Research on World Agricultural Economy

Assessing Land Use and Land Cover (LULC) Change and Factors Affecting Agricultural Land: Case Study in Battambang Province, Cambodia

Taingaun Sourn

1. A.K Assessment Co., Ltd, Phnom Penh, 120604, Cambodia
2. Faculty of Land Management and Land Administration, Royal University of Agriculture, Phnom Penh, 120501, Cambodia

Sophak Pok

Faculty of Land Management and Land Administration, Royal University of Agriculture, Phnom Penh, 120501, Cambodia

Phanith Chou

Faculty of Development Studies, Royal University of Phnom Penh, Phnom Penh, 12156, Cambodia

Nareth Nut

Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture, Phnom Penh, 120501, Cambodia

Dyna Theng

Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture, Phnom Penh, 120501, Cambodia

Lyhour Hin

Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture, Phnom Penh, 120501, Cambodia

DOI: https://doi.org/10.36956/rwae.v4i4.925

Received: 15 August 2023; Received in revised form: 13 October 2023; Accepted: 20 October 2023; Published: 3 November 2023

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Abstract

This study analyzed land use and land cover (LULC) change from 1998 to 2018 in Battambang, Cambodia, and determined factors and constraints affecting agricultural production. Landsat satellite images in 1998, 2008, and 2018 were used to identify the changes in LULC. In combination, a social survey was conducted in August 2021 using purposive sampling, selecting a total sample of 200 from two wealth classes: the poor (65) and the better off (135) based on the Cambodia poverty assessment by the World Bank, from uplands to lowlands of Battambang Province, Cambodia. Household characteristics, farm size, and constraints were compared between the classes. T-tests, the analysis of variance (ANOVA), and Likert scale analysis were adopted using the R Program and RStudio, while Pearson's correlation test was used to determine the factors affecting agricultural land. The results show that between 1998 and 2018, the forest cover decreased by 79%. In contrast, agricultural land expansion was the highest (54%). The average household size and age of the respondents were 5.0 persons/household and 50.1 years, respectively. Of all the interviewees, about 80% attended no higher than primary school. The total farm size of the better-off (7.0 ha/household) was larger than that of the poor (5.2 ha/household). The population growth, machinery use, and improved infrastructure were found to be positive and strongly related to agricultural land use. The highest constraints of the poor and the better-off households were the same: chemical fertilizer use. Then, drought and flooding were also challenges for all. In terms of land, credit, and labor, they were not the main constraints. Thus, it is recommended that the involvement of interdisciplinary stakeholders and policy frameworks is really important from both biophysical and social perspectives.

Keywords: Agricultural production; Chemical fertilizer; Drought; Flooding

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References

[1] Desalegn, T., Cruz, F., Kindu, M., et al., 2014. Land-use/land-cover (LULC) change and socioeconomic conditions of local community in the central highlands of Ethiopia. International Journal of Sustainable Development & World Ecology. 21(5), 406-413. DOI: https://doi.org/10.1080/13504509.2014.961181

[2] Field, C.B., Barros, V.R., 2014. Climate change 2014—Impacts, adaptation and vulnerability: Regional aspects. Cambridge University Press: Cambridge.

[3] Manandhar, R., Odeh, I.O., Ancev, T., 2009. Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sensing. 1(3), 330-344. DOI: https://doi.org/10.3390/rs1030330

[4] Montalván-Burbano, N., Velastegui-Montoya, A., Gurumendi-Noriega, M., et al., 2021. Worldwide research on land use and land cover in the amazon region. Sustainability. 13(11), 6039. DOI: https://doi.org/10.3390/su13116039

[5] Shi, Z., Crowell, S., Luo, Y., et al., 2018. Model structures amplify uncertainty in predicted soil carbon responses to climate change. Nature Communications. 9(1), 2171. DOI: https://doi.org/10.1038/s41467-018-04526-9

[6] Xu, X., Xie, Y., Qi, K., et al., 2018. Detecting the response of bird communities and biodiversity to habitat loss and fragmentation due to urbanization. Science of the Total Environment. 624, 1561-1576. DOI: https://doi.org/10.1016/j.scitotenv.2017.12.143

