Characterization of drivers of agricultural land use change

Keywords: Agricultural land use, Principal Component Analysis, Spatial data


Major factors driving agricultural land use in Malaysia were characterized with Principal Component Analysis (PCA). Discrete variables assumed to drive agricultural land use were converted into spatial data. Vector data subsequently obtained from these conversions were later rasterized before being disaggregated. ASCII data of each of the disaggregated was derived using ArcGIS 10.3.1. A MatLab program was thereafter used to convert the ASCII data into vector column where systematic sampling was performed after Moran I test to select the samples for PCA analysis in SPSS/IBM version 23. The result of the PCA analysis finally aggregated variables driving agricultural land use into: urbanization, availability, ageing and cross sectoral mobility of labour, geophysical, accessibility, and climatic factors. These factors explained about 88 % of the cause of agricultural land use in the study area. The proposed transition of Malaysia to a high income nation will no doubt put additional pressures on the identified drivers (factors) of the agricultural land use, therefore, it is expected that the policy makers put in place measures that will minimize environmental effects of these pressures in order to make the proposed transition sustainable.


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Author Biographies

Akeem Olawale Olaniyi, Department of Environmental Management, Kaduna State University, Nigeria

He is a lecturer at the Department of Environmental Management of Kaduna State University. He bagged his PhD in Environmental Analysis and Modelling at the Universiti Putra Malaysia in 2013. He has published several papers, attended conferences/workshops and has collaborated with other professionals in some global studies, reviews and publications.

Ahmad Makmom Abdullah, Department of Environmental Sciences, Universiti Putra Malaysia

He is a Professor of Ecophysiology and Air Pollution Modelling at the Department of Environmental Sciences, Faculty of Environmental Management, Universiti Putra Malaysia, Malaysia. He has to his credit publications of articles in frontline journals in his field. He has attended several conferences, partner with professionals in conducting studies (international and national) in ecophysiology and air pollution and has supervised several Bachelor, Masters and PhD students.


Abdullah, A.S. & Hezri, A.A. (2008). From forest landscape to agricultural landscape in the developing tropical countries of Malaysia: Pattern, Process and their Significance on Policy. Environmental Management, 42, 907 – 917.

Abdullah, S.A. & Nakagoshi, N. (2007). Forest fragmentation and its correlation to human land use change in the state of Selangor, Peninsular Malaysia. Forest Ecology and Management, 241, 39 - 48.

Abdullah, F.A. & Abu Samah, B. (2013). Canadian Center of Science and Education 120 Factors Impinging Farmers’ Use of Agriculture Technology. Asian Social Science, (9), 3.

Alias, N.Z., Abdul Rashid, M.A. & Kok - Chye, J.F. (2010). State of Selangor. Economic Research, Vol. ER/001/2011. Malaysian Rating Corporation Berhad (364803-V). KDN No: PP 14787/11/ 2011(026546).

Agilent Technologies (2004). Principal components analysis.

Al – Amin, A.Q. & Siwar, C. (2008). The economic dimensions of climate change: impacts and adaptation practices in Malaysia”, Proceedings of the 9th international business research conference, Novotel Hotel, Melbourne, Australia, 24 – 26th November, 2008.

Alexander, P. et al. (2015). Drivers for global agricultural land use change: Te nexus of diet, population, yield and bioenergy. Global Environmental Change, 35, 138 – 147 (2015).

Anete, A.A. & Amusa, T.A. (2010). Challenges of agricultural adaptation to climate change in Nigeria: a synthesis from the literature. Journal of Field Action, 4.

Anselin, L. (2001). Spatial Effects in Econometric Practice in Environmental and Resource Economics. In: American Journal of Agricultural Economics, Vol. 83 (3), pp. 705 – 710.

Anselin, L. (2002). Under the hood: issues in the specification and interpretation of spatial regression models, Agricultural Economics, 27: 247 – 267.

Anselin, L. & Griffith, D.A. (1988). Do spatial effects really matter in regression analysis ? Pap. Regional Sci. Assoc. 65, 11 – 34.

