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Title Carbon capture by the cultivation of Nopal Succlent, Succlent ficus-indica L.
Concept Type New methodology
Date of Issue 07-June-2021
Sectoral Scope

Sectoral scope(s) applicable to the methodology.

For AFOLU; ALM (ICM And CGLC)

Developer Bret Consultores SAPI de CV.
Prepared By Teresa Tattersfield
Contact

[email protected]

525515117290 +12267571085

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Methodology Concept Note: VCS Version 4.0

Contents

1 Summary Description of the Proposed Methodology 4
2 Relationship to Approved or Pending Methodologies 6
3 Project Activities and Applicability Conditions 7
4 Demonstration of Additionality 8
5 Quantification of Emission Reductions 9
6 Monitoring 10
7 Associated Projects and Emission Reduction Potential 10
8 Development Team 28
9 Funding 32
10 Signature 32
Appendix 34
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Methodology Concept Note: VCS Version 4.0
1 Summary Description of the Proposed Methodology
Additionality and Crediting Method
Additionality Project
Crediting Baseline Project

This methodology describes steps for estimating and monitoring carbon dioxide capture and biomass in Succlent, trees, agricultural crops. The protocol is based on guidance provided in the IPCC 2003 Good Practice Guidance for Land Use, Land-Use Change and Forestry. The methodology has been designed to be applicable to estimate CO2 s sequestration by Crassulacean Acid Metabolism (CAM) plant under field growing conditions. It is important to mention that CAM metabolism can be very variable depending on the taxonomic scale to which we are limiting (e. g, families, subfamilies, genera and species) (see Nobel and Harstock 1986); in particular, among Succlent species, the CAM metabolism can present important changes that affect the CO2 capture rates, therefore not conforming to the models and equations described here. This is very important, since around the world not only Succlent species is cultivated, in a much smaller proportion other congenerics are also used. Because of these reasons, it is recommended to use this method for crops. If the methodology is used for other species, genera or some other taxonomic category of CAM plants, the results should be interpreted with caution considering the above.

The intention of the developers has been to create an easy methodology which includes sufficient detail on methods to allow to evaluate a wide range of environmental and crop conditions in which this member of Succlent is cultivated. However, accurately estimating and projecting the values of the various Succlent carbon pools does require a level of technical ability on the part of the project proponent team. It is therefore expected that in many cases landowners and farmers may need to work with people with specific technical skills to complete the development of an Succlent carbon project using this methodology.

All the operations involved in the estimation method described here are based on data consulted from the scientific articles cited in this report (Acevedo et al 1983; Ligouri et al 2013; Nobel and Harstock 1983, 1986; Nobel and Valenzuela 1987; Nobel 1988; Nobel et al.1992, 1993, 2002; Nobel and Israel 1994; Nobel and Bobich 2002; Pimienta-Barrios et al., 2000, 2001, 2005). The CO2 capture values in Succlent correspond to theoretical data that were obtained under "controlled" or more stable conditions than those that can be found directly in the field. Although in these studies the effect of different factors that can affect the capture of CO2 in Succlent ficus-indica is already part of the evaluation, -environmental (temperature, humidity, photosynthetic active radiation, CO2 concentration), growth (development stage) and of the life cycle (in vegetative or sexual reproduction) - these factors, particularly the environmental ones, represent a small range of variation compared to the spectrum to which crops can be confronted at bigger spatial scales (throughout the geography) and much more extensive temporary (from one to ten years).

The output values of method VM000XX, Version 1. 0. must be interpreted as a conservative estimate based on the average CO2 capture under controlled conditions, with adjustments that involve some factors that potentially affect the capture efficiency of this plant, since closely related to their physiology. To have a much more accurate data and in accordance with the conditions of the problem plantation, different studies were carried out in the field considering the spatial and temporal environmental variation, making capture measurements on the cladodes and trees.

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The estimates of captured CO2 and biomass are derived from the morphological variables of the cladodes. With the width, length and thickness of the cladodes, the respective calculation of the area of the two faces of each cladode, of the surface corresponding to the edge and its volume is performed. This methodology is focused on addressing the following key variables:

· Estimating the amount of carbon in the Succlent crops at the start of the project.
· Estimating the amount of CO2 captured by total photosynthetic surface in the crop.
· Estimating the value of CO2 and biomass per crop of Succlent (base line) for year during for ten years.
· Monitoring and documenting changes in Succlent carbon for ten years under the project scenario

The methodology has been designed using a modular approach. This methodology document lays out the steps required to fulfil estimation, projection and quantification requirements for projects wishing to register credits under the VCS program.

The methodology requires the completion of 7 main tasks, each of which is comprised of a number of sub-tasks:

1. Task 1: Identification of limits and extension of project area.
2. Task 2: Determining the baseline scenario
3. Task 3: Measuring morphological variables of cladodes and trees of project area.
4. Task 4: Estimating biomass (tons at T0) corresponding to all plants in the project area
5. Task 5: Estimating CO2 capture.
6. Task 6: Projecting Carbon accumulation per year in project area for 10 years. The overall process used by the methodology is shown in the following methodology map.
7. Task 7. Monitoring and estimation.


An Succlent's methodology for accrediting carbon sequestration in crops (OMC) project involves management activities that maintain and increase the carbon stocks in the Succlent crops, in relation to the levels of carbon stored at the zero time of the project. Thus, initial conditions at cero time of the project are estimated: the amount of carbon in the Succlent crops at the start of the project, the amount of CO2 captured by total photosynthetic surface in the crop, the value of CO2 and cacti biomass per crop of Succlent (base line) for year during the project and monitoring and documenting changes in Succlent carbon during the project scenario.

Furthermore, actions are carried out that focus on maintaining the structure of the crops, i.e. dimensions of the project area, number of plants per unit area, baseline of plants within the project area; the latter corresponds to the number of cladodes that have to be maintained per plant so that it ensures with an appropriate volume and baseline area that it maintains the growth, capture and storage of carbon, as well as allows the harvest of the plant for human consumption (without affecting the estimates during the project's accreditation period).

Indicate using the above table whether the proposed methodology would use a project, performance or activity method for determining additionality, and a project or performance method for determining the crediting baseline.

Provide a brief summary description of the proposed methodology, including a description of the project activity(s) to which the methodology would apply. The summary should be concise.

