1. - Lukas Brader Fellowship

2. Ethiopia is one of the mega centres of diversity
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3. TIGRAY REGIONAL STATE
(The study region)
Tigray is located in the northern highlands of Ethiopia,
covering 80,000 square km
Topography: 500 - 4000 meters above sea level
Population: 4.3
4 3 million
Crop calendar:
June to September rainy season;
October & November harvest season; and
May & early June land preparation and sowing.
y y p p g
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4. Study Area
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5. Research objectives:
1.
1 To detect land use and land cover (LULC) changes based on a time series
of remote sensing data and identify drivers of the changes at a regional
scale.
2. To identify and analyze factors affecting agro-biodiversity and soil
erosion, focusing on relationships between agro-biodiversity, physical
environment, crop (farm) characteristics and measures of wealth at farm
and regional scales.
3. To study spatial and temporal variation in agro-biodiversity and soil
degradation in relation to farm, productivity, wealth, social, development
d d i i l i f d i i lh i l d l
and topographic characteristics between 2000 and 2005 at farm and
regional scales.
4. To investigate the effects of F. albida based land use systems on crop
productivity at field and landscape scales.
- Lukas Brader Fellowship

6. How was the research done?
Regional +
Component 2 (2000)
-Road & town maps
R
-Elevation and slope
El ti d l
-Farm & wealth Component 1 (1964-2005)
Component 3 -LULC change detection
(151 farms)
(2000-2005) -Driving factors of change
-Road & town maps
oad to aps
Field scale
-Elevation and slope
Farm
-Farm, wealth &
social (151 farms)
F
+
Component 4 (2005)
-Road & town maps
Road
-Elevation and slope
-Field (77) and farm (81)
Field
productivity & soil
F
Productivity Agro-Biodiversity Land use
Level of study

7. Remote sensing based land use/ land cover change
detection and associated driving factors for the period
1964 – 2005 in the highlands of Tigray, Ethiopia.
- Lukas Brader Fellowship

8. Problem statement
No d
N understanding of where, when and why l d use/land cover (LULC)
di f h h d h land /l d
changes took place in relation to drivers of the changes which may have serious
implications on biodiversity loss, land degradation and declining agricultural
productivity.
productivity
Changing land use policies with changing governments/regimes (three different
land use policies in the whole study period: 1964 – 2005)
2005).
Challenge on how to ensure food security while conserving biodiversity and
minimizing land degradation.
g g
Objectives
To assess the dynamics of LULC for the period 1964 – 2005 in the highlands of
Tigray, northern Ethiopia using remote sensing techniques, and
To identify and quantify the drivers associated with LULC changes.
- Lukas Brader Fellowship

9. Specific study area description
The specific study area is located in Tigray, northern Ethiopia (40 82’ - 50 10’ N
p y g y, p (
and 150 66’ - 150 28’ E), and covers an area of 30 x 40 km at an elevation of
1300 - 2800 metres above sea level (m.a.s.l.).
- Lukas Brader Fellowship

10. Materials used:
Data Year + month Path/row Resolution/
Scale
Landsat ETM+ 2005,10 169/050 30 meter
Landsat TM 1994,10 169/050 30 meter
Aerial Photograph 1964 and 1994,11
Topographic map 1994
Shuttle Radar 2000 90 meter
Topographic
Mission (SRTM)
Softwares:
ERDAS IMAGINE 9 1 9.1
ArcGIS 9.2
SAS statistical package
- Lukas Brader Fellowship

