Collating available Information and Designing Field ...

Collating available Information and Designing Field Trials for Energy Plantations K. v. Gadow1, L. Fehrmann1, D. Murach2 and P. Walotek2 Manuscript prepared for the International IUFRO Conference 3-7 April 2006, University of Valladolid, Spain

Abstract The sudden upsurge in the demand for timber as a source of energy in Central Europe comes as a surprise to those who expected that all forest areas would eventually be turned into national parks. Consequently, many half-forgotten experiences of the past and additional know-how developed mainly in commercial timber plantations outside of Europe, are relevant again. Especially important are methods of data collection, growth modelling and sustainable harvest control of bio-energy plantations. - Credible estimates of timber yields are based on empirical research, and one objective of field experiments is to assess tree growth rates and timber yield potentials under varying site conditions and in response to different treatments. Many forest yield plots have been remeasured for extended periods of time, providing valuable information on long-term developments. However, changing environmental and economic conditions sometimes confront us with new questions requiring immediate answers. This contribution presents some background on efforts to collate existing research results and discusses principles of designing field experiments related to bio-energy issues. A field trial may be costly, but it provides essential information for management decisions. What kind of data are already available about the productive potential of energy plantations? What are the limits of collating existing data? What is the difference between a manipulated experiment and a comparative observational study? What are the comparative advantages and disadvantages of long-term experiments, chronosequences and interval studies? These are the questions that will be addressed in this contribution.

Keywords: manipulated experiment ; comparative observational study; chronosequence; interval study; boundary line method.

1 2

Institute of Forest Management, Georg-August-University, Göttingen, Germany University of Applied Sciences of Eberswalde, Faculty of Forestry

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1. Three related Research Issues Three more or less related issues which make political headlines, stimulate renewed interest in an almost forgotten science known as Forest Management. The first issue deals with the Impacts of Forest Management on Carbon Budgets; the second one is concerned with Forests as a Source of Renewable Energy, concentrating on production potentials and sustainable use. The third major issue deals with the possible implications of expected climate change on forest management, with particular emphasis on tree species selection. Forests play a significant role in the climate system. They are important carbon sinks and sources, and the assessment of their carbon budgets has received much attention in recent years (Apps and Price, 1996; IPCC 2000, 2001). Losses or gains of carbon may be due to afforestation, reforestation or deforestation. These activities are explicitly included into Article 3.3 of the Kyoto Protocol as “accountable activities” in the national commitments to reduce net greenhouse gas emissions (UNFCCC, 1997). In contrast, “additional human-induced activities” related to forest management of existing forests in Article 3.4 of the Kyoto Protocol are less obvious with respect to their impact on the global carbon budget (Mund and Schulze, 2006). Carbon trading within the Kyoto Protocol is a way of assigning significant monetary value to woody biomass. Wood is the dominant source of renewable energy, particularly in households in developing countries and relevant scenarios indicate increasing demands for woodfuels of poor rural and urban households in developing countries. In developed countries, wood energy (mainly for heat and power generation) is being increasingly used as an environmentally sound source of energy that provides a potential substitute for fossil fuels and has the ability to help reduce greenhouse gas emissions. According to FAO3 statistics, approximately 60 percent of the world's total wood removals from forests and trees outside forests are used for energy purposes. Already, the main use of woody biomass is to generate energy and the importance of wood as a source of energy is expected to increase in the future. Fuel wood may originate from very different sources, including residual wood from thinnings in long rotation forests or clearfellings in short rotation plantations. The importance of wood as a source of bio-energy, bio-fuel and biobased products is expected to increase in the future4. Biomass-to-liquid-fuels (BtL-fuels) and biorefineries point the way for future use of dendromass5. Furthermore, as firms in the European Union will initially not be allowed to use credits from carbon sink-projects such as forestry to meet their emission targets under the Kyoto Protocol, the transformation of wood from energy plantations into bio-energy via JI projects will be the only way to raise credits through forestry projects. A third vital research issue deals with the impacts of climate change. The anticipated warming of the Earth's atmosphere caused by rising atmospheric carbon dioxide levels is expected to affect the main environmental drivers - temperature, water availability and wind. see www.fao.org/forestry/site/14067/en See www.bioproducts-bioenergy.gov/pdfs/BioVision_03_Web.pdf 5 See www.dendrom.de 3 4

