Evaluation of the costs and benefits of soil and water conservation

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1.3.2 The current use of Soil and Water Conservation structures in Mayleba . ...... The second translator graduated in industrial engineering and had experience in ...... 2008.pdf. Duke, J.M. et al., 2012. Sustainable agricultural management ...

FACULTY OF SCIENCE

FACULTY OF SCIENCE ENGINEERING SCIENCES

AND

Evaluation of the costs and benefits of soil and water conservation techniques in the Northern Ethiopian Highlands

Marthe WENS

Supervisor: Prof. Dr. J. Poesen Division of Geography, Department of Earth and Environmental Sciences, KULeuven Co-supervisor: Prof. Dr. E. Mathijs Division of Bio-economics, Department of Earth and Environmental Sciences, KULeuven

Mentor: Dr. G. W. Taye

Department of Land Resource Management and Environmental protection, Mekelle University

Thesis presented in fulfillment of the requirements for the degree of Master of Science in Geography Academic year 2015-2016

BIO-

© Copyright by KU Leuven Without written permission of the promoters and the authors it is forbidden to reproduce or adapt in any form or by any means any part of this publication. Requests for obtaining the right to reproduce or utilize parts of this publication should be addressed to KU Leuven, Faculteit Wetenschappen, Geel Huis, Kasteelpark Arenberg 11 bus 2100, 3001 Leuven (Heverlee), Telephone +32 16 32 14 01. A written permission of the promoter is also required to use the methods, products, schematics and programs described in this work for industrial or commercial use, and for submitting this publication in scientific contests. © Copyright by Vrije Universiteit Brussel Without written permission of the promoters and the authors it is forbidden to reproduce or adapt in any form or by any means any part of this publication. Requests for obtaining the right to reproduce or utilize parts of this publication should be addressed to Vrije Universiteit Brussel, Faculteit Wetenschappen en Bio-ingenieurswetenschappen, Pleinlaan 2, 1050 Brussel, Telephone +32 (0)2 629 33.57. A written permission of the promoter is also required to use the methods, products, schematics and programs described in this work for industrial or commercial use, and for submitting this publication in scientific contests.

PREFACE

I

Table of Contents PREFACE ................................................................................................................................................................ I List of figures ................................................................................................................................................... IV List of tables .................................................................................................................................................... VII List of abbreviations ...................................................................................................................................... VIII Abstract ............................................................................................................................................................ IX Acknowledgements.......................................................................................................................................... X INTRODUCTION ................................................................................................................................................... 1 1.1

Sustainable development in the Ethiopian Highlands of Tigray ................................................... 2

1.1.1

The role of Agricultural development in Tigray .......................................................................... 2

1.1.2

Soil as exhaustible natural resource, requiring protection ....................................................... 4

1.1.3

Economics of soil and water conservation in degraded areas ................................................. 5

1.2

Problem statement .............................................................................................................................. 7

1.2.1

Land degradation in Tigray ........................................................................................................... 7

1.2.2

Starting point ................................................................................................................................... 9

1.2.3

Research question ........................................................................................................................ 11

1.2.4

Justification, academic and social relevance ............................................................................ 12

1.3

Background soil and water conservation management in Tigray .............................................. 13

1.3.2

History of Soil and Water Conservation structures in Tigray .................................................. 13

1.3.2

The current use of Soil and Water Conservation structures in Mayleba .............................. 15

3.1.3

The role of the Productive Safety Net Program on the current SWC ................................... 22

MATERIALS & METHODS................................................................................................................................. 24 2.1

Study area .......................................................................................................................................... 25

2.1.1

Geography ..................................................................................................................................... 25

2.1.2

Climatology .................................................................................................................................... 27

2.1.3

Geology .......................................................................................................................................... 28

2.1.4

Geomorphology ............................................................................................................................ 28

2.1.5

Pedology ........................................................................................................................................ 29

2.1.6

Land use and land cover ............................................................................................................. 30

2.1.7

Hydrology....................................................................................................................................... 32

2.2

Data collection and Analysis ............................................................................................................ 33

2.2.1

To make an inventory the on- and off-site effects of SWC measures ................................... 33

2.2.2

To get an insight in the interests and perceptions of farmers in Mayleba and of students

from Tigray.................................................................................................................................................. 34 2.2.3

To value the alleged costs and benefits qualitatively via the WTC labour of farmers and the

WTP taxes by the society ......................................................................................................................... 39 2.2.4

To verify the economic efficiency of stone bunds, stone-faced trenches, conservation

trenches and check dams in the Mayleba Catchment ......................................................................... 49

II

RESULTS & DISCUSSION ................................................................................................................................ 50 3.1

Inventory of the on- and off-site effects of SWC in Mayleba ....................................................... 51

3.1.1

Determinants of the adoption of SWC ....................................................................................... 51

3.1.2

Costs of Soil and water conservation structures ...................................................................... 52

3.1.3

Benefits of Soil and Water Conservation structures ................................................................ 53

3.2

Insight in the perception of farmers concerning soil degradation and SWC management ... 58

3.2.1

Characteristics of farmer respondents ...................................................................................... 58

3.2.2

Farmers’ knowledge concerning soil erosion ........................................................................... 63

3.2.3

Farmers’ opinion concerning soil and water conservation ..................................................... 68

3.3

Value of SWC effects via discrete choice experiments ............................................................... 81

3.3.1

DCE about onsite (dis) benefits of SWC measures - farmers................................................. 81

3.3.2

DCE about offsite (dis)benefits of SWC - SOCIETY................................................................. 87

3.3.3

Comments on the valuation of the on- and offsite benefits of SWC in Tigray ...................... 91

3.4

Discussing the present-day integrated watershed management in the Mayleba catchment,

Tigray ............................................................................................................................................................. 92 3.4.1

Summarizing the attitude of farmers towards general SWC management .......................... 92

3.4.2

Empirical Scenario analysis of realistic SWC management in Mayleba ............................... 94

3.4.3

Examining the limiting factors in agricultural development .................................................... 98

CONCLUSIONS ................................................................................................................................................ 100 4.1

Summary of main results ............................................................................................................... 101

4.2

Policy implications........................................................................................................................... 105

4.3

research implications...................................................................................................................... 106

REFERENCES ................................................................................................................................................... 107

III

LIST OF FIGURES Figure 1 LEFT added value of Agriculture in the GDP of Ethiopia. World Bank, 2012 ............................... 2 Figure 2: RIGHT: Distribution of urban and rural population in Ethiopia. World Bank, 2014...................... 2 Figure 3: A Deep gully reaching the limestone bedrock poses a threat to the surrounding fertile fields. Mayleba 08/2015. ................................................................................................................................................. 3 Figure 4: Farmer showing his fields dammed by stone bunds. Mayleba 08/2015. ..................................... 3 Figure 5: Degraded rangeland Landscape dissected by a developing gully. Mayleba 08/2015. ............ 7 Figure 6: Distribution of the different SWC practices per land use type in the Mayleba Catchment. Taye (2014) ..................................................................................................................................................................... 8 Figure 7: Terraced landscape in Mayleba. Stone Bunds delineate the boundaries. Steep slopes are converted to exclosures. Mayleba, 08/2015. .................................................................................................... 9 Figure 8: Tigrigna family weeding on a field, bounded by large stone bunds. Mayleba 8/2015 ............ 12 Figure 9: Steep Landscape converted to rangeland and protected with stone bunds. Mayleba 08/2015. .............................................................................................................................................................................. 14 Figure 10: Loose stones check dams (left) and a gabion check dams (right). The loose stones left below are proven effective in revegetating the gully, the gabion right below is damaged. Mayleba 08/2015.. 16 Figure 11: Stone bunds in cropland. Left: Recent one, low influence on the slope. Right: Older one, terracing effect visible. Mayleba, 08/2015. .................................................................................................... 17 Figure 12: Trenches in rangeland. Left: large trenches. Right: small trenches with embarking soil. Below: normal conservation trenches. Mayleba, 08/2015. ........................................................................................ 18 Figure 13: Left: Filled up stone-faced trench on cropland, density difference in crops visible. Right: Muddy stone-faced trench in rangeland. Borders clearly more densely vegetated. below: old stone-faced trench, filled up witsh soil and crops. Mayleba 08/2015. ............................................................................. 19 Figure 14: Increase of trench and bund length in km in Mayleba from 2010 to 2015. (BoARD 2015) ... 21 Figure 15: Increase of check dam volume in Mayleba from 2010 to 2015. (BoARD 2015) ..................... 21 Figure 16: Share of each type of work in the construction of trenches. (Regional Agricultural office 2015) .............................................................................................................................................................................. 23 Figure 17: Share of each type of work in the construction of stone bunds. (Regional Agricultural office 2015). ................................................................................................................................................................... 23 Figure 18: Share of each type of work in the construction of check dams. (Regional Agricultural office 2015) .................................................................................................................................................................... 23 Figure 19: Digital elevation model of ethiopia. (intercarto.com) ................................................................. 25 Figure 20: Localisation of the Mayleba catchment in the Dogu’a Tembien district in Tigray, northern Ethiopia. (Taye 2014) ........................................................................................................................................ 26 Figure 21: The precipitation and potential evapotranspiration (left) and min., mean and max. Temperatures (right) for the dogu’a tembien district. Data from new locclim (FAO, 2013). .................... 27 Figure 22: Geology of the mayleba catchment (Van der Wauw, 2008) ...................................................... 28 Figure 23: Soil map of the mayleba catchment. (Van der Wauw, 2008)..................................................... 29

IV

Figure 24: Land use map of the Mayleba catchment. (Taye 2012) ............................................................. 30 Figure 25: Land use in Mayleba: foreground: rangeland. Background: cropland. Hills: exclosures. Mayleba 08/2015. ............................................................................................................................................... 31 Figure 26: View on the mayleba reservoir,sedimentation and microdam delineating the catchment. Mayleba, 08/2015. .............................................................................................................................................. 32 Figure 27: Translator conducting a semi-structured interview with a woman of a household. Mayleba 08/2015 ................................................................................................................................................................ 33 Figure 28: Concept of Choice Experiments .................................................................................................... 34 Figure 29: Locations of the conducted interviews on a land use map of Mayleba of 2012. CUrrently, less rangeland is present. LU Data Taye 2012 ....................................................................................................... 35 Figure 30: Location of The conducted interviews on a Google Earth image. Contour data Taye 2014 . 36 Figure 31: Translator and farmer doing an interview on the field. Mayleba, 08/2015. .............................. 38 Figure 32: Experimental Design and analysis of the Discrete Choice Experiments .................................. 42 Figure 33: Translator executing an interview with one farmer, several farmers are listening out of interest. Mayleba, 08/2015 ............................................................................................................................................... 47 Figure 34: Nexus of the process of SWC adoption (Teshome 2015) .......................................................... 51 Figure 35: Field full of stone bunds. severe rill erosion is still present. . Mayleba 08/2015 .................... 52 Figure 36: On-site effects of SWC on the fields, resulting from SB, CT and CDs. Arrows indicate increasing (up) versus decreasing (down) effect. .......................................................................................... 54 Figure 37: on-site effects of SWC on the fields, resulting from SB, CT and CDs. Arrows indicate increasing versus decreasing effect. ............................................................................................................... 56 Figure 38: Maps with indication of the conducted interviews. Left: Geology of Mayleba. Right: Pedology of Mayleba. Data: Taye 2012............................................................................................................................. 58 Figure 39: young family of 4 working on the field. Mayleba, 08/2015. ........................................................ 59 Figure 40: House with nearby field, BADLY maintained bench terraces. Mayleba 08/2015. .................. 62 Figure 41: changes in productivity of cropland (yield) and rangeland (biomass) during 2010-2015. HH=105 ................................................................................................................................................................ 63 Figure 42: effects ranked according to their importance in influencing the crop or biomass productivity. (left: crop yield; right: grass biomass). Rank 1 has the largest influence. HH=105 ................................... 64 Figure 43: The erosion phenomena in Mayleba as perceived by the respondents. HH=105 .................. 65 Figure 44: Types of erosion indicated by the respondents. HH=105 .......................................................... 65 Figure 45: Processes that cause erosion according to the answers of the farmers. HH=105 ................. 67 Figure 46: Use of SWC structures on the different land covers. HH = 105 ................................................ 68 Figure 47: Change of the state of the soil after implementation of swc techniques. HH=105 ................. 69 Figure 48: Stone bund well maintained and fortified by the farmer with alien weed and crop residu. Mayleba 08/2015. ............................................................................................................................................... 70 Figure 49: Effects induced by the use of swc measures, as mentionned by the respondents. HH=105 73

V

Figure 50: Mean value of importance for benefits of swc structures on cl. 0: not important  5: extremely important. ............................................................................................................................................................. 76 Figure 51: Mean value of importance for benefits of swc structures on rl. 0: not important  5: extremely important .............................................................................................................................................................. 76 Figure 52: drawbacks of SWC structures ....................................................................................................... 77 Figure 53: Zigzag pattern in stone bunds to make ploughing more easy. Mayleba, 08/2015 ................. 78 Figure 54: Mean value of importance for drawbacks and benefits of SWC structures in gullies. 0: not important. 5: Extremely important effect ......................................................................................................... 79 Figure 55: Rare case of a farmer who constructed loose stone check dams in a developing gully by own initiative and own labour force. Mayleba, 08/2015......................................................................................... 79 Figure 56: The main characteristics of swc structures on cl ranked according to the perceived importance.(HH=105) ........................................................................................................................................ 80 Figure 57: The main characteristics of swc structures on rl ranked according to the perceived importance. (HH=105) ....................................................................................................................................... 80 Figure 58: WTW for Soil Loss Reduction on CL .................................................................................... 87 Figure 59: WTW for Soil fertility on CL…………………….. .......................................................................... 86 Figure 60: WTW for soil ploughing on CL .............................................................................................. 87 Figure 61: WTW for soil loss reduction on RL ................................................................................................ 86 Figure 62: WTW for flora increase on RL ............................................................................................... 87 Figure 63: WTW for wood production on RL .................................................................................................. 86 Figure 64: WTW for biomass growth on RL ........................................................................................... 87 Figure 65: WTW for fauna increase on RL ...................................................................................................... 86 Figure 66: Satellite image of Tigray. Yellow pins indicate the origin of the responding students. Red circle displays location of study area. Yellow line shows boundary of Ethiopia, grey line the boundary of Tigray. Google Earth 2013.............................................................................................................................................. 87 Figure 67: Wealth status of the student respondents .................................................................................... 87 Figure 68: : The start of a gully is protected by the farmers himself. He states to need more help to protect his fields more efficient. 08/2015 Mayleba. ....................................................................................... 99

VI

LIST OF TABLES Table 1: Choice Experiment Design - Cropland ............................................................................................. 41 Table 2: Choice Experiment Design - Off-site ................................................................................................ 41 Table 3: Choice Experiment Design – Rangeland .......................................................................................... 41 Table 4: (part of the) literature body dealing with effects of SWC on the fields, resulting from SB, CT and CDs. Effects refer to the effects in the figure above. ..................................................................................... 56 Table 5 : Summary of the farmer household survey in Mayleba. The total number of households is 105. .............................................................................................................................................................................. 60 Table 6: Summary of livestock holding. Mean and max amount of animals KEPT BY the respondents (percentage of 105) having at least one animal of that species................................................................... 61 Table 7: Summary OF CROP production. Expected YIELD OF the farmers summed over all their Cropland .............................................................................................................................................................. 61 Table 8: Ordered Probit model on the perceived erosion problem in TIGRAY (0=no erosion; 1=minor erosion; 2=erosion; 3=strong erosion)............................................................................................................. 66 Table 9: Ordered Probability Model on the perceived effectivity of SWC structures on CL and RL. ..... 71 Table 10: Ordered Probability Model on putting own labour forces in the SWC structures. (0=no effort, 1= maintaining, 2= reconstructing) .................................................................................................................. 72 Table 11: Random Parameter Logistic Econometric model for SWC benefits on CL (HH=105). Result using NLogit. ....................................................................................................................................................... 81 Table 12: Random Parameter Logistic Econometric model for SWC benefits on RL (HH=105). Result using NLogit. ....................................................................................................................................................... 83 Table 13: Mixed logistic ‘willingness to work’ model for SWC benefits on CL (HH=105). Result using NLogit. .................................................................................................................................................................. 85 Table 14: Random Parameters (mixed) Logit Model for off-site effect of SWC management. Result using NLogit. .................................................................................................................................................................. 89 Table 15: Latent Class Logit Model for off-site effect of SWC management ............................................. 90

VII

LIST OF ABBREVIATIONS                     

ASC CBA CBPWD CD CL Clogit CT DCE HH IWM LClogit MU-WAREP RPlogit PSNP REST RL SFT SB SWC WTC WTP

alternative-specific constant combining all not-considered effects cost-benefit analysis community-based participatory watershed development check dam, SWC structure in gullies cropland (farmland, owned by farmers to produce food) conditional logistic econometric model conservation trench, SWC structure on rangeland discrete choice experiments, contingent valuation method (number of) households that are considered integrated watershed management latent class logistic econometric model Mekelle University ‘s water resource planning project random parameters (or mixed) logistic econometric model the productive safety net program of Ethiopia Relief society of Tigray (large NGO in the north of Ethiopia) rangeland (communal grazing area, owned by the government) stone-faced trench, SWC structure combining trenches and stone bunds stone bund, most applied SWC structure on RL and CL in Mayleba soil and water conservation willingness to contribute (labour), by the farmers (private benefit) willingness to pay (extra taxes), by the students (society’s benefit)

VIII

ABSTRACT In Ethiopia, fragile environmental conditions, a high population pressure and limited success in adopting a sustainable agricultural systems makes the region very prone to degradation. Land degradation is devastating for subsistence farmers and poses a threat to food security in the rural regions of Ethiopia. Sheet- rill and gully erosion rates in the highlands of Tigray are large and bring high costs with it. Since 1970, intensive efforts are done to rehabilitate the environment. While the effectivity of the implemented soil and water conservation techniques has been thoroughly studied, the economic profitability has received only limited attention. This research analyses the perceived economic efficiency of stone bunds, stone-faced trenches, trenches and check dams in Mayleba, a sub-watershed in Tigray. It is tried to link the biophysical effects of soil and water conservation (SWC) structures with the monetary value for these effects, as perceived by farmers and the society. A literature-based list of profits and drawbacks is completed with a local farmer survey, discrete choice experiments (DCE) with farmers and students of Tigray are conducted to investigate the preferences of both groups. This innovative valuation approach gives insight in the attributes of SWC that influence the alleged on-site benefits from farmers and the alleged off-site benefits from the society. Results of the farmer study indicate that crop yield increase is marked as most important benefit from SWC structures on cropland, soil loss reduction and soil moisture increase follow. Soil loss reduction is the largest benefit of the current rangeland SWC structures, soil moisture and wood production are next. The state of the soil is (strongly) improved by installing SWC structures in both land use types. Econometric models show that SWC scenarios on cropland with a large reduction in soil loss and increase in future fertility, that does not disturbs the ploughing convenience, are valued the most. Large families are willing to work more for such scenario than others. On rangeland, there is a general aversion to SWC but scenarios effective in reducing soil loss, improving biodiversity and increasing wood and biomass production are more likely to be adopted. Farmers with more animals are willing to work more on rangeland. A scenario analysis examines if the large labour costs for constructing SWC structures are compensated by the biophysical benefits farmers and society experience. The contingent valuation method fails to show a significant conclusive answer but this study is able to expose the major limitations in the adoption of SWC in Mayleba: limited productive area, a labour shortage and financial constraints. Resolving this first one can allow people to install the more effective but space-filling structures. Extra financial support can help to secure sufficient food and relieve the amount of work by enhancing a labour market.

IX

ACKNOWLEDGEMENTS Working on a master thesis as a variate project requiring knowledge, inspiration, creativity and motivation. It is a process that cannot be accomplished alone. Therefore, I want to express my utmost gratefulness to everyone who -in one way or another- - helped me succeeding this project. Besides, delivering a successful thesis also means graduating as a MSc in geography, which makes this chapter a great opportunity to thank some important people who were indispensable in achieving this. A big THANK YOU to my promotors prof. Jean Poesen and prof. Erik Mathijs, for guiding me though the whole process, to equip me with the needed knowledge and provide me with feedback; to dr. Pieter Vlaeminck and drs. Goedele Van den Broeck, for their kind and indispensable assistance during the preparation, analysis and interpretation of the DCE; to all professors from the Earth and Environment Department of the KULeuven and the geography professors of the VUB, for teaching me all necessary geography-skills; to dr. Vranken, dr. Adgo, dr. Teshome, dr Balana, dr. Smitter, drs. Monsieurs, for sharing all their professional insights and tips and tricks concerning my research. Bet’ami ĀMESEGINALEHU to dr. Gebeyehu Taye, for preparing and guiding the fieldwork, for taking me to the congress, and for giving me a lot of inspiration for my thesis; to dr. Amanuel Zenebe and VLIRIUS for helping to finance my fieldwork, without which the whole project would not have been possible; to dr. Angesom, drs. Tesfakiros, drs. Jemana, drs. Tigistu and others from Mekelle university for translating the questionnaires and having interesting lunch breaks; to Getachu and Haile, for being my guide on the field, for teaching me Tigrigna and Amharic, for learning me about Tigray and off course for translating all the interviews. Hgus YEKENYELLEI to Tesfy, Kiros, Naom, Zenawi and Fortuna, for the food, cosy evenings, for showing me how to become an Habesha and for being the sweetest Ethiopian family I could imagine; to all responding farmers from Mayleba, for never refusing an interview, for being interested in my research and for all making time for injera, sowa, kollo, buna and embebba; to dr. Halefom, mr. Gedina, mr. Tesfy and mr. Arefe to make time for a discussion and provide me with their field experience and information about my thesis topic; to Eden, Rafaël, Elias, Samuel, Tesfy and Flower, and many more to let me feel at home in Mekelle, to treat me with nice delicious dinners, joyful evenings and lots of friendship. Een warme DANKJEWEL to Merkator, for being a great student association, enhancing the educational productivity of all geography students in Leuven, and bringing so much friendship; to my colleagues and friends of 2nd master, Adriaan, Bieke, Celina, Dami, Enyo, Eva, Janna, Joost, Liane, Michiel, Pieter, Ward, Willem, Wim and Yasmine, for being an inspiration and source of courage for me; to my parents and sisters, for caring for me, supporting me in everything I do and for bringing me so far, and last but not least; to my boyfriend Niels, who’s direct and indirect, unconditional support has pulled me through all hard moments at University and who showed me to embrace all nice moments as student.

