This document provides growth projections for eucalyptus plantations in Bahia, Brazil over a period of years based on climate and soil data. It describes two scenarios - one with average soil fertility and management, and another with increased irrigation, fertility, and optimized practices. The first scenario projects average growth rates similar to other studies in Brazil. The second scenario with improved practices projects significantly higher growth, with potential yields exceeding reports from other regions. Projections are based on a forest growth model run with climate data from a nearby weather station and soil properties.
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Biofuels offer one approach for reducing carbon emissions in transportation. However, the agricultural expansion needed to produce biofuels may endanger tropical forests and thus offset the benefits of fossil fuel substitution. Whether this occurs depends on the extent to which increases in biofuels supply arise from gains in yields per acre or expansion in growing areas. I use a dynamic model of land use to disentangle the roles played by acreage expansion and yield increases in the supply of sugarcane ethanol in Brazil. The model is estimated using a panel of 1.8 million fields, which is built using remote sensing (satellite) information of sugarcane activities. My estimates imply that, at the margin, 94% of new ethanol comes from increases in area planted and only 6% from increases in yield. Direct deforestation accounts for 12% of area expansion. Balancing carbon
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The paper deals with the status of the land use/land cover change taken place in a part of Ramganga River Basin, at Bareilly district, Uttar Pradesh, India, by using remote sensing satellite data. The present study area is confined to latitude 28°10′ to 28054ʹ North and longitude 78°58′ to 78o58ʹ East, covering an area of 4120 km2. The satellite images of 1979 and 2009 have been obtained from global land cover facility (GLCF) and examined by unsupervised classification method. The general classification level has been adopted. The identified classes include Settlements, Croplands, Vegetation/Plantations, Water Bodies, and Waste Land. The result shows increasing trend of crop land and built up area and decreasing trend of vegetation and Plantation land.
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Presented by Adam Komarek (IFPRI), Belhouchette H. (CIHEAM-IAMM), Blanco M. (UPM-ETSIA), Chenoune R. (CIHEAM-IAMM), El Ansari L. (CIHEAM-IAMM) and Flichman G. (CIHEAM-IAMM) at the Africa RISING Monitoring and Evaluation Meeting, Arusha, Tanzania, 13-14 November 2014
Decision support tools for forestry using open source softwareRoss McDonald
Stephen Bathgate from Forest research on how implementing a GIS delivered savings and efficiencies to a small workforce. Presented at 6th Scottish QGIS UK user group meeting.
Biofuels offer one approach for reducing carbon emissions in transportation. However, the agricultural expansion needed to produce biofuels may endanger tropical forests and thus offset the benefits of fossil fuel substitution. Whether this occurs depends on the extent to which increases in biofuels supply arise from gains in yields per acre or expansion in growing areas. I use a dynamic model of land use to disentangle the roles played by acreage expansion and yield increases in the supply of sugarcane ethanol in Brazil. The model is estimated using a panel of 1.8 million fields, which is built using remote sensing (satellite) information of sugarcane activities. My estimates imply that, at the margin, 94% of new ethanol comes from increases in area planted and only 6% from increases in yield. Direct deforestation accounts for 12% of area expansion. Balancing carbon
emissions from deforestation and the carbon saved by fossil fuel substitution, I find that it would take about 20 years for the lower emissions from sugarcane ethanol to “pay back” the added emissions from deforestation. As an illustrative policy experiment, I
consider the effects of a 5 billion gallon sugarcane ethanol mandate (~ 3% of US gasoline consumption). Such policy would lead to a 1% price increase and deforestation of about 9,000 sq. km. ( ~3/4 the size of Connecticut).
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Presentation by Olatoye T. A. (FRIN, Nigeria) and Odularu G. O. (FARA, Ghana) for the Agrifood chain toolkit conference: Livestock and fish value chains in East Africa, Kampala, 9-11 September 2013.
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Private sector participation in commercial forest plantation development in S...Pabasara Gunawardane
The man-made forests occupy a significant place in the national forest estate and generally fall into the category of multiple use reserved forests as defined in the Forest Policy, unless being classified in the categories of strict conservation or conservation forests due to specific reasons. At present, the forest plantations consist of even-aged monocultures, except in the case of the mixed mahogany plantations, and stocked with species such as teak (Tectona grandis), several eucalypt species (E. grandis, E. microcorys, E. robusta etc.), Caribbean pine (Pinus caribaea), African mahogany (Khaya senegalensis) and with several other local species. Forest plantations were established for the first time in the 1870s, although most of the planting has taken place since the 1950s. Within that period, about 89,000 ha of forest plantations of varying quality have been established.