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2 Relationship to Approved or Pending Methodologies

Approved and pending methodologies under the VCS and approved GHG programs, that fall under the same combination of sectoral scopes or AFOLU project categories, were reviewed to determine whether an existing methodology could be reasonably revised to meet the objective of the proposed methodology. Three methodologies were identified, and are set out in Table 1 below.

Table 1: Similar Methodologies

Methodology Title GHG Program Comments
VM0017 Adoption of Sustainable Agricultural Land Management VCS This methodology focuses on a specific set of management practices
VM0042

Methodology for Improved Agricultural Land Management, v1.0

VCS This methodology quantifies the greenhouse gas (GHG) emission reductions and soil organic carbon (SOC) removals resulting from the adoption of improved agricultural land management (ALM) practices.

At this time, there´s no methodology approved or under development for estimating and monitoring carbon dioxide capture and biomass in Succlent crops in the VCS Program or in another approved GHG program. Therefore, it was determined that development of a new methodology was most appropriate.

3 Project Activities and Applicability Conditions

This methodology is global in scope and applies to estimate CO2 capture correspond to the CAM metabolism of the Succlent ficus-indica species under growing conditions. CAM metabolism can be very variable depending on the taxonomic scale to which we are limiting (e. g, families, subfamilies, genera and species).

All projects using this methodology must meet the following conditions:

a. Projects must meet the most recent VCS requirements for one of the following two Agricultural Land Management activities:
· Improved Cropland Management (ICM)
· Cropland and Grassland Land-use Conversions (CGLC)
b. Project activities must be implemented on land that is cropland at the project start date and remains cropland throughout the project crediting period (i.e., land use change is not eligible, including conversion from cropland to grassland and grassland to cropland).
c. As of the project start date entire project area consists in croplands of Succulent . Crops may include Succulent grown for food products (Succulent , fruits, vegetable, prickly pear) or other derivate products of this cacti.
d. The conditions of project (number of trees per area unity, levels of growing in all trees, trees of the same age, equidistant crop lines) must be homogenously maintained by the next ten years to ensure the CO2 assimilation calculated by mean the methodology.
e. Project activities must not include changes in project area, and the conditions of the crop at the moment of the accreditation: e. g. number of trees in the project, number of growth levels per tree (no more tree levels), soil water regimes or other significant anthropogenic changes in the crop as changes in fertilization.
f. The project must meet with an annual evaluation to verify the biomass and CO2 accredited and to ensure that the project area maintain homogeneous conditions respect to those at moment of accreditation.
g. The biomass accredited in the project area excludes that which is harvested per season to human use (Succulent , fruits, vegetable, or prickly pear).
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4 Demonstration of Additionality

Nopal Succlent methodology offers a cost-effective option for projects to yield surplus GHG reductions that and demonstrate additionality to what would have occurred in the absence of the project.

The approach to additionality for Succlent's methodology for accrediting carbon sequestration in crops projects recognizes increases in the amount of CO2 removed from the atmosphere relative to "business as usual" management. It also considers the long-term risks to carbon sequestered in the project area presented by "business as usual" management and the potential emissions of carbon into the atmosphere. Under this approach, additionality takes into account the following:

· Onsite carbon stocks are at risk on a 20-year time scale.
· Land ownership and management direction are not permanent, except in cases where binding commitments limit management options, such as conservation easements (CA).
· Management goals and objectives are likely to change over time, especially as: ownership of a crop changes hands, as often happens between generations of family crop owners or between entities owning crops as a financial investment.
· Over the length of a project lifetime and in the absence of a long-term commitment to a project and associated conservation easement, emissions may have resulted from the clearing of Succulent trees to convert a crop to another crop type or from harvest.
· Activities that reduce average onsite carbon stocking.
· Committing a site to crop cover in perpetuity

Furthermore, this methodology acknowledges that the project's baseline, as the way "business as usual" management is represented for quantification purposes, is a counterfactual scenario, i.e., a representation of what may have actually occurred if the project had never happened. Additionality is assured over the 20-year crediting period, during which the terms of the CA ensure carbon stocks increase compared to the baseline.

The methodology has been designed using a modular approach and lays out the steps required to fulfill estimation, projection and quantification requirements for projects wishing to register credits under the VCS program.

The methodology requires the completion of 11 main tasks, each of which is comprised of a number of sub-tasks:

· Task 1: Identification of limits and extension of project area.
· Task 2: Determining the baseline scenario
· Task 3: Measuring morphological variables of cladodes and trees of project area. (Quantification)
· Task 4. Social and Environmental Safeguards
· Task 5: Estimating biomass (tons at T0) corresponding to all plants in the project area
· Task 6: Estimating CO2 capture.
· Task 7: Projecting Carbon accumulation per year in project area for 10 years. The overall process used by the methodology is shown in the following methodology map.
· Task 8. Monitoring and estimation
· Task 9. Permanence
· Task 10. Verification process
· Task 11. Credits emission
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5 Quantification of Emission Reductions

This section provides requirements and guidance for quantifying an OMC project's net GHG reductions. The Verified Carbon Units (VCUs) will be issued to an OMC project upon confirmation for ISO- accredited and Verra-approved verification body that the OMC project's GHG reductions and removals have been quantified following the applicable requirements of this methodology.

The estimates of captured CO2 and biomass are derived from the morphological variables of the cladodes. With the width, length and thickness of the cladodes, the respective calculation of the area of the two faces of each cladode, of the surface corresponding to the edge and its volume is performed. It has to be mentioned that for these calculations, + the formula of the area of a regular ellipse was considered, because in general the cladodes are oval (like an ellipse); however, its shape is much more complex, especially in the proximal region, so the calculated values of area and volume do not represent the actual area of a racket. In this sense, the VM000XX, Version 1. 0 include a size adjustment based in a geometric morphometric study in thousands of cladodes of this species (in process).

This methodology is focused on addressing the following key variables:

· Estimating the amount of carbon in the Succlent crops at the start of the project.
· Estimating the amount of CO2 captured by total photosynthetic surface in the crop.
· Estimating the value of CO2 and biomass per crop of Succlent (base line) for year during for ten years.
· Monitoring and documenting changes in Succulents carbon for ten years under the project scenario
6 Monitoring

The monitoring plan must be prepared using module VMD0034 Methods for Developing a Monitoring Plan. This module includes specifications on quality assurance and quality control that must be followed during development of the project description and other project documents.

7 Associated Projects and Emission Reduction Potential

Currently there are no projects planned or waiting for this proposed methodology.