3 Broadmeadow (2002) discusses the potential impacts on forest growth and species choice, and the incidence of pests and diseases. Iverson et al. (1999) have published an atlas of current and potential future distributions of common trees of the eastern United States under several CO2 climate change scenarios (see also Prasad and Iverson, 1999; Walker, 1999). These studies do not always consider the significant impact of forest management activities which modify the radiation regime, species composition and tree size distribution. Among the few studies highlighting the importance of forest management is the report by Kellomäki and Leinonen (2005) which addresses strategies to adapt to climate change and to mitigate the adverse impacts of the anticipated climate change. This contribution presents some background on efforts to collate existing research results and discusses principles of designing field experiments related to bio-energy issues. A field trial may be costly, but it provides essential information for management decisions. What kind of data are already available about the productive potential of energy plantations? What are the limits of collating existing data? What is the difference between manipulated experiments and comparative observational studies? What are the comparative advantages and disadvantages of long-term experiments, chronosequences and interval studies? Are long-term experiments always necessary? These are the main questions that will be addressed in this contribution.

2. Collating available Information Before establishing a new field experiment, all existing information should be surveyed to make sure that the effort is really necessary. Surprisingly, researchers do not always act in accordance with this obvious requirement. In many cases, it is not necessary to collect new data, because the desired information has already been published. A number of recent papers have collated available models related to the productive potential of bio-energy plantations in Europe.

2.1 Stem Volume and above-ground Biomass Zianis et al. (2005) present a review of 230 stem volume and 607 biomass equations for tree species growing in Europe. The mathematical forms of the empirical models, the associated statistical parameters and information about the size of the trees and the country of origin were collated from scientific articles and from technical reports. Most of the biomass equations were developed for aboveground tree components. A relatively small number of equations were developed for southern Europe. Most of the biomass equations were based on a few sampled sites with a very limited number of sampled trees. The volume equations were, in general, based on more representative data covering larger geographical regions and the major tree species in Europe. The collected information provides a basic tool for estimation of carbon stocks of forest ecosystems across Europe as well as for validation of theoretical models of biomass allocation.

4 There have been a many attempts to generalize in biomass studies. One example of a general model is the power function:

M = a ⋅ Db where M is the total aboveground dry biomass of a tree, a and b are the scaling coefficients and D is the tree diameter at breast height. In most cases the variability of M is explained by the variability of D. Based on metadata sets, Zianis and Mencuccini (2004) found that the parameters a and b were correlated in several studies. Based on this finding they conclude that it may be possible to derive generalized biomass functions. However, values of b and a are reported to vary greatly with species, stand age, site quality and stand density (see Wirth et al., 2004). Fehrmann and Kleinn (2005) has shown a great variety of relationships for Spruce (Fig. 1). 400

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oberirdische Biomasse [Kg]

300

250

200 gemessen Briggs

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Poeppel Brække

100

Johansson Woods et al.

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Cerny et al. Ter-Mikaelian, Korzukhin

0 10

15

20

25

30

BHD [cm]

Figure 1. Allometric above-ground Biomass equations for Spruce show the differences (Fehrmann and Kleinn 2005). Similar results are reported for short rotation forestry. The correlation between the tree diameter and the above-ground biomass under otherwise more or less equal conditions may be close (Liesebach et al. 1999). However, the position within the stand (interior versus border row, Verwijst and Telenius 1999), age, site or clone (Friedrich 1999) have been found to cause variation in the weight-diameter relations. These differences need to be explained by further analysis of the data. If they cannot be explained, and if the predictions are essential, then there may be good reason for establishing a new field trial.

2.2 Below-ground Biomass Based on 42 spruce and 27 beech trees, Bolte et al. (2004) presented a set of equations for estimating coarse root biomass from breast height diameter in European beech (Fagus sylvatica)

5 and Norway spruce (Picea abies), two of the most common tree species in Germany’s forests. The model parameters are shown in Tab. 1. Table 1. Parameters for estimating coarse root biomass (M, kg dry weight) from breast height diameter (D, cm), using the equation ln M = β0 + β1 ln D , for beech and spruce, with or without stump cylinder. R² = coefficient of determination. Norway spruce without stump with stump cylinder (kg) cylinder (kg) ß0 ß1 Number of trees assessed R²

-5.90 2.85 42 0.92

-5.59 2.79 42 0.92

European beech without stump with stump cylinder (kg) cylinder (kg)

