By Aboubacar Mariko, Reda Mokere, T. Scott Murrell, Kokou Adambounou Amouzou, and Hakim Boulal
Thanks to its potential life span of hundreds of years, olive can develop great biomass and sequester large amounts of carbon (C). It may therefore play a critical role in reducing atmospheric C and improving farmer livelihoods by providing additional revenue from C credits. Predicting olive’s impacts on C cycling relies on allometric models. In our specific conditions, such models reliably predicted biomass accumulation with a minimum of three dendrometric measurements: tree age, height, and diameter at 10 cm.
Olive orchards have been a cultivated crop in the Mediterranean region for over 5,000 years (Zohary and Spiegel-Roy, 1975). The tree holds important societal and economic significance (Arenas-Castro et al., 2020; Montanaro et al., 2017), and today olives are cultivated on over 11 million ha worldwide (Vilar et al., 2018). Production techniques have become more intensive, moving from traditional, rainfed approaches with minimal inputs to irrigated systems with higher planting density, increased productivity per ha, greater use of fertilizers and pest and disease control practices, and mechanization (Rato-Nunes et al., 2024).
Biomass and C accumulation in olive trees is important for soil health and mitigating climate change.
The tree C reserve must be accurately characterized to assess the sustainability of an agroecosystem containing olives (Funes et al., 2022). Such information is the foundation for C markets that are a potential source of additional on-farm income for olive growers.
Our objective was to develop a reliable method for quantifying the biomass stock of an olive grove containing trees of varying ages. This article focuses only on biomass, but C is also being measured in the main study.
Study description
An area containing olive trees was scheduled to be cut and cleared at the UM6P Experimental Farm, Benguerir, Morocco. The grove had trees that were 2, 4, and 16 years old. All trees had been transplanted to the farm within 2-3 years of sampling, with a plantation density of 10 m x 6 m. Trees were not managed for olive production and were occasionally irrigated. Their primary function was soil erosion control. Researchers were granted permission to sample selected trees prior to the clearing operation.
This study was designed to (1) directly measure olive tree (Olea europaea L.) above- and below-ground biomass using destructive sampling, and (2) build and calibrate allometric equations to estimate biomass using nondestructive, dendrometric measurements. Researchers selected 9 olive trees across the 3 age classes (2-, 4-, and 16-years-old), with 3 trees from each age group.
Dendrometric measurements
Before cutting the trees and excavating the roots, several dendrometric measurements were taken. These measurements came from the forestry literature.
Trunk diameter was measured at different heights from the base of the tree. Trees were marked at the basal part while paying attention to any roots that were above ground and ensuring the start of the height measurement was above the above-ground roots. A digital calliper was used to measure the diameters of the small trees, but for the older trees, the circumference was measured with a flexible tape and divided by π to calculate the diameter (diameter = circumference / π). For multi-stemmed trees, measurements were taken on each stem and then averaged. Diameters at the following heights from the base were measured: “breast height” (DBH) or 120 cm, 80 cm (D80), 30 cm (D30), and 10 cm (D10).
Tree and trunk heights were measured using a stick with 1 cm increments. Tree height was measured from the soil to the highest leaf of the tree. Four separate measurements were made, one on each side (every 90 degrees), and then averaged. Since the sampled trees were not single-trunk trees, the height of each stem per single tree was measured to calculate the average trunk height (assuming the stem height was the height from the ground to the point where the first primary branches emerged).
Crown measurements were also taken. Crown height was calculated as the difference between average tree height and average trunk height (Fig. 1). Average crown diameter (dave) was calculated from two crown diameters measured using tape or a rope from the north end of the tree’s canopy to the south end, and from the west end to the east end. Average canopy radius was the average canopy diameter divided by 2 (rave = dave/2). Canopy surface area was calculated assuming the canopy was spherical (4πrave2).
To count the number of branches, the primary and secondary branches were identified (Fig. 1). The primary branches are the main branches that emerge from the tree’s trunk, and the secondary branches are the ones that emerge from each primary branch. To determine the total number of branches per olive tree, both primary and secondary branches were counted, and the sums of these counts were combined.