11. Land use/land cover classes used in the classification
S Class Description
N Name
1 Woodland It is composed of trees, bushes, shrubs and herbs. Canopy cover of this unit is estimated to be 65%.
2 Grassland This is open grassland with some shrubs and occasional trees.
3 Shrub land Land supporting a stand of shrubs, usually not exceeding 3m in height with a canopy cover of more
shrubs height,
than 30%.
4 Scrubland It is mainly characterized by strata of shrubs and grasses or herbs growing here and there.
5 Intensively It is estimated that of this mapping unit over 70% of the land is under annual and perennial crops
Cultivated
land
6 Moderately It is estimated that of this mapping unit 40-70% of the land is under annual and perennial crop.
cultivated
land
7 Sparsely It is classified as sparsely cultivated (only 20-40%) of the entire mapping unit is under cultivation.
Cultivated
land
8 Water body Water in Micro Dams
9 Settlement Residential/industrial areas with a population of more than 2000 households
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12. Description of Land Use/Land cover classes of Tigray, Ethiopia
Woodland
Shrub land
Scrubland
Sparsely cultivated
Moderately cultivated
Intensively cultivated
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13. Cont…
Grassland
Waterbody
Settlement
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14. Methods
Topographic map of the Aerial photographs
Corrected ETM+ 2005 SRTM
area (1964)
image
Scanning Geometric correction
Further correction
from Topomap & field data Scanning Geo-referencing
Geo-referenced DTM
topomap
Geo-referenced aerial
Geo-referenced recent photographs
image (2005)
Digitize ground control points
Image-to-image registration
Corrected aerial photos
Resample
1994
Unsupervised
classes Resampled aerial photos
Topographic Unsupervised
p
Normalization classification
-Training sample collection from field for
the signature editor
Normalized images -Supervised classification by MLKH Gluing
classifier
Accuracy assessment (2005 Land cover map of Transfer interpreted aerial
& 1994) each year Landover map of photographs to Ortho-photo
1964 mosaic
Overall accuracy
Vectorize Change Statistical
detection analysis and
l i d
Kappa statistics interpretation
Land cover map of
each year (2005 &
1994)
- Lukas Brader Fellowship

15. Cont…
Spatially explicit multiple logistic regression model was used to estimate the
probability of occurrence of LULC class change as affected by a set of
independent variables:
•elevation (continuous)
•slope (continuous)
p ( )
•distance to major river (buffered)
•distance to major road (buffered)
•distance to settlement (buffered) and
• population density (continuous)
Dependent LULC classes (a total of 2000 samples: 1000 changed; 1000
unchanged)
h d)
•Woodland (binary: 0 - 1)
•Shrub land (binary: 0 - 1)
•Scrubland (binary: 0 - 1)
•Agricultural land (binary: 0 - 1)
The general formula of the multiple logistic regression model was:
Logit (p) = log [p/1-p] = α + β1 X1 +β2 X2 … + βn Xn
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16. Results
Across 41 years (1964 - 2005), the results reveal a sharp reduction in natural
habitats and an increase in agricultural land.
g
1964 1994 2005
road
d
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17. Regional scale - results
1964
Sparsely cultivated
Shrub land
Woodland
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18. Regional scale - results
1994
Moderately cultivated
Scrubland
Sparsely cultivated
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19. Regional scale - results
2005 Intensively cultivated
Shrub land
Moderately cultivated
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20. Cont…
In 1964, shrub land was dominant (covering 46% of the study area) followed by
woodland (covering 28% of the study area)
area).
In 1994 and 2005, agriculture was dominant covering 34 and 40 % of the study
area respectively.
p y
50.00
45.00
40.00
35.00
entage
30.00 1964
25.00
25 00 1994
Perce
20.00 2005
15.00
10.00
5.00
0.00
Wd Sh Sc SCu MCu ICu Gr W Se
Land use/land cover type
- Lukas Brader Fellowship

21. Cont…
Over the study period (1964 – 2005), there was conversion of one land cover
type to another For example, 32.4 and 33.1 % of shrub land was converted into
another. example 32 4 33 1
combined agricultural land in 1964 – 1994 and 1994 – 2005, respectively.
Moreover, 59.3 and 50.1 % of grassland was converted into agricultural land in
, g g
1964-1994 and 1994-2005, respectively.
There was even conversion of sparsely cultivated into moderately cultivated by
27.7 % in 1964-1994 and 37.3 % in 1994-2005.
- Lukas Brader Fellowship

22. Cont…
Accuracy assessment
Validation
V lid ti was carried out f
i d t from random validation points collected from field
d lid ti i t ll t d f fi ld
for the 2005 Landsat ETM+ and from the same spatial and temporal scale of
1994 aerial photographs for the 1994 Landsat TM.
The overall accuracy and overall Kappa statistic for the Landsat 1994 image
were 78 and 71 %, respectively.
For the Landsat 2005 image, overall accuracy and Kappa statistic were 74 and
70 %, respectively.
- Lukas Brader Fellowship