The private tree growers can be categorized as: Private individuals, Regional Plantation Companies and Private companies offering small plots of lands stocked with Teak or Mahogany seedlings to the public on long-term leases. When considering about the percentage of forest lands in relation to agricultural and other land uses it is clearly can be seen that the share of forest plantations are nearly 1.4%. However when consider about the contribution of wood sources as a percentage of total supply forest plantations accounts for 11%.
Popularizing the concept ‘Forestry as a business’ among the general public, providing required intensive management for the plantations (In order to gain profits), technical knowledge is applied intensively, introduction of novel species to the trade (Sandalwood, Aquillaria, Vanilla) and improvement in the research and development aspect are the major advantage of involvement of private sector in plantation forestry.
Providing non- legal land deeds, some planting companies can be bankrupted Due to financial instability and the investors will be affected, social and environmental impacts and risks (i.e. some private owners are not obligated to consider regional ecological concerns) and misleading future projections of growth and related statistics in order to attract more investors are the major issues of having private sector in plantation forestry.
Establishment of a planning and monitoring body for the private sector institutes, providing insurance and tax benefits for the investors can be recommended for the future improvement of the field. Much coordination will be needed with other sectors such as wildlife conservation, land use planning, water management, environmental management, the timber industry, paper manufacturing and disaster management etc. Raising the productivity of forest plantations using new technology and within the sector itself, much collaboration will be needed among all stakeholders in planning and implementation.
Focal themes SOILS Research Area:
Restoring degraded lands
Restoring soils and landscapes to increase ecosystem services
Soils and climate change
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Sustaining soil fertility and health
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Crop is defined as an “Aggregation of individual plant species grown in a unit area for economic purpose”.
Growth is defined as an “Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant”.
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GW Group reforestation Licenses Farm Development Site S. Maria IGW Group
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GW Group reforestation Licenses Farm Site Development BuritiGW Group
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GW Group reforestation Licenses Farm Site Barrinhas 2GW Group
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GW Group reforestation Licenses Farm site Barrinhas 1GW Group
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2. Table of Contents
Rationale, pg. 3
Site conditions, pg.4
Soils, pg. 4
Agro-ecological Zoning, pg. 4
Trees per Hectare, pg. 6
Climatic data, pg.6
Growth projection notes - Conclusions, pg. 9
Conversion Factor - Wood volume to charcoal MDC, pg. 13
Growth projections based on Barreiras Meteo Station data, pg.12
References - Resources, pg.19
Software resources, pg. 20
Acknowledgments, pg. 20
3. Rationale
This document is an independent assessment developed for GW Group, describing the results of a
software growth model for the production of Eucalyptus wood volume and charcoal for the plantation
sites located in the Municipalities of Baianopolis, Cristopolis and Catolandia, Bahia, Brazil. Specific
data for the sites was provided by GW Group and also collected from
international and national data sources as described in this document and in the References.
Results are given based on data provided by GW Group, assumptions and conditions as mentioned in
this document. Scenarios 1 and 2 describe 2 hypothetical model projections per hectare of Eucalyptus
plantations, based on resource availability and management practices. Please note that this document
describes customized modeled results which, depending also on model parametrization, can under or
overestimate the actual site conditions and future plantation productivity.
This is a theoretical assessment of Eucalyptus growth, based on scientific methodology and data
available, and can present variations of the actual future Eucalyptus plantation growth . While the utmost
care has been taken to ensure the validity of the results, the author can not be held responsible for
variations between the current projections and actual future Eucalyptus plantation growth, due to
managerial, anthropogenic, or other reasons.
3
4. Site conditions
Soils
According to the soil maps reviewed (Ref: Embrapa Solos & IBGE), the soils at the areas of north
Baianopolis Municipality, south west Cristopolis Municipality and west Catolandia Municipality - were the
sites are located - have medium/prominent clay texture and belong at the class of “LVd4: Latosolo
Vermelho Amarelo Distrofico”.
Soil fertility in literature (ZANE) as also presented on relevant maps is considered low, but suitable for
agroforestry use.
