Information

Relation between biomass and radius of roots system


Is there a known relation between the mass of a plant to the size of its roots?

For example, can I take the root size to be proportional to the biomass of the plant like in this relation: $$R = R_0 (1+EB) $$, where $R_0$ is the minimal size of the root (i.e. the diameter of the seedling), $B$ is the biomass of the plant, $E$ is some parameter, and $R$ is the length of a typical root?


Your question specifically asks for an equation which gives root radius and your sample equation is for typical root length, neither of which I'm aware of. However, there is a known relation between the above-ground biomass and the below-ground biomass of a plant, which varies with species and environmental factors. This is the root:shoot ratio.

This is a linear model where the constants represent differences in starting conditions (the intercept) and biomass partitioning (slope).

Roots and shoots are functionally interdependent and these two systems maintain a dynamic balance in biomass which reflects relative abundance of above-ground resources (light and CO2) compared with root-zone resources (water and nutrients). Whole-plant growth rate and root : shoot ratio are thus an outcome of genotype × environment interaction, but source of control is ambiguous.

The actual size of the roots - as in their extent - will depend on the soil characteristics, water table, the plant species, etc.

Source: http://plantsinaction.science.uq.edu.au/edition1/?q=content/6-3-1-biomass-distribution


Above- and below-ground biomass relationships of Leucaena leucocephala (Lam.) de Wit in different plant stands

Affiliations Research Institute of Resources Insects, the Chinese Academy of Forestry, Kunming, Yunnan Province, China, Desert Ecosystem Research Station in Yuanmou County of Yunnan Province, State Forestry Administration of China, Yuanmou, Yunnan Province, China

Contributed equally to this work with: ChengJie Gao, Min Chen

Roles Investigation, Methodology, Software

Affiliations Research Institute of Resources Insects, the Chinese Academy of Forestry, Kunming, Yunnan Province, China, Desert Ecosystem Research Station in Yuanmou County of Yunnan Province, State Forestry Administration of China, Yuanmou, Yunnan Province, China

Contributed equally to this work with: ChengJie Gao, Min Chen

Roles Investigation, Methodology, Writing – original draft

Affiliation College of Life Science, Southwest Forestry University, Kunming, Yunnan Province, China

Roles Funding acquisition, Methodology, Supervision

Affiliations Research Institute of Resources Insects, the Chinese Academy of Forestry, Kunming, Yunnan Province, China, Desert Ecosystem Research Station in Yuanmou County of Yunnan Province, State Forestry Administration of China, Yuanmou, Yunnan Province, China


Abstract

The plant root system traverses one of the most complex environments on earth. Understanding how roots support plant life on land requires knowing how soil properties affect the availability of nutrients and water and how roots manipulate the soil environment to optimize acquisition of these resources. Imaging of roots in soil allows the integrated analysis and modeling of environmental interactions occurring at micro- to macroscales. Advances in phenotyping of root systems is driving innovation in cross-platform-compatible methods for data analysis. Root systems acclimate to the environment through architectural changes that act at the root-type level as well as through tissue-specific changes that affect the metabolic needs of the root and the efficiency of nutrient uptake. A molecular understanding of the signaling mechanisms that guide local and systemic signaling is providing insight into the regulatory logic of environmental responses and has identified points where crosstalk between pathways occurs.


Methods

Study site

The trial site was located on a low-lying, even-surfaced alluvial terrace adjacent to the Taraheru River, in Gisborne City (Fig. 1). The same site has also been used to measure ‘plant growth performance’ of: (i) 12 early (colonising) indigenous species considered typical of riparian margins (Marden et al. 2005) (ii) different clones of poplar and willow (Phillips et al. 2014) and (iii) a range of exotic forest species (Phillips et al. 2015). Temperatures over summer average 23 °C and over winter 12 °C, and mean annual rainfall is 1000 mm. The soil is a naturally fertile, free-draining Typic Sandy Brown Soil of the Te Hapara soil series (Hewitt 2010). Although not used, a drip-irrigation was installed to ensure survival of the plants in the event that drought could jeopardise the longer-term aims of this trial.


Contents

It is important for plants to be able to balance their absorption and utilization of available resources and they adjust their growth in order to acquire more of the scarce, growth-limiting resources: sunlight, carbon dioxide, mineral nutrients, and water. [4] The equilibrium of biomass partitioning can be explained by Liebig's Law of the Minimum and modeled through the metaphor of Liebig's Barrel in which the limiting resource of plant growth is like the shortest slat on a barrel full of water. [5] The barrel can only hold water up to the level of the shortest slat and, likewise, plants can only grow at a rate allowed by the limiting resource. To continue growing, biomass must be partitioned to help sequester these resources.

Sunlight Edit

The main light-sensing mechanism for plants is the phytochrome system with pigments located throughout the plant to detect changes in red and far-red light. [2] The phytochromes' detection of light quality is what helps trigger changes in biomass partitioning. Plants grown in low light conditions have been shown to allocate more biomass to shoots (mainly leaves). By measuring leaf area of plants at different levels of irradiance or sunlight, it has been determined that lower levels of light cause total plant leaf surface to increase. [6] If sunlight is limiting the plant increases growth to the shoot and decreases the energy partitioned to the roots because the lower rates of photosynthesis lessen need for water and mineral nutrients. [2] As low light causes greater allocation to the shoot, the same correlation is made for plants in high light higher irradiance causes smaller total lower surface area because less surface area is needed to absorb sunlight. When a plant has more than enough photosynthetic capacity, it will instead prioritize growth in the roots to absorb water and nutrients.

