Skip to main content
NCBI home page
Annals of Botany logoLink to Annals of Botany
. 2011 May 13;108(1):221–230. doi: 10.1093/aob/mcr102

Is the simple auger coring method reliable for below-ground standing biomass estimation in Eucalyptus forest plantations?

Joseph Levillain 1,2, Armel Thongo M'Bou 1, Philippe Deleporte 2, Laurent Saint-André 2,3, Christophe Jourdan 2,*
PMCID: PMC3119613  PMID: 21572093

Abstract

Background and Aims

Despite their importance for plant production, estimations of below-ground biomass and its distribution in the soil are still difficult and time consuming, and no single reliable methodology is available for different root types. To identify the best method for root biomass estimations, four different methods, with labour requirements, were tested at the same location.

Methods

The four methods, applied in a 6-year-old Eucalyptus plantation in Congo, were based on different soil sampling volumes: auger (8 cm in diameter), monolith (25 × 25 cm quadrate), half Voronoi trench (1·5 m3) and a full Voronoi trench (3 m3), chosen as the reference method.

Key Results

With the reference method (0–1m deep), fine-root biomass (FRB, diameter <2 mm) was estimated at 1·8 t ha−1, medium-root biomass (MRB diameter 2–10 mm) at 2·0 t ha−1, coarse-root biomass (CRB, diameter >10 mm) at 5·6 t ha−1 and stump biomass at 6·8 t ha−1. Total below-ground biomass was estimated at 16·2 t ha−1 (root : shoot ratio equal to 0·23) for this 800 tree ha−1 eucalypt plantation density. The density of FRB was very high (0·56 t ha−1) in the top soil horizon (0–3 cm layer) and decreased greatly (0·3 t ha−1) with depth (50–100 cm). Without labour requirement considerations, no significant differences were found between the four methods for FRB and MRB; however, CRB was better estimated by the half and full Voronoi trenches. When labour requirements were considered, the most effective method was auger coring for FRB, whereas the half and full Voronoi trenches were the most appropriate methods for MRB and CRB, respectively.

Conclusions

As CRB combined with stumps amounted to 78 % of total below-ground biomass, a full Voronoi trench is strongly recommended when estimating total standing root biomass. Conversely, for FRB estimation, auger coring is recommended with a design pattern accounting for the spatial variability of fine-root distribution.

Keywords: Eucalypt plantation, Eucalyptus urophylla, E. grandis, root biomass, auger, monolith, Voronoi trench, accuracy and precision, labour requirements, Congo

INTRODUCTION

Below-ground biomass is an important part of the biosphere and may amount to approx. 30 % of above-ground biomass (Grier et al., 1981; Van Noordwijk et al., 1996; Canellas Rey de Vinas and San Miguel Ayanz, 2000). In addition, fine-root production has been found to be equivalent to, or greater than, above-ground litterfall in a large number of forests, and may account for more than half of plant net primary production (NPP) (Keyes and Grier, 1981; Burke and Raynal, 1994; Fahey and Hughes, 1994). For example, in northern America, >66 % of the NPP of conifer stands has been attributed to root production (Grier et al., 1981). Furthermore, while net ecosystem exchange can be estimated from eddy–flux data (Papale et al., 2006), it can also be assessed from NPP and heterotrophic CO2 respiration (from both above- and below-ground litter decomposition). In the second approach, root production and root turn-over are the key quantities to be assessed to get proper values of NPP and net ecosystem exchange (Matamala et al., 2003; Norby et al., 2004).

One of the most common methods for root biomass estimation is the root : shoot ratio, where root biomass is estimated from easily measured shoot biomass (Mokany et al., 2006). This method is now widely used to estimate below-ground biomass and carbon stocks (Cairns et al., 1997; Snowdon et al., 2000; Eamus et al., 2002), but it is not really precise enough for this purpose due to the considerable variability encountered in the data (Brown, 2002). This variability is probably due not only to natural variability in forests or plantations or the use of different sampling methods, but also to the lack of a systematically, statistically rigorous experimental design executed with the same sampling methods (Brown, 2002). Moreover, reliable estimation of root biomass requires that the root : shoot ratio applied is representative in all systems being studied (Snowdon et al., 2000), which is not the case in all studies (Mokany et al., 2006).

Root biomass can also be evaluated by another indirect method, without digging, using allometric equations or models (Saint-André et al., 2005), but such equations/models need to be calibrated on large amounts of data collected by reliable excavation methods.

Despite the importance of below-ground parts in plant production, estimations of root mass and its distribution in the profile by direct methods are still very difficult and time consuming, and no single reliable study methodology is available (Vogt et al., 1998). Difficulties in harvesting roots in their totality, particularly for deep root systems (Stone and Kalisz, 1991; Nepstad et al., 1994, Canadell et al., 1996) may lead to global underestimates of root mass in forest ecosystems. Consequently, depths are not standardized, but the depth selected in a given study is assumed to capture practically all roots. Moreover, no distinctions are usually made between living and dead roots, so root biomass is generally reported as total living and dead roots. Furthermore, it is very difficult to compare, generalize and model root systems due to the scarcity of data and lack of accuracy and precision in the methodology used.

Part of the problem lies in substantial below-ground spatial heterogeneity (Haynes and Gower, 1995; Vogt et al., 1998), and the highly variable allocation of photosynthates to roots (Vogt et al., 1996). In addition, fine-root dynamics are subject to many biotic and abiotic factors that vary in time and space. These factors include tree age and species, soil type, soil temperature and moisture, nutrient availability, as well as the impacts of insects, fungi and other soil organisms (Nadelhoffer et al., 1985; Hendrick and Pregitzer, 1993; Haynes and Gower, 1995; Gill and Jackson, 2000).

Two types of direct methods have been used to estimate root biomass and involve sampling individuals or multi-tree plots (Snowdon et al., 2002). Single-tree excavation methods consist in removing the tree root system from the soil and tracing each root individually from the stump to root tip. Volumetric soil-root sample methods consist in excavating a given volume of soil and sorting the roots contained in that volume (Pierret et al., 2005). These volumetric samples range from traditional auger cores and monoliths to Voronoi polygons (Honda, 1978; Snowdon et al., 2002; Saint-André et al., 2005). With in situ imaging methods, roots are observed through a transparent tube (Lopez et al., 1998; Smit et al., 2000; Tierney and Fahey, 2002; Hendricks et al., 2006; Mainiero and Kazda, 2006) or a transparent pane of glass (Thongo M'bou et al., 2008) inserted into the soil. Biomass can be estimated by these imaging methods (Metcalfe et al., 2008) but it remains inaccurate, because they require the application of a correction factor to convert length to root mass (Steele et al., 1997).

