Presentation given by Robert Zomer at ICRAF’s Science Week, 5-9 September 2016 in Nairobi. During Science Week, researchers with the World Agroforestry Centre also discussed their work under the CGIAR Research Program on Forests, Trees and Agroforestry.
Semelhante a Trees on Farm: Global Extent and Socio-Ecological Characteristics and the Contribution of Agroforestry to Global and National Carbon Budgets
Land restoration, climate change and why cheap stuff doesn't get done. Patrick Worms
Semelhante a Trees on Farm: Global Extent and Socio-Ecological Characteristics and the Contribution of Agroforestry to Global and National Carbon Budgets (20)
Feature-aligned N-BEATS with Sinkhorn divergence (ICLR '24)
Trees on Farm: Global Extent and Socio-Ecological Characteristics and the Contribution of Agroforestry to Global and National Carbon Budgets
1. Land
Robert Zomer, Antonio Trabucco, Jianchu Xu, Mingcheng Wang
Frank Place, Rick Coe, Henry Neufeldt, Deborah Bossio,
Miene van Noordwyk, Antje Ahrends
Center for Mountain Ecosystem Studies
Kunming Insitute of Botany /
World Agroforestry Centre – East and Central Asia Region
Kunming, Yunnan Province, China
r.zomer@cgiar.org
Sept 5, 2016
ICRAF Science Week
Nairobi, Kenya
Global Tree Cover and Biomass Carbon
on Agricultural Land:
Trees on Farm: Global Extent and Socio-Ecological Characteristics
and the
Contribution of Agroforestry to Global and National Carbon Budgets
3. Agroforestry is Globally Important
• Increasingly cited in sustainable development, adaptation
and mitigation strategies and policies, in all regions, biomes
• Estimates needed to ensure realistic policy attention
“During preparation of the IAAST report, USA referees said
that everyone knew there were only 50,000 ha of agroforestry
in the world and that they were a failure”
• Global estimates based on expert opinion
“…we propose that 20% of the arable and permanent cropped area
and 15% of the pasture lands in the world is under silvopastoral
combination…” Nair , Kumar and Nair (2009)
4. Issue: What is agroforestry
Landuse Category
• Many definitions of AF,
– systems, typologies, technologies
• Many types of AF systems
– spatial and temporal scales
• Plot to landscape,
• Short-rotations to historic
• Cropping - Livestock Based
Key mapping problem:
• Not easily categorized or classified within traditional agriculture /
forestry typologies, as used in remote sensing and landuse
mapping
• Small holder farming systems are not easily mapped using RS
The result: Partial area estimates for some systems
5. Agroforestry defined as
trees in agricultural landscapes
Use remote sensed estimates of:
• Location of agricultural land
– GLC 2000 Dataset
– 1 km resolution – Year: 2000
• Tree cover %
– VCF - Hanson et al 2003
– 500m MODIS data – Year: 2000
Add:
• Population Density
• (CIESIN 2004) – GRUMP v1
• Bioclimate – Aridity Wetness Index
• (Zomer et al 2007)
6. The 1 km x 1 km scale of analysis
Example – a few
km from here.
- classified as
‘agricultural’
- 10% tree cover
- 400 people
One
observation
in the global
database of
22 million
1 km
1 km
Statistical analysis:
counting pixels in
different categories
7. Disclaimers and
Sources of Uncertainity
• A global analysis showing large scale patterns,
not predictions of specific localities.
