D.J., Izaurralde R.C., Williams J.R., Gassman P.W., Morrison M., Bergentine R., and Zentner R.P., 1995. EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Can. J. Plant Sci., 75, 679-688.
KMA (Korea Meteorological Administration), 2014. Korean Climate Change Assessment Report 2014: Climate Change Impacts and Adaptation. Incheon, Republic of Korea: National Institute of Environmental Research.
KMA, Korea Meteorological Administration, 2020. Weather pattern in Korea. (accessed on September 8th 2020).
Ko J., Ng C.T., Jeong S., Kim J.H., Lee B., and Kim H.Y., 2019. Impacts of regional climate change on barley yield and its geographical variation in South Korea. Int. Agrophys., 33, 81-96.
Lee H. and Cho S., 2016. Hitejinro Sustaining Success in the Korean Beer Market. IJBMR, 6(3),11-22.
Lee M., Kim K., Kim Y., Choi J., Park K., and Kim H., 2013. Quality characteristics and antioxidant activity of noodle containing whole flour of Korean hull-less barley cultivars. Korean J. Crop Sci., 58(4), 459-467.
Monsi M. and Saeki T., 1953. Uber Den Lichtfaktor in Den Pflanzen-Gesellschaften Und Seine Bedeutung Fur DieSto_produktion. Jpn. J. Bot.,14, 22-52.
Munns R. and Tester M., 2008. Mechanisms of salinity tolerance. Ann. Rev. Plant Biol., 59, 651-681.
Naju-si, 2020. Naju agriculture. (accessed on November 12th, 2020).
Nevo E., Fu Y.B., Pavlicek T., Khalifa S., Tavasi M., and Beiles A., 2012. Evolution of wild cereals during 28 years of global warming in Israel. PNAS, 109, 3412-3415.
Newman C.W. and Newman R.K., 2006. A brief history of barley foods. Cereal Foods World, 51(1), 4-7.
Newton A.C., Flavell A.J., George T.S. et al., 2011. Crops that feed the world 4. Barley: a resilient crop? Strengths and weaknesses in the context of food security. Food Sec., 3, 141.
Statistics Korea, 2020a. KOSIS: Production of Barley. (accessed on Oct. 11th 2020).
Statistics Korea, 2020b. Production of barley, garlic, and onions in 2020. Online available at file:///C:/Users/ksumi/Downloads/pbgo2020.pdf (accessed on November 12th, 2020).
Takác Ĵ. and Šiška B., 2009. Climate change impact on spring barley and winter wheat yields on Danubian Lowland. In: Bioclimatology and Natural Hazards (Eds K. Střelcová et al.). Springer, Dordrecht, 283-288.
USDA-FAS, USDA Foreign Agricultural Service, 2019. Barley market brief in Korea. (accessed on September 8th 2019).
Yi Y.S., Kang H.G., and Jun H., 2016. Build to last, discount, or defer?: Examining lotte’s entry into the beer industry. Asian Case Res. J., 20(02), 429-454.