Carbon Dioxide Edit

In a situation where carbon is the limiting resource, increasing the levels of CO2 increases photosynthetic rates. [7] This will also cause increases in nutrient uptake and water use, focusing more growth towards the roots. In low CO2 concentrations, plants create larger and more numerous leaves to bring in more CO2. [7] The impacts of atmospheric carbon dioxide concentration on biomass partitioning is important to understanding in impacts on plants in the face of climate change. Plant cells have increased carbon to nitrogen ratios when CO2 concentrations are higher, which decreases levels of decomposition. The decrease in decomposition caused by high carbon dioxide concentrations has the effect of decreasing nitrogen availability for plants. [8]

Mineral Nutrients Edit

Overall, nutrient availability has a strong effect on partitioning, with plants growing in poor nutrient areas partitioning most of their biomass to underground structures. [9] Soil nitrogen availability, for example, is a strong determinant of biomass allocation. For example, in low productivity systems (low levels of soil nitrogen) such as boreal forests, trees devote a large portion of their biomass to roots. But as soil productivity increases, biomass is primarily allocated to aboveground structures such as leaves and stems. [9] As an overall pattern, the lengths of roots decrease as nutrient concentrations increase. [4] High concentrations of a mineral nutrient that lead to toxicity can have a strong effect on growth and partitioning as well. For instance, in toxic concentrations of cadmium and lead, Fagus sylvatica was shown to develop a more compact and less branching root system while establishing few root hairs. [10] This altered structure functions to reduce surface area of the roots and the effects of toxic nutrients while also conserving biomass for parts of the plant where growth would be more beneficial.

Water Edit

Plants grown in dry conditions often have a decreased total biomass production but they also contribute more of their biomass to the roots and develop a higher root to shoot ratio. [4] When plants allocate more of their biomass to their roots, they are able to enhance water absorption by tapping further down into the water table and extending root mass further laterally. Increased root hairs also aid in increasing absorption. When there is an extreme soil drought, there is not an increase in root to shoot biomass, because a state of dormancy is adopted. [11] Water availability also impacts leaf surface area as too much surface area could allow for excessive transpiration in low water conditions.

Interactions between organisms can also alter partitioning due to competition for resources, sharing of resources, or reduction of plant biomass.

Competition Edit

Interspecific and intraspecific competition can cause a decrease in the available resources for an individual plant and alter how it partitions biomass. For instance, competition between plants causes decreased radial growth in branches and the stem while increasing growth of the roots and height of the stem. [12] This provides reasoning for the importance of phenotypic plasticity in maintaining fitness in a competitive environment the better a plant is at altering its morphology, the more competitive it will be.

Mutualism With Mycorrhizae Edit

The relationship between plants and mycorrhizal fungi is an example of mutualism because plants provides fungi with carbohydrates and mycorrhizal fungi help plants absorb more water and nutrients. Since mycorrhizal fungi increase plants' uptake of below-ground resources, plants who form a mutualistic relationship with fungi have stimulated shoot growth and a higher shoot to root ratio. [13]

Herbivory Edit

Herbivory causes a short-term reduction in leaf mass and/or stem mass that shifts stability and proportions of plant parts. To restore the balance between uptake of nutrients/water and photosynthetic rates, plants send more energy to the stems and leaves. Herbivory causing high levels of defoliation (greater than 25% of leaf area) increases growth to the shoot, seeking to achieve the same shoot to root ratio as before defoliation. [14] Therefore, defoliation also reduces root growth and nutrient uptake until the before-defoliation root to shoot ratio is restored.

As different plants have different structures and growth forms, their partitioning of biomass is not identical either. For example, evergreen trees have higher leaf mass fractions (LMFs) compared to deciduous trees. Additionally, the stem to root ratio varies between species more greatly in herbaceous plants than woody ones as the plasticity for roots in herbaceous plants is higher. Furthermore, herbaceous monocots or graminoids compared with herbaceous dicots have larger root to total mass ratios (RMFs). [15] The difference in herbaceous monocots and dicots may be explained by differences in efficiency of nutrient uptake or by the need for graminoids to store more starch and nutrients to regrow after fire or grazing. Biomass partitioning can also be affected by a plant's method of carbon fixation: C3 or C4. For herbaceous monocots, the type of photosynthesis does not affect the percentile LMF or RMF, but in herbaceous dicots, C4 species have lower RMFs. [15] The life cycle of plants can also cause different allocation strategies and ratios for leaves, roots, and stems compared to same-size eudicotelydonous perennials, annuals put more energy into growing leaves and stems than perennial species. [15]

Biomass partitioning patterns can also change as plants age. In pinus strobus stands of 2, 15, 30, and 65 years old, the root to shoot ratio was 0.32, 0.24, 0.16, and 0.22 respectively showing a decrease in root mass compared to shoot mass in the first few decades of growth. [16] Age also affects how trees partition biomass to different components of the stem. Growth of branches and leaf surface area decreases with age as partitioning to the trunk increases. [16]

Many biomass partitioning studies consist of manipulative experiments that increase or reduce levels of growth-limiting resources and observe the effects. Often-times these studies use potted plants grown in a greenhouse to measure effects of density, excess nutrients, low light etc. Other studies may focus on observation and analysis of naturally occurring plants or use data analysis of previous measurements. The methods and measurements for determining biomass partitioning can be quite difficult due to the weight and volume of larger plants. Furthermore, measuring size and weight of roots entails washing and careful removal of soil to get accurate measurements. [2] Plant biomass is often measured in terms of leaf mass fraction (LMF), stem mass fraction (SMF), and root mass fraction (RMF) where the dry mass of the plant part is set over the total dry mass of the plant. [2] Lateral, radial, and height increases are used to quantify rates of growth.


3 Types of Plant Tissue System and their Function (With Diagram)

Some of the most important types of plant tissue system and their function are as follows:

1. Epidermal Tissue System 2. Ground Tissues System 3. Vascular Tissue System.

All the tissues of a plant which perform the same general function, regardless of position or continuity in the body, constitute the tissue system. The tissues of a plant are organized to form three types of tissue systems: the dermal tissue system, the ground tissue system, and the vascular tissue system.