Marthe WENS

X

Chapter 1 INTRODUCTION

1

1.1 SUSTAINABLE DEVELOPMENT IN THE ETHIOPIAN HIGHLANDS OF TIGRAY 1.1.1 THE ROLE OF AGRICULTURAL DEVELOPMENT IN TIGRAY Agriculture in Ethiopia – as in many developing countries - embodies the leading economic sector, accounting for 49% of the GDP (figure 1; The world bank 2014). 82% of the population lives in rural areas (figure 2; The world bank 2014) and 90% of Ethiopia’s agricultural production emanates from small-scale subsistence production systems (Haregeweyn et al. 2015). Likewise, rain fed subsistence agriculture is the dominant land use type in Tigray, the northernmost province of Ethiopia. It serves as base for the socioeconomic settings of the rural communities in this region (Nyssen et al. 2014; Taye 2014). Agricultural land is seen as a cornerstone upon which the welfare of the society is built (Bekele and Drake 2003). However, fragile environmental conditions as a low vegetation cover, erosive rains and steep slopes make Tigray to be very prone to land degradation (Taye et al. 2013). In addition, the region is one of the most drought- prone region of the country; it suffers from seasonal moisture stress for eight months due to the high concentration of rainfall during a limited period of the year (Haregeweyn, 2008). Inadequate water availability and excessive soil erosion reduces the agricultural potential; the average cereal production does only reach up to 2 ton/ha/yr, leaving a yield gap of 2 ton/ha/yr. (Getachew et al. 2008) and is heavenly dependent on the annual rainfall and natural soil fertility (Taye 2014).

FIGURE 1 LEFT ADDED VALUE OF AGRICULTURE IN THE GDP OF ETHIOPIA. WORLD BANK, 2012 FIGURE 2: RIGHT: DISTRIBUTION OF URBAN AND RURAL POPULATION IN ETHIOPIA. WORLD BANK, 2014

Furthermore, the high countrywide population growth of 2.6% (World Bank, 2015) causes a high pressure on the agricultural land (Martens 2013). With 91 persons per km² and 277 animals per km², Tigray has the highest human and livestock population density of Ethiopia(Central Statistical Agency 2014). To ensure that food production follows the demographic expansion, cultivated land is extended at the expense of rangeland and forest (FAO 2015). This resulted in past times and still results in deforestation and overgrazing and 2

leads to the exploitation of fields that are less suitable for agriculture (Esser & Haile 2002; Ayele et al. 2015). Moreover, poor agricultural practices are in use: the animal manure replaces sparse firewood as resource for cooking and heating. The manure thus cannot be used as a fertiliser. Crop residues are given to animals as fodder or used to reinforce stone bunds, stopping any organic residues to be returned to the soil (Desta et al. 2005b; Amsalu and de Graaff 2006). The described alarming situation exposes the landscape of Tigray to physical, chemical and biological degradation (Nyssen et al. 2014). As a consequence, “this crucial natural resource is under continuous threat and its long-term productive potential is being impaired” (Nyssen et al. 2014). FIGURE 3: A DEEP GULLY REACHING THE LIMESTONE BEDROCK POSES A THREAT TO THE SURROUNDING FERTILE FIELDS. MAYLEBA 08/2015. FIGURE 4: FARMER SHOWING HIS FIELDS DAMMED BY STONE BUNDS. MAYLEBA 08/2015.

The dependence of agriculture makes the economy of Tigray very vulnerable to natural shocks (Ayenew 2015). Severe soil depletion and limited water availability, together with recurrent droughts and high climate variability in the region, pose a threat to livelihoods and food security (REST 1991; Andersson et al. 2009). Barbier & Bishop (1995) estimated the annual cost of land degradation in Ethiopia around 6%-9% of the GNP. Nationally around 8.3 million people are chronically food insecure 36% is undernourished (The world bank 2012). In Tigray more than 50% of the rural population depends on food aid 6 months after harvest in years with normal rainfall distribution (Van Wesemael et al. n.d.). Last year, a drought period due to the El Niño effect caused a livelihood crisis putting thousands of extra people on food aid (FAO 2016). Besides, limited access to services and a lack of employment opportunities are pushing the urban population into shortages (Slater & Tefera 2006). Poverty is both a result and a cause of the slow agricultural development, triggered by a complex blend of biophysical, socioeconomic and policy factors (Taye et al. 2013). A lack of information and insufficient financial capacity confines the incentive of farmers to adopt sustainable agriculture, increasing the food insecurity and the risk on extreme poverty (Taye 2014).

3

1.1.2 SOIL AS EXHAUSTIBLE NATURAL RESOURCE, REQUIRING PROTECTION The natural stock of fertile soil can be considered as a renewable resource and counts as the main productive asset of subsistence farmers (Esser & Haile 2002). Soil ecosystem services range from water regulation and purification to carbon fixation, nutrient recycling and food production (Pimentel et al. 1995). Ensuring sustainable use of the soil implies that the exploitation of this renewable resources does not exceed the natural rate of soil regeneration (Haregeweyn et al. 2015). However, both wind and water erosion threaten the sustainable use of the soil in the Tigray Highlands (Taye 2014). Erosion, together with intensive use of the land, has resulted in reduced soil organic matter which further increases soil erosion and reduces land productivity (Webb et al. 1992). Hurni (1988) estimated the soil loss in the cultivated highlands of Tigray up to 42 ton/ha/yr. (Hurni et al. 2015). Haregeweyn et al. (2006) measured a lower rate of soil loss of 9.1 ton/ha/yr. on catchment level using reservoir survey. This is close to the measured soil loss by sheet and rill erosion as reported by Nyssen et al., being 9.7 ton/ha/yr. on cropland and 17.4 ton/ha/yr. on rangeland (Nyssen et al. 2009). Recently, Taye et al. assessed soil loss rates of 38.7 t/ha/yr. from a degraded rangeland and 7.2 t/ha/yr. from cropland based on plot-scale measurements in the study area (Taye 2014). Authors attribute the higher soil loss in rangeland to increased runoff resulting from intensive grazing, a general low vegetation cover and soil compaction, whereas soil tillage supports infiltration (Taye et al. 2013; Haregeweyn et al. 2015b). Besides, gully erosion adds an extra soil loss of 1.1 t/ha/yr. in northern Ethiopia (Nyssen et al. 2006). In any case, these erosion rates exceed the soil formation rate by a factor of 4 to 10 (Hurni et al. 2015), indicating that soil must be seen as an exhaustible resource, prone to fertility and depth losses as a result of uncontrolled and accelerated erosion. Soil and water conservation (SWC) structures (figure 3) play a crucial role in restoring the precious equilibrium between soil loss and soil regeneration, aiming to maintain its productive capacity while still using it (Esser & Haile 2002; Pimentel & Burgess 2013). Proper SWC structures and management of agricultural land implies improving land productivity through encouraging different conservation and rehabilitation mechanisms and rational utilization of the land resource (Desta et al. 2005a). Physical SWC structures such as stone or soil bunds, check dams, water ponds, trenches and diversion ditches reduce the ‘recurrent wastage of rainwater due to surface runoff’, thereby improving the soil moisture content and reducing the soil erosion (Haregeweyn et al. 2015). The intent is to mitigate water scarcity and the malicious circle of nutrient depletion - vegetation loss - moisture stress.

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1.1.3 ECONOMICS OF SOIL AND WATER CONSERVATION IN DEGRADED AREAS In the past, economic considerations did not play a major role in the analysis of SWC (Bekele 2005). Yet, decisions concerning SWC measures should also depend on the costs relative to the total expected value of the environmental and private benefits of these measures (Bekele 2004). The implementation of SWC management is often enforced using top-down approaches and is only selected on its effectiveness in improving crop productivity and reducing the soil erosion risk (Kassahun & Jacobsen 2015). However, it is crucial that these management projects are economically efficient as well (Teshome et al. 2014a). A community supported bottom-up adoption of SWC requires a net positive economic and ecological impact. Hence, it is important to consider the potential biophysical effects of SWC structures as well as the related economic benefits and costs (Haregeweyn et al. 2015). However, some methodological limitations make it hard to quantify and value many of the costs and benefits associated with SWC (Bekele 2003). Many studies only address the impacts of SWC on crop yield, while non-marketable values of ecosystem services of soils are largely neglected in such research (Haregeweyn et al. 2015b). SWC structures change the physical, biological and chemical qualities of the soil that influence biomass production – like water holding capacity, soil organic matter, bulk density and mineral content – (Gunatilake & Vieth 2014). It is interesting to assess the monetary value values of each of these effects separately. Changes in ecosystem services such as the influence of soils on water availability, reservoir sedimentation, plant nutrient enrichment, biodiversity and carbon sequestration are far more challenging to estimate (Haregeweyn et al. 2012; Mirzabaev et al. 2015). A conventional costbenefit analysis (CBA) of the on-site effects of SWC is thus insufficient to evaluate the viability of SWC scenarios (FAO 2015). A comprehensive CBA requires the integration of quantifiable data such as the costs for SWC measures implementation and crop yield with data about environmental conditions including biodiversity and landscape sensitivity (Haregeweyn et al. 2012). Besides, it is more and more clear that those ecological off-site effects of SWC measures are not compensated by a market mechanism (Esser & Haile 2002). Since the (labour) costs of SWC are largely borne by the smallholder farmers, there exist an inequity in the covering of the costs and the widespread distribution of the benefits of SWC. Markets fail due to a deficiency in information to the subsistence farmers, uncertainty about property rights, nonexistence of credit markets and other institutional factors (Haregeweyn et al. 2013). This limits individual farmers’ incentives to practice SWC (Bekele 2003; Kassahun & Jacobsen 2015). Smallholder farmers usually cannot afford the high initial investment costs of the structures

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(Esser & Haile 2002) and have a strong short time preference and a risk-averse behaviour. This contrasts with the limited short-term effect on crop yield and merely long term effect on soil fertility of SWC measures (Haregeweyn et al. 2015). The result is a sub-optimal resource allocation (Maertens n.d.). Hence, appropriate government intervention in SWC management is required. In addition to the biophysical effectiveness, the economic efficiency of SWC practices should be taken into account in environmental protection policies, considering both on- and off-site effects, on the short- and long term, to ensure individual and social optimal SWC scenario’s (Esser & Haile 2002; Abebe & Bekele 2014; Shiferaw & Holden 2014; Bekele 2003). Given that SWC management requires a considerable labour input, there is need for solid approaches to evaluate the economic value of SWC measures (Bekele 2004). Although, a lack of consensus about the methods to evaluate the profitability of different SWC structures prevents good natural resource management policies (Haregeweyn et al. 2015). Recently, a new approaches in the economics of SWC is developing: Ayenew (2015) uses a contingent valuation method (CVM) in the form of a discrete choice experiment. His goal is to “to elicit households’ Willingness to Pay (WTP) for soil and water conservation practices” in terms of labour contribution (Ayenew 2015). In their search for economic and institutional incentives for the adoption of SWC measures. Kassahun & Jacobsen (2015) use a latent class approach to evaluate the results of a discrete choice experiment (DCE). They were able to calculate the effect of e.g. subsidies, extension services, land reform on the willingness to contribute (WTC) labour to SWC management (Kassahun et al. 2015). The use of the WTC labour or the WTP taxes can also be used to estimate the economic value of different on- and offsite effects of SWC. In this research, a survey-based stated preference elicitation method is use to model perceived values of hypothetical soil and water conservation (SWC) scenarios. By means of DCE’s with farmers and with student, the characteristics of these SWC scenarios that have a higher likelihood to be (both privately and community) supported, will be revealed. SWC effects can be valued in order to compare the total benefit of the SWC scenarios with their costs. This should be a new method to estimate the economic viability of the current SWC management in Tigray, Ethiopian Highlands.

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1.2 PROBLEM STATEMENT 1.2.1 LAND DEGRADATION IN TIGRAY The region of Tigray is considered to be the most degraded part of Ethiopia (Esser & Haile 2002). Severe erosion due to prevalence of steep slopes, high erosive rainfall and low vegetation cover caused by overexploitation and deforestation characterises the Tigray region (Mayor et al 2010). Soil erosion and human-influenced environmental changes, triggered by accelerated population growth and resulting expansion of cultivated land, are responsible for the current degradation of this dry northern part of Ethiopia (Bekele & Drake 2003; Desta et al. 2005;). Nyssen et al. (2009) states that more than 50% of northern Ethiopian highlands suffer from extreme loss of topsoil. In the Mayleba catchment, a yearly sediment loss of 7.2 ton/ha for cropland (CL) and 38.7ton/ha for rangeland (RL) was estimated by Taye et al. (2013). Next to a depletion of topsoil nutrients, there is a risk on crop damage caused by runoff flows, because rainfall exceeds the water holding capacity of the soil (Taye et al. 2013). Soil erosion is a threat to the food security and development of Ethiopia inducing on-site costs to individual farmers next to off-site costs for the whole society (Bekele & Drake 2003; Adgo et al. 2013). The dire environmental situation is pushing a vulnerable society into a vicious circle of extreme poverty with a high risk on food shortages every year. Besides, low education is posing difficulties to adopt sustainable agricultural systems (Taye 2014; Hurni et al. 2015). FIGURE 5: DEGRADED RANGELAND LANDSCAPE DISSECTED BY A DEVELOPING GULLY. MAYLEBA 08/2015.

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Serious investments in Soil and Water Conservation (SWC), irrigation, revegetation, reversing resources degradation and enhancing agricultural production are necessary to escape the vicious cycle of poverty and land degradation (Desta et al. 2005b). Currently the Ethiopian government spends more than 90% of the total public works budget to natural resource projects in order to reverse the trend of land degradation and assure future soil fertility (REST 1991). The aim of these SWC strategies is to build natural and social resilience in Tigray (Haregeweyn et al. 2015). Research by Kraaijvanger et al. (2014) in Tigray computed that -next to environmental characteristics (31%)-, the management of fields account for 45% in the yield variability. This supports the importance of good SWC techniques in assuring food security. As an answer on this fragile environmental and social situation, soil and water conservation structures have been widely spread over the study area of this master thesis research; the Mayleba catchment, over the past 25 years, (Nyssen et al. 2007). To fight water erosion and to conserve soil and water in-situ, check dams, stone bunds, grass strips, micro basins, gully revegetation, contour tillage, drainage ditches and conservation trenches are widely installed. Most of this is accomplished through (mandatory) voluntarism where farmers contributed 20 days free labour per year (Halefom, pers.com; Kumasi & Asenso-Okyere 2011) and through paid Public Works included in food for work or cash for work Cash transfer projects, starting from the 1980s (Nyssen et al. 2007; Desta et al. 2005). Vegetation cover undeniable reduces runoff and soil erosion but it is not able to protect the ground from excessive runoff during times of extreme rainfall. Though, this are the periods that soil loss, flooding and crop burial occur. High runoff from upland areas cannot be protected by biological interventions alone (Herweg & Ludi 1999). Hence, more expensive and labourrequiring physical SWC structures are indispensable to protect the land from soil erosion and related problems (Haregeweyn et al. 2012). The drawbacks for such structures are more evident, making them more interesting as subject of a cost benefit analysis. Subsequent smart graph (figure 6) indicates the presence of the 5 most prominent physical SWC structures in the Mayleba catchment per land use type. Stone bunds and stone-faced trenches are built in all types of land uses, whereas trenches only occur in rangeland. Gullies are protected by the construction of check dams, both by assembling loose stones as by using gabions.

FIGURE 6: DISTRIBUTION OF THE DIFFERENT SWC PRACTICES PER LAND USE TYPE IN THE MAYLEBA CATCHMENT. TAYE (2014)

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1.2.2 STARTING POINT Fruitful research in Tigray indicates that physical SWC structures such as stone bunds, conservation trenches and check dams can effectively reduce erosion and improve crop yields (Taye 2014; Nyssen et al. 2007). Between 2008 - 2014, multiple researchers contributed to the “Water Resource Planning” project of the Mekelle University: “Improving water resource planning at the scale of micro-dam catchments (ca. 20km³) in Tigray, Northern Ethiopia: learning from success and failure”. Case study analysis illustrates the positive effects of SWC measures at various spatial scales (Adgo et al. 2013; Hengsdijk et al. 2005; Herweg & Ludi 1999). The introduced SWC structures are proven to be effective in improving the soil structure and soil moisture, rain infiltration, crop yield and biomass production. Besides, they reduce sheet, rill erosion and gully to prevent flooding and reservoir siltation (Taye 2014; Nyssen 1995; Nyssen et al. 2014; Teshome et al. 2014b). There are also negative effects: the presence of SWC structures can induce rodent plagues in the fields and the reduction of runoff leads to less water inflow into the reservoirs (Meheretu et al. 2014; Haregeweyn et al., 2008).

FIGURE 7: TERRACED LANDSCAPE IN MAYLEBA. STONE BUNDS DELINEATE THE BOUNDARIES. STEEP SLOPES ARE CONVERTED TO EXCLOSURES. MAYLEBA, 08/2015.

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The overall goal of the MU-WAREP project was to build the capacity of the local people in planning and management of water resources at scale of micro-catchment. However, there are no detailed studies focusing on the economic costs and benefits of these SWC structures so far (Balana et al. 2012; Bojö 1996). This contrasts with the fact that overall profitability is a necessary condition for the wide adoption of SWC (Teshome et al. 2015; 2014; 2013). At present, only two studies concerning economic efficiency are done in the region: An assessment estimates a 50% return rate from stone bund investments (Gebremedhin et al. 1999), while Nyssen et al. (2007) reports no household profitability, arguing that the costs of stone bunds is nearly the same as the value of the induced crop yield increases. By merely calculating the benefit for the farmers, they ignore other biophysical effects. There is a lack of attention on the off-site effects such as changes in ecosystem services, water availability, sediment control and carbon sequestration, (Teshome et al. 2013) which causes ignorance about the distribution of costs and benefits among space and society (Hanley et al. 1997). Recent research in the Northern Highlands of Ethiopia indicates that sustaining adoption of SWC measures is positively associated with farmers’ perceived profitability of SWC measures (Teshome et al. 2015) and uncovers the household characteristics that influence adoption and non-adoption (Kassahun & Jacobsen 2015). Successful SWC programs are often connected with technical feasibility and adaptability, ecological soundness, social acceptance and the economic viability of the programs (Herweg & Ludi 1999). It is known that policies focussing on subsidizing the investment costs improve farms incentive to conserve the soil (Shiferaw & Holden 1999). Thus, investigating the economic efficiency of the different measures and their characteristics influencing the probability of adoption is of paramount importance to select feasible SWC measures. To have an impact on the decision making on natural resource management, the SWC structures not only have to be effective but also equitable, flexible and efficient (Hanley et al, 1997). This master thesis research will focus on the economic valuation of the effects of SWC structures by trying to assess the costs of installing SWC structures and the perceived environmental benefits that they bring along. In contrast with previous research only focussing on crop yield or biomass increase, the aim of this master thesis research is to take into account as much profits of SWC structures as possible. In section 2.3, some of these profits are listed. Field work improved this list, and made it possible estimate of the costs and benefits of these SWC structures. By doing so, information about the adoption of SWC measures by farmers and the knowledge gap about economic efficiency of SWC structures will shrink.

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1.2.3 RESEARCH QUESTION The general research question of this master thesis can be noted as: “Is the implementation of stone bunds, check dams and conservation trenches economic efficient in the Mayleba catchment in the Northern Ethiopian Highlands of Tigray, considering the perception of farmers and the society of Tigray concerning both off-site and on-site effects of these soil and water conservation measures?” The overall objective of this master thesis research is to gain a better understanding of the advantages and disadvantages of stone bunds, check dams, stone-faced trenches and trenches in the Mayleba Catchment, in the Northern Ethiopian Highlands of Tigray. It aims to link the profitability of SWC structures with their biophysical effects on the environment and tries to evaluate what institutions can contribute to the adoption process. The basic idea of profitability is that investments and yearly maintenance costs is justified in terms of high positive benefits and low negative drawbacks resulting from SWC. Entry point is the local-specific effectivity analysis of SWC by Taye (2014). To achieve this objective, an economic valuation of the effects of SWC structures needs to be done. Discrete choice experiments are used to investigate the perceived (dis)advantages and to assess the costs and the value of the benefits of a SWC structure. The challenge is to examine which conservation approach can help to meet both the short-term needs of the farmers and the long-term conservation objectives of the community simultaneously. Which benefits and costs are more prominent on a certain parcel or catchment, depends on several factors. The type of soil substrate (basalt, marl, sandstone, limestone) (Taye et al. 2013), the stone cover (Nyssen et al. 2001) and the slope (Taye et al. 2013) can have an influence on the magnitude of the on-site effects of SWC structures. In addition the land use type (cropland, pasture and rangeland) and the spacing of the implemented SWC structures will have an effect on the economic valuation of the effects. This master thesis research falls apart in several steps: I.

To make an inventory of the onsite and offsite effects of SWC measures in Mayleba

II.

To get an insight in the interests and perceptions of farmers in Mayleba and by students from Tigray concerning land degradation and SWC management.

III.

To value the alleged costs and benefits qualitatively via the willingness to contribute labour of farmers and the willingness to pay taxes of the community

IV.

To verify the economic efficiency of conservation scenarios with stone bunds, stone-faced trenches, conservation trenches and check dams via a financial cost-benefit analysis Aforementioned sub questions will be discussed in detail in the methods section.

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1.2.4 JUSTIFICATION, ACADEMIC AND SOCIAL RELEVANCE This master thesis research is a follow-up of the studies that were accomplished by the Mekelle University –Water Resources Planning project (Van Wesemael et al. n.d.) in order to improve and facilitate the environmental management and decision making in Tigray. Information from farmers and stakeholders about the profits and drawbacks of SWC measures is essential in this master thesis research. Local farmers, NGO’s and governmental stakeholders such as the Relief Society of Tigray (REST), the Tigray Bureau of Agriculture and Natural Resources (BoANR) and Mekelle University Natural Resource Economics and Management department (MU-NREM) can provide information on the perception of people regarding land degradation and conservation.

FIGURE 8: TIGRIGNA FAMILY WEEDING ON A FIELD, BOUNDED BY LARGE STONE BUNDS. MAYLEBA 8/2015

This master thesis research tries to merge two disciplinary fields by linking biophysical effectivity with socioeconomic efficiency. The use of Discrete Choice Experiments in the valuation of biophysical effects is a new method. Data about the extent of the implementation of SWC measures, their costs and their benefits can improve the academic knowledge about the (dis)benefits of SWC measures. Moreover, an assessment of the profitability of the stone bunds, check dams and conservation trenches is developed. Better understanding of the factors influencing adoption of SWC structures by farmers is vital for the planning and management of water resources in semi-arid environments (Bekele & Drake 2003). By evaluating the perceived efficiency of stone bunds, conservation trenches and check-dams, the stimulus to install the most profitable SWC structure can be strengthened (Teshome et al. 2013). The knowledge gained can support the incentive to enhance sustainable agriculture in the region and ideally, land degradation in Tigray can be halted more expedient. The results of this thesis can be of great relevance for land managers, farmers as well as local NGO’s.