– Base layers are imperfect
• Uncertainity associated with remote sensing data
– No info on configuration of trees and agric land in
each pixel
– No info on population interaction with the land and
trees
– Estimates of tree crown cover only, not of number of
trees
• Land not classified as ‘Agricultural’ is excluded
– Tree crops
– Agroforests
9. Agricultural land and tree cover
0
20
40
60
80
100
0
5
10
15
20
0 20 40 60 80 100
%agriculuralarea
cumulativearea(millionkm2)
tree cover %
46% of global agric land (gal)
= 10.1 Million km2
has more than 10% tree cover
46% gal (10.1 M km2) has > 10% tree cover
27% gal ( 6.0 M km2) has > 20% tree cover
18% gal ( 3.9 M km2) has > 30% tree cover
8% gal ( 1.7 M km2) has > 50% tree cover
10. Tree cover varies by region
0
20
40
60
80
100
0 20 40 60 80 100
cumulative%agricland
% tree cover
Central America
South America
East Asia
South Asia
SouthEast Asia
Sub-Saharan Africa
11. People in agric land with tree cover
0
20
40
60
80
100
0
300
600
900
1200
1500
1800
0 20 40 60 80 100
%population
cumulativepopulation(millions)
Tree cover %
Of 1.8 billion people in agric land…
31% (558 M) have > 10% tree cover
18% (330 M) have > 20% tree cover
10% (187 M) have > 30% tree cover
12. Global pattern of trees and people in
agricultural land
1.Every combination of
+/- tree cover and +/- population
occurs
2. There are large scale patterns
13. Aridity is a biophysical determinant
0
5
10
15
20
25
30
35
40
45
50
Averagetreecover(%)
Aridity Wetness Index
Central America
South America
Africa
South Asia
East Asia
SouthEast Asia
Global
dry wet
14.
15. Tree cover on agricultural land in
sub-saharan Africa varies
16. Feasible tree cover = observed on top 20%
of land with that population and climate
18. Key messages - 2009
• Tree cover is a common feature on agricultural land
– Must be recognized by all involved in agricultural
production, planning and policy development.
• There is large variation at every scale from continental
to 1 km2
• Tree cover increases with humidity – but with many
exceptions.
• There is no general tradeoff in agricultural landscapes
between people and trees.
• Large scale tree cover patterns cannot be fully
explained by humidity, population density or region
19. • Improved Data
– 250 m MODIS
– Improved accuracy
• Temporal Analysis
– Annual Data
– 2000 to 2010
• Change Analysis
– Avg 2000-2002
– Avg 2008-2010
• Global estimate of
land under at least
10% tree cover in
2000 revised to 40%
from 46%
Update and Re-analysis - 2014
20. Change in Amount of Agricultural
Area with Tree Cover
From 2000 to 2010
• Globally, percent of land under at
least 10% tree cover increased
from 40% to 43%, > 1 billion ha
• Almost all regions increased the
amount of land with at least 10%
tree cover
• South America showed the largest
increase
• Only North and Central Asia
decreased area
• South Asia increased from 21% to
28%, East Asia from 43% to 48%
• Central America increased to 96%
of all agricultural land with at least
10% tree cover
21. Change in Population of
Agricultural Area with Tree Cover
From 2000 to 2010
• Globally, percent of population
under at least 10% tree cover
increased from 41% to 46%,
increasing by 90 million, to more
than 900 million persons
• Almost all regions increased the
population living with at least 10%
tree cover
• South Asia showed the largest
increase, 44 million more people,
to 34% of all persons in ag area
• Only North and Central Asia
decreased population
• Central America increased to 95%
of all population in agricultural
22. Above and Below Ground
Biomass Carbon on Agricultural Land
Estimating The Contribution of Agroforestry to
Global, Regional, and National Carbon Accounting
• IPCC Tier-1 Global Biomass Carbon Map
• Ruesch and Gibbs (2008)
• World stratified into 124 carbon zones by eco-
floristic/ bio-climatic region
• Each landuse type in the GLC2000 dataset (which
we also used), within each carbon zone, has a
carbon estimate specific for that landuse within
that carbon zone
• However, globally, all agricultural land was
estimated with one relatively low value of 5 tC / ha
• Tree cover (agroforestry) component missing from
this map, and from global and national carbon
budgets and carbon accounting generally
23. Above and Below Ground
Biomass Carbon on Agricultural Land
Adding the missing trees !!