[7] Llerena-Montoya, S., Velastegui-Montoya, A., Zhirzhan-Azanza, B., et al., 2021. Multitemporal analysis of land use and land cover within an oil block in the Ecuadorian Amazon. ISPRS International Journal of Geo-Information. 10(3), 191. DOI: https://doi.org/10.3390/ijgi10030191

[8] Perz, S.G., Qiu, Y., Xia, Y., et al., 2013. Trans-boundary infrastructure and land cover change: Highway paving and community-level deforestation in a tri-national frontier in the Amazon. Land Use Policy. 34, 27-41. DOI: https://doi.org/10.1016/j.landusepol.2013.01.009

[9] Sourn, T., Pok, S., Chou, P., et al., 2021. Evaluation of land use and land cover change and its drivers in Battambang Province, Cambodia from 1998 to 2018. Sustainability. 13(20), 11170. DOI: https://doi.org/10.3390/su132011170

[10] Toure, S.I., Stow, D.A., Shih, H.C., et al., 2018. Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sensing of Environment. 210, 259-268. DOI: https://doi.org/10.1016/j.rse.2018.03.023

[11] Velastegui-Montoya, A., Lima, A.D., Adami, M., 2020. Multitemporal analysis of deforestation in response to the construction of the Tucuruí Dam. ISPRS International Journal of Geo-Information. 9(10), 583. DOI: https://doi.org/10.3390/ijgi9100583

[12] Mwavu, E.N., Witkowski, E.T.F., 2008. Land-use and cover changes (1988-2002) around Budongo forest reserve, NW Uganda: Implications for forest and woodland sustainability. Land Degradation & Development. 19(6), 606-622. DOI: https://doi.org/10.1002/ldr.869

[13] Agriculture and Food Security [Internet]. USAID; 2021. Available from: https://www.usaid.gov/agriculture-and-food-security

[14] Lambin, E.F., Meyfroidt, P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences. 108(9), 3465-3472. DOI: https://doi.org/10.1073/pnas.1100480108

[15] Barona, E., Ramankutty, N., Hyman, G., et al., 2010. The role of pasture and soybean in deforestation of the Brazilian Amazon. Environmental Research Letters. 5(2), 024002. DOI: https://doi.org/10.1088/1748-9326/5/2/024002

[16] Bonilla-Moheno, M., Aide, T.M., 2020. Beyond deforestation: Land cover transitions in Mexico. Agricultural Systems. 178, 102734. DOI: https://doi.org/10.1016/j.agsy.2019.102734

[17] Le Polain de Waroux, Y., Garrett, R.D., Heilmayr, R., et al., 2016. Land-use policies and corporate investments in agriculture in the Gran Chaco and Chiquitano. Proceedings of the National Academy of Sciences. 113(15), 4021-4026. DOI: https://doi.org/10.1073/pnas.1602646113

[18] Nut, N., Mihara, M., Jeong, J., et al., 2021. Land use and land cover changes and its impact on soil erosion in Stung Sangkae catchment of Cambodia. Sustainability. 13(16), 9276. DOI: https://doi.org/10.3390/su13169276

[19] Kong, R., Diepart, J.C., Castella, J.C., et al., 2019. Understanding the drivers of deforestation and agricultural transformations in the Northwestern uplands of Cambodia. Applied Geography. 102, 84-98. DOI: https://doi.org/10.1016/j.apgeog.2018.12.006

[20] Annual Research Report for 2015 [Internet]. Cambodian Agricultural Research and Development Institute; 2016. Available from: https://www.cardi.org/cardi-publications-2/annual-reports-2/

[21] Pheap, S., Lefevre, C., Thoumazeau, A., et al., 2019. Multi-functional assessment of soil health under Conservation Agriculture in Cambodia. Soil and Tillage Research. 194, 104349. DOI: https://doi.org/10.1016/j.still.2019.104349

[22] New Casava Policy to Transform Production of Crucial Crop [Internet]. UNDP; 2021. Available from: https://www.undp.org/cambodia/press-releases/new-cassava-policy-transform-production-crucial-crop

[23] Lambin, E.F., Meyfroidt, P., 2010. Land use transitions: Socio-ecological feedback versus socio-economic change. Land Use Policy. 27(2), 108-118. DOI: https://doi.org/10.1016/j.landusepol.2009.09.003

[24] Angelsen, A., Kaimowitz, D., 2001. Agricultural technologies and tropical deforestation. CAB International: Wallingford, UK.

