A system of equations was developed and incorporated into the PTAEDA2 loblolly pine stand simulator to provide growth projections from time of planting. Annual height growth is predicted using a two-parameter Weibull function, where distribution parameters are estimated from equations that utilize site index and age as predictor variables. Allometric equations are employed to estimate tree diameter and height-to-crown attributes. First year after planting mortality estimates are based on physiographic region and drainage class, with adjustments for bedding or discing site preparation treatments. Thereafter, a simple mortality function is used. The onset of competition is defined through a point density measure, which was conditioned to correspond with inflection points of basal area growth curves from observed data. Early silvicultural treatment response functions were also developed. These equations modify growth for shearing and piling, discing, and bedding site preparation methods, fertilization with phosphorous, nitrogen, and/or potassium, and 1-year or 2-year herbaceous weed control treatments. Differential responses due to drainage class and physiographic region are included in the response functions where necessary. Equations that account for interactions between certain treatments are used to adjust response levels where treatments have similar effects site conditions. Analyses of pre-competitive growth projections where no treatments are specified reveal that a small amount of over-prediction is present when compared with observed data. Predicted values in the post-competitive growth phase confirm that the addition of the pre-competitive growth system did not significantly affect the predictive behavior of the PTAEDA2 model. The simulated growth responses attributed to early silvicultural treatments are consistent with response levels reported in other studies. / Ph. D.

This study develops forestry growth and yield models for two economically important tree species in Manitoba, black spruce [Picea mariana (Mill.) BSP] and jack pine [Pinus banksiana Lamb]. The growth and yield models developed include regression-based individual tree height growth and site index, tree diameter (basal area) growth, tree bole taper, and individual tree mortality models. These regression-based models were developed empirically, using stem analysis, growth and mortality data from 80 permanent sample plots located within the commercially important boreal forests of Manitoba. Model development involved the exploration, comparison and testing of numerous potential regression models and predictor variables. Statistical issues commonly encountered in forest growth and yield modeling, particularly data autocorrelation and variable multicollinearity, were addressed using nonlinear least squares (NLS), generalized nonlinear least squares (GNLS), and nonlinear mixed-effects model regression (NLMM) approaches. Height growth and site index of black spruce and jack pine was modelled using a three-parameter generalized logistic function. NLMM regression was used since the data were spatially autocorrelated. The inclusion of prior measures from individual trees produced more accurate predictions. In the tree diameter (basal area) growth models, tree size variables were significant predictors for black spruce and managed jack pine stands. Site index (a measure of site productivity) was positively correlated, and basal area of trees larger than the target tree (a relative measure of competition) negatively correlated, with diameter increment. Thiessen polygon area, a spatial measure of competition, was a significant predictor for natural jack pine and upland black spruce stands. Tree bole taper was modeled by NLMM approach using a five-parameter equation based on dimensional analysis, with breast height diameter, total height and relative height as predictor variables. The inclusion of a single prior measure from each tree improved model prediction. Black spruce and jack pine mortality was modeled using logistic regression. The black spruce models predicted high survivorship for larger, fast-growing trees in less crowded stands. In the jack pine model, highest survivorship was predicted for larger, less locally crowded trees.

This study develops forestry growth and yield models for two economically important tree species in Manitoba, black spruce [Picea mariana (Mill.) BSP] and jack pine [Pinus banksiana Lamb]. The growth and yield models developed include regression-based individual tree height growth and site index, tree diameter (basal area) growth, tree bole taper, and individual tree mortality models. These regression-based models were developed empirically, using stem analysis, growth and mortality data from 80 permanent sample plots located within the commercially important boreal forests of Manitoba. Model development involved the exploration, comparison and testing of numerous potential regression models and predictor variables. Statistical issues commonly encountered in forest growth and yield modeling, particularly data autocorrelation and variable multicollinearity, were addressed using nonlinear least squares (NLS), generalized nonlinear least squares (GNLS), and nonlinear mixed-effects model regression (NLMM) approaches. Height growth and site index of black spruce and jack pine was modelled using a three-parameter generalized logistic function. NLMM regression was used since the data were spatially autocorrelated. The inclusion of prior measures from individual trees produced more accurate predictions. In the tree diameter (basal area) growth models, tree size variables were significant predictors for black spruce and managed jack pine stands. Site index (a measure of site productivity) was positively correlated, and basal area of trees larger than the target tree (a relative measure of competition) negatively correlated, with diameter increment. Thiessen polygon area, a spatial measure of competition, was a significant predictor for natural jack pine and upland black spruce stands. Tree bole taper was modeled by NLMM approach using a five-parameter equation based on dimensional analysis, with breast height diameter, total height and relative height as predictor variables. The inclusion of a single prior measure from each tree improved model prediction. Black spruce and jack pine mortality was modeled using logistic regression. The black spruce models predicted high survivorship for larger, fast-growing trees in less crowded stands. In the jack pine model, highest survivorship was predicted for larger, less locally crowded trees.