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1.3 BACKGROUND SOIL AND WATER CONSERVATION MANAGEMENT IN TIGRAY 1.3.2

HISTORY OF SOIL AND WATER CONSERVATION STRUCTURES IN TIGRAY

As a response to the vulnerable environmental and social situation and to combat rill, inter-rill and gully erosion, soil and water conservation (SWC) structures have been widely spread in Tigray. This started four decades ago after the large land reform. Physical SWC structures have the potential to reduce soil loss by decreasing overland flow and mitigate seasonal yield variability by increasing the soil moisture through the retention of rainwater (Bekele & Drake 2003; Taye, 2014). The institutionalize approach follows the severe drought of 1973/74, at the beginning of a military junta, the DERG regime (Bekele 2005; Adgo et al. 2013). Indigenous soil conservation methods date back from 400 AC but the activities were local and insignificant (Haregeweyn et al. 2015). Many SWC structures were built in the 70’s, partly supported by the World Food Program (WFP) under the Food-for-work approach (Amsalu & de Graaff 2006). However, the absence of continuous financial support, a lack of awareness by the farmers and the top-down requisite of not-adapted structures prevents the SWC practices to be widely accepted and adopted on sustainable bases (Esser & Haile 2002; Teshome et al. 2013; Haregeweyn et al. 2015). Active and more viable programs to implement SWC on catchment level - supported by the World Food Program (WFP) and the Ethiopian-Swiss Soil Conservation Research Program (WCRP) - started around 1980 (Amsalu & de Graaff 2007). Increased land management investments both by government (the Tigray Peoples Liberation Front TPLF) and NGO’s (e.g. Relief Society of Tigray REST) are visible since 1988 (Adgo et al. 2013). After the overthrow of the Derg regime (1991), a new land distribution phase started and important conservation efforts were made to intensify the agricultural production (Lanckriet et al. 2015; Lanckriet et al. 2012). SWC structures were implemented at large scales, sometimes with help of NGO’s (Adimassu et al. 2012). Over this forty years on human interventions, the status of natural resources, the land management and overall vegetation have improved in Tigray (Haregeweyn et al. 2015). However, the efforts of terracing 800 000ha have succeeded neither in triggering voluntary adoption nor in significantly mitigating soil erosion (Esser & Haile 2002; Bekele 2003). Reasons are the collective approach, the continuing land insecurity and inadequate policy measures (Amsalu & de Graaff 2007).

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More recent, there arose many initiatives like the ‘Community Mobilisation through free-labour days’ movement and the Sustainable Land Management Project – that tries to improve the watershed planning in a multi-disciplinary and multi-institutional way (Amede & Belachew 2001; Haregeweyn et al. 2015b). It obliges each adult between 16 and 40 years old to work for 20 days uncompensated in group for natural restoration on community level (Kumasi & AsensoOkyere 2011). This community labour mobilisation is still functional at present. While taking responsibility for common-property resources and public goods, this new policy also yields critics concerning the limited monitoring and maintenance of the implemented structures and the free labour-contribution (Amsalu & de Graaff 2006; Haregeweyn et al. 2015). From 2005 on, a new SWC campaign started with a renewed focus on watershed level (Edwards et al. 2007). A more participatory approach, with a focus on land tenure security and technical knowledge, is applied to improve the awareness of soil erosion and increase the knowledge of sustainable agricultural practices (Teshome et al. 2015). There are for example the MERET program (Managing Environmental Resources to Enable Transition to sustainable livelihoods) and the PSNP (Productive Safety Net Program). Both programmes link natural rehabilitation to income generation. This recent wave of renewed active SWC intervention by authorities and by local farmers is still yielding successes. Detailed in situ studies demonstrate the significant improvements in terms of soil structure, rain infiltration, crop yield, biomass production, groundwater recharge and prevention of flood hazard. (Nyssen, et al. 2009).

FIGURE 9: STEEP LANDSCAPE CONVERTED TO RANGELAND AND PROTECTED WITH STONE BUNDS. MAYLEBA 08/2015.

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1.3.2

THE CURRENT USE OF SOIL AND WATER CONSERVATION STRUCTURES IN MAYLEBA

A widely-used Community-based Participatory Watershed Development – guideline (Desta et al. 2005) provides information for the implementation of SWC structures concerning the spacing, width, depth and height of the SWC structures. Various SWC structures will not have exactly the same influence on the different erosion processes. Depending on the need of a field, different SWC will be the most suitable (Haregeweyn et al. 2015). Scientific comparisons of different SWC techniques are provided in this aforesaid governmental manual used by watershed managers and community committees. Defining best practices is a location specific task requiring the participation of local communities and scientific experiments, to include both local preferences and farming-related variability as characteristics of the regional environment (Kraaijvanger et al. 2014). The current plan for the Mayleba watershed (Gedina, REST officer of Dogua Tembien, pers. Com) is to reforest the upper watershed. Exclosures are implemented and larger areas are protected with conservation trenches (CT) and revegetated with the intention to enhance water infiltration and recharge the ground water table. On the steeper slopes, mainly stone-faced trenches (SFT) need to be installed to enhance infiltration. Irrigation and water channels can be created and gullies have to be protected with check dams (CDs) in order to improve the productivity of this part of the watershed. On the lower flat areas on clay soils, bigger water plants can be built, stone bunds (SB) can protect against flooding and besides being a producing area, these places can function as sources of drinking water too. Slopes up to 10% can be protected by grass strips of 1m wide. In a sub-humid area with insecure rainfall like the Mayleba catchment, it is of main importance that the excesses of rainfall and the shortages of water are brought into a compromise (Herweg & Ludi 1999). One has to implement unbreakable structures that are capable of water retention during dry spells. The construction requirements of the five SWC structures studied in this master thesis research are explained in detail in the next paragraphs, followed by the recent SWC construction history of the Mayleba catchment in specific.

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C HECK D AMS (CD S ) Check dams (figure 10) are built in gullies and have an embanking function to decrease the erosive power of the runoff (Sorbie 2012).and can effectively reduce the gully depth by one third (Frankl et al. 2013). CDs constructed from gabions, loose stone, revegetation of the gully and stabilized sections to counter the further incision of the gully. Gabion CDs are more expensive than loose stone check dams and only needed in the most critical positions. Areas protected by CDs have an average soil loss of 1.1 t/ha/yr. instead of 6.5 t/ha/yr. The structures are 1-2 m high and control runoff and peak discharges (Nyssen et al. 2007; 2009). CDs reduce the loss of land due to gully formation and decrease micro-dam sedimentation (increasing reservoir lifetime). By slowing down the velocity of water in the channel, infiltration is stimulated and regreening of the gully encouraged (Nyssen, 2009). Steep slopes and clay soils are very prone to gullies and thus the building design needs to be flawless in order to not collapse. The spillway of the lower CD should always be higher than the foot of the upper CD. The distance between two CDs is thus sloape-related. (Nyssen et al. 2007). Nyssen et al. (2004) estimated that almost 40% of the dams breaks down after two years.

FIGURE 10: LOOSE STONES CHECK DAMS (LEFT) AND A GABION CHECK DAMS (RIGHT). THE LOOSE STONES LEFT BELOW ARE PROVEN EFFECTIVE IN REVEGETATING THE GULLY, THE GABION RIGHT BELOW IS DAMAGED. MAYLEBA 08/2015.

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S TONE B UNDS (SB) Stone bunds (figure 11) are embankments of stones that enhance sediment and runoff deposition, resulting in a terraced landscape (Desta et al. 2005; Vancampenhout et al., 2006). They are (0.8 m wide, 0.7 m high) walls of large (> 10 cm) rock fragments, built along the contour across sloping land (Nyssen et al. 2009; Taye 2014). The spacing is dependent on the slope steepness (Desta et al. 2005). The stone walls are slightly dug in the ground and filled with smaller stones acting as filter for sediments (Martens 2013). SB in Mayleba are the most preferred on clay soils, because they keep space to let the water pass through it (Tillahun, pers.com). On steeper Cambisols soils, they can be very effective too if built strong and high. According to a study by Gebremichael et al. (2005), SB can reduce soil loss by 68% after 3 to 20 years of age and slope gradients decrease 1 % every 3 years (Nyssen et al. 2007). Moreover they create a higher soil moisture on both sides of the soil bund (Martens 2013). The highest SB density is found on CL, to improve the in-situ soil moisture conservation (Taye et al. 2015). Even though there are recommended dimensions for SB in the Ethiopian Highlands, there is a lot of variation in height and depth of the stony walls (Desta et al. 2005). Often, the availability of stones plays a major role in the stone bund frequency along a slope. Picking stones from the CL reduces the infiltration in situ and is thus discouraged. Crop burial by the breakdown or overtopping of SB can occur but is rare, it occurs mainly when the bunds that are not constructed parallel to the contours (Nyssen et al. 2007).

FIGURE 11: STONE BUNDS IN CROPLAND. LEFT: RECENT ONE, LOW INFLUENCE ON THE SLOPE. RIGHT: OLDER ONE, TERRACING EFFECT VISIBLE. MAYLEBA, 08/2015.

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C ONSERVATION T RENCHES (CT) As part of catchment management, conservation trenches (figure 12) were dug on small and medium slopes in RL (Taye, 2014). CT capture runoff water and sediments. They are made by replacing soil downslope of the trench and are typically 0.5 m wide and deep and 3m long (Taye, 2014). CT frequently occur in successive formation along the contours (Martens, 2012) on soils that are not too shallow. It is proved that they intensely improve infiltration (Sorbie 2012) and can reduce soil loss till 88% (Taye, 2014), during the first year after installation. Besides, they strongly enhance vegetation regrowth (Balana et al. 2012). However, they are dangerous for cattle, which can demolish the dug pits and are not found to be suitable in CL; Tillage would fill up the pits too easily (Nyssen et al. 2000). Besides, on grassland with a shallow soil, it is not possible to dig sufficient deep CT (Taye, 2014).

FIGURE 12: TRENCHES IN RANGELAND. LEFT: LARGE TRENCHES. RIGHT: SMALL TRENCHES WITH EMBARKING SOIL. BELOW: NORMAL CONSERVATION TRENCHES. MAYLEBA, 08/2015.

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S TONE -F ACED T RENCHES (SFT) Trenches are widely used in combination with SB; stone-faced trenches (figure 13) are structures where a trench is placed upslope of a stone bund (Martens 2013). The soil material of the trench is used to reinforce the stone bund, increasing its runoff and sediment trapping effectiveness (Nyssen et al. 2009). The stony ground that is dug out is used to reinforce the stone bund, increasing the effectiveness of it (Martens 2013). The practice occurs both on CL and on RL (Taye 2014) and appears to be more efficient then SB ( Nyssen et al. 2007) On loamy soils, this is the most effective technique according to the watershed manager in Mayleba. Steep slopes require a spacing of 7m, medium soils 10 and flat slopes 15m (Tillahun, pers.com). A short-term effect of SFT is the creation of small retention basins for runoff and sediment, reducing the volume and erosivity of overland flow (Nyssen et al. 2007). The medium- and long-term effects include the development of vegetation cover on the soil-SB themselves (Nyssen et al., 2007). A drawback is the large amount of space that these structures occupy and the huge effort to construct them (Goose, 2010).

FIGURE 13: : LEFT: FILLED UP STONE-FACED TRENCH ON CROPLAND, DENSITY DIFFERENCE IN CROPS VISIBLE. RIGHT: MUDDY STONE-FACED TRENCH IN RANGELAND. BORDERS CLEARLY MORE DENSELY VEGETATED. BELOW: OLD STONE-FACED TRENCH, FILLED UP WITH SOIL AND CROPS. MAYLEBA 08/2015.

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As told before, farmers often choose constructions with their own preferred construction i.e. spacing and material. For example, because ploughing of the fields is done by oxen; the width of the terraces must be sufficient to allow easy turning at each end (Esser & Haile 2002). Besides, some farmers see a problem in the space that the structures occupy and thus install less than prescribed (Bewket 2007). Via intensive extension services, REST (Relief society of Tigray NGO) tries to align the scientifically proven best setup with the farmers’ preferences (Tillahun, pers. com.). Further, maintenance of the present SWC is of profound importance. Taye et al. (2015) measured the evolution of the effectiveness in reducing runoff and soil loss of some SWC structures, concluding that they are only very effective in the first years of their construction; After 4 years without reconstruction, they become ineffective (Taye et al. 2015; Haregeweyn et al. 2015). This maintenance – including redigging CT, distributing the sediment over the fields and progressively increasing the height of the bunds or dams - is one of the major issues in the SWC management (Adgo, per.com). Constructing in group goes well but maintenance of communal land is not controlled nor monitored so it cannot be assured that this is done (Shirmay, SWC technician, pers.com). Farmers even sometimes dismantle the structures because they don’t see the benefits (Adgo, pers.com.). This is some key problem that still requires a solution. Unfortunately, geo-data about the recent implementation of SWC structures and the Mayleba watershed management plan could not be accessed, so the current state of SWC management cannot be shown. It would have been a great opportunity to create scenarios, analyse geo differences and make predictions. Nevertheless, an excel sheet of the Woreda Agricultural office provides some insight in the recent changes in SWC use. Besides, the interview with the Watershed coordinator, who personifies the link between REST and the societies living in the villages of the study area, was of great help exploring the current and future management plans. It is clear that, in the Mayleba Catchment, SWC structures are actively been built till present day. Relatively large areas RL or communal land are being converted to exclosures (Tillahun pers.com.) and vigorously protected with CT (graph 4). With a peak of more than 250km of CT in 2014, this structure is the most adopted technique. SB and SFT have the same trend over the years and fluctuate between 75-175km newly build structures per year. In comparison with gabion CDs, loose stone CDs are installed three times more, with almost 25000m³ new loose stone CD material in 2014. Anyway, this are large amounts for a sub-watershed of only 17km².

20

300 250

Km

200 Stone bund

150

Stone faced trench

100

Conservation trench

50 0 2010

2011

2012

2013

2014

2015

2016

Year

FIGURE 14: INCREASE OF TRENCH AND BUND LENGTH IN KM IN MAYLEBA FROM 2010 TO 2015. (BOARD 2015)

30000 25000



20000 15000

Stone checkdam Gabion checkdam

10000 5000 0 2010

2011

2012

2013

2014

2015

Year

FIGURE 15: INCREASE OF CHECK DAM VOLUME IN MAYLEBA FROM 2010 TO 2015. (BOARD 2015)

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2016

3.1.3 THE ROLE OF THE PRODUCTIVE SAFETY NET PROGRAM ON THE CURRENT SWC Before 2005, the typical response to food insecurity in this country was emergency food relief, which is an inefficient, unpredictable and non-structural method to cope with short-term emergency situation (Andersson et al. 2009; Gilligan et al. 2008; Arnold et al. 2011). This contrasts with the thousands of households in Ethiopia which are in a chronical food shortage situation (World food programme 2012), meaning that they are ‘structural poor’ and have a food depletion gap of at least 3 months in normal years (Devereux et al. 2008). New programmes like the PSNP and the MERET try to reverse the trend of land degradation by linking environmental rehabilitation with income generation for food insecure households (PSNP 2009). Particularly the PSNP is significantly present in the poverty-prone rural regions of Tigray (Sabates-Wheeler & Devereux 2010). It is a joined government- and NGO-supported project (co-partnership with i.e. REST (relief society of Tigray NGO), UNICEF and WFP) ( World food programme, 2012) and tries to increase families’ long-term resilience to food shortages by ensuring food consumption and preventing asset depletion (Arnold et al. 2011; REST 1991; Andersson et al. 2009). PSNP achieves this by providing a fixed, predictable income for working on natural resource management. Per day of work, they can get 3kg wheat, 0.45lr oil or 25 ETB (Halefom, pers.com.). This may shift the risk-aversion of subsistence farmers away from ‘survival’ mode, and allow them future planning and investments which is proven to be beneficial for SWC management (World food programme 2012; Davis 2012; Davis & Gaarder 2012). By providing seasonal job opportunities, it connects Social Protection with investments in sustainable natural resource management. It allows the transition away from emergency relief and enables the rural poor to resist shocks, create assets and become food self-sufficient (Andersson et al. 2009). In the framework by this programme, REST (relief society of Tigray NGO) organises a watershed committee with one watershed manager per sub-watershed (+-20km²). This committee discusses the watershed management plan together with representatives of the government and then mobilizes farmers to work on SWC management (Halefom, REST, pers.com.). This method of mass mobilisation, whereby 10-30 farmers gather in ‘development groups’ and collectively work on other farmers’ CL and on communal land – voluntary and in exchange for money/food - is still effective nowadays (Halefom, REST, pers. Com.). While farmers are obliged to work 40 days (around February) for free in group on the protection of the environment, most of the present-day structures are funded by the PSNP programme.

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The graphs below (figure 25-26-27) indicate the percentages of the use of Food for work (FFW)– Cash for work (CFW)– free labour days in the construction of SWC techniques in Mayleba, between 2011-2015. Around 50% of the SWC structures are built in the framework of the Food for work programme. Cash for work accounts for around 20% of the total constructed SWC techniques, the remaining 30% is done for free in the obliged 40 free work days. These percentages do not differ too much between the different types of SWC structures, as is visible in the charts below. 100% 80% 60%

Free labor

40%

CFW FFW

20% 0%

2011

2012

2013

2014

2015

FIGURE 16: SHARE OF EACH TYPE OF WORK IN THE CONSTRUCTION OF TRENCHES. (REGIONAL AGRICULTURAL OFFICE 2015)

100% 80% 60%

Free labor

40%

CFW FFW

20% 0%

2011

2012

2013

2014

2015

FIGURE 17: SHARE OF EACH TYPE OF WORK IN THE CONSTRUCTION OF STONE BUNDS. (REGIONAL AGRICULTURAL OFFICE 2015).

100% 80% 60%

Free labor

40%

CFW FFW

20% 0%

2011

2012

2013

2014

2015

FIGURE 18: SHARE OF EACH TYPE OF WORK IN THE CONSTRUCTION OF CHECK DAMS. (REGIONAL AGRICULTURAL OFFICE 2015)

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CHAPTER 2: MATERIALS & METHODS

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2.1 STUDY AREA 2.1.1 GEOGRAPHY This master thesis research is conducted in Ethiopia, an eastern African country in ‘the horn of Africa’, surrounded by Eritrea, Djibouti, Somalia, Kenya, South Sudan and Sudan (figure 9). Having its lowest point in the hot Danakil Depression at 125 m b.s.l. and its highest peak Rash Dashen at 4533 m a.s.l in a cold-arid climate, Ethiopia is characterised by diverse physiographic conditions, climates and elevations (Bekele 2003). With a population growth rate of 2.6%, Ethiopia is one of the fastest growing countries of the world (World Databank, 2015).

FIGURE 19: DIGITAL ELEVATION MODEL OF ETHIOPIA. (INTERCARTO.COM)

Tigray (figure 10) is the northernmost province of Ethiopia and its capital city, Mekelle, is located at 45 km to the east of the study area. The area of interest for this thesis is the Mayleba Catchment (figure 10), situated in the North-Eastern Ethiopian Highlands (at 13°41’N (lat), 39°15’E (lon)) and elevation ranges from 2290 - 2835 m a.s.l.. It delineates a watershed of 17, 3 km², lying within the Dogu’a Tembien woreda (district), in Central Tigray, and covers part of the Ayin Birikrkin and Arebayi kebelles (neighbourhoods). The Mayleba Catchment contains eight rural kushets (villages): Raeset (877 inhabitants, 280ha), Adi Koilo + Adexiat (627 inhabitants, 220ha), Adiwerat (555 inhabitants, 380 ha), Alaesa (442 inhabitants, 300 ha), Togla (unknown) and Medaek (unknown). All villagers are populated with self-sufficient farming households (Data Local administration office, 2015). The Tigray region is relatively dense populated, housing 80 persons/km², yet only 19% of the land is suitable for agriculture (FAO 2015; Nyssen et al. 2014; Haregeweyn et al. 2006).

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The Mayleba Catchment - also named May Leva, Mayleiba, Myleba, Mai Leba …- is covered with rangeland, exclosures and cropland and closed off with the Mayleba micro-dam (May = water, leba = thief in Tigrigna language) producing a water reservoir of 0.98 million m³. It accumulates the runoff water from several (permanent) gullies during the rainy season (Nyssen et al., 2004). Mayleba is representative for other catchments in the Tigray highlands considering demography, geomorphology, elevation, geology, land use type, water management and SWC structures (Nyssen et al. 2007; Taye 2014; Van de Wauw et al. 2008).

Dogu’a Tembien

Tigray

Tigray

Ethiopia

Mayleba sub-watershed

Mayleba

FIGURE 20: LOCALISATION OF THE MAYLEBA CATCHMENT IN THE DOGU’A TEMBIEN DISTRICT IN TIGRAY, NORTHERN ETHIOPIA. (TAYE 2014)

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2.1.2 CLIMATOLOGY Due to a high topographic variability and the changing position of the Intertropical Convergence Zone (ITCZ), Ethiopia’s climate is complex. The country can be divided into 5 climate zones (Alemayehu 2003). The Behera hot arid zone (desert lowlands, average annual temperature over 30°C, annual rainfall less than 510 mm), the Kolla Tropical zone (midaltitude; average annual temperature of 28°C, annual rainfall of 510mm), the Woina Dega Subtropical zone (medium altitude; average annual temperature of 22°C, annual rainfall of 5101530 mm), the Dega Cool zone (high altitude; average annual temperature of 16°C, annual rainfall of more than 1275 mm) and the Worch Afro-Alpine zone (mountainous; chilly climate).The Mayleba catchment – situated in the northern Highlands - has a cool tropical semiarid climate and belongs to the Woina Dega climate zone. The mean average temperature in the study area varies between 12 and 19°C, the diurnal range lies between 5 and 28°C. The yearly average rainfall at Hagere Selam -10 km to the west of Mayleba - is measured at 716 mm (Nyssen 1995). The yearly distributions of temperature and precipitation are given in figure 11. Precipitation in the Northern Ethiopian Highlands falls in a bimodal pattern; Initiated by a northmoving ITCZ, the Belg season (march-may) is characterized by convergence rains, while in the Kiremt (June- September) the ITCZ is at is most northerly position starting the rainy season in the highlands (Frankl et al. 2013). The Kiremt delineates the growing season for crops and other vegetation, at this time precipitation exceeds 50% the potential evapotranspiration (figure 11). The Easterlies winds in this season are representative for more than 80% of the total annual precipitation (Vancampenhout et al. 2006). Besides, the region has a diurnal rainfall pattern (Nyssen et al. 2009), controlled by valley effects. The convective cloud formation in the morning results in daily precipitation during the afternoon (Nyssen et al. 2005). In addition to a strong temporal variability, the rainfall pattern in Tigray shows significant spatial differences resulting from the wide range of slope orientations and slope gradients at catchment scale.

