Ecosystem Assessment

Comprehensive Evaluation of the Suitability of Agricultural Land in Myanmar

  • 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China

Received date: 2018-04-02

  Revised date: 2018-07-10

  Online published: 2018-11-30

Supported by

The Key Research Program of the Chinese Academy of Sciences(ZDRW-ZS-2016-6-4); Strategic Priority Research Program of Chinese Academy of Sciences (XDA20040200); National Natural Science Foundation of China (41761144081;41671104).


Myanmar is a country with an economy based on agriculture. It has rich agricultural resources and great potential for development. The development of agriculture in Myanmar is becoming increasingly important to international food security. Assessments of agricultural land resources in Myanmar are the basis for the country’s agricultural development and for food security evaluations. In this paper we used the MaxEnt model to analyze the relationship between the suitability of land for agricultural reclamation and the main environmental variables in Myanmar, and then constructed a model to comprehensively evaluate the suitability of land for agriculture in Myanmar. The results show that: 1) the overall accuracy of the MaxEnt model is high (AUC>0.8), which means there is a high correlation between the database of selected environmental indicators and the true distribution of cultivated land in Myanmar. 2) Soil depth is the most important factor affecting the suitability of land for agriculture in Myanmar. When the thickness of soil layer is less than 100 cm, the suitability of land for agriculture is low. With respect to topographic conditions, slope is the main factor affecting suitability. When the slope is greater than 20 degrees, the suitability of land for agriculture is low. With respect to climate conditions, precipitation is the main influencing factor. There is a positive correlation between river network density and land suitability. 3) Currently, 400 000 km² of the land resources in Myanmar are suitable for agriculture, and of this amount 290 000 km² are highly suitable, accounting for nearly 40% of the country's land area. The highly suitable land is distributed mainly in Magway, Sagaing, Ayeyarwady and Yangon provinces. The provinces are also important grain production areas in Myanmar, and this serves to validate the effectiveness of the method used in this paper.

Cite this article

GU Changjun, ZHANG Yili, LIU Linshan, LI Lanhui, ZHANG Binghua . Comprehensive Evaluation of the Suitability of Agricultural Land in Myanmar[J]. Journal of Resources and Ecology, 2018 , 9(6) : 609 -621 . DOI: 10.5814/j.issn.1674-764x.2018.06.004