All the parameters were successfully obtained from the 16-year-old trees; however, DBH, D80, and D30 were not measurable for some of the younger trees due to their shorter height. Both linear and non-linear regression were employed to build allometric models from the dendrometric measurements. Variables such as tree age, tree height, D10, crown diameter (north-south and east-west), crown surface area, average canopy diameter, and average canopy radius was selected for their ease of measurement, non-destructive nature, and representativeness. Allometric models were validated using cross-validation leave-one-out (CV LOO), and evaluated with R2, root mean squared error (RMSE), and mean absolute error of the cross-validation (MAE CV).
Destructive measurements
The destructive measurements consisted of cutting down the selected trees into separate components (leaves, twigs, branches, trunk, root ball, medium and/or fine roots; Fig. 1).
The below-ground portion (root ball, medium and/or fine roots) was collected using a backhoe to excavate the soil. The excavated areas were marked with gypsum to carefully guide the backhoe operator. For the 16-year-old age group, 2 zones were marked: 1) the irrigation zone under the canopy area at a 1.2 m radius from the center of the tree, and 2) an outside the canopy area at a 2.5 m radius from the center of the tree. For the 2- and 4-year-old age groups, only the irrigation zone was sampled. The excavation was done progressively in 3 depths: 0-20, 20-40, and 40-60 cm. The excavated soil of each layer was subsequently sieved to collect the remaining roots.
The above-ground tree biomass was separated into different components: primary, secondary, and tertiary branches; twigs; and leaves. Each component was weighed fresh using a scale of 10 or 1,000 kg capacity based on its size and quantity, and subsampled (3 subsamples per component). The subsamples were weighed and oven-dried at 70C until constant weight. The oven-dried samples were weighed again, and the dry matter (DM) content was first extrapolated to kg DM tree-1 and later to kg DM ha-1 using the plantation density (167 tree ha-1).
The biomass of the olive tree components (Bc) was calculated using the following equation: Bc = FWc X (DWs/FWs)
Where, FWC is the fresh weight of the component; FWS is the average fresh weight of the subsamples; and DWS is the average dry weight of the subsamples. Total biomass equals the sum of each of these components (Huynh et al., 2021). Totals were calculated for both above-ground biomass (AGB) and below-ground biomass (BGB).
Biomass accumulation and partitioning trends
Fig. 2 illustrates the relationship between the above and below-ground olive tree biomass differences within different age groups. These trends are specific to the conditions of the study. The sampled olive trees had been transplanted and randomly pruned, likely affecting the natural growth of the trees and the root system.
The average biomass of the olive trees varied as follows: (1) for the 2-year-old group, the AGB was 0.5 kg tree-1 (77.1 kg ha-1) and the BGB was 0.2 kg tree-1 (33.1 kg ha-1); (2) for the 4-year-old group, the AGB was 2.8 kg tree-1 (468.1 kg ha-1) and the BGB was 1.6 kg tree-1 (263 kg ha-1); and (3) for the 16-year-old group, the AGB was 30.3 kg tree-1 (5120.4 kg ha-1) and the BGB was 47.8 kg tree-1 (7981.7 kg ha-1).
Additionally, the average tree heights were 1.2 m, 1.8 m, and 3.3 m for the 2-, 4-, and 16-year-old olive trees, respectively. The average canopy dimensions were: (1) 0.9 m in height, 0.8 m in diameter for the 2 age groups; (2) 1.6 m in height, 1.5 m in diameter for the 4 age groups; and (3) 2.4 m in height, 2.6 m in diameter for the 16 age groups.
At 2 years of age, the olive trees had significantly greater biomass above-ground than below-ground. As the trees age, there is a tendency for biomass allocation to shift more towards the root system, particularly noted at 16 years. However, there is not enough evidence to demonstrate the significance of this shift, neither at 4 years (where biomass still predominates above-ground) nor at 16 years. This is likely due to the limited number of observations (9 in total) with 3 replications per age group. This underscores the importance of planning future studies with a larger sample size. Subsequent power analysis (not shown) indicated that 5 samples from each age group would be required to draw statistically significant differences.
Above-ground biomass partitioning
The biomass partitioning of the above-ground portion of olive trees across different age groups is shown in Fig. 3.
At 2 years of age, biomass partitioning across the above-ground components was similar, with a minimum of 22% of the biomass allocated to branches and a maximum of 30% allocated to twigs. At 4 and 16 years, the maximum percentages of biomass were allocated to twigs (36%) and the trunk (46%), respectively. For the 16-year-old trees, only 7% of the biomass was allocated to the leaves. This could be attributed both to orchard management practices, since trees were not pruned for olive production, and the timing of the measurements, taken in late March 2023 before flowering.
