Research on World Agricultural Economy

[1] Che Omar, S., Shaharudin, A.A., Tumin, S.A., 2019. The Status of the Paddy and Rice Industry in Malaysia. Khazanah Research Institute: Kuala Lumpur, Malaysia.

[2] Dorairaj, D., Govender, N.T., 2023. Rice and Paddy Industry in Malaysia: Governance and Policies, Research Trends, Technology Adoption and Resilience. Frontiers in Sustainable Food Systems. 7, 1093605. DOI: https://doi.org/10.3389/fsufs.2023.1093605

[3] Anwar, M.R.K., Kamisan, N.A.B., Ahmad, A.A., 2024. Time Series Analysis of Paddy Production in Malaysia. Proceedings of the Science and Mathematics Conference. 22, 1–9.

[4] Ibrahim, N.F., Abdullah, M.A.A., Olaniran, O.R., 2025. Unraveling the Impact of Climate Change on Food Security in Malaysia: Insights from Vector Error Correction Modeling. Engineering, Technology & Applied Science Research. 15(2), 20811–20818. DOI: https://doi.org/10.48084/etasr.9768

[5] Mansoor, S., Iqbal, S., Popescu, S.M., et al., 2025. Integration of Smart Sensors and IoT in Precision Agriculture: Trends, Challenges and Future Prospectives. Frontiers in Plant Science. 16, 1587869. DOI: https://doi.org/10.3389/fpls.2025.1587869

[6] Kumar, V., Sharma, K.V., Kedam, N., et al., 2024. A Comprehensive Review on Smart and Sustainable Agriculture Using IoT Technologies. Smart Agricultural Technology. 8, 100487. DOI: https://doi.org/10.1016/j.atech.2024.100487

[7] Pham, V.B., Diep, T.T., Fock, K., et al., 2021. Using the Internet of Things to Promote Alternate Wetting and Drying Irrigation for Rice in Vietnam’s Mekong Delta. Agronomy for Sustainable Development. 41, 43. DOI: https://doi.org/10.1007/s13593-021-00705-z

[8] Wee, G.W.W., Lim, A.L.S., 2022. Factors Influencing the Behavioural Intention for Smart Farming in Sarawak, Malaysia. Journal of Agribusiness Marketing. 9(1), 37–56. DOI: https://doi.org/10.56527/jabm.9.1.4

[9] Alfred, R., Obit, J.H., Chin, C.P.-Y., et al., 2021. Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks. IEEE Access. 9, 50358–50380. DOI: https://doi.org/10.1109/ACCESS.2021.3069449

[10] Gulzar, Y., Reegu, F.A., Soomro, A.B., et al., 2025. Exploring the Economic Implications of IoT Adoption in Agriculture: A Cost-Benefit Study in Jazan, Saudi Arabia. Sarhad Journal of Agriculture. 41(1), 51–65. DOI: https://doi.org/10.17582/journal.sja/2025/41.1.51.65

[11] Liu, H., Chen, Z., Wen, S., et al., 2025. Impact of Digital Literacy on Farmers’ Adoption Behaviours of Green Production Technologies. Agriculture. 15(3), 303. DOI: https://doi.org/10.3390/agriculture15030303

[12] Saifan, S., Shibli, R., Hamid, J.A., et al., 2021. Agriculture Technology Adoption in Malaysia: The Extension Service’s Role, Rural Financing, and the Lender’s Institutional Context. AgBioForum. 23(2), 92–101.

[13] Bulut, C., Wu, P.F., 2024. More Than Two Decades of Research on IoT in Agriculture: A Systematic Literature Review. Internet Research. 34(3), 994–1016. DOI: https://doi.org/10.1108/INTR-07-2022-0559

[14] Dibbern, T., Romani, L.A.S., Massruhá, S.M.F.S., 2024. Main Drivers and Barriers to the Adoption of Digital Agriculture Technologies. Smart Agricultural Technology. 8, 100459. DOI: https://doi.org/10.1016/j.atech.2024.100459

