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- Lukas Brader Fellowship
Ethiopia is one of the mega centres of diversity
- Lukas Brader Fellowship
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 millionPopulation: 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
- Lukas Brader Fellowship
Study AreaStudy Area
- Lukas Brader Fellowship
Research objectives:
1 To detect land use and land cover (LULC) changes based on a time series1. 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 d d i i l i f d i i l h i l d ldegradation in relation to farm, productivity, wealth, social, development 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
+
How was the research done?
Reg
iona
l
Component 2 (2000)-Road & town mapsEl ti d lR
e
Component 1 (1964-2005)-LULC change detection-Driving factors of change
-Elevation and slope-Farm & wealth(151 farms) Component 3
(2000-2005)-Road & town maps
Farm
Fiel
d sc
al
oad & to aps-Elevation and slope-Farm, wealth &social (151 farms)
F
+
Component 4 (2005)Road & town maps
Fiel
d
-Road & town maps-Elevation and slope-Field (77) and farm (81) productivity & soil
Productivity Agro-Biodiversity Land use
F
Level of study
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
Problem statement
N d di f h h d h l d /l d (LULC)No understanding of where, when and why land use/land cover (LULC) changes took place in relation to drivers of the changes which may have serious implications on biodiversity loss, land degradation and declining agricultural productivityproductivity.
Changing land use policies with changing governments/regimes (three different land use policies in the whole study period: 1964 – 2005)land use policies in the whole study period: 1964 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
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
Data Year + month Path/row Resolution/
Materials used:
Data Year + month Path/row Resolution/Scale
Landsat ETM+ 2005,10 169/050 30 meterLandsat TM 1994,10 169/050 30 meterAerial Photograph 1964 and 1994,11Topographic map 1994Topographic map 1994Shuttle Radar Topographic Mission (SRTM)
2000 90 meter
Mission (SRTM)
Softwares:
ERDAS IMAGINE 9 1ERDAS IMAGINE 9.1ArcGIS 9.2SAS statistical package
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SN
Class Name
DescriptionLand use/land cover classes used in the classification
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 more3 Shrub land Land supporting a stand of shrubs, usually not exceeding 3m in height, with a canopy cover of more than 30%.
4 Scrubland It is mainly characterized by strata of shrubs and grasses or herbs growing here and there.
5 IntensivelyCultivated
It is estimated that of this mapping unit over 70% of the land is under annual and perennial crops Cultivated land
6 Moderately cultivated land
It is estimated that of this mapping unit 40-70% of the land is under annual and perennial crop.
7 Sparsely Cultivated land
It is classified as sparsely cultivated (only 20-40%) of the entire mapping unit is under cultivation.
8 Water body Water in Micro Dams
9 Settlement Residential/industrial areas with a population of more than 2000 households
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Description of Land Use/Land cover classes of Tigray, Ethiopia
Woodland
Shrub land
Scrubland
Sparsely cultivated
Moderately cultivated
Intensively cultivated
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Cont…
GrasslandGrassland
Waterbody
Settlement
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MethodsTopographic map of the
areaCorrected ETM+ 2005 image
Aerial photographs (1964) SRTM
Geo-referenced topomap
ScanningFurther correctionfrom Topomap & field data Scanning
DTM Geo-referencing
Geometric correction
Geo-referenced aerial Geo-referenced recent
image (2005)
Image-to-image registration
photographs
Digitize ground control points
Unsupervised classes
Topographic
1994
Unsupervised
Corrected aerial photos
Resampled aerial photos
Resample
Normalized images
Topographic Normalization
pclassification
Gluing
-Training sample collection from field for the signature editor-Supervised classification by MLKH classifier
Accuracy assessment (2005 & 1994)
Overall accuracy
Transfer interpreted aerial photographs to Ortho-photo
mosaicLandover map of 1964
Vectorize Change Statistical l i d
Land cover map of each year
Kappa statistics detection analysis and interpretationLand cover map of
each year (2005 & 1994)
- Lukas Brader Fellowship
Cont…Spatially explicit multiple logistic regression model was used to estimate the probability of occurrence of LULC class change as affected by a set ofprobability 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 h d)unchanged)
•Woodland (binary: 0 - 1)•Shrub land (binary: 0 - 1)•Scrubland (binary: 0 1)•Scrubland (binary: 0 - 1)•Agricultural land (binary: 0 - 1)
The general formula of the multiple logistic regression model was: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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ResultsAcross 41 years (1964 - 2005), the results reveal a sharp reduction in natural habitats and an increase in agricultural land.1964 1994 2005
g
droad
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Regional scale - results1964
Sparsely cultivated
Shrub land
Woodland
Shrub land
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Regional scale - results1994
Moderately cultivated1994
Scrubland
Sparsely cultivated
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Regional scale - results
2005 Intensively cultivated2005
Shrub land
Moderately cultivated
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Cont…In 1964, shrub land was dominant (covering 46% of the study area) followed by woodland (covering 28% of the study area)woodland (covering 28% of the study area).
