Impacts of Environmental Variables on Rice Production in Malaysia
Abstract
:1. Introduction
2. An Overview of Literature
3. Material and Method
3.1. The Dynamic ARDL Model
3.2. The Frequency Domain Causality Test
4. Results and Discussion
4.1. The ARDL Model Result
4.2. Results for the Dynamic ARDL
4.3. Causality Analysis
5. Conclusions and Recommendations
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Janjua, P.Z.; Samad, G.; Khan, N. Climate Change and Wheat Production in Pakistan: Autoregressive distributed lag approach. NJAS—Wagening. J. Life Sci. 2014, 68, 13–19. [Google Scholar] [CrossRef] [Green Version]
- Environmental Protection Agency (EPA). Overview of Greenhouse Gases; Environmental Protection Agency (EPA): Washington DC, USA, 2023. Available online: https://www.epa.gov/ghgemissions/overview-greenhouse-gases (accessed on 5 October 2021).
- Intergovernmental Panel on Climate Change-IPCC. Summary for Policymakers, Emissions Scenarios. In A Special Report of IPCC working Group3; IPCC: Geneva, Switzerland, 2007; ISBN 92-9169-113-5. [Google Scholar]
- Dang, A.T.N.; Kumar, L.; Reid, M. Modelling the Potential Impacts of Climate Change on Rice Cultivation in Mekong Delta, Vietnam. Sustainability 2020, 12, 9608. [Google Scholar] [CrossRef]
- Blocher, J.M.; Bergmann, J.; Upadhyay, H.; Vinke, K. Hot, Wet, and Deserted: Climate Change and Internal Displacement in India, Peru, and Tanzania. Potsdam Institute for Climate Impact Research (PIK). 2021. Available online: https://www.internal-displacement.org/global-report/grid2021/downloads/background_papers/background_paper-climatechange.pdf (accessed on 24 June 2023).
- Malhi, G.S.; Kaur, M.; Kaushik, P. Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review. Sustainability 2021, 13, 1318. [Google Scholar] [CrossRef]
- Amirnejad, H.; Asadpour Kordi, M. Effects of Climate Change on Wheat Production in Iran. J. Agric. Econ. Res. 2017, 9, 163–182. Available online: http://jae.miau.ac.ir/article_2520_a2bcfd520f9181ece63f0f6d71d3a516.pdf (accessed on 24 June 2023).
- Hussain, S.; Huang, J.; Ahamd, S.; Nanda, S.; Anwar, S.; Shakoor, A.; Zhu, C.; Zhu, L.; Cao, X.; Jin, Q. Rice Production under Climate Change: Adaptations and Mitigating Strategies; Environment, Climate, Plant and Vegetation Growth; Fahad, S., Hasanuzzaman, M., Alam, M., llah, H., Saeed, M., Khan, A.I., Adnan, M., Eds.; Springer: Cham, Germany, 2020. [Google Scholar] [CrossRef]
- Arshad, M.; Kächele, H.; Krupnik, T.J.; Amjath-Babu, T.S.; Aravindakshan, S. Climate variability, farmland value, and farmers’ perceptions of climate change: Implications for adaptation in rural Pakistan. Int. J. Sustain. Dev. World Ecol. 2017, 24, 532–544. [Google Scholar] [CrossRef]
- Rabbany, M.G.Y.; Mehmood, F.; Hoque, T.; Sarker, A.A.; Khan, K.Z.; Hossain, M.S.; Hossain, R.; Roy Luo, J. Effects of Partial Quantity Rationing of Credit on Technical Efficiency of Boro Rice Growers in Bangladesh: Application of the Stochastic Frontier Model. Emir. J. Food Agric. 2021, 33, 501–509. [Google Scholar] [CrossRef]
- Rabbany, M.G.; Mehmood, Y.; Hoque, F.; Sarkar, T.; Zulfik, K.; Ahmad, A.; Hossain, K.Z.; Khan, A.A.; Hossain, M.S.; Roy, R.; et al. Do credit constraints affect the technical efficiency of Boro rice growers? Evidence from the District Pabna in Bangladesh. Environ. Sci. Pollut. Res. 2022, 29, 444–456. [Google Scholar] [CrossRef]