Combine Tree Cover Analysis with
the CDIAC Biomass Carbon Map
Assumptions:
• If agric. land had 0% tree cover,
then: biomass = 5 tC/ha
• (IPCC Tier-1 default value)
• If agric. land had 100% tree cover,
then:
• biomass = mixed forest type
• Biomass carbon increases linearly
from 0 to 100 % tree cover
• i.e., from 5 tC/ha to value of
mixed forest
24. Total Global Biomass Carbon on Agricultural Land
• IPCC Default Value: 11.08 PgC
• 2000 : 45.30 PgC 2010 : 47.37 PgC Increase : 2.07 PgC
• Increase of 4.6 % in total global biomass carbon on agricultural land
Average Biomass Carbon on Agricultural Land
• IPCC Default Value: 5 tC/ha
• 2000 : 28.0 tC/ha 2010 : 29.0 tC/ha Increase : 0.95 tC/ha
Above and Below Ground Biomass Carbon on Agricultural Land
The Contribution of Agroforestry to
Global, Regional, and National Carbon Accounting
CO2 emissions from deforestation and other land-use change were 0.9±0.5 PgC on
average during 2005-2014, accounting for about 9% of all emissions from human
activity (fossil fuel, cement, land use change).
Source: Carbon Project
25. Biomass Carbon on Agricultural Land
Total Biomass Carbon Average Biomass Carbon
Total Agricultural
Area (km2)
Pg C Increase
as % of
Total C
t C / ha
Region 2000 2010 Change 2000 2010 Change
Australia/Pacific 2.11 2.28 0.17 8.06 26.7 28.9 2.2 790,658
Central America 1.42 1.52 0.09 6.45 52.9 56.3 3.4 269,235
Central Asia 0.48 0.47 0.00 -1.04 5.7 5.7 -0.1 830,949
East Asia 2.37 2.53 0.16 6.95 13.2 14.1 0.9 1,795,893
Eastern and Southern Africa 2.31 2.30 0.00 -0.17 14.7 14.6 -0.0 1,573,527
Europe 2.13 2.15 0.02 0.96 9.3 9.4 0.1 2,299,766
North Africa 0.11 0.11 0.00 -0.01 7.3 7.3 -0.0 155,948
North America 3.31 3.40 0.09 2.68 16.0 16.4 0.4 2,073,033
Russia 1.07 1.07 0.00 0.02 6.4 6.4 0.0 1,669,166
South America 11.34 12.13 0.79 6.95 29.2 31.2 2.0 3,888,792
South Asia 2.30 2.48 0.18 7.85 12.6 13.6 1.0 1,827,025
South East Asia 10.03 10.69 0.66 6.59 60.8 64.8 4.0 1,648,268
West and Central Africa 5.57 5.45 -0.12 -2.18 23.3 22.8 -0.5 2,390,980
Western Asia 0.75 0.79 0.04 4.72 7.9 8.2 0.4 955,689
Global 45.30 47.37 2.07 4.57 28.0 29.0 0.95 22,168,929
Agricultural Baseline 11.08 11.08 5.0 5.0
Contribution by Trees 34.22 36.29 2.07 4.57 23.03 23.97 0.95
Above and Below Ground Biomass Carbon on Agricultural Land
The Contribution of Agroforestry to
Global, Regional, and National Carbon Accounting
26. Above and Below Ground
Biomass Carbon on Agricultural Land
The Contribution of
Agroforestry to
National Carbon Accounting
• Brazil, the greatest total amount, 6.8
PgC in 2000, increased by 14% to
7.7 PgC by 2010.
• Indonesia (5.5 PgC) increased more
than 9%.
• 60 countries have < 10t C/ha
• 26 countries have > 50t C/ha
• Chile, New Zealand, Ghana, and
Bangladesh’s stocks all showed
increases near or in excess of 20%.
• 23 countries declined more than 1%,
• Sierra Leone (25%), Argentina
(20%), Guinea (14%), and
Myanmar (10%).
27. • Brazil increasing by 14%
• Argentina’s stocks showed
the largest total decline
decreasing 20%, (0.18 PgC)
• On a per hectare basis,
Agentina’s decrease from
17.8 to 14.2 tC/ha
represents a 3.6%
decrease biomass carbon
over nearly a half million
km2 of agricultural land.
