Published on International Journal of Agriculture & Agribusiness
Publication Date: March 20, 2019
Michael Jide Nworji
Department of Forestry and Wildlife Management, Faculty of Agriculture, Chukwuemeka Odumegwu
Bangor University, North Wales, United Kingdom
Ojukwu University, Igbariam, Anambra State, Nigeria
Allometric equations derived from trees grown in forestry systems are most often used in estimating biomass of open-grown trees typically found in agroforestry systems despite the known differences in their growth forms and possible errors in estimating their biomass and carbon stock potentials. To circumvent these limitations, this study developed species specific allometric equations for the estimation of biomass for two forms (‘good’ form and ‘poor’ form) of open-grown red alder (Alnus rubra Bong) trees through destructive sampling in a lowland silvopastoral agroforestry system in North Wales, United Kingdom. Allometric models were developed for stem, branch and twig biomass using only diameter at breast height (DBH) as the independent variable in a simple linear regression while a stepwise multiple linear regression analysis was performed to determine which of the variables, DBH, total height (HT), crown area (CA), wood density (WD), and bifurcation ratio (BR), were most significant to predict aboveground biomass of the two forms of open-grown red alder. Results showed that mean aboveground biomass differed significantly (p < 0.05) between good form trees ( = 245.43, = 51.23, n = 10) and poor form trees ( = 129.52, = 42.19, n = 10), t (18) = 5.52, p < 0.05 as well as between their components. In 2012, 20 years after field planting, the mean AGB were found to vary from 130 kg tree-1 (26 Mg ha-1) to 246 kg tree-1 (49 Mg ha-1) in poor form and good form red alder trees, respectively, based on a stocking density of 200 stems ha-1. Furthermore, biomass was shown to be largest in stems (69.97%), intermediate in branches (23.02%), and lowest in twigs (7.00%) for the two forms of red alder. The optimal stepwise regression model for predicting aboveground biomass was Ln(AGB) = [1.385 + 1.011 * Ln(DBH) + 0.174 * Ln(CA)] * 1.001] for good form red alder trees, Ln(AGB) = [-1.339 + 1.495 * Ln(DBH) + 0.430 * Ln(CA)*1.003] for poor form trees, and Ln(AGB) = [-0.903 + 1.373 * Ln(DBH) + 0.429 * Ln(CA)*1.009] for both forms of trees. Though the equations in this study were developed specifically for open-grown red alder trees in Silvopasture, the models and procedure for these equations have valuable applications to other open-grown tree species and can provide a reference to development of biomass equations for other open-grown tree species in silvopastoral agroforestry settings.
Keywords: Agroforestry system, silvopasture, biomass, allometric equation, open-grown tree, good form red alder, poor form red alder.
Tree allometry relates easily measurable variable such as tree stem diameter and height to other structural and functional tree characteristics (Wang, 2006). Though the scientific literature is replete with allometric equations developed for temperate, tropical, sub-tropical and semi-arid tree species and forests (e.g. Zianis and Mencuccini 2003; Jenkins et al., 2004; Navar 2010), there is scarcity of literature presenting biomass equations for trees grown in open conditions. Allometric equations used in estimating biomass of trees in agroforestry systems are most often derived from trees grown in forest systems that are totally different in their growth form from open-grown trees typically found in agroforestry systems. The use of those broad scale allometric models on open-grown trees can be challenging as they generally lack accuracy because they are either too location-specific or much generalised (Nair et al., 2009). This can introduce errors in estimating not only biomass production potential, but carbon sequestration as well (Nair et al., 2009). This highlights the imperative need for the development of species specific allometric equations for trees grown in agroforestry systems to circumvent this limitation.
Although several allometric equations have been developed for temperate forests (e.g. Wang, 2006) and specifically for red alder, little is known about red alder production in agroforestry plantations. Again, despite the compilation of a wealth of allometric equations for European tree species that relate stem volume and biomass of the various tree components to diameter at breast height and/or to tree height (Zianis et al., 2005), allometric equation for red alder (Alnus rubra Bong.) is conspicuously absent from the compiled equations. Furthermore, despite more than 20 years of agroforestry research at the UK’s Silvopastoral National Network Experiment (SNNE) site at Henfaes, North Wales, no allometric equations have yet been developed for red alder grown in agroforestry configurations.
Red alder is known for its fast growth rate and potential to produce a range of quality wood (Mmolotsi and Teklehaimanot 2006). In 8 to 10-year-old naturally established red alder plots, annual biomass production can reach 29 Mg ha-1 (Smith, 1977). The aboveground biomass of red alder growing in a natural stand increased very rapidly during the first 15-20 years and reached about 240 metric tons/ha by the age of 33 years under ideal soil-moisture conditions (Zavitkovski and Stevens, 1972).
The objective of this study was to assess the aboveground biomass of two forms of open-grown red alder (Alnus rubra Bong) trees in a lowland silvopastoral system. Specifically, the study determined the total aboveground biomass of samples of ‘good’ and ‘poor’ form open-grown red alder trees through destructive sampling, and developed yield prediction models for the two forms of open-grown red alder trees. It is hypothesised that the estimates of dendrometric measurements and aboveground biomass of the good form red alder samples do not differ significantly from that of the poor form red alder samples. Since there is a dearth of information on allometric equations for red alder in the UK, this study partially fills that deficiency and falls into site-specific studies as it is focused on the determination of allometric relationships in a silvopastoral national network experiment setting. Allometric equations developed in this study can therefore be useful not only in the estimation of tree biomass in agroforestry landscapes but also serve as tool for policy makers for the formulation of appropriate environmental policy decisions.