- Solaymani, S. Global Energy Price Volatility and Agricultural Commodity Prices in Malaysia. Biophys. Econ. Sustain. 2022, 7, 11. [Google Scholar] [CrossRef]
- Pickson, R.B.; He, G.; Boateng, E. Impacts of climate change on rice production: Evidence from 30 Chinese provinces. Environ. Dev. Sustain. 2022, 24, 3907–3925. [Google Scholar] [CrossRef]
- Saud, S.; Wang, D.; Fahad, S.; Alharby, H.F.; Bamagoos, A.A.; Mjrashi, A.; Alabdallah, N.M.; AlZahrani, S.S.; AbdElgawad, H.; Adnan, M.; et al. Comprehensive Impacts of Climate Change on Rice Production and Adaptive Strategies in China. Front. Microbiol. 2022, 13, 926059. [Google Scholar] [CrossRef]
- Hossain, M.S.; Arshad, M.; Zhao, L.Q.M.; Mehmood, Y.; Kächele, H. Economic impact of climate change on crop farming in Bangladesh: An application of Ricardian method. Ecol. Econ. 2019, 164, 106354. [Google Scholar] [CrossRef]
- Tang, K.H.D. Climate change in Malaysia: Trends, contributors, impacts, mitigation and adaptations. Sci. Total Environ. 2019, 650, 1858–1871. [Google Scholar] [CrossRef]
- Siwar, C.; Ahmed, F.; Begum, R.A. Climate change, agriculture and food security issues: Malaysian perspective. J. Food Agric. Environ. 2013, 11, 1118–1123. [Google Scholar]
- Zhang, Q.; Akhtar, R.; Saif, A.N.M.; Akhter, H.; Hossan, D.; Alam, S.M.A.; Bari, M.F. The symmetric and asymmetric effects of climate change on rice productivity in Malaysia. Heliyon 2023, 9, e16118. [Google Scholar] [CrossRef]
- Department of Statistics Malaysia (DOSM). Time Series Data; DOSM: Putrajaya, Malaysia, 2021. [Google Scholar]
- Firdaus, R.B.R.; Tan, M.L.; Rahmat, S.R.; Gunaratne, M.S.; Casadevall, S.R. Paddy, rice and food security in Malaysia: A review of climate change impacts. Cogent Soc. Sci. 2020, 6, 1818373. [Google Scholar] [CrossRef]
- Dorairaj, D.; Govender, N.T. Rice and paddy industry in Malaysia: Governance and policies, research trends, technology adoption and resilience. Front. Sustain. Food Syst. 2023, 7, 1093605. [Google Scholar] [CrossRef]
- Azlan, A.A.A.; Zulkifi, N.; Fawwaz, A.M.; Bakri, Y.M. Food security in Malaysia: Literature review. RES Mil. Soc. Sci. J. 2022, 12, 905. Available online: https://resmilitaris.net/menu-script/index.php/resmilitaris/article/view/905 (accessed on 24 June 2023).
- Herath, G.; Hasanov, A.; Park, J. Impact of Climate Change on Paddy Production in Malaysia: Empirical Analysis at the National and State Level Experience. ICMSEM 2019. In Advances in Intelligent Systems and Computing, Proceedings of the Thirteenth International Conference on Management Science and Engineering Management, Shanghai, China, 30 July–1 August 2021; Xu, J., Ahmed, S., Cooke, F., Duca, G., Eds.; Springer: Cham, Germany, 2020; p. 1001. [Google Scholar] [CrossRef]
- Tan, B.T.; Fam, P.S.; Firdaus, R.B.R.; Tan, M.L.; Gunaratne, M.S. Impact of Climate Change on Rice Yield in Malaysia: A Panel Data Analysis. Agriculture 2021, 11, 569. [Google Scholar] [CrossRef]
- Ariff, E.; Elini, E. Economics Assessment and Impact of Climate Change on Rice Production in Selected Granary Area in Malaysia. Ph.D. Thesis, University of Nottingham, Nottingham, UK, 2016. Available online: https://eprints.nottingham.ac.uk/31220/ (accessed on 24 June 2023).