[15] Pedersen, S.M., Erekalo, K.T., Christensen, T., et al., 2024. Drivers and Barriers to Climate-Smart Agricultural Practices and Technologies Adoption: Insights from Stakeholders of Five European Food Supply Chains. Smart Agricultural Technology. 8, 100478. DOI: https://doi.org/10.1016/j.atech.2024.100478

[16] Samsudin, M., Sumin, V., 2025. Bridging Youth Technology Readiness and Internet of Things (IoT) Adoption in Agriculture: Perceived Benefits and Risks as Mediators in the TRI Framework. RSF Conference Proceeding Series: Business, Management and Social Science. 5(2), 541–546. DOI: https://doi.org/10.31098/bmss.v5i2.1055

[17] Adrian, A.M., Norwood, S.H., Mask, P.L., 2005. Producers’ Perceptions and Attitudes toward Precision Agriculture Technologies. Computers and Electronics in Agriculture. 48(3), 256–271. DOI: https://doi.org/10.1016/j.compag.2005.04.004

[18] Koundouri, P., Nauges, C., Tzouvelekas, V., 2006. Technology Adoption under Production Uncertainty: Theory and Application to Irrigation Technology. American Journal of Agricultural Economics. 88(3), 657–670. DOI: https://doi.org/10.1111/j.1467-8276.2006.00886.x

[19] Hensher, D.A., Ho, C., Mulley, C., 2015. Identifying Preferences for Public Transport Investments under a Constrained Budget. Transportation Research Part A: Policy and Practice. 72, 27–46. DOI: https://doi.org/10.1016/j.tra.2014.12.002

[20] Scarpa, R., Rose, J.M., 2008. Design Efficiency for Non-Market Valuation with Choice Modelling: How to Measure It, What to Report and Why. Australian Journal of Agricultural and Resource Economics. 52(3), 253–282. DOI: https://doi.org/10.1111/j.1467-8489.2007.00436.x

[21] Wolfert, S., Ge, L., Verdouw, C., et al., 2017. Big Data in Smart Farming: A Review. Agricultural Systems. 153, 69–80. DOI: https://doi.org/10.1016/j.agsy.2017.01.023

[22] Klerkx, L., Rose, D., 2020. Dealing with the Game-Changing Technologies of Agriculture 4.0: How Do We Manage Diversity and Responsibility in Food System Transition Pathways? Global Food Security. 24, 100347. DOI: https://doi.org/10.1016/j.gfs.2019.100347

[23] Bakang, J.-E.A., Wongnaa, C.A., Tham-Agyekum, E.K., et al., 2024. Determinants of Maize Farmers’ Willingness to Pay for Private Extension Services in Ejisu Municipality, Ghana. Heliyon. 10, e37464. DOI: https://doi.org/10.1016/j.heliyon.2024.e37464

[24] Train, K.E., 2009. Discrete Choice Methods with Simulation, 2nd ed. Cambridge University Press: Cambridge, UK. pp. 1–400.

[25] McFadden, D., 1972. Conditional Logit Analysis of Qualitative Choice Behavior. In: Zarembka, P. (Ed.). Frontiers in Econometrics. Academic Press: New York, NY, USA. pp. 105–142.

[26] Ben-Akiva, M.E., Lerman, S.R., 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press: Cambridge, MA, USA. pp. 1–400.

[27] Lancaster, K.J., 1966. A New Approach to Consumer Theory. Journal of Political Economy. 74(2), 132–157. DOI: https://doi.org/10.1086/259131

[28] Louviere, J.J., Hensher, D.A., Swait, J.D., 2000. Stated Choice Methods: Analysis and Applications. Cambridge University Press: Cambridge, UK. pp. 1–400.

[29] Hensher, D.A., 2010. Hypothetical Bias, Choice Experiments and Willingness to Pay. Transportation Research Part B: Methodological. 44(6), 735–752.