Growing season

FIGURE 21: THE PRECIPITATION AND POTENTIAL EVAPOTRANSPIRATION (LEFT) AND MIN., MEAN AND MAX. TEMPERATURES (RIGHT) FOR THE DOGU’A TEMBIEN DISTRICT. DATA FROM NEW LOCCLIM (FAO, 2013).

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2.1.3 GEOLOGY The Ethiopian Rift valley divides the country in a north-western and a south-eastern part and the tectonic activity in this area resulted in very steep slopes in the Ethiopian Highlands (Nyssen et al. 2009). The Mayleba catchment is part of the Mekelle outlier, a distinct geological feature on the western shoulder of the Ethiopian Rift. It consists of alternating hard and soft Antalo limestone layers overlain by Ambo Aradam sandstone (both of Mesozoic age) and shale of Jurassic Age, as shown in figure 12 (Nyssen et al. 2007; Van der Wauw et al. 2008). Basalt lava flows of the Tertiary – alternated with silicified lacustrine deposits- cover these Mesozoic sedimentary layers. The south eastern corner of Mayleba is composed of Agula Shale, while Dolerite sills are outcropping in southern-Mayleba (Van der Wauw et al., 2008).

FIGURE 22: GEOLOGY OF THE MAYLEBA CATCHMENT (VAN DER WAUW, 2008)

2.1.4 GEOMORPHOLOGY Due to the sub-horizontal layering of the rock formations, a tectonic uplift in the Mio-PlioPleistocene and differential erosion, the landscape has a tabular, stepped (Haregeweyn et al. 2006). The resistant basalt flows overlying the sandstone mark the highest points of the Mayleba catchments. The sandstone shapes the cliffs in the landscape, resulting in a wide range of slopes.

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This typical Ethiopian Highlands landform is one of the reasons why the study area is very prone to landslides. Besides, the downwards flow of vertic clays (a degradation product of limestone and basalt) can cause mass movement. The frequency of this geomorphological feature has a significant impact on the soil map of the Mayleba catchment (Nyssen 1997; Van de Wauw et al. 2008).

2.1.5 PEDOLOGY The occurrence of soil types (figure 13) can be closely linked to the geology and the relief of the Mayleba Catchment. The occurrence of mass movements, intense erosion and sediment deposition and rugged topography results in a complex soil variability with Vertisols, Luvisols, Calcisols, Leptosols and Regosols (Van de Wauw et al. 2008). Vertisols develop on basaltic parent materials and in lower landscapes where water accumulates such as valley bottoms, while Calcaric Leptosols, calcisols and Luvisols can be found on limestone-derived materials (Frankl et al, 2013). Steep slopes are characterised with regosols; on foot slopes– often buried by colluvium- Cambisols are present. (Van de Wauw et al., 2008). The soils on basalt are the most fertile and mostly used for cropland but also the flatter parts of the Luvisols, Colluvic Cambisols and Regosol are used for cultivation.

FIGURE 23: SOIL MAP OF THE MAYLEBA CATCHMENT. (VAN DER WAUW, 2008)

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2.1.6 LAND USE AND LAND COVER Small scale rain-fed agriculture is the main economic activity in the Northern Ethiopian Highlands. The main cultivations on cropland (63% of the Mayleba catchment) are teff (Eragrostis teff), wheat (triticum aestivum), barley (hordeum vulgare), maize (zea mais), grass pea (Lathyrus sativus) and lentils (Lens culinaris) (Taye et al. 2013), all tilled with an ox-drawn plough (Vancampenhout et al. 2006). Most cultivated area is situated in the northern part of Mayleba and managed by individual farmers (see figure 14). 30% of the catchment is used as rangeland (Van de Wauw et al. 2008). Livestock are mainly cattle; oxen, sheep, goats, donkeys, mules. Shortage of crop area to fulfil the food requirements of an increasing population causes a conversion of rangeland to cropland (Vancampenhout et al. 2006). This few grazing areas – owned by the community - are situated on the stony and steep slopes (Descheemaeker et al. 2006). The use of fertilisers (Diamoniumphosphate and urea) on intensively used cropland is limited, most of the animal manure and crop residues are used as fuel for households and this, together with the past clearance, consequences in resource degradation (Vancampenhout et al. 2006). The soil has a low level of soil fertility and a declining productivity (Taye, 2014).

FIGURE 24: LAND USE MAP OF THE MAYLEBA CATCHMENT. (TAYE 2012)

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A small part of the Mayleba catchment is used for housing (figure 15). High population and livestock pressure in this region has led to the conversion of all fertile grounds to cropland, decreasing the amount of rangeland (Tillahun, pers.com.). Overgrazing has as result that there is not a lot of the natural vegetation left and that the land carrying capacity is exceeded in the study area.. Forest is only remained around churches, where it is protected for religious reasons. Besides, there are exclosures (5% of the catchment), protected areas aiming to fight land degradation and restore the vegetation and soils (Descheemaeker et al. 2006). Degraded and marginal former crop- and rangeland is converted to these exclosures in order to enhance tree growth and soil recovering (Babulo et al. 2012)

FIGURE 25: LAND USE IN MAYLEBA: FOREGROUND: RANGELAND. BACKGROUND: CROPLAND. HILLS: EXCLOSURES. MAYLEBA 08/2015.

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2.1.7 HYDROLOGY The Mayleba catchment is situated in the Atbara-Takaze river basin system, draining to the Blue Nile. The Dogu’a Tembien district contains many ephemeral rivers fed by the seasonal gullies from different catchments such as Mayleba. They are deeply incised and in a juvenile stadium because of the on geological timescale –recent uplift (Nyssen et al. 2007; 2007b). The region has perched water tables, acting as aquicludes or aquitards, causing the presence of active spring on top of the Amba Aradam sandstone. This is also illustrated by freshwater deposits on the sandstone cliffs. Besides, perched water tables can be found on the impermeable Antalo limestone. (Nyssen et al. 2010) Micro-dams and household ponds are installed for water harvesting in the area. The Mayleba micro dam was built in 1998 by REST to accommodate the rainfall unpredictability in the area. It was designed to irrigate 50 ha of agricultural land while it currently is only capable to irrigate 15 ha mainly through base flow in the downstream areas (Asmelash et al. 2006). The reason for this decline is sedimentation of eroded material that hinders the outflow. It is now used as livestock drinking pool and as ground water recharging site through seepage and base flow feeding perennial spring some 3 km down the stream.

FIGURE 26: VIEW ON THE MAYLEBA RESERVOIR,SEDIMENTATION AND MICRODAM DELINEATING THE CATCHMENT. MAYLEBA, 08/2015.

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2.2 DATA COLLECTION AND ANALYSIS As mentioned before, the method used to answer the different sub questions of this master thesis research will be explained – per sub question - in this chapter.

FIGURE 27: TRANSLATOR CONDUCTING A SEMI-STRUCTURED INTERVIEW WITH A WOMAN OF A HOUSEHOLD. MAYLEBA 08/2015

2.2.1 TO MAKE AN INVENTORY THE ON- AND OFF-SITE EFFECTS OF SWC MEASURES First, an extensive literature study is done about land degradation and SWC measures in Tigray. Articles about the effects of SWC were sought and analysed in order to understand what has been addressed so far. This master thesis research focussed on the biophysical effects of stone bunds, stone-faced trenches and conservation trenches, and check dams in sub-tropic highlands. A list with on- and -off-site effects is established and completed during the fieldwork via dialogs scholars and stakeholders and a farmer survey with semi-structured interviews was organized and held in the Mayleba catchment. Several discussions with representatives of NGO’s and government officials were arranged. Three meetings were held with representatives of Relief Society of Tigray (REST), which is the local most-influencing NGO in Tigray. REST helps the Tigrinian people by providing both a social and economic safety net and by helping to develop a thorough natural resource management and sustainable agricultural practices. The head of the SWC Division for Tigray region, the head of the SWC division for the Dogu’a Tembien district and the watershed coordinator of Mayleba, were interviewed. They explained, from their perspectives, the situation of the SWC management practices in Mayleba: where are the SWC structures situated, who organizes the installation of them, which people have to work, which techniques are applied and how are people paid for this work? Besides, the head of SWC of the Bureau of Agriculture and Natural Resources was reached and a SWC technician linked to the Environmental Office of Mekelle was met, offering information about the building guidelines and organizational practices in SWC management. Starting with some history of SWC management, both gave a clear view on the governmental opinion about sustainable agriculture and natural resource management.

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2.2.2 TO GET AN INSIGHT IN THE INTERESTS AND PERCEPTIONS OF FARMERS IN MAYLEBA AND OF STUDENTS FROM TIGRAY Figure 18 conceptualizes the strategy that has been applied to examine all perceived benefits and drawbacks that come along with SWC management. The first step is a literature review, as explained above, and then the preferences of society and the private preferences need to be investigated. The goal is to discover to what extent profit from SWC measures is experienced by both groups. Getting an insight in the interests of the society of Tigray is achieved by collective choice experiments and will be explained in section 2.3. Evaluating the perception of farmers in Mayleba concerning land degradation and SWC management is realized by extending the structured interviews mentioned above. This approach will be explained in what follows.

FIGURE 28: CONCEPT OF CHOICE EXPERIMENTS

M ETHOD USED TO EXECUTE THE F ARMERS SURVEY Semi-structured interviews were carried out between 14 August and 20 September 2015 with mostly self-sufficient farmers in Mayleba. The interview locations are geographically spread as wide as possible, which is visualized in figure 8. If the household head was interviewed, else the wife or oldest child answered or assisted the interviews. When it was clear that the respondent was unknowing or uninterested and not able to answer questions in a proper way, the interview was interrupted early and neglected. In the end, 105 fruitful interviews were conducted; 75% of the respondents are male, the mean age is 40.27 years old. The results of interviews will be used to investigate which effects are the most important for the farmers and which effects are nearly negligible. Besides, the perception of farmers regarding land degradation and SWC measures will be explored, providing knowledge about the benefits of investment and conservation of the SWC structures.

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With a mean interview time of 50 minutes, it took 25 full days to survey the whole area. Considering the limited time and the frequency of religious holidays among the EthiopianOrthodox Christians, interviews were held both on working and on non-working days. This made sampling in transects over the crop fields of the catchment not possible since farmers were alternatingly present in their houses and on their fields. As there were no economic nor demographic data nor parcel maps available at the local administration office, rational sampling based on those numbers could also be crossed out. Hence, a logical sampling strategy was not practical and a random sampling technique was applied. The goal was - based on a googleearth imagery of the area- a complete and homogenous geographical coverage of the Mayleba Catchment, a strategy closely related to Adgo (Adgo et al. 2013). Even this technique was challenging, because often – when the interview took place inside - the farmers’ description of the location of their fields was of limited quality. Visiting the fields together with the farmers was not possible (too time consuming), given the walking times of often more than one hour.

FIGURE 29: LOCATIONS OF THE CONDUCTED INTERVIEWS ON A LAND USE MAP OF MAYLEBA OF 2012. CURRENTLY, LESS RANGELAND IS PRESENT. LU DATA TAYE 2012

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FIGURE 30: LOCATION OF THE CONDUCTED INTERVIEWS ON A GOOGLE EARTH IMAGE. CONTOUR DATA TAYE 2014

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A translator was hired to assist the farmer surveys. His indispensable job was to introduce the master thesis research, ask all questions, explain things if needed, translate all answers and being a guide in the study area. A first translator originates from Adi Koilo, the village where the field work started every morning. The translator is a 19 years old man studying third year civil engineering. Although his knowledge about the region, the people and the farming practices is quite good, he failed to reach a required level of English. The accuracy of that part of the data is thus questionable. After 10 field days (45 interviews), another translator was addressed. The second translator graduated in industrial engineering and had experience in translating to English for research purposes. He is born in Hagere Selam, 10km from the study area and is not familiar with the catchment. This made convincing farmers to join interviews more challenging but his communication was far more professional. It is clear that the quality of the later interviews is higher; they went more fluently, there was almost no misunderstanding and farmers were never confused. C ONTENT OF THE F ARMERS ’ QUESTIONNAIRES The questionnaire for the farmer’s survey (see appendix 1) was prepared in advance and tested on field. The questions are build up carefully, because next to data about the use of SWC measures, also some demographic and economic data must be acquired. Every interview started with a short introduction about the researcher, the research goal and the role of the translator. Complete anonymity and confidentiality were assured. During the interview, the researcher could make some observations about the state of the present SWC structures, the soil texture, the slope steepness and so on. Besides, it was mentioned to the respondent how the interview would develop. After some improvements based on the test interview, the questions were translated to Tigrigna by different Natural Resource Management PhD students of Mekelle University. Moreover, pearls and colour pictures were used to show to the (illiterate) respondents to clarify the concept of percentage and illustrate different biophysical processes. It facilitated the explanations, estimation of quantities and reduces translation errors. First, some general information was gathered to get to know the background of the farmer, their age, their farming experience, which crops they are producing, which livestock the farmer has. Then the perception of the farmers regarding erosion problems was surveyed; the evolution of their crop production and the biomass production, together with the perceived reasons for this evolution, were interrogated. The extent of erosion problems was qualitatively rated, the type of prevalent erosion noted and the causes for erosion interrogated. Further, the adoption of SWC structures was gauged, as well as the mean age of the present SWC structures. After that, questions about the benefits and drawbacks of SWC were questioned.

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Using a Likert-scale, farmers were asked to rate the different characteristics for each SWC structure, thus quantifying the importance and extent of change on this characteristic by implementing a particular SWC structure. This was done for two structures on cropland (SB and SFT), two on gullies (LS and Gb) and three structures on rangeland (TR, SB, SFT), assuming that the interrogated farmer has this structure on/close to his fields. Ordered logistic regressions were used to find relationships between answers of respondents. Besides, different characteristics of soils had to be ranked according to importance. Some extra questions were asked to have an idea about the costs and labour time of constructing and maintaining SWC structures but these questions seemed to be too difficult to answer for most –illiterate- farmers. The respondents’ opinion was requested on “Farmers should be paid for constructing and maintaining SWC structures.” To conclude, different factors concerning the poverty were assessed by asking yes/no questions. With these, a multi-dimensional livelihood index is created, enabling to mark the poverty situation of a household as severely poor, poor, nearly poor, deprived or normal in comparison with the other households.

FIGURE 31: TRANSLATOR AND FARMER DOING AN INTERVIEW ON THE FIELD. MAYLEBA, 08/2015.

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2.2.3 TO VALUE THE ALLEGED COSTS AND BENEFITS QUALITATIVELY VIA THE WTC LABOUR OF FARMERS AND THE WTP TAXES BY THE SOCIETY The advantages and disadvantages of SWC structures- resulting from literature review and the answers of the farmers’ survey - must be valued in monetary terms to design economically efficient management scenarios. On one hand, the on-site costs of constructing SWC structures can easily be extracted from the labour time needed to build these structures. Clear governmental guidelines exist about the expected construction or maintenance time and the appropriate levels of payment. On the other hand non-market valuation techniques must be applied to get an idea about monetary value of the social and private benefits of SWC structures and the off-site negative effects (Duke et al. 2012). Some discussions with scholars working in the ‘Environmental Economics and Development’ fields were held, providing knowledge about the possible methods to investigate the economic efficiency of natural resources management. Many valuation methods have a market-based approach which tries to commodify different direct and indirect effects (Colombo et al. 2007; Colombo et al. 2005). Examples are calculating the costs of all nutrients in the soil by looking at the mean price of fertilizers (hedonic pricing) or approximating the effect of crop yield increase solely based on average market price of different crops. Indeed, when measuring the total economic benefit of SWC, one often only the use value is considered and in particular the private effects for the farmer (Teshome et al. 2013; Balana et al. 2012; Prabuddh & Suresh 2013; Bekele 2004). However, other societal costs and benefits are of the neglected. The net benefits for the society include not only the direct use value but also the direct and indirect use value of SWC structures for the non-farming population, as well as the option (opportunity to use soil in the future)- and non-use values (value of existence, bequest, altruism) for the population (Kjær 2005; Kassahun et al. 2015). Contrarily, in this master thesis research, the valuation method is based on the view that it is not only vital to assess the adoption of SWC structures by farmers; the people enjoy the private benefits and costs. One should also consider the preferences of the people who take the management decisions and who enjoy public (or social) benefits and who bears part of the costs by paying taxes. In Ethiopia, more than 90% of the total governmental public works budget is reserved for natural resource projects (REST 1991). This implies that taxpayers partly provide the money for SWC management. Besides, farmers gather in ‘Development groups’ and join these public works to protect the land. The preferences of these two groups need to be included in the value of private and social benefits (Duke et al. 2012). This is accomplished by adding Discrete Choice experiments as an extra part to the farmers’ survey and organise collective discrete choice experiment discussions with young, educated people from Tigray.

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Discrete Choice Experiments (DCE) are an attribute-based survey method, suitable to deal with multi-dimensional choices and accounting for the tree types of economic values (Birol et al. 2006). By looking to trade-offs between different characteristics of management policies, DCE uncover preferences of people for these characteristics of the management policies. Respondents are expected to make a sequence of choices between two different alternative scenarios or a ‘no change’ option. DCE models are able to statistically relate the choices that the respondents made to the characteristics of these respondents and to the characteristics of the alternative scenarios (Kjær 2005). Assuming that people try to maximize their own utility, it allows researchers to uncover how individuals value these different characteristics (Colombo et al. 2006). By including the number of labour days or the tax increase for each scenario, the willingness to contribute labour or willingness to pay for certain characteristics can be assessed (Colombo et al. 2006). More specifically, the DCE designed for this master thesis research tries to evaluate the willingness to work or pay for different biophysical characteristics of SWC, based on the choices of respondents between different hypothetical SWC management scenarios (Kjær 2005). Respondents are asked to choose between alternative SWC scenarios, representing packages of a priori defined on- or off-site effects of SWC management. DCE appear to be extremely suitable to assess the monetary values of non-marketable characteristics of SWC management, such as soil moisture or herb diversity (Amaya-amaya et al. 2008). C HOICE E XPERIMENT DESIGN The design of the choice experiments consists of a few steps (after Kjær (2005)) and is visualised in a flowchart presented in figure 21. First, all relevant attributes were identified. Those differ for the farmer survey, assessing the private benefits and the students survey, assessing the social preferences. A trade-off of must be made between the amount of attributes and the complexity of the choice experiments (Kjær 2005). A limitation of DCE is that one can only link a restricted amount of attributes to a management scenario. It is for a human brain not possible to make clear and fast decisions about scenarios with more than 6 attributes (Amayaamaya et al. 2008), thus not all effects found in the semi-structured interviews, could be included. Following literature, the most important benefits and disadvantages of SWC were chosen. Then, possible levels of those key attributes were selected; The choice design is based on literature and found in the following tables. They indicate all possible attributes and levels, both for cropland, rangeland as off-site. Table 2, 3 and 4 show all possible combinations of attributes and their levels (Kjaer 2005).

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TABLE 1: CHOICE EXPERIMENT DESIGN - CROPLAND

Attribute

Level 1

Level 2

Level 3

No measure

Change in soil loss due to sediment interception

-20% soil loss

-40% soil loss

-60% soil loss

Decreasing; high soil loss

Change in soil moisture due to runoff interception

+40% runoff interception

+60% runoff interception

+80% runoff interception

Decreasing; high runoff

Change in crop damage caused by rodents or flooding

-5% crop yield

Current situation

+5% crop yield

Current situation

Ploughing convenience, the easiness to plough the fields**

More easy

Current situation

More difficult

Current situation

Future state of the soil, yearly change of fertility level

0 % crop yield

-1 % crop yield

+1 % crop yield

Large Reduction

Labour for construction and 5 day / man 10 day / man 15 day / man maintenance per year, tsimidi ** These attributes are coded as dummies instead of continuous variables

No extra work

TABLE 2: CHOICE EXPERIMENT DESIGN - OFF-SITE

Attributes

Level 1

Level 2

Level 3

No measure

Soil degradation considering soil + nutrient loss **

Stabilising degradation

Worsening degradation

Reducing degradation

Worsening soil fertility

Water quality, possible use of the ground- + reservoir water **

Purification possible

Water for irrigation

Only suitable for cattle

Decreasing purity

Water availability, defined by springs, wells, groundwater **

Less accessible

No change

More accessible

Decreasing availability

Biodiversity, the healthiness of insects and plants **

Reduction

Stabilisation

Improvement

Decreasing biodiversity

The rural employment, Strong Small Same amount farming and –related jobs ** decrease decrease farming and -related jobs The willingness to pay extra taxes +1% birr +5% birr +10% birr for improvement ** These attributes are coded as dummies instead of continuous variables

Small decrease No extra costs

TABLE 3: CHOICE EXPERIMENT DESIGN – RANGELAND

Attribute

Level 1

Level 2

Level 3

No measure

Change in soil loss due to sediment interception

-20% soil loss

-40% soil loss

-60% soil loss

Decreasing; high soil loss

Change in diversity and amount of herbs and shrubs **

No change

Slight increase

Strong increase

Decrease in diversity

Change in diversity and amount of bees and insects **

No change

Slight increase

Strong increase

Decrease in diversity

Change in production of fuelwood (presence of trees)

+0% extra wood

+3% extra wood

+5% extra wood

No extra fuelwood

Future state of the soil, yearly change fertility level

0 % biomass

-1 % biomass

+1 % biomass

Large. reduction

Labour for construction and 5 day / man 10 day / man 15 day / man maintenance per year, tsimidi ** These attributes are coded as dummies instead of continuous variables

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No extra work

A set of choice cards can be formed by combining attribute levels into alternative choices, building possible hypothetical scenarios. The design thus includes (36)²=531 441 possible full factoral choice pairs. Given the limited sample size of respondents, it is not possible to take into account all choice combinations. Via the specialized Ngene software, a set of choice cards was formed in a statistically efficient way using a validity test (optimized D-efficiency; Duke, 2012). This approach results in a fractional factorial design and accounts for level balance, meaning that each level of an attribute occurs with equal frequency in the design (Kjær 2005). For the question about on-site effects of rangeland and cropland, each time 20 choice card need to be developed by Ngene. Both must be separable in two groups of 8 equally significant cards. The questions about off-site effects required a choice set of 50 cards, of which 2 significant blocks must be made. To finish the experimental design, the card sets must be customized: only the realistic choice cards were preserved. The sets are constructed so that respondents will have to make reasonable trade-offs between the hypothetical scenarios (Amaya-amaya et al. 2008). In the end, two times twelve different choice cards were retained for farmers: the farmers were separated in two groups and had to choose between six cropland- and six rangeland scenarios. Forty choice cards were used for the students: 2 groups of students had to give their opinion about 20 scenarios.