Forestry in the UK is dominated by coniferous plantations. Sitka spruce (Picea sitchensis) and Scots pine (Pinus sylvestris) are the most prevalent species and are mostly grown in single age mono-culture stands. Forest strategy for Scotland, England, and Wales all include efforts to achieve further afforestation. The aim of this afforestation is to provide a multi-functional forest with a broad range of benefits. Due to the time scale involved in forestry, accurate forecasts of stand productivity (along with clearly defined uncertainties) are essential to forest managers. These can be provided by a range of approaches to modelling forest growth. In this project model comparison, Bayesian calibration, and data assimilation methods were all used to attempt to improve forecasts and understanding of uncertainty therein of the two most important conifers in UK forestry. Three different forest growth models were compared in simulating growth of Scots pine. A yield table approach, the process-based 3PGN model, and a Stand Level Dynamic Growth (SLeDG) model were used. Predictions were compared graphically over the typical productivity range for Scots pine in the UK. Strengths and weaknesses of each model were considered. All three produced similar growth trajectories. The greatest difference between models was in volume and biomass in unthinned stands where the yield table predicted a much larger range compared to the other two models. Future advances in data availability and computing power should allow for greater use of process-based models, but in the interim more flexible dynamic growth models may be more useful than static yield tables for providing predictions which extend to non-standard management prescriptions and estimates of early growth and yield. A Bayesian calibration of the SLeDG model was carried out for both Sitka spruce and Scots pine in the UK for the first time. Bayesian calibrations allow both model structure and parameters to be assessed simultaneously in a probabilistic framework, providing a model with which forecasts and their uncertainty can be better understood and quantified using posterior probability distributions. Two different structures for including local productivity in the model were compared with a Bayesian model comparison. A complete calibration of the more probable model structure was then completed. Example forecasts from the calibration were compatible with existing yield tables for both species. This method could be applied to other species or other model structures in the future. Finally, data assimilation was investigated as a way of reducing forecast uncertainty. Data assimilation assumes that neither observations nor models provide a perfect description of a system, but combining them may provide the best estimate. SLeDG model predictions and LiDAR measurements for sub-compartments within Queen Elizabeth Forest Park were combined with an Ensemble Kalman Filter. Uncertainty was reduced following the second data assimilation in all of the state variables. However, errors in stand delineation and estimated stand yield class may have caused observational uncertainty to be greater thus reducing the efficacy of the method for reducing overall uncertainty.

Undoubtedly, the advancement of IoT technology has created a plethora of new applications and a growing number of devices connected to the internet. Among these developments emerged the novel concept of smart farming. In this context, sensor nodes are used in farms to help farmers acquire a deeper insight into the environmental factors affecting their productivity. In recent years, we have witnessed an emerging trend of scholarly literature focused on smart farming. Some focus has been on system architecture for monitoring purposes, while another area of interest includes yield prediction. Humidity, air and soil temperature, solar radiation, and wind speed are some key weather elements monitored in smart farms. We introduce a mechanistic crop growth model to predict crop growth and subsequent yield, subject to weather, soil parameters, crop characteristics and management practices. We also seek to measure the influence of nitrogen on yield throughout the growing season. The machine learning models are trained to emulate the crop growth model in the state of Iowa (US). The multilayer perceptron (MLP) is chosen to evaluate the model prediction as it generates fewer errors. Furthermore, the MLP optimization model is used to maximize corn yield. The experiment was performed using different scenarios, stochastic gradient descent (SGD), and adaptive moment estimation (Adam) optimizers. The experiment results revealed that the SGD optimizer and the dataset with the scenario of unchanged parameters provided the highest crop yield compared to the mechanistic crop growth model.

The ability to predict wood properties would aid in the growing of southern pine timber for specific end uses. Three wood properties, specific gravity, shrinkage, and knottiness, were chosen as the focus of this study. Silvicultural studies focusing on southern pine management were researched for any information on their impacts on wood properties. The information from silvicultural studies was then used to evaluate growth and yield models for ease of adaptation to predict wood properties. The information necessary to predict all wood properties is not currently available. Although, specific gravity has significantly more information available than the other properties and it is probably the most predictable.