Arshad, F.M., Abdullah, R.M.N., Kaur, B. & Abdullah, M.A. (2007). 50 years of Malaysian agriculture: transformational issues, challenges and direction. Penerbit UPM, Serdang.

Arshad, M.A., Armanto, E.M. & Zain, M.A. (2012). Evaluation of climate suitability of oil palm (Elaeis guineensis Jacq.) cultivation. Journal of Environmental Science and Engineering, 272 – 276.

Aspinall, R.J. (2002). Use of logistic regression for vali¬dation of maps of the spatial distribution of vegetation species derived from high spatial resolution hypers¬pectral remotely sensed data. Ecological Modelling, 157, 301 - 312.

Backlund, P., Janetos, A.C. & Schimel, D. (2008). The effects of climate change on agriculture, land resources, water resources, and biodiversity. Climate Change Science Program Synthesis and Assessment Product 4.3, Washington, DC.

Bakar, B.B. (2021). The Malaysian agricultural industry in the new millennium: issues and challenges. Retrieved from: htpp// accessed on 2021/03/16.

Batty, M. & Longley, R. (1994). Urban modeling in computer-graphic and geographic information system environments. Environment and Planning B, 19, 663 – 688.

Bender, O., Boehmer, H.J., Jens, D. & Schumacher, K.P. (2005). Using GIS to analyze long term cultural landscape change in Southern Germany. Landscape Urban Planning, 70(2), 111 – 125.

Bockstael, N.E. (1996). Modeling economics and ecology:The importance of a spatial perspective. Am. J. Agric. Econ., 78, 1168 – 1180.

Burnside, C. & Dollar, D. (2000). Aid, policies and growth. American Economic Review, 90, 847 – 868.

Carmel, Y., Kadmon, R. & Nirel, R. (2001). Spatiotemporal predictive models of Mediterranean vegetation dynamics. Ecological Applications, 11: 268 – 280.

Cheng, J., Masser, I. (2003). Urban growth pattern modelling, a case study of Wuhan, P.R.China. Landscape and Urban Planning, 62(4), 199 - 217.

Chou, Y.H. (1991). Map resolution and spatial autocorrelation Geogr. Anal., 23, 228 – 246.

Cliff, A. & Ord, J.K. (1973). Spatial autocorrelation. London: Pion Press.

Cochard-Picon, C., Pilon, R., Tarroux, E., Pages, L., Robertson, J. & Dawson, L. (2012). Effect of species, root branching order and season on the root traits of 13 grass species. Plant and Soil, 353, 47 – 57.

Costello A.B. & Osborne J.W. (2005). Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis, practical assessment, research & evaluation, 10,(7). Retrieved from: (accessed: 2021/01/30).

Cu, P.V., Charrette, P. & Dieu, D.T. (2009). Application of the principal component analysis to explore the relation between land use and solid waste generation in the Duy Tien district, Ha Nam Province, Vietnam. Journal of Science, Earth Sciences, 25, 65 – 75.

Dai, E., Wu, S., Shi, W., Cheung, C. & Shaker, A. (2005). Modeling change-pattern-value dynamics on land use: An integrated GIS and artificial neural networks approach. Environmental Management, 36(2), 1 – 17.

De Pinto, A. & Nelson, G.C. (2002). Correcting for spatial effects in limited dependent variable regression: Assessing the value of “Ad-Hoc” techniques. Paper prepared for the American Agricultural Economics Association annual meeting, Long Beach, California. 10.22004/ag.econ.19782

Dendoncker, N., Rounsevell, M. & Bogaert, P. (2007). Spatial analysis and modelling of land use distributions in Belgium. Computers, Environment and Urban Systems, 31(2), 188 – 205.

Deng, X.Z., Huang, J.K., Rozelle, S. & Uchida, E. (2006). Cultivated land conversion and potential agricultural productivity in China. Land Use Policy, 23(4), 372 – 384.

Deng, X.Z., Su, H.B. & Zhan, J.Y. (2008). Integration of multiple data sources to simulate the dynamics of land systems. Sensors, 8(2), 620 – 634.

Deng, X. (2011). Modeling the dynamics and consequences of land system change. Higher Education Press, China. 250.