Of the direct methods, single-tree excavation is now considered as a standard for coarse-root biomass (Snowdon et al., 2002). Conversely, for fine and medium roots, it is well documented that all the above-mentioned techniques result in highly variable biomass estimates. Furthermore, comparing two different techniques often leads to contradictory results, because most of the studies were not carried out at the same sites (one method per site). For example, Millikin and Bledsoe (1999) found that the root mass density of blue oak using the monolith method was at least 50 % higher than that obtained by the core method for the youngest trees, while the opposite trend was obtained for larger trees. Conversely, Nissen et al. (2008) recorded a good agreement between the soil core and monolith methods for soybean root biomass, and a similar result was obtained by Jose et al. (2001) in a temperate alley cropping system. For fine and medium roots, the choice is somewhat determined from the researcher's personal preference, experience, equipment, the time taken and/or available finances, rather than accuracy and precision.

In this study, the objective was to carry out a comparison in the same area, within the same plot, over the same time span and on the same trees. The experiment took place in large plantations of eucalypt trees near Pointe Noire in Congo (6 years old which is nearly the age of harvesting in that area). Four direct root excavation methods were used to assess fine (<2 mm), medium (2–10 mm) and coarse (>10 mm) root biomass: two methods using small-volume soil samples (8-cm diameter auger and 25 × 25 cm2 monolith coring method) were compared with two methods using high-volume soil samples (half and full 3-m3 trench excavation technique). Lastly, the accuracy and precision of the four methods were assessed with regard to labour requirements and financial costs.

MATERIALS AND METHODS

Study site area and plant material

The experimental site was located at Kondi, in the Kouilou region, 30 km north-east of the town of Pointe-Noire, south-western Congo (4 °S 12 °E). The ecological context was previously described in detail by Laclau et al. (2000). In brief, the site was located on a plateau, 100 m a.s.l., 10–20 km from the sea. The soils were classified as Ferralic Arenosols (FAO); they were deep (>10 m), homogeneous in colour (grey on the surface and ochre at depth), structure (particulate) and texture (>85 % sand content) and chemically poor, except for phosphorus. The climate was characterized by a marked dry season from June to September and 1200 mm of annual rainfall (average of 50 years). The relative humidity was approx. 85 % on average (±2 %) and the average temperature was 25 °C with 5 °C of seasonal variations. The site was planted in April 2001 with the highly productive clone 18-52 resulting from a cross between Eucalyptus urophylla and E. grandis, from a tree breeding programme. This clone was chosen for its wide distribution in the local region and its numerous biomass and biogeochemical studies. The stand density was 800 stems ha−1. The present study was carried out when the trees were 6 years old.

Sampling design

Four trees were selected after a forest inventory to cover the full range of basal area variations within the stand. Root excavations were performed in the Voronoi polygon (Honda, 1978, Snowdon et al., 2002, Saint-André et al., 2005), which is the elementary space defined by the half distances between the sampled tree and its neighbours (Fig. 1D). The hypothesis is that all the roots of the sampled tree that grow outside the Voronoi polygon are balanced by those of neighbouring trees growing inside the polygon. Variability between trees was taken into account by the number of replicates (four trees) and variability along the sampled tree was taken into account by subdividing the Voronoi space into four equal parts and by sampling each of them. A sampled tree was thus delineated by four Voronoi quarters hereafter called Voronoi trench. Four root biomass excavation methods were studied: auger sampling method (auger), monolith sampling method (monolith), half Voronoi-trench excavation (half trench), and full Voronoi-trench excavation (full trench). Each method was randomly allocated to each quarter.

Fig. 1.

Fig. 1.

Open in a new tab

Design patterns of the different root sampling methods: (A) auger, (B) monolith and (C) half and full Voronoi-trench excavation methods with three option designs in one single Voronoi quarter. All sampling methods were set up around the same sampled tree with the following organization (D): three quarters were selected for replication of methods (auger, monolith and half and full Voronoi trench) and one-quarter was selected to superimpose all the methods. All the quarters were chosen randomly. For the auger method (A), option T1 consisted of five soil sampling cores located at the four corners and in the centre of the Voronoi quarter (ABCDE). Option T2 was the same as T1 plus four soil sampling cores (G, I, K and M) set in the centre of gravity of triangles AED, DEC, BEC and AEB, respectively. Option T3 was the same as T2 plus four soil sampling cores (H, J, L and F) set in the middle of each diagonal [DE], [CE], [BE] and [AE], respectively. For the monolith method (B) two main lines [Ax] and [Ay] were chosen (x: middle of [CD], y: middle of [BC]). Options P1 and P2 were set up with five monolith sampling locations (P1, P12, P13, P14 and P5) and (P1, P22, P23, P24 and P5), respectively, and the P option design with eight monolith sampling locations (P1, P12, P13, P14, P22, P23, P24 and P5). For the Voronoi method (C), half Voronoi trenches (V1 or V2) were set up on half of the Voronoi quarter separated by the sampling tree diagonal. The full Voronoi trench (V = V1 + V2) was a Voronoi quarter.

Sampling methods

Auger-core method

Coring was done with a root auger (8-cm inner diameter) in three soil horizons (0–10, 10–50 and 50–100 cm called H1, H2 and H3, respectively). Root mat biomass (+3–0 cm, called H0; see Laclau et al., 2004) was measured above H1, in the soil litter layer above ground. Three different sampling intensities were tested: T1 (five cores: A, B, C, D and E), T2 (nine cores: T1 + G, I, K and M) and T3 (13 cores: T2 + F, H, J and L) (Fig. 1A).

Monolith method

For the H1 and H2 soil horizons 25 × 25 cm monoliths were sampled, and for the H3 soil horizon two auger cores (8-cm inner diameter) were collected in each monolith. The Voronoi trench was separated into two equal parts. Two samples (P1 and P5) were set up on the diagonal and were common to the two half trenches. Two lines were then drawn on each side of the diagonal (Fig. 1B) and three additional monoliths were equally distributed on each of these two lines starting from the edge of the trench.

Half Voronoi trench

The half Voronoi trench was set up by dividing the entire Voronoi trench into two equal parts (Fig. 1C). The section to be excavated was randomly chosen. The three soil horizons were fully removed. Fine-root biomass values were then corrected by applying a conversion factor to account for losses due to manual sorting compared with the sieving techniques. The conversion factor was calculated by estimating the percentage of roots lost during sieving procedures. The average conversion factor was applied to the H1 soil horizon where the root density was assumed to be higher than the deeper horizons (Bouillet et al., 2002).

Full Voronoi trench

This was the reference trench. All soil volume was excavated on the entire Voronoi trench (3 m3). Roots were manually separated from the soil for all horizons without sieving. The same average conversion factor used for the half Voronoi trench was also used here for fine-root biomass estimation.

Superimposing of methods

The other three excavation methods were superimposed over the quarter allocated to the full Voronoi trench, for a strict comparison between them: same tree, same place and same period of observations (Fig. 1D). The auger-coring method was applied first, followed by the monolith method and, finally, the half Voronoi trenches were performed. The root biomass measured in the core and monolith samples was added to the other excavation methods when necessary.