[30] Johnston, R.J., Boyle, K.J., Adamowicz, W., et al., 2017. Contemporary Guidance for Stated Preference Studies. Journal of the Association of Environmental and Resource Economists. 4(2), 319–405. DOI: https://doi.org/10.1086/691697

[31] Bhat, C.R., 2001. Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model. Transportation Research Part B: Methodological. 35(7), 677–693. DOI: https://doi.org/10.1016/S0191-2615(00)00014-X

[32] Carson, R.T., Groves, T., 2007. Incentive and Informational Properties of Preference Questions. Environmental and Resource Economics. 37(1), 181–210. DOI: https://doi.org/10.1007/s10640-007-9124-5

[33] Scarpa, R., Gilbride, T.J., Campbell, D., et al., 2009. Modelling Attribute Non-Attendance in Choice Experiments for Rural Landscape Valuation. European Review of Agricultural Economics. 36(2), 151–174. DOI: https://doi.org/10.1093/erae/jbp012

[34] Liesivaara, P., Myyrä, S., 2014. Willingness to Pay for Agricultural Crop Insurance in the Northern EU. Agricultural Finance Review. 74(4), 539–554. DOI: https://doi.org/10.1108/AFR-06-2014-0018

[35] Mariel, P., Hoyos, D., Meyerhoff, J., et al., 2021. Environmental Valuation with Discrete Choice Experiments: Guidance on Design, Implementation and Data Analysis. Springer: Cham, Switzerland. pp. 1–136.

[36] Department of Agriculture Malaysia (DOA), 2024. Crop Statistics (Food Crops Subsector) 2024. Department of Agriculture Malaysia: Putrajaya, Malaysia. (in Malay)

[37] Orme, B.K., 2010. Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research, 2nd ed. Research Publishers LLC: Madison, WI, USA. pp. 1–207.

[38] Valarmathi, K., Mageshwari, M., Prabakaran, N., et al., 2025. Food Wastage Analysis Framework Using Comprehensive IoT-Based Multi-Sensor. Global NEST Journal. 27(10), 1–12. DOI: https://doi.org/10.30955/gnj.07826

[39] Maruthai, S., Selvanarayanan, R., Thanarajan, T., et al., 2025. Hybrid Vision GNNs Based Early Detection and Protection against Pest Diseases in Coffee Plants. Scientific Reports. 15(1), 11778. DOI: https://doi.org/10.1038/s41598-025-96523-4

[40] Davis, F.D., 1989. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 13(3), 319–340. DOI: https://doi.org/10.2307/249008

[41] Venkatesh, V., Morris, M.G., Davis, G.B., et al., 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 27(3), 425–478. DOI: https://doi.org/10.2307/30036540

[42] Tao, H., Xiong, H., You, L., et al., 2024. Farmers’ Willingness to Pay for Smart Farming Technologies: Evidence from a Smart Drip Irrigation Technology in North China. China Agricultural Economic Review. 16(1), 114–134. DOI: https://doi.org/10.1108/CAER-03-2023-0050

[43] Blasch, J., van der Kroon, B., van Beukering, P., et al., 2022. Farmer Preferences for Adopting Precision Farming Technologies: A Case Study from Italy. European Review of Agricultural Economics. 49(1), 33–81. DOI: https://doi.org/10.1093/erae/jbaa031

[44] Osuafor, O.O., Ude, K.D., 2021. Valuation of Rice Farmers’ Preferences and Willingness to Pay for Climate-Smart Agricultural Technologies in Southeast Nigeria. Asian Journal of Economic Modelling. 9(1), 48–57. DOI: https://doi.org/10.18488/journal.8.2021.91.48.57

[45] Paudel, G.P., KC, D.B., Rahut, D.B., et al., 2019. Smallholder Farmers’ Willingness to Pay for Scale-Appropriate Farm Mechanization: Evidence from the Mid-Hills of Nepal. Technology in Society. 59, 101196. DOI: https://doi.org/10.1016/j.techsoc.2019.101196

[46] Obaideen, K., Yousef, B.A.A., AlMallahi, M.N., et al., 2022. An Overview of Smart Irrigation Systems Using IoT. Energy Nexus. 7, 100124. DOI: https://doi.org/10.1016/j.nexus.2022.100124

[47] Li, L., Li, W., Zhang, M., et al., 2025. Study of Vegetable Farmers’ IoT Technology Adoption Decision Making Based on Heckman Model. Smart Agricultural Technology. 11, 101019. DOI: https://doi.org/10.1016/j.atech.2025.101019