The components and functions of the tissue systems are summarized below:

1. Epidermal Tissue System:

The cells of epidermis are parenchymatous having protoplasm and nucleus without intercellular spaces. Epidermis possesses numerous minute openings called stomata. Main function of stomata is exchange of gases between the internal tissues and the external atmosphere. Cuticle is present on the outer wall of epidermis to check evaporation of water. Epidermis forms a Protective layer in leaves, young roots, stem, flower, fruits etc.

2. Ground Tissues System:

It includes all the tissues of the plant body except epidermal and vascular tissues.

It is divided into following parts:

It is situated below the epidermis. It is multilayered and made up of parenchymatous and sclerenchymatous cells.

This consists of parenchymatous cells with or without chloroplasts.

Endodermis is single layered made up of parenchymatous cells. The radial and internal walls of endodermal cell are thickened a band of lignin or suberin knows as casparian strip is sometimes found on the radial and transverse wall of every cell.

It is single or multilayered and is situated in between endodermis and vascular bundles. It is made up of sclerenchymatous and parechymatous cells.

The central portion in stems and roots is called pith or medulla. It is made up of parenchymatous cells with intercellular spaces. In dicot stem the pith is large and well developed in dicot roots the pith is either absent or small in monocot roots large pith is present in monocot stem the vascular bundles are scattered and the ground tissue is not marked into different parts.

3. Vascular Tissue System:

It consists of xylem and phloem tissues which are found as strands termed as vascular bundles. The main function of xylem is to conduct water, materials to different parts of the plant body. The main function of phloem is transportation of food materials in different parts of the plant.

There are three different types of vascular bundles (Fig. 3.5):

Xylem and phloem strands are located on alternate radii in radial vascular bundles. These are mainly found in roots.

Xylem and phloem combine together into one bundles, Xylem lies towards the centre and phloem towards the periphery. There are two types of conjoint bundles.

Xylem and phloem lie on the same radius, xylem towards the centre and phloem towards the periphery. When cambium is present in collateral bundles, such bundle is called open, e.g. in dicot stems and collateral bundle without cambium is called closed, e.g. in monocot stems.

In this type of bundle, the phloem strands are present on both outer and inner side of xylem.

(iii) Concentric Bundles:

In this type of vascular bundle, one tissue is completely surrounded by the other. These are of two types Amphivasal and Amphicribral.


Discussion

A new allometric tool for coffee perennial components

Assessing root biomass is usually complex, so allometric models of below-ground components are a promising alternative to time consuming fieldwork and can also be used to screen root biomass over large areas. Allometric models can be used to, for example, support the baseline for NAMA certifications or Intergovernmental Panel on Climate Change (IPCC) evaluations, where root biomass is usually not taken into account. The below-ground component is crucial in small trees like coffee plants that are coppiced and where perennial roots are major C storage organs. Thus, we developed allometric equations to complement the few existing relationships for the aerial parts of coffee plants ( Segura et al., 2006 Charbonnier, 2013). Allometric equations have been shown as efficient for predicting below-ground biomass of mature forests because the method is quick, simple and non-destructive ( Saint-André et al., 2005 Segura et al., 2006 Kenzo et al., 2009). We demonstrated here that it can also be applied to stands of coffee plants, despite inherent technical complications brought about by diverse management practices (e.g. several plants at one location, fused stumps and coppicing). Linear regressions satisfactorily predicted the biomass of perennial components, although intercepts were significantly greater than zero, except for the aerial stump and for perennial components. This anomaly may have arisen due to estimates in locations containing young plants that had been used to replace dead plants: it is possible that the neighbouring older plants developed roots in the non-occupied soil volume, thus biasing the linear relationship at lower values. Therefore, this factor may have induced an error in the allometric relationship, explaining why we chose to force the intercept through zero when calculating biomass and NPP.

Deep root biomass in heterogeneous agroforestry conditions

Fine root biomass.

In our study, fine root biomass amounted to 2·29 t ha −1 estimated from the sequential coring method (taking into account temporal variability). In tropical evergreen forests, Jackson et al. (1997) found fine root biomass up to 3·30 t ha −1 , and in temperate broadleaved and deciduous forests, Nadelhoffer et al. (1985) and Cox et al. (1978) estimated it in a range of 2·70–8·00 t ha −1 . We estimated that 75 % of fine root biomass of coffee plants was in the top 70 cm of soil, similar to Siles et al. (2010), who found that 75 % of biomass was located in the upper 60 cm in a coffee plantation also in Costa Rica. However, other studies found that the percentage of coffee fine root biomass was higher in the upper soil layers, possibly because deeper roots were not measured in these studies (see Table 4 for a literature review).

Literature review of coffee (Coffea arabica L.) root distribution and biomass according to soil depth sampled and sampling method