The sites soil fertility for the projection modelling is classified as in similar Eucalypt plantantion sites in
Minas Gerais with the same soil class (Latosolo Vermelho Amarelo Distrofico) [Filho et al. 2006; Couto et
al. 2004]. The 1:100.000 soil maps of IBGE (See References) were the highest resolution source of
information, and thus these were used for the site projections.
Agro-ecological Zoning
The Zoneamento Agroecologico de Nordestre (ZANE), reports that the Municipalities of interest are
classified as “Chapadas Altas” suitable also for agroforestry land use (agroflorestas). Chapadas altas are
reported having an alitutde of more than 800 m above sea level, while from local analysis the areas are
at somewhat lower location, 750 meters approximately. The sites have a dry period during which a
precipitation deficit occurs. The climate is considered semi-humid with a dry period from April to October.
The Growing Season Length for the area of Barreiras is approximately 200 days, starting on the
beginings of October and ending at the first days of May.
Precipitation deficit during the dry period is the limiting factor for plant growth based on the Miami Model
Climatic Net Primary Productivity [Lieth, 1972] simulated for these sites. This is something already taken
into management consideration, since, as mentioned before (Data Provided by GW
Group) the plantantion sites are irrigated.
4
5.
6. Trees per Hectare
Their number is defined as in data provided by
GW Group, 1333 per hectare.
Climatic data
The mean monthly data used for the area as input
to projections is provided as temeperature/rainfall
graphs in this report. Several climatic data
sources were reviewed and examined, either from
Brazil
(CPTEC/INPE,
SUDENE,
INMET),
international (FAOCLIM,GHCN), and several web
weather forecast resources (Ref. ClimateCharts).
Mean
monthly
data
(temperature
and
precipitation) and literature data (radiation and
evapotranspiration) for the City of Barreiras, BA (12.09, -45.00) were used
for the growth
projections, and also interpolated mean climatic
data for points close to the plantation site
locations.
From literature sources, such as the Zoneamento
Agroecologico de Nordestre (ZANE) , the
Cristopolis area receives a mean precipitation of
1067 mm. The closest meteorological station
(~20km) to Cristopolis and Baianopolis, Mariquita,
receives a mean annual precipitation sum of 970
mm approximately. Again in comparison with
literature sources, the meteorological station of
Barreiras, although in larger kilometric distance
(~45km) and lower altitude than the two sites
(450m above sea level, in comparison to
approximate of 750m
for the site locations)
presents more precipitation annual amount
similarity with Cristopolis as found on literature
(ZANE) – than the closer in distance Mariquita
meteo station.
Recorded (measured) data from meteorological
stations is usually preferred in scientific
assessments over estimated/modeled data. For
the development of this document, both recorded
and estimated data was used, as an input to the
growth model.
- The (measured) recorded meteorological data of
Barreiras city
was used in separate calculations,
applying again the same growth-management
scenarios.
- Since measured meteorological station data for
the specific areas
of plantations were not
available according to the review made for this
assessment,
several
meteorological
data
6
7.
8.
9. interpolations methods were tested, and the Thin Plate Spline interpolation method was selected for
providing the best predictions of the literature (ex. ZANE) precipitation data.
Assessment results vary depending on the climatic data used in the simulations, but since the climatic
data variation is low between the stations (Barreiras) and the interpolated points the MAI results are
very slightly influenced (See Growth Projections Chapter). That said, the projection tables and graphs
developed based on Barreiras climatic data can be considered representative of all others.
Growth projection notes - Conclusions
The objective of this assessment was to customize theoretical projections as much as possible to local
conditions. The results rely also on data, parameters and methodological growth projections from the
neighboring region of Minas Gerais (methods which are also widely applied and to other regions of
Brazil) for several the Eucalyptus species such as E. camaldulensis x E. urophylla (which is the clonal
variety of interest to GW Group ), E. urophylla and others.
The widely applied 3-PG Model software [Landsberg et al, 1997], as also specifically referenced in the
References and Acknowledgments Chapters, was used for the growth projection analysis, since it is
deployed in numerous scientific and commercial projects for Eucalyptus plantations in Brazil, and globally
for other species and biomes. The local parametrization of the modelfor the climate and soil variables
used are outlined below and in the other relevant chapters of this report.