Stump and coarse-root system excavation

After completing the volume-sampling methods, the stump and the remaining root system were excavated to assess the total below-ground biomass (up to 1 m in depth). Above-ground biomass was also measured according to the protocol described in Saint-André et al. (2005). The root : shoot ratio was calculated from the measured above- and below-ground biomass.

Sample treatments

Once collected, root samples were brought to the laboratory where they were gently washed in a standardized sieve (2 mm) to separate roots from soil particles. Three diameter classes were considered (fine roots, 0–2 mm; medium roots, 2–10 mm; coarse roots, >10 mm). For the stump, a sub-sample was taken by cutting a longitudinal slice. Roots were oven-dried to a constant weight at 65 °C (approx. 2 d for fine roots, 3 d for medium roots and 1 week for coarse roots and stump sub-samples). For the root mat system (H0), after air drying, the samples were sieved with a 2-mm sieve to remove soil debris. The root mat was then gently and manually separated from the decomposed litter and was dried at 65 °C to constant weight. Weeds and understorey shrubs were controlled by herbicide and no confusion between eucalypt roots and those of other species was possible.

Method comparison

Labour requirements and cost benefits

The time required for every task in the field and in the laboratory was recorded: setting up the sampling design on each tree, excavation (coring or digging), sorting roots from soil (sieved or manually), washing and root-size classification. Oven drying and weighing were not taken into account since these operations were performed on sub-samples that were strictly equivalent between the four methods. Labour requirements were transformed into person-days (on a basis of 6 h of field work per day). The cost/benefits analysis between the different methods was applied to the raw dataset without taking account of tree size distribution within a stand.

Accuracy and precision

Taking full Voronoi-trench excavation as the reference value, accuracies for auger-core, monoliths and the half Voronoi-trench methods were calculated as the relative differences between those methods and the reference method.

Confidence intervals at 95 % (IC95), which give the precision of the method, were calculated for each method assuming a Gaussian distribution:

graphic file with name mcr102eqnU1.jpg

where σ is the measured standard deviation and N the number of samples collected for a given method. The formula was reversed to calculate Npredicted, the number of samples to be collected to achieve 10 % precision (i.e. IC95 = 10 % of the average biomass; Chave et al., 2003). The differences between methods were statistically tested using the GLM procedure of SAS software (SAS® Institute Inc., Cary, NC, USA, 2004) combined with Tukey's test. In the Results section, mean root biomass are given with their standard deviation (s.d.) and expressed in term of dry matter (t ha−1).

RESULTS

Below-ground biomass

With the reference trench, 1·8, 2·0 and 5·6 t ha−1 were measured for fine-root (FRB), medium-root (MRB) and coarse-root biomass (CRB), respectively, from the root mat to a depth of 1 m (Table 1). Total root biomass varied greatly with tree size. A root density index was calculated by dividing fine and medium-root biomass by the thickness of the soil layer (Fig. 2). The density of fine-root biomass was very high in the top soil horizons (0·19 ± 0·04 t ha−1cm−1 for H0 and 0·04 ± 0·02 t ha−1cm−1 for H1) and decreased with depth (5·7 × 10−3 ± 1·3 × 10−3 t ha−1cm−1 for H3).

Table 1.

Tree characteristics [circumference (cm) at 130 cm (C130) and height (m)] and below-ground standing biomass (t ha−1) of a 6-year-old eucalypt plantation in Congo

H0
 H1
 H2
 H3
 All profile
 Total
Tree C130 (cm) Height (m) Root mat FRB MRB CRB FRB MRB CRB FRB MRB CRB FRB MRB CRB RB Stump Below-ground biomass Root : shoot ratio
A1 44·0 22·35 0·62 0·67a 0·33b 0·00a 0·70a 1·06a 3·99b 0·30a 0·37b 0·12b 2·28a 1·75b 4·11b 8·14 4·89 13·03 26 %
A2 59·4 27·20 0·44 0·30c 0·36ab 0·28a 0·45b 0·96a 2·84bc 0·33a 0·61b 0·88b 1·51b 1·92b 4·00b 7·43 9·10 16·53 14 %
A3 25·6 15·53 0·70 0·41bc 0·42ab 0·00a 0·42b 0·48b 0·26c 0·27a 0·26b 0·02b 1·80b 1·16b 0·28b 3·24 1·47 4·71 36 %
A4 71·0 29·10 0·48 0·22c 0·52a 1·50a 0·40b 1·31a 7·75a 0·30a 1·39a 4·61a 1·39b 3·22a 13·85a 18·46 11·86 30·32 17 %
Mean 50·0 23·55 0·56 0·40 0·41 0·45 0·49 0·95 3·71 0·30 0·66 1·41 1·75 2·01 5·56 9·32 6·83 16·15 23 %
Open in a new tab

Fine- (FRB), medium- (MRB), coarse-root (CRB) and total root biomass (RB) were calculated on all datasets (t ha−1) for the full Voronoi trenches (reference method) as a function of soil depth horizons (H0, +3–0; H1, 0–10; H2, 10–50; H3, 50–100 cm).

The total below-ground standing biomass of the trees was calculated by adding total RB and stump biomass (t ha−1).

Means of tree root biomass are compared by tree; values with the same superscript letters are not significantly different (Tukey test, P < 0·05).

Fig. 2.

Fig. 2.

Open in a new tab

Fine- and medium-root densities (in t ha−1 cm−1 of excavated soil) within four soil horizons. H0, pure organic top soil horizon (+3–0 cm); H1, 0–10 cm; H2, 10–50 cm; H3, 50–100 cm. Vertical bars represent s.d.

The same pattern was observed for medium-root biomass. Conversely, CRB was more abundant in the H2 and H3 soil horizons than in H1 (Table 1), without taking into account the below-ground stump. The root : shoot ratio ranged between 14 % and 36 % with an average of 23 % (Table 1). Coarse-roots and stumps amounted to 78 % of total below-ground biomass.

Comparison between methods

There were no significant differences between the four methods in estimating mean fine-root biomass (Table 2A), meaning that the auger-core and monolith methods were as accurate as the full Voronoi-trench excavation method. This suggests that fine-root distribution is horizontally uniform around the tree in a 6-year-old stand. The variability was mainly driven by a ‘tree’ effect in the surface horizon for the monolith method (H1, P < 0·0001; H2, P <0·0002), and for the auger-core method (H1, P < 0·0001; H2, P < 0·0001), with smaller trees producing fewer fine roots than larger trees. In the deeper horizon (H3, 50–100 cm), root density decreased with the distance from the tree for the auger-core (P < 0·0028) and monolith methods (P < 0·0001).

Table 2.