[48] Hidrobo, M., Palloni, G., Gilligan, D.O., et al., 2022. Paying for Digital Information: Assessing Farmers’ Willingness to Pay for a Digital Agriculture and Nutrition Service in Ghana. Economic Development and Cultural Change. 70(4), 1367–1402. DOI: https://doi.org/10.1086/713974

[49] Selvaggi, R., Lusk, J.L., Pappalardo, G., 2025. Eliciting Dairy Farmers’ Willingness to Pay for Digital Devices for Precision Livestock Farming. Journal of Rural Studies. 119, 103772. DOI: https://doi.org/10.1016/j.jrurstud.2025.103772

[50] Ambali, O.I., Areal, F.J., Georgantzis, N., 2021. Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference. Agriculture. 11(8), 691. DOI: https://doi.org/10.3390/agriculture11080691

[51] Olum, S., Gellynck, X., Juvinal, J., et al., 2020. Farmers’ Adoption of Agricultural Innovations: A Systematic Review on Willingness to Pay Studies. Outlook on Agriculture. 49(3), 187–203. DOI: https://doi.org/10.1177/0030727019879453

[52] Yue, M., Li, W.J., Jin, S., et al., 2023. Farmers’ Precision Pesticide Technology Adoption and Its Influencing Factors: Evidence from Apple Production Areas in China. Journal of Integrative Agriculture. 22(1), 292–305. DOI: https://doi.org/10.1016/j.jia.2022.11.002

[53] Dao, D.D., Nguyen, D.T., Le, N.L., et al., 2022. Estimated the Willingness to Pay Levels for the Adoption of the Internet of Things Technology: An Empirical Study in Swiftlet Farming in Binh Thuan Province, Vietnam. Asian Social Science. 18(11), 5–16. DOI: https://doi.org/10.5539/ass.v18n11p5

[54] Shee, A., Azzarri, C., Haile, B., 2020. Farmers’ Willingness to Pay for Improved Agricultural Technologies: Evidence from a Field Experiment in Tanzania. Sustainability. 12(1), 216. DOI: https://doi.org/10.3390/su12010216

[55] Lawi, M.B., Yakusak, E., Oshadumo, D., 2024. Assessment of Poultry Farmers’ Willingness to Pay for Agricultural Insurance in Osun State, Nigeria. Journal of Agribusiness and Rural Development. 73(3), 312–326. DOI: https://doi.org/10.17306/J.JARD.2024.01752

[56] Anugwa, I.Q., Onwubuya, E.A., Chah, J.M., et al., 2022. Farmers’ Preferences and Willingness to Pay for Climate-Smart Agricultural Technologies on Rice Production in Nigeria. Climate Policy. 22(1), 112–131. DOI: https://doi.org/10.1080/14693062.2021.1953435

[57] Onugo, T.N., Onyeneke, R.U., 2022. Farmers’ Preference and Willingness to Pay for Climate-Smart Rice Varieties in Uzo-Uwani Local Government Area of Enugu State, Nigeria. Ekológia (Bratislava). 41(3), 262–271. DOI: https://doi.org/10.2478/eko-2022-0027

[58] Alemu, M.H., Mørkbak, M.R., Olsen, S.B., et al., 2013. Attending to the Reasons for Attribute Non-Attendance in Choice Experiments. Environmental and Resource Economics. 54(3), 333–359. DOI: https://doi.org/10.1007/s10640-012-9597-8

[59] Campbell, D., 2007. Willingness to Pay for Rural Landscape Improvements: Combining Mixed Logit and Random-Effects Models. Journal of Agricultural Economics. 58(3), 467–483. DOI: https://doi.org/10.1111/j.1477-9552.2007.00117.x

[60] Badura, T., Ferrini, S., Burton, M., et al., 2020. Using Individualised Choice Maps to Capture the Spatial Dimensions of Value within Choice Experiments. Environmental and Resource Economics. 75(2), 297–322. DOI: https://doi.org/10.1007/s10640-019-00358-3