- Zed, Z.; Nurfarhana, R.; Mukhtar, J.A.; Amirparsa, J.; Ahmad, S.M.S.; Farrah Melissa, M.; Khairudin, N.; Mohamed, B.R.; Chung, J.X.; Liew, J.; et al. Historical and projected future hydroclimatic risk on seasonal yield in the irrigated rice paddies of Malaysia. In EGU General Assembly Conference Abstracts 2021; EGU General Assembly: Vienna, Austria, 2021; p. EGU21-15844. [Google Scholar] [CrossRef]
- Vaghefi, N.; Shamsudin, M.N.; Radam, A.; Abdul Rahim, K. Impact of climate change on food security in Malaysia: Economic and policy adjustments for rice industry. J. Integr. Environ. Sci. 2016, 13, 19–35. [Google Scholar] [CrossRef]
- Siddig, K.; Stepanyan, D.; Wiebelt, M.; Grethe, H.; Zhu, T. Climate change and agriculture in the Sudan: Impact pathways beyond changes in mean rainfall and temperature. Ecol. Econ. 2020, 169, 106566. [Google Scholar] [CrossRef] [Green Version]
- Solaymani, S. Impacts of climate change on food security and agriculture sector in Malaysia. Environ. Dev. Sustain. 2018, 20, 1575–1596. [Google Scholar] [CrossRef]
- Abbas, S. Climate change and major crop production: Evidence from Pakistan. Env. Sci. Pollut. Res. 2022, 29, 5406–5414. [Google Scholar] [CrossRef]
- Wakjira, M.T.; Peleg, N.; Anghileri, D.; Molnar, D.; Alamirew, T.; Six, J.; Molnar, P. Rainfall seasonality and timing: Implications for cereal crop production in Ethiopia. Agric. For. Meteorol. 2021, 310, 108633. [Google Scholar] [CrossRef]
- Zainal, Z.; Shamsudin, M.N.; Mohamed, Z.A.; Adam, S.U.; Kaffashi, S. Assessing the Impacts of Climate Change on Paddy Production in Malaysia. Res. J. Environ. Sci. 2014, 8, 331–341. [Google Scholar]
- Chandio, A.A.; Gokmenoglu, K.K.; Ahmad, F. Addressing the long- and short-run effects of climate change on major food crops production in Turkey. Environ. Sci. Pollut. Res. 2021, 28, 51657–51673. [Google Scholar] [CrossRef]
- Baig, I.A.; Chandio, A.A.; Ozturk, I.; Kumar, P.; Khan, Z.A.; Salam, M. Assessing the long- and short-run asymmetrical effects of climate change on rice production: Empirical evidence from India. Env. Sci. Pollut. Res. 2022, 29, 34209–34230. [Google Scholar] [CrossRef]
- Kumar, P.; Sahu, N.C.; Ansari, M.A.; Kumar, S. Climate change and rice production in India: Role of ecological and carbon footprint. J. Agribus. Dev. Emerg. Econ. 2023, 13, 260–278. [Google Scholar] [CrossRef]
- Bhardwaj, M.; Kumar, P.; Kumar, S.; Dagar, V.; Kumar, A. A district-level analysis for measuring the effects of climate change on production of agricultural crops, i.e., wheat and paddy: Evidence from India. Env. Sci. Pollut. Res. 2022, 29, 31861–31885. [Google Scholar] [CrossRef]
- Nasrullah, M.; Rizwanullah, M.; Yu, X.; Jo, H.; Sohail, M.T.; Liang, L. Autoregressive distributed lag (ARDL) approach to study the impact of climate change and other factors on rice production in South Korea. J. Water Clim. Chang. 2021, 12, 2256–2270. [Google Scholar] [CrossRef]
- Senanayake, S.; Pradhan, B.; Huete, A.; Brennan, J. Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka. Sci. Total. Environ. 2022, 806, 150405. [Google Scholar] [CrossRef]
- Gumel, D.Y.; Abdullah, A.M.; Sood, A.M.; Elhadia, R.E.; Jamalani, M.A.; Youssef, K.K.M.B. Assessing Paddy Rice Yield Sensitivity to Temperature and Rainfall Variability in Peninsular Malaysia Using DSSAT Model. Int. J. Appl. Environ. Sci. 2017, 12, 1521–1545. Available online: https://www.ripublication.com/ijaes17/ijaesv12n8_05.pdf (accessed on 24 June 2023).