Comparison of different methods for fine- (FRB, A), medium- (MRB, B) and coarse-root (CRB, C) biomass estimation superimposed on the same Voronoi trench

(A) Fine root
H1
 H2
 H3
 Total
Methods Options FRB RD FRB RD FRB RD FRB RD
Auger T1 0·37a ± 0·27 –20·4 0·55a ± 0·18 5·7 0·33a ± 0·08 9·7 1·25a ± 0·41 –2·4
T2 0·49a ± 0·36 5·9 0·58a ± 0·13 11·1 0·36a ± 0·05 18·9 1·42a 0·50 11·1
T3 0·49a ± 0·38 7·3 0·57a ± 0·14 8·1 0·35a ± 0·07 17·8 1·41a ± 0·53 10·1
Monolith P1 0·46a ± 0·21 1·1 0·47a ± 0·12 –9·8 0·31a ± 0·11 2·4 1·24a ± 0·21 –3·1
P2 0·43a ± 0·24 –5·5 0·44a ± 0·14 –15·0 0·25a ± 0·11 –16·8 1·13a ± 0·28 –12·0
P 0·45a ± 0·23 –2·2 0·46a ± 0·12 –12·4 0·28a ± 0·11 –7·2 1·18a ± 0·24 –7·5
Half trench V1 0·49a ± 0·30 7·2 0·58a ± 0·30 10·1 0·29a ± 0·07 –4·8 1·35a ± 0·55 5·6
V2 0·43a ± 0·23 –7·2 0·47a ± 0·16 –10·1 0·31a ± 0·08 4·8 1·21a ± 0·36 –5·6
Full trench V 0·46a ± 0·26 0·0 0·52a ± 0·23 0·0 0·30a ± 0·07 0·0 1·28a ± 0·23 0·0
(B) Medium root
Method Option H1
 H2
 H3
 Total
MRB RD MRB RD MRB RD MRB RD
Auger T1 0·12a ± 0·14 –73·0 0·54a ± 0·41 –46·0 0·68a ± 0·93 41·3 1·34a ± 0·86 –30·6
T2 0·30ab ± 0·13 –32·2 0·68a ± 0·74 –31·9 0·48a ± 0·55 –0·1 1·47a ± 0·94 –23·8
T3 0·25ab ± 0·10 –44·6 0·89a ± 0·67 –11·1 0·43a ± 0·39 –9·4 1·58a ± 0·89 –18·1
Monolith P1 0·45ab ± 0·14 1·1 0·77a ± 0·61 –23·8 0·30a ± 0·25 –36·3 1·52a ± 0·71 –21·2
P2 0·57b ± 0·38 27·7 0·75a ± 0·45 –25·3 0·34a ± 0·32 –30·0 1·66a ± 0·76 –14·0
P 0·51ab ± 0·16 14·4 0·76a ± 0·42 –24·6 0·32a ± 0·28 –33·1 1·59a ± 0·73 –17·6
Half Trench V1 0·43ab ± 0·10 –3·1 0·98a ± 0·42 –2·1 0·49a ± 0·30 3·3 1·91a ± 0·63 –1·0
V2 0·46ab ± 0·18 3·1 1·03a ± 0·50 2·1 0·46a ± 0·24 –3·3 1·95a ± 0·63 1·0
Full trench V 0·45ab ± 0·14 0·0 1·01a ± 0·43 0·0 0·48a ± 0·25 0·0 1·93a ± 0·45 0·0
(C) Coarse root
Method Options H1
 H2
 H3
 Total
CRB RD CRB RD CRB RD CRB RD
Monolith P1 0·00a ± 0·00 –100·0 4·05a ± 3·91 –4·7 1·22a ± 2·45 –47·3 5·27a ± 6·05 –26·0
P2 0·00a ± 0·00 –100·0 3·43a ± 3·50 –19·2 1·65a ± 3·29 –29·1 5·08a ± 6·28 –28·7
P 0·00a ± 0·00 –100·0 3·74a ± 3·45 –11·9 1·43a ± 2·70 –38·2 5·18a ± 6·16 –27·4
Half trench V1 0·64a ± 1·07 14·3 4·12a ± 4·02 –2·9 2·09a ± 3·52 –10·0 6·85a ± 8·53 –3·9
V2 0·48a ± 0·81 –14·3 4·37a ± 3·16 2·9 2·55a ± 5·03 10·0 7·41a ± 7·95 3·9
Full trench V 0·56a ± 0·88 0·0 4·25a ± 3·35 0·0 2·32a ± 2·70 0·0 7·13a ± 8·21 0·0
Open in a new tab

Mean root biomass ± s.d. are expressed in t ha−1 and relative differences (RD) from the Full Voronoi trench as a percentage.

Means are compared by method; the values with the same superscripted letters are not significantly different (Tukey's test, P < 0·05).

H1, H2 and H3 represent soil horizons that were 0–10, 10–50 and 50–100 cm thick, respectively.

As for FRB, there were no statistical differences between methods for estimating MRB (Table 2B). However, standard deviations were higher than for FRB. For H1, the monolith and auger methods showed a ‘distance from tree’ effect (P = 0·0375 and 0·0378, respectively). For H2 and H3, a ‘distance from tree’ effect was only found for the monolith method (P = 0·0010 and 0·0176, respectively). For H2, there was also a ‘tree’ effect (monolith, P = 0·0005; auger, P = 0·0198).

The auger-core method was not suitable for assessing coarse-root biomass and only the other three methods are given in Table 2C. As for FRB and MRB, there were no statistical differences between the monolith, half and full Voronoi-trench methods. However, variability was very high and the monolith method was found to be less accurate than the half Voronoi-trench method (27 % of underestimation). A ‘distance from tree’ effect was found for H2 (P < 0·0001), with more CRB near the tree than on the edge of the Voronoi.

Comparison of the methods based on precision and cost

For FRB, it was necessary to sample 312 auger cores (or 24 trees × 13 cores) to achieve 10 % precision for the soil surface horizon (0–10 cm). This figure fell to about 130 auger cores for H2 and H3. It took 36 person-days to complete this task (Table 3A) on a whole 0–1 m profile. Assuming a Gaussian distribution (Chave et al., 2003), reducing the desired precision from 10 % to 30 % reduced labour costs by 75 %. Compared with auger cores, use of the monolith method reduced the total number of samples by >2 for H1 but the time required for sieving operations increased by 30 % and total time for a whole 0–1 m profile was approximately double, at 62 person-days. Lastly, the full Voronoi-trench method was the least efficient (in terms of cost/precision) because it required 100, 50 and 20 soil samples to reach 10 % precision for H1, H2, and H3, respectively, and a total of 207 person-days for sampling, sieving and sorting operations.

Table 3.