[1] Akıncı H, Özalp A Y, Turgut B.2013. Agricultural land use suitability analysis using GIS and AHP technique.Computers and Electronics in Agriculture, 97: 71-82.
[2] Bandyopadhyay S, Jaiswal R K., Hegde V al.2009. Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach.International Journal of Remote Sensing, 30(4): 879-895.
[3] Chen J, Ban Y, Li S.2014. China: Open access to Earth land-cover map.Nature, 514(7523): 434.
[4] Chen Y, Yu J, Khan S.2010. Spatial sensitivity analysis of multi- criteria weights in GIS-based land suitability evaluation.Environmental Modelling & Software, 25(12): 1582-1591.
[5] El Baroudy A A.2016. Mapping and evaluating land suitability using a GIS-based model.CATENA, 140: 96-104.
[6] Elith J, Phillips S J, Hastie T,et al.2011. A statistical explanation of MaxEnt for ecologists.Diversity and Distributions, 17(1): 43-57.
[7] Elsheikh R F A., Ahmad N, Shariff A R M, et al.2010. An agricultural investment map based on geographic information system and multi-criteria method.Journal of Applied Sciences, 10(15): 92-94.
[8] Gao X, Xie Y, Liu G, et al.2015. Effects of soil erosion on soybean yield as estimated by simulating gradually eroded soil profiles.Soil & Tillage Research, 145: 126-134.
[9] Grover M, Drossman D A, Oxentenko A S.2015. Integration of artificial neural network and geographical information system for intelligent assessment of land suitability for the cultivation of a selected crop.Neural Computing & Applications, 26(6):1-10.
[10] Hanley J A, Mcneil B J.1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve.Radiology, 143(1): 29-36.
[11] Hengl T, Mendes De Jesus J, Heuvelink G B M, et al.2017. SoilGrids250m: Global gridded soil information based on machine learning.Plos one, 12(2): e169748.
[12] Jaynes E .1957. Information Theory and Statistical Mechanics.Physical Review, 106(4): 620-630.
[13] Jessica B, Christine R, Karen V.2014. Developing macrohabitat models for bats in parks using maxent and testing them with data collected by citizen scientists.International Journal of Biodiversity & Conservation, 6(2), 171-183.
[14] Jobbagy E G, Jackson R.B.2001. The distribution of soil nutrients with depth: global patterns and the imprint of plants.Biogeochemistry, 53(1): 51-77.
[15] LI F, Cheng S K, YU H L, et al.2016. National geopolitical vulnerability: A Myanmar case and implications for China’s geopolitical policy.Progress in Geography, 35(6):737-746. (in Chinese).
[16] Liu Y, Jiao L, Liu al.2004. Research on land evaluation based on fuzzy neural network.Proceedings of SPIE-The International Society for Optical Engineering, 5232: 565-574.
[17] Lobell D B, Schlenker W, Costa-Roberts J.2011. Climate trends and global crop production since 1980.Science, 333: 616-620.
[18] Malczewski J.2006. GIS‐based multicriteria decision analysis: a survey of the literature.International Journal of Geographical Information Science, 20(7): 703-726.
[19] Mann K K, Schumann A W, Obreza T A.2011. Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data.Precision Agriculture, 12(4): 457-472.
[20] Montgomery B, Dragićević S, Dujmović J, et al.2016. A GIS-based Logic Scoring of Preference method for evaluation of land capability and suitability for agriculture.Computers and Electronics in Agriculture, 124: 340-353.
[21] Myanmar.2015. Myanmar Statistical Yearbook.
[22] Pereira J M C, LucienDuckstein.1993. A multiple criteria decision-making approach to GIS-based land suitability evaluation.International Journal of Geographical Information Systems, 7(5): 407-424.
[23] Peterson A T, Cohoon K P.1999. Sensitivity of distributional prediction algorithms to geographic data completeness.Ecological Modelling, 117(1): 159-164.
[24] Phillips S J, Dudík M.2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation.Ecography, 31(2): 161-175.
[25] Phillips S J, Anderson R P, Schapire R E.2006. Maximum entropy modeling of species geographic distributions.Ecological Modelling, 190(3-4): 231-259.
[26] Rabia A H.2012. Mapping Soil Erosion Risk Using Rusle, Gis and Remote Sensing Techniques.The 4th International Congress of ECSSS, EUROSOIL, Bari (p. 1082).
[27] Saaty T L.1977. A scaling method for priorities in hierarchical structures.Journal of Mathematical Psychology, 15(3): 234-281.
[28] Saaty T L.1980. The analytic hierarchy process: Planning, priority setting, resource Allocation. McGraw-Hill,London, 287.
[29] Saaty T.L.1990. An Exposition on the AHP in Reply to the Paper “Remarks on the Analytic Hierarchy Process”.Management Science, 36(3): 259-268.
[30] Steinitz C.1976. Hand-drawn overlays: their history and prospective uses.Landscape Architecture, 66:444-455.
[31] Troeh F R, Thompson L M.1993. Soils and Soil Fertility. New York: Oxford University Press
[32] Wang C, Myint S.2016. Environmental Concerns of Deforestation in Myanmar 2001-2010.Remote Sensing, 8(9): 728.
[33] Wang J, Chen Y, Shao X, et al.2012. Land-use changes and policy dimension driving forces in China: Present, trend and future.Land Use Policy, 29(4): 737-749.
[34] Weidong L.2015 Scientific understanding of the Belt and Road Initiative of China and related research themes.Progress in Geography, 34(5): 538-544. (in Chinese)
[35] Wheeler T, von Braun J.2013. Climate Change Impacts on Global Food Security.Science, 341(6145): 508-513.
[36] Xing D, Hao Z.2011. The principle of maximum entropy and its applications in ecology.Biodiversity Science, 19(3): 295-302.
[37] Zhang G L, Wu Y J, Zhan Y G.2010. Physical suitability evaluation of reserve resources of cultivated land in China based on SOTER. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(4): 1-8. (in Chinese)
[38] Zhao C, Piao S, Wang X, et al.2017. Plausible rice yield losses under future climate warming.Nature Plants, 3(1): 16202.
[39] Zolekar R B, Bhagat V S.2014. Use of IRS P6 LISS-IV data for land suitability analysis for cashew plantation in hilly zone.Asian Journal of Geoinformatics, 14(3): 23-35.
[40] Zolekar R B, Bhagat V S.2015. Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach.Computers and Electronics in Agriculture, 118: 300-321.