- Entezari, A.F.; Wong, K.K.S.; Ali, F. Malaysia’s Agricultural Production Dropped and the Impact of Climate Change: Applying and Extending the Theory of Cobb Douglas Production. J. Agribus. Rural. Dev. Res. 2018, 7, 127–141. [Google Scholar] [CrossRef]
- Chizari, A.; Mohamed, Z.; Shamsudin, M.N.; Seng, K.W.K. The Effects of Climate Change Phenomena on Cocoa Production in Malaysia. Int. J. Environ. Agric. Biotechnol. IJEAB 2017, 2, 2599–2604. Available online: https://ijeab.com/upload_document/issue_files/42%20IJEAB-OCT-2017-8-The%20Effects%20of%20Climate%20Change.pdf (accessed on 24 June 2023). [CrossRef]
- Wan Mohd Jaafar, W.S.; Abdul Maulud, K.N.; Muhmad Kamarulzaman, A.M.; Raihan, A.; Md Sah, S.; Ahmad, A.; Saad, S.N.M.; Mohd Azmi, A.T.; Jusoh Syukri, N.K.A.; Razzaq Khan, W. The Influence of Deforestation on Land Surface Temperature—A Case Study of Perak and Kedah, Malaysia. Forests 2020, 11, 670. [Google Scholar] [CrossRef]
- Solaymani, S. Agriculture and poverty responses to high agricultural commodity prices. Agric. Res. 2017, 6, 195–206. [Google Scholar] [CrossRef]
- Solaymani, S.; Yusma, N.B.M.Y. Poverty effects of food price escalation and mitigation options: The case of Malaysia. J. Asian Afr. Stud. 2018, 53, 685–702. [Google Scholar] [CrossRef]
- Wong, K.K.S.; Lee, C.; Wong, W.L. Impact of climate change and economic factors on Malaysian food price. J. Int. Soc. Southeast Asian Agric. Sci. 2019, 25, 32–42. Available online: https://www.cabdirect.org/cabdirect/abstract/20193361897 (accessed on 24 June 2023).
- Tan, K.K.; Loh, P.N. Climate change assessment on rainfall and temperature in Cameron highlands, Malaysia, using regional climate downscaling method. Carpathian J. Earth Environ. Sci. 2017, 12, 413–421. Available online: http://www.cjees.ro/actions/actionDownload.php?fileId=1114 (accessed on 24 June 2023).