Precision and labour requirements for auger coring, monolith, half and full Voronoi-trench excavation methods for fine- (FRB), medium- (MRB) and coarse-root biomass (CRB) estimation (t ha−1)

Sampled
 Precision 10 %
 Precision 30 %
Root type Horizon Method Mean ± s.d. Nsampled Precision Time N10 % Time N30 % Time
Mat H0 Auger 0·557 ± 0·288 51 14 % NC 105 NC 12 NC
FRB H1 Auger 0·420 ± 0·374 103 17 % 2 312 7 35 1
Monolith 0·435 ± 0·247 64 14 % 3 127 6 14 1
Half trench 0·396 ± 0·206 16 26 % 8 106 52 12 6
Full trench 0·396 ± 0·203 8 36 % 8 103 101 11 11
H2 Auger 0·546 ± 0·319 100 12 % 5 134 12 15 0
Monolith 0·445 ± 0·252 64 14 % 13 125 25 14 3
Half trench 0·503 ± 0·187 16 18 % 31 54 106 6 12
Full trench 0·503 ± 0·181 8 25 % 31 50 247 6 22
H3 Auger 0·345 ± 0·214 99 12 % 11 150 17 17 2
Monolith 0·321 ± 0·181 61 14 % 15 125 31 14 3
Half trench 0·287 ± 0·065 16 11 % 39 20 49 2 5
Full trench 0·287 ± 0·065 8 16 % 39 20 98 2 11
MRB H1 Auger 0·340 ± 0·752 104 43 % 2 1912 43 212 5
Monolith 0·460 ± 0·365 64 20 % 3 246 12 27 1
Half trench 0·398 ± 0·132 16 16 % 8 43 21 5 2
Full trench 0·398 ± 0·092 8 16 % 8 21 21 2 2
H2 Auger 1·013 ± 1·723 104 33 % 5 1133 103 126 3
Monolith 1·085 ± 1·123 64 26 % 13 420 83 47 9
Half trench 1·043 ± 0·353 16 17 % 31 45 88 5 10
Full trench 1·043 ± 0·270 8 18 % 31 26 103 3 11
H3 Auger 0·684 ± 2·622 103 75 % 12 5762 652 640 72
Monolith 0·541 ± 1·124 61 53 % 15 1688 416 188 46
Half trench 0·595 ± 0·323 16 27 % 39 116 283 13 31
Full trench 0·595 ± 0·301 8 35 % 39 100 491 11 55
CRB H1 Monolith 0·019 ± 0·128 64 170 % 3 18578 915 2064 102
Half trench 0·880 ± 1·979 16 111 % 8 1982 1615 220 108
Full trench 0·880 ± 1·437 8 114 % 8 1046 1025 116 114
H2 Monolith 4·820 ± 13·976 64 72 % 13 3297 650 366 72
Half trench 3·960 ± 3·366 16 42 % 31 283 555 31 62
Full trench 3·960 ± 3·217 8 57 % 31 259 1014 29 113
H3 Monolith 1·302 ± 4·775 61 91 % 15 5011 1234 586 144
Half trench 1·875 ± 3·145 16 83 % 39 1104 2703 123 300
Full trench 1·875 ± 2·995 8 112 % 39 1001 4902 111 545
Open in a new tab

Nsampled is the number of samples collected.

Precision is calculated with a confidence index of 95 % with the hypothesis of normal distribution of the dataset; N10 % and N30 % are the numbers of samples needed to achieve, respectively, 10 % or 30 % precision on average; labour time requirement (Time) is expressed in person-days.

H0, H1, H2 and H3 represent soil horizons that were +3–0, 0–10, 10–50 and 50–100 cm thick, respectively.

The monolith and auger-core methods were not accurate for estimating medium and coarse-root biomass. Only the half or full Voronoi-trench methods provided a good estimate. However, given the high variability in the spatial distribution of these roots, a desired precision of 30 % would have been more realistic in terms of cost/precision than 10 %.

Sieving versus manual sorting

Most of time spent in the field was devoted to separating roots from soil (manually or with a sieve): 40 %, 55 % and 71 % for the auger-core, monolith and full Voronoi-trench methods, respectively. For example, for FRB estimation in the top soil horizon (H1), the time for sieving soil reached 62 % of the total sorting time for a 12 % gain in precision.

DISCUSSION

Root biomass and distribution

Estimating root biomass under field conditions is challenging because of unknown root distribution (Costa et al., 2000). Within plant species, root biomass and architecture depend on genetic and environmental factors. The main environmental factors affecting root biomass may be soil fertility, water content and temperature (Delitti et al., 2001; Green et al., 2005).

Several studies on eucalypt root biomass and distribution have been conducted in tropical areas (Fabiao et al., 1995; Laclau et al., 2001; Bouillet et al., 2002; Eamus et al., 2002; Teixeira et al., 2002; Harmand et al., 2003), but it is not easy to compare the results because of a diversity of root sizes (stump and coarse roots were not systematically integrated in most below-ground biomass studies), soil depth, plant ages and sampling methods. Another difficulty in comparing root biomass data from diverse studies is to account for differences in methodologies and sites. Despite these difficulties, fine-root biomass from the reference trench of the present study was close to that obtained for fine roots (<1 mm in diameter) of a 7-year-old Eucalyptus urophylla stand in Brazil sampling to a depth of 60 cm (0·7 t ha−1; Teixeira et al., 2002), and a 7-year-old eucalyptus PF1 stand (clone 1-41) in Congo sampling to a depth of 1 m (1·4 t ha−1; Saint-André et al., 2005). However, these results were in a lower range than those obtained at 0–30 cm in depth on a 2-year-old Eucalyptus globulus stand in Portugal and a 5-year-old Eucalyptus camaldulensis stand in Cameroon (2 t ha−1, Jones et al., 1999; Harmand et al., 2003). In addition, the fine-root biomass data of the present study were also lower than those obtained in other temperate and tropical species (Ruess et al., 1996; Jackson et al., 1997; Hertel and Leuschner, 2002; Hendricks et al., 2006; Hwang et al., 2007; Valverde-Barrantes et al., 2007). For example, Jackson et al. (1997) recorded a mean fine-root biomass (0–30 cm depth) of 3 t ha−1 in tropical species.

The fine-root biomass density was affected by the soil depth effect in the present study. It was higher in H0 and H1 than in H2 and H3. This result has been observed in many other studies (Fabiao et al., 1990; Castellanos et al., 2001; Bouillet et al., 2002; Resh et al., 2003; Claus and George, 2005) and may be related to a reduction in fine-root formation and mortality (Mainiero and Kazda, 2006) or differences in resource availability (Goransson et al., 2008) along the soil profile.

The ‘distance from tree’ effect on FRB from the monolith and auger methods was not significant for the H1 and H2 horizons. This result was consistent with other studies on tree plantations that have shown no decrease in fine-root density from the position of the row to the middle of the inter-row (Fabiao et al., 1995; Bouillet et al., 2002). However, the opposite trend was found in a radiata pine stand (Nambiar, 1983).