[25] Trisurat, Y., Shirakawa, H., Johnston, J.M., 2019. Land-use/land-cover change from socio-economic drivers and their impact on biodiversity in Nan Province, Thailand. Sustainability. 11(3), 649. DOI: https://doi.org/10.3390/su11030649

[26] Teck, V., Poortinga, A., Riano, C., et al., 2023. Land use and land cover change implications on agriculture and natural resource management of Koah Nheaek, Mondulkiri province, Cambodia. Remote Sensing Applications: Society and Environment. 29, 100895. DOI: https://doi.org/10.1016/j.rsase.2022.100895

[27] Baig, M.F., Mustafa, M.R.U., Baig, I., et al., 2022. Assessment of land use land cover changes and future predictions using CA-ANN simulation for selangor, Malaysia. Water. 14(3), 402. DOI: https://doi.org/10.3390/w14030402

[28] Santiphop, T., Shrestha, R.P., Hazarika, M.K., 2012. An analysis of factors affecting agricultural land use patterns and livelihood strategies of farm households in Kanchanaburi Province, Thailand. Journal of Land Use Science. 7(3), 331-348.

[29] Tran, H., Tran, T., Kervyn, M., 2015. Dynamics of land cover/land use changes in the Mekong Delta, 1973-2011: A remote sensing analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam. Remote Sensing. 7(3), 2899-2925. DOI: https://doi.org/10.3390/rs70302899

[30] Yüksel, A., Akay, A.E., Gundogan, R., 2008. Using ASTER imagery in land use/cover classification of eastern Mediterranean landscapes according to CORINE land cover project. Sensors. 8(2), 1237-1251. DOI: https://doi.org/10.3390/s8021287

[31] Greiner, C., Vehrs, H.P., Bollig, M., 2021. Land-use and land-cover changes in pastoral drylands: Long-term dynamics, economic change, and shifting socioecological frontiers in Baringo, Kenya. Human Ecology. 49, 565-577. DOI: https://doi.org/10.1007/s10745-021-00263-8

[32] Guyer, J.I., Lambin, E.F., Cliggett, L., et al., 2007. Temporal heterogeneity in the study of African land use: Interdisciplinary collaboration between anthropology, human geography and remote sensing. Human Ecology. 35, 3-17.

[33] Sertel, E., Findik, N., Kaya, S., et al., 2008. Assessment of landscape changes in the Kizilirmak Delta, Turkey, using remotely sensed data and GIS. Environmental Engineering Science. 25(3), 353-362. DOI: https://doi.org/10.1089/ees.2006.0149

[34] Toh, F.A., Angwafo, T.E., Ndam, L.M., et al., 2018. The socio-economic impact of land use and land cover change on the inhabitants of Mount Bambouto Caldera of the Western Highlands of Cameroon. Advances in Remote Sensing. 7(1), 25. DOI: https://doi.org/10.4236/ars.2018.71003

[35] Gashaw, T., Tulu, T., Argaw, M., et al., 2017. Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Environmental Systems Research. 6(1), 1-15. DOI: https://doi.org/10.1186/s40068-017-0094-5

[36] Sandewall, M., Ohlsson, B., Sawathvong, S., 2001. Assessment of historical land-use changes for purposes of strategic planning: A case study in Laos. Ambio. 30, 55-61. DOI: https://doi.org/10.1579/0044-7447-30.1.55

[37] Maro, P., 2011. Environmental change in Lesotho: An analysis of the causes and consequences of land-use change in the Lowland Region. Springer International Publishing: Dordrecht.