Bottomland hardwood stands of the Mid-South region of the United States are some of the most productive forests in the country. A large percentage of these stands are owned by nonindustrial private forest landowners, who have little information on which to base management decisions. These stands are, therefore, a largely unmanaged and under-utilized reserve of high quality hardwoods. To provide landowners with a decision-making tool for comparing management scenarios, a growth and yield study was initiated in 1981. One hundred and fifty permanent plots were installed in red oaksweetgum stands. The study has been remeasured three times over the past 35 years. New plots were added when losses occurred due to natural disasters or harvesting. Stand level (Iles 2008), log grade volume distribution (Banzhaf 2009), and diameter distribution (Howard 2011) models were developed as component models of the overall growth and yield system. This study completes the modeling effort by developing individual tree equations for percent annual diameter growth and survival. Equations were constructed using linear, non-linear, and logistic regression techniques. The best set of developed equations was selected based on biological consistency, joint behavior when inserted into the growth and yield computer model, and the performance of each plot’s predicted future yield when compared to its observed data at the next projection period. Final independent stand level variables for the two models included age, diameter at breast height, trees per acre, and average height of dominant trees. Percent diameter growth and survival equations exhibited high fit statistics and when coupled with the other equations in the computer model, produced estimates for trees per acre, basal area, arithmetic and quadratic mean diameters with low bias and root mean squared error. The resulting growth and yield simulator implemented in Microsoft Visual Basic® Editor within Microsoft Excel® enables forest professionals and landowners to make better management decisions for their red oak-sweetgum mixture bottomland hardwood stands by projecting current forest inventories into the future, predicting average yields, and evaluating and comparing forest management scenarios.

This study explores the biophysical potential and economic attractiveness of black spruce (Picea mariana) regeneration in eastern Canada under future climate changes. It integrates process-based ecosystem model simulated forest productivities from three major global climate models (GCMs), growth and yield formulations specific to black spruce and economic analyses to determine the overall investment value of black spruce, both including and excluding carbon sequestration benefits. Net present value (NPV) was estimated to represent the financial attractiveness of long-rotation forest plantations through time. It was assumed that stands would not be harvested at volumes less than 80 m3 ha-1. The price of stumpage was set to $20 m-3, stand establishment cost was set to $500 ha-1, and the discount rate was considered at 4%, with sensitivity analyses conducted around these assumptions. The growth and yield of black spruce was simulated for an extreme future climate scenario – IPCC-RCP 8.5. The results suggested a general North-South gradient in forest productivity where gross merchantable wood volumes increased with decreasing latitudes. This pattern was also observed in NPVs, with higher values projected for the southern portion of the study area. Based on the base economic assumptions and sensitivity analyses, study results suggested that black spruce plantations are not economically attractive, unless carbon sequestration benefits of at least $5 ton-1 CO2 are realized. Further sensitivity analyses showed that discount rate plays a significant role in determining the optimal harvest age and value. Furthermore, the optimal harvest rotation age increases with increasing carbon price by approximately 9 to 18 years. / Thesis / Master of Science (MSc)

Southern bottomland hardwood forests lack effective growth and yield predictive models primarily due to the complexity of the ecosystems. These models are important tools for relative comparison of management schemes and making sound management decisions to obtain optimal future yields. Starting in 1982, 150 red oak-sweetgum bottomland hardwood growth and yield plots were established in northern and central parts of Mississippi. These plots were remeasured in 1988, 1992, 1993, 2005, 2006, and 2007 along with the addition of new plots. A diameter distribution model was developed from stand level component equations constructed in a previous study (Iles 2008; Schultz et al. 2010). The equations created performed well when testing the predicted survival and diameter growth against the observed data. The resulting growth and yield system will be a basis for better decision making in the comparison of management alternatives as well as increased conservation and efficient utilization of wood products.

Southern bottomland sites are among the most productive areas for producing high quality grade hardwood, yet the ability to estimate the quantity and quality of standing grade hardwood is almost non-existent. Measurements and observed log grades were recorded on standing trees to construct volume prediction models for individual trees. Several different modeling techniques were explored and compared during development. Developed equations predict merchantable sawtimber volume and volume by grade category in trees by species group. Two separate sets of equations were developed for each species group using either total height or merchantable height. Models were chosen based on significance of variables, index of fit, RMSE, bias, ease of use, and biological trends. The models developed to predict merchantable sawtimber and grade volumes were designed to be implemented in a larger hardwood growth and yield system.