[61] Utai, K., Narjes, M., Krimly, T., et al., 2022. Farmers’ Preferences for Fertilizers Derived from Domestic Sewage and Kitchen Waste—A Discrete Choice Experiment in Germany. German Journal of Agricultural Economics. 71(4), 169–183. DOI: https://doi.org/10.30430/gjae.2022.0235

[62] Alcon, F., Tapsuwan, S., Brouwer, R., et al., 2019. Modelling Farmer Choices for Water Security Measures in the Litani River Basin in Lebanon. Science of the Total Environment. 647, 37–46. DOI: https://doi.org/10.1016/j.scitotenv.2018.07.410

[63] Ferrini, S., Scarpa, R., 2007. Designs with a Priori Information for Nonmarket Valuation with Choice Experiments: A Monte Carlo Study. Journal of Environmental Economics and Management. 53(3), 342–363. DOI: https://doi.org/10.1016/j.jeem.2006.10.007

[64] Miriti, P.K., Lambarraa-Lehnhardt, F., 2025. Understanding Farmer Preferences and Trade-Offs for Adopting Sustainable Crop Production: A Systematic Review. Discover Sustainability. 6, 760. DOI: https://doi.org/10.1007/s43621-025-01591-1

[65] Schaafsma, M., Ferrini, S., Turner, R.K., 2019. Assessing Smallholder Preferences for Incentivised Climate-Smart Agriculture Using a Discrete Choice Experiment. Land Use Policy. 88, 104153. DOI: https://doi.org/10.1016/j.landusepol.2019.104153

[66] Miriti, P., Regassa, M.D., Ojiewo, C.O., et al., 2023. Farmers’ Preferences and Willingness to Pay for Traits of Sorghum Varieties: Informing Product Development and Breeding Programs in Tanzania. Journal of Crop Improvement. 37(2), 253–272. DOI: https://doi.org/10.1080/15427528.2022.2079038

[67] Islam, M.H., Anam, M.Z., Hoque, M.R., et al., 2024. Agriculture 4.0 Adoption Challenges in the Emerging Economies: Implications for Smart Farming and Sustainability. Journal of Economy and Technology. 2, 278–295. DOI: https://doi.org/10.1016/j.ject.2024.09.002

[68] Abdul-Majid, M., Zahari, S.A., Othman, N., et al., 2024. Influence of Technology Adoption on Farmers’ Well-Being: Systematic Literature Review and Bibliometric Analysis. Heliyon. 10(2), e24316. DOI: https://doi.org/10.1016/j.heliyon.2024.e24316

[69] Singh, B.J., Chakraborty, A., Sehgal, R., 2023. A Systematic Review of Industrial Wastewater Management: Evaluating Challenges and Enablers. Journal of Environmental Management. 348, 119230. DOI: https://doi.org/10.1016/j.jenvman.2023.119230

[70] Greene, W.H., Hensher, D.A., 2010. Does Scale Heterogeneity across Individuals Matter? An Empirical Assessment of Alternative Logit Models. Transportation. 37(3), 413–428. DOI: https://doi.org/10.1007/s11116-010-9259-z

[71] Nguyen, L.L.H., Khuu, D.T., Halibas, A., et al., 2024. Factors That Influence the Intention of Smallholder Rice Farmers to Adopt Cleaner Production Practices: An Empirical Study of Precision Agriculture Adoption. Evaluation Review. 48(4), 692–735. DOI: https://doi.org/10.1177/0193841X231200775

[72] Day, M., Haider, M.Z., Datta, A.K., 2022. Modern Technology Adoption by the Rice Farmers in the South-West Region of Bangladesh. Khulna University Studies. Special Issue, 175–188. Available from: https://ku.ac.bd/publication-details/617

[73] Ren, C., He, L., Ma, Y., et al., 2024. Trade-Offs in Agricultural Outcomes across Farm Sizes. Earth Critical Zone. 1(1), 100007. DOI: https://doi.org/10.1016/j.ecz.2024.100007

[74] Yeo, M.L., Keske, C.M., 2024. From Profitability to Trust: Factors Shaping Digital Agriculture Adoption. Frontiers in Sustainable Food Systems. 8, 1456991. DOI: https://doi.org/10.3389/fsufs.2024.1456991