[38] Klintenberg, P., Seely, M., Christiansson, C., 2007. Local and national perceptions of environmental change in central Northern Namibia: Do they correspond?. Journal of Arid Environments. 69(3), 506-525. DOI: https://doi.org/10.1016/j.jaridenv.2006.10.015

[39] Mertens, B., Sunderlin, W.D., Ndoye, O., et al., 2000. Impact of macroeconomic change on deforestation in South Cameroon: Integration of household survey and remotely-sensed data. World Development. 28(6), 983-999. DOI: https://doi.org/10.1016/S0305-750X(00)00007-3

[40] Sourn, T., Pok, S., Chou, P., et al., 2022. Assessment of land use and land cover changes on soil erosion using remote sensing, GIS and RUSLE model: A case study of battambang province, Cambodia. Sustainability. 14(7), 4066. DOI: https://doi.org/10.3390/su14074066

[41] Global Digital Elevation Map Announcement [Internet]. NASA and METI ASTER; 2011. Available from: https://asterweb.jpl.nasa.gov/gdem.asp

[42] MoWRM, 2019. Climate Data. Ministry of Water Resource and Meteorology. Phnom Penh, Cambodia.

[43] SoilGrids—Global Gridded Soil Information [Internet]. ISRIC; 2020. Available from: https://www.isric.org/explore/soilgrids

[44] BBDAFF, 2019. Agricultural Report in 2019; Battambang Department of Agricultural Forestry and Fisheries: Battambang, Cambodia.

[45] Geographic Department of Cambodia, 2015. Administrative Boundary. Geographic Department of Cambodia: Phnom Penh, Cambodia.

[46] The Study on the Establishment of GIS Base Data for the Kingdom of Cambodia [Internet]. Japan International Cooperation Agency; 2002. Available from: https://openjicareport.jica.go.jp/pdf/11730835_01.PDF

[47] Congalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. 37(1), 35-46. DOI: https://doi.org/10.1016/0034-4257(91)90048-B

[48] Congalton, R.G., Green, K., 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC Press: Boca Raton.

[49] Patil, M.B., Desai, C.G., Umrikar, B.N., 2012. Image classification tool for land use/land cover analysis: A comparative study of maximum likelihood and minimum distance method. International Journal of Geology, Earth and Environmental Sciences. 2(3), 189-196.

[50] Smith, J.H., Stehman, S.V., Wickham, J.D., et al., 2003. Effects of landscape characteristics on land-cover class accuracy. Remote Sensing of Environment. 84(3), 342-349. DOI: https://doi.org/10.1016/S0034-4257(02)00126-8

[51] Foody, G.M., 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment. 80(1), 185-201. DOI: https://doi.org/10.1016/S0034-4257(01)00295-4

[52] Rwanga, S.S., Ndambuki, J.M., 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences. 8(4), 611. DOI: https://doi.org/10.4236/ijg.2017.84033

[53] Ministry of Agriculture, Forestry, and Fisheries, 2019. Cambodian Strategy Plan on Agricultural Sector Development 2019-2023. MAFF: Phnom Penh, Cambodia. Available online: https://web.maff.gov.kh/books/zZI8dVG3pQ?lang=kh (accessed on 30th November 2021)

[54] Sisodia, P.S., Tiwari, V., Kumar, A. (editors), 2014. Analysis of supervised maximum likelihood classification for remote sensing image. International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014); 2014 May 9-11; Jaipur, India. New York: IEEE. p. 1-4.

[55] Gomarasca, M.A., 2010. Basics of geomatics. Applied Geomatics. 2, 137-146. DOI: https://doi.org/10.1007/s12518-010-0029-6

[56] Wickham, H., Chang, W., Henry, L., et al., 2023. Package ‘ggplot2’: Create Elegant Data Visualizations Using the Grammar of Graphics [Internet]. Available from: https://ggplot2.tidyverse.org

[57] Akoglu, H., 2018. User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine. 18(3), 91-93. DOI: https://doi.org/10.1016/j.tjem.2018.08.001

[58] Kassambara, A., 2023. Package ‘rstatix’: Pipe-Friendly Framework for Basic Statistical Tests [Internet]. Available from: https://cran.r-project.org/web/packages/rstatix/rstatix.pdf

[59] Fielding, W.J., Arnold, J.G., Oyejola, B.A., 2001. Ranks Are Statistics: Some Advice for Their Interpretation [Internet]. Available from: https://www.iied.org/sites/default/files/pdfs/migrate/G01797.pdf

[60] Lee, S., Lee, D.K., 2018. What is the proper way to apply the multiple comparison test?. Korean Journal of Anesthesiology. 71(5), 353-360.