Allometric equations
After testing different dendrometric measurements, age, tree height, diameter at 10 cm, basal diameter (BD), wood density, and canopy diameter north to south were shown to be the best predictors of above, below, and total biomass.
However, to construct an equation that requires the minimum number of variables and utilizes nondestructive measurements, only age, tree height, and diameter at 10 cm were selected. This set of variables ensures the accuracy of biomass predictions and makes the equation practical and non-destructive for field use. In each model, the variables have different coefficients, reflecting the importance of each variable in predicting above-ground, below-ground, or total biomass. Age is the most important parameter across all models (the most influential predictor), followed by diameter at 10 cm, and height.
The linear regression model for AGB demonstrated a high goodness of fit, explaining 99% of the observed variability (R2 = 0.99). The very low RMSE (0.22 kg DM tree-1) and MAE (0.16 kg DM tree-1) indicated that predicted values were close to those observed. The low MAE CV (0.27 kg DM tree-1) indicated expected good model performance for independent datasets. On the other hand, the BGB linear regression model has a higher error level, as indicated by the RMSE value of 11.01 kg DM tree-1. A cross-validated MAE of 9.78 kg DM tree-1 also indicated the possibility of model overfitting, where the algorithm fits the training data too closely, or even exactly, and cannot predict any data other than the training data. The high level of error in the BGB model is due to the significant variability in biomass amounts between younger and older trees. Additionally, the older trees were transplanted from another orchard, which could have resulted in the loss of some belowground biomass. The total biomass linear model (TotBM = AGB + BGB) had a performance similar to that of BGB resulting from the larger variability of the BGB included in its calculation.
The root-to-shoot ratio (i.e., belowground/aboveground biomass) is the parameter that most directly reflects biomass allocation by plants (Qi et al., 2019). The average belowground biomass to aboveground biomass ratio was 0.4 for the 2-year-old, 0.6 for the 4-year-old, and 1.6 for the 16-year-old olive trees. Unlike our results, Ilarioni et al. (2013) found a root-to-shoot ratio of 0.139 in a nonirrigated 11-year-old olive tree study. Brunori et al. (2017) investigated 14 olive trees (Leccino cultivar) in six different experimental orchards and found root-to-shoot ratios from 0.15 to 0.97. Ferreira et al. (2018) found that P application increased the ratio of root-to-shoot from 0.4 to 0.7 of a 3-year-old rainfed “Cobrançosa’’ olive grove in northeast Portugal. Results show a major difference between the default values of 0.5 for the root-to-shoot ratio for woodland and savanna used in C accounting (IPCC, 2003). In our study, our root-to-shoot ratios tended to be higher than other studies, and for the 16-year-old trees, they were over three times as high as the default values used by the IPCC. This has significant implications for the C payment in C-based projects. The reasons for these discrepancies need to be understood and will require sampling more trees representing more situations.
Summary
This work provides an overview of olive tree biomass estimations for differently aged trees. Only the 2-year-old trees had significantly greater biomass in the aboveground portions than below-ground portions. The 4- and 16-year-old trees had similar biomass above- and belowground. Although data were sparse, age and diameter at 10 cm were identified as the most significant predictors of aboveground and belowground biomass separately, while age and height were the most important predictors for total biomass. Notably, age emerged as the most influential factor across all biomass categories.
Mr. Mariko (e-mail: aboubacar.mariko@um6p.ma) is a Doctoral Student at Mohammed VI Polytechnic University (UM6P), Benguérir, Morocco. Mr. Mokere is a Doctoral Student at UM6P, Benguérir. Dr. Boulal is Senior Scientist, African Plant Nutrition Institute (APNI), Settat, Morocco. Dr. Amouzou is Scientist at APNI, Yamousskro, Cote d’Ivoire., Dr. Murrell is Principal Scientist at APNI, Benguérir, Morocco.
Cite this article
Mariko, A., Mokere, R., Murrell, T.S., Amouzou, K.A., Boulal, H. 2024. A Case Study on Biomass Assessment in a Semi-Arid Olive Rainfed System in Morocco, Growing Africa 3(1), 2-6. https://doi.org/10.55693/ga31.FDQW7437
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