-4.04 2.27 27 0.93

-4.00 2.32 27 0.94

Root biomass data are scarce and difficult to obtain, even for such common species as European beech and Norway spruce. Regarding short rotation forestry, there is very little detailed information about root growth in coppice systems. Rytter (2001) calculated that nearly 40% of annual NPP was allocated belowground into fine root production. Tufekcioglu et al. (1999) found fine-root biomass of 6 year old poplar in the top 35 cm of soil with over 6 Mg ha–1 and a rooting depth extending to at least 180 cm. Heilman et al. (1994) reported similar fine root biomass of four-year-old hybrids of Populus trichocarpa × Populus deltoides and a vertical root systems which extended to depths beyond 3.2 m.

2.3 Tree Growth, Volume Yield and Biomass Expansion Factors Another attempt to generalize deals with the so-called biomass expansion factors (BEFs) which describe the ratio of dry weight (W) over stem volume (V):

BEF =

W V

The expansion factors may vary greatly with age and stand density. Examples are the biomass expansion factors for Scots pine, Norway spruce and Birch which vary according to stand age for boreal forests (Lehtonen et al., 2004). The essential basis for deriving these factors is provided by traditional growth studies which allow estimation of stem volume per unit area, as a function of site quality, silvicultural treatment (soil preparation; planting espacement; fertilizer application) and age. Suitable stand volume models for fast-growing timber plantations were developed in the USA, Chile, South Africa and New Zealand and have been in use for several decades (Gadow and Bredenkamp, 1992). Renewed interest in the production of bio-energy has stimulated similar research in other parts of the world. A typical example is the equation proposed by Hui (1998) for estimating the volume per ha (V) of Cunninghamia lanceolata plantations in China as a function of site index (SI, the mean stand height at age 25 years) and the number of trees per ha planted (NP):

6

V = 39.9 ⋅ SI

0.83

[1- 1.14⋅ e

- 0.072 ⋅age⋅ NP 0.081

]

3.11

Yield models like this one can be used to derive the age at which the mean annual increment culminates. They enable foresters to determine the minimum land area required for producing a given amount of biomass per unit of time. Such models are essential for evaluating the effects of different harvest levels on the sustainability of future timber supplies, and they are indispensable for optimizing the flow of roundwood of integrated timber growing and processing systems.

2.4 Distribution of Biomass and Element Content Relatively few papers have been published dealing with age-related biomass allocation and element content in different tree species and tree compartments. Tab. 2 shows a typical pattern of biomass partitioning over age for Scotch pine (Pinus sylvestris) in Northern Germany, presented by Rademacher (2002). young stand (25-years) tree compartment BM (t/ha) N (kg/ha) Needles 3,2 44,7 Branches < 7cm diameter 31,1 79,3 Bark > 7cm 3,3 11,1 Stemwood >7cm 16,3 14,9 Root stock 2,2 2,9 Coarse roots 4,9 7,1 Fine roots 4,1 24,1 Total 65,0 184,1

P (kg/ha) 4,4 6,6 1,1 0,5 0,2 0,9 2,7 16,2

K (kg/ha) 14,8 36,2 3,5 10,6 1,6 5,4 7,2 79,2

Ca (kg/ha) 7,3 42,7 21,3 30,3 5,7 3,7 3,4 114,5

Mg (kg/ha) 2,3 12,2 1,1 3,3 0,5 1,3 1,3 22,0

medium age stand (46-years) tree compartment BM (t/ha) N (kg/ha) Needles 6,5 99,2 Branches < 7cm diameter 27,5 126,2 Bark > 7cm 8,0 22,8 Stemwood >7cm 77,5 68,2 Root stock 9,5 10,1 Coarse roots 18,9 32,6 Fine roots 4,1 24,1 Total 152,0 383,2

P (kg/ha) 8,9 10,6 2,2 2,1 0,5 3,6 2,7 30,5

K (kg/ha) 35,6 46,7 9,9 22,7 3,6 21,8 7,2 147,4

Ca (kg/ha) 19,9 99,2 47,9 54,8 11,4 14,4 3,4 251,1

Mg (kg/ha) 5,4 14,3 3,7 14,6 2,0 4,7 1,3 46,1

old stand (115-years) tree compartment BM (t/ha) N (kg/ha) Needles 2,9 50,0 Branches < 7cm diameter 15,2 55,9 Bark > 7cm 7,9 31,7 Stemwood >7cm 104,3 47,9 Root stock 11,9 8,2 Coarse roots 22,2 39,7 Fine roots 4,1 24,1 Total 168,4 257,3