In 1994 and 2005, agriculture was dominant covering 34 and 40 % of the study area respectively.p y
45.0050.00
25 0030.0035.0040.00
enta
ge
19641994
10.0015.0020.0025.00
Perc
e 19942005
0.005.00
Wd Sh Sc SCu MCu ICu Gr W Se
Land use/land cover type
- Lukas Brader Fellowship
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 intotype to another. For example, 32.4 and 33.1 % of shrub land was converted into 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 g1964-1994 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
Cont…
Accuracy assessment
V lid ti i d t f d lid ti i t ll t d f fi ldValidation was carried out from random validation points collected from field 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
Cont…
Drivers of LULC changeDrivers of LULC change
In the first period (1964 – 1994), distance to road was important driver of LULC change The further thedistance to road was important driver of LULC change. The further the location was from a road so much the greater was the probability of change (reductions) in wood and shrub lands and associated increase in scrublandscrubland.
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 l ti d itpopulation density.
- Lukas Brader Fellowship
Conclusion
• The study over a period of 41 years (1964 -2005) reveals LULC changesThe study over a period of 41 years (1964 2005) reveals LULC changes 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.heterogeneous landscapes of tropical highlands.
- Lukas Brader Fellowship
Agro-biodiversity and soil erosion on farmlands in Tigray, northern Ethiopia.
- Lukas Brader Fellowship
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
l b f h i d d LULC hnatural components because of human induced LULC changes.
However, there was no information on status and spatial distribution of agro-biodiversity and soil erosionbiodiversity and soil 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
Materials and Methods
Study area
- Lukas Brader Fellowship
Soil type
Non-spatial datasetData collection and analysis
Farm chara.per farm Insect species
Crop type
Weed species
Agro-biodiversityCrop selectionCriteria
Inorganic fertilizer
per farmCrop diversity
Wealth chara.per farm
fertilizer
Number of livestock holding
Number of creditSoil erosion per
Tree/shrub species Statistical analysis
•Multiple regression•Chi-square test
Number of credit sources
Erosion classes
Spatial dataset
farm •Correlation analysis•Redundancy analyses (RDA)
p
Topographic/Development
Distance from road
Distance from DevelopmentDrivers
town
Elevation of farms
Results
Factors related to agro-biodiversityFactors related to agro biodiversity
- The higher the number of tree and shrub species, the higher was the crop diversity.
Th th il i th l di ifi d b th th d t / h b i- The worse the soil erosion, the less diversified were both the crops and tree/shrub species.
-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.