Dinç, G. & Gül, A. (2021). Estimation of the future land cover using Corine Land Cover data. Tema. Journal of Land Use, Mobility and Environment, 14 (2), 177 - 188.

Easterly, W., Levine, R. & Roodman, D. (2003). New data, new doubts: a comment on Burnside and Dollar’s aid, policies and growth’s. National Bureau of Economic Research, Cambridge, Mass. NBER Working Paper 9846,

EoN, (2010). Agriculture in Malaysia. Encyclopedia of Nations. Retrieved from: (accessed on 16/03/2021)

Ezhar, T., Rahim, M. S., Zainal Abidin, M., Mohamed Rezal, H. & Zamre, Y. (2007). Micro agro - based entrepreneurs’ readiness in facing agricultural challenges. IPSAS Monograph Series Bil. 1/2007, University Putra Malaysia Publisher, Serdang, Malaysia.7.

Grifith, D.A. (2003). Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization New York, Springer-Verlag.

Guo, G., Wen, Q. & Zhu, J. (2015). The impact of ageing agricultural labor population on farmland output: from the perspective of farmer preferences. Mathematical Problems in Engineering,

Food and Agriculture Organization FAO (1976). A framework for land evaluation (Soils Bulletin No. 32). Rome: Food and Agriculture Organization of the United Nations.

FAO, 1996. Report of the World Food Summit (Rome: FAO).

FAO, (2002). The role of agriculture in the development of least-developed countries and their integration into the world economy. Retrieved from: (accessed on 2021/01/03)

FAO, (2020). Agriculture at a crossroad: International assessment of agricultural knowledge, science and technology for development. Retrieved from: (accessed on 06/10/2021)

Franklin, S.B., Gibson, D.J., Robertson, P.A., Pohlmann, J.T. & Fralish, J.S. (1995). Parallel analysis: a method for determining significant principal components. Journal of Vegetation Science, 1, 99 – 106.

Fujii, T. (2008). How well can we target aid with rapidly collected data? Empirical results for poverty mapping from Cambodia. World Development, 36(10), 1830–1842.

Gao, Z.Q. & Deng, X.Z. (2002). Analysis on spatial features of LUCC based on dataset of land use and land cover change in China. Chinese Geographical Science, 12(2), 107 – 113.

Ge Q.S., Dai, J.H., He, F.N., Pan, Y. & Wang, M.M. (2008). Land use changes and their relations with carbon cycles over the past 300 years in China. Science in China Series D – Earth – Sciences, 51(6), 871 – 884.

Gobin A., Camping, P. & Feyen, J. (2002). Logistic modelling to derive agricultural land use determinants: A case study from Southeastern Nigeria. Agriculture, Ecosystems and Environment, (89), 213 – 228.

Goetzke, F. (2008). Network effects in public transit use: evidence from a spatially autoregressive mode choice model. Urban Studies, (45), 407 – 417.

Greenaway, D., Upward, R. & Wright, P. (2000). Sectoral transformation and labour market flows. Oxford Review of Economic Policy, 16,(3), 57 - 75.

Hassanzadeh, Z., Ghavami, R. & Zareh, M.K. (2016). Radial basis function neural networks based on the projection pursuit and principal component analysis approaches: QSAR analysis of fullerene[C60]-based HIV-1 PR inhibitors. Medicinal Chemistry Research, (25), 19–29. 10.1007/s00044-015-1466-x

Hayrol Azril, M. S., Md Salleh, H. & Bahaman, A.S. (2009). Level of Agro-based Website Surfing among Asian Social Science Vol. 9, No. 3; 2013 124 Malaysian Agricultural Entreprenuers: A Case Study of Malaysia. Journal of Agriculture and Social Science, (5), 55 - 60.

Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, (24), 417–441.

Hu, Y., Batunacun, X., Zhen, L. & Zhuang, D. (2019). Assessment of Land-Use and Land Cover Change in Guangxi, China. Scientific Reports, 9: 2189 |

Huang, Q.H., Cai, Y.L. & Peng, J. (2007). Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China. Environmental Model Assessment, (12), 55 – 61.

Ioffe, G., Nefedova, T. & Zaslavsky, I. (2004). From spatial continuity to fragmentation: the case of Russian farming. Annals of the Association of American Geographers, (94), 913 – 943.

Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services [IPBES] (2018). Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services. Scholes, R., Montanarella, L., Brainich, A., Barger, N., ten Brink, B., Cantele, M., Erasmus, B. , Fisher, J., Gardner, T., Holland, T.G., Kohler, F., Kotiaho, J.S., Von Maltitz, G. , Nangendo, G., Pandit, R., Parrotta, J., Potts, M.D., Prince, S., Sankaran, M. & Willemen L. (eds.). IPBES secretariat, Bonn, Germany. 44 pgs. IPBES.

IPBES (2019). Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services. Díaz, S., Settele, J., Brondízio, E.S., Ngo, H.T. Guèze, M., Agard, J., Arneth, A., Balvanera, P. Brauman, K.A., Butchart, S.H.M., Chan, K.M.A., Garibaldi, L.A., Ichii, K., Liu, J., Subramanian, S.M., Midgley, G.F., Miloslavich, P., Molnár, Z., Obura, D., Pfaff, A., Polasky, S., Purvis, A., Razzaque, J., Reyers, B., Roy-Chowdhury, R., Shin, Y.J., Visseren-Hamakers, I.J., Willis, K.J. & Zayas, C.N. (eds.). Bonn: IPBES. https://

Jackson, D.A. (1993). Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology, 74(8), 2204 – 2214.

Jasim, M.R., Mat Jafri, M.Z. & Lim, H.S. (2013). Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia. Atmospheric Environment. (71), 36 – 43.

Jayawardana, D.T., Ishiga, H. & Pitawala, H.M.T.G.A. (2012). Geochemistry of surface sediments in tsunami-affected Sri Lankan lagoons regarding environmental implications. International Journal of Environmental Science and Technology, 9, 41 – 55.

Jobson, J.D. (2002). Applied multivariate data analysis. New York, NY: Springer Verlag

Jolliffe, I.T. (2002). Principal component analysis (2nd eds.). Springer series in statistics. New York, NY: Springer.

Jongman, R.H.G., Ter Braak, C.J. & Van Tongren, O.F. (1995). Data analysis in community and landscape ecology (p. 299). Cambridge: Cambridge University Press

Kaphegyi, T.A.M., Dees, M., Zlatanova, D., Ueffing, C., Dutsov, A. & Kaphegyi, U. (2012). Rapid assessment of linear transport infrastructure in relation to the impact on landscape continuity for large ranging mammals. Biodiversity and Conservation, 22,(1), 153 – 168.

Kleemann, J., Baysal, G., Bulley, H.N.N., & Fürst, C. (2017). Assessing driving forces of land use and land cover change by a mixed-method approach in North - Eastern Ghana, West Africa. J. Environ. Manag. 196, 411 – 442.

Kok, K. & Veldkamp, T.A. (2001). Evaluating impact of spatial scales on land use pattern analysis in Central America. Agriculture, Ecosystems and Environment, 85(1- 3), 205 – 221.

Koulouri, M. & Giourga, C. (2007). Land abandonment and slope gradient as key factors of soil erosion in Mediterranean terraced lands. Catena, 69, 274 – 281.

Kuemmerle, T., Levers, C., Erb, K., Estel, S., Jepsen, M.R., Müller, D., Plutzar, C., Stürck, J., Verkerk, P.J., Verburg, P.H. & Reenberg, A. (2016). Hotspots of land use change in Europe. Environmental Research Letters, 11.

Lambin, E.F., Geist, H.J. & Lepers, E. (2003). Dynamics of Land-Use and Land-Cover Change in Tropical Regions. Annual Review of Environment and Resources, 28, 205 – 241.

Legendre, P. & Legendre, L. (1998). Numerical ecology. Amsterdam: Elsevier. pgs. 853.

Lennon, J.J. (2000). Red-shifts and red herrings in geographical ecology.Ecography, 23:101 – 113.

Lesschen, J.P., Verburg, P.H., & Staal, S.J. (2005). Statistical methods for analyzing the spatial dimension of changes in land-use and farming systems, LUCC Report Series No. 7. The International Livestock Research Institute, Nairobi Kenya and LUCC Focus 3, Office, Wageningen University, The Netherlands.