P (kg/ha) 3,3 3,6 2,4 2,7 0,5 2,3 2,6 17,5

K (kg/ha) 12,6 15,4 10,6 27,3 3,9 19,0 7,2 96,1

Ca (kg/ha) 9,2 30,1 66,2 71,4 14,3 18,8 3,4 213,3

Mg (kg/ha) 2,1 5,0 3,8 16,7 2,1 5,4 1,3 36,4

Table 2. Age-related distributions of biomass (BM) and five elements in seven different tree compartments of Scotch pine (Pinus sylvestris) in Northern Germany (Rademacher, 2002).

7 The biomass allocation and element distribution in the different tree compartments (needles, branches, bark, stemwood, root stock, coarse roots and fine roots) is typical for forest trees. The proportion of stemwood biomass increases whereas the element content (mg element per g of biomass) decreases exponentially with increasing age. The effort of collecting this kind of information is considerable and therefore such data are scarce. Sampling is destructive, thus the rates of change of the biomass and element distributions cannot be measured directly. A similar example for Norway spruce (Picea abies) was published by Wirth et al. (2004), showing the average partitioning of the total biomass of 17 test sites ranging in age from 16 to 172 years. Equations are presented that permit prediction of biomass as a function of tree diameter, for different individual tree compartments. A paper published by Bartelink (1996) presents a general model for dry matter partitioning in trees.

3. Designing Growth and Yield trials Attempts to generalize are often counterproductive, because they tend to reduce the inherent variability of the data, resulting in loss of information and reduced accuracy of predictions. There seems to be general agreement that some basic relationships exist between the productive potential, site conditions and management practice. Different tree species, different environmental conditions and silvicultural treatments may produce greatly diverse volume-age relations, which is an indication of the uncertainties that still exist despite the great number of yield studies that have been established and evaluated in the past. For this reason new field trials are often essential. Credible estimates of timber yields are based on empirical research, and the aim of the early field experiments established during the 19th century was to measure timber yields on different growing sites in response to specific planting espacements and thinning treatments. Some of these experiments have been remeasured for over a century, providing valuable information on long-term developments (Pretzsch, 2001). Figure 2 gives an impression of the development of field experiments, guided by different objectives. Growth and yield experiments which evaluate tree growth and provenance trials which test the suitability of certain tree species on given sites, exist since the 19th century. During the 20th century, fertilizer trials which evaluate the effect of fertilizer applications on tree growth, catchment studies which assess the effect of afforestation on water yield and multi-disciplinary silvicultural experiments which test new approaches to managing forest ecosystems were conducted.

8 Catchment studies Fertilizer experiments Provenance trials Growth/Yield studies IUFRO

1800

1850

1900

1950

2000

Figure 2. Diagram showing the development of different types of forest field experiments (after Mårell and Leitgeb, 2004). The information gathered in forest experiments has to be weighed against the estimated cost of collecting it. Not only are the available resources limited, but time is also a major constraint. Furthermore, the validity and effectiveness of an experiment is influenced by its design and execution. Thus, attention to the planning of field experiments is important. We may distinguish two types: manipulated experiments and comparative observational studies (Gadow and Kleinn, 2004).

3.1 Manipulated Experiments A manipulated experiment is an investigation that attempts to establish a particular set of conditions under a specified protocol with the aim of testing a hypothesis. The adjective manipulated implies the establishment of a set of predefined treatments which allow comparison of the effects/responses resulting from these treatments (Fisher, 1935; Cox, 1958). Thus, the experiment deliberately imposes a treatment on a group of objects in the interest of observing the response. Establishment and maintenance is usually rather costly because – particularly in silvicultural and growth-and-yield research – relatively large experimental plots are needed and relatively long time periods (Kleinn and Köhl, 1999). Selection of sites for manipulated experiments is not random as in forest inventories, but based on criteria which reflect the objective of the study, such as “homogeneous conditions” or “minimum size”. Examples include medium- and longterm tree growth experiments in response to different fertilizer applications and stand densities in Australia (O’Hehir, 2001) and growth studies on different sites, including marginal ones, in Europe involving poplar clones (Bungart and Hüttl, 2004; Hofmann, 2005). Manipulated field experiments involve uncertainties in controlling ceteris paribus conditions, which are necessary for obtaining “dose/response” relations. There are a lot of examples showing that environmental factors are not independent. Thus, manipulation of a single environmental factor may induce changes in other factors: rising soil temperature may increase CO2 in the soil, fertilizing KCl may result in Al+++-stress for fine root growth in acid soils with properties of Al