The average root : shoot ratio was estimated at 0·23, which tallied with previous studies carried out at the same site but on another clone (Saint-André et al., 2005). This ratio was similar to those (0·26) calculated by Cairns et al. (1997) in more than 160 studies covering tropical, temperate and boreal forests. These authors highlighted the great variability in root biomass estimates, due to the different methods used at a great number of diverse sites and they assumed that the integration of parameters such as latitude, soil texture, tree age and type could reduce that variability. In the present study, the great variability found (range 0·14–0·36) could not be assigned to these parameters as the study was performed in the same 6-year-old plantation, but rather on the great variability in tree height and diameter at breast height of the sampled trees as they were representative of the clonal eucalypt stand. Given the labour requirements of this study, variability between trees associated with a given diameter or height size class (only one tree per diameter at breast height class was sampled) was not investigated, despite its relevance for fitting relationships between tree size and the corresponding root biomass. For an accurate root biomass estimation of the stand, this aspect should be taken into account, but for the purpose of the paper (method comparison), and considering the work was on clonal plantations (same genotype) established on sandy soils (>90 % sand) with low variability on a stand scale, tree size was the first and main factor to be considered.

Comparison between methods

Few studies have compared the different sampling methods for root biomass estimation, probably because of the time-consuming experiments (Jose et al., 2001) and laborious field excavation work required. When different root sizes are included, studies are rather scarce (Millikin and Bledsoe, 1999). Kücke et al. (1995) compared four commonly used direct field methods for estimating root biomass (i.e. core method, core-break method, trench-profile wall method and root-extraction method). They concluded that the core-break and trench-profile methods delivered no reliable data for comparing rooting intensities between soils and between different crops. On the other hand, the core and root extraction methods were more appropriate for estimating root biomass. Of the four direct field methods tested in the present study, all of them involved root excavations (i.e. by auger cores, monoliths or large trenches) in a known soil volume, and a distinction was made between root diameter classes (i.e. fine, medium, coarse roots and stumps).

The study showed that using an auger core is not a suitable method for estimating coarse-root biomass. This was consistent with previous studies on eucalypt and others species (Millikin and Bledsoe, 1999; Resh et al., 2003; Saint-André et al., 2005; Macinnis-Ng et al., 2010). In young and old eucalypt stands in Congo, the half Voronoi-trench and the auger-core methods were unsuitable for correct estimation of coarse-root biomass (Saint-André et al., 2005). In addition, Millikin and Bledsoe (1999) obtained at best 25 % of coarse-root biomass with soil coring compared with excavation methods in a Quercus douglasii stand. That result may have been due to heterogeneous distribution of coarse roots around the tree (Macinnis-Ng et al., 2010). Resh et al. (2003) showed that with an increase in coarse-root diameter, their lateral distribution became highly spatially variable. Thus, by enhancing the sampling soil volume, the full Voronoi trench enabled better sampling of the heterogeneity of coarse roots growing in all directions from the stump. This method more effectively took into account the variability of coarse-root distribution and therefore reduced the subsequent biases generated by standard auger-sampling methods (Macinnis-Ng et al., 2010).

When the root system is uniformly established in the horizontal soil profile, biomass can be estimated with less risk of error by the auger-core or monolith methods as observed for fine roots in the present study. This result tallied with those found for maize, black walnut and red oak alley cropping systems, which showed a significant linear relationship between fine-root biomass from auger cores and minirhizotrons (Jose et al., 2001). The study of spatial distribution of root systems in Congo showed that at 1 year old, fine roots extended beyond a depth of 3 m and up to the middle of the inter-row (Bouillet et al., 2002). From this age, we assumed homogeneous colonization of the horizontal horizons by fine roots in undisturbed soil.

Fine-root biomass required 312 auger cores to achieve 10 % precision in the 0–10 cm horizon and around one third of that for the 10–50 cm and 50–100 cm horizons. In the surface horizon, fine-root biomass variability may explain this large number of samples. This variability may be introduced locally by factors such as micro-site spatial soil topography or mechanical soil preparation before planting. Consequently, the amounts of fine-root biomass may vary with environmental conditions, crop genotypes, and with soil physical, chemical and biological properties (Bingham and Bengough, 2003).

The root mat biomass in the above-ground forest floor amounted to 32 % of the total fine-root biomass (assessed in the 0–1 m soil layer) of a 6-year-old eucalypt stand. This result was consistent with previous studies at the same site (Laclau et al., 2004). In addition, roots may preferentially explore the planting row, where soil compaction is lower, resulting from the ripping treatment carried out during land preparation (Laclau et al., 2000). The smaller number of samples required in the deepest soil layers to achieve a given level of precision may be related to the reduction in biomass variation, as observed in three European forest chronosequences (Claus and George, 2005).

Lastly, this study showed that the full Voronoi-trench method is highly recommended for estimating total standing root biomass, due to the relative share (78 %) of coarse roots and stumps in total below-ground biomass. Moreover, the Voronoi-trench method required a smaller number of samples to achieve good precision for fine-root biomass, but it needed a greater number of person-days for sampling, sieving and sorting operations. This was consistent with the large soil volume of this sampling method.

Based on the absence of significant differences in FRB between the different sampling methods, the auger coring and monolith methods were the best compared with the Voronoi trench for fine-root estimation in a 6-year-old eucalypt plantation and older. However, previous studies have shown uniform fine-root colonization in the root profile (Bouillet et al., 2002) from 1 year old onwards, but radial uniformity was not tested and additional studies need to be carried out in young eucalypt plantations.

ACKNOWLEDGEMENTS

The study was funded by the EU STREP – CARBOAFRICA project. Technical assistance was provided by CRDPI and we thank the ‘nkumbi’ root team members. We also thank Peter Biggins for revising the English.