- Li, W.; Ruiz-Menjivar, J.; Zhang, L.; Zhang, J. Climate change perceptions and the adoption of low-carbon agricultural technologies: Evidence from rice production systems in the Yangtze River Basin. Sci. Total Environ. 2021, 759, 143554. [Google Scholar] [CrossRef]
- Shamshiri, R.R.; Ibrahim, B.; Ahmad, D.; Che Man, F.; Wayayok, A. An Overview of the System of Rice Intensification for Paddy Fields of Malaysia. Indian J. Sci. Technol. 2018, 11, 2–16. [Google Scholar] [CrossRef]
- Pesaran, M.H.; Shin, Y. An autoregressive distributed lag modelling approach to cointegration analysis. In Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium; Strom, S., Ed.; Cambridge University Press: Cambridge, UK, 1999. [Google Scholar]
- Li, Y.; Solaymani, S. Energy consumption, technology innovation and economic growth nexuses in Malaysian. Energy 2021, 232, 121040. [Google Scholar] [CrossRef]
- Jordan, S.; Philips, A.Q. Cointegration testing and dynamic simulations of autoregressive distributed lag models. STATA J. 2018, 18, 902–923. [Google Scholar] [CrossRef] [Green Version]
- Breitung, J.; Candelon, B. Testing for short- and long-run causality: A frequency-domain approach. J. Econom. 2006, 132, 363–378. [Google Scholar] [CrossRef]
- Ghysels, E.; Hill, J.B.; Motegi, K. Testing for Granger causality with mixed frequency data. J. Econom. 2016, 192, 207–230. [Google Scholar] [CrossRef]
- Nurul Ain, A.B.; Mohammad Hariz, A.R.; Shaidatul Azdawiyah, A.T.; Azizi, A.A.; Mardhati, M.; Mohd Fairuz, M.S.; Mohd Saufi, B.; Fauzi, J. Indirect Estimation of Agricultural Nitrous Oxide Emission in Malaysia. Malays. J. Soil Sci. 2021, 25, 171–193. Available online: https://www.msss.com.my/mjss/Full%20Text/vol25/V2/V25_12.pdf (accessed on 24 June 2023).
- Abdul Rahman, M.H.; Chen, S.S.; Abdul Razak, P.R.; Abu Bakar, A.N.; Shahrun, M.S.; Zin Zawawi, N.; Muhamad Mujab, A.A.; Abdullah, F.; Jumat, F.; Kamaruzaman, R.; et al. Life cycle assessment in conventional rice farming system: Estimation of greenhouse gas emissions using cradle-to-gate approach. J. Clean. Prod. 2019, 212, 1526–1535. [Google Scholar] [CrossRef]
- Elsoragaby, S.; Yahya, A.; Mahadi, M.R.; Nawi, N.M.; Mairghany, M. Analysis of energy use and greenhouse gas emissions (GHG) of transplanting and broadcast seeding wetland rice cultivation. Energy 2019, 189, 116160. [Google Scholar] [CrossRef]
- Harun, S.N.; Hanafiah, M.M.; Aziz, N.I.H.A. An LCA-Based Environmental Performance of Rice Production for Developing a Sustainable Agri-Food System in Malaysia. Environ. Manag. 2021, 67, 146–161. [Google Scholar] [CrossRef]
- Wang, J.; Vanga, S.K.; Saxena, R.; Orsat, V.; Raghavan, V. Effect of Climate Change on the Yield of Cereal Crops: A Review. Climate 2018, 6, 41. [Google Scholar] [CrossRef] [Green Version]
- Fei, L.; Meijun, Z.; Jiaqi, S.; Zehui, C.; Xiaoli, W.; Jiuchun, Y. Maize, wheat and rice production potential changes in China under the background of climate change. Agric. Syst. 2020, 182, 102853. [Google Scholar] [CrossRef]
- Shabbir, G.; Khaliq, T.; Ahmad, A.; Saqib, M. Assessing the climate change impacts and adaptation strategies for rice production in Punjab, Pakistan. Environ. Sci. Pollut. Res. 2020, 27, 22568–22578. [Google Scholar] [CrossRef] [PubMed]
- Abbas, S.; Kousar, S.; Shirazi, S.A.; Yaseen, M.; Latif, Y. Illuminating Empirical Evidence of Climate Change: Impacts on Rice Production in the Punjab Regions, Pakistan. Agric. Res. 2021, 11, 32–47. [Google Scholar] [CrossRef]
- Duasa, J.; Mohd-Radzman, N.A. Impact of climate change on food security of rice in Malaysia: An empirical investigation. IOP Conf. Ser. Earth Environ. Sci. 2021, 756, 012003. Available online: https://iopscience.iop.org/article/10.1088/1755-1315/756/1/012003/meta (accessed on 24 June 2023).
