Spatiotemporal Asymmetric Modeling of the Long Run Response of Corn Productivity to Environmental Variabilities

Abstract

A spatiotemporal statistical model for the corn productivity of Bulacan using local and global variables was generated. The data from January 1995–September 2019 for a total of 99 quarters were used. Necessary imputations and conversions aside from a transformation technique were done to match the data and generate the best fitting model. The nonlinear autoregressive distributed lag was used to determine the asymmetric long-run effects on the corn production volume of the province to the potential positive and negative changes in the amount of rainfall, minimum temperature, maximum temperature, the water level of Angat Dam, atmospheric carbon dioxide concentration, and sea surface temperature anomaly. It also included the land area harvested as a deterministic factor. The findings show that the corn production of the selected area responds to both negative and positive shocks to the rainfall amount and availability of water supply in the dam in the long run. It also shows that the long-term effects of an increase in the minimum temperature are detrimental, whereas rising levels of carbon dioxide concentration in the environment could be profoundly contributory to the dependent variable, at least in a direct manner. Several diagnostic tests substantiated the fitness of the econometric model. The empirical findings of this study extend the generally collective results of existing literature. It may also be used as a basis for plans of action for sustainable production practices of the grain, as well as other crops in the uncertain future amidst the threat of environmental stresses.