The methodology for completion of the tasks is as follows:

7.1 Task 1 Identification of limits and extension of project area
7.1.1 Project boundary determination

Requirement: For all projects

Goal: To determine the project boundary for baseline scenario and additionality purposes.

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Method:

1.1.1 The procedures described in the sub-sections below entail the following inputs and outputs.

Inputs:

1) a. General knowledge of project area and expected conditions under the baseline and project Scenarios.
2) b. Maps of the region within which the project occurs, ideally consisting of layers in a GIS showing geographic and cultural features.
3) c. Geo-referenced data points delineating the project area; and,
4) d. Knowledge of the range of the permissible project crediting periods.

Outputs:

a. Geo-referenced definition of the project area.
b. Documented project start date.
c. Documented choice of project crediting period.
d. Documented projection of the monitoring periods.
e. Documented choice of Succulents carbon pools to be accounted.
f. Documented choice of sources of GHG emissions to be accounted.
1.1.2 Spatial boundaries

Define the boundaries of the following spatial feature:

1.1.3 Project area:

The project area is the area or areas of land on which the project proponent will undertake the project activities. Lands on which the project activity will not be undertaken cannot be included in the project area.

Describe and justify the criteria used to define the boundary of the project area. Use appropriate sources of spatial data for each of these criteria, such as remotely sensed data, field information, and other verifiable sources of information meeting the requirements laid out in the latest version of the Standard.

Provide project location in KML file and geodetic polygons, as well as additional shape files, maps, GPS coordinates or any other location information that allows the identification of the boundaries unambiguously and with a reasonable level of certainty.

1.2. Temporal boundaries

Define the temporal boundaries listed below:

1.2.1 Project start date and end date of the project activity

The duration of the project activity must fall within the permissible range as set out in the most recent version of the Standard.

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1.2.2 Starting date and end date of the project crediting period

The crediting period must fall within the permissible range as set out in the most recent version of the Standard.

1.2.3 Monitoring period

The minimum duration of a monitoring period is one year.

1.3 Carbon pools

In this section only are considered as carbon pools to be accounted, the trees of O. ficus-indica. This species reproduces sexually and propagates vegetatively. The accredited trees and respective biomass correspond only to vegetative reproduction and biomass accumulation in the first four levels of growing (i.e. cladodes corresponding to the first three generations from mother cladode). Higher growing levels are excluded because they are intended for harvest.

7.2 Task 2. Determining the baseline scenario
7.2.1 Calculating total number of trees of Succulents in the project area

Requirement: Required for all projects.

Goal: To determine the number of trees in baseline scenario.

Method: Determine the number of trees of Succulents that integrate the project area using methodology described here.

The protocol to quantify the number of trees of Succulents in project area has been designed and supported by field studies performed by ECOPLAN in more than 2000 ha crops in Mexico under different growing conditions trough of a wide geographic range.

7. 2. 1. 2. Sampling unit. Quadrants of 9m2 of area (3 X 3 m). These could be delimited on project area using, rope, string or other material.

7. 2. 1. 3. Number of quadrants to sampling. The number of quadrants to measure was determinate for two types of project areas. One of them considering a project with "homogeneous" conditions of growing (i. e. low variability in number of trees per area unity, trees of the same age, equidistant crop lines), and the other one for areas with heterogeneous conditions (i. e. high variability in number of trees per area unity, trees of different age, non-equidistant crop lines), or constituted by a landscape of multiples crops with different growing conditions.

In both cases the parameter to evaluate was the number of trees per area unity. The formula to achieve a desired precision in estimating the mean of trees per area unity was taken of Zar (2010):

Where:

S2= sample variance of number of trees per quadrant

ta(2)v= two tailed critical values of Student's t, with u = n - 1 degrees of freedom

d2= width of the desired confidence interval.

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7. 2. 1. 3. 1 Number of quadrants to sampling in the project areas with homogeneous conditions. Sample size was calculated with data from a sampling from a landscape of multiples crops with low variability in number of trees per area unity, trees of similar age and with equidistant crop lines. Sample variance of number of trees per area unity was calculated from 100 sampled quadrants (n= 100). From this data the next basic statistics were calculated:

N= 100 quadrant sampled

Minimum value= 21 trees per quadrant

Maximum value= 24 trees per quadrant

Mean= 22.6 trees per quadrant

Std. error= 0.091

Variance (S2)= 0.83

Stand. Dev= 0.91

Substituting the variance value in the formula, considering the value two as the desired interval confidence (only two trees):

ngh= number of quadrants to sampling in homogeneous project areas

S2=0.83

ta(2).v= 1.67

d2= 2.0

Thus, it conclude that a sample size greater than 18 quadrants is required to achieve the confidence interval of two trees in estimating the medium value of number of trees per unit of area in a project area with "homogeneous conditions".

7. 2. 1. 3. 2 Number of quadrants to sampling in the project areas with heterogeneous conditions. Sample size was calculated with data from a sampling from a landscape of multiples crops with high variability in number of trees per area unity, trees of different age, non-equidistant crop lines. Sample variance of number of trees per area unity was calculated from n= 150 (quadrant number). From this data the next basic statistics were calculated:

N= 150 quadrant sampled

Minimum value= 6 trees per quadrant

Maximum value=30 trees per quadrant

Mean= 17.54 trees per quadrant

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Std. error= 0.58

Variance (S2)= 20.04

Stand. Dev= 4.47

Substituting the variance value in the formula, considering the value two as the interval confidence (only two trees):

nght= number of quadrants to sampling in heterogeneous project areas

S2=20.04559

ta(2).v= 1.67

d2= 2.0

Thus, it conclude that a sample size greater than 56 quadrants is required to achieve the confidence interval of two trees in estimating the medium value of the crop of the variable number of trees per unit of area.

7. 2. 2. Projection of the quadrants on project area. Unities sampling (quadrants) must be randomly displayed throughout the project area. Total area of project must be divided by a grid of squares of desirable size; which will be listed in ascending order, for its later selection by means of random numbers. The sampling quadrants must be displayed on the selected squares of the grids.

Once the points to be sampled have been displayed, string quadrants must be extend over the site to be sampled, making sure that the sides of the square are perfectly straight, forming angles of 90 ° between sides.