Study site . Sampling method . Soil sampling depth (max.) (cm) . Root distribution (%) . Root biomass (t ha −1 ) . Reference(s) .
Amani, Tanzania Full excavation 60 (150) 406 56 Nutman (1933a, b, 1934)
Kenya Partial excavation 270 100 Trench (1934)
Catalina, Puerto Rico Full excavation 30 (50) 91 95 Guiscafré-Arrillaga and Gomez (1938, 1940, 1942)
Campinas, Brazil Full excavation 30 (300) 250 70 Franco and Inforzato (1946)
Chinchina, Columbia Core sampling (0.3 × 0.3 m) 10 (160) 160 47 Suarez de Castro (1953)
20 (160) 69
30 (160) 90
70 (160) 98 0·53
Veracruz, Mexico Partial excavation 20 (110) 110 44–58 Garriz (1978)
(8.3 %) 110 (110) 100 14·90
Miranda State, Venezuela Core sampling (0.5 × 0.5m) 10 (50) 33* Cuenca et al. (1983)
30 (50) 73
50 (50) 100 4·30
Chipinge, Zimbabwe Full excavation 50 (300) 350 57 Cassidy and Kumar (1984)
Juan Viñas, Costa Rica Auger sampling* 10 (40) 40 Schaller et al. (2003), Van Kanten et al. (2005)
San Pedro de Barva, Costa Rica Auger sampling* 60 (100) 100 75 Siles et al. (2010)
100 (100) 100 9·30
Masatepe, Nicaragua Root impacts 30 (200) 200 51 Padovan et al. (2015)
Trench wall 170 (200) 100
Aquiares, Costa Rica Auger and 50 (405) 405 72 13·20 This study
Voronoi trench 100 (405) 87 15·95
110 (405) 89 16·30
405 (405) 100 18·34
Study site . Sampling method . Soil sampling depth (max.) (cm) . Root distribution (%) . Root biomass (t ha −1 ) . Reference(s) .
Amani, Tanzania Full excavation 60 (150) 406 56 Nutman (1933a, b, 1934)
Kenya Partial excavation 270 100 Trench (1934)
Catalina, Puerto Rico Full excavation 30 (50) 91 95 Guiscafré-Arrillaga and Gomez (1938, 1940, 1942)
Campinas, Brazil Full excavation 30 (300) 250 70 Franco and Inforzato (1946)
Chinchina, Columbia Core sampling (0.3 × 0.3 m) 10 (160) 160 47 Suarez de Castro (1953)
20 (160) 69
30 (160) 90
70 (160) 98 0·53
Veracruz, Mexico Partial excavation 20 (110) 110 44–58 Garriz (1978)
(8.3 %) 110 (110) 100 14·90
Miranda State, Venezuela Core sampling (0.5 × 0.5m) 10 (50) 33* Cuenca et al. (1983)
30 (50) 73
50 (50) 100 4·30
Chipinge, Zimbabwe Full excavation 50 (300) 350 57 Cassidy and Kumar (1984)
Juan Viñas, Costa Rica Auger sampling* 10 (40) 40 Schaller et al. (2003), Van Kanten et al. (2005)
San Pedro de Barva, Costa Rica Auger sampling* 60 (100) 100 75 Siles et al. (2010)
100 (100) 100 9·30
Masatepe, Nicaragua Root impacts 30 (200) 200 51 Padovan et al. (2015)
Trench wall 170 (200) 100
Aquiares, Costa Rica Auger and 50 (405) 405 72 13·20 This study
Voronoi trench 100 (405) 87 15·95
110 (405) 89 16·30
405 (405) 100 18·34

The age of plants varied between studies, and ranged from 7 to 40 years. The maximum depth of soil sampling is indicated in parentheses. The maximum rooting depth is underlined when available.

Literature review of coffee (Coffea arabica L.) root distribution and biomass according to soil depth sampled and sampling method

Study site . Sampling method . Soil sampling depth (max.) (cm) . Root distribution (%) . Root biomass (t ha −1 ) . Reference(s) .
Amani, Tanzania Full excavation 60 (150) 406 56 Nutman (1933a, b, 1934)
Kenya Partial excavation 270 100 Trench (1934)
Catalina, Puerto Rico Full excavation 30 (50) 91 95 Guiscafré-Arrillaga and Gomez (1938, 1940, 1942)
Campinas, Brazil Full excavation 30 (300) 250 70 Franco and Inforzato (1946)
Chinchina, Columbia Core sampling (0.3 × 0.3 m) 10 (160) 160 47 Suarez de Castro (1953)
20 (160) 69
30 (160) 90
70 (160) 98 0·53
Veracruz, Mexico Partial excavation 20 (110) 110 44–58 Garriz (1978)
(8.3 %) 110 (110) 100 14·90
Miranda State, Venezuela Core sampling (0.5 × 0.5m) 10 (50) 33* Cuenca et al. (1983)
30 (50) 73
50 (50) 100 4·30
Chipinge, Zimbabwe Full excavation 50 (300) 350 57 Cassidy and Kumar (1984)
Juan Viñas, Costa Rica Auger sampling* 10 (40) 40 Schaller et al. (2003), Van Kanten et al. (2005)
San Pedro de Barva, Costa Rica Auger sampling* 60 (100) 100 75 Siles et al. (2010)
100 (100) 100 9·30
Masatepe, Nicaragua Root impacts 30 (200) 200 51 Padovan et al. (2015)
Trench wall 170 (200) 100
Aquiares, Costa Rica Auger and 50 (405) 405 72 13·20 This study
Voronoi trench 100 (405) 87 15·95
110 (405) 89 16·30
405 (405) 100 18·34
Study site . Sampling method . Soil sampling depth (max.) (cm) . Root distribution (%) . Root biomass (t ha −1 ) . Reference(s) .
Amani, Tanzania Full excavation 60 (150) 406 56 Nutman (1933a, b, 1934)
Kenya Partial excavation 270 100 Trench (1934)
Catalina, Puerto Rico Full excavation 30 (50) 91 95 Guiscafré-Arrillaga and Gomez (1938, 1940, 1942)
Campinas, Brazil Full excavation 30 (300) 250 70 Franco and Inforzato (1946)
Chinchina, Columbia Core sampling (0.3 × 0.3 m) 10 (160) 160 47 Suarez de Castro (1953)
20 (160) 69
30 (160) 90
70 (160) 98 0·53
Veracruz, Mexico Partial excavation 20 (110) 110 44–58 Garriz (1978)
(8.3 %) 110 (110) 100 14·90
Miranda State, Venezuela Core sampling (0.5 × 0.5m) 10 (50) 33* Cuenca et al. (1983)
30 (50) 73
50 (50) 100 4·30
Chipinge, Zimbabwe Full excavation 50 (300) 350 57 Cassidy and Kumar (1984)
Juan Viñas, Costa Rica Auger sampling* 10 (40) 40 Schaller et al. (2003), Van Kanten et al. (2005)
San Pedro de Barva, Costa Rica Auger sampling* 60 (100) 100 75 Siles et al. (2010)
100 (100) 100 9·30
Masatepe, Nicaragua Root impacts 30 (200) 200 51 Padovan et al. (2015)
Trench wall 170 (200) 100
Aquiares, Costa Rica Auger and 50 (405) 405 72 13·20 This study
Voronoi trench 100 (405) 87 15·95
110 (405) 89 16·30
405 (405) 100 18·34

The age of plants varied between studies, and ranged from 7 to 40 years. The maximum depth of soil sampling is indicated in parentheses. The maximum rooting depth is underlined when available.