• ”Hot spots” of biomass
loss are evident along the
coast of Ecuador, northeast
Brazil
Above and Below Ground
Biomass Carbon on Agricultural
Land
The Contribution of
Agroforestry to
National Carbon Accounting
28. • Hot spot of of biomass
carbon loss along NW
coast of Myanmar
• Decreases southern
Vietnam, central Laos,
northeast Thailand, parts
of northern Malaysia,
northern Vietnam
• Increases in southern
China, Thailand, Malaysia
Above and Below Ground
Biomass Carbon on Agricultural
Land
The Contribution of
Agroforestry to
National Carbon Accounting
29. • Hot spots of of biomass
carbon loss in West Africa
• Sierra Leone - 25% decrease
• Guinea – 14% decrease
• Cameroon – 7% decrease
• Nigeria – 6% decrease
• Tanzania – 16% decrease
• Equatorial Guinea – 18%
• Cote de Ivoire – 7% increase
• Ghana – 23% increase
• Madagascar – 24% increase
Above and Below Ground
Biomass Carbon on Agricultural
Land
The Contribution of
Agroforestry to
National Carbon Accounting
30. Above and Below Ground Biomass Carbon on Agricultural Land
The Contribution of Agroforestry to National Carbon Accounting
Increase in biomass carbon stock: Bangaldesh 20% - Indonesia 9 %– Malaysia 10 % - China 8%
India 7%– Thailand 6% - Papua New Guinea 4%
31. • Approximately 43% of agricultural land in 2010 had >10% tree cover
• Nearly one-billion hectares supporting more than 900 million persons
• IPCC default value of 5t C/ha of biomass for agric land is a gross under-estimate
• Off by a factor of 4 - 75% of biomass on agricultural land is tree-based
• Agroforestry provides not just adaptation, but also mitigation benefits
• Amount of carbon is significant, .. enough that it should be accounted for !!!
• Current focus is on delivering the (I)NDCs, countries are looking for
evidence/analyses, practical solutions and increased capacities to include, or
not, tree-based solutions.
• This type of analysis can provide a basis for targeting, guiding adaptation strategies
and policy development.
• Can provide insight to impact of enabling environments and national policy context
• This is a rich set of spatial data available for understanding broad geographic
patterns of agroforestry and the implications of national policy environments.
Key messages - 2016
We are here because AF is globally important. Few in this audience will need convincing of that. But can we quantify it? Without objective data, this sort of view, recounted by Roger Leakey, is to common. There are several estimates in the literature, but if you look closely you will find that they depend on expert opinion at some crucial point. For example, a recent paper on AF and carbon sequestration assumed 20% of cropland globally to be under AF. This assumption was explained, but was not supported with any objective, global measurement. There is a reason for this lack…
The reason we have not had good estimates lies in the problem of viewing AF as a series of technologies – arrangements of trees and crops in space and time. If this is the starting point, then you quickly get bogged down in definitions, in deciding what area is under the technology (what area of AF do we have with a boundary planting?), whether to count a plot in some rotation system that does not currently have trees, etc. But most importantly, every bit of agricultural land needs assessing on the ground. It is simply not feasible.
Rather than focusing on technologies and specific arrangements of trees, much current work on AF, particularly when thinking of environmental interactions, emphasises the landscape. AF can be defined as ‘trees in agricultural landscapes’ and that gives a way of estimating global extent. Others have developed global classifications of landuse based on RS data which identify where agricultural land is. They have also developed estimates of percentage tree cover. Putting these together will allow us to say where we have trees in agric land. We add two more layers, population, as we want to know about people in AF, not just land area, and climate (an aridity or humidity index) as it is a key in patterns of trees
How does it work? This scene is from very near here, a few km up the road. It 1 km x 1 km and is clearly a landscape with agriculture the main land use, and so it is classified as agricultural. The trees are visible in this picture, and with a crown cover of maybe 10% of the landscape. You can guess that there is also a high population here, based on the density of houses along the roads. So we say about 400 people are associated with this agricultural land which has 10% tree cover. Such a scene constitutes one pixel or observation in the global analysis of 22 million – that is about 22 million km2 of land classified as agricultural in the database. Note that on all maps, it is agricultural land that is coloured. Non-agricultural land remains the background brown shade. Getting the results now needs no more than counting.