7.2. 2. 1. Tree quantification. Count and enumerate in ascending order all the trees (label it) that were inside the area of quadrant. Total number of trees per quadrant must be recorded (Tq1, Tq2, Tq3, Tq4, Tq (n)……….).

From the total of trees, some of them will be selected for its measurement (see Task 3. 2. 6). The selection of the tree will be done through the random numbers. For each quadrant, the procedure described in the previous paragraphs must be followed until all quadrants to be sampled are completed.

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7.2.2. 1. Average number of trees per quadrant in project area. The number of trees counted per quadrant are used to estimate this value using this formula:

Where:

µtpa = Average value of number of trees per quadrant in project area (9m2)

Tg(n) = Total number of trees within quadrants (Tq1, Tq2, Tq3, Tq4, Tq (n)……….)

nq=Total number of sampled quadrants

7. 2. 3. Total number of trees in project area. To estimate total number of trees the next formula will be used:

Where:

TT= Total number of tress in project area

µTpa= Average value of number of trees per m2 in project area (9m2)

ATP=Total area of the project (m2)

7.3 Task 3 Measuring morphological variables of cladodes and trees of project area.
7.3.1. Input data

Requirement: Required for all projects

Method: The estimates of captured CO2 and biomass are derived from the morphological variables of the cladodes. With the width, length and thickness of the cladodes, surface of CO2 capture per tree is calculated. Furthermore, every cladodes per sampled tree are characterized according to their phenological state, i. e. photosynthetic and non-photosynthetic (less than one year and older than one year).

7.3.1 For each selected tree in a quadrant, all cladodes must be accounted and measured.

7.3. 1. 1. Measurements (morphometrics data). Each cladode must be labeled and characterized by three measures: length, width and thickness. The data for each cladode must be written in a data table.

"Length" (L): the most extreme points between bases of cladode until its distal edge;

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"Width" (W): the most extreme points between the lateral edges.

"Thickness" (T): This attribute must be measured using Vernier at the lateral medium side of cladode.

7.3. 1. 2. Cladode classification according its phaenological events. Cladodes present a decrease in CO2 capture associated with its age; the youngest cladodes (one year old) capture on average 10% more than the cladodes of two years or more (Pienta-Barros et al 2005)

Because in the calculation of CO2 capture is considered a conservative adjustment to quantify these differences, the cladodes of selected trees must be classified according to three categories (Reyes-Agüero et al. 2005):

a) green cladodes smaller than 25 cm in length corresponding to less than two years

b) those green larger 25 cm, to cladodes older than two years without;

c) cladodes brown, larger 25 cm, correspond to cladodes older than two years and non-photosynthetically. These cladodes only are involved in biomass estimation, but they are not used to calculate Carbon dioxide captured.

7.3.2. Output data

Requirement: Required for all projects

Method: Perimeter, area of the two faces of each cladode, the surface corresponding to its edge, total surface per cladode and tree are calculated from morphological variables of the cladodes (width, length and cladode thickness).

7. 3. 2. 1. Perimeter. To each cladode (C1, C2, C3, C4, Cn……) in each tree sampled (T1, T2, T3, T4, Tn…..) the next formula was performed:

Where:

Pc1-T1= Cladode perimeter of cladode "1"of tree"1".

AFp= adjustment factor for perimeter

Π= 3.1416

Lc1-T1= length of cladode "1" of tree"1" (cm)

Wc1-T2= width of cladode "1" of tree"1" (cm)

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7. 3. 2. 2. Area of the two faces of each cladode. To each cladode (C1, C2, C3, C4, Cn……) in each tree sampled (T1, T2, T3, T4, Tn…..) the next formulas were performed:

Where:

Asc1-T1=Cladode area of a side of cladode "1" of tree"1" (cm2)

Assc1-T1=Cladode area of both sides of cladode "1" of tree"1" (cm2)

AFa= adjustment factor for area

Π= 3.1416

Lc1= cladode length of cladede "1" (cm)

Wc1= cladode width of cladede "1" (cm)

7. 3. 2. 3. Area of edge surface. To each cladode (C1, C2, C3, C4, Cn….) in each tree sampled (T1, T2, T3, T4, Tn…..) the next formula was performed:

Where:

Atc1-t1=Area of cladode thickness of cladode "1" of tree "1"(cm2)

Pc1= Cladode perimeter of cladode "1" of tree "1" (cm)

Tc1 =Tickennes of cladode "1" of tree "1" (cm)

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7.3. 2. 4. Total photosynthetic surface of per cladode. To each cladode (C1, C2, C3, C4, Cn….) in each tree sampled (T1, T2, T3, T4, Tn…..) the next formula was performed:

Sc1-T1= Total photosynthetic surface of cladode "1" of tree "1" (cm2)

Assc1-t1=Cladode area of both sides of cladode "1" of tree "1" (cm2)

Atc1-t1=Area of cladode thickness of cladode "1" of tree "1" (cm2)

7.3. 2. 5. Total surface per tree. To each tree the next formula was performed:

STA(T1) = Total photosynthetic surface per tree "1"(T1, T2, T3, T4, Tn…..) (cm2)

S(n)(T1)= Total photosynthetic surface per cladode (C1, C2, C3, C4, Cn….) of tree "1" (cm2)

7. 3. 2. 6. Number of sampling trees in project area. The number of trees to measure was determinate for projects areas with heterogeneous conditions, or constituted by a landscape of multiples crops with different growing conditions. Sample size was calculated with data from a sampling from a landscape of multiples crops with high variability in number of trees per area unity, trees of different age, no equidistant crop lines. Parameter to evaluate was the total photosynthetic surface per tree (ST A (n)). Sample variance of mean total photosynthetic surface of trees was calculated from n= 200 (Succulents trees). From these data, the next basic statistics were calculated:

N= 200 trees

Minimum value= 0.16 m2

Maximum value= 5.83m2

Mean= 1.38 m2

Std. error= 0.09

Variance (S2)= 1.03

Stand. Dev= 1.019

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Substituting the variance value in the formula of 2. 1. 2, considering the value 0.1m2 the interval as desired confidence interval:

n= number of trees to sampling in heterogeneous project areas

S2=1.03

ta(2)v= 1.67

d2= 0.1 m2

Thus, it conclude that a sample size greater than 148 Succulents trees is required to achieve the confidence interval of 0.1 m2 in estimating the medium value of mean total photosynthetic surface of trees.

