ASSESSMENT OF VEGETATION DYNAMICS AS FEED RESOURCE AND IMPROVEMENT OF GRAZING LANDS IN SHEEP DOMINATED AREAS OF WEST SHOA ZONE, OROMIA REGION, ETHIOPIA

MSC THESIS

FEKADU NEMERA

OCTOBER 2016 HARAMAYA UNIVERSITY, HARAMAYA Assessment of Vegetation Dynamics as Feed Resource and Improvement of Grazing Lands in Sheep Dominated Areas of West Shoa Zone, Oromia Region, Ethiopia

A Thesis Submitted to the Department School of Animal and Range Sciences

DIRECTORATE OF POSTGRADUATE PROGRAM HARAMAYA UNIVERSITY

In Partial Fulfillment of the requirements for the Degree of Master Sciences in Range Ecology and Biodiversity

Fekadu Nemera

OCTOBER 2016

Haramaya University, Haramaya

ii HARAMAYA UNIVERSITY POSTGRADUATE PROGRAM DIRECTORATE

I hereby certify that I have read and evaluated this Thesis entitled Assessment of Vegetation Dynamics as Feed Resources and Improvement of Grazing Lands in Sheep Dominated Areas of west Shoa Zone, Oromia Region, Ethiopia prepared under my guidance by Fekadu Nemera Kitila, We recommend that it be submitted as fulfill the thesis requirement. Tessema Zewdu (PhD) ______Major Advisor Signature Date Abule Ebro (PhD) ______Co-Advisor Signature Date As a member of the board of the Examiners of the MSc Thesis Open Defense Examination. I certify that I have read and evaluated the Thesis prepared by Fekadu Nemera and examined the candidate, I recommend that the Thesis be accepted as fulfilling the Thesis requirement for the degree of Master of Sciences of Range Ecology and Biodiversity. ______Chair Person Signature Date ______Internal Examiner Signature Date ______External Examiner Signature Date

Final approval and acceptance of Thesis is contingent upon the submission of its final copy to the Council of Graduate Studies (CGS) through the candidate’s department or school graduate committee (DGC or SGC).

iii STATEMENT OF THE AUTHOR

By my signature below, I declare and affirm that this Thesis is my own work. I have followed all ethical and ethical principles of scholarship in the preparation, data collection, data analysis and compilation of this Thesis. Any scholar matter that is included in this Thesis has been given recognition through citation.

This Thesis is submitted in partial fulfillment of the requirements for an advanced MSc degree at Haramaya University. The Thesis is deposited in the Haramaya University Library and made to available to borrowers under the rules of the Library. I solemnly declare that this Thesis has not been submitted to any other institution anywhere for the award of any academic degree, diploma or certificate. Brief quotations from the Thesis may be made without special permission provided that accurate and complete acknowledgment of the source is made. Requests for permission for extended quotations from the reproduction of this Thesis in whole or in part may be granted by the Head of the School or Department when in his or her judgment the proposed use of the material is the interest of scholarship. In all other instances however, permission must be obtained from the author of the Thesis.

Name: Fekadu Nemera Kitila Signature: ______Date: October 2016 School /Department: School of Range and Animal Sciences

iv BIOGRAPHICAL SKETCH

The researcher was born in 1975 at Gimbi district, west Wellega zone, Oromia Regional State, Ethiopia. He attended his elementary and secondary school at Horest Spinings and Lalo Aira secondary school, respectively. He earned his honored diploma in animal sciences from Alage Agricultural College in 2004.

Then after, he served for 3 years at Gimbi District Agricultural Office as supervisor and then, he joined Ambo University in 2007 and graduated with BSc degree in Animal production in 2009. Following receipt of his BSc, he worked as dairy expert at Gimbi district livestock development, health and marketing agency for one year. After one year service he again joined Adami Tulu Agricultural research center and assigned as forage agronomist junior researcher and served as forage agronomist and team leader from 2010 to 2014. Then after four years services in the research center he has got a MSc opportunity by Oromia Agricultural research Institute to fill gap of human power in the field of study in his case team Animal feed resources and range land management team in the field of Range Ecology and Biodiversity and stayed one year for class course and finally he has conducted his research thesis with a title of “ Assessment of Vegetation Dynamics as Feed Resources and Improvement of Grazing Lands in Sheep Dominated Areas of West Shoa Zone Oromia Regional State” at Meta Robi district in mandate area of LIVES/ILRI project with financial support of organization.

v ACKNOWLEDGEMENTS

Above all my greatest thank goes to the Almighty God who helped me in all directions, time to be strong and patient to finalize, this work even in terrible situations I faced during my study period. This thesis would not have been possible without the help and support of many individuals and organizations. First of all, I would like to express my sincere gratitude and heartfelt thanks to my major advisor Dr. Tessema Zewdu at Haramaya University and Dr. Abule Ebro who is working in ILRI/LIVES at Addis Ababa, for his limitless guidance and encouragement since the early time of proposal development to this time. His support and guidance was not limited to the working days and hour but any time and place Dr. Abule was available for help, I would like to say him God bless you ! My appreciations and thanks also go to my mother institute “Oromia Agricultural Research institute” for giving me this MSc. Study chance and providing me all the necessary supports including the payments of my salary and tuition fees.

I would like to, appreciate and thank ILRI/LIVES for funding my research work by financial support during my study period. Without the support of ILRI/LIVES, I couldn’t finalize this piece of work. I am highly thankful to Haramaya University, especially school of Animal and Range Sciences department of Range Ecology and Biodiversity, for all the supports and guidance during the study period. Since we were the first batch of MSc. Students for the department, the department and all staff members helped and guided us very jealously. Thanks all!

I wish to express my sincere word of thanks to Meta Robi District Agricultural Office Livestock Development and Health Agency and Land Management Offices for providing all the necessary information and supports that I needed during the study period. I also thank the owners of grazing land farmers for providing me all the necessary information by sacrificing their time. Finally I also like to express my gratitude to Mr. Bediru Roba for his valuable support during field vegetation species identification and all those helped me in contribution of pieces of effort and time to deserve me while carried out the study.

vi ACRONYMS AND ABBREVIATIONS

ADF Acid Detergent Fiber ADL Acid Detergent Lignin Ca Calcium CEC Cation Exchange Capacity CF Crude Fiber CP Crude Protein DM Dry Matter EC Electric Conductivity GDP Gross Domestic Product GIS Geographic Information System IFPRI International Food Policy Research Institute ILRI International Live Stock Research Institute IVDMD In Vitro Dry-Matter Digestibility IVI Importance value Index K Potassium LIVES Livestock and Irrigation Value Chain for Ethiopian Small Holder Farmers Mg Magnesium MOA Ministry Of Agriculture N Nitrogen NDF Neutral Detergent Fiber NRCS Natural Resources Conservation OM Organic Matter P Phosphorous RCBD Randomized Complete Block Design SDDP Small Dairy Development Project SI Similarity Index SPSS Statistical Package for Social Science UNEP United Nation Education Program USDA United State Department of Agriculture

vii TABLE OF CONTENTS

STATEMENT OF THE AUTHOR iv BIOGRAPHICAL SKETCH v ACKNOWLEDGEMENTS vi ACRONYMS AND ABBREVIATIONS vii TABLE OF CONTENTS viii LIST OF TABLES xii LIST OF FIGURES xiii LISTS OF TABLES IN APPENDICES xiv LISTS OF FIGURES IN APPENDICES xv ABSTRACT xvi 1. INTRODUCTION 1 2. LITERATURE REVIEW 4 2.1.Grasslands 4 2.1.1.World grasslands 4 2.1.2.Ethiopian grasslands 5 2.1.3. Grasslands of west Shoa zone 6 2.1.4. Grasslands of Meta Robi 6 2.2. Effects of Altitude Gradients on Forage Yield 7 2.3. Effects of Altitudinal Gradients on Species Diversity 7 2.4. Grasslands Rehabilitation and Improvement 8 2.5. Effect of Organic and Inorganic Applications Fertilizer on Forage Botanical Composition, Yield and Quality of Natural Pasture 8 2.5.1.Botanical composition 10 2.5.2.Forage yield 10 2.5.3. Forage quality 11 2.6. Sheep Vegetation Preferences in Relation of other Livestock Classes 12 2.7. Land Use Land Cover Changes 12 3. MATERIALS AND METHODS 13 continues…

viii 3.1. Description of the Study Area 13 3.1.1. Location of the study area 13 3.1.2. Climatic condition and topography 14 3.1.3. Soil types 14 3.1.4. Farming system 14 3.1.5. Vegetation of the study area 14 3.2. Vegetation Assessment as Feed Resource 15 3.2.1. Site selection and field layout 15 3.2.2. Experimental design and treatments 15 3.2.3. Sampling procedures 16 3.2.3.1. Botanical composition 16 3.2.3.2. Dry matter yield 16 3.2.4. Soil samples collection and analysis 16 3.2.4.1. Soil sample collection Procedure 16 3.2.4.2. Soil analysis 17 3.3. Sheep Vegetation Preference in Relation to Other Livestock Class 17 3.3.1. Site selection and design of treatments 17 3.3.2. Method of data sampling 17 3.4. Land use/ Land Cover of the Study Area 18 3.4.1. Procedures of land-cover classification 18 3.4.2. Method of data extraction and analysis 18 3.4.3. Sampling Procedure 19 3.5. Improvement of Degraded Grassland 20 3.5.1. Site selection and field layout 20 3.5.2. Experimental design and treatments 20 3.5.3. Sampling procedures 21 3.5.3.1. Botanical composition 21 3.5.3.2. Dry matter yield 21 3.5.4. Chemical analyses and in vitro dry matter digestibility (IVDMD) 21 3.5.5. Soil samples collection and analysis. 22 3.5.5.1. Soil samples collection procedure 22 continues

ix 3.5.5.2. Soil analysis 22 3.6. Statistical Analysis 22 4. RESULTS AND DISCUSSION 23 4.1. Soil Physical and Chemical Characteristics of the Study Area 23 4.2. Effects of Altitudinal Gradient on the Botanical Composition and Dry Matter Yield of Herbaceous species 25 4.2.1.Characteristics of herbaceous species Botanical composition 25 4.2.2. Botanical composition 25 4.2.3. Dry matter yield 26 4.3. Effects of Altitudinal Gradient on Woody Species 27 4.3.1. Woody composition 27 4.3. 2. Species diversity index 28 4.3.3. Similarity index of woody species 28 4.3.4. Importance value index (IVI) 29 4.3.5. Sheep Vegetation Preference in Relation to other Livestock classes 30 4.4. Land Use and Land Cover Changes of the Study Area 31 4.4.1. Nature of the land-cover units 31 4.4.2. Types and extent of land cover changes 32 4.4.3. Causes of the land cover changes 33 4.4.4. Consequences of the land use cover changes 34 4.4.5. Variations on detected changes, interpretations and limitations 34 4.5. Degraded Grassland Rehabilitation and Improvements 35 4.5.1. Physical and chemical characteristics of soil of the experimental site 35 4.5.2. Rain fall pattern of the study area 37 4.5.3. Effects of organic and inorganic fertilizer applications physical and chemical properties of soil of degraded grasslands 37 4.6. Effect of Organic and Inorganic Fertilizer Application on Botanical Compositions and Dry Matter Yield of Herbaceous Species 41 4.6.1. Characteristics of the botanical composition of herbaceous species 41 Continues…

x 4.6.2. Botanical compositions of herbaceous species 41 4.6.3. Dry matter yield 42 4.6.4. Effect of organic and inorganic fertilizer application on nutritional composition of natural pasture 43 5. SUMMARY AND CONCLUSIONS 45 5.1. SUMMARY 45 5.2. CONCLUSIONS 45 6. REFERENCE 46 7. APPENDICE 62 7.1. Appendix in table 63 7.2. Appendix Figure 67

xi LIST OF TABLES

Tables Pages 1. Physical properties of soil at the study site 23 2. Chemical properties of soil at the study site 24 3. Botanical composition and dry matter yield of natural pasture 26 4. woody species diversity indices 28 5. Similarity index of woody species 29 6. Land use land cover change 35 7. Soil property before conducting the experiment 36 8. Soil physical property 38 9. Soil chemical properties of improved grasslands after experiment 40 10. Botanical composition of herbaceous species 43 11. Feed chemical composition of improved natural grassland 44

xii LIST OF FIGURES

Figure page 1. Location of study area 13 2. Sheep vegetation preferences 31 3. Rain fall data of study 37

xiii LISTS OF TABLES IN APPENDICES

Appendix Table Page 1. Herbaceous species of the study area 63 2. Wood species of the study area 64 3. Vegetation species desirability livestock class 64 4. Description of the experimental treatment setup 65 5. Tree cove 65 6. Shrub cove 65 7.Tree Species diversity 65 8. Shrub diversity 65 11.Location of sampling area 66 10. Rain fall data of Inchini Station 67

xiv LISTS OF FIGURES IN APPENDICES

Appendix Figure page 1. Experimental design setup 67 2. Site Selection and sampling Procedure for vegetation Assessment 68 3. Land Use land cover of Meta Robi district (1986, 2000 and 2013) 69 4 .Questionnaires prepared on Land use land cover dynamics and sheep herbaceous species and browse vegetation preferences in the study area 70

xv ABSTRACT

ASSESSMENT OF VEGETATION DYNAMICS AS FEED RESOURCE AND IMPROVEMENT OF GRAZING LANDS IN SHEEP DOMINATED AREAS OF WEST SHOA ZONE, OROMIA REGION, ETHIOPIA