I l l d d ht i t1620
f far
ms
HighlandIntermediateIn lowlands, drought resistance was
first choice
In highlands, straw quality was mosti t t
048
12Yield e lue ity lity lity
Num
ber o
f IntermediateLow land
important
In intermediate altitude, esp. close to towns, high market value was first
l ti it i
YieInsect r
esistance
Weed resis
tanceMarke
t value
Early matu
rity
D rought r
esistance
Treshab ility
Beverage s
uitab ility
Straw qua lity
Selection criteriacrop selection criterion. Selection criteria
- Lukas Brader Fellowship
Cont…Development drivers and altitude
a ba ba
- Tree/shrub species diversity and crop diversity decreased as buffer distance of farms from roadsdistance of farms from roads decreased.
- Higher agro-biodiversity was observed in farms far from roadsobserved in farms far from roads.
-Road type was also important- Both tree/shrub and crop diversity were reduced close to
a ba b b
diversity were reduced close to all weather roads than dry weather roads.
-Diversity of tree/shrub and crops-Diversity of tree/shrub and crops were negatively influenced by proximity of farms to urban areas.
-Both diversity components were
c dc d d
-Both diversity components were higher at higher altitude.
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Cont…RDA analysis
RDA analysis clearly separated diversity, soilerosion and other explanatory variables.
-The lowland region (region 3) was distinctg ( g )from the others because of minimal
agricultural activities and sparse naturalvegetation.
-Region 5, with the highest altitude, was also separated from the intermediate regionand had high agro-biodiversity, as it waslocated far from towns and roads.
1.0
R1
-Region 1, located close to town, was also somehow separated from the others because of relatively high inorganic fertilizer use.
Var/6yr
ErosionNo.SoilT
crops/yrcrops/6y
SelCr
Fert/kg
Weed_yr
weed_6yrpest ind
SPECIES
ENV. VARIABLES
SAMPLESy g g
-RDA analysis showed agro-biodiversitywas significantly (p<0.001) related to each ofthe explanatory variables, but mainly 0
TotTSppT_Ratio
Var/yrSelCr
Town Dist
Road Dist SAMPLES
Region 1
Region 2
Region 3
Region 4
R i 5
R3 R5
p y , ywith distance to road and town (positively)and fertilizer and soil erosion (negatively).
-1.0 1.0
-1. Region 5
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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 use ( 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) andp ( ; )
-Animals per farm household ( r = -0.21; P < 0.01)
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Conclusion
Higher agro-biodiversity was associated with farms located far from road and towns, often associated with indigenous farming , g gpractices.
Agricultural technology packages (inorganic fertilizer) wereAgricultural technology packages (inorganic fertilizer) were important to increase food production
but were often associated with removal of landraces and i l ( d h b i )native plants (trees and shrub species).
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 speciesaccount locally available land races and native tree/shrub species.
- Lukas Brader Fellowship
Spatial variation in agro-biodiversity, soil degradation and productivity in agricultural landscapes in the highlands of Tigray, northern Ethiopia.
- Lukas Brader Fellowship
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
Materials and Methods
Study areaStudy area
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DatasetFarm Wealth/Social
•Soil type, OM, Avail. P, N•Crops planted/year•Animal manure
•Inorganic fertilizer•Livestock holding•Number of credit sourcesFarmer’s education level•Weed, insect species/farm
•Caloric crop yield & sel.sri.•Farmer’s education level•Farmer’s off farm employment opportunity
Agro BiodiversityAgro-Biodiversity
Soil erosion
•Distance to major road•Distance to major
•Elevation of farms•Slope of farms
town/marketSlope of farms
Development drivers Topographic
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Topographic
Data Collection Method
Development driversTopographic
Geog. locationsGPS GPS
Development drivers
Feature delineation Aerial photos
Aerial
photos
Slope/AltitudeAlti t / SRTM
Crossing of fieldsDouble trackTransect walk
Slope/AltitudeAltimeter/ SRTM
Wealth/SocialFarm Field Measurement & interview
Agro-Biodiversity &Soil erosion
Wealth/SocialFarm
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Data AnalysisSpatial data Non-spatial data
Development drivers
Topographic
Field data
Interviews
Agro-biodiversity
LULC classes
Farm characteristics
GIS
Socio-economicdata
GIS
Overlay Analysis
GIS output mapsData integration
Statistical Analysis- Discriminant analysis (2005) andComparison between 2000 & 2005
Outcome
Co pa so be wee 000 & 005for agro-biodiversity & soil erosion.