9 buffer range. In contrast, with soils in the exchange or silicate buffer range the same treatment will result in a release of nutrients like Ca or Mg . Thus, the experimentalist has to be aware of the risk of uncontrolled conditions in manipulated field experiments. The effect of a particular growth factor may change under varying conditions due to interactions with other growth factors. Thus, critical values established under conditions where only a single growth factor is varied may not hold under different conditions. This is another problem often encountered in manipulated experiments.

3.2 Chronosequences and Boundary Line analyses A manipulated experiment deliberately imposes treatments on experimental plots with the aim of observing a particular response. In contrast, a comparative observational study involves collecting and analyzing data from different site conditions but without actively pre-defining these conditions (Kuehl, 1994)6. Comparative observational studies are also known as quasi-experiments (Campbell and Stanley, 1963; Cook and Campbell, 1979). Examples of quasi-experiments in forest research are chronosequences. A chronosequence includes a set of field plots which may cover a wide range of ages and growing sites, but are measured only once. Thus, the sequence of remeasurements in time is substituted by simultaneous point measurements in space. This method has been used extensively during the 19th century (Kramer, 1988, p. 97; Assmann, 1953; Wenk et al., 1990, p. 116). Chronosequences may be combined with stem analyses to reconstruct the development of a particular variable, e.g. tree height (Lee, 1993; Biber, 1996). This method is currently being applied by the third author in short rotation plantations stocked with Salix spec. clones in Poland, where growth data are not available. Chronosequences may provide information relatively quickly, but they do not capture the response of a target variable to a given initial state. A chronosequence evaluates differences between different conditions. It does not provide evidence that can be used to test an effect, or data that can be used to develop a tree growth model. A chronosequence is the only possible approach, however, in studies requiring destructive sampling. Destructive sampling is particularly relevant in biomass studies where individual trees, or a cohort of trees within a given area, have to be cut up, dried and weighed. Obviously, the selection of the particular individuals or the particular cohorts will greatly influence the results, i.e. the function parameters. The boundary line analysis is a particular approach that is often, though not necessarily, based on chronosequence data. An attempt is made to detect relations between plant growth variables and environmental factors under field conditions using inventory data. The investigation is complicated by the complexity of the relationship between tree growth and an unknown set of 6

comparative observational study: select 50 smokers and 50 non-smokers and compare their health condition. manipulated experiment: select 100 smokers, request 50 of these to stop smoking and observe the response 3 years later.

10 environmental factors determining plant performance. Any attempt to fit regression models to the total data set, i.e. to explain all the variation of the dependent growth variable with an incomplete set of measured environmental factors will result in a poor fit if one or more limiting variables are neglected, even if there is a strong influence of the measured variables on plant growth. This could imply that the measured environmental factors do not have a significant influence on plant growth and thus limiting growth factors may be overlooked. The boundary line method is handling the complexity of the relationship between tree growth and environmental factors by applying Liebig’s Law of the Minimum to the evaluation. With scatter diagramms of yield against one environmental factor, covering a wide range of this independent variable and representing the natural variability of growth conditions for the dependent variable a regression model is fitted to the upper edge of the body of the data. Liebig’s theory states that the exploitation of the genetically fixed yield potential of crops is limited by the variable, which is insufficiently supplied to the greatest extent. Thus, possible interactions between growth factors are neglected, but the approach is assumed to be sufficiently accurate to estimate the response of a growth limiting factor. This assumption is implemented in “the upper boundary line approach” (Webb 1972 and Walworth et al. 1986). Webb (1972) has shown that for a given set of data where there is a cause and effect relationship between two variables, there exists a line at the edge of the data set representing the best growth performance under the given effect of the measured independent variable. For these values of the dependent variable other environmental factors – not taken into account- are not restricting plant growth. There is a theoretical maximum potential that can be achieved by the dependent variable at any given value of the independent variable and optimal other growth conditions. The boundary line approach is mainly used in agriculture (Haneklaus and Schnug 2002), but has also ben used in forest research (Schübeler, 1997; Sloboda and Leuschner 2002; Black and Abrams, 2003; Gadow, 2004, p. 16 et sq).