- Anh, D.L.T.; Anh, N.T.; Chandio, A.A. Climate change and its impacts on Vietnam agriculture: A macroeconomic perspective. Ecol. Inform. 2023, 74, 101960. [Google Scholar] [CrossRef]
- Xiang, X.; Solaymani, S. Change in cereal production caused by climate change in Malaysia. Ecol. Inform. 2022, 70, 101741. [Google Scholar] [CrossRef]
Unit | Mean | Median | Maximum | Minimum | Std. Dev. | Obs. | |
---|---|---|---|---|---|---|---|
RICE | Kg/hectares | 14.477 | 14.519 | 14.884 | 13.901 | 0.245 | 59 |
HARV | Hectares | 13.412 | 13.428 | 13.549 | 13.155 | 0.082 | 59 |
AGLF | people | 3.821 | 3.916 | 4.287 | 3.152 | 0.353 | 59 |
GFCF | Million Ringgit | 6.63 | 7.064 | 9.149 | 2.434 | 2.029 | 59 |
RAIN | mm | 8.02 | 8.015 | 8.222 | 7.838 | 0.093 | 59 |
TEM | °C | 3.236 | 3.237 | 3.27 | 3.21 | 0.015 | 59 |
CO2 | Kilo tonne | 10.826 | 10.936 | 12.387 | 8.294 | 1.227 | 59 |
FERT | tonnes | 13.317 | 13.721 | 14.458 | 11.219 | 1.022 | 59 |
9 | ADF Test | PP Test | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Level | First Difference | Level | First Difference | |||||||||
Int. | Int. and t | Non | Int. | Int. and t | Non | Int. | Int. and t | Non | Int. | Int. and t | Non | |
LRICE | −1.90 | −3.72 ** | 1.62 | −10.59 * | −10.53 * | −10.31 * | −1.93 | −3.60 ** | 1.90 | 11.08 * | 11.16 * | −10.39 * |
LTEM | −0.98 | −6.24 * | 1.83 | −7.55 * | −7.49 * | −7.17 * | −1.63 | −6.26 * | 2.95 | −30.67 * | −30.32 * | −11.36 * |
LRAIN | −5.80 * | −5.96 * | 1.62 | −8.74 * | −8.65 * | −7.13 * | −5.84 * | −5.90 * | 1.36 | −17.97 * | −17.50 * | −7.33 * |
LHARV | −3.89 * | −3.69 ** | 0.85 | −10.75 * | −10.87 * | −10.75 * | −3.85 * | −3.69 * | 0.90 | −10.75 * | −11.09 * | −10.79 * |
LFERT | −4.14 * | −0.03 | 0.06 | −9.40 * | −6.35 * | −8.83 * | −3.75 * | −0.65 * | −0.17 | −9.46 * | −23.17 * | −18.40 * |
LCO2 | −3.73 * | −1.71 | 5.64 | −7.54 * | −8.92 * | −3.18 * | −4.19 * | −1.00 | 5.20 | −7.55 * | −9.37 * | −5.15 * |
LGFCF | −1.89 | −1.98 | 2.02 | −5.35 * | −5.61 * | −4.32 * | −2.57 | −1.75 | 2.84 | −5.33 * | −5.58 * | −4.19 * |
LAGLF | −3.05 ** | −1.97 | 1.62 | −7.43 * | −8.20 * | −7.13 * | −3.01 ** | −1.96 | 1.36 | −7.56 * | −8.17 * | −7.33 * |
Function | F-Statistic | |
---|---|---|
FLRIC(LRICE|LHARV, LAGLF, LGFCF, LFERT, LRAIN, LTEMP, LCO2) | 3.385 ** | |
Level of significance | I(0) | I(1) |
10% | 1.92 | 2.89 |
5% | 2.17 | 3.21 |
1% | 2.73 | 3.90 |
Long-Run Results | Short-Run Results | |||||