- Chi-square (Educat. & Employment
P i d t t t (b t 2000 & 2005)Outcome - Paired t test (between 2000 & 2005)
- Lukas Brader Fellowship
Results
Status of agro-biodiversity in 2005
I. Agro-biodiversity and social characteristicsg 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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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 classesand were positively associatedwith the first canonical function.
- Lukas Brader Fellowship
Cont…
Caloric crop yield (Mcal/farm)Caloric crop yield (Mcal/farm), Animal ownership (number/farm), Farm distance from the nearest town (km) andElevation (m)
also significantly (P<0.05) discriminated agro-biodiversity classes andwere positively associated with the first canonical function.
Compared to low agro biodiversity classes farms with high agro biodiversity class hadCompared to low agro-biodiversity classes, farms with high agro-biodiversity class had52 % higher available P, 39 % higher total N,47 % more crop types,71 % higher animal manure71 % higher animal manure,53 % more animals,42 % more crop selection criteria and 19 % caloric crop yield.
Inorganic fertilizer use (kg/farm) and credit sources (number/farm) werenegatively associated with the first canonical function,but significantly discriminated the three agro-biodiversity classes.
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Cont…
III. Agro-biodiversity and land use types
-Low agro-biodiversity class wasstrongly associated with intensively cultivated land use type (Icu).
-High agro-biodiversity class coincidewith the sparsely cultivated land use type (Scu).
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Cont…
Status of soil degradation in 2005
Soil erosion classes were categorizedinto 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 associatedwith the first canonical functionand contributed significantly (p<0.001)t th di i i ti f th il ito the discrimination of the soil erosionclasses.
-The higher the slope of farms,th hi h th il ithe higher was the soil erosion.
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Cont…
Soil OM (%) and crop selection criteria (number/farm) were negativelyassociated 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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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 inorganic fertilizer increased significantly (Paired t test t 3 40 P < 0 01 n 151)Whereas, inorganic fertilizer increased significantly (Paired t-test, t = -3.40, P < 0.01, n=151)
No significant change for the other variables between 2000 and 2005.
- Lukas Brader Fellowship
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),
i f ( )-Farm distance from the nearest town (km), and -Farm distance from the nearest road (km)
significantly (P<0.05) separated agro-bi di i h l (dbiodiversity change classes (decrease, no change and increase) and were positivelyassociated with the first canonical function.
Wh i i f ili (k /f )Whereas, inorganic fertilizer (kg/farm)was negatively associated with the firstcanonical function but significantly (P < 0.05) separated the agro-biodiversityh lchange classes.
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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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Cont…Spatial distribution of agro-biodiversity (2000 and 2005)
Tree and shrub species diversity didnot change significantly between 2000 and 2005.
Number of crop diversity decreased(a) No. of tree and shrub species
overlaid with road buffers in 2000
(b) No. of tree and shrub species overlaid with road buffers in 2005 Number of crop diversity decreased
significantly mainly on farmslocated close to the nearest major roads.
Proximity of farms to the nearest townProximity of farms to the nearest town was strongly associated with lowagro-biodiversity (mainly with cropdiversity), both in 2000 and 2005.
(c) No. of crop varieties overlaid with road buffers in
(d) No. of crop varieties overlaid with road buffers in
2000 2005
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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 d hi h b f ditand higher number of credit sources.
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
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
ObjectiveObjective
To investigate influence of traditional F. albida based land use systems on barley productivity at field and regional scales in Tigray northern Ethiopiabarley productivity at field and regional scales in Tigray, northern Ethiopia.
