Application of Time Series Analysis to Investigate Crop and Environment Relationships 1

Correlation techniques are important tools for investigating relationships between crop growth and environment. However when applied to a time series the presence of autocorrelation affects the estimates of correlations between two observed series, an effect which is sometimes overlooked in studies reported in the literature. Appropriate statistical techniques are needed to ensure that proper inferences can be made from these observations. Empirical time series modeling has the potential for removing autocorrelations in many of these cases. To test the feasibility of this technique the relationship between boll retention in cotton ( Gossypium hirsutum L.) and 5‐day average minimum temperature during the growing season was investigated with 3 years of data. The best fitting autoregressive moving average models were selected for the observed time series for each of the 3 years. Residual series, observed minus predicted values, were computed from the estimated time series models for boll retention and minimum temperature and were free of autocorrelation. Negative crosscorrelations were found between the residual series for boll retention and average minimum temperature for the 5‐day periods beginning with 10 days and 1 day prior to anthesis, respectively. The negative relationship suggests that high nighttime temperatures accumulated over 5‐day periods prior to and during anthesis increase boll shedding. We conclude that time series modeling is an effective technique to aid the identification of relationships between agronomic variables measured in time sequences.