A direct approach of causal detection for agriculture related variables via spatial and temporal non-parametric analysis

Understanding the causality between biological variables or their related variables is beneficial in environmental or biological policy making. The usual approaches revealing the relations between them are traditional ANOVA or regression models. These models normally resort to a plethora of assumptions regarding the population, the covariance or the error distributions. Checking the validity of these assumptions might in turn rely on other batches of assumptions. This shall cause a huge burden on the interpretation and calculation. Even if all the assumptions are taken for granted or validly checked, the traditional approaches reveal more on the correlation or association properties and less on the causality, because of the fundamental reasoning is based on distance functions or the least squared methods, which are symmetric indicators. We devise a method which directly measures the causality between vectors, which in turn measures the causal relation between agriculture-related variables. The measure takes monotonicity, temporal properties, asymmetry and additivity into consideration. It is then implemented by a set of simulated data and two sets of agriculture-related data. This method could validate or invalidate the existence of positive or negative causal relations between agriculture-related variables. In the end, we analyze the advantages and disadvantages of this method.