- Lukas Brader Fellowship
Materials and Methods
Study areaStudy area
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RRA & PRATopographic map
F lbid densit
Landscape scale-farm datasetTour, interview &
group discussion
Landscape scale
S l ti b l d ith 77 fi ld
F. albida density
Eucalyptus density
Li k d i
Selected site
Selecting sub-landscape with 77 fields Livestock density
Inorganic fertilizer
Land use system
A. albida +Livestock
A. albida aloneStatistical
analysisM d t ti lLow spatial A. albida
Land use system
Di t f t Livestock
High spatial A. albida
Moderate spatial A. albida
Overlay
1 m from A. albida trunk
2 f A
A. albida +Eucalypt
Mixed model
Multiple i
CCAanalysis
Distances from tree
Added Ecosystem Services(Added barley yield)
Overlay
LULC map
Elevation map
50 m from A. albida trunk
25 m from A. albida trunk
regression
Regional scaleField scale
( y y )Elevation map
ResultsProductivity and land use systems at field scale
Barley yield estimate
Th F lbid l d id dThree F. albida land use systems were considered: F. albida alone (AA),F. albida + livestock (AL) and F. albida + Eucalyptus (AE).yp ( )
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Cont…Significantly (P < 0.05) higher barleyyield was found at 1 m from A. albida trunk than 25 and 50 m for the AA andAL land use systems.
In contrast barley yield did not changeIn contrast, barley yield did not changesignificantly with distance from the A. albida trunk for the AE land use system.
1400
1600 a a a
LUS
800
1000
1200
1400
ld (k
g/ha
)
AAAL
ab b
a
b b
a
LUS
200
400
600
800Ba
rley
yiel AL
AE
01 25 50
Distance from A. albida trunk (m)
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Cont…
Soil properties (Mixed Model Analysis)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)- Soil moisture ( P < 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.
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Cont…a b
GIS analysis
AA and AL land use systems werei l i d i h lmainly associated with sparsely
cultivated and moderately cultivatedland use classes, respectively.
The AE was not clearly associated with distinct land use class.
Ho e er most AE ere associatedHowever, most AE were associatedwith higher inorganic fertilizer useand irrigation practices.
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Cont…
Productivity and farm characteristics at landscape scale
Canonical correspondence analysis (CCA)h d l i bshowed clear separation between
barley yield classes and farmcharacteristics (F. albida density,Eucalyptus dominance, Livestockyp ,density and Inorganic fertilizer).
- Barley yield was positively associatedith F lbid densitwith F. albida density.
- Higher yield (Class 3) at higherF. albida density (HA)
- Barley yield was negatively associated with Eucalyptus dominance (HE), locatedclose to townsclose to towns.
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Cont…
Land use classes and spatial distribution of A. albida at regional scalep g
Sparsely cultivated land use class (Scu)was strongly associated with farms havingHigh F albida density but low EucalyptusHigh F. albida density but low Eucalyptusdominance.
Intensively cultivated land use class (Icu)was related with low F. albida density and higher Eucalyptus dominance.
50
60
acia
and
nt
20
30
40
farm
s un
der A
capt
us m
anag
eme
HALAHELE
0
10
20
S M I
Perc
enta
ge o
f E
ucal
y p LE
Scu Mcu Icu
Agricultural land use types
- Lukas Brader Fellowship
Cont…
Added ecosystem service (barley yield benefit)y ( y y )
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
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 aroundIt indicates F. albida trees should be maintained and promoted in and aroundfarmlands 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 (addedwhich in turn was important to enhance added ecosystem service (added barley yield).
- Lukas Brader Fellowship
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 productivitycrop diversity at 151 farms) was positively correlated with crop productivity.
- In contrast, soil erosion was higher at lower relative agro-biodiversity (at the 151 farms).
- Lukas Brader Fellowship
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 humanHow 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.
A f ti t i bl i lt l d ti d f d itAs one way of promoting sustainable agricultural production and food security, 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, pollination and pest control., , p p
- Lukas Brader Fellowship
A O !THANK YOU!
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