FIGURE 32: EXPERIMENTAL DESIGN AND ANALYSIS OF THE DISCRETE CHOICE EXPERIMENTS

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C HOICE E XPERIMENT REALIZATION By conducting 105 DCEs with farmers, local on-site effects of SWC measures (private benefits) can be valued. Farmers have the best experimental knowledge about the effects of SWC measures on the field and are thus an ideal sample group to estimate the values of the on-site benefits. After explaining the concept and working method to the respondents, the visually clear scenarios were shown. Each time, it was asked to point to the preferred scenario, so that the translator only had to read what was written in Tigrigna and no translations to English were needed. Around 20 farmers chose the ‘wrong’ answer in the test question, indicating that they didn’t yet understand what to do. For them, the explanation was given a second time. For the other respondents it is assumed that they fully understand the choice experiment. The on-site choice cards concern soil loss, soil moisture, ploughing convenience, crop damage, future fertility, biodiversity, wood production and labour days (see table 1 and 3). Farmers were asked to choose 6 times between 2 SWC scenarios or ‘no measures’ on cropland and 6 times between 2 SWC scenarios or ‘no measures’ on rangeland. Tilahun (2013) and other studies conclude that respondents are willing to contribute more in labour than in money if it concerns natural resource management interventions with public benefits, since they are often income-poor (39% of the Ethiopian population lives below the absolute poverty line (World Bank, 2010)) but labour abundant. Since the contribution of farmers in SWC consists of participating in the works rather than providing money, this attribute is chosen above a monetary one. The choice experiment results will be more informative than the Likertscale opinions from the structured interviews– as discussed before- because DCE reveal trade-offs that respondents accept to make between SWC attributes, including cost (Duke et al. 2012). Nonetheless, the interviews can serve as a help to identify the characteristics of the respondents, which will have an influence on their preferences. Thus, the interview and DCE results are complementary for the analysis rather than redundant. Moreover, CE were held with 95 students of Mekelle University, Department of Natural Resource Management, assuming that they represent a sample of the society (Social benefits). The students are supposed to give a reliable approximation of the values of the off-site benefits of SWC and were asked to choose 20 times between 2 scenarios or ‘No measures’. This last one implies a status quo, indicating that they are not willing to pay for both scenarios. These choice cards question about biodiversity, water accessibility and quality, rural employment and tax increases (see table 2). In this case, tax increase represents the monetary attribute. They will provide tax money for the government and – as highly educated people- probably have some influence on the future governmental decision making.

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The conduction of the DCE were held in group, supported by a PowerPoint presentation. Student got a form with a blank table, only indicating the choice card number and alternative code and had to cross the preferred alternative. Besides, the respondents also received five questions about their socioeconomic background to get an idea about characteristics that can impact their decisions. Errors in the DCE technique can occur when it is not well explained to respondents how the technique work and what they are supposed to do. To avoid the bias of illiterate farmers or uninterested students, the order of choice cards changed each time. A systematic error in the first and last card caused by inexperience or annoyance with the choice experiment is thus mitigated. D ISCRETE C HOICE E XPERIMENT ANALYSIS DCE models specify the probability that an individual respondent chooses a certain scenario among a set of alternative scenarios. The probability is derived from the utility of the scenario’s (the higher the utility, the higher the probability to be preferred), which is of interest in this master thesis research. Correct coding using a specialized software called NLogit can resolve different econometric estimation techniques for the collected dataset. In what follows, the Conditional Logit, the Mixed Logit and the Latent Class model will be explained. Two factors challenge the prediction of preferences: the unobservable properties related to the choice alternatives and the taste variation of the different respondents because they cannot be modelled. Models also include an Alternative-Specific constant (ASC). This ASC summarizes the average effect of all characteristics on the utility of a scenario, which are not included in the model. A negative ASC means that respondents are inherent not willing to pick up the no measures scenario (Lambrecht et al. 2013). The significance of the models can be calculated via the Chi² test or the Akaike R² test, a measure of the relative quality of statistical models, analysing the trade-off between model complexity and goodness- of- fit for a given set of data. AIC provides a means for selecting models (Tilahun, 2015), so that the most explaining model for the collected dataset can be sought and used in the further analysis. The structure of the DCE analysis is visualized in a flow chart presented in figure 22.

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The conditional logit model accepts that the utility of a scenario can be estimated by means of a logistic regression with all the attributes of that scenario. The model assumes that all respondents have homogeneous preferences (Sagebiel 2011; Tesfaye & Brouwer 2012; Brouwer et al. 2015). The utility function (equation 1) consists of a systematic component and a random variation error term. This systematic component (Vin) is the representative utility function, which is a linear sum of the attribute values (𝐴𝐴𝑛𝑛𝑛𝑛 ) and their weight coefficients (𝛽𝛽),

indicating the importance of the attributes (Hole, 2013). Further, the error term (eni ) represents

mean effect of the utility sensitive elements that are unobserved but are known to the respondent (Sagebiel 2011) and has to be identically distributed and independent from the variables (Brouwer et al. 2015). In DCE, respondents i have a probability Prim to choose alternative m, a relation that is given in equation 2. eq.1 eq.2 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝐴𝐴𝑖𝑖𝑖𝑖 , 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ𝑡𝑡 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝛽𝛽, 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑘𝑘, 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑛𝑛 𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑖𝑖

To resolve assumption of homogeneity the random parameter logit model allows for variance in preferences (coefficients) on attributes between the respondents (Hole 2013). This heterogeneity is accommodated as a specified continuous distribution around𝛽𝛽𝑘𝑘 , depending on a set of (socioeconomic) parameters characterizing the respondent (Sagebiel 2011). Hence,

the variable 𝛽𝛽𝑖𝑖𝑖𝑖 is calculated as shown in equation 3, where 𝜂𝜂𝑘𝑘𝑘𝑘 is an error term with normal distribution 𝑓𝑓(𝜂𝜂𝑘𝑘𝑘𝑘 ) , mean 0 and variance 𝜙𝜙². The varying 𝛽𝛽𝑖𝑖𝑖𝑖 contrasts with the fixed value of 𝛽𝛽

in the conditional logit model (Kassahun & Jacobsen 2015). For this purpose, the utility function of the conditional logit model must be slightly adjusted to calculate the total utility of the random parameter logit model (equation 4). eq.3

eq.4

𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑘𝑘 𝑎𝑎𝑎𝑎𝑎𝑎 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑖𝑖 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑛𝑛 𝑡𝑡ℎ𝑒𝑒 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

The random parameter logit choice probability can be found as the weighted average of Pr im , which is calculated as shown in equation 5.

eq.5

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A third model, the latent class logit model tackles the respondent-characteristics heterogeneity in a more detailed way. It defines discrete classes of respondents and assumes that people in the same class make homogenous choices about scenario’s, while respondents of different classes will have altered parameter estimates (Tilahun et al. 2013). Recent research by Teshome et al. (2015) concludes that the most important respondent characteristics influencing the SWC management preferences are “size of the cultivated land”, “land security”, “perception of erosion problems”, “resource endowments” and “technical support”. The first three will be used in this analysis because they can be extracted from the structured interviews. As the SWC management in Mayleba is done in group under the leadership of one responsible, the two other factors are less relevant to use as classifier. In addition, Grosjean & Kontoleon (2009) found the following determinants of allocation decisions of farmers: ”education level of spouse”, “off-farm labour”, “household size”, “agricultural yield”, “time since the first installation”, “easiness to rent”, “number of Cattle”. Those will be included in this analysis as well. Tilahun et al. (2013) points to the effect of “household size” and “age” in the willingness to contribute labour. Besides, crop yield productivity, a poverty-factor (see later), the perception and perceived benefit of SWC and the use of SWC are completing the respondentcharacterization. Whereas the random parameter model is a mixed logit with continuous weight distributions, the latent class model is a mixed logit with discrete weight distributions (Hole & Kolstad 2012; Hole 2013). The latter can thus be interpreted as a semi-parametric version of the former, where the coefficient 𝛽𝛽𝑘𝑘 can take a finite number of values; namely one per respondent class

(Sagabiel 2011). The utility for a specific choice made by an individual class is calculated the

same way as the conditional logit model, but will differ between the respondent classes (Sagebiel 2011). The unconditional probability Pr im to choose for different SWC management scenarios is defined as the weighted average of the class-specific 𝛽𝛽𝑘𝑘/𝑠𝑠 parameters, (equation,

6). Pr im/s is the conditional logit probability per respondent class, where 𝛽𝛽𝑘𝑘 is replaced by the class-specific 𝛽𝛽𝑘𝑘/𝑠𝑠 (equation 7).

eq.6

eq.7

eq.8 𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑋𝑋𝑋𝑋 𝑡𝑡ℎ𝑒𝑒 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑡𝑡ℎ𝑎𝑎𝑎𝑎 ℎ𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑜𝑜𝑜𝑜 𝑡𝑡ℎ𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝐶𝐶𝐶𝐶 𝑡𝑡ℎ𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑓𝑓𝑓𝑓𝑓𝑓 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑠𝑠 46

Classes are defined within the calculation procedure. Variables that can explain the most likely distinction between respondent classes are chosen and probability membership functions h s can be calculated using a multi nominal logit formula (equation 8; Kassahun & Jacobsen 2015 and Hole, 2013). To identify the optimal number of classes, Sagebiel proposes to use statistical measures like the Akaike information criterion AIC (Sagebiel 2011). FIGURE 33: TRANSLATOR EXECUTING AN INTERVIEW WITH ONE FARMER, SEVERAL FARMERS ARE LISTENING OUT OF INTEREST. MAYLEBA, 08/2015

All three models give insight in the decision behaviour of the respondents and are used in the analysis of the DCE concerning off-site effects (student respondents) as well as the analysis of the DCE concerning on-site benefits on cropland and rangeland. Running the three models – for each DCE- generates an output in NLogit specifying the influence (weight) that each attribute has on the probability to be the preferred scenario. Dummy variables are made for attributes with discrete levels (such as biodiversity or ploughing convenience), giving each attribute level a certain weight. The Conditional logit model calculates one weight per attribute, not allowing in any variance of taste between the respondents. The Random Parameter logit model shows an interval of weights per attribute, indicating a distribution of preferences, caused by different socioeconomic backgrounds of the respondents. The Latent Class logit model presents one weight per attribute per class of respondents. This last model thus distinguishes the decision behaviour of the respondents according to their socioeconomic background. Comparing the representativeness of the models and their output allows to have an idea of the accuracy of the technique as well as the influence of the socioeconomic characteristics on the preferences of the respondents.

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E XTRACTING THE W ILLINGNESS TO C ONTRIBUTE (WTC) FOR THE ATTRIBUTES These models output estimates the individual influence (weight coefficients,𝛽𝛽𝑘𝑘 ) of the different

attributes (on- and off-site effects,𝐴𝐴𝑛𝑛𝑛𝑛 ) on the probability of being the preferred scenario (Pr im ).

Assuming that the respondents want to maximize their utility, they balance the improvements of one attribute with a possible decline of another (Amaya-amaya et al. 2008). This assumption supposes perfect information for the respondents. Since farmers are experts through experience in the management of their fields, this assumption can be made. If the cost of choosing an alternative is included in the model, the relative importance of models’ attributes can be used to evaluate the rate of substitution between an attribute and the ‘monetary’ attribute (labour or tax cost in this master thesis research). This is defined as the marginal willingness to contribute (WTCm) (Colombo et al. 2006) and computed for a certain attribute solving the next formula (Sagebiel 2011).

eq.9

𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 = −1 𝑥𝑥 �

𝛽𝛽𝑖𝑖

𝛽𝛽𝑀𝑀



𝑤𝑤𝑤𝑤𝑤𝑤ℎ 𝑖𝑖 𝑡𝑡ℎ𝑒𝑒 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎 𝑀𝑀 𝑡𝑡ℎ𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎

Equation 9 denotes the WTC for a standard conditional logit model. When applying a random parameter logit model, this formula needs to be adjusted to account for unobserved preference heterogeneity of the attributes and observed preference heterogeneity (Brouwer et al. 2015). The LC logit model allows to estimate the WTC per class, using the standard WTC formula with the different weight coefficients per class. Finally, the willingness to contribute (WTC) for a certain management scenario is calculated by multiplying the WTCm for each attribute by the corresponding attribute levels. This WTC expresses the amount of recompense that the people want to make to have a certain level of a beneficial attribute (Amaya- Amaya 2008). The WTC enables to value all benefits of a SWC management scenario that are taken into account in the DCE (Brouwer et al. 2015). In the end, the total WTC for a scenario can be obtained for different SWC scenario’s by summing up the multiple WTC of the different attributes for that scenario (Colombo et al. 2006). The costs for the community is rendered in amount of tax increase. For calculating the private costs, labour contribution doesn’t need to be converted to monetary units it can be expressed in labour days (provided in guidelines about SWC – public works of the Government and REST). When analysing the community costs and benefits, these guidelines can be used to convert labour days in wages (which can be assumed to be a proxy for the monetary contribution that farmers want to make to improve SWC). Then it must be summed up with the amount of ETB that taxpayers want to pay. However, this labour-wage conversion introduces a bias as it supposes a perfect labour market. (Kassahun & Jacobsen 2015).

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2.2.4 TO VERIFY THE ECONOMIC EFFICIENCY OF STONE BUNDS, STONE-FACED TRENCHES, CONSERVATION TRENCHES AND CHECK DAMS IN THE MAYLEBA C ATCHMENT When all benefits are valued in the same unit as the costs via the DCE, they can be compared via a financial Cost-Benefits Analysis, which is “… an analysis to quantify in monetary terms the costs and benefits of a policy intervention or project” (Birol et al. 2006; Kjær 2005) For different management scenarios, a simple CBA will be produced to check whether the investigated SWC scenarios are economically efficient: the required (opportunity) costs of investments (in labour time) in a certain scenario should be lower than the benefits which can be assessed through the willingness to work or pay taxes for it. The most efficient scenario can be found by comparing the differences in required and supplied labour days. If this value is negative, then the scenario is not perceived to be profitable. The costs are not compensated by the benefits and adoption of the structure thus is not cost-effective. There is a pile of effects resulting from SWC measures. Hence, the resulting impacts will differ geographically. A SWC structure can be beneficial on plot-scale but have negative consequences on catchment scale. Based on the interviews with the NGO’s and the information from the farmers, five hypothetic but with expert-knowledge and field-experience constructed scenarios will be discussed. It is not possible to do an exact Cost-Benefit analysis since there is no unequivocally between authors about the biophysical effectivity of SWC measure. The exact quantities of the effects show mixed results, a large influence is attributed to the type of SWC measures and the agroecology under which they were implemented and subject to the slope, soil type, climate and age of the fields (Haregeweyn et al. 2015). Therefore, the perceived profits per SWC structures and per local context will differ as well.

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CHAPTER 3: RESULTS & DISCUSSION

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The aim of this chapter is (I) to review the on- and off-site effects of SWC structures on microcatchment scale, (II) to investigate the perception of farmers and the society concerning the degradation of the landscape and its solutions, (III) to get a better insight in the cost and benefits of both on- and off-site effects of the SWC structures in Tigray and (IV) to try to value these profits and drawbacks based on marketable indicators and indirect valuation techniques.

3.1 INVENTORY OF THE ON- AND OFF-SITE EFFECTS OF SWC IN MAYLEBA 3.1.1 DETERMINANTS OF THE ADOPTION OF SWC As stated by many scholars, the SWC decision behaviour of farmers is influenced by the physical and socioeconomic characteristics of farm households (Shiferaw and Holden, 1998, Bekele & Drake 2003; Gebremedhin, 2003; Amsalu and De Graaf, 2007; Bewket, 2007). Shortterm agricultural practices and the preference of farmers for fast income generating activities contrasts with a SWC approach yielding higher and sustainable returns mostly in the long-term (Bekele & Drake 2003). This makes investments for poor and risk-averse smallholder farmers less attractive. Besides, weak markets in Ethiopia fail to reflect this scarcity of fertile soil, reducing the incentives of farmers to practice SWC (Bekele & Drake, 2003).

FIGURE 34: NEXUS OF THE PROCESS OF SWC ADOPTION (TESHOME 2015)

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Recent research by Teshome (2015) indeed concludes that the implementation of SWC follows a ‘sequential decision process’ where, in each phase, different biophysical, socioeconomic and institutional factors influence the decision behaviour of the farmers (see figure 28). A distinction is made between an initial adoption (acceptance of the structures), actual adoption (maintenance of the structures) and final adoption (maintenance and expansion of the structures) in addition to dis-adoption of SWC structures (Teshome et al, 2015). Their nexus can be linked to the research of Bewket et al. (2007) that showed that education (influencing the perception on erosion and its effect on productivity), age and land tenure security (linked with experience and planning horizon), slope and soil type (the vulnerability to erosion), farm size and land-holding size (affecting labour supply), access to information and perceived profitability (knowledge about (dis)benefits) and income are the most important factors in the adoption process (Bewket et al. 2007). This master thesis research focusses on the profitabilityfactor found in the framework of Teshome et al. (2015) and investigates the biophysical effects influencing this perceived profitability. FIGURE 35: FIELD FULL OF STONE BUNDS. SEVERE RILL EROSION IS STILL PRESENT. MAYLEBA 08/2015

3.1.2 COSTS OF SOIL AND WATER CONSERVATION STRUCTURES The labour costs of constructing SWC is described in the community-based participatory watershed guideline of Desta (2005) and expresses this cost in PD. A PD is a full day of labour for an adult and equal to 700Birr or 3kg grain. The work norm for SB is 250 PD per kilometre. To control erosion by SB on a land of one tsimidi, around 50 PD are needed to construct and stabilize the structures. The vegetative stabilisation of such structures is estimated to 30PD/km. It is possible to construct 3 CT per PD. For stony CDs, constructing 0.5m³ can be done in one PD. Constructing gabions is even a tougher job, only 0.25m³ can be done per PD. 1m³/PD is charged for maintaining CDs and the revegetation of gullies is worth 500PD per hectare.

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3.1.3 BENEFITS OF SOIL AND WATER CONSERVATION STRUCTURES The goal of the described physical SWC structures is the creation of terrace-like features, controlling soil erosion by lowering the slope of the land and decreasing the runoff length (Adgo pers.com.). These cross-slope barriers aim at decreasing surface runoff and enhancing infiltration, sediment deposition and vegetation (Nyssen et al. 2014; Esser & Haile 2002). SWC structures allude to make themselves quasi redundant in the long-term, since the resulting bench-terraced landscape with an adequate vegetation cover is less prone to erosion than the initial environment. The effects of the different SWC structures (based on the literature shown in table 4) are both far-reaching and widespread and will be discussed in what follows. However, there is a pile of literature about SWC in sub-tropical highlands (Haregeweyn et al. 2015) and it was not possible to review every article in this master thesis research. O N - SITE The physical removal of productive soil results in an immediate decline of potential crop and biomass yield (Bekele & Drake 2003). By reducing the runoff speed, SWC structures are capable to decrease the sheet and rill erosion by 27% and 89% (Haregeweyn et al. 2012). Investing in SWC causes fewer nutrients to be washed away and less soil organic carbon to be dissipated, thus toning down the increasing need to use costly fertilizers (Balana et al. 2012). A study by Mekuria et al. (2007) in exclosures, protected by SWC structures, shows significant higher levels for SOM, nitrogen and available phosphorus compared to RL. The cost of N and P loss due to erosion in Tigray is assessed on 34.2 million euros (Haregeweyn et al. 2006). Protecting the fields with SWC can effectively reduce this loss of fertile topsoil. Nyssen et al. 2007 measures an average sediment accumulation rate of 58 t/ha/yr. behind 3-21 year old SB. In May-zeg-zeg – a micro catchment close to Mayleba-, a soil loss decrease from 14.3 to 9.0 t/ha/yr. was noted in only 6 years due to the protection with SB (Nyssen, 2009). Compared with control plots, installation of SB, CT and SFT in Mayleba causes a reduction of soil loss of respectively 63%, 90% 97% on RL. In the same area, on CL and averaged over different slopes, SB resulted in 40% reduction and SFT in 85% reduction of soil loss (Taye 2014). In the study area, the introduction of SB, SFT and CT led to runoff reduction by 17%, 85% and 62% respectively for RL and 11% and 61% for CL (Taye, 2014). Consequently, the infiltration of runoff water is stimulated, decreasing flooding threats (Balana et al. 2012). SWC structures can be used to plant trees on it and enhance the effect of flood regulation (Teshome et al. 2015). Increased infiltration leads to a reduction in crop damage and amplifies the soil moisture and soil structure, leading to a better soil fertility (Adgo et al. 2013). Physical SWC structures are thus beneficial for the catchment water balance (Haregeweyn et al 2015).

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Tillage erosion and water erosion displace soil material downhill within one field. Downslope of the terracing structure, topsoil in constantly removed, while this fertile soil is deposited uphill of the SWC structure, resulting in a lowering of the slope. The deposition of ‘foreign soil’ can be both a blessing (accumulation of fertile topsoil) or a curse (diseases can be spread) (Vancampenhout et al. 2006). The net effect on soil fertility is not deeply investigated and the resulting effect on crop/biomass yield is indecisive as well. Vancampenhout et al. (2006) found a spatial variability in the crop yield increase after the construction of SWC structures: 53% more grain yield right above SB compared to the central and upper parts of the plot. Taye (2014) also made note of this differences on plot scale and concludes that the net effect on crop yield is field-specific but insignificant in the Mayleba catchment (Taye et al. 2015). This opposes the average yield increase of 7% for teff, cereals and peas brought up by Vancampenhout et al. (2006) and the rise of a half quintal per hectare computed by Nyssen (2007), both measured close to the Mayleba study area. Gebremedhin (1999) considers the effect of SWC on the temporal variability in crop yield: in semi-arid climates the crop yields in the soil accumulation zone are more stable and thus more food secure. The on-site benefits of soil erosion (shown in figure 36) in the first place affects the farmers themselves, giving them vested interest in managing this erosion problem (Bekele 2003). However, construction does require a large amount of labour and some SWC structures are not convenient for ox-ploughing (Firew 2014). Besides, while this is argued to be only a minor problem by some authors (e.g. Nyssen et al. 2007), recent research shows that fields with higher bund densities harbour more rodents causing significantly more crop damage (Meheretu et al. 2014). In addition, the absence of land and accessible credit markets in Tigray limits the economic incentive of farmers to invest in sustainable SWC.