7.3. 2. 7. Number of sampling trees for measuring per quadrant. The number of trees for measuring in each sampling quadrant is calculated by the next formula:

Where:

ntr= number of trees to sampling in heterogeneous project areas

nqht= number of quadrants to sampling in heterogeneous project areas

nt/c = number of sampling trees for measuring per quadrant

Thus, it concludes that a sample size greater than 2. 5 Succulents trees per quadrant is required to achieve the confidence interval of 0.1 m2 in estimating the medium value of mean total photosynthetic surface of trees in project areas with heterogeneous conditions.

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In project areas with homogeneous conditions number of trees per quadrant correspond to::

A sample size greater than 2. 5 Succulents trees per quadrant is required to achieve the confidence interval of 0.1 m2 in estimating the medium value of mean total photosynthetic surface of trees in project areas with homogeneous conditions

7.4 Task 4: Estimating biomass (tons at T0) corresponding to all trees in the project area

7.4.1 Task 4.1 Carbon in the project area.

Requirement: Required for all projects.

Goal: to calculate the carbon retained in the project area in T0 (at the moment of the accreditation).

Methods: To have greater accuracy about carbon retained in project area, two methods were involved. The first based on the study by Garcia and Nobel (1992), where the relationship between the surface area of the cladodes and their dry weight is evaluated, which has the behavior of an exponential function. This function and its respective constants is used to estimate biomass in dry weight from the calculated surface of each cladode.

In the second method, from the morphological data of the rackets, its volume is calculated in cm3. With this data the wet biomass of the cladode is calculated considering that the cladodes have a density of 1.06 g / cm3 (Machado-Velasco and Velez-Ruiz, 2008). Using the wet weight, dry biomass can be calculated in three different conditions of humidity (94%, 96%, 97%) (Maki-Díaz et al. 2105, Yang et al 2015). In both methods, from the dry biomass, the proportion of Carbon retained in grams is calculated, based on the fact that 35% of the biomass of Succlent ficus indica is Carbon (Yang et al 2015).

Biomass estimation by these methods was verified measuring directly the weight of the Cladodes in the field.

7. 4. 1. 1. Calculating dry weight from surface area per cladode. Calculate weight (Wtc) is estimated for each cladode using the next formula:

Where:

Wtc1-t1= Dry weight calculated per cladode (C1, C2, C3, C4, Cn….) per tree "1" (T1, T2, T3, T4, Tn…..) (g)

Sc1-t1= Total photosynthetic surface per cladode (C1, C2, C3, C4, Cn….) per tree "1" (T1, T2, T3, T4, Tn…..) (cm)

A= equation factor of García and Nobel (1992)= 88.3

B= equation factor of García and Nobel (1992)= 2779

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7. 4. 1. 2. Calculating total dry weight from surface area per tree. Calculate dry weight per tree (Wtt) is estimated for each tree using the next formula:

Where:

Wtt1= Dry weight calculated per tree "1" (T1, T2, T3, T4, Tn…..) (g)

Wtc1-T1= Dry weight calculated per cladode (C1, C2, C3, C4, Cn….) (g)

7. 4. 1. 3. Calculating average dry weight from surface area of trees in project area. Average dry weight is estimated for each tree using the next formula:

Where:

µWt= average dry weight of sampled trees in project area.

Wtt1= Dry weight calculated per tree (T1, T2, T3, T4, Tn…..) (g)

ntr= number of trees to sampling in heterogeneous project areas……………….3. 2. 6

7. 4. 1. 4. Calculating average carbon content from surface area of trees in project area. Calculate average carbon content (µCt) is estimated for each tree using the next formula:

Where:

µCt= average carbon weight of trees in project area.

µWt= average dry weight of trees in project area.

7. 4. 2. 1. Calculating wet weight from volume per cladode. Weigh (Wvc1-T1) is estimated for each cladode (C1, C2, C3, C4, Cn….) for each tree (T1, T2, T3, T4, Tn…..) using the next formula:

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Where:

WvC1= wet weigh of cladode "1" (C1, C2, C3, C4, Cn….) in tree 1 (T1, T2, T3, T4, Tn…..) (g)

d= Cladode density= 1.06 g/cm3

Asc1=Cladode area of a side of cladode "1" (1, 2, 3, n…..) in tree 1 (T1, T2, T3, T4, Tn…..) (cm2)

T c1=Tickennes of cladode "1" (C1, C2, C3, C4, Cn….) in tree 1 (T1, T2, T3, T4, Tn…..) (cm)

VC1=Cladode volume of cladode "1" (C1, C2, C3, C4, Cn….) in tree 1 (T1, T2, T3, T4, Tn…..) (cm3)

6. 4. 2. 2. Calculating dry weight from volume per cladode. Dry Weigh (Wvdc1) is estimated for each cladode (C1, C2, C3, C4, Cn….) per tree (T1, T2, T3, T4, Tn…..) using the next formula:

Where:

WvdC1-T1= dry weigh of cladode "1" (C1, C2, C3, C4, Cn….) in tree "1" (T1, T2, T3, T4, Tn…..) (Considering 96% of water content)

WvC1-T1= wet weigh of cladodes of cladode "1" (C1, C2, C3, C4, Cn….) in tree "1" (T1, T2, T3, T4, Tn…..).

7. 4. 2. 3. Calculating dry weight from volume per tree (T1, T2, T3, T4, Tn…..). Dry Weigh (WvdT) is estimated for each tree using the next formula:

Where:

Wvdt1= dry weigh of tree (T1, T2, T3, T4, Tn…..) (considering 96% of water content)

WvdC1= dry weigh of cladodes (C1, C2, C3, C4, Cn….) (considering 96% of water content)

7. 4. 2. 4. Calculating average dry weight from volume of trees in project area. Average dry Weigh (µ_Wvd) is estimated for each tree using the next formula:

µWvd= average dry weight from volume of trees in project area.

Wvdt1= Dry weight from volume calculated per tree 1 (T1, T2, T3, T4, Tn…..) (g)

ntr= number of trees to sampling in heterogeneous project areas…………….3. 2. 6

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7. 4. 2. 5. Calculating average carbon content from surface area of trees in project area. Calculate average carbon content (µCv) is estimated for each tree using the next formula:

Where:

µCv= average carbon weight of trees from volume in project area.

µWvd= average dry weight from volume of trees in project area.

7.5 Task 5: Estimating CO2 capture

Requirement: Required for all projects.