We observed a significant increase in fine root biomass between 1·5 and 2·5 m depth, which is unusual in a rooting profile, given that the global tendency of this parameter is a strong decrease from 30 cm downwards ( Schenk and Jackson, 2002). This phenomenon could be explained by the change in physical soil properties such as a rapid decrease in the volumetric stone fraction within the same soil layer ( Fig. 6). Another explanation could be the change in the chemical properties and nutrient accumulation in deeper soil layers. A previous study performed in Costa Rica showed that nitrate content increased significantly from topsoil downwards, reaching a maximum at 2 m ( Harmand et al., 2007). Lysimeters installed at that depth in the same site did not indicate high concentrations of NO 3 − in the soil solution or groundwater, indicating that NO 3 − adsorption was particularly high in the subsoil at that depth. Soil mineral analyses revealed a high content of positively charged mineral surfaces at 2 m ( Harmand et al., 2007), which may explain the greater root proliferation at these depths.

With regard to the temporal variability of fine root biomass we found major fluctuations, ranging seasonally by a factor of two. Fine root biomass estimated through sequential coring (0–30 cm), and scaled up to the whole rooting profile at stand level (2·29 t ha −1 ), was lower than that estimated via shallow excavations using Voronoi polygons (3·81 t ha −1 ). Integrating the temporal variability is a clear advantage of the sequential coring method over trench excavations and could explain the difference we obtained between the two methods. We encountered higher values of fine root biomass during the wet months, i.e. periods where LAI was also higher ( Taugourdeau et al., 2014). We observed that the lowest value of fine root biomass was observed in February, which is the driest month of the year and also corresponds to the minimum LAI. In addition, a decrease in fine root biomass in September corresponded to the driest month within the rainy season. Hence, there appears to be a certain synchronicity between leaf and fine root dynamics, which is possibly affected by the fruiting period (the harvest period ranges from August to January) and its biennial pattern. Fine root dynamics are therefore important to consider when adjusting schedules of fertilizer application, due to possible high leaching rates when roots are less numerous or less active.

In the top 30 cm of soil, we found a clear effect of distance to the row with more than twice as much coffee fine root biomass within the row compared to within the adjacent inter-row. Similar results were also found in the top 20 cm and above the depth of 1·0 m in comparable soil conditions in Costa Rica ( Table 4). This distribution could be a consequence of fertilizer being distributed close to the stump, creating heterogeneity in the soil, and increasing fertility along the row. An increase in soil fertility in the row would have several consequences for the components of SOM build-up (fine root litter, decomposition and stabilization), soil water content and biology, soil compaction and respiration. Our results also show that the inter-row remains under-exploited in terms of root colonization. Charbonnier et al. (2013) reported that 30 % of the incoming light to the plot reaches the soil, mostly in the inter-row. The inter-row could therefore be managed more intensively, in order to exploit these important but neglected resources.

Surprisingly, we found no effects of Erythrina trees on fine root biomass in the top 0–30 cm of soil, indicating that root competition was low. Charbonnier (2013) reported that NPP of coffee resprouts was similar in full sun or under shade, but that biomass allocation was biased toward the above-ground vegetative component when grown in shady conditions. Surprisingly, Schaller et al. (2003) and van Kanten et al. (2005) even showed that the presence of coffee fine roots induced shallower (0–10 cm depth) distributions of fine roots in shade-inducing trees (Terminalia or Eucalyptus) in the same region of Costa Rica, although competition with these trees may be stronger than with Erythrina. However, a study performed in a coffee agroforestry system in Nicaragua showed that tree roots occupied deeper soil layers, beneath the roots of coffee plants ( Padovan et al., 2015). Conversely, Siles et al. (2010) did not observe any effects of shade-inducing trees (Inga densiflora) on the fine root biomass of coffee plants in Costa Rica. Therefore, the fine root system of coffee plants dominates in surface layers, even when grown in conjunction with shade-inducing trees, regardless of whether the trees are pruned (most studies) or not (this study).

Deep root biomass.

Difficulties in harvesting roots, particularly deeper in the soil, may lead to severe global underestimations of root mass and productivity in plantation or forest ecosystems ( Canadell et al., 1996). Consequently, depths are not standardized, but the depth selected in a given study is expected to capture practically all roots. In our study, no roots were observed below 4·05 m because bedrock was continuous below this depth ( Fig. 6). This result confirmed those from the first complete description of Coffea arabica root distribution performed in Tanganyika (former Tanzania) by Nutman (1933a, b, 1934) where the maximum rooting depth was observed at 4·06 m ( Table 4). Since then, most studies on the distribution of fine roots of coffee plants have been limited to the top soil ( Table 4) where most fine roots were distributed ( Cuenca et al., 1983 Cassidy and Kumar, 1984 Schaller et al., 2003 van Kanten et al., 2005 Siles et al., 2010). Finally, it has been reported that the maximum rooting depth of coffee plants can be averaged at around 300 cm ( Barros et al., 1999). Obviously, the maximum rooting depth depends on the coffee variety, its origin (seed, grafting or somatic embryogenesis) and soil chemical and physical characteristics, such as volumetric stone fraction (our study).