Since, as reported by GW Group, detailed local data on plantation growth is not always available for
Bahia for the specific Eucalyptus species, the projections were locally adjusted based also on
parameters of sites in Minas Gerais with similar climatic, soil and clonal Eucalyptus plantation
characteristics [Embrapa Solos, 1973; IBGE; Filho et al. 2006; Couto et al. 2004]. The soil at these sites
is also a Latosolo Vermelho Amarelo Distrofico [Filho et al. 2006; Couto et al. 2004] and with mean
min/max temperature of 17 to 29 Degrees Celsius, but with higher precipitation annual sum. These
growth projections were customized for the plantation sites in Bahia using local and interpolated climatic
data. Soil fertility and available soil water were also adjusted according to the site and soil characteristics,
based on literature parametrization for the similar ecological conditions reported above.
After adapting parameters, several simulations were run with the 3-PG model, and 2 representative
scenarios are described and tabulated results are provided:
- The first scenario (Scenario No1) assumes site treatments to bring soil fertility to the medium level
(0.5) which is gradually decreasing after the third year (0.4), reaching a lower level (0.3) at year 5
and afterwards, to simulate nutrient loss. Available soil water -calibrated as in literature for similar soil
types in the area- is assumed to be at its highest level at the time of planting.
- The second scenario (Scenario No 2) represents a different management regime and soil fertility
class, assuming that additional actions are taken to further increase site characteristics and available
water to plants. Irrigation is assumed to be 8ml/Ha/year for the first 6 years. Site fertility rating for the
3-PG model is assumed to be at a higher level (0.8) for the first 5 years, slightly reducing to a lower
level (0.7) for the next 2 years, and gradually reducing (0.3) after year 7. This is a rather optimal
theoretical growth scenario.
The rotation used in many commercial plantations and experiments in Brazil is between year 6 and 9 for
biomass production in agroforestry systems, and also in scientific reports for plantations for energy
biomass production the 7nth year is the recommended one for harvest in certain cases. Model
simulations present similar results such as the graph intersection of Mean Annual Increment (MAI) and
Periodic Annual Increment (PAI). Mean and Periodic Annual Increment (MAI and PAI) in this case are,
the mean annual volume growth per stand hectare, and the periodic (annual) change on volume per
plantation hectare, respectively.
As mentioned before, assuming a certain site quality scenario, but substituting the Barreiras meteo data
with the point interpolated climatic one, has no significant difference to growth projections. However, this
simulation was made to further customize the projections and examine any hypothetical variability of
such influence.
9
10. The tabulated results, using the Barreiras meteorological mean monthly data, for each of
the two scenarios, no1 & no2.
However, significant differences are obtained when simulating different site fertility and management
scenarios (Scenario no1 or no2). The first scenario, is based on assumptions discussed above and its
results are similar with studies made for neighboring regions in Brazil [Filho et al. 2006; Couto et al.
2004], where fertilization and nitrogen fixing crops are part of the agroforestry system management
practices. Significant variation is observed between the response of the growth projections to the 2nd
management scenario discussed, with simulated growth to increase substantially given that field
conditions and applications (ex. nitrogen fixing plants, fertilization, irrigation, clonal species
characteristics, silviculture) can decrease significantly growth limitations to plantation trees.
Local management practices and site conditions, such as increased irrigation, mixed cultivation of
10
11. The graphs based on the tabulated results or the previous page, using the Barreiras
meteorological mean monthly data, for each of the two scenarios, no1 & no2. These
results are the representative of the projection runs made for the Eucalyptus plantation
sites.
11
12.
13. nitrogen fixing plants and/or higher levels of fertilizer input and selected clonal Eucalyptus varieties
(similar to Scenario No2), up to a level where tree growth limiting factors are significantly reduced, can
increase the volume production.
Yields higher than 40 cubic meters per ha per year are widely reported in Brazil Eucalyptus plantation
literature [BEPP and citations within; Rossi et al, 2003]. On a recent published scientific study in Bahia,
Brazil, an Eucalyptus plantation was established on “Typic Hapludox” soils, and treated for increased
fertility and irrigated experimental plot reached an MAI of 90 m3/ha/year, “which is one of the highest
ever reported on forests”, not just in Eucalyptus plantations [Reference: Stape et al, 2007].
Conversion Factor - Wood volume to charcoal MDC
The conversion factor of solid wood volume to charcoal which was used for the projection results is
defined by literature of Brazilian eucalyptus forestry, and is provided below:
Solid wood cubic meter/MDC of charcoal = 2,2
Please note that this conversion factor applies not to stacked wood volume, but to solid wood volume dry wood- (Stacked wood volume to MDC has a conversion factor of 1,47. This conversion factor (2.2)
above is for solid wood volume, and the mean annual increment is used to calculate charcoal MDC).
