An autoregressive distributed lag-bounds testing approach in determining impacts of climate change indicators to the rice yield of Central Luzon
Crop production, among many others, is threatened by climate change. In the Philippines, rice (Oryza sativa) is the staple food that also serves as an indicator of nutritional and economic status. Various models have been developed already for the production of this grain but were done on a larger s...
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| Format: | text |
| Language: | English |
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Animo Repository
2020
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| Online Access: | https://animorepository.dlsu.edu.ph/etd_masteral/5990 https://animorepository.dlsu.edu.ph/context/etd_masteral/article/13096/viewcontent/Pantolla_Hernan_11290684_Partial.pdf |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | Crop production, among many others, is threatened by climate change. In the Philippines, rice (Oryza sativa) is the staple food that also serves as an indicator of nutritional and economic status. Various models have been developed already for the production of this grain but were done on a larger scale. Further, local and international organizations call for the creation of models that are specific to locations as well as its response to the changes in the environment. Majority of produced rice in the country comes from Central Luzon. Hence, a model is proposed for the region but made use of the yield as the dependent variable instead to account for the harvested land area. This study applied the Bounds Testing feature of the Autoregressive Distributed Lag Model (ARDL) to determine the long-run and short-run effects of the selected climate change variables namely precipitation, temperature, atmospheric carbon dioxide (CO2) concentration, and sea surface temperature anomaly (SSTA) to the rice yield of Central Luzon. The findings show that (a) the speed of adjustment is 1.26%, (b) precipitation, temperature, and CO2 have long-run impacts, (c) the lagged differences of the yield itself and temperature as well as the first difference temperature have short-run effects, (d) while the SSTA has no significant contributions. The fitness of the generated ARDL model was also substantiated by every diagnostics test applied. In addition, the comparison with a baseline model showed gains in forecasting accuracy. The results of this study could be used in decision-making and policy developments. |
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