We showed that the total root biomass of a coffee root system (18·34 t ha −1 ) represents almost the same amount as the above-ground perennial organs (aerial stumps: 22·52 t ha −1 ). This below-ground biomass is greater than what was found in other studies that used destructive methods without taking into account the whole below-ground component and especially the taproot. Using an auger core in Venezuela, Cuenca et al. (1983) measured 4·3 t ha −1 at a depth of 0–0·5 m, but in Costa Rica, Siles et al. (2010) measured 9·3 t ha −1 and in Mexico, Garriz (1978) estimated 14·9 t ha −1 at a depth of 0–1·1 m ( Table 4). With regard to the distribution of coffee root biomass along the soil profile, our results showed that 30–55 % of total root biomass was found in the upper 10–30 cm of soil, in agreement with Schaller et al. (2003), who found that 40 % of total root biomass was concentrated within the upper 10 cm. Although total fine root biomass of coffee plants in our study (18·34 t ha −1 ) was similar to other tropical plantations also assessed with the Voronoi method, for example 16-year-old oil palm (Elaeis guineensis) in Ivory Coast with 25·4 t ha −1 ( Jourdan and Rey, 1997), 20-year-old coconut (Cocos nucifera) in Vanuatu with 10·8 t ha −1 ( Navarro et al., 2008), 6-year-old Eucalyptus in Congo with 19·9 t ha −1 ( Saint-André et al., 2005), 5-year-old Eucalyptus in Brazil with 27·5 t ha −1 ( Laclau et al., 2013) and 13-year-old rubber tree (Hevea brasiliensis) in Thailand with 11·5 t ha −1 ( Chairungsee, 2011), it is low compared to tropical rainforests (70–100 t ha −1 Grace et al. 2001). Coffea arabica is a shade-tolerant plant, originating from eastern Africa where it grows within the forest and its functional traits are characteristic of shade-adapted plants, notably low photosynthetic and growth rates. The cultivar used in this study (‘Caturra’) is a dwarf variety and the pruning frequency was quite high (approx. every 5 years), so a high fine root biomass was not expected.

To calculate an accurate root/shoot ratio, estimates of the whole-plant biomass (above and below ground) are required. Shoot biomass is not detailed in our results, because specific methods are required to account for the dynamics of the shoot resprouts after coppicing and their intra-plot heterogeneity ( Charbonnier, 2013). When including the ephemeral above-ground components (resprouts, leaves, flowers, fruit), the total coffee plant biomass amounted to around 55 t ha −1 , with 49 % of below-ground parts and 29 % of perennial roots, higher than the reported 20 % of perennial roots by ( Siles et al., 2010). Moreover, in that same study, the root/shoot ratio was assessed at 40 % (49 % in our study). In tropical forests, Jackson et al. (1997) and Deans et al. (1996) reported root/shoot ratios between 20 and 33 %, consistent with 30 % previously reported ( Van Noordwijk et al., 1996 Canellas Rey de Vinas and San Miguel Ayanz, 2000). Coffee plants are pruned approximately every 5 years, possibly creating an imbalance to the benefit of the aerial stump and below-ground perennial components, and a very high root/shoot ratio for older ages. Thus old coffee is clearly much out of this range and this is probably due to its management (coppicing). We conclude that the root system is very large in older coffee plantations, as compared to the development of productive resprouts. A proportionally large root system might bring some advantages in terms of resistance to root diseases, such as nematodes, or tolerance to drought, being able to explore a very large volume of soil. It might also be considered as an advantage for grafting new varieties on old stumps to resist drought for instance. A strong root system is probably an advantage also for mobilizing the reserves required at the time of resprouting. However, pruning might bring some imbalances to the plant’s carbon budget, due to a high proportion of photosynthesis going to autotrophic respiration, and the carbon use efficiency of old coffee plants could be low compared to younger plants. The growing imbalance between vegetative and reproductive parts during plant ageing could also affect yield.

Growth ring analysis

Recently, Pereira et al. (2014) described precisely the anatomical structure of wood and dark growth rings of coffee plants, but to our knowledge, there are no growth ring studies available to date.

In our study, we observed that ring width stops increasing linearly at about 12 years old, typical of juvenile rings in many woody tree and shrub species. This transition could also possibly correspond to the age of stabilization of biomass in the compartments subjected to pruning. Moreover, ring width varied around 2·5 mm yr −1 from 12 to 44 years old, indicating a steady and large perennial biomass accumulation until 40 years or even more. Indeed, a fairly constant ring width means an increasing allocation to the perennial compartments (increasing NPP of aerial stumps, tap and coarse roots). This is perhaps related to the fact that fruit production decreases with age: competition between vegetative and reproductive parts would progressively become favourable to the growth of perennial compartments, to the detriment of fruit production. Indeed, replanting is generally programmed between 20 and 40 years after planting. A coffee growth allocation model would allow testing such questions.

Growth rings, combined with the inventory of the distribution of basal area, also allowed us to calculate the average age of a whole plot. We estimated an average plant age of 28 years for our plot. The partitioning of a given plot into age classes and the overall demography of the plot should depend on parameters such as year after planting (normally after a clear-cut and extraction of remnants), mortality per age classes and rate of replacement of dead plants. Characterizing plot average age and cohorts could be very useful to estimate plot vigour and productivity, or to standardize comparisons that are usually made between plantations, or even to evaluate the actual market value of a plantation. It could also be used to determine the optimum time to apply the rotation.

The study of growth rings should allow us to compare historical growth, according to soil and climatic conditions and management, and to look for correlations with fruit production. Alternatively, it would be interesting to study δ 13 C in growth rings of coffee, and link results to the efficiency of water use, especially in dry areas ( Gessler et al., 2014),

Below-ground NPP, root turnover and decomposition

Below-ground NPP amounted to 4·26 t ha −1 y −1 , i.e. around 24 % of total coffee plant (NPPr/NPP – calculated using unpublished results of NPP, F. Charbonnier, pers. comm.), similar to that of Cocos nucifera plantations (5·0 t ha −1 yr −1 , Navarro et al., 2008). In tropical broadleaved evergreen forests, Gower et al. (1992) estimated a much higher range, between 10 and 40 %. The major resource allocation to the below-ground component in coffee plants could be a consequence of the high fertilization rates used, which promote above- rather than below-ground productivity.