Leta, M.K., Demissie, T.A., & Tränckner, J. (2021). Modeling and Prediction of Land Use Land Cover Change Dynamics Based on Land Change Modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin ethiopia. Sustainability, 13, 3740.

Lordan, G., Soto, E. J., Brown, R. P. C., & Correa-Valez, I. (2012). Socioeconomic status and health outcomes in a developing country. Health Economics, 21, 178 – 186.

Luijten, J., Miles, L., & Cherrington, E. (2006). Land use change modeling for three scenarios for the MAR region ICRAN – MAR project. UNEP WCMC, 2006 Technical Report. 3.

Malaysian Department of Statistic (MDoS) (2010). Yearbook of Statistics, Malaysia. Malaysian Department of Statistics (MDoS), 2010. Yearbook of Statistics, Malaysia, 2010. Malaysian Department of Statistics, Kuala Lumpur, Malaysia.

Malaysian Meteorological Department (MMD), (2009). Climate change scenarios for Malaysia 2001 - 2099 scientific report by numerical weather prediction. Technical Development Division. Malaysian Meteorological Department Ministry of science, Technology and Innovation.

Marchant, R., Richer, S., Boles, O., Capitani, C., Courtney-Mustaphi, C.J., Lane, P., Prendergast, M.E., Stump, D., De Cort, G., Kaplan, J.O. et al. (2018). Drivers and trajectories of land cover change in East Africa: Human and environmental interactions from 6000 years ago to present. Earth-Sci. Rev. 178, 322 – 378.

Mazzeo, G. & Russo, L. (2016). Aspects of Land Take in the Metropolitan Area of Naples. Tema Journal of Land Use, Mobility and Environment, 9 (1), 89 - 107. doi: http://10.6092/1970-9870/3727.

Demirci, A., McAdams, M.A., Alagha, O. & Karakuyu, M. (2006). The relationship between land use change and water quality in Kucukcekmece Lake watershed. 4th GIS Days in Turkey, Istanbul, 27-34.

McMorrow, J., & Talip, M.A. (2001). Decline of forest area in Sabah, Malaysia: Relationship to state policies, land code and land capabilities. Global Environmental Change, 11, 217 – 230.

Menard, S. (2002). Applied logistic regression analysis:quantitative applications in the social sciences. Sage Publication, Thousand Oak, CA, USA.

Millington, J.D.A., Perry, G.L.W. & Romero – Calcerrada, R. (2007). Regression techniques for examining land use/land cover change: A case study of a Mediterranean landscape. Ecosystems, 10: 562 – 578.

Mottet, A., Ladet, S., Coque, N. & Gibon, A. (2006). Agricultural land use change and its drivers in mountain landscapes: A case study in the Pyrenees. Agriculture, Ecosystems and Environment, 114, 296 – 310.

Muller, D. & Zeller, M. (2002). Land use dynamics in the central highlands of Vietnam: a spatial model combining village survey data with satellite imagery interpretation. Agricultural Economics, 27, 333 – 354.

Müller, D. & Munroe, D.K. (2008). Changing rural landscapes in Albania: cropland abandonment and forest clearing in the post socialist transition. Annals of the Association of American Geographers, 98, 855 – 876.

Müller, D., Kuemmerle, T., Rusu, M. & Griffiths, P. (2009). Lost in transition: determinants of post-socialist cropland abandonment in Romania. Journal of Land Use Science, 4, 109 – 129.

Munroe, D.K., Southworth, J., & Tucker, C.M. (2002). The dynamics of land-cover change in western Honduras: exploring spatial and temporal complexity. Agricultural Economics, 27, 355 - 369.

Näschen, K., Diekkrüger, B., Evers, 1 , M., Höllermann, B., Steinbach, S. & Thonfeld, F. (2019). The impact of land use/land cover change (LULCC) on water resources in a Tropical Catchment in Tanzania under different climate change scenarios. Sustainability, 11, 7083; http://doi:10.3390/su11247083

Nelson, G.C. (2002). Introduction to the special issue on spatial analysis for agricultural economists. Agricultural Economics, 27, 197 – 200.

