A modelling system is described that indicates the extent to which day-to-day variations in nitrogenase activity in young Alnus incana (L.) Moench, grown in defined conditions in the field, may be affected by weather conditions both during and prior to the day of measurement. Nitrogenase activity (acetylene reduction activity, ARA) was measured weekly on intact field-grown grey alder (A. incana) plants, 0.15–0.42 m tall at planting, nodulated with Frankia. The measurements were done at noon on two groups of plants in 1987 and on two other groups in 1988. Each group was made up of five or six plants. Seven weather variables: daily sunshine hours, daily mean, maximum and minimum air temperature, daily mean and 1300 h relative humidity, and daily rainfall were used. The relation between log(ARA/leaf area) and the weather variables were analysed using a PLS model (partial least squares projection to latent structures). The advantage of PLS is that it can handle x-variables that are correlated. Data from 1987 were chosen as a training set. Multivariate PLS time series analysis was made by adding, in a stepwise manner, the weather data up to 5 d before the day of measurement. This procedure gave six models with n * 7 x-variables (n= 1–6). With the models from the time series analysis of 1987 data, true predictions of ARA per leaf area were made from weather data 1988 (test set 1) and from ‘early-season’ weather data from 1987 and 1988 (test set 2). The variation in ARA/leaf area could be predicted from the weather conditions. The predictions of the two test sets improved when the weather conditions one and two days before the day of measurements were added to the model. The further addition of weather data from 3 to 5 d before the day of measurement did not improve the model. The good predictions of ARA/leaf area show that the alders responded to the variable weather conditions in the same way in 1988 as in 1987, despite the ten-fold difference in size (leaf area) at the end of the growing season. Among the weather variables, air temperature and the daily sunshine hours were positively correlated to ARA, while relative air humidity and rainfall were negatively correlated to ARA. The daily minimum temperature and rainfall appeared to have least impact on ARA. By use of PLS, we could extract information out of a data set containing highly correlated x-variables, information that is non-accessible with conventional statistical tools such as multiple regression. When making measurements of nitrogenase activities under field conditions, we propose that attention should be paid to the weather conditions on the days preceding the day of measurement. The day-to-day variation in nitrogenase activity is discussed with reference to known effects of stress factors under controlled conditions.
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Mar 1, 2009
권태연 
Open Access