The study was conducted in Meta Robi district, west Shoa zone of Oromia Regional State with the objectives to assess the sheep grazing lands vegetation dynamics as feed resources in sheep dominated areas and to improve grasslands through improvement methods. The sheep vegetation preferences was studied using check list and group discussion. The land use land cover of the study area was studied using 1986, 2000 and 2013 year’s satellite images. Vegetation assessment was conducted in 270 quadrat 90 from each three altitudes having 10mx10m (for trees layer), 5mx5m (for shrubs layer) and1mx1m (for herbs layer) quadrant size. A reconnaissance survey was conducted to identify and select the degraded grassland. The treatments were chemical fertilizer, cattle manure, wood ash (k2co3), lime (Caco3) and control. The plot sizes was 4m x 4m with three replication arranged in RCBD design. The space between plots and replications were 1m and 2m respectively. Composite soil samples were collected and analyzed. Pre- experiment. Then after harvest DM yield was determined by (ILCA, 1990).Chemical composition of the feed samples were determined by (AOAC, 1990). Additionally fifteen soil samples from each plot and three composite soil samples from three altitudinal gradients were collected and analyzed. The soil lab analysis results before the experiment indicated that the soil was salt free, slightly acidic pH (5.9), greater in CEC (20.37), low in available P (1.6) the organic carbon and total nitrogen were rich (0.98 and 0.081), higher in exchangeable Ca (0.98) and Mg, Na and K were (3.6 0.46 and 12) respectively. Highest botanical composition of grass was recorded with chemical fertilizer (59%), and highest legume components was recorded with wood ash application (57.9%). The highest total dry matter recorded with chemical fertilizer application (4.5 ton ha-1). The DM and total ash of the feed composition were affected at (P<0.05) while, CP, ADF, ADL and IVDMD were affected at (P<0.001). Wood ash was recommended as grasslands liming material for farmers. Key words: Assessment, Botanical composition, Cattle manure, Chemical fertilizer, Total dry matter, Lime, Meta Robi and Wood ash.

xvi 1.INTRODUCTION

More than 85% of the Ethiopian population live in the rural area and agriculture is the dominant sector of the Ethiopian economy, accounting for more than 45% of the gross domestic products (GDP), 80% of the exports, and 80% of the total employment (Belay et al., 2013). The livestock sector also contribute an immense of contribution for the country. 30- 40% of Agricultural Growth Domestic Product (ADP), 16-20% of national GDP and 14-16% of foreign exchanges comes from this sector (Gebregziabhare, 2010).

Ethiopian livestock population estimated to be 55.03 million cattle, 27.35 million sheep, 28.16 million goats, 1.96 million horses, 6.95 million donkeys, 0.36 million mules, and about 1.1 million camels and 5 1.35 million poultry (CSA, 2013).

From agro ecological point of view the distribution of sheep and goat in the country is closely associated with either of climatic extremes, sheep mostly concentrated in the highlands while goats in the arid and semi-arid lowlands. Agro-ecologically greater 3000 masl, 2000-3000 masl and 1500 to 2000 masl are suitable for sheep production. The feeding habit of sheep is grazing herbage close to the ground while goats predominantly browse diverse flora. Therefore, the type of forage to be needed need to be viewed in relation to the ecological situations and feeding behavior of sheep and goats (Solomon et al., 2008).

The country has 111.5 million hectares of land, and although 74 million hectares are arable, only 13 million hectares are being used for agricultural activities (Davis et al., 2010). According to CSA (2011), Ethiopian grass lands account more than 30% of coverage and constitute 57% of livestock feed resource. There are different types of grasslands that serve as livestock grazing in the highlands of Ethiopia. These include privately owned grazing areas, communally owned grazing areas, riverside and lakeshore grazing areas, roadside grazing areas and to some extent rested areas, which are reserved for dry season grazing (Tessema, 2005).

However the natural pasture grazing land which is a feed resource for live stack reduced from time to time due to fast growth of population, land demand for crop production and 2 over grazing (Yayneshet, 2010). Well managed grassland is essential to optimize cattle and sheep performance. The proven benefits of using sward heights to manage grass for sheep and cattle have led to the advice that sward heights in set-stocked systems of 4-6 cm for ewes and lambs and 6-8cm for cattle are ideal. For sheep in particular maintaining a sward height of around 4cm will ensure grass quality is at its highest (Lovina et al., 2011).

The grasslands which cover 57% of the animal feeds portion characterized by seasonal fluctuation of total dry matter production and nutritional quality. This was due to, the distinct seasonal fluctuations of annual rainfall pattern (CSA, 2013). Therefore, evaluation of herbage yield and quality of natural pasture helps to arrive at the correct carrying capacity and stocking rate of the ruminants. This help in solving problems of over stocking. Estimation of yield and chemical composition also give information to species composition and quality of pasture (Birnin et al., 2014). The main problems of natural pasture grasslands as a source of feed for livestock is their low production of dry matter. The dry matter production depend on the seasonality of Plant growth, which inter-related to the annual rainfall distribution pattern , which restrict the availability of herbage for the grazing animal to four or five months of the wet season over most of the natural grasslands of the country (Ulfina et al., 2013). The limitations associated with different factors such as cutting frequency, species compositions, and stage of maturity, climatic conditions, soil fertility status and season of harvesting (Tessema et al., 2010). The use of native hay is limited in coverage and it is better in terms of its feeding value than crop residues if timely cut, proper handling and storage measures are applied. Even during years of good rainy season, forage is not sufficient to feed livestock in the highlands for reasons associated with restricted grazing land and poor management (Melese et al., 2014).

According to the Livestock and Irrigation Value Chain for Ethiopian Smallholder framers (LIVES), (2013), environmental assessment, it was reported that the grazing land was degraded by sheep and goats overgrazing due to lack of proper grazing land management. The numbers of sheep in Meta Robi district exceed the numbers of sheep population in neighbor districts Adea Berga and Ejere. Similarly, Endale (2015) indicated that the grazing lands which contributes 58.9 of animal feeds resources had been shrinking due to allocation of communal grazing lands for crop production and the major constraint of the study area 3 was critical shortage of animal feed. It also indicated that the available small portion of grazing lands which were left for grazing are un productive and degraded due farmers knowledge gap in grazing lands improvement techniques especially in manure and fertilizer applications. Therefore in consideration of the research gaps identified by the researchers the vegetation of the grasslands needed to be assessed as feed resources, in sheep vegetation preferences and land use land over changes as it indicate where the resources more affected or declined and direct when to start the interventions to be implemented. After assessment of vegetation and identification degraded grassland the improvements methods planed and designed and undertake by using fertilizer, manure, wood ash and lime application treatments to identify and recommend sounding and feasible grassland improvement method for users.

The general objectives of study was to assess the grazing land vegetation resources and improving degraded sheep grazing lands in sheep dominated grazing areas of West Shoa. Specific Objective o To assess the sheep grazing lands vegetation as feed resources in the study area. o To improve grazing lands through grazing lands improvement methods in the study area. 4

2. LITERATURE REVIEW

2.1.Grasslands

Grasslands are defined as a natural vegetation composed mainly by the members of gramineae family of plants that are grazed by livestock (Pathak and Dagar, 2015). Grass lands are the largest single component of the earth’s 117 million km2 of vegetated lands. Generally grass lands are too arid to support crop lands or dense forests and they contribute for livestock to the earth’s human carrying capacity.

A grassland is defined as a non-wetland type with at least 10% vegetation cover, dominated or co-dominated by gramineae and forbs growth forms, and where the trees form a single- layer canopy with either less than 10% cover and 5m height (temperate) or less than 40% cover and 8m height (tropical) (Dixon et al., 2014).

2.1.1. World grasslands

According to Abule (2015) about 52.50 million km2 grasslands found globally of which 28, 23, 20 and 19% are found in semi-arid, humid, cold and arid regions respectively. Sub- Saharan Africa and Asia share the largest portion of grasslands 14.5 and 8.9 million km2 respectively. Owing to the abandonment of traditional practices in this part of Europe, the number of species-rich semi natural grasslands has decreased and plant species declined, requiring the reintroduction of grazing in order to restore these important ecosystems.

World grasslands are prone to degradation due to natural and anthropogenic factors such as overgrazing, deforestation, agricultural mismanagement, over exploitation and industrial activities. And this degradations problems are more accelerating in developing countries because livestock population increase is parallel to human population which exacerbated the rates of degradation by decreasing carrying capacity of grazing lands (Pathak and Dagar, 2015). Grasslands ecosystems are most threatened ecosystems around the world due to anthropogenic mediated factors. Thus, conservation and restoration of species rich grasslands is an urgent tusk to be implemented. According to Malatinszky et al. (2013), understanding mechanism of sustaining grasslands biodiversity is vital for effective 5 planning of conservation. The plant composition and species richness of permanent grasslands are determined by management practices and site characteristics such as topography, water and nutrient availability, and light conditions (Marie et al., 2014). The productivity and quality of the grasslands could be increased by different fertilization regimes and types of mineral/organic fertilizers. A typical characteristic of fertilizers is that they affect (directly and indirectly) the growth and development of plants (Solomon and Alemayehu, 2015).

2.1.2. Ethiopian grasslands

The grasslands of Ethiopia found in Afro-monotone and Afro-alpine grasslands regions, which covers 490,000 km2 or 43% of the total high lands of Africa, the humid Savannah of west and south western of Ethiopia and the eastern rangelands and rift valley (Solomon and Alemayehu, 2015). According to CSA (2011), Ethiopian grass lands account over 30% of the land cover and constitute to 66% percent of feed resources for livestock. However, according to Solomon and Alemayehu (2015) the grasslands of Ethiopia cover about 25% of land surface and contribute a lot to lively hoods of millions of pastoral and agro pastoral communities. It was estimated that 57% of livestock feed resources comes from this natural grasslands (CSA, 2013).

Afro-alpine vegetation, found at altitudes above 3,000 meters, is characterized by heaths and lobelia with cold-resistant short grasses. But much of this area is overgrazed. The highland areas between 2,200 and 3,000 masl are characterized by mixture pastures of grasses and legumes, with the legume component decreasing as the altitude decrease. Natural grasslands of the highland areas are rich in legume species, while grasslands of the mid and low land zones have lower proportion of legume; this is due to as altitudes decrease the proportion of legumes decrease due to edaphic differences (Mekonnen and Ali, 2013). The natural grazing lands of the country, which support the livestock as a feed resources are reduced from time to time due to fast growth of population demanded land for crop production and over grazing (Yayneshet, 2010).

Natural pasture supply the bulk of livestalk feed which is composed of indigenous forage species and is subjected to overgrazing. Grazing occurs on permanent area, fallow land and 6 a land following harvest. Both fallow land and crop stubble provide poor grazing for a very short period just after harvest of crops. The availability and quality of native pasture varies with altitude, rainfall and, soil type (Yayneshet, 2010).

2.1.3. Grasslands of west Shoa zone

Grazing land contributes about 67% of livestock feed resources in west Shoa zone in Oromia, Ethiopia, making it an important resource that deserves attention. Nowadays however, private grazing land and hay preparation are more common in west Shoa zone. ‘If a person is herding animals in June in one place, you will find them in the same place in September’ explains a community member in west Shoa zone; showing the high level of private use of grazing lands and the disruption of the previous system that rotated grazing between wetland and upland areas. At the moment, private grazing lands (0.25 to 0.5ha/household on average) are used for hay making and/or grazing. Furthermore, private grazing lands are larger in size than communal ones and the latter are diminishing (Abule, 2015).

2.1.4. Grasslands of Meta Robi

According to Endale (2015), the grazing land of Meta-Robi district was about 12,979.5ha of grazing lands is available and the total dry matter production from natural pasture was 2 tons /ha/ year. The natural grazing land in the district comprised 58.9% of feed resources for livestock. The average house hold grazing land was 0.77 to 1.45ha. Available communal grazing land was 28.9% and the size of communal grazing land was decreasing overtime due to allocation to land less youth and expansion of crop lands. This could be major challenging for animal feed production since only 31% was left for grazing.

Dry matter production in the district was below requirements of livestock and sustain the animals for only 5 to 6 months. This critical animal feed shortage coupled with animal’s disease and affecting the farmers’ economy. These constraints of animal feed emerged from mismanagement of grazing lands due lack of an awareness of management techniques by farmers. 74.6% of the respondent in the district do not know how to manage their grazing lands and only 31.8% and 9% of the respondents were practiced manure and fertilizer 7 application respectively. Similarly, Livestock and Irrigation Value Chain for Smallholder farmers (LIVES), (2013) identified that the grazing lands of the study area was degraded by sheep and goats (small ruminants) due to lack of improper grazing management.

2.2. Effects of Altitude Gradients on Forage Yield

Forage yield of natural pastures are influenced by altitude, rainfall and soil fertility status (Adane, 2005). However, the average yield of natural pasture in Northwest lowlands of Ethiopia was 5.4 tones ha-1 (Bilatu et al., 2013). Another finding reported that pasture productivity in upland grazing or degraded lands of sloppy area was very low (0.5-1t DM ha-1), in arable land grazing areas 1-3t DM ha-1 and in valley bottomland grazing areas ranged from 3-5t DM ha-1 (Gezahegn et al., 2015). The biomass production of herbaceous species depends on climate, available water and altitude (Chollet et al., 2014). The altitude becomes a limiting factor for plant growth. The higher the altitude is the lower the atmospheric temperature and higher precipitation, which is more likely to limit plant growth (Cheng et al., 2013).