FIGURE 36: ON-SITE EFFECTS OF SWC ON THE FIELDS, RESULTING FROM SB, CT AND CDS. ARROWS INDICATE INCREASING (UP) VERSUS DECREASING (DOWN) EFFECT.

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O FF - SITE The supply of topsoil by deposition can lead to an increase the fertility of the neighbouring fields but moving soil also causes negative effects like inundation, spreading diseases and mud flooding. Strong SWC structures shrink the risk of burial of present-day crops on adjacent fields and protect the future productivity of these fields against the siltation caused by water logging (Haregeweyn et al 2015). SWC structures impacts the environment both positively and negatively, as summarized in figure 37 It is stated that increased infiltration –triggered by SWCwill lead groundwater recharge. (Bekele). Research in India shows that 63% of the water stored in check dams seeps to the ground water table (Parimalarenganayaki and Elango, 2015). A massive implementation of SB can lead led to a yearly rise in water table and emerging springs, prolonging the crop growing period and intensifying diversity (Bombino et all, 2013; Nyssen et al. 2010). New irrigation schemes can develop and the micro-climate around the watershed improves (Woldemariam 2012) Haregeweyn et al. (2006) argues that reservoir sedimentation is the most serious off-site consequence of soil erosion in the Ethiopian highlands. More sediment deposition in gullies due to CDs and less soil loss on CL and RL due to SWC results in less reservoir sedimentation, increasing not only the lifespan of many irrigation reservoirs but also the productive lifecycle of hydroelectric dams downstream. In Tigray, the annual total capacity loss of reservoirs during 1997–2005, 1997–2007, and 2005–2007 is estimated by (Haregeweyn et al. 2012) at 4.02%, 3.16%, and 3.03%, respectively. On the other hand, the reduced runoff can cause a problem for the downstream irrigation reservoirs, which encounter an unforeseen decline in water inflow (Taye, 2014). The capacity of hydropower plantation declines, irrigation basins can get polluted and dams can be filled reducing their protection against floods (Teshome et al, 2015). Loss of the top soil reduces the soils natural ability to control diseases. By preserving this decapitation of the soils, both fauna diversity (insects - worms in the soil) and flora diversity (increased vegetation cover) will increase. (Al-ani et al. 2012) SWC provide numerous ecosystem services that can also mitigate environmental damage. They are not yet described well in literature but include e.g. more dust trapping (good for the health of the society), carbon sequestration (good for climate regulation) and drought mitigation (good for reducing the vulnerability of the society) (Bekele & Drake 2003; Haregeweyn et al. 2015). Those SWC-associated impacts have not been studied well in Ethiopia (Haregeweyn et al, 2015). By protecting the land against degradation, the pressure on terrestrial biodiversity alleviated. The net effect of carbon sequestration is not known (Bekele & Drake 2003).

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FIGURE 37: ON-SITE EFFECTS OF SWC ON THE FIELDS, RESULTING FROM SB, CT AND CDS. ARROWS INDICATE INCREASING VERSUS DECREASING EFFECT. TABLE 4: (PART OF THE) LITERATURE BODY DEALING WITH EFFECTS OF SWC ON THE FIELDS, RESULTING FROM SB, CT AND CDS. EFFECTS REFER TO THE EFFECTS IN THE FIGURE ABOVE.

Effect Erosive power

Water infiltration

Ploughing convenience

Literature Taye 2014; Prabuddh & Suresh 2013; Haregeweyn et al. 2008; Sorbie 2012; Alan & Dawson 1999; Mishra et al. 2014; Haregeweyn et al. 2015; Sharpley 2007 Lanckriet et al. 2012; Descheemaeker et al. 2009; Frankl et al. 2013; Nyssen et al. 2008; Taye 2014; Zenebe 2012; Sharpley 2007; Nyssen et al. 2006; Haregeweyn et al. 2015; Sorbie 2012; Gebremichael et al. 2005; Descheemaeker et al. 2006; Firew 2014; Nyssen et al. 2007; Nyssen et al. 2007; Martens 2013; Nyssen 1998; Descheemaeker et al. 2006; Mekuria et al. 2009; Pimentel et al. 1995; Nyssen et al. 2009; Waters-bayer et al. 2006; Haregeweyn et al. 2012 Taye 2006; Bewket 2007; Amede & Belachew 2001; Amsalu & de Graaff 2007; Descheemaeker et al. 2009; Teshome et al. 2012

Rodent habitat

Sharpley 2007; Meheretu et al. 2014; Nyssen et al. 2007; Taye 2006; Hoben 1995; Bekele & Drake 2003; Teshome et al. 2013; Bewket 2007; Esser & Haile 2002; Desta et al. 2005; Tesfaye et al. 2014; Teshome et al. 2014; Balana et al. 2012; Hengsdijk et al. 2005; Smit & Goshu 2011

Soil depth

Vancampenhout et al. 2006; Descheemaeker et al. 2006; Opolot et al. 2014

Soil loss

Taye 2014; Amdihun et al. 2014; Gebremichael et al. 2005; Herweg & Ludi 1999; Nyssen et al. 2007; Martens 2013; Teshome et al. 2013; Mekuria et al. 2009; Nyssen et al. 2009; Adimassu et al. 2012; Gebremedhin et al. 1999; Haregeweyn et al. 2008; Nyssen et al. 2009; Yitbarek et al. 2012; Haregeweyn et al. 2012; Nyssen et al. 2008; de Graaff et al. 2013; Amede & Belachew

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2001; Sharpley 2007; Haregeweyn et al. 2015; Ayele et al. 2015; Desta et al. 2005; Nyssen et al. 2007b; Tillahun 1996 Soil moisture

Prabuddh & Suresh 2013; Descheemaeker et al. 2006; Taye 2014; Monsieurs 2014; Teshome et al. 2013; Walraevens et al. 2009; Bewket 2007; Kassie et al. 2009; Amede & Belachew 2001; Adgo et al. 2013; Herweg & Ludi 1999; Nyssen et al. 2009; Vancampenhout et al. 2006; Haregeweyn et al. 2015; Descheemaeker et al. 2006; Desta et al. 2005; Nyssen et al. 2007; Martens 2013; Balana et al. 2012; Frankl et al. 2014; Mekuria et al. 2009; Edwards et al. 2007; Adimassu et al. 2012

Water logging

Opolot et al. 2014; Shiferaw & Holden 1999; Esser & Haile 2002; Firew 2014; Teshome et al. 2014; Nyssen et al. 2007; Taye 2006; Hengsdijk et al. 2005; Bojö 1996; Bekele 2003

Sediment trap

Descheemaeker et al. 2006; Gebremichael et al. 2005; Taye 2014; Haregeweyn et al. 2015; Nyssen et al. 2007; Nyssen et al. 2000; Nyssen et al. 2009; Zegeye et al. 2011

Nutrient loss

Adimassu et al. 2012; Prabuddh & Suresh 2013; Taye 2014; Hoben 1995; Monsieurs 2014; Amede & Belachew 2001; Hurni et al. 2015; Sharpley 2007; Gebremedhin et al. 1999; Ayele et al. 2015; Vancampenhout et al. 2006; Teshome et al. 2014; Yitbarek et al. 2012; Haregeweyn et al. 2012

Soil structure Storage foreign soil Crop burial Fertility

Alien weeds Flooding

Reservoir siltation Woody species biodiversity Reservoir inflow Spring development

Amede & Belachew 2001; Sharpley 2007; Esser & Haile 2002; Descheemaeker et al. 2011; Descheemaeker et al. 2006 Fieldwork Nyssen et al. 2007; Nyssen et al. 2008; Tesfaye et al. 2014; Nyssen et al. 2007b; Bekele 2003 Vancampenhout et al. 2006; Amede & Belachew 2001; Nyssen et al. 2007; Shiferaw & Holden 1999; Amsalu & de Graaff 2006; PSNP. 2009; Teshome et al. 2014; Amsalu & de Graaff 2007 Bekele & Drake 2003 ; fieldwork Nyssen et al. 2007; Monsieurs et al. 2015; Nyssen et al. 2008; Girmay et al. 2009; Birol et al. 2006; Haregeweyn et al. 2015; Adgo et al. 2013; Nyssen 2009; Vancampenhout et al. 2006; Firew 2014; Nyssen et al. 2007; Nyssen et al. 2007; Nyssen 1998; den Biggelaar et al. 2003; Woldemariam 2012 Course ‘Soil geography on world scale’ (Deckers, 2015) Babulo et al. 2009 Fieldwork Course ‘Soil geography on world scale’ (Deckers, 2015) Nyssen et al. 2007

Nutrient cycling

Course ‘Soil geography on world scale’ (Deckers, 2015)

Carbon sequestration

Course ‘Soil geography on world scale’ (Deckers, 2015)

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3.2 INSIGHT IN THE PERCEPTION OF FARMERS CONCERNING SOIL DEGRADATION AND SWC MANAGEMENT 3.2.1 CHARACTERISTICS OF FARMER RESPONDENTS Hundred and seven in-depth interviews were conducted with farmers in the Mayleba catchment. Two of them served as test interviews - to assure the comprehensibility of the interview questions – which are not included in the analysis. As the interviews were semistructured, the results could be mostly analysed quantitatively (similar to Lanckriet 2012). Visible in figure 28 is the dispersion of interviews in the study area; all types of soils, geology, elevation and slope are covered by the hundred five interviews. A summary of the household characteristics is given in table 6 and will be discussed in the following paragraphs. As data was collected in an area with a high level of illiteracy, there is a chance on irrational responses due to a limited- or mis-understanding. This can be a source of data inaccuracy but it is not possible to identify these ‘wrong’ answers, since they are not discernible from the ‘rational’ ones (Sagebiel 2011). On average, the age of the respondents – often the household head - is 41 years old; the youngest respondent counts 18, the oldest 80. Three quarter of the respondents are male, which is a logical consequence of the attempt to interview the household heads. Still, the interrogated women were very aware of the farm practices as well as the erosion problem and subsequent SWC measures. Often, women took more time to think about questions, which resulted in apparent more profound answers.

FIGURE 38: MAPS WITH INDICATION OF THE CONDUCTED INTERVIEWS. LEFT: GEOLOGY OF MAYLEBA. RIGHT: PEDOLOGY OF MAYLEBA. DATA: TAYE 2012.

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S OCIOECONOMIC CHARACTERISTICS The mean size of the households that were visited is 6 persons, in line with the 5.8 found by Teshome (2014) in the north-western Highlands. 56% of the respondents never went to school, which is somewhat higher than the mean 46% female and 33% male no-education rate in Tigray (World Data Atlas, 2014). The difference can be assigned to the fact that the interviews took place in a rural area, where a lower literacy rate than in cities can be expected. 20% of the respondents were beneficiary of the Productive Safety Net Programme and received food aid during a certain period of the year. 50% has extra crop yield to sell on the market in average years. In 17% of the cases, there was at least one household member who has an off-farm job. Other proxies for a sustainable livelihood were asked, like the presence of their own toilet (30 % of the households) and the ownership of a mobile phone (36%), a radio (32%) or electricity (7.6%). None of the respondents had a transport vehicle. A livelihood factor is constructed from this data, to discretize between the relatively more poor and more wealthy farmers in the study area. Based on socioeconomic indicators, land resources excluded, this factor enables to account for financial, social and human capital. The livelihood factor includes assets (0 if electricity, +1 if mobile and radio, +2 if mobile or radio, +3 if none), accessibility (to water: +travel time/30 and to credits: +1 if difficult to get), household (+1 if no toilet, +1 if no charcoal) and income (+1 if self-sufficient, +1 if no off-farm job, +1 if food aid). According to this selfconstructed definition, the ‘capital-richest’ household in the study area has a factor 3.3, the ‘poorest’ totals 17. After standardizing this poverty factor, it can be useful for classifications in further analysis of this master thesis research.

FIGURE 39: YOUNG FAMILY OF 4 WORKING ON THE FIELD. MAYLEBA, 08/2015.

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TABLE 5 : SUMMARY OF THE FARMER HOUSEHOLD SURVEY IN MAYLEBA. THE TOTAL NUMBER OF HOUSEHOLDS IS 105.

VARIABLES DESCRIPTION HOUSEHOLD CHARACTERISTICS AND LABOUR RESOURCES Age Age of the respondent - household head (in years) Sex Percentage of male respondents Education Level of education of the respondent (in years) Family size Size of the household (in numbers) Poverty factor Index related to multi-poverty (non-farm related) AGRICULTURAL LAND RESOURCES Average farm size Size of the land in ownership (in hectare) Cultivated land Size of the land in cultivation (in hectare) Tenure security Respondents possessing of a land certificate (in %) Time of use Age of the CL in use (in years) Crop income Respondents selling crop yield on the market (in %) Livestock Amount of cattle per household (in numbers) Productivity Value of the total annual crop yield (in ETB) Crop yield Total crop yield per year (in kg per ha) OTHER SOCIOECONOMIC RESOURCES Food aid Beneficiaries of the Productive Safety Net Off-farm income Respondents having HH members with off-farm job Credit access Respondents with easy access to credit markets SWC extension Respondents getting info about SWC management PERCEPTION OF SWC MANAGEMENT Erosion problems Perception on erosion problems (0 no erosion < 3 severe) SWC profitability Perception on the profitability of SWC on CL CL (0 if no effect. 3 if strongly improved soil state) SWC profitability Perception on the profitability of SWC on RL RL (0 if no effect. 3 if strongly improved soil state) ADOPTION OF SWC STRUCTURES SB CL Respondents with SB on their CL SFT CL Respondents with SFT on CL SB RL Respondents with SB on the RL nearby SFT RL Respondents with SFT on RL nearby CT RL Respondents with CT on RL nearby

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MEAN SD 40.73 76.2% 2.49 6.05 10.38

15.49 3.38 5.25 2.68

0.92 0.97 89.5% 20.29 49.5% 3.22 11759 1040

0.48 0.59 13.12

20.0% 17.1% 75.2% 88.6%

-

2.58

1.03

2.27

0.80

2.01

0.97

92.4% 8.6% 51.4% 26.7% 31.4%

-

2.00 8691 720

A GRICULTURAL CHARACTERISTICS The mean farm size is slightly smaller than the mean cultivated area, indicating that the respondents are net land borrowers. On average, the farmers cultivate 3.88 tsimidi (0.97ha), which is almost 4 ‘Tigrigna’ fields equalling 0.25ha. 24% of the households possesses less than 1 hectare. Farmers’ crop yield varies between 100 and 2000 kg/ha, with most of them between the region-characteristic 500-1500 kg/ha (Lanckriet et al. 2012). The respondents used their fields on average 20.3 years. Since young couples in Ethiopia get fields from the governments when they are around 20 years old, this is an expected consequence of the mean respondent age of 41. The modus year of acceptance however, is 1982 EC. This is the year of a large land reform in this area (Teklu, 2014) and thus a logical result. Almost 90% of the respondents has a land certification, the other ones were expecting one. Not even 50% of the respondents has extra crop yield that is not used for consumption and can be sold on the market. The most consistent crops in the study area are wheat, teff and barley, grown by respectively 94, 85 and 78 farmers. The mean yields by those farmers is 350kg for wheat, 150kg for teff and 375kg barley, wheat and barley are mostly cultivated alternatingly every other year. Other cultivated crops are sorghum, chick pea, grass pea, maize, lentils, horse beans and flax, resembling the list of crops by Taye (2014). The agricultural production is summarized in table 6, showing the percentage of respondents cultivating the crop type and the mean amount that they cultivate. Based on the mean price per kg of June-July-august 2015 (CSA 2015), the productivity per farmer can be expressed in ETB. On average, the surveyed farmers in Mayleba produced crops worth 11759 ETB per year, equivalent to about 500 euro (exchange rate august 2015). Table 7 summarizes the livestock holding; the percentage of respondents having the animal type and the mean number of animals that they have. It can be calculated that on average, the respondents have 3.19 cattle animal. This is comparable to other research in the area (Lanckriet, 2010). Only 22.9% of the respondents has less than the two animals that are needed to pull the ox-plough to cultivate the fields and thus have to borrow an ox/cow/(donkey). .

TABLE 7: SUMMARY OF CROP PRODUCTION. EXPECTED YIELD OF THE FARMERS SUMMED OVER ALL THEIR CROPLAND

CROP FARMERS MEAN * (KG) TEFF 84% 150,00 WHEAT 93% 343,88 BARLEY 76% 374,38 HORSE BEAN 42% 79,26 LENTILS 38% 118,75 CHICK PEA 27% 151,79 FLAX 19% 62,50 GRASS PEA 11% 143,75 MAIZE 10% 136,25 SORGUM 10% 250,00 *Mean of the per farmer expected total yield in kg for the coming year, for all fields

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TABLE 6: SUMMARY OF LIVESTOCK HOLDING. MEAN AND MAX AMOUNT OF ANIMALS KEPT BY THE RESPONDENTS (PERCENTAGE OF 105) HAVING AT LEAST ONE ANIMAL OF THAT SPECIES

ANIMAL OX GOAT SHEEP DONKEY COW

FARMERS 80% 22% 8% 62% 64%

MEAN 1,64 5,85 3,00 1,65 1,40

MAX 4 20 5 4 3

D ISCUSSING THE SOCIOECONOMIC AND AGRICULTURAL RESOURCES OF THE FARMERS Since the study area encloses only 17km², the diversity between the respondents is quite small. Their general accessibility is comparable and they have the same main market town, Hagere Selam – thus the same trade possibilities. All respondents are subsistence farmers – less than one in five has a small source of non-farm income - using the same cultivation practices (mahresha ard plough, contour tillage) and get similar extension service from the same Agricultural office and SWC managers (88% indicates to have access to this information). They are governed by one administration office, which divides the land ownership between all young men. This means that almost each respondent has cropland on steep and less steep fields, on clay and on loamy soil, on shallow/stony and deeper soils and that they have comparable knowledge concerning agricultural practices and soil and water conservation. Only a selfconstructed livelihood-factor is able to discover some differences in poverty, proving to be a good tool in further analysis. However, making geographical distinctions based on the interviews – while this originally was a goal of this master thesis research - is thus out of the question. Besides, since most rangeland recently converted to exclosures, many farmers were not able to answers questions related to the status of the rangeland.

FIGURE 40: HOUSE WITH NEARBY FIELD, BADLY MAINTAINED BENCH TERRACES. MAYLEBA 08/2015.

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3.2.2 FARMERS’ KNOWLEDGE CONCERNING SOIL EROSION C AUSES OF PRODUCTIVITY CHANGES Different graphs are made to show the answers of respondents on questions related to the perception of soil erosion and the effects of SWC. Farmers were asked if – seen over a longer period - their crop yield and/or the grass biomass was increasing, decreasing or fluctuating (figure 41). Most of the time, a decrease was determined. When the answer ‘increase’ was given, often the previous three ‘good-rain’ years, the closed areas or the newly introduced fertilizers urea and Di-ammonium Phosphate (DAP, containing N and P) were mentioned (24 respondents). Farmers were of course well aware of the need for sufficient water and a high organic matter to increase soil fertility.

decreases fluctuates

Grass biomass Crop yield

increase 0%

10%

20%

30%

40%

50%

percentage of respondents FIGURE 41: CHANGES IN PRODUCTIVITY OF CROPLAND (YIELD) AND RANGELAND (BIOMASS) DURING 2010-2015. HH=105

Reasons for the alleged production changes are presented in figure 42, ranked according the perceived importance by the respondents. Shortage or rainfall– with an average rank of 1.1 – is clearly the most influencing factor on the crop yield. With an average of 2.6, water erosion is situated on rank 2, closely followed with the presence of pests (diseases and animals) having an average of 2.8. Alien weeds (3.0) and overexploitation (3.3) are of less importance. Besides, a lack of SWC structures (4 respondents), crop diseases (10 respondents), fertility decline due to dung/DAP shortage (11 respondents) and hail (11 respondents) are frequently mentioned as production-limiting factors. Amsalu and De Graaf (2006) asked the same question to farmers in the Beressa Watershed (Central Ethiopian Highlands). They conclude that farmers assign productivity decline to rainfall shortage (rank1), fertility decline (rank2), overexploitation (rank3), soil erosion (rank4) and frost (rank5). While overexploitation in Beressa (2006) is seen as a reason for soil fertility changes causing productivity decline by respectively 46% of the farmers, this is ranked very low by the farmers in Mayleba.

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On grassland in Mayleba, rainfall shortage (average rank 1.2) is also the factor perceived to have the biggest impact on the production. Overexploitation and water erosion are of more importance on this land use type, with respectively an average rank of 2.6 and 2.8. In addition, the presence of pest animals (with an average rank of 3.2) stays important, while alien weeds appear to influence the biomass production of fodder grass or wood production only slightly. Deforestation is given as an extra factor having a significant influence on the grass production in RL.

Factors influencing crop yield

Factors influencing biomass 100%

Percentage of respondents

Percentage of respondents

100% 90% 80% 70% 60% 50% 40%

80% 70% 60% 50% 40% 30%

30%

20%

20%

10%

10% 0%

90%

0% Rank 1 Rank 2 Rank 3 Rank 4 Rank 5

Rank 1 Rank 2 Rank 3 Rank 4 Rank 5

alien weeds pest animals Water erosion Overexploitation Rainfall shortage

alien weeds pest animals Water erosion Overexploitation Rainfall shortage

FIGURE 42: EFFECTS RANKED ACCORDING TO THEIR IMPORTANCE IN INFLUENCING THE CROP OR BIOMASS PRODUCTIVITY. (LEFT: CROP YIELD; RIGHT: GRASS BIOMASS). RANK 1 HAS THE LARGEST INFLUENCE. HH=105

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P ERCEPTION OF EROSION PROBLEMS When asked about the seriousness of erosion (figure 43), most respondents specify the erosion on their land as ‘moderate’. More than 80% answered that there is an erosion problem on their farm. Amsalu and De Graaf (2006) derived somewhat lower values from their survey (severe: 10%, moderate: 33%, minor: 25%) and found out that farmers only perceive erosion as ‘severe’ if there are visible erosion marks (such as rills). Compared to the North-western Highlands, where only 1.6%-3% of the farmers mention to be free of erosion, in Mayleba fewer farmers are aware of the erosion problem on their fields (Bewket 2007; Teshome et al. 2012). However, some of the surveyed farmers in this master thesis research included the presence of SWC structures in their response or describe the difference in erosion on steep versus flat slopes, indicating that they have knowledge about erosion. Water erosion is clearly the most prevalent type of erosion, specified by 85% of the respondents (figure 44). No distinction was made between inter-rill and rill erosion. While signs wind erosion are present in the study area, farmers clearly are not disturbed by this type of erosion or not everyone is aware of it. One farmer explains that – when it is dry - the Vertisols contains cracks, the wind can go through it and this results in an erosion problem. Around 30 % of the respondents was threatened by gullies (an extreme type of water erosion) next to their fields or next to the places used as grazing land for their livestock.