This manuscript is part of my Doctor of Philosophy (Ph.D.) dissertation in Agroforestry from Bangor University, North Wales, United Kingdom, which may be available from Bangor University’s Library or from services like ProQuest but has not been traditionally published (Nworji, 2017).
2. LITERATURE REVIEW
2.1 Forest tree biomass estimation
Biomass, in relation to the forest biomass issue, is a vital indicator of ecosystem energy potential and productivity. Typically expressed in terms of dry weight of organic matter, biomass refers to the weight or mass of its living plant tissue and is generally expressed in units of metric tonnes (t) or oven-dry tonnes of matter per unit area (individual plant, hectare, region or country). Biomass, in general, includes the above ground biomass (leaves, branches and stems) and below ground biomass (roots) components. Estimates may be restricted to the aboveground section of trees only, or to tree components (such as leaves, wood, etc.), or to belowground portions. Most past research studies on biomass estimation centred on aboveground biomass because of the difficulty in collecting field data of below ground biomass (Lu, 2006). It is therefore most common to estimate the aboveground live dry biomass of a tree, which is the weight of the living aboveground plant tissue after all the water has been removed, i.e., after the leaves, branch¬es, and stems have been dried thoroughly, often using a special laboratory oven. In general, water accounts for approximately 50% or ½ of the weight (or wet biomass) of a live tree.
In addition to widespread use in estimating the carbon stocks of forest and CO2 dynamics and their greenhouse effect (Rokityanskiy et al., 2007; Wulder et al., 2008), biomass estimates are important for a broad range of applications, including: characterizing forest structure, conditions and processes (Wulder et al., 2008); assessing forest productivity, timber extraction and sustainability; modelling impacts of fire and other disturbances; modelling the environmental and economic consequences of energy production from biomass; Monitoring changes in biomass over time; and for studying biogeochemical cycles (e.g., Zianis and Mencuccini 2003; Cole and Ewel 2006; Vashum and Jayakumar 2012). The usual approaches to estimating aboveground biomass (AGB) are through traditional field-based measurement and remote sensing and geospatial information system (GIS) methods (Brown and Gaston 1995; Schroeder et al., 1997; Houghton et al., 2001; Santos et al., 2003; Lu 2006; Vashum and Jayakumar 2012).
There are two field-based methods of measuring forest biomass and carbon storage in the forest ecosystems: the destructive and the non-destructive methods. The destructive, also known as the harvest method, is the most direct and accurate method of biomass estimate and consists of felling the trees in an area and measuring the weight of the different components like the roots, stem, branches, and foliage (Zianis and Mencuccini 2003; Segura and Kanninen 2005; Vashum and Jayakumar 2012) and measuring the weight of these components after they are oven dried. The biomass of an area can be accurately measured. However, this approach is destructive, strenuous, time and resource consuming, and expensive, and is limited to small area or small tree samples destructive, difficult to implement, especially in remote areas, and are only feasible for a small-scale analysis (Vashum and Jayakumar 2012), and cannot be applied to degraded forests having rare or protected species (Montes et al., 2000).
The aboveground forest biomass can also be estimated directly but non-destructively by climbing the tree to measure the various component parts or by simply measuring the diameter, height, volume, and wood density of the tree and applying biomass expansion factors, as well as by using available generalized or species-specific allometric equations (Brown et al., 1989; Aboal 2005; Ravindranath and Ostwald 2008; Vashum and Jayakumar 2012). Again, this method is limited to a small area or to small tree samples and are often labour and time intensive, and expensive, and climbing can be strenuous and risky.
To avoid the challenges associated with destructive sampling and climbing of trees, indirect approaches have been conceived. The indirect method estimates the biomass of a tree without felling (non-destructive) and is usually used when the tree has large dimensions and in environments where the harvesting of rare or protected tree species is not very practical or feasible (Vashum and Jayakumar 2012). Indirect methods include use of allometric relationships (Brown, 1997), functional branch analysis (van Noordwijk and Mulia, 2002), photographic techniques (Jonckheere et al., 2004), remote sensing, and geospatial information system (Suarez et al., 2005; Gibbs et al., 2007; Wulder et al., 2008; Vashum and Jayakumar 2012). Although indirect methods have many advantages over direct methods and since there is no felling of tree species, it is not easy to validate the reliability of these methods.
2.2 Modelling of total aboveground biomass
The most common tools for estimating biomass of a given forest stand is using tree allometric equations combined with forest inventories (Henry et al., 2013). Tree allometric equation relates aboveground biomass (AGB), wood volume or that of several tree components to stem diameter at breast height DBH and/or to tree height (HT) and/or other dendrometric variables. An area or a few trees are destructively sampled and the weight of each component determined and related by regression to some dimensions of the standing tree. DBH is commonly used to estimate AGB because it is easy to measure accurately, repeatedly and conventionally (Kuyah et al., 2012). Consequently, in specific forest ecosystem studies, allometric equations based on DBH can be refined by including other variables such as HT, crown area (CA) or wood density (WD) to improve the tree AGB accuracy (Ketterings et al., 2001; Chave et al., 2005). Sometimes, regression may be calculated using combinations of some of the variables (usually DBH2HT) to obtain a linear relation in arithmetic units. Logarithm transformation and back transformation to arithmetic units are usually employed when the necessary assumptions of regression analysis are violated (Baskerville 1972).