Niu, L., Luo, W., Jiang, M. & Lu, N. (2018). Land - use degree and spatial autocorrelation analysis based on big data in Kunming City. In 97–100, (IEEE, 2018).

Nuissl H. & Siedentop S. (2021) Urbanisation and Land Use Change. In: Weith T., Barkmann T., Gaasch N., Rogga S., Strauß C., Zscheischler J. (eds) Sustainable Land Management in a European Context. Human-Environment Interactions, Vol 8. Springer, Cham.

Nusret, D. & Dug, S. (2009). Applying the inverse distance weighting and kriging methods of the spatial interpolation on the mapping the annual precipitation in Bosnia and Herzegovina. International Environmental Modelling and Software Society (iEMSs) 2012. International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.)

Nyaga, E.K. & Doppler, W. (2009). Combining principal component analysis and logistic regression models to assess household level food security among smallholder cash crop producers in Kenya. Quarterly Journal of International Agriculture, 48, 5 – 23.

Olaniyi A.O., Abdullah, A.M., Ramli, M.F. & Alias, M.S. (2013). Agricultural land use in Malaysia: an historical overview and implication for food security. Bulgarian Journal of Agricultural Science., 19(1).

Olaniyi, A.O., Abdullah, A.M., Ramli, M.F. & Alias, M.S. (2012). Assessment of drivers of coastal land use change in Malaysia. Ocean and Coastal Management, 67: 113 – 123.

Olaniyi, A.O., Abdullah, A.M., Ramli, M.F. & Alias, M.S. (2011). Effects of socio – economic factors on agricultural land use in Malaysia. Elixir Agriculture 37, 3790 – 3797.

Omar, S.C., Shaharudin, A. & Tumin, S.A. (2019). The status of the paddy and rice industry in Malaysia, Kuala Lumpur. Khazanah Research Institute. Agricultural transformation and inclusive growth: The Malaysian experience. Retrieved from: (accessed on 2021/03/12)

Overmars, K. (2000). Quantification of spatial autocorrelation and an application of spatial autoregressive model in land use change analysis. Internal Report. Wageningen University and Research Centre, Wageningen.

Parker, M. L., Reeves, J.N., Matzeu, G.A., Buisson, D.J.K. & Fabian, A.C. (2018). Using principal component analysis to understand the variability of PDS 456. Monthly Notices of the Royal Astronomical Society, 474, (1), 108 – 114.

Prishchepov, A.V., Radeloff, V.C., Müller, D., Dubinin, M. & Baumann, M. (2011). Determinants of agricultural land abandonment in Post-Soviet European Russia “Will the „ Brics decade‟ continue? – prospects for trade and growth” 23 - 24 June 2011 Halle (Saale), Germany.

Prishchepov, A.V., Radeloff, V.C., Müller, D., Dubinin, M. & Baumann, M. (2013). Determinants of Agricultural Land Abandonment in Post-Soviet European Russia. Land Use Policy, 30(1):873 – 884.

Qasim, M., Hubacek, K., Termansen, M. & Fleskens, L. (2013). Modelling land use change across elevation gradients in district Swat, Pakistan. Reg Environ Change, https//www.doi 10.1007/s10113-012-0395-1.

Richards, C. L., Rosas, U., Banta, J., Bhambhra, N. & Purugganan, M. D. (2012). Genome-wide patterns of arabidopsis gene expression in nature. Genetics, 8, 482 – 495.

Rigg, J. & Nattapoolwat, S. (2001). Embracing the global in Thailand: Activism and pragmatism in an era of de-agrarianisation. World Development, 29(6), 945–960.

Rigg, J. (2006). Land, farming, livelihoods and poverty: Rethinking the links in the rural South. World Development, 34(1), 180 – 202.

Sidique, S.F. & Shaharudin, A. (2019). Malaysia’s Agricultural Transformation: National Food Security. Draft, Institute of Agricultural and Food Policy Studies, Universiti Putra Malaysia.

Simone Z.D., Armando, M. & Barbara S. (2010). Simulation of urban development in the City of Rome. Framework, methodology, and problem solving. The Journal of Transport and Land Use, 3(2), 85 – 105.

