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White spruce and trembling aspen are two important commercial species in British Columbia. They often grow in association, particularly in the Boreal White and Black Spruce and Sub-Boreal Spruce biogeoclimatic zones. Site index conversion equations are useful for estimating the site index of one species from the site index of another species. This study fills a need for site index conversion equations for mixed spruce/aspen stands. Seventy 0.01 ha study plots were established in mixed spruce/aspen stands. One site tree of each species was selected from each plot. The height and breast height ages of the site trees were measured and the site index was estimated with these data. The correlation between the site index of spruce and aspen was 0.6. Geometric mean regression was used to estimate the parameters of a linear site index conversion equation. The analysis did not reveal any differences in the conversion equations across the three major biogeoclimatic units (BWBSmw1, BWBSmw2, and SBS) that were sampled. Therefore, only one conversion equation is required.

The taper equations used by the Ministry of Forests, Lands and Natural Resource Operations in British Columbia (BC), Canada, date back to the mid-1950s. Very little work has been done on examining the effect of climate on taper, particularly for BC but elsewhere as well. The objective of our research was to determine whether climate has an effect on tree taper for lodgepole pine (Pinus contorta Dougl. ex Loud.) in BC. The data for this project consisted of multiple diameter inside bark measurements along the stems of 270 trees across eight biogeoclimatic zones. In addition, 20 climate variables for the sample sites were predicted from the ClimateWNA model. Kozak's variable-exponent taper model was refitted with the climate variables in the exponent of the model. The single temperature- and precipitation-related variables that provided the best fit were incorporated into the final taper model. The model was analysed as a mixed-effects model, with spatial correlation and heteroscedastic errors being explicitly modelled. Mean annual precipitation and the Julian date of the first frost after the summer growing period were the best predictors of taper. Further work is required to understand why these variables are important predictors of taper, but a possible linkage is through the tree's crown. [ABSTRACT FROM PUBLISHER]

Non-linear mixed effects (NLME) models are now common in the forestry literature. The fixed effects parameters are estimated in the model fitting stage. For new individuals, the random effects parameters are predicted when the model is used to predict the response variable. Predicting both the random effects parameters and the response variable requires the underlying model to be linearized using a Taylor series expansion. This is done by expanding around the expected or predicted value of the random effects parameters. Whichever way is chosen, it should be consistent. However, mismatches between the predictors for the random effects and predictors for the future response are sometimes made in forestry growth and yield applications. We consider the implications of using mismatched predictors on a single-level NLME model. We analyzed and empirically compared a total of four combinations of two types of predictors for both the random effects and the response variable. Diameter at breast height was predicted for 140 loblolly pine trees using these four combinations of predictors. The predictors were evaluated with the mean squared error. Model accuracy was best when the predictors for the random effects and the response variables were based on the same expansion method. [ABSTRACT FROM AUTHOR]

Empirical growth and yield models for forest management are evolving toward individual-tree models that are capable of simulating the growth of mixed and uneven-aged stands. Spatially explicit (i.e., distance-dependent) models usually modify the growth of trees by means of competition indices; however, these competition indices rarely simulate the light available for tree growth explicitly. We used tree growth data from an even-aged, unthinned, 50-year-old Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stand in British Columbia to test the hypothesis that the amount of absorbed light is a good predictor of diameter at breast height, height, and bole volume growth of an individual tree. We also explored the relationships between these variables. A spatially explicit light model was used to simulate photosynthetically active radiation absorbed by individual trees during a growth period (APAR) based on detailed canopy architecture information. For the purpose, we used a weighted leaf area (WLA) that is linearly related to APAR. Because of the integration of light absorption by a tree crown, estimates of WLA were highly correlated with leaf area for dominant trees. For suppressed trees, leaf area was a poor estimator of WLA. The relationship between WLA and bole volume growth was nonlinear, indicating a higher light-use efficiency in suppressed trees than in dominant trees. This relationship was strong enough to be useful for growth modeling. Only height growth of suppressed trees was affected by WLA. We conclude that single-tree WLA can be used as a process-oriented competition index in growth models for forest management. [ABSTRACT FROM PUBLISHER]

Understanding the irregular yield pattern of greenhouse-grown sweet peppers (Capsicum annuum L.) has been a challenge to researchers and greenhouse producers. Experimental data from 4 years, each consisting of 26 production weeks, were used in a time series analysis, neural network (NN) modeling, and regression analysis. Time series analysis revealed that weekly yield was influenced by yields from the preceding 2 weeks (Yd_1 and Yd_2), cumulative light 2 and 4 weeks prior (L_2 and L_4), and average 24-h air temperature 5 weeks prior (T_5). Cumulative light (L) data were transformed into kL by dividing by 1000 for subsequent NN modeling and regression analysis. These five inputs were used to establish a NN model, which illustrated the positive influence of Yd_1, kL_4, and kL_2 and negative influence of Yd_2 and T_5. Again, these five inputs were used in a regression analysis illustrating the positive influence of Yd_1 and the negative influence of Yd_2. Each input was further modified to include its squared value before entering the regression, which resulted in significant inputs of Yd_1, Yd_1 squared, and Yd_2 squared. Among these three analyses, the most consistent parameters were Yd_1 and Yd_2, confirming that the irregular yield pattern of greenhouse-grown peppers is of a biological nature. Environmental factors kL_2, kL_4, and T_5 did not show a consistent effect on yield in all three analyses, indicating yield pattern is less influenced by growing environment. [ABSTRACT FROM AUTHOR]