Browse > Article
http://dx.doi.org/10.5338/KJEA.2019.38.1.4

Classification of Agro-Climatic Zones of the State of Mato Grosso in Brazil  

Jung, Myung-Pyo (Climate Change & Agroecology Division, Department of Agricultural Environment, National Academy of Agricultural Sciences)
Park, Hye-Jin (Division of Atmospheric Environmental Science, College of Natural Sciences, Pusan National University)
Hur, Jina (Climate Change & Agroecology Division, Department of Agricultural Environment, National Academy of Agricultural Sciences)
Shim, Kyo-Moon (Climate Change & Agroecology Division, Department of Agricultural Environment, National Academy of Agricultural Sciences)
Kim, Yongseok (Climate Change & Agroecology Division, Department of Agricultural Environment, National Academy of Agricultural Sciences)
Kang, Kee-Kyung (Climate Change & Agroecology Division, Department of Agricultural Environment, National Academy of Agricultural Sciences)
Ahn, Joong-Bae (Division of Atmospheric Environmental Science, College of Natural Sciences, Pusan National University)
Publication Information
Korean Journal of Environmental Agriculture / v.38, no.1, 2019 , pp. 34-37 More about this Journal
Abstract
BACKGROUND: A region can be divided into agroclimatic zones based on homogeneity in weather variables that have greatest influence on crop growth and yield. The agro-climatic zone has been used to identify yield variability and limiting factors for crop growth. This study was conducted to classify agro-climatic zones in the state of Mato Grosso in Brazil for predicting crop productivity and assessing crop suitability etc. METHODS AND RESULTS: For agro-climatic zonation, monthly mean temperature, precipitation, and solar radiation data from Global Modeling and Assimilation Office (GMAO) of National Aeronautics and Space Administration (NASA, USA) between 1980 and 2010 were collected. Altitude and vegetation fraction of Brazil from Weather Research and Forecasting (WRF) were also used to classify them. The criteria of agro-climatic classification were temperature in the hottest month ($30^{\circ}C$), annual precipitation (600 mm and 1000 mm), and altitude (200 m and 500 m). The state of Mato Gross in Brazil was divided into 9 agro-climatic zones according to these criteria by using matrix classification method. CONCLUSION: The results could be useful as information for estimating agro-meteorological characteristics and predicting crop development and crop yield in the state of Mato Grosso in Brazil.
Keywords
Agro-climatic zone; Mato Grosso; Precipitation; Solar radiation; Temperature;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Barnes, S. L. (1964). A technique for maximizing details in numerical weather-map analysis. Journal of Applied Meteorology, 3(4), 396-409.   DOI
2 Brown, J. C., Kastens, J. H., Coutinho, A. C., de Casto Victoria, D., & Bishop, C. R. (2013). Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sensing of Environment, 130, 39-50.   DOI
3 Choi, D. H., & Yoon, S. H. (1989). Agroclimatic zone and characters of the area subject to climatic disaster in Korea. Journal of Korean Society of Crop Science, 34(2), 13-33.
4 Jung, M. P., Hur, J. N., Park, H. J., Shim, K. M., & Ahn, J. B. (2015). Classification of agro-climatic zones in Northeast district of China. Korean Journal of Agricultural Forest Meteorology, 17(2), 102-107.   DOI
5 Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., & Foley, J. A. (2012). Closing yield gaps nutrient and water management. Nature, 490, 254-257.   DOI
6 Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., & Solomon, A. M., (1992). Special paper: a global biome model based on plant physiology and dominance, soil properties and climate. Journal of Biogeography, 19(2), 117-134.   DOI
7 Reddy, M. C., & Madiwalar, S. L. (2014). Productivity assessment and economic analysis of teak plantations in different agroclimatic zones of Karnataka. Indian Forester, 140(3), 287-290.
8 Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G. K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., & Woollen, J. (2011). MERRA: NASA's modern-era retrospective analysis for research and applications. Journal of Climate, 24(14), 3624-3648.   DOI
9 Shim, K. M., Kim, Y. S., Jung, M. P., Kim, S. C., Min, S. H., & So, K. H. (2013). Agro-climatic zonal characteristics of the frequency of abnormal air temperature occurrence in South Korea. Climate Change Research, 4, 189-199.
10 van Wart, J., van Bussel, L. G. J ., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H.B., Gerber, J., Mueller, N. D., Claessens, L. van Ittersum, M. K., & Cassman, K. G. (2013). Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research, 143, 44-55.   DOI