---|---|---|---|---|---|---|
Variables | Coefficient | Std. Error | t-Statistic [Prob.] | Coefficient | Std. Error | t-Statistic [Prob.] |
C | 5.291 | 5.141 | 1.029 [0.309] | 3.033 | 2.935 | 1.033 [0.307] |
LHARV | 1.648 * | 0.172 | 9.568 [0.000] | 1.313 * | 0.110 | 11.982 [0.000] |
LAGLF | −0.531 * | 0.141 | −3.753 [0.000] | −0.304 * | 0.092 | −3.308 [0.002] |
LGFCF | 0.073 ** | 0.033 | 2.196 [0.033] | 0.112 * | 0.031 | 3.588 [0.001] |
LFERT | −0.052 | 0.059 | −0.882 [0.382] | −0.080 | 0.062 | −1.297 [0.201] |
LRAIN | −0.140 | 0.107 | −1.312 [0.196] | −0.030 | 0.035 | −0.857 [0.396] |
LTEM | −2.807 ** | 1.359 | −2.066 [0.044] | −1.609 ** | 0.784 | −2.052 [0.046] |
LCO2 | −0.044 | 0.091 | −0.486 [0.629] | −0.025 | 0.052 | −0.484 [0.631] |
ECMt−1 | -- | -- | --- | −0.573 * | 0.114 | −5.034 [0.000] |
Diagnostic Tests | ||||||
Adj. R2 | 0.977 | statistic [p-value] | D-W stat | 1.922 | ||
F-Stat. F(10, 47) | 238.743 [0.00] | |||||
Serial Correlation | Chi-square (1) | 0.77 [0.38] | ||||
Functional Form | Chi-square (1) | 1.14 [0.29] | ||||
Normality | Chi-square (2) | 4.18 [0.12] | ||||
Heteroscedasticity | Chi-square (1) | 0.02 [0.88] |
DLRICE | Coefficient | Std. Err. | t [p > t] |
---|---|---|---|
ECT(−1) | −0.63 c | 0.13 | −4.72 [0.00] |
L1_LHARV | 1.02 c | 0.23 | 4.44 [0.00] |
L1_LAGLF | −0.36 c | 0.12 | −2.86 [0.01] |
L1_LGFCF | 0.05 e | 0.03 | 1.77 [0.08] |
L1_LTEM | −2.79 d | 1.31 | −2.13 [0.04] |
L1_LFERT | −0.04 | 0.05 | −0.73 [0.47] |
L1_LRAIN | −0.16 | 0.11 | −1.51 [0.14] |
L1_CO2 | −0.02 | 0.07 | −0.32 [0.75] |
D_LHARV | 1.30 c | 0.12 | 11.22 [0.00] |
D_LAGLF | −0.68 | 1.68 | −0.41 [0.69] |
D_LGFCF | 0.12 c | 0.04 | 3.30 [0.00] |
D_LFERT | −0.04 | 0.04 | −0.99 [0.33] |
D_LRAIN | −0.07 | 0.07 | −1.05 [0.30] |
D_LTEM | −1.87 d | 0.93 | −2.02 [0.05] |
D_LCO2 | −0.03 | 0.08 | −0.35 [0.73] |
_cons | 7.48 | 4.79 | 1.56 [0.13] |
Adj. R-sq. | 0.77 | F(15, 42) | 13.97 [0.00] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Solaymani, S. Impacts of Environmental Variables on Rice Production in Malaysia. World 2023, 4, 450-466. https://doi.org/10.3390/world4030028
Solaymani S. Impacts of Environmental Variables on Rice Production in Malaysia. World. 2023; 4(3):450-466. https://doi.org/10.3390/world4030028
Chicago/Turabian StyleSolaymani, Saeed. 2023. "Impacts of Environmental Variables on Rice Production in Malaysia" World 4, no. 3: 450-466. https://doi.org/10.3390/world4030028