We used measurements of necromass in our calculations of NPPfr, which results in a more accurate estimation ( Brunner et al., 2013). Even if it is difficult to distinguish dead root fragments from live fragments ( Vogt et al., 1998 Persson and Statenberg, 2007 Persson and Stadenberg, 2010), in coffee plantations, the sorting of live and dead biomass can be performed quite easily. Other uncertainties in the estimation of NPPfr arise from the choice of calculation method that can significantly influence the outcome ( Hendricks et al., 2006). In our study, we compared two of the most commonly used methods ( Brunner et al., 2013), the Decision Matrix (DM) and Max-Min (MM) methods. Our results showed no major differences in NPP between these two methods, with higher values for DM, as expected, as the MM method can largely underestimate fine root production, as often reported ( Lehmann and Zech, 1998 Nadelhoffer, 2000 Hendricks et al., 2006 Jourdan et al., 2008). Brunner et al. (2013) recently summarized the results of studies on NPP in European forests by comparing DM and MM methods: estimates of NPP of fine roots were doubled when using DM. However, DM requires a higher number of variables, including root necromass and decomposition rate MM could be more useful if necromass measurements or estimations are not available or if large errors on the calculation of total necromass are suspected. DM and MM are both flawed and are particularly sensitive to the options chosen. In addition, both methods provide only a single annual NPP estimation of fine roots and therefore do not allow for statistical comparisons (i.e. among plots, or between sites).

Using DM, we calculated NPPfr of 2·96 t ha −1 yr −1 for the whole rooting profile (4 m). NPPfr in the top 0–30 cm was 2·04 t ha −1 yr −1 , i.e. around twice as much as for other perennial tropical trees [Eucalyptus in Brazil: 1·12 t ha −1 yr −1 Jourdan et al. (2008), Gliricidia in Ivory Coast: 1·1 t ha −1 y −1 Schroth and Zech (1995) or Acacia in northern Kenya: 0·95 t ha −1 y −1 Lehmann and Zech (1998)]. However, our results were lower than those found in tropical dry ecosystems of India (2·8 t ha −1 yr −1 Singh and Singh, 1981) or were comparable to tropical agroforestry systems of Acacia intercropped with Sorghum in Kenya (2·1 t ha −1 y −1 Lehmann and Zech, 1998). Compared to temperate ecosystems, our calculations were much lower: 6·5–8·1 t ha −1 yr −1 for sugar maple trees (Acer saccharum) in North America ( Aber et al., 1985 Hendrick and Pregitzer, 1993) and 3·2–10·9 t ha −1 yr −1 found for red pine trees (Pinus resinosa) ( McClaugherty et al., 1982).

Finally, fine roots can tally a substantial proportion to the total NPP in tropical plants: 16 % in coconut ( Navarro et al., 2008), approx. 30 % in rubber tree, 33 % in oil palm ( Dufrêne, 1989) and approx. 30 % on average for all terrestrial ecosystems ( Vogt et al., 1996 Jackson et al., 1997 Ostonen et al., 2005). In our study, fine roots contributed 17 % to total NPP (using unpublished results), which seems relatively low compared to the results cited above. Optimal nutrient conditions here could explain such differences, given the plasticity of root systems in general.

Estimation of fine root turnover rates were not much influenced by the methodology chosen, contrary to several previous studies ( Hertel and Leuschner, 2002 Hendricks et al., 2006 Jourdan et al., 2008). In our study, fine root turnover ranged from 1·25 to 1·00 yr −1 estimated from both DM and MM, respectively, and both rates are low for tropical humid perennial plantations. In plantations in a similar climate, turnover rates have been reported in the order of 2·0–2·5 yr −1 [e.g. Larix in Korea ( Son and Hwang, 2003), Acacia in Kenya ( Lehmann and Zech, 1998), Gliricidia in Ivory Coast ( Schroth and Zech, 1995), Eucalyptus in Brazil ( Jourdan et al., 2008)]. Our results were more similar to turnover rates in deciduous (0·1–1·5 yr −1 ) or coniferous (0·5–0·7 yr −1 ) forests in temperate ecosystems ( Nadelhoffer et al., 1985 Burke and Raynal, 1994 Ruess et al., 1996 Brunner et al., 2013). In their review, Gill and Jackson (2000) concluded that fine root turnover decreased from tropical to high-latitude ecosystems for all plant functional groups. Therefore, coffee seems to be an exception, probably because it grows in very well-watered, drained and fertilized conditions.

We found a discrepancy between fine root lifespan and necromass concentrations determined by sequential coring, and fine root decomposition rates in litter bags. Fine root lifespan from sequential coring ranged from 0·61 to 1·03 years (7–12 months), but we did not encounter corresponding large necromass amounts in the cores: either fine root necromass decomposed very rapidly, or the separation of live and dead fine roots severely underestimated necromass. In the root decomposition experiment, 54 % of fine root necromass did not decompose at all over a 6-month incubation period. If these results were representative for the field, then we should have recovered around half of the lost biomass in the cores during seasonal biomass drops. However, necromass always remained far less than biomass, by two orders of magnitude. We already mentioned the possibility of underestimation of necromass during fine root sorting. It is, however, also possible that the fine root decomposition rates in the litter bags were artificially low due to a very fine mesh size this mesh size ensured that root material could not leave the bag unless at least properly fragmented to a microscopic level, but also prevented the contact of the roots with macro- and mesofauna and reduced contact with soil to micro-organisms. Mesh size may therefore have slowed down decomposition considerably. Also, the timing of the decomposition experiment (February–August 2012) and the sequential coring (April 2012 to July 2013) only match partially. Climatic conditions (rainfall, soil humidity and temperature) did not vary much over the period of measurement, with the exception of some drier months in February and April 2013 (2012 had an unusually wet dry season – data not shown). This would provide an argument for the decomposition rates being representative, but repeated fine root incubations at different times over the period of root sampling would have been more accurate. In future studies, we recommend putting a greater emphasis on necromass distinction and fine root decomposition, using litter bags with a larger mesh size and assessments repeated along seasons.