23. Cont…
Drivers of LULC change
In the first period (1964 – 1994),
distance to road was important driver of LULC change. The further the
change
location was from a road so much the greater was the probability of
change (reductions) in wood and shrub lands and associated increase in
scrubland.
scrubland
Change in location (increase) in agricultural land was primarily
associated with an increase in human population density.
p p y
In the second period (1994 – 2005),
woodland locations changed (decreased) primarily by settlement,
particularly at high elevation and steep slopes.
Similar to the first period, agricultural land changed (increased) by
population density.
l ti d it
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24. Conclusion
• The study over a period of 41 years (1964 -2005) reveals LULC changes
2005)
particularly expansion and intensification of agricultural lands at the
expense of natural habitat reductions.
•Reductions in extent and location of natural habitats (woodland and shrub
land) was higher as locations were further from a road in the first study
period (1964-1994).
•In the second period (1994-2005), natural habitats were reduced closer to
settlements, particularly at high elevation and steep slopes.
•Expansion and intensification of agricultural lands was associated with an
increase with human population.
•This study provides a spatially explicit approach that can help to improve
the understanding of LULC dynamics in relation to their drivers in
heterogeneous landscapes of tropical highlands.
- Lukas Brader Fellowship

25. Agro-biodiversity and soil erosion on farmlands
in Tigray, northern Ethiopia.
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26. Problem statement
Previous research results, in northern Ethiopia, indicated that there is expansion
and intensification of agriculture (even in steep slopes) at the expense of
natural components because of human induced LULC changes.
l b fh i d d h
However, there was no information on status and spatial distribution of agro-
biodiversity and soil erosion.
erosion
Objective
To identify and analyze factors affecting agro-biodiversity and soil erosion,
and relate them to physical environment, farm characteristics, wealth
characteristics and topographic/development drivers in Tigray, northern
Ethiopia.
- Lukas Brader Fellowship

27. Materials and Methods
Study area
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28. Data collection and analysis
Non-spatial dataset
Soil type
Crop type
Farm
Weed species
chara.per
Insect species
farm Agro-biodiversity
Crop selection per farm
Criteria
Crop
diversity
Inorganic
fertilizer
Tree/shrub
Wealth Number of species Statistical analysis
livestock holding •Multiple regression
chara.per
farm Soil erosion per •Chi-square test
Number of credit
sources
farm •Correlation analysis
•Redundancy analyses
Erosion classes (RDA)
Spatial dataset
p
Distance from
road
Topographic/
Distance from
Development town
Drivers Elevation of
farms

29. Results
Factors related to agro biodiversity
agro-biodiversity
- The higher the number of tree and shrub species, the higher was the crop diversity.
- Th worse th soil erosion, th l
The the il i the less di
diversified were both the crops and t / h b species.
ifi d b th th d tree/shrub i
-Farmers with few credit sources planted a great variety of crops (χ2 = 18.6, DF = 6,
P=0.01).
- Crop selection criteria was positively
associated with crop diversity.
N u m b e r o f fa r m s
20
16 Highland
In lowlands, d
I l l d drought resistance was
ht it 12 Intermediate
Low land
first choice 8
4
0
In highlands, straw quality was most
el d
important
i t t
e
ty
i ty
i ty
Yi
a lu
a li
rk e e
ra y
ce
b il
t ur
Tr n ce
c
S t b i li t
tv
qu
an
ta n
ha
ma
a
ist
i ta
es
w
s is
is t
rly
su
es
Ma
es
t re
Ea
dr
ge
tr
ec
In intermediate altitude, esp. close to ee
gh
ra
In s
ve
W
ou
Be
Dr
towns, high market value was first
Selection criteria
crop selection criterion.
l ti it i
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30. Cont… a
a
b
Development drivers and
altitude
- Tree/shrub species diversity and
crop diversity decreased as buffer
distance of farms from roads
decreased.
- Higher agro-biodiversity was
observed in farms far from roads.
roads
-Road type was also important a b b
- Both tree/shrub and crop
diversity were reduced close to
all weather roads than dry
weather roads.
-Diversity of tree/shrub and crops d
c d
were negatively influenced by
proximity of farms to urban areas.
-Both diversity components were
higher at higher altitude.
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31. Cont…
RDA analysis
RDA analysis clearly separated diversity, soil
erosion and other explanatory variables.
-The lowland region (region 3) was distinct
g ( g )
from the others because of minimal
agricultural activities and sparse natural
vegetation.
-Region 5, with the highest altitude, was
1.0
also separated from the intermediate region
and had high agro-biodiversity, as it was
R1
located far from towns and roads.
Fert/kg Var/6yr
Erosion SPECIES
Weed_yr
No.SoilT
-Region 1, located close to town, was also pest ind
weed_6yr ENV. VARIABLES
somehow separated from the others because of crops/yr
crops/6y
relatively high inorganic fertilizer use.
y g g Road Dist SelCr SAMPLES
Var/yr
T_Ratio
TotTSpp Region 1
-RDA analysis showed agro-biodiversity R3 R5
Region 2
Town Dist
was significantly (p<0.001) related to each of Region 3
Region 4
the explanatory variables, but mainly
p y , y
-1.0
Region
R i 5
with distance to road and town (positively) -1.0 1.0
and fertilizer and soil erosion (negatively).
- Lukas Brader Fellowship