2.3.Effects of Altitudinal Gradients on Species Diversity

According to Rathod (2014), species diversity differs along altitudinal gradient in different layers at different scales in distributions pattern. Natural grasslands constitute the main highland pastures such as grasses, they contain 28 Trifolium species out of which eight are endemic (Alemayehu, 2006). Altitude is a factor that determines the distribution of climatic factors and land suitability, this influences the crops to be grown, rate of crop growth, natural vegetation types and their species diversity. Taking the proportion of legumes tends to increase with increasing altitude; particularly above 2200 msl, there is a wide range of annual and perennial Trifolium spp and annual Medicago spp. At lower altitudes native legumes are less abundant and commonly have a climbing or sprawling habit with a large variation in their range and density in wet bottomlands. This appears to be only partly due to edaphic differences.

According to Alemayehu (2006), in the lowlands browse and shrubs are dominant plants. The components of species diversity that determine the expression of traits include the 8 number of species present (species richness), their relative abundance (species evenness), presence of the particular species (species composition), the interactions among species (non-additive effects) and the temporal and spatial variation in these properties. The permanent grasslands species composition and richness are influenced by management practices, and site characteristics such as topography, water, nutrient availability and light conditions (Araya et al., 2013).

2.4.Grasslands Rehabilitation and Improvement

In human used lands degradation is clearly correlated with species loss, but it is not a clear that degradation is driven by species loss. Instead, soil degradation simultaneously reduces rain use efficiency and species richness. The loss of keystone species, the species critical to ecosystem and ecosystem functioning is a final indicator that irreversible land degradation has occurred. Strategies for rehabilitation should be address the maintenance or restoration of soil rather than species conservation (Pathak and Dagar, 2015).

Scientific plan needed to be developed for a realistic successful restoration of degraded grasslands. Assessments of the current status of the grasslands and delineating the areas needing intervention is point of start. The restoration is only possible after demarcation of area, scientific protocol to be followed by the implementing agency be identified. This includes removal of unwanted and unpreferred species, protection of the area from grazing during periods of restorations by fencing, trenching to keep wild animals, land leveling, provision of slopes and trenching to ensure drainage to leach out excess water, addition of manure and fertilizers to improve soil fertility and sowing the seeds (Pathak and Dagar, 2015).

2.5.Effect of Organic and Inorganic Applications Fertilizer on Forage Botanical Composition, Yield and Quality of Natural Pasture

The most common deficient nutrients for pastures are phosphorus, potassium, and nitrogen. The amounts of each nutrient needed will depend on the soil type, previous use of field, and previous nutrients added either through fertilizer or manure. Phosphorus is necessary to establish new seedlings (Manitoba, 2013). So the amount of nitrogen fertilizer needed 9 depends amount of legumes in the pastures. Pastures with over 40% legume generally need no additional nitrogen fertilizer. Too much nitrogen fertilizer will cause the legumes to be less competitive and be crowded out by the grasses in the pasture (Tesfay et al., 2015). Improving pastures require fertile soils for optimal herbage production. Basal applications of the micronutrients especially, nitrogen (100 to 150kg/ha) urea and phosphate are helpful for successful establishment. However, considering the economic status of farmers and using farmyard manure is advisable at the rate of 5–10 tones /ha (Asmare et al., 2015).

According to Abdi (2015), application of nitrogen fertilizer improved both quantity and quality of forages. Ashagire (2008) also reported that quantity and quality of natural pasture was improved with application of fertilizer coupled with stage of harvesting. Adane (2003) reported that, the yield of the natural grasslands increased with increasing levels of fertilizer applications up to 125 kg/ha regardless of decline in overall production due to frequent grazing and cuttings in one growing season.

Wood ash is the inorganic and organic residue remaining after combustion of wood or unbleached wood fiber (Mark, 2013). According to Asmare et al. (2015), application of lime and wood ash increased the soil pH and available phosphorous (P), reduced soil exchangeable acidity and aluminum and also increased plant height, dry shoot biomass, dry root biomass and P uptake. However, wood ash application gave a better yield than the lime application because of the additional nutrients such as P, potassium (K), magnesium (Mg) and micronutrients essential to plant growth. This implies that combined application of wood ash with lime may enable resource poor farmers of Ethiopia, who use wood as their source of fuel, can give chance to use lower levels of commercial lime and mineral P fertilizers in strongly acidic soil which has high tendency to fix P. The experiments have proved that wood ash can successfully be used for purposes of nutrient supply. The ash must be applied and evenly mixed with the upper level of the soil before sowing; the recommended wood ash dose is 1 to 5 t/ha. In nitrogen deficient soils, it is recommended to combine the application of wood-ash with N fertilization (Manitoba, 2013).

However Fernandez al. (2012), reported that wood ash application affected soil properties in similar way with lime but does not affected plant growth. Field and greenhouse research 10 confirms the safety and practicality of recycling wood ash on agricultural lands. Wood ash has a liming effects of between 8 and 90 percent of the total neutralizing power of lime, and can increase plant growth up to 45 percent over traditional limestone.

The major constraints to land application of wood ash are transportation costs, low fertilizer analysis, and handling constraints. With ever increasing disposal costs, land application of wood ash will probably be the disposal choice in the coming century as it results in savings for the industry, an opportunity for agriculture and conservation of our resources (Mark, 2013).

According to Nasedjanov (2012) different liming rates for grasslands in targeting soil pH levels. 3.7 ton per ha was established to adjust soil to pH 5.5 (0.01 M CaCl2) over a 6 year periods on pastureland sown ryegrass and subterranean clover (Trifolium subterraneum) in south-east Australia. In England, Wales and Scotland the liming rate was at 2.5 ton per ha for grassland to maintain soil acidity and to avoid yield loss.

2.5.1. Botanical composition

The relative proportions of the plant components (species and morphological units) in a canopy above a defined sampling height, preferably ground level and calculated based on forage mass, cover, density or frequency (Allen et al., 2011). Appropriate grassland management through the number of cuts and fertilization makes it possible to improve botanical composition, and amount and quality of the forage (Marie et al., 2014). According to Alemayehu (2004), the highlands of Ethiopia is a center of origin and diversity for many tropical grasses and herbaceous legumes of the world, and a total of 736 grass and 358 herbaceous legumes species have been documented in the country. Tessema et al. (2010) reported that, stages of harvesting influenced botanical composition than application of fertilizer.

2.5.2. Forage yield

The application of fertilizers on natural pasture has been clearly shown to improve the herbage yields (Adane, 2003). Similarly, Yossif and Ibrahim (2013) reported application of fertilizer increased the forage yield of natural pasture. Chemical fertilization reduced the 11 legume content and increased grass proportion and total yield (Hanife, 2010). The dry matter yield of fertilized plots of natural pasture has been shown to be 9.47 ton/ha as compared to unfertilized plots 5.67ton/ha at 90 days of harvest (Adane, 2003). This was due to .nitrogen fertilizer fasten the growth of grass to be competitive over legume.

2.5.3. Forage quality

When the commercial fertilization was applied with aeration, hay yield increased more, but forage quality decreased (Hanife, 2010). It was reported that grass quality indices were mostly influenced by the rate of nitrogen(N) fertilizer by what the protein content in grass dry matter and its total yield per hectare increased considerably (Bumane, 2010). Wide variations in weather conditions from year to year results in variable forage yield at similar N fertilizer rates. Also, yield in response to N varied positively during wet seasons. However, despite the wide variations in yield, N fertilization consistently increased crude protein (CP) and total digestible nutrient (TDN) and decreased acid detergent fiber (ADF) and neutral detergent fiber (NDF) (Kerning, 2011). In contrary Mohammed and Hassan (2011), reported that urea fertilizer application reduced the crude protein content of pasture.

According to Marie et al. (2014), the cattle slurry application significantly, increased the concentration of crude protein and dry matter compared to unfertilized or control. These results, indicates that collecting manure from grazed plots resulted in reduced biomass production in comparison with manure applied plots. The biomass yield on manure applied plots that were moderately and heavily grazed was 23.6% and 23.2% higher, respectively, than on plots with no manure. Species richness was greater on moderately grazed plots with manure than on plots with other treatments (Girma et al., 2003).

. Application of different types of organic manures and nitrogen, phosphorous and potassium (NPK) fertilizer enhanced availability of soil nutrients and cations exchange capacity considerably, in both acid soils and nutrient depleted soils (Adeniyan et al., 2011). To maintain the required amount of dry matter (DM) yield, crude protein (CP) and digestibility for ruminants, the presence of herbaceous legumes in the grassland is paramount importance, and this can be achieved through an appropriate stage of harvesting coupled with application of an optimum level of N fertilizer (Tessema, 2005). 12

2.6. Sheep Vegetation Preferences in Relation of other Livestock Classes

In Ethiopia most production systems, agro-ecologies and geographic regions, extensive free grazing in communal grazing lands and stubble grazing are the most common practices of feeding sheep, while browses are used for goat flocks by almost all farmers and pastoralists (Tsedeke, 2007). According to Lemmon (2016), natural pasture treated with season of intensive sheep grazing tend to varied in vigor and reproductive capability. According to (Meg et al., 2007) sheep preferred vegetation species were Festuca ovina, Deschampsia flexuosa, Danthonia decumbens, Luzula campestris, Potentilla erecta, Carex demissa, Galium saxatile, Carex binervis, Nardus stricta and Juncus effuses.

The effects of sheep grazing on mountain vegetation diversity, biomass and functional groups Olivia that heavy sheep grazing that both plant height and biomass decreases as the altitude increases and biomass decreased with altitude increased. Woody vegetation is mostly found on south facing aspects and lower altitude.

2.7. Land Use Land Cover Changes

Like elsewhere, parts of the world and Africa land use land cover in Ethiopia are dramatically impacted by anthropogenic effects rather natural phenomenon. Recent investigations and findings show that the land use land cover of the country undergoing under influence of these factors.

The land use land cover changes in Ethiopia as studied and reviewed by several researchers indicated that human driven forces and natural factors influencing the pattern of land use land cover.

A large extent of land was converted to farms and settlements, but the individual farm size has been declining (Amare, 2015). In appropriate farming practice and terracing were another human induced factors shape land use land cover. Landslide are the outcome of this factors in the study area (Tarun et al., 2015). Land-cover analysis provides the baseline data required for proper understanding of how land was used in the past and what types of changes are to be expected in the future. 13

3. MATERIALS AND METHODS

3.1.Description of the Study Area

3.1.1. Location of the study area

The study was carried out in Meta Robi district of west Shoa Zone of Oromia Regional   State, study area is situated at 9 19 599’(9,3333) N latitude and 38 10’ 01’(38,1667) E longitude geography with average elevation 2473 meters above sea levels. The study area is located at 101kms from the capital city of the country Addis Ababa at west. The altitudes ranges of the study area is 1,376–2,904 meters above sea levels (Figure1). The total human population of the district is 166,472 (82,482) are male and 83,990 are female (CSA, 2015). The total land area of the study area, is about 93,769 ha (crop land=51,762.9 ha, grazing land=11,775.94 ha, forest land = 6,792.75 ha and land used for other purposes= 23,437.4ha), the district has 41 rural kebeles and 5 peri-urban towns (Endale. 2015).

Figure 1.Location of study area (Source: Ethio GIS 2015) 14

3.1.2. Climatic condition and topography

The minimum and maximum temperatures of the district are 15 and 31C, respectively. The district receives bimodal rainfall an average of annual rainfall ranging from 750–1,300mm (high land =950-1300mm, midland =800-950mm and lowland =750-800 mm). The main rainy season is from June to end of September. According to Endale (2015) the topography of the district is characterized by flat land, valley, mountains and rugged area estimated to be 60%, 8%, 9% and 23% respectively.

3.1.3. Soil types

The soil types of the district are classified into Humic Nitosols (one of the best and most fertile soil, can suffer acidity and phosphorous-fixation, and it becomes very erodible). Processes (FAO, 1974).

3.1.4. Farming system

The majority of the population of the district depends on rain-fed agriculture. The farming system is characterized by mixed crop-livestock production system with a wide ranges of cereals and livestock husbandry practice.

The livelihood of the farmers depend on the production of cereals, pulses and oil crops along with livestock that is kept on natural pasture and crop residues (FAO, 2011).

3.1.5. Vegetation of the study area

Andropogon abysincus, Cynodon dactylon (L.) Pers., Eragrostis tenuifolia, Sporobolus pyramidalis P. Beauv, Eleusine africana, Chiloris pichynotrics, Pennisetum spp, Hyparrhenia rufa (Nees) Stapf, Lolium multiflorum, Digitaria abyssinica, Cerastium Octandrum, Snowdenia polystachya, Setaria sphacelata (Schumach.) M.B, Eleusine jagrie, Eleusine flocci folia, Eleusine indica, Cyperus rotundus, Trifolium ruppellianum, Guizotia scabra, Bidens pilosa, Bidens biternata, Amarhantus aspera, are major herbaceous species found in the study area and similar with those reported by (Alemayehu, 2006; Gezahegn et al., 2015). 15

3.2.Vegetation Assessment as Feed Resource

3.2.1. Site selection and field layout

Site selection of vegetation assessment was conducted with discussions apprehended with the community members, elders in the kebeles and agricultural experts in the office who know about the major grazing areas and their locations. The numbers of sites in the district were decided based up on proportional basis of the available grazing lands in the district. The study district was divided into three based on altitude variations using geographical point system (GPS) 1650-2000 msl, lower altitude, 2000-2350 msl, as mid altitude and 2350- 2700 msl as higher altitude (Holechek et al., 1998), as it is widely argued, altitude has an important influence on the distribution, growth, and diversity of rangeland plants ( Getachew et al., 2008). However the classification of altitudes are not on the basis of Ethiopian agro ecological classification (Alemayehu, 2006).