Goal: To estimate CO2 captured with relation to photosynthetic surface

Methods: Succlent studies report the capture of CO2 in millimoles per day, per square meter of photosynthetic surface, using the cladodes as a unit of study (e. g. Nobel and Harstock 1983, Pimienta-Barros et al. 20000, Ligouri et al. to 2013). Carbon dioxide (m2) is obtained by adding the instantaneous capture of CO2 in micromoles per second per square meter in periods of 24 hours of plant activity, from measurements made under different environmental conditions, to which CAM metabolism of this species is susceptible, such as different temperature, humidity and active photosynthetic ranges, among others. In this methodology, an average of 309 millimoles of CO2 per square meter per day of photosynthetic surface (µCO2c), equivalent to 13. 596 g of CO2 was considered. This value was obtained from the data reported in the studies cited last. Data corresponds to a value obtained from cladodes greater than two years, without fruits and under the optimal environmental conditions for a greater efficiency of CO2 capture process. Based in this value, and with the total photosynthetic surface per cladode and per tree, CO2 captured per day was estimated for the trees measured.

7. 5. 1 Estimating CO2 capture per cladode. An increase of 10% in CO2 capture was considered in youngest cladodes (one year old). To calculate CO2 captured in measured cladodes considering this factor the next formula will be performed:

Formula to younger cladodes

Formula to older cladodes

Where:

CO2c1-T1 = Carbon dioxide captured in cladode "1" (C1, C2, C3, C4, Cn….) in tree "1" (T1, T2, T3, T4, Tn…..) (g)

µCO2c= average of CO2 per square meter per day of photosynthetic surface 13. 596 (g)

Sc1-T1= Total photosynthetic surface in cladode "1" (C1, C2, C3, C4, Cn….) in tree "1" (T1, T2, T3, T4, Tn…..)

(cm).

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fp(x)= 1.1= adjust factor to consider an increase of 10% in CO2 capture in younger cladodes. This factor only must be taken in account for cladodes shorter than 25cm.

7. 5. 2 Estimating CO2 capture per tree. To estimate CO2 captured per tree (T1, T2, T3, T4, Tn…..) the next formula will be used:

CO2 T1= CO2 captured in tree "1" (T1, T2, T3, T4, Tn…..) (g)

CO2c1-T1 = Carbon dioxide captured in cladode "1" (C1, C2, C3, C4, Cn….) in tree "1" (T1, T2, T3, T4, Tn…..) (g)

7. 5. 2. 1. Adjusted value of CO2 capture per tree. To estimate a conservative value of CO2 captured per tree an adjust was performed based on that CO2 capture values per photosynthetic surface measured from one tree are up to three times smaller (33%) compared to the measurements by cladode (Ligouri et al. 2013). Thus the next formula will be used:

aCO2 T1= adjusted value of CO2 captured in tree "1" (T1, T2, T3, T4, Tn…..) (g)

7. 5. 3 Estimating average CO2 capture per tree in project area. To estimate average CO2 captured per tree in project area the next formula will be used:

µC02T= average CO2 capture per tree in project area (g)

aCO2 T1= adjusted value of CO2 captured in tree "1" (T1, T2, T3, T4, Tn…..) (g)

ntr= total number of sampled trees

7. 5. 4 Estimating CO2 capture in project area per day. To estimate CO2 captured in project area in tons the next formula will be used:

CO2pa= CO2 capture in project area per day (Tons)

µC02T= average CO2 capture per day and per tree in project area (g)

TT= Total number of tress in project area

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7. 5. 5 Projecting CO2 capture in project area for a year. Using CO2 capture in project area per day, CO2 capture for a year is calculated. To obtain conservative values of captured CO2 in project area throughout the year some seasonal adjustments were included, taking into account the following factors:

Temperature. The optimum day / night temperature ranges at which the greatest CO2 capture occurrs in O. ficus indica are 24/16 ° C and 26 / 16''C. After 35 ° C / 23 ° C, capture of CO2 per day decreases to 50% (Inglese et al., 1994).Thus in the projection of CO2 capture in project area for a year an adjust was taken into account, based on the number of months of the year with a daily average temperature greater than 35 ° c. During the days of these months it was considered that the capture of CO2 is reduced to 50%. It is important to mention that the number of months that average a temperature equal or higher to 35 ° C is variable latitudinal. For this, it is recommended to consult the data of the climatological stations closest to the project area to determine how many and which months averaged values of 35 ° C or higher.

CO2 captured in project area corresponding for days of the months with mean temperatures equal or higher to 35 ° C must be calculated with the next formula:

Where

CO2pa-t= CO2 captured in project area per day or days with temperatures equal or higher to 35 ° C (Tons)

CO2pa= CO2 captured in project area per day (Tons)

Environmental humidity. An additional factor that significantly influences the capture capacity of CO2 in Succlentwas considered, the availability of water. According to the studies, the average CO2 capture is 50% lower after 50 days that the plant stops having access to water (Liguori et al., 2013). For this reason, for estimate a conservative value of catch of CO2 for each day of the year, an adjustment was included that reduces 50% of capture for days out of rainy season. The next formula will be used:

Where:

CO2pa-r= CO2 capture in project area per day (ton) for days out of rainy season with temperatures equal or higher to 35 ° C (Tons)

CO2pa= CO2 capture in project area per day (Tons)

Its important to mention that in some season of year there are days where both factors can be interact. -i. e. months with average values of 35 ° C or higher and without rains. In these cases both adjust factors must be used. The next formula will be used.

Where:

CO2pa-T-r= CO2 capture in project area per day for days out of rainy season (Tons)

CO2pa= CO2 capture in project area per day (Tons)

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CO2 capture in project area per year must be calculated (CO2y) with the next formula:

Where:

CO2y =CO2 capture in project area per year (Tons)

CO2pa-t= CO2 capture in project area per day for days with temperatures equal or higher to 35 ° C (Tons)

dT= number of days in the year with temperatures equal for higher to 35 ° C

CO2pa-T-r= CO2 capture in project area per day or days out of rainy season (Tons)

dr= number of days in the year of rainy season

CO2pa-r= CO2 capture in project area per day (ton) for days out of rainy season with temperatures equal or higher to 35 ° C (Tons)

dT-r= number of days in the year of rainy season with temperatures equal or higher to 35 ° C

7.5.1

7.6 Task 6 Projecting Carbon accumulation per year in project area for 10 years

Requirement: Required for all projects.