LITERATURE CITED

  1. Bingham IJ, Bengough AG. Morphological plasticity of wheat and barley roots in response to spatial variation to soil strength. Plant and Soil. 2003;250:273–282. [Google Scholar]
  2. Bouillet JP, Laclau JP, Arnaud M, et al. Changes with age in the spatial distribution of roots of Eucalyptus clone in Congo: impact on water and nutrient uptake. Forest Ecology and Management. 2002;171:43–57. [Google Scholar]
  3. Brown S. Measuring carbon in forests: current status and future challenges. Environmental Pollution. 2002;116:363–372. doi: 10.1016/s0269-7491(01)00212-3. [DOI] [PubMed] [Google Scholar]
  4. Burke MK, Raynal DJ. Fine root phenology, production, and turnover in the northern hardwwod forest ecosystem. Plant and Soil. 1994;162:135–146. [Google Scholar]
  5. Cairns MA, Brown S, Helmer EH, Baumgardner GA. Root biomass allocation in the world's upland forests. Oecologia. 1997;111:1–11. doi: 10.1007/s004420050201. [DOI] [PubMed] [Google Scholar]
  6. Canadell J, Jackson RB, Ehleringer JB, Mooney HA, Sala OE, Schulze ED. Maximum rooting depth of vegetation types at the global scale. Oecologia. 1996;108:583–595. doi: 10.1007/BF00329030. [DOI] [PubMed] [Google Scholar]
  7. Canellas Rey de Vinas I, San Miguel Ayanz A. Biomass of root and shoot systems of Quercus coccifera shrublands in Eastern Spain. Annals of Forest Science. 2000;57:803–810. [Google Scholar]
  8. Castellanos J, Jaramillo VJ, Sanford RL, Jr, Kauffman JB. Slash-and-burn effects of fine root biomass and productivity in a tropical dry forest ecosystem in México. Forest Ecology and Management. 2001;148:41–50. [Google Scholar]
  9. Chave J, Condit R, Lao S, Caspersen JP, Foster RB, Hubbell SP. Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama. Journal of Ecology. 2003;91:240–252. [Google Scholar]
  10. Claus A, George E. Effect of stand age on fine-root biomass and biomass distribution in three European forest chronosequences. Canadian Journal of Forest Research. 2005;35:1617–1625. [Google Scholar]
  11. Costa C, Dwyer LM, Hamilton RI, Hamel C, Nantais L, Smith DL. A sampling method for measurement of large root systems with scanner-based image analysis. Agronomy Journal. 2000;92:621–627. [Google Scholar]
  12. Delitti WBC, Pausas JG, Burger DM. Belowground biomass seasonal variation in two neotropical savannahs (Brazilian Cerrados) with different fire histories. Annals of Forest Science. 2001;58:713–721. [Google Scholar]
  13. Eamus D, Chen X, Kelley G, Hutley LB. Root biomass and root fractal analyses of an open Eucalyptus forest in a savanna of north Australia. Australian Journal of Botany. 2002;50:31–41. [Google Scholar]
  14. Fabiao A, Madeira M, Steen E. Effect of water and nutrient supply on root distribution in an Eucalyptus globulus plantation. Water, Air and Soil Pollution. 1990;54:635–640. [Google Scholar]
  15. Fabiao A, Madeira M, Steen E, Kätterer T, Ribeiro C, Araujo C. Development of root biomass in an Eucalyptus globulus plantation under different water and nutrient regimes. Plant and Soil. 1995;168/169:215–223. [Google Scholar]
  16. Fahey TJ, Hughes JW. Fine root dynamics in a northern hardwood forest ecosystem, Hubbard Brook Experimental Forest, NH. Journal of Ecology. 1994;82:533–548. [Google Scholar]
  17. Gill RA, Jackson RB. Global patterns of root turnover for terrestrial ecosystems. New Phytologist. 2000;147:13–31. [Google Scholar]
  18. Goransson H, Ingerslev M, Wallander H. The vertical distribution of N and K uptake in relation to root distribution and root uptake capacity in mature Quercus robur, Fagus sylvatica and Picea abies stands. Plant and Soil. 2008;306:129–137. [Google Scholar]
  19. Green J, Dawson L, Proctor J, Duff E, Elston D. Fine root dynamics in a tropical rain forest is influenced by rainfall. Plant and Soil. 2005;276:23–32. [Google Scholar]
  20. Grier CC, Vogt KA, Keyes MR, Edmonds RL. Biomass distribution and above- and below-ground production in young and mature Abies amabilis zone ecosystems of the Washington Cascades. Canadian Journal of Forest Research. 1981;11:155–167. [Google Scholar]
  21. Harmand JM, Donfack P, Forkong Njiti C. Tree-root systems and herbaceous species-characteristics under tree species introduced into grazing lands in sub-humid Cameroon. Agroforestry Systems. 2003;59:131–140. [Google Scholar]
  22. Haynes BE, Gower ST. Belowground carbon allocation in unfertilized and fertilized red pine plantations in northern Wisconsin. Tree Physiology. 1995;15:317–325. doi: 10.1093/treephys/15.5.317. [DOI] [PubMed] [Google Scholar]
  23. Hendrick RL, Pregitzer KS. The dynamics of fine root length, biomass, and nitrogen content in two northern hardwood ecosystems. Canadian Journal of Forest Research. 1993;23:2507–2520. [Google Scholar]
  24. Hendricks JJ, Hendrick RL, Wilson CA, Mitchell RJ, Pecot SD, Guo D. Assessing the patterns and controls of fine root dynamics: an empirical test and methodological review. Journal of Ecology. 2006;94:40–57. [Google Scholar]
  25. Hertel D, Leuschner C. A comparison of four different fine root production estimates with ecosystem carbon balance data in a Fagus–Quercus mixed forest. Plant and Soil. 2002;239:237–251. [Google Scholar]
  26. Honda M. Description of cellular pattern by Dirichlet domains: the two dimensional case. Journal of Theoretical Biology. 1978;72:523–543. doi: 10.1016/0022-5193(78)90315-6. [DOI] [PubMed] [Google Scholar]
  27. Hwang J, Son Y, Kim C, et al. Fine root dynamics in thinned and limed pitch pine and japanese larch plantations. Journal of Plant Nutrition. 2007;30:1821–1839. [Google Scholar]
  28. Jackson RB, Mooney HA, Schulze ED. A global budget for fine root biomass, surface area, and nutrients contents. Ecology. 1997;94:7362–7366. doi: 10.1073/pnas.94.14.7362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jones HE, Madeira M, Herraez L, et al. The effect of organic matter management on the productivity of Eucalyptus globulus stands in Spain and Portugal: tree growth and harvest residue decomposition in relation to site and treatment. Forest Ecology and Management. 1999;122:73–86. [Google Scholar]
  30. Jose S, Gillespie AR, Seifert JR, Pope PE. Comparison of minirhizotron and soil core methods for quantifying root biomass in a temperate alley cropping system. Agroforestry Systems. 2001;52:161–168. [Google Scholar]
  31. Keyes MR, Grier CC. Above- and below-ground net production in 40-year-old Douglas-fir stands on low and high productivity sites. Canadian Journal of Forest Research. 1981;11:599–605. [Google Scholar]
  32. Kücke M, Schmid H, Spiess A. A comparison of four methods for measuring roots of field crops in three contrasting soils. Plant and Soil. 1995;172:63–71. [Google Scholar]
  33. Laclau JP, Bouillet JP, Ranger J. Dynamics of biomass and nutrient accumulation in a clonal plantation of Eucalyptus in Congo. Forest Ecology and Management. 2000;128:181–196. [Google Scholar]