Each altitudinal site was further classified in to the three grazing sites, moving from four to five Kms, considering the vegetation status of the study area discussing with district livestock agency further each altitudes classified in to three locations or sites. A 200m x 50m at 4 Kms interval transect area was divided sampled using 1m x 1m, 5m x5m and 10m x 10m quadrat for herbaceous species, shrubs and tree species (Karami et al., 2015).

3.2.2. Experimental design and treatments

The study was conducted using a three altitudinal gradient variations with 350 msl as (1650-2000 msl, 2000-2350 msl and 2350-2700 msl (Holechek et al., 1998). The design of this experiment was factorial experiment arranged as randomized complete block design with three replications. The altitudinal gradients variations, transects and quadrat numbers were considered as treatments replications and plots.

Transect were laid out with 200m x50m at 4 Kms interval (Figure appendix 1). The total number of treatments during the study were three treatments (3 altitudinal variations) as indicated in (Appendix figure 4). 16

3.2.3. Sampling procedures

3.2.3.1. Botanical composition

The botanical composition was determined by harvesting the herbaceous randomly selected samples (1m × 1m quadrat) within each transect at height of 0–5cm above the ground. Immediately after harvesting, the total fresh weight of the pasture in each plot was measured using a sensitive balance and then the sample in each plot was further classified into different botanical compositions based on biomass (grass, legume and forbs) and each botanical component was weighed separately to determine the contribution of each component in the total dry matter yield of the pasture (ILCA, 1990). Furthermore all species were listed, recorded and identified based on, their morphological, structural and floristic characteristics of each botanical component. Nomenclature was done following (Cufodontis, 1953–1972; Fromann and Persson, 1974; Edwards et al., 2000; Hedberg et al., 2003).

3.2.3.2. Dry matter yield

The DM yield of each botanical component in each plot was determined by drying a representative sample in an oven at 70 °C for 48 hour until constant weight followed by weighing (ILCA, 1990). The DM yield of each botanical component was calculated separately and added together to provide the total dry matter yield of the plot, and the final dry matter yield was reported in tons per hectare (t ha−1).

3.2.4. Soil samples collection and analysis

3.2.4.1. Soil sample collection Procedure

The soil samples from vegetation assessment site were collected by randomly selected soil sample taking area by considering to attain the target representative soil sample of the study area. Accordingly three soil samples were collected from three transects of each altitudinal gradient at a depth of 0-20cm using augur. Then after, the samples were pooled together to form one composite sample representing the site (transect) under one altitudinal gradient. 17

The soil samples collection procedure from a plot was followed a” zigzag” method to get a reliable soil test (Estefan, 2013).

3.2.4.2. Soil analysis

The collected soil samples were, dried and thoroughly mixed (composited) and prepared to determine the soil properties; pH, organic carbon (OC), electro-conductivity (EC), available phosphorus(P), exchangeable potassium (K), total nitrogen (N), exchangeable calcium sodium, magnesium and texture.

Accordingly, the soil textural analysis was performed using the hydrometer method, total nitrogen (N) found in the soil samples was analyzed using Kjeldahl method. Soil organic matter (SOM) was determined by multiplying% OC by the factor of 1,724 (Brady, 1990). Available P using Olsen method (Olsen and Dean, 1965). EC in water suspension with soil to water ratio 1:2.5 by electro conductivity meter and in the same manner the pH of the soil was determined in water suspension with a soil to water ratio of 1:2.5. Cation exchange capacity (CEC) was analyzed by ammonium acetate. Exchangeable cations: sodium (Na), potassium (K) were extracted by flame photometer. Magnesium (Mg) and calcium (Ca) were extracted by EDTA titration. The soil analysis was undertaken at Ziway soil laboratory.

3.3.Sheep Vegetation Preference in Relation to Other Livestock Class

3.3.1. Site selection and design of treatments

The site selection was carried out in collaboration with Woreda livestock agency experts, DAs and local communities. Based on their experience, three sheep grazing sites which are frequently grazed by sheep were identified and assessment was undertaken using quadrat (Lukas et al., 2016).

3.3.2. Method of data sampling

A combination of vegetation assessment and a focal group discussion were undertaken in sampling of data. The vegetation was sampled using 1 m x 1 m quadrat thrown in the field 18 and the herbaceous species included in a quadrat were identified, counted, and named in local name with support of herders and filed technician knowledgeable about taxonomy of the plant species from Adami Tulu Agricultural center. Finally the identified plants local names were translated in botanical names. Ten key informants were selected purposely from sheep grazing sites. The knowledge and experiences of the farmers were considered to attain target individual rearing sheep knowledgeable and experienced in species more preferences.

3.4.Land use/ Land Cover of the Study Area

3.4.1. Procedures of land-cover classification

A simple classification scheme comprising seven land-cover types was developed for the purpose of this study and a mapping legend was prepared for each of the cover types (Hussein, 2009). A combination of information collected from the field and a satellite image (resolution 30m x 30m x 180m), have been effectively used in the preparation of the map legend.

3.4.2. Method of data extraction and analysis

To study trends and dynamics of, satellite image and group discussion were used. A total of three land sat satellite images, Digital Elevation Model 30m by 30m by 180m resolution and 30 households were used. These three referenced images were obtained from Ethiopian Mapping Agency. Field survey and group discussion used to collect the history of land use/ land over data of the study area. The land use/ land cover change map of the study area was correlated to the information collected from the questioners to study factors triggering the change.

Data Analysis: The contribution of land use/ land cover types on the recorded change in the study area is the primary determinant factor. In order to clearly assess the changes of land use and land cover in the study area, determination of type and size of classes is significant. Accordingly, based on the information’s obtained from the key informants past knowledge, 19 visual interpretation of remotely sensed satellite image and using field observation over all seven land use and land cover classes were identified for this study.

Before the classification was accomplished, correcting the data for sensor irregularities and atmospheric noise was performed using ERDAS IMAGINE 10.2 software. To facilitate supervised classification, unsupervised classification and aerial photo image fusion was done. For each land use and land cover class, signature was prepared using training area and significant spear ability is obtained. The classification of land use/land cover was undertaken based on supervised classification.

The comparison of land use and land cover statistics assisted to identify the amount of changes per hectares, percentages, extent, and rate of changes between 1986 and 2013. Observed change was calculated by subtracting the recent data from the former/previous one (1986-2000 and 2000-2013 years). Geographical extent of each land use and land cover type was computed for each time interval and changes in the trend dynamics of different periods were traced. Land use/ land cover conversation rate and extent were computed in terms of percentage and area change. The land cover conversion matrix analysis was conducted in ERDAS IMAGINE 10.2. (ERDAS IMAGINE, 2010).

3.4.3. Sampling Procedure

A group discussion was undertaken in October 2015 to generate information on level of land use land cover of the study area to obtain insight in to social and environmental factors that forced to change land use land cover of the area and to identify the extent of changes in land use land cover in the last three decades.

Thirty key informants were selected purposely from study area in collaborations with development agents in consideration to attain target individual pre prepared questionnaires relevant to land use land cover of the study area in touch the past and current scenario were openly discussed with key informants knowledgeable about land use land cover of the study area in past decades (Hussein, 2009). 20

3.5. Improvement of Degraded Grassland

3.5.1. Site selection and field layout

Reconnaissance survey had conducted in June 2015 to collect baseline information to have a mental image and visual information on the study area in relation to its vegetation distribution and topographic nature. To get an impression of the site conditions to identify the possible sampling sites and the number of transect lines to be laid down across the altitudinal gradients and to select degraded grassland to take intervention measures. This preliminary survey was aimed at identifying the plant community types and their distribution and to have a better familiarization with the study area. The degraded grassland with slop 6-10% by considering run off in the area to reduce the leaching of nutrient was selected at one location from sheep grazing sites to conduct the experiment of improvement of degraded grassland.

3.5.2. Experimental design and treatments

The study was conducted using a 4m × 4m factorial experiment arranged in a randomized complete block design with three replications. The space between plots and replications were 1m and 2m, respectively. The distance of experimental plots from boarder was 2m to reduce boarder effects. The total experimental site was 20m x 30m (Figure appendix 1). The total number of treatments during the study was 4 excluding control or without treatment. The treatments for the experiments were chemical fertilizer (a combination of urea and (DAP) diammonium phosphate fertilizer), cattle manure, and wood ash and lime applications. The proportion or amount were 100 &150kg, 7.5, 3 and 2 tones/ ha-1 for chemical fertilizer, cattle manure, wood ash and lime respectively (Anderson et al., 2013; Asmare et al., 2015 and Ritchey et al., 2015). The chemical fertilizer and lime were acquired by purchasing while both cattle manure and wood ash collected from farmers in the study area.

Degraded grazing land selected to be improved was ripped to incorporate the treatments in to the soil, to prevent nutrients leaching. After land preparation and plot lay out, the amount of treatments needed for each plots were weighed using sensitive balance and applied. Then 21 diammonium phosphate (DAP) and urea mixed and sown over and mixed using rack except plots for control. Cattle manure was dissolved in to water and applied in the form slurry. Wood ash was slightly rinsed with water to prevent blowing and uniformly scattered over the plot and lime also slightly rinsed with water and applied and after application of treatments was accomplished, mixing in to soil took place to incorporate in to soil (Mark, 1990).

3.5.3. Sampling procedures

3.5.3.1. Botanical composition

The botanical composition of herbaceous species was carried out by harvesting three randomly selected samples (0.5m x 0.5m quadrat) within each plot at height of 0–5cm above the ground at 90 days in the same procedure with section (3.2.3.1).

3.5.3.2. Dry matter yield

Dry matter determination undertaken in the same procedure described in section (3.2.3.2).

3.5.4. Chemical analyses and in vitro dry matter digestibility (IVDMD)

Representative samples were taken from the whole harvested biomass (bulk sample) for each replication of each treatment (plot) and oven-dried at 105 °C overnight. The dried samples were ground to pass through a 1 mm sieve and were then stored individually in airtight containers for chemical analyses and IVDMD assessment each treatment was done in duplicate to increase the precision of the analysis. Ash was determined by igniting the samples in a muffle furnace at 550 °C overnight (AOAC, 1990 Nitrogen was determined Kjeldahl method; CP was calculated as N × 6.25, Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were determined according to Goering and Van Soest (1985). Hemicelluloses and cellulose were calculated as NDF minus ADF, and ADF minus acid detergent lignin (ADL), respectively. The IVDMD was determined by the modified Tilley and Terry system (Van Soest and Robertson 1985). All the chemical compositions and IVDMD analyses were carried out at the Holeta Agricultural research center animal feed analytical laboratory 22

3.5.5. Soil samples collection and analysis.

3.5.5.1. Soil samples collection procedure

One composite soil sample and fifteen soil samples were collected in the same procedure described in section (3.2.4.1).

3.5.5.2. Soil analysis

The soil sample prepare and analysis to determine the grasslands soil property was undertaken in the same procedure described in section (3.2.4.2) soil analysis of vegetation assessment.

3.6.Statistical Analysis

The data acquired from assessments of vegetation as feed resources and improvement of grazing lands (dry matter, botanical composition feed nutritional composition and soil analysis results) were analyzed by analysis of variance (ANOVA) by using the General Linear Model (GLM) Procedure of SAS (SAS, 2002). The species diversity and similarity index were calculated by using a mathematical measure of species a species diversity in a community Shannon winner index was used to determine the diversity index (Eshaghi et al., 2009). Species richness, diversity and evenness were summarized using PAST software (Koleff et al., 2003). The sheep vegetation preferences data was computed using Microsoft excel and presented in graph. Vegetation attributes of grasslands were calculated to sum up and obtain importance value index and can be specified as: Importance value (IV) = Rci + RDi + RFi Where, Abundance (Ai) = total number of individual species i, Cover (Ci) = total% cover of specie I, Relative cover (RCi) = cover of species i/total plant cover, Density (Di) =Ai/Area, Relative Density (RDi) = Di/total plant Density and Frequency (Fi) number of Quadrats with species i/ total number of quadrats sampled (Baxter, 2014). 23

4. RESULTS AND DISCUSSION

4.1.Soil Physical and Chemical Characteristics of the Study Area

Analytical results of the composite soil samples indicated that the soil was sandy (51 to 55%) was sandy, (10 to 22% clay), and 25 to 35% silt and the soil textural class was sandy clay loam (Table1).

Table 1. Physical properties of soil at the study site Parameter Treatments Sand Silt Clay Textural class Higher altitude 51 27 22.2 Loam Mid altitude 53 25 22.2 Sandy clay loam Lower altitude 55 35 10.2 Sandy clay loam

Depending on the classification of electro conductivity of the Netherlands commissioned by the Ministry of Agriculture (1985) and Tekalign et al. (1991), the electro-conductivity (EC) of soil indicated the amount of salt in the soil was low. According to the classification given for electro conductivity the soil of study area was free of salts. Soil samples with electro-conductivity (EC) greater than 4ms/cm generally indicate the occurrences of excess salts in the soil hence need for reclamation. In this study the soil sample result indicates that total salt content was < 4 mmohs/cm which is salt free soil and EC value of 0.21 ms/cm as illustrated by (Table 2). The soil pH of the study area was ranged from strongly acidic to moderately alkaline (pH 4.94 to 8.15) which was in agreement with (Tolera et al., 2015). The soil pH showed a decrease trend in acidity with altitude decrease. This was due to high precipitation of high land caused to increase leaching of base cations and the soil pH was lowered.

