The study analyzed dynamic time relationships and time warping between weight of highland chinese cabbage and growth environment variables using an arbitrary-lag causality method. During an early stage of growth, the relation between cabbage and growth environment variables is formed in arbitrary times. The bigdata used in the study are collected from the open field samrtfarm system operated by Rural Development Administration. The study applied for a dynamic time warping algorithm to measure dynamic time distances between weights of highland chinese cabbage and growth and weather variables, respectively. After that, we compared arbitrary-lag causality and fixed-lag causality. We found the existence of arbitrary-time lags between variables. In addiction, we found that the arbitrary-lag causality analysis is performed better than fixed-lag causality. Finally, we found that growth and weather variables caused cabbage weight under the arbitray-time lags, but not vice versa.