Goal: To estimate Carbon accumulated using data of CO2 capture in project area per year.

Methods: according to previous, in a tree, mother cladodes in O. ficus indica only retain 60% of biomass generated by CO2 cached by younger cladodes (García and Nobel 1992).Therefore; to generate a conservative projection in the year only was considered a 60% of CO2 capture in project area per year for the accumulation of Carbon in baseline. The next formula must be used.

Where:

CO2y =CO2 capture in project area per year (Tons)

CO2y1 =CO2 capture in project area in year "1" (y1, y2, y3………y10) (Tons)

Total CO2 must be calculated by the next formula:

CO2Total = Total CO2 capture during project activities time (Tons)

CO2y1 =CO2 capture in project area in year "1" (y1, y2, y3………y10) (Tons).

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The Carbon amount by year in project area can be estimated by the next formula:

Total Carbon amount during project activities time must be calculated by the next formula:

8 Development Team

Provide a description of the qualifications and expertise of the members of the development team. In particular, include professional and/or academic experience related to the development of methodologies and projects.

Dr Víctor Manuel Mayoral

Dr. Victor Manuel Mayoral is a chemical engineer from the National Polytechnic Institute of Mexico with a Master of Science and a PhD in Chemical Engineering from the Institut du Génie Chimique de Toulouse and the Paul Sabatier University in France. He has a Post-doctorate in Biotechnology from the University of LEEDS, UK. He is considered one of the most recognized Mexican experts in applied chemistry and is a professor at the National Polytechnic Institute of Mexico. Dr. Mayoral's research work has generated in the last twenty years publications in international science journals as well as the generation of seven registered patents

MSC Teresa Tattersfield

María Teresa Tattersfield, is currently the forest carbon Manager of WRI Mexico. Responsible for designing the largest forest carbon capture project in Mexico; develop the necessary alliances with key actors for the field projects component, as well as for monitoring and influencing public policy in Mexico.

For the past 10 years, she has been involved in the design and development of methodologies and protocols that have been implemented in Mexico with great success in the sale of offsets in the international market; such as The Climate Action reserve Forest Carbon Protocol for Mexico (released in 2014), Carbon Capture for Succlents (currently in validation process with CAR), The Mexican Forest Carbon Standard from de Mexican Ministry of Environment (Semarnat), among others.

Previously, she was in charge of the National Program of Natural Solutions of the Tecnológico de Monterrey University, promoting conservation programs through economic instruments, advising the Neutralízate Program of the Pronatura México organization, in the years when the voluntary forestry market in Mexico was consolidated with Pronatura pilot projects that are now the benchmarks in Latin America.

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She has been part of the special team of advisers in sustainable development and climate change for the Foreign Ministry of the British Commonwealth Government, and was in charge of the relationship with the priority states on mitigation and adaptation issues, promoted the realization of the first Plans State of Climate Change, as well as political analysis and preparation of recommendations for decision making in the design of agreements and memoranda of understanding in the Mexico-United Kingdom relationship on issues such as low carbon economy.

On the other hand, she has been responsible for the elaboration of strategies that combine the efforts of local and federal governments in the development of international initiatives such as Methane to Markets. In her work at the Ministry of Environment and Natural Resources, she coordinated initiatives such as the restoration of the Lerma Chapala Basin. She has also coordinated cooperation projects for Mexico in the field of rural training with different international organizations and institutions such as the CEC, USAID, UNDP, etc., and has had the opportunity to participate in several publications of the same.

Teresa is an expert in project execution and evaluation, with extensive experience in the federal government, non-governmental organizations, international organizations and academia.

Biologist Alberto Ramírez

Alberto Ramírez is the Forest Carbon Coordinator at WRI Mexico. He is responsible for coordinating CO2 capture forestry projects; through the formation of community technical capacities; linking forest communities at the national level; advice on removal inventories, monitoring, reporting and verification. He actively participates in the implementation of activities aimed at influencing public policy on issues of biodiversity, management of forest resources and mitigation of climate change.

He has collaborated as a consultant for entities such as CONAFOR, PROBOSQUE; SEDEMA; WWF Mexico; Climate Action Reserve (CAR); PRONATURE; ClimateSeed, among others, for the development and evaluation of the viability of CO2 capture forestry projects; He has experience as a verifier in compliance with forestry regulations in matters of sustainability with an emphasis on biodiversity and social aspects.

He was the first Mexican verifier accredited by CAR and has participated in the review and proposal of GHG removal quantification protocols such as the CAR Forest Protocol for Mexico v.2.0 and the Draft Forest Protocol for the Emissions Trading System (SCE MX).

His main activities are focused on the sustainable management of natural resources through the participation of rural communities, technical training and national and international dissemination of the current status of the potential of Mexican forests for carbon markets.

Alberto is a Biologist graduated from UNAM.

Biologist René Ibarra

René worked as a specialist in certification of national and international standards focused on the sustainable management of natural resources, in the Association of Normalization and Certification A.C.

He served as a certification engineer for the evaluation of the NMX-AA-143-SCFI-2015 for the sustainable management of forest resources; standard in which he led the works of certification of 156 forest properties in 13 states of the republic, managing to certify 914, 720 hectares until 2019. He was also a leading verifier in the NMX-AA-170-SCFI-2016 for the certification of forest nurseries; NMX-AA-169-SCFI-2016 for the establishment of production units and forest germplasm management and as Substitute Technical Manager of the Verification Unit, accredited by the Mexican accreditation entity for the evaluation of the NMX-AA-144- standards. SCFI-2008 and NMX-N-107-SCFI-2010 related to the sustainable production and free of elemental chlorine of paper.

He has been accredited as a leading verifier by the Climate Action Reserve to verify carbon capture projects under the standard of the Mexico Forest Protocol since 2016, participating in the evaluation of 48 projects developed in the State of Mexico, Mexico City, Puebla, Oaxaca, Veracruz, Hidalgo and Durango under the Mexico Forest Protocol. He has also participated in national and international forums such as the North American Carbon World conference in its editions from 2017 to 2019, the year in which he was the recipient of the CARROT award granted by the Climate Action Reserve to its most outstanding partners and collaborators for promoting and encouraging the establishment of forest carbon sequestration projects in Mexico.