[61] Midway, S., Robertson, M., Flinn, S., et al., 2020. Comparing multiple comparisons: Practical guidance for choosing the best multiple comparisons test. PeerJ. 8, e10387. DOI: https://doi.org/10.7717/peerj.10387

[62] de Mendiburu, F., 2023. Package ‘agricolae’: Statistical Procedures for Agricultural Research [Internet]. Available from: https://cran.r-project.org/web/packages/agricolae/agricolae.pdf

[63] The World Bank, 2022. GDP per Capita (Current US$) - Cambodia: World Bank National Accounts Data, and OECD National Accounts Data Files. Available from: https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.CD?downloadformat=excel

[64] World Economic Outlook (WEO) Database, October 2022: Cambodia—Gross Domestic Product per Capita, Current Prices (Purchasing Power Parity; International Dollars). IMF; 2021. Available from: https://www.imf.org/en/Publications/WEO/weo-database/2022/October

[65] Cambodian Agriculture in Transition: Opportunities and Risks [Internet]. The World Bank; 2021. Available from: https://www.worldbank.org/en/country/cambodia/publication/cambodian-agriculture-in-transition-opportunities-and-risks

[66] Mottet, A., Ladet, S., Coqué, N., et al., 2006. Agricultural land-use change and its drivers in mountain landscapes: A case study in the Pyrenees. Agriculture, Ecosystems & Environment. 114(2-4), 296-310. DOI: https://doi.org/10.1016/j.agee.2005.11.017

[67] Joshi, A., Kale, S., Chandel, S., et al., 2015. Likert scale: Explored and explained. British Journal of Applied Science & Technology. 7(4), 396-403. DOI: https://doi.org/10.9734/BJAST/2015/14975

[68] Lançon, J., Wery, J., Rapidel, B., et al., 2007. An improved methodology for integrated crop management systems. Agronomy for Sustainable Development. 27, 101-110. DOI: https://doi.org/10.1051/agro:2006037

[69] Braimoh, A.K., Vlek, P.L., Stein, A., 2004. Land evaluation for maize based on fuzzy set and interpolation. Environmental Management. 33, 226-238. DOI: https://doi.org/10.1007/s00267-003-0171-6

[70] Govindaraj, G., Jyoti, K., Anamika, M., et al., 2010. Problem identification and prioritisation of research options: The PRA and AHP approach. Journal of Rural Development (Hyderabad). 29(4), 449-455.

[71] Kraaijvanger, R., Sonneveld, M., Almekinders, C., et al., 2015. Comparison of methods to identify crop productivity constraints in developing countries. A review. Agronomy for Sustainable Development. 35, 625-637. DOI: https://doi.org/10.1007/s13593-014-0254-1

[72] Waddington, S.R., Li, X., Dixon, J., et al., 2010. Getting the focus right: Production constraints for six major food crops in Asian and African farming systems. Food Security. 2, 27-48. DOI: https://doi.org/10.1007/s12571-010-0053-8

[73] General Population Census of Kingdom of Cambodia [Internet]. National Institute of Statistics; 2019. Available from: https://www.nis.gov.kh/index.php/en/15-gpc/79-press-release-of-the-2019-cambodia-general-population-census

[74] Cambodia Agriculture, Natural Resource, and Rural Development Sector Assessment, Strategy and Road Map [Internet]. ADB; 2021. Available from: https://www.adb.org/sites/default/files/publication/718806/cambodia-agriculture-rural-development-road-map.pdf

[75] Tunon, M., Rim, K., 2013. Cross-Border Labour Migration in Cambodia: Considerations for the National Employment Policy [Internet]. Available from: https://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/documents/publication/wcms_228484.pdf

[76] Yen, B.T., Visser, S.M., Hoanh, C.T., et al., 2013. Constraints on agricultural production in the northern uplands of Vietnam. Mountain Research and Development. 33(4), 404-415. DOI: https://doi.org/10.1659/MRD-JOURNAL-D-13-00015.1

[77] Floods in Cambodia [Internet]. HRF; 2021. Available from: https://reliefweb.int/report/cambodia/floods-cambodia-situation-report-no-1-humanitarian-response-forum-29-september-2021