32. Cont…
Relationship with soil erosion
- Soil erosion (measure of un-sustainability) was positively correlated with inorganic
fertilizer use ( r = 0.44; P < 0.001),
e e 0. ; 0.00 ),
- Soil erosion was negatively correlated with
-Crop diversity ( r = -0.44; P < 0.001),
p y ; ),
-Tree/shrub species diversity ( r = -0.74; P < 0.001),
-Crop selection criteria ( r = -0.42; P < 0.001) and
p ; )
-Animals per farm household ( r = -0.21; P < 0.01)
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33. Conclusion
Higher agro-biodiversity was associated with farms located far
from road and towns, often associated with indigenous farming
, g g
practices.
Agricultural technology packages (inorganic fertilizer) were
important to increase food production
but were often associated with removal of landraces and
native plants (trees and shrub species).
i l ( d h b i )
Soil erosion was worse on less diversified farms.
Improved agricultural production should, therefore, take in to
account locally available land races and native tree/shrub species.
species
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34. Spatial variation in agro-biodiversity, soil
degradation and productivity in agricultural
landscapes in the highlands of Tigray, northern
Ethiopia.
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35. Objective
To compare the spatial and temporal variations in agro-biodiversity and soil
degradation in relation to agricultural productivity in Tigray, northern Ethiopia
between 2000 and 2005.
Hypothesis
Based on previous research results, we hypothesized agro-biodiversity and crop
productivity have declined in recent years, whereas soil erosion has increased.
Aim
The aim was to understanding the drivers of agro-biodiversity loss and soil
erosion, and relate them to agricultural productivity.
- Lukas Brader Fellowship

36. Materials and Methods
Study area
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37. Dataset
Farm Wealth/Social
•Soil type, OM, Avail. P, N •Inorganic fertilizer
•Crops planted/year •Livestock holding
•Animal manure •Number of credit sources
•Weed, insect species/farm •Farmer’s education level
Farmer’s
•Caloric crop yield & sel.sri. •Farmer’s off farm employment
opportunity
Agro-Biodiversity
Agro Biodiversity
Soil erosion
•Distance to major road •Elevation of farms
•Distance to major •Slope of farms
Slope
town/market
Development drivers Topographic
- Lukas Brader Fellowship

38. Data Collection Method
Topographic Development drivers
GPS GPS
Geog. locations
Aerial Aerial
Feature delineation photos
photos
Double track
Transect walk Crossing of fields
Alti t / SRTM
Altimeter/ Slope/Altitude
Field Measurement & interview
Farm Wealth/Social
Agro-Biodiversity &
Soil erosion
- Lukas Brader Fellowship

