Development of models to predict Pinus radiata productivity throughout New Zealand

Abstract

Development of spatial surfaces describing variation in productivity across broad landscapes at a fine resolution would be of considerable use to forest managers as decision support tools to optimize productivity. In New Zealand, the two most widely used indices to quantify productivity of Pinus radiata D. Don are Site Index and 300 Index. Using an extensive national data set comprising a comprehensive set of national extent maps, multiple regression models and spatial surfaces of these indices for P. radiata were constructed. The final models accounted for 64% and 53%, respectively, of the variance in Site Index and 300 Index. For Site Index, variables included in the final model in order of importance were mean annual air temperature, fractional mean annual available root-zone water storage, mean annual windspeed, length and slope factor, categories describing Land Environments of New Zealand (LENZ), and major soil parent material. The variables included in the final model of 300 Index in order of importance included the degree of ground frost during autumn, fractional mean annual available root-zone water storage, categories describing LENZ, vegetation classification, foliar nitrogen, taxonomic soil order, and major soil parent material. These results highlight the utility of thematic spatial layers as driving variables in the development of productivity models.

Characterising prediction error as a function of scale in spatial surfaces of tree productivity

“…However, regression models of site productivity seldom explain much more than 50% of the variability due to the difficulty in measuring many of the environmental variables which affect tree growth, particularly variables associated with soil fertility. In the current study, PLS regression explained 56% of variation in the 300 Index and 63% of the variation in the Site Index, comparable to the levels of variation explained in previous studies using regression models such as those described by (Watt et al 2010). Where high-density datasets are available, kriging can often provide better predictions than regression models as spatial dependence between points is greater when data points are located closely together.…”

“…Temperaturerelated variables have generally been found to have the greatest influence on P. radiata productivity within New Zealand (Watt et al 2005;Watt et al 2008;Jackson and Gifford 1974;Hunter and Gibson 1984;Watt et al 2010) and were found here to be important determinants of both the 300 Index (summer degree frost days) and Site Index (max temperature in summer). The positive relationship often found between air temperature and tree growth is thought to be principally driven by the lengthening of the growing season (Lieth 1973;Kerkhoff et al 2005).…”

Multi-sensor modelling of a forest productivity index for radiata pine plantations

“…This is consistent with previous research that showed air temperature to be the most important determinant of P. radiata growth in New Zealand (Jackson and Gifford 1974;Hunter and Gibson 1984;Watt et al 2010), with air temperature in most locations in New Zealand, including the central North Island, sub-optimal for growth under current climatic conditions (Kirschbaum and Watt 2011). Models of Site Index for plantation species growing outside of New Zealand have also frequently found air temperature to be an important determinant of Site Index (Sharma et al 2012), but rainfall is at least as important as air temperature in many drier regions (Mohamed et al 2014;Sabatia and Burkhart 2014).…”

“…The 300 Index can be estimated from plot measurements of basal area, mean top height and stand density at a known age when the stand silvicultural history has been recorded (Watt et al 2010). The productivity index is estimated using numerous models including a standlevel basal area growth model, a height/age function, a mortality function, a stand-volume function and a thinning function.…”

Modelling spiral grain angle variation in New Zealand-grown radiata pine

“…approximately 7.3 % of the total variation), it is still considered worthwhile to investigate this further as previous studies have shown that SGA may be affected by exposure to wind (Eklund and Säll 2000;Fonweban et al 2013). Other site factors, such as soil type, temperature and rainfall distribution may also contribute and these vary widely across New Zealand, are strong determinants of stand productivity (Watt et al 2010) and have also been shown to affect other wood properties, most notably density (Palmer et al 2013). While there are known genetic differences in radiata pine SGA (Burdon and Low 1992;Gapare et al 2007;Wu et al 2008), the genetic origin of the trees in the dataset used to develop these models is very diverse it is unlikely for such differences to be observed over and above the other sources of variation in the dataset.…”