The total nitrogen percentage and organic carbon of the study area were higher whereas, the available P was medium (Table 2). The total nitrogen and organic carbon percentage of the study area were categorized as higher in reference to classification of Murphy (1968) who classified the soil total nitrogen content less than 0.10% as low, 0.10-0.15% as medium, 0.15-0.25% as high and greater than 0.25% as very high. Similarly the classification of The Netherlands Commissioned by Ministry of Agriculture and Fisheries (1985) and 24

Tekalign et al. (1991), indicated that both total nitrogen percentage and organic carbon were higher.

The available phosphorous of the natural grasslands soil of the study area was medium based on classification that categorize a relative range of extractable phosphorous in (ppm) of 0.5, 6-10 ppm, 11-15 ppm, 16-20ppm and 21-25 ppm as very low, low, medium, high and very high, respectively (Driven et al., 1973).

Table 2. Chemical properties of soil at the study site Attitudes Parameters Unit Higher Mid Lower Electro conductivity MS/cm 0.77 0.277 0.21 pH of the soil (1:2.5 H20) 4.94 7.61 8.15 Total nitrogen % 0.296 0.228 0.161 -1 Cation exchange capacity Cmol (+)kg 10.64 13.83 10.94 Available phosphorous (ppm 1.16 7.08 1.26 Organic carbon % 7.22 6.44 6.44 -1 Exchangeable calcium Cmol (+)kg 4 12.8 8 -1 Exchangeable Magnesium Cmol (+)kg 3.2 1.2 2 -1 Exchangeable sodium Cmol (+)kg 0.348 0.37 0.261 -1 Exchangeable potassium Cmol (+)kg 1.189 1.01 1.368 Note: Higher altitude= 2350 -2700 msl, mid=2000-2350 msl and lower=1650-2000 msl

As indicated in the table 2 the cations exchange capacity of the grasslands soil of the study -1 area was within arrange of 10.64 -13.83 Cmol (+) kg which indicated that grasslands with such amount of cations exchange capacity are categorized as low in reference cations exchange capacity of 19.7 to 36.5 what was recorded at Bako by (Tolera et al., 2015). However the cations exchange capacity of the grasslands of the study area were ideal for grasslands as it falls within a rating of (Cornel University soil analysis, 2007). Such soils are clay soil and have nutrient holding capacity as it considered as high clay soils hold high capacity of cations greater than (greater than 20 cmolc/kg) while soils with less than (3 cmolc/kg) considered as low CEC and CEC (5-25 cmol/kg).

Soil results in (Table 2) indicated that exchangeable bases (calcium, magnesium, sodium and potassium) were varied with the variation of the altitudinal gradients. The lowest value of exchangeable calcium was recorded at upper altitude. This was due to higher 25 precipitation at higher altitude made leaching. However it was higher than the favorable range of calcium for plant growth relatively in reference what was noted by (Abdenna et al., 2007). Similarly the value recorded for exchangeable magnesium (Mg) was similar with what was reported by (Abdenna et al., 2007), whereas the value of exchangeable potassium was considered as good since it was higher than the critical values (0.2-0.5 cmol(+)/kg or 80-250 mg/kg (ppm) noted by (Anderson et al., 2013).

4.2.Effects of Altitudinal Gradient on the Botanical Composition and Dry Matter Yield of Herbaceous species

4.2.1. Characteristics of herbaceous species Botanical composition

A total of 24 herbaceous species are identified, in the study area. Among them 62.5% were different grasses, 8.3% were legume, 25% were forbs or weeds and 4.2% were sedges. In life form 75% were annuals, while 25% were perennials in life forms (Appendix table 1).

The major herbaceous species identified were; Andropogon abysincus, Cynodon dactylon, Eragrostis tenuifolia, Sporobolus pyramidalis, Eleusine africana, Chloris pycnothrix, Pennisetum clandestinum, Hyparrhenia rufa, Lolium multiflorum, Digitaria abyssinica, Cerastium octandrum, Snowdenia polystachya, Setaria acromelacea, Eleusine jagrie, Eleusine flocci folia, Eleusine indica, Cyperus rotundus, Trifolium ruppellianum, Guizotia scabra, Bidens biternata, Amarhantus aspera and Bidens pilosa; were major herbaceous species identified in the study area which were similar what was indicated by (Alemayehu, 2006; Gezahegn et al., 2015).

4.2.2. Botanical composition

The SAS analytical results of the botanical composition of herbaceous species disclosed that, the legume botanical composition of natural grasslands were highly influenced by variations of altitudinal gradients than grass herbaceous species (Table 3). The effects of altitudinal gradients had affected significantly, legume herbaceous species at (P<0.01) as in lustrated in (Table 3). The herbaceous legume botanical composition was affected at (P<0.01). The current results indicated that more grass botanical composition was found 26 between altitudes 1650-2000 msl. Whereas, botanical composition of herbaceous legume was higher at higher (2350-2700 msl). Legume component showed a decrease trend towards altitude decrease and this confirmed in other similar agro ecologies of high and mid altitudes of Horo and Guduru districts in Oromia region Ethiopia reported by (Gezahegn et al., 2015). According to Alemayehu (2006), the trends increasing and decreasing of legume botanical composition along altitudinal gradients due to high grazing intensity. As revealed from this study, the fact that legume species less at lower altitude might be due to lower altitude rich in organic matter which concentrated and deposited at bottom due erosion from higher altitude. The organic matter availability favors the growth of grass species and dominate over legumes. Higher nutrient availability made a few species so robust that the less vigorous species were excluded, the ability of a species to utilize resources becomes more important in the presence of high nutrient availabilities, so the plant species that can grow taller and more quickly will become more abundant (Chang Wang et al., 2007).

4.2.3. Dry matter yield

Table 3. Botanical composition and dry matter yield of natural pasture Botanical compositions (%) Treatment % of Grasses % of Legumes % of Forbs DMY 2300-2700msl 53.922b 37.578a 8.433a 2.05556b 2000-2300msl 57.067a 35.189a 8.022b 2.13333b 1650-2000msl 59.922a 31.578b 8.433a 2.7000 a Mean 58.20000 28.55926 13.30370 2.296296 CV 20.55159 39.19492 63.10475 28.11777 LSD 0.035127 0.032874 0.024655 0.1896 SEM 5.901930 4,776946 0.822514 0.602726 SL 0.05 <0.01 <0.01 <0.01 a,b means in a column with the same category having different supper scripts differ (P<0.05); CV= Coefficient of variations; DMY= Dry matter yield; LSD= Least significance difference; SEM = Standard error of the mean and SL= Significance level.

The analytical results from (Table 3) disclosed that the altitudinal gradients variation had high significantly affected the herbaceous species dry matter yield per of the grassland at (P<0.01). The higher forage biomass yield was recorded at lower altitude (Table 3) and this was due to higher soil organic matter and less acidity whereas the lowest dry matter was recorded at higher altitude because of high precipitation causes soil nutrient leaching. The 27 current study is in agreement with Gezahegn et al. (2015) at Horo and Guduru districts revealed that, highest DMY was reported at mid-altitude than higher altitude due to hilly topography, large number of equines which have deep grazing habit and larger population density of livestock which selectively feed preferred species of grassland.

The study by Fekede et al. (2013), confirms that grasslands are more productive at valleys and bottomlands grazing areas ranged from 3-5 t DM ha-1 , whereas grasslands of upper lands and degraded sloppy area were in ranges of (0.5-1 t DM ha-1), which considered as very low. Another findings by Bilatu et al. (2013), confirmed that lowlands are productive grassland in dry matter yield and which was 5.4 tones ha-1 in northwest lowlands of Ethiopia.

However the current study was against, Endale (2015), who reported that 2 tons per hectare dry matter yield in each altitudinal gradients and Alemu (2015), who reported that the biomass from higher altitude was higher than both mid and lower altitude. The current study indicated that the total dry matter yield was lower at higher altitude and increased lower ones.

4.3.Effects of Altitudinal Gradient on Woody Species

4.3.1. Woody composition

A total of 19 wood species belonging to 11 families were identified in the study area. Of which eleven were trees (> 2cm dbh and > 5m height), whereas, the remaining rest eight were shrubs (< 2cm dbh and less than 5m height). According to their palability to animals (42.1%) were preferred, (21.1%) were highly preferred and the rest (36.8) were unpreferred (Appendix table 2). The major wood species recorded in the study area were, Eucalyptus tree, Cordia africana, Croton megelocapus, Acacia siberiana, Acacia tortolis, Rhus natalensis and Vernonia amygdalina and the major shrub functional groups recorded in the study area were Carissa edulis, Calpurina subdecandra, Solanum incanum, Rumex usambarensis, Dodonaea viscosa, Maytenus ovatus, cactus and Vernonia auriculifera. Vernonia amygdalina was the dominant species in private grazing lands of the study area. This species dominated the 28 upper and mid altitudes of the study area. Among the shrubs, the dominant species were Maytenus ovatus, Carissa edulis, Solanum incanum and Calpurina subdecandra. And Solanum species were the dominant species in mid altitude where there is higher grazing pressure which indicate degradation desirable vegetation species.

4.3.2. Species diversity index

The table for Shannon winner index disclosed that, altitudinal gradient had affected the woody species. The highest diversity index mean value was recorded at lower altitude (1650-2000 msl) for shrubs and trees woody species (Table 4). This might be due to high soil organic matter and nutrient availability at lower altitude whereas the higher altitude (2350-2700 msl) soil was acidic due to high precipitation. The other factual evidence for lower existence of diversity might be the intensity of human activity impacted the woody species for livelihoods. The other justification fewer diversity at higher altitude the relationship of altitude and soil depth which probably acted upon decrease of species occurrence because of highest soil depth inhibited the species to utilize beneficial soil nutrients as with increased altitude (Zakir, 2014).

Table 4.woody species diversity indices Woody species 2350-2700msl 2000-2350msl 1650-2000msl

Shrubs 2.3 2.6 2.81 Trees 1.71 2.1 2.24

4.3.3. Similarity index of woody species

The computation from the vegetation analysis results of species across altitudinal gradients disclosed that the distribution of trees woody composition of the study the grasslands area were dissimilar (Table 5). Similarity index values in species composition were ranges from 18% to 85%. The adjacent ecological background of the area helped both higher and mid altitude to have more similarities and similarly mid and lower altitude to be similar in species diversities than upper versus mid altitude. In consequence of the adjucent location of the communities was enabled to have common environmental factors and additionally, these environmental factors such as aspects, slopes soil physical and chemical properties 29 have sound effects to change the patterns of plants communities (Dereje, 2007). The comparison of altitudinal gradients disclosed that higher and lower altitudes had highest dissimilarity indices whereas lower dissimilarity indices were observed between lower versus mid and mid versus upper altitudes. The sounding evidence for the similarity indices were the occurrence of most frequent and common species between two locations. Therefore altitudinal gradient played sound full role in determining the similarity indices.

The similarity indices of shrubs woody composition of the grasslands of the study area was within a range of 0.62 to 0.86 (Table 5). The current study showed that in tree woody composition the similarity indices was found to be highest between upper and mid altitudes 86% against upper and lower altitudes to be highest dissimilarity indices 18%.

Table 5. Similarity index of woody species Locations Tree species Shrubs species Similarity Dissimilarity Similarity Dissimilarity Higher and mid 0.85 0.15 0.86 0.14 Higher and lower 0.18 0.82 0.62 0.38 Mid and lower 0.20 0.80 0.77 0.23

Higher= 2350 -2700msl, mid=2000-2350msl and lower=1650-2000msl.

In case the shrubs the highest similarity indices was 86% found between uppers and mid altitudes. While the lowest similarity indices 62% was found between upper and lower altitudes. From the ecological points of view the grasslands of the study area showed highest similarities indices as long as they have adjacent locations and the highest dissimilarity indices as far as they have separated location, similar finding with (Getachew, 2015).

4.3.4. Importance value index (IVI)

The importance value index is the combined results of the three parameters of relative frequency, relative density and relative dominance (Kent and Coker, 1992). Therefore the importance value index of this woody composition was calculated by adding all these three parameters. As displayed in (Appendix table 2), the nineteen woody species recorded from three altitudinal gradients were found to have importance value index of 24.74 to 77.25% 30 for shrubs of woody composition at upper altitude, 29.97 to 75.25% for mid altitude and 11.3 to 92.13% for lower altitudes (Appendix table 6).

The dominant shrub species found to be recorded highest importance value in the three locations were P. africana, S. nigrum and S. incanum for upper altitude, M. ovatus, R. usambarensis and P. africana at mid altitude and P. africana, C.edulis and M. ovatus at lower altitude respectively (Appendix table 6). Similarly the tree woody composition of the grasslands of the study area found to be 15.44 to 64.12, 22.93 to 78.33 and 49.3 to 67.81 for upper, mid and lower altitudes respectively (Appendix table 5). The tree woody species found to have highest in importance value at upper altitude with the dominant tree species C. subdecandra, D. einerven and C. megelocapus. While the rest two altitudinal gradients were found to have dominant tree species D. einerven, V. amygdalina and C. subdecandra and R. natalensis, A. siberiana and V. amygdalina respectively (Appendix table 5).

The highest importance value index recorded for the particular woody species show that the species is said to be dominant species in reference to other plant species within the same particular area (Curtis and McIntosh, 1951). Therefore C. subdecandra, D. einerven and C. megelocapus were dominant tree species and P. africana, S. nigrum and S. incanum were dominant shrub species for upper altitude of the grasslands of the study area.