39. Data Analysis
Spatial data Non-spatial data
Interviews
Topographic
Development drivers
Field data
Farm characteristics
Socio-economic
LULC classes
data
Agro-biodiversity GIS
Overlay Analysis
GIS output maps
Data integration
- Discriminant analysis (2005) and
Statistical Analysis Co pa so be wee 000
Comparison between 2000 & 2005 005
for agro-biodiversity & soil erosion.
- Chi-square (Educat. & Employment
Outcome - P i d t t t (b t
Paired test (between 2000 & 2005)
- Lukas Brader Fellowship

40. Results
Status of agro-biodiversity in 2005
I. Agro-biodiversity and social characteristics
g y
-More off-farm employment, less agro-biodiversity (χ2 = 30.8, DF = 4, P=0.001).
-Farmer’s education was not significantly associated with agro-biodiversity.
g y g y
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41. Cont…
II. Agro-biodiversity and quantitative explanatory variables
For the discriminant analysis, combined (both tree/shrub species and crop diversity)
average agro-biodiversity was categorized into three classes:
low (<7), medium (7-12) and high (>12).
- Total N (%),
- Available P (mg/kg),
- Crop types (number/farm),
- Animal manure (kg/ha) and
- Crop selection criteria (number/farm)
significantly separated (P <0.05)
the agro-biodiversity classes
and were positively associated
with the first canonical function.
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42. Cont…
Caloric crop yield (Mcal/farm)
(Mcal/farm),
Animal ownership (number/farm),
Farm distance from the nearest town (km) and
Elevation (m)
also significantly (P<0.05) discriminated agro-biodiversity classes and
were positively associated with the first canonical function.
Compared to low agro-biodiversity classes, farms with high agro-biodiversity class had
agro biodiversity classes agro biodiversity
52 % higher available P,
39 % higher total N,
47 % more crop types,
71 % higher animal manure
manure,
53 % more animals,
42 % more crop selection criteria and
19 % caloric crop yield.
Inorganic fertilizer use (kg/farm) and credit sources (number/farm) were
negatively associated with the first canonical function,
but significantly discriminated the three agro-biodiversity classes.
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43. Cont…
III. Agro-biodiversity and land use types
-Low agro-biodiversity class was
strongly associated with intensively
cultivated land use type (Icu).
-High agro-biodiversity class coincide
with the sparsely cultivated land use type (Scu).
- Lukas Brader Fellowship

44. Cont…
Status of soil degradation in 2005
Soil erosion classes were categorized
into four: low (<10 tons/ha), moderate (10-20 tons/ha), high (20-40
tons/ha) and extremely high (>40 tons/ha).
Farm slope (%) was positively associated
with the first canonical function
and contributed significantly (p<0.001)
to th discrimination f th
t the di i i ti of the soil erosion
il i
classes.
-The higher the slope of farms,
the higher
th hi h was th soil erosion.
the il i
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45. Cont…
Soil OM (%) and crop selection criteria (number/farm) were negatively
associated with the first canonical function but significantly (p<0.001) separated
the soil erosion classes.
-The higher OM content of farms, the less soil erosion.
-The higher crop selection criteria per farm, the less the soil erosion.
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46. Cont…
Temporal changes (between 2000 and 2005)
I. Changes in farm and wealth characteristics
Paired t-test comparison between 2000 and 2005 resulted in significant decrease in
- crop diversity (Paired t-test, t = 6.46, P < 0.001, n=151)
- animal ownership (Paired t-test, t = 4.23, P < 0.001, n=151)
- crop selection criteria (Paired t-test, t = 2.05, P < 0.05, n=151)
Whereas,
Whereas inorganic fertilizer increased significantly (Paired t test t = -3.40, P < 0.01, n=151)
t-test, 3 40 0 01 n 151)
No significant change for the other variables between 2000 and 2005.
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47. Cont…
II. Agro-biodiversity and soil degradation (2000-2005)
Agro-biodiversity (2000-2005)
Agro-biodiversity was compared between 2000 and 2005 and categorized into: decrease (<0),
no change (=0) and increase (>0).
-Crop type (number/farm),
-Crop selection criteria (number/farm),
-Animal ownership (number/farm),
-Farm distance from the nearest town (km), and
i f ( )
-Farm distance from the nearest road (km)
significantly (P<0.05) separated agro-
biodiversity change classes (decrease, no
bi di i h l (d
change and increase) and were positively
associated with the first canonical function.
Whereas, i
Wh inorganic f ili
i fertilizer (kg/farm)
(k /f )
was negatively associated with the first
canonical function but significantly
(P < 0.05) separated the agro-biodiversity
change classes.
h l
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48. Cont…
Soil degradation (2000-2005)
Classes for changes in soil erosion (decrease, no change and increase)
between 2000 and 2005 were not significantly separated by the explanatory variables.
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49. Cont…
Spatial distribution of agro-biodiversity (2000 and 2005)
Tree and shrub species diversity did
not change significantly between 2000 and 2005.
(a) No. of tree and shrub species (b) No. of tree and shrub
overlaid with road species overlaid with road
buffers in 2000 buffers in 2005
Number of crop diversity decreased
significantly mainly on farms
located close to the nearest major roads.
Proximity of farms to the nearest town
was strongly associated with low
agro-biodiversity (mainly with crop
diversity), both in 2000 and 2005. (c) No. of crop varieties (d) No. of crop varieties
overlaid with road buffers in overlaid with road buffers in
2000 2005
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50. Conclusion
Significant loss of agro-biodiversity, mainly crop diversity, between 2000 and 2005.
Higher loss of agro-biodiversity was contributed from higher use of inorganic fertilizer
and higher number of credit sources.
d hi h b f dit
Proximity to towns and roads reduced agro-biodiversity, both in 2000 and 2005.
Agro-biodiversity loss was also facilitated by higher soil erosion.
Higher agro-biodiversity was associated with increased caloric crop yield.
Higher agro-biodiversity was favored by sparsely cultivated land use (with higher
trees/shrubs).
The information on the relationships among agro-biodiversity, productivity and soil
erosion can improve the understandings on increasing food security while maintaining
locally available agro-biodiversity resources.
- Lukas Brader Fellowship