Percentage of respondents

40% 30% 20% 10% 0%

No erosion

Minor Erosion

Moderate Erosion

Severe erosion

FIGURE 43: THE EROSION PHENOMENA IN MAYLEBA AS PERCEIVED BY THE RESPONDENTS. HH=105

100% Percentage of respondents

80% 60%

Cropland

40%

Grazing land

20% 0%

water

gully

wind

FIGURE 44: TYPES OF EROSION INDICATED BY THE RESPONDENTS. HH=105

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An ordered logit regression is used in a try to estimate the probability of farmers to perceive the erosion problem as more severe. This extended logistic model can be applied to predict ordinal dependent variables, in this case the severity of erosion. The result is shown in table 8 but fails in finding much significance. Farmers who put effort in renewing their own structures, generally have the opinion that the erosion problem is less severe and thus under control on their fields. This is probably a result-cause relation instead of vice versa, which is confirmed by answers in the survey like “if we put enough effort, we can stop erosion”. The elevation slightly influences the perceived erosion severity too. While in the low lying clayey areas erosion forms less of a problem, the higher areas near the cliffs collect runoff form the steep slopes. Water erosion indeed must be a larger problem there. TABLE 8: ORDERED PROBIT MODEL ON THE PERCEIVED EROSION PROBLEM IN TIGRAY (0=NO EROSION; 1=MINOR EROSION; 2=EROSION; 3=STRONG EROSION)

Log likelihood Pseudo R-squared

-133.860 0.653 Coefficient Error Index function for probability Work put in SWC on own initiative Maintenance -0.601 0.612 Renewing -0.808* 0.485 Perception effect SWC techniques Normal 0.215 0.570 Strong -0.050 0.542 Perception evolution crop yields Increasing -0.033 0.255 Age SWC struct -0.004 0.022 Socioeconomic variables Age of respondent 0.004 0.015 Area in cultivation -0.043 0.134 Productivity (ETB) 0.244D-4 -0.930 Household size 0.139 0.101 Livelihood factor -0.084 0.072 Biophysical variables Slope (%) 0.061 0.065 Elevation (m a.s.l) 0.001* 0.001 Shale 0.860 0.707 Limestone 0.319 0.658 Sandstone 0.266 0.858 Cambisol -0.568 0.761 Leptosol 1.091 1.109 Vertisol -0.068 0.888 Threshold parameter Mu 1.386*** 0.192 Note: ***, **, * Significance at 1%, 5%, 10%.

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C AUSES OF SOIL EROSION The farmers were asked about the causes of erosion (figure 45). More than 75% percent is aware of the erosive effect of strong rains and the second-most mentioned cause is a lack of (good) SWC structures. The importance of poor management in soil erosion was already proven by Mekuria et al. (2007). This is closely followed by overgrazing, deforestation and the presence of steep slopes, while the effect of a green soil cover or intensive cultivation is barely mentioned. In studies in the Beressa watershed (Amsalu and De Graaf, 2008) and the Dilgil watershed (Bewket 2007), strong rains were respectively mentioned by 28% - 14% of the farmers, slope steepness by 23% - 40%, damaged structures by 7% - N/A and overgrazed soil by N/A - 3% of the respondents (Amsalu and De Graaf, 2006, Bewket 2007). They are less aware of the causes of erosion than the farmers in Mayleba, were these causes were answered by 78%, 25%, 35% and 27% of the farmers. It is clear that the human impact on soil erosion is known (overgrazing, deforestation), while ten years ago the majority of farmers (60%) did not consider population growth as a cause of soil erosion (Mekuria et al 2007).

Intensive cultivation Lack of vegetation Footpaths Steep slopes Deforestation Overgrazing No/bad SWC Strong rains 0%

10%

20%

30%

40%

50%

60%

Percentage of respondents FIGURE 45: PROCESSES THAT CAUSE EROSION ACCORDING TO THE ANSWERS OF THE FARMERS. HH=105

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70%

80%

90%

3.2.3 FARMERS’ OPINION CONCERNING SOIL AND WATER CONSERVATION U SE OF SOIL AND WATER CONSERVATION TECHNIQUES Figure 35 shows the use of SWC structures. Almost all farmers reported having SWC structures on their CL. 92% of the respondents has SB whereas SFT are adopted in smaller amounts. 51% mentioned SB on the RL where they graze their cattle or close to their houses, again SFT and normal CT are less in use. 53% of the respondents indicated having loose stones CDs or gabion CDs near their fields. Besides, contour ploughing (all respondents), flood diversion (15 respondents), planting Aloe or Chisel to reinforce the structures (14 respondents) and creating grass buffers (5 respondents) appeared to be common practices in the Mayleba Catchment. Tillahun (2013) already mentioned that - despite living in a low-income country - Ethiopian farmers are clearly aware of the importance of conservation of the resource. He found that the farmers in Central Tigray were interested to contribute to the conservation of forests (Amede & Belachew 2001; Tilahun et al. 2013). This master thesis research proves that this conclusion can be made about the conservation of soils as well: On average, respondents work yearly 9.3 days per tsimidi on SWC structures on their CL, which is already quite a though job. On CL, the modus year of protection is 1992, which is after a land reform following the fall of a military dictatorshop. On RL, the modus is 2008. In gullies, the median year to start protection is 2007, the most frequently mentioned year 2014. Farmers who did not have any SWC structures (5 respondents) on CL all helped constructing elsewhere. They perceive that the erosion is not bad on their fields, yet two of them are planning to install SWC structures in the coming year. Two other have the opinion that disadvantages are larger than the advantages and are not intending to construct any SWC. 100%

92%

90% 80% 70% 60%

53%

51%

50% 40%

31%

30% 20%

9%

10% 0%

SB cropland

27%

9%

SFT cropland SB rangeland TR rangeland

SFT Loose stones Gabion CD rangeland CD

FIGURE 46: USE OF SWC STRUCTURES ON THE DIFFERENT LAND COVERS. HH = 105

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P ERCEPTION ABOUT THE EFFECTIVITY OF SWC STRUCTURES A picture analysis by Nyssen et all compares the state of the vegetation cover and land management in Tigray between 1975-2006. In this research, 80% of the pictures showed an improved or strongly improved vegetation cover and in 72% of the cases, the land management was (strongly) improved. The vegetation cover and land management was deteriorated in respectively 13% and 6% of the pictures. They state that there is no significant change in the climate, thus these changes must be linked to SWC conservation (Nyssen et al. 2007). During the interviews of this master thesis research, a similar question gauges the state of the land since SWC management. While on CL 47% of the people indicates that the state of the soil is ‘strongly improved’ after constructing SWC, on RL this is only 38% (figure 36) . The percentage of respondents answering ‘normally improved’ or ‘slightly improved’ is almost the same for the two land use types but 10% of the respondents indicates that SWC had no effect on RL, while on CL this is only 2% of the farmers. In general, that the effect of SWC is perceived more positive on CL. Often, respondents indicate that soil erosion can be completely controlled by installing SWC: “Every farmer knows how to treat their land; if you work hard, erosion can be stopped”. In the North-western highlands, 97% of the respondents believed that erosion can be under control.

100% 90% 80% 70%

Strongly improved

60% 50%

Improved

40%

Slightly improved

30%

No change

20% 10% 0%

CROPLAND

RANGELAND

FIGURE 47: CHANGE OF THE STATE OF THE SOIL AFTER IMPLEMENTATION OF SWC TECHNIQUES. HH=105

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An ordered probability model is made on the experienced soil fertility improvements since the implementation of SWC structures on CL (table 9 left). it shows that people who saw the crop yields increase in the last 5 years, have a more positive attitude to the SWC structures. This is an intuitive result. Likewise farmers having fields on limestone and sandstone, mostly on higher areas, are more probable to have the opinion that SWC structures strongly improve the fertility on CL. It must be that the effect of SWC on the sandstone cliffs and low lying limestone areas is more explicit than in intermediate shale areas. When analysing the perceived effect of SWC on the soil fertility in RL (table 9 right), it becomes clear that farmers with more cattle generally are more likely to have a positive perception. An ordered logit model also points to the significant impact of perceived increase in grass biomass over the last 5 years. Both results are logical, although other variable does not seem to have any influence. Slope does not result in a significant factor while one would expect this. The spatial resolution of the DEM of 30*30m is probably not high enough to distinguish cliff areas from normal slopes.

FIGURE 48: STONE BUND WELL MAINTAINED AND FORTIFIED BY THE FARMER WITH ALIEN WEED AND CROP RESIDU. MAYLEBA 08/2015.

65% of the responding farmers often (re)constructs the present structures on CL, while 27% only improves the present structures. This sums up to a total free maintenance rate of 92% of the responding farmers in Mayleba. Research in the same area in 2002 reports a lower maintenance rate of 85% (Naudts 2002). 31% of the households constructed the present SB on CL with own labour forces, quite in line with the 26% that Esser (2004) reports in his analysis of SWC in Tigray. For example in Adiwerat, farmers work for free on each other’s land on own initiatives (without PSNP) because “sharing the cost results in sharing the benefits’. In RL and gullies, the lions’ share of the respondents only acts in group (obliged by the government). However, 3 farmers made note of restoring SWC there themselves because ‘it is needed but no one does it”.

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TABLE 9: ORDERED PROBABILITY MODEL ON THE PERCEIVED EFFECTIVITY OF SWC STRUCTURES ON CL AND RL.

CROPLAND Log likelihood Pseudo R-squared

-92.240 0.170

RANGELAND Log likelihood Pseudo R-squared

-95.052 0.161

Standard Standard Coefficient Error Coefficient Error Index function for probability Constant -21.628** 10.577 Constant 5.619 10.054 Perception erosion problem in Tigray Perception erosion problem in Tigray Minor 0.156 0.696 Minor 0.457 .694 Moderate -0.866 0.629 Moderate -0.029 .621 Severe -0.496 0.359 Severe -0.119 .642 Work put in SWC on own initiative Perception evolution biomass production Maintenance Increasing -0.639 0.634 1.486** .619 Renewing Decreasing 0.713 0.588 0.309 .571 Stone-faced CT 0.726 0.937 Age structures 0.035 .023 Perception evolution crop yields Socioeconomic variables Increasing Age of respondent 1.107* 0.613 -.017 .015 Decreasing Livelihood factor -1.137** 0.548 -.045 .086 Age structures 0.006 0.047 Number of cattle .327*** .124 Socioeconomic variables Household size .087 .085 Age of respondent 0.018 0.017 Slope .150 .098 Livelihood factor -0.081 0.082 Elevation -.004 .004 Productivity (in 0.285D-4 -1.590 ETB) Household size -0.011 0.038 Biophysical variables Slope (%) 0.040 0.095 Elevation (m a.s.l) 0.007* 0.004 Shale 0.305 1.009 Limestone 2.387** 1.108 Sandstone 1.995* 1.184 Cambisol 1.007 0.973 Leptosol 1.091 1.109 Vertisol

1.584

1.082 Threshold parameter Mu 1.958*** 0.302 Mu Note: ***, **, * ==> Significance at 1%, 5%, 10% level.

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1.53123***

.259

An ordered logit analysis with the respondent characteristics is done to get more insight in the decision of farmers to (re)construct or improve the SWC structures themselves. The less severe the erosion is estimated by the respondent, the more chance on working alone on SWC. This result is counter-intuitive. Besides, farmers who acknowledge the beneficial (light or strong) effect of SWC structures generally are more likely to install structures on their own. Other variables appear to be insignificant. While literature suggests that age of the household head and household size influence the adoption of SWC structures (Teshome et al 2015), this is not visible here. TABLE 10: ORDERED PROBABILITY MODEL ON PUTTING OWN LABOUR FORCES IN THE SWC STRUCTURES. (0=NO EFFORT, 1= MAINTAINING, 2= RECONSTRUCTING)

Log likelihood Pseudo R-squared

-91.612 0.1451 Coefficient Error Constant 2.263 11.011 Perception erosion problem in Tigray Minor 1.588** 0.749 Moderate -0.102 0.600 Severe -0.172 0.669 Perception effect SWC techniques Slight 1.946* 1.112 Normal 0.792 0.973 Strong 2.014** 0.965 Perception evolution crop yields Increasing -0.936 0.593 Decreasing -0.422 0.546 Age SWC 0.001 0.026 structures Socioeconomic variables Age of respondent -0.024 0.017 Area in cultivation -0.043 0.134 Productivity (in 0.386D-4 0.297D-4 ETB) Household size 0.057 0.048 Livelihood factor 0.092 .088 Biophysical variables Slope (%) -0.046 0.107 Elevation (m a.s.l) -0.001 0.005 Shale 0.305 1.009 Limestone -0.904 1.020 Sandstone 0.751 1.056 Cambisol 1.076 0.978 Leptosol 1.091 1.109 Vertisol -0.042 1.051 Treshold parameter 1.501*** 0.266 Note: ***, **, * Significance at 1%, 5%, 10%

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P ERCEIVED BENEFITS FROM SWC STRUCTURES It is asked to the interviewed farmers to sum up all benefits they could think of, that are linked with installing SWC. More than 70% percent of the respondents points to crop yield increase as the dominant benefit of implementing SWC (figure 37). This is remarkable since authors are still debating on the net impact of SWC techniques (mostly SB) on the crop yield (Hengsdijk et al. 2005; Adimassu et al. 2015; Araya et al. 2015; Gebremedhin & Swinton 2003; Kassie et al. 2009; Bekele 2004; Gebremichael et al. 2005; Vancampenhout et al. 2006; Nyssen et al. 2007; Tillahun 1999 and 1996,). Crop yield increase is crop-related, soil-, slope-dependent, affected by the rainfall depth and by many other environmental conditions. A recent study in Mayleba (Taye, 2014) indicates that the crop yield increase is almost negligible (not significant) if space and spatial variability are taken into account. Studies investigating the geographical difference in crop yield at plot scale caused by SWC structures, conclude that the yield increases above the structures but decrease downstream of the structures. This is due to erosion and sedimentation of nutrient-rich soil on plot scale (Descheemaeker 2007) and is also often answered during the interviews in Mayleba. When asked about the quantity of crop yield increase in their fields, the answer is almost always 1 quintal (100kg), which is the smallest unit they know. Maybe farmers do feel a small difference but cannot distinguish between the effects of good rains or the use of DAP and the real effect of SWC. On the other hand, one farmer strongly stated that ‘without any SWC, we just have no land, so no crop yield’, demonstrating that SWC are a conditionality to have fertile and productive CL.

crop yield increases soil loss reduces soil fertility improves better soil moisture extra fodder for… plowing is easier slope decreases lower risk on floods 0%

20%

40%

Percentage of respondents

60%

FIGURE 49: EFFECTS INDUCED BY THE USE OF SWC MEASURES, AS MENTIONNED BY THE RESPONDENTS. HH=105

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80%

Figure 48 also specifies that the reduction of soil loss, the improvement of soil fertility and the increase in soil moisture are frequently stated as benefits from SWC. Besides, farmers also mentioned extra effects like afforestation, extra soil deposition, sustainability, grass/wood increase, protection against gullies and less necessity for DAP fertiliser as benefits. When asked which SWC structure they like the most, neglecting the time and space consumption of the structure, 42% answered SB. Hence, 52% prefers SFT arguing that this last one is the best to increase soil moisture. Another benefit proposed by some farmers is the extra fodder they can grow in the stone bund and SFT in CL and the structures are also a good place to stock compost. However, most farmers are not able to install FT, because they are very labour intensive; “it is too much work” or “we need extra help for this” are frequently (8 times) given comments to the question why they did not install their preferred structure. Also the fact that it takes too much space is a major disadvantage of this most efficient structure, as this is remarked by 17 respondents. Farmers preferring the SB answer that this is easy to maintain (it fills not rapidly) and less space-taking. One farmer argues that SB are the best when there are enough stones available/ In the other case, the soil from the trench in a SFT structure is needed to reinforce the stone wall and back filling material. Also the soil type does play a role in choosing which structure is perceived as best, as mentioned before. Esser (2004) states that “since reducing soil erosion is likely to be a less important objective for farmers than securing immediate food needs, recommended changes (i.e. SWC structures) should be paid with food or other input (fertilizer, seed, … to increase yield). Thus, the question was posed to the respondents if they think it is a good idea to support SWC management. Although in Tigray, this is often done via the food-for-work programme, farmers have to work at least 40 days for free. Only 7 respondents answered “No” on the statement “Farmers should be paid for constructing and maintaining soil and water conservation structures”. They argue that “the benefit form saving soil is worth more than the work it takes” or “SWC structures are beneficial for everyone, so there is no payment needed”. All other farmers would be pleased to see extra support, either to hire extra labour forces or to compensate for the opportunity cost of not being able to hire out own labour forces while working on the SWC structures. 73 respondents found that food/money inputs should be given as an extra resource when working on SWC structures on RL. 48 respondents were positive for SWC on CL and 83 respondents think ‘working to protect gully systems’ is worth a compensation.

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E FFECTS OF SWC STRUCTURES AS PERCEIVED BY THE FARMERS Lastly, the farmers were asked to give a number from 1 (not important) to 5 (very important) to different literature-based effects of SB, SFT and CT. The presented graphs show the mean level importance of each biophysical effect per land use type. On CL, the effects on soil loss, soil moisture, need for dung/DAP fertiliser, crop yield, flooding and ox-ploughing are scaled while on RL soil loss, soil moisture, grass density, herb diversity, insect diversity and wood production are rated. Respondents had – for example- to indicate if SB had ‘no important’ – ‘a quite important’ – ‘a very important effect’ on the ‘reduction of soil loss’ in CL. Ultimately, the same concept of scaling is used to value the effect of CDs and gabions in gullies. Questioned is their ability to stop gully growth, reduce reservoir sedimentation, limit water inflow, and increase the biodiversity and wood production. Only respondents who reported to have the given structure on/near their fields, were asked to fill in the Likert-scales for that structure. Consequently, the number of answers from household heads (HH) differs noteworthy between the different SWC structures. This is indicated in the legend of each graph. Scaling the importance which farmers assign to different biophysical benefits and drawbacks of the stone bunds, stone-faced trenches, trenches and check dams appears to be a challenging job. Not only is it tough for (illiterate) farmers to allot a certain value to a certain effect, it is also challenging to compare the different answers of the respondents because the interpretation of ‘not important’ of ‘very important’ can differ from farmer to farmer. Nevertheless, since the results seems quite reasonable and following literature, it can be assumed that the mean of the given answers represents a fair value for the different effects. Figure 50 compares the effects of SB and SFT on CL. One of the significant results here is that SFT have a quite - to a very important effect on the crop yield but ‘no effect’ to an effect of little importance on the use of dung or fertiliser. SFT have the largest effect on crop yields and SB are most effective in reducing soil loss according to the interrogated the farmers. For stone bunds on cropland, reducing soil erosion has a mean value of respectively 4.13/5 soil moisture increase gets 3.99/5 and crop yield increase is valued 3.97/5. This order is the same as what Nyssen et al. (2007) conclude based on their farmer survey in the area. Stone-faced trenches have an effect of 4.69/5 for flood protection, 4.62/5 for improving the productivity, 4.54/5 for keeping the soil more wet and 4.38/5 for erosion protection and thus scores overall higher on cropland.

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Figure 51 shows the differences in importance for the SWC measures used on RL. SFT’ most important characteristic is reducing soil loss; it is also indicated as the best structure to achieve that. CT are more effective in increasing grass density and are overall the highest scorer. Farmers did indicate that SB are damaged by cattle very easily, which makes it an undesirable on grazing area. CT are filled fast but still perceived as having the best benefits on RL if livestock is avoided. For rangeland, the stone bunds again are scaled having less large effects compared with stone-faced trenches: soil loss is protected for 3.63/5 when using stone bunds, for 4.61/5 using stone-faced trenches. The grass density increases with 4.03/5 opposing 4.18/5 for stone bunds with trenches. As last, the effect of soil moisture increase is valued 3.63/5 for SB while this is 4/5 for the more advanced technique. Less soil is flowing away 5 4 It is easier to oxplough the fields

3 2

The soil moisture is improved

1

Stone Bunds (HH=98)

0

Stone-Faced Trenches (HH=13) There is less dung needed

There is less flooding of crops

There is a crop yield increase FIGURE 50: MEAN VALUE OF IMPORTANCE FOR BENEFITS OF SWC STRUCTURES ON CL. 0: NOT IMPORTANT  5: EXTREMELY IMPORTANT.

Less soil is flowing away 5 There is a (fire)wood increase

4 3 2

The soil moisture is improved

1

Stone faced Trenches (HH=33)

0

Stone Bunds (HH=40) Trenches (HH=7) The grass density is higher

There are more bees/insects

There are more herbs FIGURE 51: MEAN VALUE OF IMPORTANCE FOR BENEFITS OF SWC STRUCTURES ON RL. 0: NOT IMPORTANT  5: EXTREMELY IMPORTANT

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The same Likert-scaling method is used to survey the drawbacks of SWC, not differentiating between land use types (figure 53). Both SB and SFT are surveyed; there were not enough respondents able to answer about CT since they are less used and less known. Questions regarding the effect of SWC on rodents and pests, area loss, labour costs, ploughing difficulties, alien weeds and water logging were posed to the respondents. The installation and maintenance costs (in labour time) is very often scaled as most important disadvantage. This is visible both for SB and for SFT. Esser (2002) already made note about the lack of the necessary labour as a serious limiting factor in the adoption of SWC. Esser determined the harbouring of rodents as a key disadvantage, followed by enhancing water logging and making land preparation difficult. In this master thesis research regarding Mayleba, only the water logging risk seems to play an essential role whereas the negative effect on ox-ploughing or rodents are less important and change significant between respondents. More rodents (mole, rats, other pests) on the fields

There is too much water logging

There is a loss of cultivated area because of the structures

Stone Bunds (HH=98)

There is more alien weed on the fields

It costs a lot of labour to install and to maintain

Stone faced trenches (HH=23)

Ox-ploughing is more difficult with the structures

FIGURE 52: DRAWBACKS OF SWC STRUCTURES

Overall, there is a better performance of stone-faced trenches compared to stone bunds and this is confirmed in literature (Taye 2014). Conversely, when comparing the negative consequences of both SWC structures, stone bunds are better off, scoring less negative on all but one drawbacks of SWC. Only the presence of alien weeds seems to be a larger nuisance on this structure. Farmers explained that stone bunds serve as a collection barrier of seeds that are blown and distributed by the wind. Between the stones, the seeds grow to become weeds and this shelter and food attracts rodents.