According to Simon and Girma (2004), the dominance and ecologically most importance of these species might be due to their good regeneration due to resisted from pathogens, adaptability to growth in the shade, competition with other trees, least preference by browsing animals, attraction of pollinators and due to seed predators that facilitated seed dispersal within the existing environmental conditions of the study area. In contrast the woody species with the least importance value may be due to adverse environmental conditions and selective disturbance of human for the available resource use and computation in the area (Feyera et al., 2007).

4.3.5. Sheep Vegetation Preference in Relation to other Livestock classes

The personal observation supplemented with focal group discussion held totally supported that sheep preferred species of legumes and forbs than grasses. The vegetation preference 31 variation from the graph indicate that sheep more preferred herbaceous forbs and legumes than other livestocks classes. Accordingly, A. aspera, A. abysincus, B. bilosa, B. biternata, C. Octandrum, C. pycnothrix, E. indica, G. scabra were those herbaceous species highly consumed by sheep in the study area. Similarly B. bilosa, B. biternata, C. Octandrum, were those species shared with goats and these species were less desirable by cattle. The study conducted by Lukas et al. (2015) in south Africa also confirmed that preferred legumes and for species were more affected in sheep grazing sites than cattle in similar manner with current study (Figure 2 and Appendix table 3).

Mara and Joao (2002), also indicated that sheep and cattle had more similarity than sheep and goat or cattle and goat in preference of vegetation. The current study in agreement with Lemmon et al. (2016), who was reported sheep grazing decreased the growth forbs species, while none significance variation with grass species which were grazed by steers Kansas State University study. Sheep and cattle computed for available pasture forage while goats more preferred the browse species which appeared similar with (Lovina et al., 2011)), stated in their studies that, sheep grazing reduced flowering forbs in Idaho University.

Figure 2.Sheep vegetation preferences

4.4. Land Use and Land Cover Changes of the Study Area

4.4.1. Nature of the land-cover units

Seven major land use land-cover types were identified 1986, 2000 and 2013 years of satellite images of the Meta Robi district (Table 6) by using supervised classification. 32

Generally, the land-covered by woody vegetation was easily identified on the satellite images by the reddish shade these cover type impart on the false color composite images. Accordingly the woody biomass was distinguished with a dark tone associated with steeper slopes. The grassland is easily distinguished as the shrubs land that are found interspersed in the grassland appear as dark spots within the light grey matrix of the grass cover. However no clear demarcation between bare land and settlements as both reflections on satellite images of bare land and settlements appeared similar in three years satellite photographs.

The swampy area or wet lands were distinguished resembling blue colored from other land cover and land use. The peculiar characteristics of crop lands in three satellite images were linear to curved features of the traditional terraces and the lines of hedges bounding the individual cultivated.

4.4.2. Types and extent of land cover changes

From the three satellites images processed seven categories of land use were recognized and observed, these categories were repeated themselves all over the three years in different dimension. The dynamic changes of land use cover growth parameters is a long term process which require investigations on land use land cover and the interaction of these components with the ecological and social components.

Analysis of land sat image 1986 showed that, the agricultural land expanded again from 1986 to 2000. Hence, it can be seen in (Appendix figure 4) that cropland was again the major land use class covering 22% of the landscape, In other way the trend show grazing lands decreased in three years land sat mage study, 17.9, 20.2, 5.9, 19, 15.3, 17 and 4.6% for agricultural land, grass land, shrub lands, forest land, rocky and valley and swampy areas respectively. Land sat image analysis of 2013 revealed that (21.6%) was agricultural land, (14.8%) was grass land (7.5%) was shrub lands (16.4%), was forest land (16.4%) was covered by settlements and bare lands,(17.3%) was rocky and valley where as the rest (6%) was wet land area (Table 6 and Appendix figure 4). Analysis of two land sat images of 1986 and 2000 crop cropland were increased by (4.8%) while grazing land was decreased by (6.6%). Similarly settlement and bare land increased by (4.4%), whereas forest and 33 shrub land altogether declined by (3.4%), Between years 2000 to 2013 forest land decreased by (1.7%) and grass land increased by (1.2%) which indicated forest lands changed to grass land due forest clearing for different purpose and use. The changes between years of 2000 to 2013 were relatively small compared to 1986 to 2000 due short time interval investigation.

4.4.3. Causes of the land cover changes

Land use and land cover changes result from various natural and human factors within social, economic and political contexts. Hence, the local human activities expressing the drivers can be determined by measuring the rates and types of changes and analyzing other relevant sources of data like demographic profiles, household characteristics and policies related to land resources administration (Hussein, 2012). Land cover changes are the results of the numbers of interacting variables and process. Due to these process and factors such as slope gradient, soil depth, terrain configuration and the demand for fuel wood the distribution of land use and land cover varied. In flat sloppier of mid and upper altitudes the shrubs and forests observed in decreasing trend while the sloppy areas and valleys appeared with dense shrubs.

According to group discussion with key informants the increasing of agricultural land with decreasing of grazing land, forestland and scrublands is the results of population growth at alarming rate and expansion agricultural land for food crop production. The decreased trend of forest land was due to clearing of trees for charcoal to sustain their livelihood. However, according to focal group discussion made, plantation of commercial trees around farm land are increasing due to seek of income generation. The land use land cover changes of study district a cumulative of natural and human induced factors. Over grazing was one factor for decreasing bush land for flat slope mid and upper altitude where large lives talk grazed as results of high population of livestock especially sheep reared in high land production system of the study area in agreement with (yadessa et al., 2016).

Inappropriate farming practice and tracing are the other factors contributed for occurrence of repeated landslides which played a role for a change of land use land cover of the study area inconsistence with study by (Tarun et al., 2015). Late season of high rainfall from 34

August to September incredible factor for change of land use land cover by raising ground water level recharge by heavy rainfall favorable for causative factors such as slope material, slope gradient and land use land cover. These heavy rainfall induced landslides in the study area possibly occurred as a result of shear strength reduction of the slope material due to saturation and development of pore water pressures within the slope mass. This fact is evident as most of the past landslides in the study area have occurred within the slopes composed of colluvial and alluvial soils.

4.4.4. Consequences of the land use cover changes

One of the immediate impacts of the thinning and destruction of the shrub land is shortage of fuel wood and construction materials for the farming community. These situations were forced the farmers to use cow dung and crop residue as fuel. The use of crop residue and cow dung leads to aggravated soil nutrient depletion and enhances soil erosion which is factor for gulley formation changes the cover and use of land. The reduced possibility for cropland-expansion and severe shortage of land has also its own impact. The land shortages had compelled farmers to practice continuous cropping and completely abandon even seasonal fallowing. Field observation and discussions with farmers suggested that cropping intensity in the Meta Robi district has been raised to almost continuous cultivation in a situation where little organic matter returns to the soils leads to severe soil erosion and land degradation. Furthermore, shortage of land has forced farmers to cultivate steep slopes and shallow soils that are vulnerable to degradation.

4.4.5. Variations on detected changes, interpretations and limitations

Variations on results from change detection analysis are expected and these could change the interpretation for the detected changes (Kashaigili, 2006). In this study some variations on the detected changes have been noted. By examine the change detected matrix (Table 6), it is clear that some of the changes were unrealistic. For instance agricultural land or forest land to swampy area, which could be as result of ecosystem dynamics responses dependent on different factors such as season of image acquired, plant paleontological effects and spectral resolutions. The different plant phonological effects are related to which season an image is acquired on the ground (Kashaigili et al., 2010). 35

Table 6. Land use land cover change NO Area 1986 2000 2013 Ha % Ha % Ha % 1 Swampy area 4135.7 4.6 5280.8 5.8 5434.7 6.0 2 Bare land and settlement 13897.0 15.3 17815.1 19.7 14828.4 16.4 3 Agricultural Land 16186.3 17.9 20585.6 22.7 19523.6 21.6 4 Grass land 18333.2 20.2 12273.0 13.6 13416.1 14.8 5 Rocky and valley 15386.5 17.0 11462.7 12.7 15655.1 17.3 6 Forest Land 17218.4 19.0 16368.6 18.1 14872.6 16.4 7 Shrub land 5377.9 5.9 6749.2 7.5 6804.5 7.5 Total 90535.0 100 90535 100 90535 100

The shrubs and forest lands of the study area concentrated to lower altitudes than higher even though the finding by Minale et al. (2012), confirms that the presence of mixed forest and shrub land increased with altitude up to the range of 2,500-3,000 msl and declined thereafter, indicating that this altitudinal range supports the optimum distribution of the mixed forest and shrub land. This finding revealed that the study area has undergone notable changes in terms of land use and land cover for the period 1986/ 2000–2000/ 2013

. The woodland areas and grass lands were found to be highly impacted notably by the increased anthropogenic activities. The settlement and cultivated land was found to have consistently increased between the two periods under investigation.

4.5.Degraded Grassland Rehabilitation and Improvements

4.5.1. Physical and chemical characteristics of soil of the experimental site

The soil samples results analysis pre experimental indicated that the physical property of the experimental site was loam with sand, silt and clay in proportion of 23%, 19% and 58%, respective. The electro-conductivity of the soil before conducting the experiment was salt free which was in agreement with standard indicated by (Tekalign et al., 1991). The mean pH of the soil of the composite sample before conducting the experiment was 5.9, slightly acidic, in agreement with (Tolera et al., 2015) reported at Bako. The mean value of cations exchange capacity of the soil before conducting the experiment was (20.37) which was in agreement with (Tolera et al., 2015), who reported 19.7 to 36.5 at Bako. And considered as high clay soils hold high capacity of cations greater than (greater than 20 36

cmolc/kg) while soils with less than (3 cmolc/kg) considered as low CEC and CEC (5-25 cmol/kg) ideal for pasture.

Clay soils are negatively charged and such soils are rich in organic matter, have good water holding capacity than sols with low CEC, whereas soils with low CEC tend to develop magnesium and phosphorous.

Table 7. Soil property before conducting the experiment Soil property Sand Silt clay Textural class Unit % % % % value 23 19 58 clay property EC pH N OC P CEC Ca Mg Na K Unit mmohs/cm 1:2.5(H2O) % ppm meq/100gm soil value 0.092 5.9 0.081 0.98 1.36 20.37 12 3.6 0.217 0.46

The available phosphorous before carried out the experiment was 1.6ppm which considered as very low based on Olsen method <3 ppm very low and >20 ppm very rich) and Bray method (<5 ppm very low and >30 ppm very rich) because soil pH between 5.5 and 7.5 limit availability of phosphorous (United state Department of Agriculture, 2001 and Driven et al., 1973). The percentage of organic carbon and total nitrogen content of the experimental site pre-experiment were (0.98 and 0.081) indicating that the study site was rich in organic carbon and total nitrogen in reference to standard range of organic carbon (Table 7) inconsistence with (Driven et al., 1973; Tekalign et al., 1991).

.As indicated in (table 7) the value of exchangeable Calcium was (0.98) which agrees with (Abdenna et al., 2007). According to Driven et al. (1973); Tekalign et al. (1991) the recorded calcium value was higher. But Abdenna et al. (2007) noted that, the exchangeable calcium in west Shoa as negligible. The exchangeable magnesium (Mg) and (K) recorded were (3.6 and 0.46) respectively. The amount of exchangeable Mg recorded was in line with Abdenna et al. (2007) and the amount K was less than what was reported by (Abdenna et al., 2006) in west Shoa. It also agrees with rages of (0.2-0.5 cmol (+)/kg or 80-250 mg/kg (ppm) the critical value for K noted by (Anderson et al., 2013). 37

4.5.2. Rain fall pattern of the study area

Ten years rain fall data of Inchini station acquired from Ethiopian Metrological Agency indicate that the rain fall of study area to be found 934.9 mm in the experimental year. The raw rain fall data from 2005-2015, shows the amount of rain fall received in one decade was fluctuating (Appendix table 3). The rain fall data received during experimental year was relatively low from usual rain fall but and it was normal for season of experimental months .As indicated by graph it revealed a decreasing pattern eventhough it was within a range of rain fall stated by Endale (2015).

Year Figure 3. Rain fall data of study Source: Ethiopia Meteorically agency (2015)

4.5.3. Effects of organic and inorganic fertilizer applications physical and chemical properties of soil of degraded grasslands

Results of the soil textural analysis indicated (Table 8) of after harvesting indicates that, soil textural class with the proportions of sand, silt and clay were significantly varied by the applications of organic and inorganic fertilizer at (P>0.05) even though, significant satirical variation was observed. The comparison of the pre and post experimental soil analysis result showed that the change of textural class was not as a results of the treatments (Table 7 & 8) as the control was showed change in similar with treatments plots.

Similarly, Variation of soil chemical properties were observed due to effects of organic and inorganic fertilizer applications. The values for soil chemical properties in (Table 9) indicated that electro conductivity of the soil property was affected at (P<0.05) by 38 application of lime treatments. Similarly, wood ash and manure increased EC comparing both pre and post experimental soil samples analysis results. Therefore the soil with such amount of electro conductivity is considered as free of salts (Tekalign et al., 1991). But application of the chemical fertilizer relatively decreased the soil electro conductivity.

Table 8. Soil physical property Treatments Parameters Sand Silt Clay Class Chemical Fertilizer 51.67a 29.00a 19.40a loam Cattle Manure 48.33a 27.67a 24.10a Sandy clay loam Wood Ash 54.33a 27.67a 18.20a Sandy clay loam Lime 49.00a 28.33a 22.80a Sandy clay loam Control 54.33a 24.33a 21.50a clay CV 9.319 12.50 28.59 Mean 51.53 27.40 21.20 SEM 23.07 11.73 36.20 LSD 9.04 6.45 11.41 LS 0.44 0.54 0.20 a,b means in a column with the same category having different supper scripts differ (P<0.05); CV= Coefficient of variations; LSD= Least significance difference; SEM = Standard error of the mean and SL= Significance level.