  34. Laclau JP, Arnaud M, Bouillet JP, Ranger J. Spatial distribution of Eucalyptus roots in a deep sandy soil in the Congo: relationships with the ability of the stand to take up water and nutrients. Tree Physiology. 2001;21:129–136. doi: 10.1093/treephys/21.2-3.129. [DOI] [PubMed] [Google Scholar]
  35. Laclau JP, Toutain F, Thongo M'bou A, et al. The function of the superficial root mat in the biogeochemical cycles of nutrients in Congolese Eucalyptus plantations. Annals of Botany. 2004;93:249–261. doi: 10.1093/aob/mch035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lopez B, Sabaté S, Gracia C. Fine roots dynamics in a mediterranean forest: effects of drought and stem density. Tree Physiology. 1998;18:601–606. doi: 10.1093/treephys/18.8-9.601. [DOI] [PubMed] [Google Scholar]
  37. Macinnis-Ng CMO, Fuentes S, O'Grady AP, et al. Root biomass distribution and soil properties of an open woodland on a duplex soil. Plant and Soil. 2010;327:377–388. [Google Scholar]
  38. Mainiero R, Kazda M. Depth-related fine root dynamics of Fagus sylvatica during exceptional drought. Forest Ecology and Management. 2006;237:135–142. [Google Scholar]
  39. Matamala R, Gonzalez-Meler MA, Jastrow JD, Norby RJ, Schlesinger W. Impacts of fine root turnover on forest NPP and soil C sequestration potential. Science. 2003;302:1385–1387. doi: 10.1126/science.1089543. [DOI] [PubMed] [Google Scholar]
  40. Metcalfe DB, Meir P, Williams M. A comparison of methods for converting rhizotron root length measurements into estimates of root mass production per unit ground area. Plant and Soil. 2008;301:279–288. [Google Scholar]
  41. Millikin CS, Bledsoe CS. Biomass and distribution of fine and coarse roots from blue oak (Quercus douglasii) trees in the northern Sierra Nevada foothills of California. Plant and Soil. 1999;214:27–38. [Google Scholar]
  42. Mokany K, Raison RJ, Prokushkin AS. Critical analysis of root:shoot ratios in terrestrial biomes. Global Change Biology. 2006;12:84–96. [Google Scholar]
  43. Nadelhoffer KJ, Aber JD, Melillo JM. Fine root production in relation to net primary production along a nitrogen availability gradient in temperate forests: a new hypothesis. Ecology. 1985;66:1377–1390. [Google Scholar]
  44. Nambiar EKS. Root development and configuration in intensively managed radiata pine plantations. Plant and Soil. 1983;71:37–47. [Google Scholar]
  45. Nepstad DC, de Carvalho CR, Davidson EA, et al. The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures. Nature. 1994;372:666–669. [Google Scholar]
  46. Nissen T, Rodriguez V, Wander M. Sampling soybean roots: a comparison of excavation and coring methods. Communications in Soil Science and Plant Analysis. 2008;39:1875–1883. [Google Scholar]
  47. Norby RJ, Ledford J, Reilly CD, Miller NE, O'Neill EG. Fine-root production dominates response of a deciduous forest to atmospheric CO2 enrichment. Proceedings of the National Academy of Sciences of the USA. 2004;101:9689–9693. doi: 10.1073/pnas.0403491101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Papale D, Reichtein M, Aubinet M, et al. Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimations. Biogeosciences. 2006;3:571–583. [Google Scholar]
  49. Pierret A, Moran CJ, Doussan C. Conventional detection methodology is limiting our ability to understand the roles and functions of fine roots. New Phytologist. 2005;166:967–980. doi: 10.1111/j.1469-8137.2005.01389.x. [DOI] [PubMed] [Google Scholar]
  50. Resh SC, Battaglia M, Worledge D, Ladiges S. Coarse root biomass for eucalypt plantations in Tasmania, Australia: sources of variation and methods of assessment. Trees. 2003;17:389–399. [Google Scholar]
  51. Ruess RW, Van Cleve K, Yarie J, Viereck LA. Contributions of fine root production and turnover to the carbon and nitrogen cycling in taiga forests of the Alaskan interior. Canadian Journal of Forest Research. 1996;26:1326–1336. [Google Scholar]
  52. Saint-André L, Thongo M'Bou A, Mabiala A, et al. Age-related equations for above- and belowground biomass of a Eucalyptus hybrid in Congo. Forest Ecology and Management. 2005;205:199–214. [Google Scholar]
  53. Smit AL, George E, Groenwold J. Root observations and measurements at (transparent) interfaces with soil. In: Smit AL, Bengough AG, Engels C, Noordwijk M, Pellerin S, Geijn SC, editors. Root methods: a handbook. Berlin: Springer-Verlag; 2000. pp. 235–271. [Google Scholar]
  54. Snowdon P, Eamus D, Gibbons P, et al. Synthesis of allometrics, review of root biomass and design of future woody biomass sampling strategies. Canberra: Australian Greenhouse Office; 2000. NCAS Technical Report No. 17. [Google Scholar]
  55. Snowdon P, Raison J, Keith H, et al. Protocol for sampling tree and stand biomass. Canberra: Australian Greenhouse Office; 2002. NCAS Technical Report No. 31. [Google Scholar]
  56. Steele SJ, Gower ST, Vogel JG, Norman JM. Root mass, net primary production and turnover in aspen, jack pine and black spruce forests in Saskatchewan and Manitoba, Canada. Tree Physiology. 1997;17:577–587. doi: 10.1093/treephys/17.8-9.577. [DOI] [PubMed] [Google Scholar]
  57. Stone EL, Kalisz PJ. On the maximum extent of tree roots. Forest Ecology and Management. 1991;46:59–102. [Google Scholar]
  58. Teixeira PC, Novais RF, Barros NF, Neves JCL, Teixeira JL. Eucalyptus urophylla root growth, stem sprouting and nutrient supply from the roots and soil. Forest Ecology and Management. 2002;160:263–271. [Google Scholar]
  59. Thongo M'Bou A Jourdan C, Deleporte P, et al. Root elongation in tropical Eucalyptus plantations: effect of soil water contents. Annals of Forest Science. 2008;65:609. [Google Scholar]
  60. Tierney GL, Fahey TJ. Fine root turnover in a northern hardwood forest: a direct comparison of the radiocarbon and minirhizotron methods. Canadian Journal of Forest Research. 2002;32:1692–1697. [Google Scholar]
  61. Valverde-Barrantes OJ, Raich JW, Russell AE. Fine-root mass, growth and nitrogen content for six tropical tree species. Plant and Soil. 2007;290:357–370. [Google Scholar]
  62. Van Noordwijk M, Lawson G, Soumaré A, Groot JJR, Hairiah K. Root distribution of trees and crops: competition and/or complementarity. In: Ong CK, Huxley P, editors. Tree–crop interaction. Wallingford: CAB International; 1996. pp. 319–364. [Google Scholar]
  63. Vogt KA, Vogt DJ, Palmiotto PA, Boon P, O'Hara J, Asbjornsen H. Review of root dynamics in forest ecosystems grouped by climate, climatic forest type and species. Plant and Soil. 1996;187:159–219. [Google Scholar]
  64. Vogt K, Vogt D, Bloomfield J. Analysis of some direct and indirect methods for estimating root biomass and production of forests at an ecosystem level. Plant and Soil. 1998;200:71–89. [Google Scholar]

Articles from Annals of Botany are provided here courtesy of Oxford University Press

RESOURCES