The MDC factors and calculations derived from literature [Álvaro Nogueira de Souza,2007] were
developed in Brazil for Eucalyptus species, and in the referenced article the clonal variety in question for
this assessment, Eucalyptus urophylla x Eucalyptus camaldulensis is mentioned.
In the pages of the “Growth Projection results” paragraph one can observe that the Periodic Annual
Increment (solid wood volume) is divided by the MDC factor of 2.2 to calculate Charcoal MDC. The
cumulative increment of charcoal MDC provides the amount of charcoal produced until that period, for
example “Year 7 of Cumulative Charcoal MDC” provides the amount of charcoal potentially produced
until that date.
13
14. The tabulated results of simulation runs made with interpolated mean climatic data for
the three locations in Baianopolis, Cristopolis and Catolandia, for the Scenario 1. These
interpollated point mean monthly data has no significant difference with the Barreiras
meteorological attributes, so it does not influence projection results significantly.
However, there is a significant diffrence between scenarios, as it can be observed in the
next page.
14
15. The tabulated results of simulation runs made with interpolated mean climatic data for
the three locations in Baianopolis, Cristopolis and Catolandia, for the Scenario 2. Please
note the differences with the Scenario 1 pesented at the previous page.
15
16.
17.
18. References - Resources
Several literature, web and other resources were reviewed for this assessment, which are mentioned in
detail below:
- Avery, T.E., B.E. Harold, 2002. Forest Measurements, fifth edition, New York; McGrow-Hill
426 p.
- J.J. Landsberg, R.H. Waring, A generalised model of forest productivity using simplified
concepts of radiation-use efficiency, carbon balance and partitioning, Forest Ecology and
Management, Volume 95, Issue 3, 1 August 1997, Pages 209-228, ISSN 0378-1127
- R.H. Waring, N.C. Coops, J.J. Landsberg, Improving predictions of forest growth using the
3-PGS model with observations made by remote sensing, Forest Ecology and Management,
Volume 259, Issue 9, Productivity in Tropical Plantations, 15 April 2010, Pages 1722-1729,
ISSN 0378-1127
- J. J. Landsberg, R. H. Waring, N. C. Coops, Performance of the forest productivity model 3PG applied to a wide range of forest types, Forest Ecology and Management, Volume 172,
Issues 2-3, 20 January 2003, Pages 199-214, ISSN 0378-1127
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do título de Doctor Scientiae)
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Software resources
Several software resources were used for the realization of this assessment:
- The 3-PG Model Software. 3-PG is the acronym of the Physilological Principles Predicting Growth,
which is a “generalized forest carbon allocation model, published by Landsberg and Waring on 1997.
http://www.csiro.au/products/3PGProductivity.html
- Microsoft Excel, http://www.microsoft.com
- Microsoft Windows XP Professional, http://www.microsoft.com
- Openoffice.org 3.2.0, http://www.openoffice.org/
- Debian Gnu/Linux 5.0.4, http://www.debian.org/
- Grass GIS 6.4.0RC6 (2010) http://grass.osgeo.org/
- Quantum GIS (QGIS) version 1.4.0 Enceladus, http://www.qgis.org
- Scribus, http://www.scribus.net
Acknowledgments
The main data sources, crucial for the realization of this assessment, have to be acknowledged:
- Landsat ETM 2000 Mosaics, from EarthExplorer, United States Geological Survey, US Department
of Interior
- IBGE: Instituto Brasileiro de Geografia e Estatística, Brazil
Acknowledgments are also owed to the authors/developers of the 3-PG model, Dr. J.J. Landsberg and
Dr. R.H. Waring.
- J.J. Landsberg, R.H. Waring, A generalised model of forest productivity using simplified concepts of
radiation-use efficiency, carbon balance and partitioning, Forest Ecology and Management, Volume
95, Issue 3, 1 August 1997, Pages 209-228, ISSN 0378-1127
Finally, acknowledgments are expressed to the Australian Commonwealth Scientific and
Research Organization (CSIRO)/Forestry Management for the distribution and provision of the 3PG model software.
20
21. Document details: This independent assessment for Eucalyptus growth projections was
developed by bio4met <www.bio4met.com> for GW Group. This document is subject to
future improvements.
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