It is common when presenting research results to follow it by discussion of limitations. I want to point out a few of those first, to reduce the chance of anyone misinterpreting what we have found. These are explained more fully in the paper. If you find the results interesting then read them! The key points are 1. The analysis can be expected to show large scale trends well, not detail, 2. The method excludes areas many think of as AF if they are not classified as agricultural land, such as land dominated by tree crops and agroforests and 3. It is one-time or cross-sectional data, and patterns (for example correlations between tree cover and population) that appear will probably not be the same as patterns that appear in changes over time.
The tree cover on agricultural land varies from 0 to 100%. This is a histogram of the global distribution. It shows the variation, with more square kilometres with low tree cover rather than high. The mode (most common) is actually at 1%. But such a picture is hard to read, so we use some alternative presentations. For summary statistics the cumulative distribution is easier. For displaying some patterns we use the mean.
Now, you choose the % tree cover that you call AF and you have an estimate of its global extent. From the graph we can read that 46% of agricultural land – about 10.1 million km2 – has at least 10% tree cover. We don’t see any virtue in choosing some cut off level of tree cover that we label AF, and would prefer to emphasise the continuous variation – all levels of tree cover occur. But however you measure it, these are very extensive areas of agricultural land with significant cover of trees in the landscape. They can not be ignored. That US reviewer of IAASTD was off by a factor of about a million.
The previous graph can be broken down by region – only some are presented here, for clarity. There are distinct patterns in different regions. There is, for example, a clear difference between S Asia and South East Asia. That may not be surprising, due to the large areas of dryland in S Asia. I will look at that influence of climate shortly.
We can do a similar thing to count the people living in those treed agricultural lands. Doing that shows that 31% of the 1.8 billion people estimated to live in agricultural landscapes have at least 10% tree cover.
These results are perhaps important, but more interesting is a look at the distribution of where these trees are, with an attempt to find factors that influence the large scale distribution.
Here we try to map the bivariate (joint) distribution of trees and people in agricultural land. The two way scale shows green when there are ‘more trees than people’, blue when ‘more people than trees’, with light shades for low density, dark for high density. Two things stand out:
That every combination of high and low tree cover with high and low population density occurs. Globally there is no sign of a trees v people trade off.
The colours come in distinct patches. There are large scale patterns
An obvious starting point is climate. We summarise this with an aridity index (based on the ratio of rainfall to PET) which is low for dry areas, high for humid areas.
Remember this is only agricultural land. Drier areas are often dominated by pastoral land, wetter by forest. The obvious pattern in mean tree cover is obtained. But note that there is much more to the pattern than that. For the SAME aridity index, there is a difference of up to 20% in tree cover in different regions. Population density is different between Central America and E Asia, for example, so we can try to build that into the analysis (condition on it) as well.
Now you can see that in E Asia there is a strong pattern with population and aridity, but uniformly high tree cover in Central America. Other factors are important.
Comparing two more regions:
S Asia shows expected trends with both aridity and population density, while there is no effect of pop density n Africa above about 100 km-2
There is much more that can be seen in this data, particularly of we look at the variation not just mean. I will finish with just one interesting one, taking Africa as an example.
The tree cover on agric land varies. We know that part of that is related to population and aridity. But still within any pop and aridity class there is much variation, from zero to high. As the high values occur, they are clearly feasible as far as the factors consider so far are concerned. Hence we map them. Actually we take the 80% point of the distribution – ‘high’ means the tree cover that only 20% of pixels with in that class exceeds.
Note this is not the ‘maximum’ or ‘potential’ tree cover meaning a landscape filled with trees. It is the tree cover which appears feasible for those conditions as 20% of the pixels with those conditions have at least that tree cover.
Now look at the difference. Where do we have fewer trees than feasible? The results are spatially coherent – there are distinct areas which are near the feasible tree cover, other patches with well below. And they do not occur in obvious places – such as in lowland W Africa, which already has high tree cover.
Distinct patchiness (rather than just a random scatter) implies that there is something further going on. The factors that explain differences in tree cover are not purely local, but affect large areas. They may thus be amenable to influence.
The key messages are here: they are maybe disappointingly vague. However we believe that this is a useful start to an objective global analysis. The paper describes some further steps. We welcome your comments and contributions to enriching those, and hope this might prompt further and more refined analyses.
South America and Southeast Asia have the highest carbon stocks.. Central America the highest tC/ha on average