51. Assessing the effect of Faidherbia albida (F. albida)
based land use systems on barley yield at field and
landscape scales in the highlands of Tigray, northern
Ethiopia.
- Lukas Brader Fellowship

52. Objective
To investigate influence of traditional F. albida based land use systems on
barley productivity at field and regional scales in Tigray, northern Ethiopia.
Tigray Ethiopia
- Lukas Brader Fellowship

53. Materials and Methods
Study area
- Lukas Brader Fellowship

54. Landscape scale-
RRA & PRA Tour, interview &
Topographic farm dataset
group discussion
map
F. lbid density
F albida densit
Landscape scale Eucalyptus density
Selecting b l d
S l ti sub-landscape with 77 fields
ith fi ld Livestock density
Li kd i
Selected site Inorganic fertilizer
Land use system
Statistical
A. albida alone
A. albida +
analysis Low spatial A. albida
Distances from tree
Di t f t Livestock Moderate
M d t spatial
ti l
Mixed model A. albida
A. albida + CCA
1 m from A. Eucalypt High spatial A. albida
analysis
albida trunk
Multiple
25 f
2 m from A A. Overlay regression
i
albida trunk
LULC map Added Ecosystem
50 m from A. Services
albida trunk
Elevation map (
(Added barley yield)
yy )
Field scale
Regional scale

55. Results
Productivity and land use systems at field scale
Barley yield estimate
Three F. lbid land
Th F albida l d use systems were considered:
id d
F. albida alone (AA),
F. albida + livestock (AL) and
F. albida + Eucalyptus (AE).
yp ( )
- Lukas Brader Fellowship

56. Cont…
Significantly (P < 0.05) higher barley
yield was found at 1 m from A. albida
trunk than 25 and 50 m for the AA and
AL land use systems.
In contrast barley yield did not change
contrast,
significantly with distance from the A. albida trunk for the AE land use system.
1600 a
a a
1400 a
a
Barley yiel (kg /h a)
1200 b b LUS
LUS
b
1000 b
AA
ld 800 AL
600 AE
400
200
0
1 25 50
Distance from A. albida trunk (m)
- Lukas Brader Fellowship