Towards a below-ground carbon storage factor for mitigation projects

In most agricultural soils, SOM is depleted as compared to the local optimum ( Hillel and Rosenzweig, 2010). Agroforestry is one of the agroecological practices recommended to recover higher values of SOM ( Arévalo-Gardini et al., 2015), a crucial goal to increase fertility while compensating for GHG emissions, e.g. NAMA and 4 per 1000 projects (http://4p1000.org). The fate of litter in long-term storage pools such as SOM remains extremely difficult to assess and most estimations rely on decomposition experiments or models ( Pansu et al., 2009 Cotrufo et al., 2013). We observed no significant variation in fine root biomass with regard to plant age. Therefore, our initial assumption that NPPfr is equivalent to the fine root litter (Lfr) is confirmed and it can be considered that Lfr is around 2·96 t ha −1 yr −1 . A conservative rule-of-thumb for estimating the conversion efficiency of litter carbon to soil carbon would be about 10 % in sandy soils ( Barthes et al., 2006), even if efficiency increases with the fraction of clay due to the formation of stable aggregates, and hence organic matter protection ( Feller and Beare, 1997). Hence, after conversion from dry mass to carbon, coffee fine roots litter could result in approx. 0·15 t C ha −1 yr −1 incorporated into SOM. This conservative estimate of 0·15 t C ha −1 yr −1 could be applied during C-balance and C-neutrality estimations, proposed here as a ‘below-ground C storage factor’ for coffee, the counterpart of the currently accepted emission factors.


Acknowledgements

We thank Wen Zeng, Zhigang Lian, Anthony McKeand, Yi Li, Hongxiu Liu, Helen Chen, Paula Zanker and Jun Lu for technical assistance, Scot Surles for tipmoth control, and Mary Topa and Bill Retzlaff for helpful discussion regarding this study. We are especially grateful to Mary Topa and Bill Retzlaff for constructive comments on early versions of this manuscript. This research was supported by the United States Department of Energy. The publication of this manuscript was approved as Journal Series No. R-10425 by the Florida Agricultural Experiment Station.


Introduction

The relative balance between the above-ground and below-ground biomass of a tree is decided by the internal hereditary characteristics and the influence of external environmental factors (Köstler et al. 1968 , Santantonio 1990 , Lacointe 2000 ). In particular, the biomass of species with the same hereditary characteristics may also change according to environmental factors. This has been proven by preceding studies on the change of root weight according to light conditions (Röhrig 1966 , Ledig et al. 1970 ), and the diverse influence of other environmental factors (Gruber 1994 ).

The biomass of the above-ground and the below-ground parts can be estimated easily using the diameter at breast height and root collar diameter as supply variables, and the relative growth-estimating equation using such a statistical method can apply to actual forestry fields easily (Lee, 2004a ). However, regional characteristics according to lumbering conditions, financial burdens and the distribution of relevant tree species should be considered when investigating the biomass of a tree (Whittaker & Marks 1975 , Alban et al. 1978 , Lee et al. 1985 , Park & Lee 1990 , Bartelink 1998 , Drexhage & Gruber 1999 , Le Goff & Ottorini 2001 ).

Harrington ( 1979 ) estimated the regression equation that could calculate the relevant carbon dioxide absorption volume using the tree height or diameter, which could be measured easily by paying attention to the fact that the biomass of the above-ground and below-ground parts had a very close correlation with the tree height and diameter, and various regression equations regarding diverse tree species have been studied since then (Na et al. 2011 ). The method for estimating biomass according to the correlation between the above-ground and below-ground biomass using the regression equation has the disadvantage that it requires a lot of time and effort to study, but due to its high reliability and economic feasibility, it is being used frequently (Lee 2004a ).

Pinus densiflora S. et Z. is highly adaptable to the environmental conditions of Korea such that it has been distributed widely throughout the country, but it shows differences in biomass even within the same age-class according to the environmental conditions (Byun et al. 2010 , Kim et al. 2012 ). Lee ( 2004b ) reported that the development of vertical roots slowed down and the development and growth of horizontal roots was enhanced when P. densiflora S. et Z. grew in poor soil conditions, which significantly reduced the biomass of the above-ground part. Lee et al. ( 2005 ) reported that significant differences in annual ring width occurred in Pinus rigida Mill. of similar age and soil conditions due to differences in slope direction.

It has been reported that the slope direction is an important factor which determines the light conditions, temperature, humidity and physical and chemical characteristics of soil, and it is closely related to the growth of biota and trees so that the biomass of the same species of trees vary 5–30 times according to the slope direction (Kutiel 1992 , Allan & Ian 2003 ), but the studies regarding such relationship between the slope direction and the growth of trees have been carried out mostly based on north-facing and south-facing slopes (Stage 1976 , Atsushi et al. 1993 , Scott & Hawkins 2005 ). Especially, studies regarding the correlation between the above-ground and below-ground biomass based on the concept of slope direction in Korea are significantly insufficient.

Therefore, the purpose of this study was to expose the influence of environmental differences such as growth environments and slope directions on the biomass growth of the above-ground and below-ground parts of P. densiflora S. et Z. The aim was to explore a comparative analysis of correlation between the above-ground and below-ground parts of a planted 13-year-old P. densiflora S. et Z. stand according to the slope direction.


Footnotes

↵ 1 Present address: School of Earth, Energy and Environmental Sciences, Department of Earth System Science, Stanford University, Stanford, CA 94305.

Author contributions: A.M., D.J.B., J.C., P.J.H., and C.M.I. designed research A.M., D.J.B., J.C., H.V.S., and C.M.I. performed research J.D.G. and P.J.H. provided data A.M., J.D.G., E.A.H., and S.C.F. analyzed data S.C.F. provided upscaling and forecasting and A.M. and C.M.I. wrote the paper.

The authors declare no competing interests.

This article is a PNAS Direct Submission.

Data deposition: All data used in this paper are archived at and available from the Spruce and Peatland Responses Under Changing Environments long-term repository https://doi.org/10.25581/spruce.077/1607860.

This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).


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