Likewise application of the treatments did not bring significant change in pH of the soil properties at (P>0.05) but the pre experimental pH mean value result of soil sample indicates that pH increase relatively by wood ash, lime and cattle manure in post experiment soil analysis respective of their results orders(Table 9). The highest PH was recorded at both wood ash and lime applications because of the wood ash and lime strong neutralizing effect (Asmare, 2015). Wood ash increased soil pH over lime in this study which was proved by several studies that the highest composition and release of calcium and magnesium which affect soil pH. This character made wood ash best alternative to be preferred as liming materials and soil amendment. The studies by Mijangos et al. (2015), had shown that wood ash application enhanced soil biological quality and fertility in acid soils and can be a valid alternative to the traditional treatment with lime.

Like other soil chemical properties the available phosphorous of soil was varied significantly at (P<0.05) by application of organic and inorganic treatments. The highest mean value for available phosphorous was scored cattle manure application (Table 9). This 39 might be due to the fact that cattle manure is rich in phosphorous, potassium, magnesium and calcium and the significant proportion of phosphorus in manure mineralize slowly and gradually to release plant available phosphorous (Manitoba, 2013). The increased amount of available phosphorus after harvest observed in cattle manure application treatments might be attributed from the release of soluble humic material or organic acids from decomposing organic residues and manures contribute greatly to decrease P adsorption capacity and increased available P that occurs in soils (Easter wood and Sartain, 1990.

The soil organic carbon and total nitrogen content were not significantly affected by application of the treatments at (P>0.05). But from comparison of pre and post soil samples analysis higher organic carbon and total nitrogen were observed at chemical fertilizers. The fact that chemical fertilizer increased total nitrogen and organic carbon was due to urea fertilizer good sources of nitrogen (Khoi et al., 2010).

From the current study slight nitrogen increment observed after harvest than pre experiment soil total nitrogen and the total nitrogen recorded in both post pre experiments were favorable for plant growth and found within a range by (Driven et al., 1973). The current study was inconsistence with findings by Ullah et al. (2008), who reported that chemical fertilizer increased organic carbon than cattle manure and Messiga et al. (2013), who also indicated that organic carbon tend to rise with increased nitrogen application. Similarly with total nitrogen percentage, the highest (CEC) was recorded by chemical fertilizer application. This was due to chemical fertilizer contribution of plant growth and return to soil through decomposition and helped to possess higher CEC and good soil texture. The highest exchangeable (Na) of the soil property was recorded at chemical fertilizer application treatment which implies that urea has a role to increase exchangeable sodium in relation to increasing soil organic carbon and total nitrogen which was previously increased the CEC of soil factor as amount of sodium depends on CEC of soil factor

Chemical fertilizer and wood ash applications increased the exchangeable magnesium than the other treatments. This was due to the soil highest composition of organic carbon and total nitrogen of the grassland contributed to increase the CEC of the soil to hold higher nutrients (Manitoba, 2013). The increase of Mg in wood ash was due to high consistence of 40 alkali metals that made to be preferred as best liming materials (Okmanis et al., 2015), that one tone sieved wood ash can bring in soil about 170 kg of liming material or 150 kg calcium and 20 kg magnesium. The exchangeable calcium for most of the treatments were higher in the study area. But the post-harvest results showed decrease trend in all applications of treatments. This might be the fact that various processes of interconnected with plant up take and reactions with fertilizer brought about the loss of calcium from the soil. The reduction of exchangeable calcium at chemical fertilizer application could be the tendency of calcium to offset the hydrolysis of the urea to reduce soil acidity by increasing the amount of cations exchange capacity (Manitoba, 2013).

Exchangeable potassium was significantly affected at (P<0.05) by wood ash application. This was due to the additional nutrients that the wood ash contained (Awodun et al., 2007)

Table 9.Soil chemical properties of improved grasslands after experiment Treat Parameters ments EC pH N P CEC OC Ca Mg Na K T1 0.07b 5.46a 0.40a 3.02ab 34.81a 8.84a 11.73a 4.93a 0.34a 0.75bc T2 0.20ab 5.96a 0.39a 9.07a 21.79a 6.96 a 12.93a 3.07a 0.28a 0.90ab T3 0.24ab 6.06a 0.34a 2.33ab 29.08a 8.12 a 12.00a 4.93a 0.29a 1.04a T4 0.27a 5.83a 0.28a 1.75b 24.02a 6.96a 11.86a 4.00a 0.19a 0.63c T5 0.15ab 5.76a 0.39a 1.54b 31.56a 8.32a 13.60a 4.40a 0.33a 0.87abc Mean 0.19 5.82 0.36 3.50 28.25 7.84 12.43 4.27 0.29 0.84 CV 54.21 10.52 41.82 54.61 29.74 49.57 26.16 31.63 32.89 15.62 LSD 0.19 1.15 0.29 6.78 15.82 7.32 6.12 2.54 0.18 0.25 SEM 0.01 0.37 0.02 12.97 70.58 15.10 10.57 1.82 0.01 0.02 SL 0.02 0.79 0.84 0.05 0.38 0.96 0.94 0.46 0.42 0.04 T1=chemical fertilizer, T2=cattle manure, T3=wood ash, T4=lime and T5=control; EC= o mmohs /cm at 25 C, PH=H2O, N&OC=%, P=ppm, CEC and exchangeable Ca, Mg Na and K = meq/100gm soil ab means in a column with the same category having different supper scripts differ at (P<0,05); CV=Coefficients of variations; SD = Least significance difference; SEM=Standard error of the mean and SL=Significance level. 41

4.6.Effect of Organic and Inorganic Fertilizer Application on Botanical Compositions and Dry Matter Yield of Herbaceous Species

4.6.1. Characteristics of the botanical composition of herbaceous species

A total of 22 herbaceous species were identified in experimental sites. Which of them, (68%) were different grasses, (9%) legumes (18%) forbs and (5%) were other herbaceous species (Appendix Table 1). In life forms the grass species recorded a total of 11 perennial and 4 annual grass species while, legume species recorded a total of 2 annuals which were tri folium species. A. abysincus and Setaria acromelacea were dominant species of grass species while, Trifolium ruppellianum dominate the legume portion. No species diversity variation among the treatments, but the species dominance observed in the plots. Higher proportion of the grass species observed in the chemical fertilizer application while the control treatments showed equal proportions of grass, legumes and forbs.

4.6.2. Botanical compositions of herbaceous species

Table 10 SAS analytical results disclosed that the application of the chemical fertilizer had high significantly affected both botanical composition of the grass species and the dry matter production at (P<0.001). This was due to high level of the nitrogen contained in the urea fertilizer, able to cause the legume to be less competitive and be crowded out by the grasses in the pasture by (Tesfay et al., 2015). According to Murdock and Edwin (2014), the chemical fertilizer gives immediate response for grasses due to release of nitrogen which fastening the growth of grass species over legumes and critical for legumes establishment. The same result reported by Stephen (2013), showed the commercial fertilizer increased the grass contents because of more intensive and fast growth of grasses. Similarly Hanife et al. (2010), also noted that in studies of two years chemical fertilizer reduced the legume content and increased grass content and total dry matter yields.

The highest mean values of the legumes species botanical composition was recorded at wood ash application treatment. This was due to wood ash higher composition of minerals which are important for growth of legume which were from wood parental materials reported by (University of Georgia, 2013). The cattle manure application showed 42 significance increase in the botanical composition of the legume species and this was confirmed in several studies, composition of phosphorous in cattle manure (Nova Scotia Department of Agriculture, 2006).

The botanical composition of forbs was significantly affected at (P < 0.05). The highest mean value of the forbs was recorded by control The reason why control treatments was higher in forbs was associated with the faster growth of annual weeds characters for competition of soil nutrients over others. The herbivorous species identified by this study were, less than those reported by (Tessema et al., 2010). However similar with, what was reported by Gezahegn et al. (2015) at mid and high lands of Horo and Guduru districts of Oromia Regional state Ethiopia. This was due overgrazing and degradation of the grazing lands due to high livestock population.

4.6.3. Dry matter yield

The total dry matter of the natural pasture of the herbaceous species had high significantly affected at (P<0.01). The highest mean value of the dry matter yield per hectare was recorded by chemical fertilizer application treatments (Table 10). Similar studies conducted in Turkey by Ahimad et al. (2013), showed hay yields of the grasslands increased by the chemical fertilizer application. The highest total dry matter yield recorded by chemical fertilizer was associated with urea fertilizer which mineralize quickly to release nitrogen which fasten the growth of high proportion of grass species. Cattle manure and wood ash applications were recorded relatively related results which showed composition minerals in both cattle manure and wood ash were promoted the growth of legumes to obtain higher dry matter yield (University of Georgia, 2013).

On the other hand the dry matter yield recorded with lime application was relatively less than that of wood ash because, wood ash improved availability of nutrients (phosphorus, potassium, zinc, manganese, copper and boron) in the soil, more than the lime does (Tarlok, 2007). The study was in agreement with Tessema et al, (2010), that dry matter yield of grass species was increased with application of nitrogen fertilizer. Even though chemical fertilizer increased the dry matter yield in the current study it was lower than what was 43 reported by (Gezahegn et al., 2015) at Holeta as the grasslands of the study area where the current study was undertaken was severely degraded.

Table 10.Botanical composition of herbaceous species Parameters Treatment Grass Legumes Forbs TDMY chemical fertilizer 59.033a 29.667b 11.333b 4.5000a cattle manure 34.443c 53.890a 11.667b 3.0667b wood ash 33.100c 57.870a 9.030b 3.3333b lime 47.667ab 38.000b 14.333b 2.7333b control 37.667bc 33.000b 29.333a 1.5633c Mean 42.38200 42.48533 15.13933 3.039333 CV 14.44874 13.65212 20.16536 14.84263 LSD 11.53 11.53 5.7481 0.8494 SEM 37.49929 37.49929 9.320207 0.20350667 SL 0.01 0.01 0.01 0.01 ab means in a column with the same category having different supper scripts differ at (P<0,05); CV=Coefficients of variations; DMY=Dry matter yield; LSD=Least significance difference; SEM=Standard error of the mean and SL=Significance level.

4.6.4. Effect of organic and inorganic fertilizer application on nutritional composition of natural pasture

Dry matter the nutritional composition of the natural pasture had affected at (P<0.05) by application of chemical fertilizer. The highest mean value of dry matter was recorded by chemical fertilizer application (93.4%).and this is in agreement with the findings of Bumane. (2010) and Ashagre. (2008), who reported that, application of nitrogen fertilizer increased pasture dry matter yield. This was due to grass vigorously responded to nitrogen application.

The total ash content of natural pasture was significantly affected at (P<0.05) by wood ash application (Table 11). This was due to high mineral contents inherited from the wood materials from which it come.

Table 11 indicated that the crude protein of feed chemical composition of the natural pasture was high significantly affected by cattle manure application at (P<0.01). Highest legume botanical composition of the treatment plots due to cattle manure taking time to give available nitrogen and rich in phosphorous reasonable to increase legume (Bumane. 44

2010). Contradictory, nitrogen in chemical fertilizer fasten the growth of grass which are less in crude protein to compute and dominate over grass. This findings was confirmed by several researchers in the past that crude protein content depends on plant species (Caddel and Allen, 2010).

The application of organic and inorganic fertilizer did not significantly affected the neutral detergent fiber content of natural pasture at (P>0.05). This might be interconnected with stage of harvest rather than treatments as plants at matured stage plants become lignified and have highest neutral detergent fibre as indicated in (Tessema et al., 2010). However the applications of organic and inorganic fertilizer significantly affected the acid detergent fiber at (P<0.01). Therefore this study in confront with Abdi et al. (2015), in case none treatment showed highest acid detergent fiber than treated ones. In similar way the acid detergent lignin (ADL) of the feed nutritional compositions of natural pasture was high significantly affected by chemical fertilizer application at (P<0.01).

Table 11. Feed chemical composition of improved natural grassland Parameters Treatment DM Ash CP NDF ADF ADL IVDM D chemical fertilizer 93.36a 7.51c 7.21b 66.50a 40.81b 5.84b 47.94bc cattle manure 91.27b 9.19bc 8.65a 64.45a 39.55b 7.46a 55.39a wood ash 91.69ab 11.69a 7.68ab 65.02a 43.08a 6.75ab 38.93d Lime 92.27ab 9.79ab 8.28a 65.12a 40.83b 7.66a 50.01b Control 91.51b 9.62abc 5.65c 66.32a 43.27a 7.56a 46.86c Mean 92.02 9.56 7.49 65.48 41.51 7.05 47.83 CV 1.07 12.66 6.91 1.68 2.68 7.22 2.77 LSD 1.85 2.28 0.98 2.07 2.09 0.96 2.49 SEM 0.98 1.21 0.27 1.099 1.24 0.26 1.76 SL 0.16 0.03 0.001 0.19 0.01 0.0110 <.0001 ab means in a column with the same category having different supper scripts differ at (P<0.05); CV=Coefficients of variations; LSD=Least significance difference; SEM = Standard error of the mean and SL=Significance level.

