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 	Temporal and spartial changes in land use patterns and biodiversity in relation to farm productivity at multiple scales in Tigray,Ethiopia
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Temporal and spartial changes in land use patterns and biodiversity in relation to farm productivity at multiple scales in Tigray,Ethiopia



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  • 1. - Lukas Brader Fellowship
  • 2. Ethiopia is one of the mega centres of diversity - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 4. Study Area - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 12. Description of Land Use/Land cover classes of Tigray, Ethiopia Woodland Shrub land Scrubland Sparsely cultivated Moderately cultivated Intensively cultivated - Lukas Brader Fellowship
  • 13. Cont… Grassland Waterbody Settlement - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 17. Regional scale - results 1964 Sparsely cultivated Shrub land Woodland - Lukas Brader Fellowship
  • 18. Regional scale - results 1994 Moderately cultivated Scrubland Sparsely cultivated - Lukas Brader Fellowship
  • 19. Regional scale - results 2005 Intensively cultivated Shrub land Moderately cultivated - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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) - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 34. Spatial variation in agro-biodiversity, soil degradation and productivity in agricultural landscapes in the highlands of Tigray, northern Ethiopia. - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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. - Lukas Brader Fellowship
  • 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 - Lukas Brader Fellowship
  • 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 !