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He currently works as Coordinator of Forest Communities for WRI Mexico's CO2munitario project; coordinating the implementation of projects at the national level and contributing its experience in the training of personnel in site and in the integration of evidence of compliance with social and environmental safeguards, distribution of benefits, calculation of removals and the eligibility and additional criteria of the Mexico Forest Protocol.

9 Funding

Private corporation that has focused on agriculture production and increasing productivity, in the agriculture sector.

The funding has been secured in the private sector by the corporation and those not required any milestones to be met.

Bret will be publicly listing company on the US over the counter market (OTC Markets). Whose focused on increasing food security and researching and developing climate mitigation in the agriculture sector.

The project is linked to project development over 5000 ha of Succulents. The founding source has already been secured in the form of private placement of major share holders of the publicly listed company.

10 Signature

By signing and submitting this methodology concept note, the Developer agrees to pay Verra the non-refundable application fee, the rate of which is set out in VCS Program document Program Fee Schedule. The Developer also acknowledges and agrees that it has read, understood and will abide by the VCS Program rules, and that the acceptance or non-acceptance of this methodology concept note shall be at the sole discretion of Verra. All information provided will be treated as confidential.

Signed for and on behalf of:

Name of entity: Bret Consultores SAPI de CV.
Signature: /s/ Teresa Tattersfield
Name of signatory: Teresa Tattersfield
Date: June 20, 2021
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Appendix

Use appendices for supporting information. Delete this appendix (title and instructions) where no appendix is required.

11LITERATURE CITED

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Acevedo, E., I. Badilla, and P. S. Nobel. 1983. Water relations, diurnal acidity changes and productivity of a cultivated Succlent, Succlent ficus-indica. Plant Physiol. 72: 775-780.

Garcia de Cortázar, V., and P. S. Nobel. 1992. Biomass and Fruit Production for the Prickly Pear Succlent, Succlent ficus-indica. J. AMER. Soc. HORT. SCI. 117(4):558-562.

Inglese, P., A. I. Alvaro, and P. S. Nobel. 1994. Growth and CO2 uptake for cladodes and fruit of the Crassulacean acid metabolism species Succlent ficus-indica during fruit development. Physiologia Plantarum. 91: 708-714.

Liguori, G., G. Inglese, F. Pernice, G. Sortino, and Paolo Inglese. 2013. CO2 uptake of Succlent ficus-indica (L.) Mill. whole trees and single cladodes, in relation to plant water status and cladode age. Italian Journal of Agronomy.8: e3.

Machado-Velasco, K.M. and J. F. Vélez-Ruiz. 2008. Study of physical properties in Mexican foods during freezing and frozen storage. Revista Mexicana De Ingeniería Química. 7: 41-54.

Maki-Díaz, G. C. B. Peña-Valdivia, R. García-Nava, M. L. Arévalo-Galarza1, G. Calderón-Zavala, and S. Anaya-Rosales. 2015. Physical and chemical characteristics of Succlent stems (Succlent ficus-indica) for exportation and domestic markets. Agrociencia 49: 31-51.

Nobel, P. S., and T. L. Hartsock. 1983. Relationships between Photosynthetically Active Radiation, Nocturnal Acid Accumulation, and CO2 Uptake for a Crassulacean Acid Metabolism Plant, Succlent ficus-indica. Plant Physiology. 71: 71-75.

Nobel, P. S., and T. L. Hartsock. 1986. Leaf and Stem CO2 Uptake in the Three Subfamilies of the Cactaceae. Plant Physiology. 80: 913-917.

Nobel, P. S. and A. G. Valenzuela. 1987. Environmental responses and productivity of the CAM plant, Agave tequilana. Agricultural and Forest Meteorology 39:319-334.

Nobel, P. S. 1988. Environmental Biology of Agaves and Cacti. New York: Cambridge University Press. Pp. 270.

Nobel, P. S., E. García-Moya, and E. Quero. 1992. High anual productivity of certain agaves and cacti under cultivation. Plant, Cell and Environment. 15:329-335.

Nobel, P. S., B. Huang, and E. García-Moya, 1993. Root distribution, growth, respiration, and hydraulic conductivity for two highly productive agaves. Journal of Experimental Botany. 44:747-754.

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Nobel, P. S., and A. A. Israel. 1994. Cladode development, environmental responses of CO2 uptake, and productivity for Succlent ficus-indica under elevated CO2. Journal of Experimental Botany 45:295-303

Nobel, P. S., E. Pimienta-Barros, J. Zaduño, and B. Ramirez-Hernandez. 2002. Historical aspects and net CO2 uptake for cultivated Crassulacean acid metabolism plants in Mexico. Ann. Appl, Biol. 140: 133 - 142.

Nobel, P. S., and E. G. Bobich. 2002. Plant frequency, stem and root characteristics, and CO2 uptake for Succlent acanthocarpa: elevational correlates in the northwestern Sonoran Desert. 130(2):165-172

Pimienta-Barrios, E., M. Loera-Q, and L. O. López. 1993. Estudio anatómico comparativo en colectas del subgénero. Succlent. Agrociencia Serie Fitociencia. 4:7-21.

Pimienta-Barrios, E., J. Zañudo-H, E. Yepez, and P. S. Nobel. 2000. Seasonal variation of net CO2 uptake for Succlent pear (Succlent ficus-indica) and (Stenocereus queretaroensis) in a subtropical environment. Journal of Arid Environments. 44:73-83.

Pimienta-Barrios, E., C. Robles-Murguia, and P. S. Nobel. 2001. Net CO2 uptake for Agave tequilana in a warm and a temperate environment. Biotropica. 33:312-318.

Pimienta-Barrios, E., J. Zañudo-Hernandez, V. C. Rosas-Espinoza, A. Valenzuela-Tapia, and P. S. Nobel. 2005. Young Daughter Cladodes Affect CO2 Uptake by Mother Cladodes of Succlent ficus-indica. Annals of Botany. 95: 363-369

Reyes-Agüero, J. A., J. R. Aguirre-Rivera, and J. L. Flores-Flores. 2005. Variación morfológica de Succlent (Cactaceae) en relación con su domesticación en la altiplanicie Meridional de México. Interciencia, 30: 476-488.

Yang, L., M. Lu, S. C., J. Mayer, J. C. Cushman, E. Tian, and H. Lin. 2015. Biomass characterization of Agave and Succlent as potential biofuel feedstocks. Biomass and Bioenergy. 73: 43-53.

Zar, J. H. 2010. Bioestatistical analysis. Prentice Hall, NJ.

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