Siwar, C., Alam, M.M., Murad, M.W. & Al – Amin, A.Q. (2011). Impacts of climate change on agriculture and food security issues in Malaysia: An Empirical Study on Farm Level Assessment. World Applied Sciences Journal, 14(3), 431 – 442.

Suhaila, J. & Deni, S.M. (2010). Trends in Peninsular Malaysia rainfall data during the southwest monsoon and northeast monsoon seasons: 1975 – 2004. Sains Malaysiana, 39(4), 533 – 542.

Theobald, D.M., Miller, J.M., & Hobbs, N.T. (1997). Estimating the cumulative effects of development on wildlife habitat. Landscape and Urban Planning, 39, 25 – 36.

Vasco, D. & Eric, K. (2010). Explaining land-use change in Portugal 1990-2000. 13th AGILE international conference on geographic information science 2010 pp 1 – 11 Guimarães, Portugal.

Veldkamp, A. & Fresco, L.O. (1999). CLUE: a conceptual model to study the conversion of land use and its effects. Ecological Modelling, 85, 253 – 270.

Velicer W.F. & Fava J.L. (1998). Effects of variable and subject sampling on factor pattern recovery, Psychological Methods, vol. 3, 231-251.

Verburg, P.H., Schot, P.P., Dijst, M.J. & Veldkamp, A. (2004). Land use change modelling: Current practice and research priorities, GeoJournal, 61, 309 – 324.

Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V. & Mastura, S.S.A. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30(3):391 - 405.

Ver Hoef, J.M., Cressie, N. & Fisher, R.N. (2001). Uncertainty and spatial linear models for ecological data. Pages 214 – 237. In C. T. Hunsaker, M. F. Goodchild, M. A. Friedl, and T. J. Case, editors. Spatial uncertainty in ecology. Springer-Verlag, New York, New York, USA.

Vincent, J.R. & Rozali, M.A. (2005). Managing natural wealth environment and development in Malaysia. (Eds.) Resources for the future press, Washington D.C. USA.

Wang, Q., Ren, Q. & Liu, J. (2016). Identifcation and apportionment of the drivers of land use change on a regional scale: Unbiased recursive partitioning-based stochastic model application. Agriculture, Ecosystems & Environment, 217, 99 – 110.

Wester-Herber, M. (2004). Underlying concerns in land-use conflicts--the role of place-identity in risk perception. Environmental Science and Policy, 7(2), 109 - 116.

Wikle, C.K. (2003). Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology, 84: 1382 – 1394.[1382:HBMFPT]2.0.CO;2

Wiktorowicz, J. (2016). Exploratory factor analysis in the measurement of the competencies of older people. Econometrics, 4(54).

World Bank (2008). World development indicators online. The World Bank, Development Data Group, Washington, DC. Retrieved from: 2021.001.030.

World Bank (2019). The status of the paddy and rice industry in Malaysia. Kuala Lumpur: Khazanah Research Institute. Agricultural Transformation and Inclusive Growth The Malaysian Experience. Retrieved from: (accessed on 2021/03/16)

Xie, Y., Mei, Y., Guangjin, T. & Xuerong, X. (2005). Socio – economic driving forces of arable land conversion: A case study of Wuxian City China. Global Environmental Change 15, 238 – 252.

Yongwei Liu, Y., Cao, X. & Li, T. (2020). Influence of accessibility on land use and landscape pattern based on mapping knowledge domains: review and implications. Journal of Advanced Transportation, 5(2).

Zhuang, D.F., Liu, M.L. & Deng, X.Z. (2002). Spatialization model of population based on dataset of land use and land cover change in China. Chinese Geographical Sciences, 12(2), 114 – 119.

Zucaro, F. & Morosini, R. (2018). Sustainable land use and climate adaptation: a review of European local plans. Tema. Journal of Land Use, Mobility and Environment, 11(1), 7 - 26.

How to Cite
OlaniyiA., & AbdullahA. (2021). Characterization of drivers of agricultural land use change. TeMA - Journal of Land Use, Mobility and Environment, 14(3), 411-432.