3.3 Interval Studies Tree growth studies are often designed as longterm experiments. A disadvantage of a longterm or “permanent” experiment is the high maintenance cost of the research infrastructure and the long wait for data. Chronosequences involve one-time measurements and consequently do not provide growth rates. A practical compromise between a longterm experiment and a chronosequence is the interval study, which maintains the advantages of a longterm experiment (gathering responses and growth rates) and a chronosequence (broad site coverage of site conditions and minimum wait for data). Interval plots are measured at least twice. The interval between the measurements is sufficiently long to absorb short-term effects of climatic fluctuations. Particular modeling techniques are used to evaluate the range of growth rates (Gadow and Hui, 1999).

11 An important question concerns the balance between the number of growth intervals that should be assessed in one particular location and the range of environmental and treatment conditions that needs to be covered. In the ideal case, one growth interval will provide sufficient response and the funds that would be required for assessing a second interval at the particular location, can be spent on gathering another set of interval data in a different location. Fig. 3 shows the typical questions that need to be asked when a decision has to be made between abandonment or continuation of an interval plot.

Objective reached?

n

y

New results expected?

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y

1 2

n

New results expected?

1 = continue trial 2 = do not continue trial

y

1

Figure 3. An interval plot may be continued after one growth period if new results are expected, or it may be abandoned and the money used in another location to increase the variety of initial states for which the response needs to be evaluated. Considering the high cost of maintaining a research infrastructure, an interval plot should be continued after one growth period only if new and essential results are expected. If this is not the case, the experiment should be abandoned and the money used in another location to increase the variety of initial states for which the response needs to be evaluated. Of course, there will probably always be a need for longterm growth studies, but such studies should be selected carefully and it should be made clear why it is necessary to maintain them for long periods of time. The ultimate aim of all field studies is external validity, the ability to generalize from a limited set of observations. No one is interested in observations that cannot be extended beyond the particular restricted data set. Generalizability depends on whether the observed response measurement is a representative one. We need to clarify whether the study sites were a representative sample and whether the results of the observations may be legitimately extended to the general population of conditions to which the research findings need to be extended. Helpful in this regard is a comprehensive description of the study sites and of the methodology

12 that was used, so that the user can judge whether the results are applicable to a particular situation.

4. Discussion and Conclusions Current forest research is dominated by three more or less related issues which may be summarized under the keywords “Impacts of Forest Management on Carbon Budgets”; “Forests as a Source of Renewable Energy” and “Implications of Climate Change on Species Selection and Silviculture”. Forests are important carbon sinks and sources and they play a significant role in the climate system. Wood is a major source of renewable energy, not only in developing countries, but also increasingly in developed countries. Wood is increasingly used as an environmentally sound source of energy that provides a potential substitute for fossil fuels and has the ability to help reduce greenhouse gas emissions. This contribution presented some background on efforts to collate existing research results. Numerous stem volume and above-ground biomass equations have been developed for most tree species in Europe, but the variation in the relationships is rather amazing, for example in the works dealing with Norway spruce. Studies covering the relationship between the tree diameter and the below-ground biomass are scarce, due to the high cost of data collection. Many tree growth and volume yield models, and biomass expansion factors are also available, but advanced predictive tools are still not too plentiful in Central Europe where there is a lack of experience in fast-growing tree plantations, when compared to other major timber growing regions in the Southern Hemisphere. Due to the high cost of data collection, few studies dealing with the distribution of biomass and element content in the leaves, branches, stems and roots of forest trees have been conducted. Although much information is already available about the productive potential of energy plantations, the existing data base appears to be rather restricted. In view of the obvious lack of essential information, field trials need to be established which are not too costly, which produce required data without undue delay and which allow accurate predictions over a wide range of environmental conditions and treatment options. Some principles of designing field experiments were discussed. It was shown that there are important differences between manipulated experiments and comparative observational studies. The former providing high precision for a limited set of conditions, while the latter involves collecting and analyzing data from different growing conditions, but without actively pre-defining these conditions. It was argued that long-term experiments are not always necessary. The ultimate aim of all field studies is external validity, the ability to generalize from a limited set of observations. People are interested in observations that can be extended beyond a given data set. Generalizability depends on whether the observation is a representative one, i.e. whether the study sites were a representative sample. If this is the case, then the results may be legitimately extended to the general population of conditions to which the findings need to be applied.

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