Species-specific and mixed-species volume and above ground biomass allometric equations were developed for 15 indigenous tree species and four tree fern species in New Zealand. A mixed-species tree equation based on breast height diameter (DBH) and tree height (H) provided acceptable estimates of stem plus branch (>10 cm in diameter over bark) volume, which was multiplied by live tree density to estimate dry matter. For dead standing spars, DBH, estimated original height, actual spar height and compatible volume/taper functions provided estimates of dead stem volume, which was multiplied by live tree density and a density modifier based on log decay class from field assessments to estimate dry matter. Live tree density was estimated using ratio estimators. Ratio estimators were based on biomass sample trees, and utilized density data from outerwood basic density surveys which were available for 35 tree species sampled throughout New Zealand. Foliage and branch ( < 10 cm in diameter over bark) dry matter were estimated directly from tree DBH. Tree fern above ground dry matter was estimated using allometric equations based on DBH and H. Due to insufficient data, below ground carbon for trees was estimated using the default IPCC root/shoot ratio of 25%, but for tree ferns it was estimated using measured root/shoot ratios which averaged 20%.

Phaeocryptopus gaeumannii, the cause of Swiss needle-cast, is widely distributed in plantations of Douglas-fir in many parts of the world. Nevertheless, information remains limited on its precise effect on stand growth, particularly in relation to regional climate, and on its consequent economic cost. In New Zealand, the spread of P. gaeumannii over a period of ≈30 years following its discovery in 1959 was closely monitored, and the timing of its arrival in different forests is known. This information was coupled with data from permanent sample plots in order to quantify the associated historical growth increment loss. Analyses revealed a steady decline in growth rate over the period from the first appearance of P. gaeumannii to a point when it stabilized at a lower increment level 14 to 20 years later. The cumulative mean reduction was 25% for mean top height, 27% for basal area, and 32% for stem volume. Volume growth rate decline was greater in the North Island (35%) than the South Island (23%) of New Zealand. These reductions in volume growth are estimated to equate to a loss in net present value of $NZ2,620 ha^–1and $NZ1,470 ha^–1for the North and South Islands, respectively, using a discount rate of 6%. Mortality did not increase as a result of infection by P. gaeumannii. The disease had less effect on cooler sites, especially those with low spring minimum temperatures (P < 0.001). Negligible growth decline occurred on sites with daily minimum October temperatures averaging <3.2°C.

Relationships between discrete-return light detection and ranging (LiDAR) data and radiata pine leaf area index (LAI), stem volume, above ground carbon, and carbon sequestration were developed using 10 plots with directly measured biomass and leaf area data, and 36 plots with modelled carbon data. The plots included a range of genetic types established on north- and south-facing aspects. Modelled carbon was highly correlated with directly measured crown, stem, and above ground biomass data, with r = 0.92, 0.97 and 0.98, respectively. LiDAR canopy percentile height (P30) and cover, based on all returns above 0.5 m, explained 81, 88, and 93% of the variation in directly measured crown, stem, and above ground live carbon and 75, 89 and 88% of the modelled carbon, respectively. LAI (all surfaces) ranged between 8.8–19.1 in the 10 plots measured at age 9 years. The difference in canopy percentile heights (P95–P30) and cover based on first returns explained 80% of the variation in total LAI. Periodic mean annual increments in stem volume, above ground live carbon, and total carbon between ages 9 and 13 years were significantly related to (P95–P30), with regression models explaining 56, 58, and 55%, respectively, of the variation in growth rate per plot. When plot aspect and genetic type were included with (P95–P30), the R² of the regression models for stem volume, above ground live carbon, and total carbon increment increased to 90, 88, and 88%, respectively, which indicates that LiDAR regression equations for estimating stock changes can be substantially improved by incorporating supplementary site and crop data.

Historically, a series of regional height-age functions have been used to predict height growth of Pinus radiata in New Zealand. However, for some regions there are no available models while other regions have more than one available model.

Empirical growth models are widely used to predict the growth and yield of plantation tree species, and the precise estimation of site quality is an important component of these models. The most commonly used proxy for site quality in growth models is Site Index (SI), which describes the mean height of dominant trees at a specified base age. Although SI is widely used, considerable research shows significant site-dependent variation in height for a given volume, with this latter variable more closely reflecting actual site productivity. Using a national dataset, this study develops and describes a stand-level growth and yield model for even-aged New Zealand-grown coast redwood (Sequoia sempervirens). We used a novel modelling approach that quantifies site quality using SI and a volume-based index termed the 300 Index, defined as the volume mean annual increment at age 30 years for a reference regime of 300 stems ha⁻¹. The growth model includes a number of interrelated components. Mean top height is modelled from age and SI using a polymorphic Korf function. A modified anamorphic Korf function is used to describe tree quadratic mean diameter (D_q) as a function of age, stand density, SI and a diameter site index. As the D_q model includes stand density in its formulation, it can predict tree growth for different stand densities and thinning regimes. The mortality model is based on a simple attritional equation improved through incorporation of the Reineke stand density index to account for competition-induced mortality. Using these components, the model precisely estimates stand-level volume. The developed model will be of considerable value to growers for yield projection and regime evaluation. By more robustly describing the site effect, the growth model provides researchers with an improved framework for quantifying and understanding the causes of spatial and temporal variation in plantation productivity.