The invitro dry matter digestibility (IVDMD) of the feed nutritional composition of improved natural pasture was high significantly affected at (P<0.01) with treatments effects. The highest mean value of the (IVDMD) recorded was by cattle manure application treatment. This was due to cattle manure higher composition of legume over grass botanical composition which indicator of good feed quality (Min et al., 2002). 45

5.SUMMARY AND CONCLUSIONS

5.1. SUMMARY

The study was undertaken in Meta Robi district with objectives of assessing vegetation dynamics and improvement of grazing lands in sheep dominated area of West shoa.

Based on this, assessments of vegetation as feed resources dynamics undertaken in three altitudinal gradients; 1650-2000msl, 2000-2350 and 2350-2700msl, sheep herbaceous vegetation species preferences in relation to other livestock class was identified using focal personal observation and focal group discussion, land use land cover of the study area was classified by supervised classification using sat images and processed by ERDAS 2010 and ARC Map 10.2 and finally degraded grassland was improved with organic and inorganic fertilizer.

5.2. CONCLUSIONS

The major findings of this study were the altitudinal variation had affected both botanical composition and total dry matter yield of the herbaceous species. And more species diversity and total forage biomass production were observed in lower altitudes where the topography of the grassland rouged and unsuitable for grazing animals around valleys.

The community focal group discussion revealed that sheep preferred legume and forbs than grasses. The land-use land-cover from satellite images of 1986, 2000 and 2013 indicated that grasslands and forest lands decreased in size whereas agricultural lands and settlements increased in area coverage due to the increase in human population as this was the main human driven force factors.

The study undertaken in improvement of degraded grassland revealed that chemical fertilizer increased the botanical composition of grass species and forage total dry matter yield. While application of wood ash improved the soil acidity, botanical composition of legume and total ash in the feed composition. Similarly cattle manure application improved available phosphorous and total crude protein of the natural grassland. 46

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7.APPENDICE 63

7.1.Appendix in table

Appendix Table 1. Herbaceous species of the study area No Local Name Botanical name Family name Functional Life prefere group forms nce 1 Qamatee A. aspera Amaranthaceae Forbs Annual P 2 Ballammi A. abysincus Poaceae Grass Annual HP 3 Keeloo B. pilosa Astraceae Forbs Annual HP 4 Adaa B. biternata Astraceae Forbs Annual HP 5 Keesuu C. octandrum Poaceae Forbs Annual LP 6 Citaa C .gayana Poaceae Grass Perennial HP 7 Migira C. pycnothrix Poaceae Grass Annual HP 8 Coqorsa C. dactylon Poaceae Grass perennial HP 9 Qunnii C. rotundus cypraceae Sedges Perennial LP 10 Marga qalla D. abyssinica Poaceae Grass Annual p 11 Sardo harre E. indica Poaceae Grass Annual p 12 Xaafii sinbiraa E. tenuifolia Poaceae Grass annual HP 13 Tufo G. scabra Compositeae Forbs Annual P 14 Dagalaa H. anthistirode Poaceae Grass Perennial P 15 Sembeliet H .rufa Poaceae Grass Annual LP 16 Qorxobbii O. gratisimum Poaceae Grass- like Annual LP 17 Mujjaa P. dustum Poaceae Grass Annual HP 18 MIgra S. acromelacea Poaceae Grass Annual p 19 Mujja S. polystachya Poaceae Grass Annual HP 20 Murii S. pyramidalis Poaceae Grass Perennial HP 21 Amekela T. terrestris Acanthaceae Forbs annual p 22 Siddisa T. ruppellianum Leguminaceae Legume Annual HP 23 Maget T. schuding Leguminaceae Legume Annual HP 24 Sardoo P. clandestinum Poaceae Grass Perennial LP 64

Appendix Table 2. Wood species of the study area No Local Name Botanical name Family name Functional Palatability group 1 Ceekaa C. subdecandra Papiliondaeae Tree P 2 Gefeto D. einerven Mimosideae Tree P 3 Tatesa R. natalensis Anacardiaceae Tree UP 4 Iticha D. viscosa Sapindaceae Tree P 5 Miessa P. africana Rosaceceae Shrub P 6 Kombolcha M. Ovatus Celastraceae Shrub P 7 Agamsa C.edulis Apocynaceae Shrub P 8 Lafto A. siberiana Leguminaceae Tree P 9 Tadecha A. tortolis Mimosideae Tree HP 10 Ceekata G. temensis Tiliaceae Shrub P 11 Hebicha V. amygdalina Astraceae Tree HP 12 Rejii V. auriculifera Astraceae Shrub UP 13 Dodota A. etbaica Astraceae Tree HP 14 Wadessa C. Africana Borageanaceae Tree HP 15 Iddii S. incanum Astraceae Shrub UP 16 Bakkanisa C. megelocapus Euphhorbiaceae Tree UP 17 Inbuach R. usambarensis Polygonaceae Shrub UP 18 Iddii Saree S. nigrum Astraceae Shrub UP 19 Kosoru A. eminens Achantaceae Shrub Hp

Appendix Table 3. Vegetation species desirability livestock class

No Treatments Sheep Goat Cattle 1 Achyranthes aspera HP HP LP 2 Andropogon Abysincus P LP HP 3 Bidens bilosa HP HP Lp 4 Bidens biternata HP HP Lp 5 Cerastium Octandrum HP P HP 6 Chiloris Pichynotrics P P HP 7 Cynodon dyctlon HP P HP 8 Eleusine indica P HP Hp 9 Guizotia scabra HP HP LP 10 Trifolium ruppellianum HP HP HP

HP=highly preferred, preferred and LP less preferred 65

Appendix Table 4. Description of the experimental treatment setup T1 T2 T3 T4 T5 Fertilizer Cattle Manure Wood Ash Lime Control

Appendix Table 5. Tree cove Altitudes Ni Cover Q MC RC D RD F FR IV 2350-2700msl 7761 3.5% 391 3.03 100 0.86 100 4.34 100 300 2000-2350msl 6618 4.1% 323 3.02 100 0.74 100 3.59 100 300 1350-2000msl 7547 3.2% 242 2.67 100 0.84 100 2.69 100 300

Appendix Table 6. Shrub cove Altitudes Ni Cover Q MC RC D RD F FR IVI 2350-2700msl 16263 1.2% 237 2.06 100 7.23 100 2.63 100 300 2000-2350msl 27463 0.9% 246 2.63 100 12.21 100 2.73 100 300 1350-2000msl 31783 0.7% 333 2.61 100 14.13 100 3.70 100 300 cover Ni Q MC RC D F RF RD and IVI represents number of individuals quadrat mean cover relative cover density relative density frequency relative frequency and importance value index

Appendix Table 7.Tree Species diversity Altitudes Ni E H’ S 2350-2700msl 1.45 2.79 233999 1.74 2000-2350msl 293716 1.58 2.02 3.29 1350-2000msl 44157 1.68 1.52 1.51 Appendix 8 Shrub diversity Altitudes Ni E H’ S

2350-2700msl 9418 2.37 1.59 1.26 2000-2350msl 8954.00 1.82 1.27 1.01 1350-2000msl 7547.00 2.22 1.55 1.29 Ni= numbers of species, E= species evenness, H’= diversity index and S= species richness 66

Appendix 9.Location of sampling area

No Elevation (msl) Longitude Latitude 1 1604 N 09o 27’135 E 038o16’09’489 2 1684 N 09o57’40’23 E 038o16'26’805 3 1790 N 09o 25’444 E 038o16’16’875 4 2324 N 09o 35'22'902 E 038o18'10'215 5 2343 N 09o 35'28'337 E 038o18'03'032 6 2346 N 09o 35'25'182 E 038o18'02'607 7 2362 N 09o 37'04'481 E 038o16'34'826 8 2362 N 09o 37'05'669 E 038o 16'30'857 9 2379 N 09o 35'36'666 E 038o 18'01'425 10 2382 N 09o 36'50'113 E 038o 16'30'582 11 2386 N 09o 38'31'876 E 038o 18'00'547 12 2392 N 09o 36'57'992 E 038o 16'30'816 13 2435 N 09o 35'37'295 E 0038o 17'48'654 14 2467 N 09o 37'17'249 E 038o 14’46’761 15 2500 N 09o 36’05’196 E 038o 16’51’992 16 2509 N 09o 23’564 E 038o 17’802 17 2515 N 09o 24’937 E 038o 16’55 18 2547.1 N 09o 49’12’35 E 038o 21’64’35 19 2548 N 09o 55’27’08 E 038o 25’65’65 20 2554.2 N 09o 52’44’48 E 038o 23’661 21 2555 N 09o 55’97’02 E 038o 26’26’55 22 2557.4 N 09o 48’62’18 E 038o 22’35’7 23 2568.3 N 09o 47’81’83 E 038o 23’24’48 24 2574.3 N 09o 48’26’72 E 038o 22’76’52 25 2591 N 09o 25’661 E 038o 18’048 26 2653 N 09o 47’25’45 E 038o 23’89’35 27 2741 N 09o 18'758 E 038o 16’211 28 2752 N 09o 18’909 E 038o 16’291 67

Appendix Table 10. Rain fall data of Inchini Station

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 2005 43.7 0 107.3 93.7 113.4 130.4 191.1 188.1 133.2 34.4 7.6 6.3 1049.2

2006 4.3 15.9 173 96.8 62.3 174.1 364.6 351.5 170.1 26.9 33.4 30.5 1503.4 2007 39.2 27 38.5 56.9 176 371.9 368.7 297.4 157.8 9.6 0 0 1543 2008 1.6 9 2.6 48.9 186.5 238.7 310.8 224.5 69 39.8 68.5 0 1199.9 2010 36.3 33.6 104.8 62 95.6 240 210.3 234 83.5 17.5 29.5 46.2 1193.3 2011 32.1 5.2 64.7 81.2 131.4 0 201.3 0 153.8 2 0 0 671.7 2012 8 0 45.1 100.2 20.4 84.5 0 245 0 0 0 0 503.2 2013 5.5 0 0 62 94.4 185.7 267.1 242 111.2 91.3 12.1 0 1071.3 2014 43.5 36.2 70 66.9 186 70.6 200.1 250.5 184.3 51.3 0 9.6 1169 2015 0 10 22.4 0 128 164 209.2 197.3 168.4 12.2 0 23.4 934.9 Source: Ethiopian meteorological agency

7.2. Appendix Figure

Appendix Figure 1Experimental design setup 68

Meta Robi

2350-2700msl 2000msl-2350msl 1650-2000msl

3 Transects 3 transects 3 Transects

30 quadrat 30 quadrat 30 quadrat

Higher altitude mid altitude Lower altitude

Appendix Figure 2.Site Selection and sampling Procedure for vegetation Assessment 69

Land -use land -cover of Meta-Robi district Appendix Figure 3. Land Use land cover of Meta Robi district (1986, 2000 and 2013) 70

Appendix Figure 4. Questionnaires prepared on Land use land cover dynamics and sheep herbaceous species and browse vegetation preferences in the study area I. Land use land cove change dynamics 1. Grazing land 1.1. What was the status of grazing lands in before thirty years? Last decade? Current? 1) Decreasing (2) increasing (3) No change 1.2. If the answer is decreasing what are the factors contributed for decreasing?

List the driving forces ______2. Agricultural land 2.1. What was the status of agricultural lands before thirty years? Last decade? Current? 1) Decreasing (2) increasing (3) No change 2.2. If the answer is decreasing what are the factors contributed for decreasing? List the driving forces; ______2.3. What are the water and soil conservation measures practiced in the area? Lists______

2.4. How the population dynamics before 30 years? Current? Describe. ______2.5. Was increasing of population a factor of increasing crop lands? How? 3. Forest and shrub lands 3.1. What was the status of Forest and shrub lands in thirty years? Last decade? Current? 1) Decreasing (2) increasing (3) No change 4.2. If the answer is decreasing what are the factors contributed for decreasing? 71

List the driving forces ______4. Water body 4.1. What was the status of river, ponds and springs thirty years? Last decade? Current? 1) Decreasing (2) increasing (3) No change 4.2. If the answer is decreasing what are the factors contributed for decreasing? List the driving forces ______5. Settlements 5.1. What was the status of settlements years? Last decade? Current? 1) Decreasing (2) increasing (3) No change 6.2. If the answer is decreasing what are the factors contributed for decreasing? List the driving forces ______6. Other land use 6.1. Was land slide ever happen in the area? When and where ______7. What are the major constraints and consequences of land use land cover change in the area?______8. What area the adaptation and mitigation mechanisms to overcome the problem? ______

II. Questionnaires prepared for key informants to identify sheep herbaceous species vegetation preference 72

No Local or name Botanical name Desirability in livestock class Wet/dry season Cattle Sheep Goat preference

1. Livestock dynamics 1.1. What is the household livestock holding in average? cattle ____ sheep _ Goat ______Equines______1.2. How you manage your livestocks? Herding/ tethering /cut and carry? 2. Grazing land dynamics 2.1. Where you graze your livestocks? On communal or private grazing lands? 2.2. Are there communal grazing lands in your area? 2.3. What are feed source for your livestock? 2.4. How you manage your grazing lands? 3. Sheep versus other livestock vegetation preference 3.1. Where you herd and graze your sheep? 3.2. How you feed? Herding? Tethering? Or other? specify 3.3. Do you know the common herbaceous and browse vegetation species consumed by each of your livestock? Vegetation type HP= high preferred. P preferred LP less preferred

4. What is the status of grazing land? 5. What are the cause of grazing land degradation in your area? 6. Do you think sheep grazing cause grazing land degradation? 7. Which livestock classes more affect grazing lands degradation? Why?