57. Cont…
Soil properties (Mixed Model Analysis)
Interaction effect of F. albida land use systems (AA, AL and AE)
and distance from F. albida trunk was significant for
g
- Total N ( P < 0.05),
- Available P ( P < 0.001),
- Soil moisture ( P < 0.001).
0 001)
In all cases, mean values decreased with increasing distance from the tree for AA
and AL land use systems, whereas they were more erratic for AE.
OM was significantly affected only by distance from the tree irrespective of the F.
albida land use systems.
Stepwise regression analysis showed soil moisture significantly affected barley yield at
the AA (P<0.01) and the AL (P<0.001) land use systems.
- Lukas Brader Fellowship

58. a b
Cont…
GIS analysis
AA and AL land use systems were
mainly associated with sparsely
i l i d ih l
cultivated and moderately cultivated
land use classes, respectively.
The AE was not clearly associated
with distinct land use class.
However,
Ho e er most AE were associated
ere
with higher inorganic fertilizer use
and irrigation practices.
- Lukas Brader Fellowship

59. Cont…
Productivity and farm characteristics at landscape scale
Canonical correspondence analysis (CCA)
showed clear separation between
h d l i b
barley yield classes and farm
characteristics (F. albida density,
Eucalyptus dominance, Livestock
yp ,
density and Inorganic fertilizer).
- Barley yield was positively associated
with F. albida densit
ith F lbid density.
- Higher yield (Class 3) at higher
F. albida density (HA)
- Barley yield was negatively associated
with Eucalyptus dominance (HE), located
close to towns.
towns
- Lukas Brader Fellowship

60. Cont…
Land use classes and spatial distribution of A. albida at regional scale
p g
Sparsely cultivated land use class (Scu)
was strongly associated with farms having
High F. albida density but low Eucalyptus
F
dominance.
Intensively cultivated land use class (Icu)
was related with low F. albida density
and higher Eucalyptus dominance. 60
acia and
50
ptus management
Percentage of farms under Aca
40 HA
LA
30
HE
LE
Eucalyp
20
10
0
Scu
S Mcu
M Icu
I
Agricultural land use types
- Lukas Brader Fellowship

61. Cont…
Added ecosystem service (
y (barley y
y yield benefit)
)
Higher overall barley yield benefit (100% at E3) in sparsely cultivated land use type
(T1).
Removing trees from inside of the field at random until T2 resulted in a reduction in
yield benefit from 100 % in E3 to 40 % in E2.
Further removal of trees down to trees at corners of the fields (T3) and complete clearing
resulted in less yield benefits.
- Lukas Brader Fellowship

62. Conclusion
The research provides field and regional scale integrated study approach to
estimate influence of F. albida land use systems on barley productivity.
It indicates F albida trees should be maintained and promoted in and around
F.
farmlands as a way of increasing crop productivity and soil fertility.
Whereas, Eucalyptus trees did not show both in yield and soil fertility
improvement.
Land use types with more trees/shrubs contributed to higher F. albida density
which in turn was important to enhance added ecosystem service (added
barley yield).
- Lukas Brader Fellowship

63. Overall contributions and implications of the research
The research contributes to the understanding of the relationships among
agrobiodiversity (agroforestry)-productivity-soil erosion in agricultural landscapes.
- Relative agro-biodiversity (compared to the maximum of tree/shrub species and
crop diversity at 151 farms) was positively correlated with crop productivity.
productivity
- In contrast, soil erosion was higher at lower relative agro-biodiversity (at the 151
farms).
- Lukas Brader Fellowship

64. Cont…
Despite the contribution of agroforestry/agro-biodiversity to productivity, expansion and
intensification of agricultural lands are continuing at the expense of natural habitats
over the past 41 years.
-mainly because of increasing population coupled with an increasing demands for
food, feed and construction materials.
Removal of natural habitats and on-farm trees/shrubs can lead to deterioration of soil
fertility and enhance soil erosion.
How to increase food production to satisfy the demand of the increasing human
population while minimizing loss of agro-biodiversity is a challenge of land use
planners and decision makers in the country.
As
A one way of promoting sustainable agricultural production and food security,
f ti t i bl i lt l d ti df d it
agroforestry needs to be considered as a natural capital from which agriculture gains
ecosystem services such as increase in productivity, soil fertility, protection against
soil erosion, water retention, p
, , pollination and pest control.
p
- Lukas Brader Fellowship

65. THANK YOU!
A O !