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Questions about ‘negative effects’ were often regaled with astonishment and arguments were given to contradict the statements. Area loss is compensated by the gain in yield thus forms no disadvantage (mentioned by 4). The possible increase of rodents caused by SB is downplayed by the fact that “rats are treatable”. However, more rats on the fields is feared, since they eat the crops and also induce gully erosion by making pipes (mentioned by 3). Farmers recognize the problem of water logging, but this only seems to be a problem on clay grounds (mentioned by 9). An extra negative consequence perceived by the farmers, is the storage of ‘foreign soil’ on their fields, sedimentation of soil that is eroded in the plots above. This soil can “contain diseases or seeds from other areas” and is not wanted. Moreover alien weed can increase due to SB or SFT because seeds dispersed by the wind are trapped in the bunds. Opinions about the ploughing convenience are variable. Some farmers mention the fact that the ox-plough needs to be removed from the cattle to come across the obstacles and that cows are scared of CT, so they turn more slowly. Both issues indicate that SWC can make ploughing more difficult. However, other farmers acknowledge that you can make a zigzag-pattern of the structures in order to avoid this problem. Moreover, ploughing is made easier because there are less large stones on the field and the soil is more ‘soft’ (moist). It is known that the energy needed to construct SB depends on the presence of stones. A farmer explains that – if there is a lack of stones on the field- “it is bad to remove stones from the field to build a stone bund, because the chance on flooding increases”. Then stones must come from somewhere else and this requires extra labour. This is indeed a negative consequence of SB discussed by Esser (2002).

FIGURE 53: ZIGZAG PATTERN IN STONE BUNDS TO MAKE PLOUGHING MORE EASY. MAYLEBA, 08/2015

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Logically, check dams have – according to the respondents - as most important effect the reduction of reservoir sedimentation (figure 52). Besides, stopping gully growth is of central importance. A (diversity) increase in fauna and flora has an average of 3.37/5, while the negative consequence of a reduced water inflow in reservoirs gains 3.56/5, indicating that this is an important drawback. The difference in effectivity between loose stone and gabion CDs is perceived to be small. However almost all farmers also mention that gabions are stronger and thus more sustainable. Nevertheless, even gabions cannot always stand the strong runoff caused by erosive rains, as often seen on the field. The gully stops growing 5 4 There is a (fire)wood increase

3 2

Less soil is filling reservoirs

1

Loose stones (HH=59)

0

Gabion (HH=9) Less water in reservoirs

There are more bees/insects

There are more herbs FIGURE 54: MEAN VALUE OF IMPORTANCE FOR DRAWBACKS AND BENEFITS OF SWC STRUCTURES IN GULLIES. 0: NOT IMPORTANT  5: EXTREMELY IMPORTANT EFFECT.

FIGURE 55: RARE CASE OF A FARMER WHO CONSTRUCTED LOOSE STONE CHECK DAMS IN A DEVELOPING GULLY BY OWN INITIATIVE AND OWN LABOUR FORCE. MAYLEBA, 08/2015.

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R ANKING OF EFFECTS OF SWC ACCORDING TO THE PERCEPTION OF FARMERS Finally, the respondents were asked to rank positive and negative effects of SWC measures, according to their indispensability for any SWC structure. With other words; “What is important to consider if you want to install a new SWC structure?”. For SWC structure on CL (figure 56), a large efficiency in reducing soil loss was the most necessary effect, with 50% of the respondents placing it on number one. Soil moisture is placed as second most important but is closely followed by a better future soil fertility. Apparently, the amount of labour days per year for a SWC does not matter that much. On RL (figure 57), soil loss again occupies the highest importance. Ranked on number two is the herb diversity, sequenced by the insect diversity. It is thus clear that the land use type has an incontrovertible influence on the effects that SWC structures are desired to have. The results of these rankings will be used to check with the DCE analysis, where implicitly the same is asked. Expert-based criteria analysis by Teshome et al (2014) learns that on steep slopes, erosion control indeed is the most important. Enhancing fertility and water retention are weighted as second and third. Crop yield and grass production are ranked after this, then labour, ploughing and pest conclude the list. The gentler the slope, the more importance is given to the crop

Percentage of respondents

yield. This is quite in line with the opinions of the farmers in this master research. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Yearly labour days per simbdi Improved plowing convenience Better future fertility of the soil Decreased risk on crop damage Soil moisture increase Soil loss reduction Rank 1

Rank 2

Rank 3

Rank 4

Rank 5

Rank 6

Percentage of respondents

FIGURE 56: THE MAIN CHARACTERISTICS OF SWC STRUCTURES ON CL RANKED ACCORDING TO THE PERCEIVED IMPORTANCE. (HH=105)

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Yearly labour days per simbdi Better future soil fertility Increase in insect diversity Improved wood production Increase in herb diversity Soil loss reduction Rank 1

Rank 2

Rank 3

Rank 4

Rank 5

Rank 6

FIGURE 57: THE MAIN CHARACTERISTICS OF SWC STRUCTURES ON RL RANKED ACCORDING TO THE PERCEIVED IMPORTANCE. (HH=105)

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3.3 VALUE OF SWC EFFECTS VIA DISCRETE CHOICE EXPERIMENTS 3.3.1 DCE ABOUT ONSITE (DIS) BENEFITS OF SWC MEASURES - FARMERS E CONOMETRIC MODELS TO ANALYSE THE PREFERENCES OF FARMERS First, a Conditional Logit model is tested; both for the choice experiments on RL and CL. Besides, interactions are tested between socioeconomic characteristics and the preferences for the labour variable in a random parameter Logit model. Results both for CL and for RL have a better pseudo log likelihood indicating that this model better fits the data. It was also tried to use a Latent Class Logistic model and to check if socioeconomic characteristics have a certain influence on the preferences and choices of the respondents. The specialised NLogit programme could not find a solution for this problem. Different amounts of classes as well as different dependent variables are tested, but no significant Latent Class model could be constructed. This means that the inquired characteristics are not able to distinguish different groups or do not play a significant role in farmers opinion about different SWC scenarios. TABLE 11: RANDOM PARAMETER LOGISTIC ECONOMETRIC MODEL FOR SWC BENEFITS ON CL (HH=105). RESULT USING NLOGIT.

CONDITIONAL MODEL MIXED MODEL Pseudo Log Likelihood = -419.0 = -383.2 Coefficient Error Coefficient Error Mean value of non-random fixed parameters Amount of labour days (/tsimidi/yr) -0.022* 0.013 -0.029* 0.016 Alternative-specific constant -100.220 0.326D+21 -50.348 0.415D+10 Mean value of normally distributed parameters Soil loss reduction (per %) -0.009*** 0.003 -0.011** 0.005 Soil moisture increase (per %) 0.003 0.002 0.004 0.003 Reduction of pest animals (per %) 0.015 0.012 0.019 0.013 Future fertility of the soil (per %) 0.271*** 0.054 0.355*** 0.077 Ploughing becomes more difficult -0.392*** 0.123 -0.486*** 0.149 Ploughing becomes more easy 0.117 0.133 0.165 0.158 Standard deviation of normally distributed parameters Soil loss reduction (per %) 0.025*** 0.007 Soil moisture increase (per %) 0.014** 0.006 Reduction of pest animals (per %) 0.001 0.021 Future crop yield (per %) 0.261** 0.142 Ploughing becomes more difficult 0.350 0.307 Ploughing becomes more easy 0.064 0.561 Variables interaction with the amount of labour days Translator 2 -0.053** 0.021 Age of the respondent -0.001 0.001 Yearly crop productivity (ETB) 0.140D-05 0.153D-05 Livelihood – poverty - Factor -0.004 0.005 Household size (nr. inhabitants) -0.008* 0.004 Perception severity erosion 0.004 0.013 Perception of effectivity of SWC -0.026 0.016 * significance level on 10%; ** significance level on 5%; *** significance level on 1% or more

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In the next passage, the result for the mixed logit model for CL will be analysed (table 11). First, it can be remarked that the translator had a significant impact on the DCE result, which is an indisputable bias in this research. Further, the mean value for soil loss reduction is small but significant. It is a negative coefficient since it was a negative variable (-20%/-40%/-80%) and it has a reasonably large and significant standard deviation. This variability probably expresses the difference in effectiveness influenced by the age of the structures. The variable ‘soil moisture increase’ has no significant mean value. Also here, the standard deviation is large, pointing to the diverse preferences of the farmers. The largest coefficient is assigned to the ploughing convenience. While easier ploughing is not significant, the fact that ploughing can become more difficult due to SWC structures is very important for the respondents. Next to this, the future fertility of the soil is of large concern of the farmers, the coefficient of this variable is also large (seven times the value for soil moisture increase) and significant to the 1% level. However, also its standard deviation is large, so some people value future fertility very high while others do not. Interaction terms are factors that can influence the WTC, Tillahun et al. 1996 showed that the age and gender can influence the WTC. Amsalu and de Graaf (2008) add i.e. farm size and perception on profitability to this list. While several interactions are tested, only the household size has a small significant negative influence on the preferences for the labour variable. Since both have a negative coefficient, this indicates that larger families have a less negative preference for labour. Now the result for the mixed logit model for RL can be scrutinised (table 12). Almost all variables are found to be significant. The largest coefficient can be found in the dummy variable expressing a strong increase in flora diversity, strong fauna diversity is valued as second most important. Both are significant on a level of 1% or more. More insects and bees to pollinate the crops and more different herbs to feed animals and to use to make tach (alcoholic drink) or bread are frequently mentioned by several farmers as something very essential, and this is accordingly visible in the choice experiments. However, the standard deviation for future biomass production and strong diversity increase of flora are large, evidencing a heterogeneity in preferences. A slight increase diversity of plants and in biomass production have also non-negligible significant positive coefficients. A less large but still significant coefficient is allotted to an increase in fuelwood or construction wood, a variable also having a significant standard deviation. The last variable, relating to reduction in soil loss, has a small coefficient on RL, opposing the large value that is given to this variable on CL. For the preferences of farmers

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concerning working days to protect the RL, it is clear that families with more animals will more likely offer more labour days. Also more poor people will prefer to work more on the RL. This can maybe point to the link between work and food/cash that poor farmers are used to make. They cannot disconnect working on RL and the compensation, although this was not the question in the choice experiments. As last, the negative and significant alternative-specific constant means that the interviewed farmers generally dislike the working on rangeland. TABLE 12: RANDOM PARAMETER LOGISTIC ECONOMETRIC MODEL FOR SWC BENEFITS ON RL (HH=105). RESULT USING NLOGIT.

CONDITIONAL MODEL Pseudo Log Likelihood Coefficient

Standard error Mean value of non-random fixed parameters Amount of labour days -0.015 0.023 (/tsimidi*year) Alternative-specific constant -3.440*** 0.642 Mean value of normally distributed parameters Soil loss reduction (per %) -0.008*** 0.003 Slight increase in diversity of flora 0.437*** 0.155 Strong increase in diversity of flora 0.808*** 0.213 Increase in wood production (per %) 0.081*** 0.026 Future biomass production (per %) 0.350*** 0.123 Slight increase in diversity of fauna 0.264 0.227 Strong increase in diversity of fauna 0.546*** 0.166 Standard deviation of normally distributed parameters Soil loss reduction (per %) Slight increase in diversity of flora Strong increase in diversity of flora Increase in wood production (per %) Future biomass production (per %) Slight increase in diversity of fauna Strong increase in diversity of fauna Variables interaction with the amount of labour days Translator 2 -0.033 0.023 Alternative-specific constant Age of the respondent Amount of animal assets Livelihood – poverty - Factor Household size (no. inhabitants) Perception severity erosion problem Perception of effectivity of SWC * significance level on 10%;

MIXED MODEL -322.82627 Coefficient Standard error -0.045*

0.025

-3.357***

0.666

-0.012*** 0.5601*** 1.030*** 0.113*** 0.454*** 0.357 0.698***

0.004 0.191 0.281 0.038 0.155 0.281 0.220

0.014** 0.015 0.689** 0.181*** 0.372** 0.513 0.258

0.006 0.448 0.319 0.051 0.162 0.329 0.433

0.001 0.020* 0.021** 0.001 -0.011 -0.001

0.001 0.011 0.008 0.003 0.018 0.020

** significance level on 5%; *** significance level on 1% or more

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E CONOMETRIC MODELS TO ANALYSE THE WTC LABOUR OF FARMERS Then the farmers’ willingness to work a certain amount of days for different effects of SWC is analysed as explained in the method section. The result for CL and RL is shown in table 13. For each variable, the estimated distribution of WTC is discussed using the figures 58-65. It is clear that some benefits have a wider range of preferences than others. On CL, the respondents were willing to work one third of a day per percentage less soil that flows away by water/wind erosion. Previous studies indicate that SWC structures are able to reduce soil loss on CL with 40%-60% (Taye, 2014), making the willingness to work around 15 days per tsimidi per year. The distribution of preferences is wide (fig 58), but the effect is significant on the 95% level. Research conducted by Taye (2014) showed that the runoff coefficient in the study area can be lowered till 80%, increasing the soil moisture very strong. However, the model did not find significance in this result. The mean willingness to work for a percentage soil moisture increase on CL is insignificant. Possibly the original state of the soil and the previous rain season affects the willingness to work for it. The willingness to work for soil fertility increase is very high (99% significance) (fig 59). However, no real significant increase caused by SB, SFT or CT could be found in the study area (Taye 2014). This means that the expectations and requirements of the farmers do not align met the present SWC structures. Besides, it is obvious that farmers do not like the fact that their fields might be harder to plough when SWC structures are present. On average, their willingness to work for a certain structure lowers with 17 days if ‘more difficult ploughing’ is a consequence (fig 60). This is a large effect that is significant on the 99% level. The effect of easier ploughing caused by SWC structures is not significant in predicting the choices of farmers. However, the willingness to work for it is significant positive on the 90% level, which is an inexplicable NLogit outcome. Also on RL, soil loss reduction is significantly worth working for (fig 61). On average, farmers are willing to spend one fifth of a day extra if soil loss reduces with 1%. This is less than on CL. For a strong increase in flora; herbs, shrubs, grasses, on RL, farmers want to spend on average around twelve days working on SWC structures (fig 62). This result is significant on the 99%. While a strong improvement of the amount of flora diversity is significant in predicting the choice of farmers, a slight improvement is not. For an extra wood production of 1% caused by the protection of RL by SWC structures, farmers are willing to work more than two and a half days extra per year (fig 63). This effect significantly influences the choices of the respondents.

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The willingness to work for a percent extra grass biomass on the RLs is quite high, around 2.5 work days per tsimidi per year (fig 64). This WTC labour is significant on the 99% level. A strong increase in diversity and amount of fauna like bees and other insects significantly affects the choices of farmers (fig 35). They are willing to work more than 7.5 days per year for this positive side-effect of SWC structures. The effect of a slight increase in fauna is not significant.

TABLE 13: MIXED LOGISTIC ‘WILLINGNESS TO WORK’ MODEL FOR SWC BENEFITS ON CL (HH=105). RESULT USING NLOGIT.

Cropland Pseudo Log likelihood choice

Rangeland

-412.71103 Coefficient

-402.3584 Std. Err.

choice

Coefficient

Std. Err.

Mean value Reduce soil * -0.227 0.128 loss 0.126 0.114 Slight herbs 6.275 2.843 increase 0.460 0.296 Strong herbs ** 11.711 5.098 increase ** -13.342 4.705 Wood * 2.521 1.486 production * 5.346 2.860 Slight insects 4.597 2.393 increase * 11.210 6.796 Strong insect ** 8.030 4.048 increase Future *** 10.366 3.743 biomass Standard deviation Reduce soil ** 0.7159 0.293 Reduce soil 0.247 0.211 loss loss Increase soil ** 0.390 0.160 Slight herbs 0.165 0.350 moisture increase Pest animal 0.317 0.471 Strong herbs 6.365 5.882 presence increase Ploughing 13.734 8.568 Wood 3.517 2.415 difficult production Ploughing *** 5.081 1.637 Slight insects 3.708 5.375 increase more easy Future crop * 8.194 4.596 Strong insect 2.619 5.038 yields increase Future 6.935 6.027 biomass * significance level on 10%; ** significance level on 5%; *** significance level on 1% or more Reduce soil loss Increase soil moisture Pest animal presence Ploughing difficult Ploughing more easy Future crop yields

**

-0.378

0.162

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FIGURE 58: WTW FOR SOIL LOSS REDUCTION ON CL

FIGURE 59: WTW FOR SOIL FERTILITY ON CL

FIGURE 60: WTW FOR SOIL PLOUGHING ON CL

FIGURE 61: WTW FOR SOIL LOSS REDUCTION ON RL

FIGURE 62: WTW FOR FLORA INCREASE ON RL

FIGURE 63: WTW FOR WOOD PRODUCTION ON RL

FIGURE 64: WTW FOR BIOMASS GROWTH ON RL

FIGURE 65: WTW FOR FAUNA INCREASE ON RL

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3.3.2 DCE ABOUT OFFSITE (DIS)BENEFITS OF SWC - SOCIETY C HARACTERISTICS OF STUDENT RESPONDENTS Likewise, a choice experiment was held with students of the Mekelle University. 96 students were reached, from which 50 summer school students (already working for government) and 46 first to third grade normal university students. The mean age is 26 years old. Whereas they are originated from all over Tigray (fig. 52), all students took classes in the same department of ‘Land resource management and Environmental protection’: 43 respondents studied Natural Forestry, 24 followed an education in Soil and Water Conservation, 13 Earth observation students were interviewed and 10 respondents took courses regarding land resource management. They all are thus aware of the threat of degradation of the Ethiopian Highlands as well as cognisant about the possibilities of SWC.

N FIGURE 66: SATELLITE IMAGE OF TIGRAY. YELLOW PINS INDICATE THE ORIGIN OF THE RESPONDING STUDENTS. RED CIRCLE DISPLAYS LOCATION OF STUDY AREA. YELLOW LINE SHOWS BOUNDARY OF ETHIOPIA, GREY LINE THE BOUNDARY OF TIGRAY. GOOGLE EARTH 2013.

very poor

6% 7% 18%

44%

25%

poor average rather well rich

Concerning socioeconomic background, 68 respondents originate for a farming family, only 33 has at least one parent with another source of income from civil services, teaching, industry or the tertiary sector. 7% of the students identifies his family as being very poor, 6% claims to be rich (fig 53). FIGURE 67: WEALTH STATUS OF THE STUDENT RESPONDENTS

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E CONOMETRIC MODELS TO ANALYSE THE PREFERENCES OF THE SOCIETY The Conditional logit model did not provide any clarification for the choice experiment problem. The random parameter model – using NLogit – does, but cannot find significance in the price variable (table 14). This should suggest that the taxes do not play a role in choosing SWC scenario’s for the respondents. However, this probably points to incorrectly chosen tax levels. On the other hand, it can be checked that the second power of the tax variable is negative, indicating that there is an invariant preference for higher tax levels but this resulted in an insignificant output as well. Since some student respondents chose the ‘status quo’ option in some cases, the proposed alternatives were not always attractive enough. The results of the random parameter model are visualised below (table 10). As the tax price variable is not significant in any model, no willingness to pay can be found. Fruitlessly removing a part of the dataset by discretising between the education and age does not help finding an useful price parameter. The last option is checking if there is any interaction between the socioeconomic variables and the tax variable. This appears to be the case: the family wealth status and the fact that a student lives/lived in a farming family both influences the preferences to pay tax in a negative way. Caution should be taken here, since the two variables are interlinked. The perceived wealth status and the amount of parents with a farm job are significantly related (slope -0.4, significance level 95%), thus have in fact the same influencing interaction with the tax variable. What can be said about the results of the off-site choice experiments, is that the stabilisation or reduction of the soil degradation are very important factors for the society. With a significance at 1% and a relative large influence on the model, it is clear that the students favour SWC scenarios which take care of the soil loss and soil water drainage. Besides, SWC plans that increase the biodiversity are chosen above others and it is significantly preferred that the decrease in rural jobs is halted. However, most notable is the very significant and large negative alternative-specific constant, so there is a general aversion towards SWC investments. As this can be expected, the result is still very prominent and model-determining. As last, the standard deviations for almost all characterising effects of SWC scenarios are significantly high, indicating that there are large differences in opinions. If the students are divided in groups per education (table 15), the Natural land management students prove to have a stronger preference for stopping soil degradation and making water not purify-able. Less weight is given to the amount of rural jobs, compared with the RP model; another division in two classes is tried, but the class membership is difficult to interpret and almost the same results as in the previous model are seen.

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TABLE 14: RANDOM PARAMETERS (MIXED) LOGIT MODEL FOR OFF-SITE EFFECT OF SWC MANAGEMENT. RESULT USING NLOGIT.

Log likelihood function -1665.26921 choice Coefficient Std. Err. Mean Stabilizing soil degradation 0.21508*** 0.08055 Reducing soil degradation 0.25863*** 0.09887 Irrigable water 0.07761 0.08662 Purify-able water -0.14195** 0.09203 Same amount of water -0.01843 0.08474 More water available 0.12016 0.09220 Stabilizing biodiversity 0.06827 0.10395 Increasing biodiversity 0.16905** 0.08553 Strong decrease of jobs 0.05115 0.09152 Same amount of rural jobs 0.22336*** 0.09205 Amount tax increase -0.05607 0.05322 ASC -1.62923*** 0.15384 Standard deviation Stabilizing soil degradation 0.09077 0.14834 Reducing soil degradation 0.46160*** 0.10280 Irrigable water 0.38580*** 0.10927 Purify-able water 0.39462*** 0.10575 Same amount of water 0.30831** 0.12554 More water available 0.42395*** 0.10040 Stabilizing biodiversity 0.58635*** 0.10187 Increasing biodiversity 0.38543*** 0.09900 Strong decrease of jobs 0.35343*** 0.10823 Same amount of rural jobs 0.35272*** 0.11427 Interaction terms Tax * natural forestry 0.03974 0.05813 Tax * soil-water manag. 0.06232 0.06221 Tax * resource manag. 0.08419 0.06429 Tax * wealth status -0.03715** 0.01732 Tax * farming family -0.04077** 0.01941 Tax * age of respondent 0.00486 0.00391 * significance level on 90%; ** significance level on 95%; *** significance level on 99% or more

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TABLE 15: LATENT CLASS LOGIT MODEL FOR OFF-SITE EFFECT OF SWC MANAGEMENT

Based on education type

CLASS 1

Log likelihood function

Coefficient

Standard Error

Coefficient

Standard Error

3.126*** 0.228*** 0.284*** 0.050 -0.183** -0.071 0.148*T 0.052 0.111 0.035 0.217*** 0.001

0.249 0.081 0.076 0.087 0.085 0.073 0.093 0.085 0.074 0.073 0.081 0.023

-0.853 0.187 -0.251 0.178 0.047 0.055 0.073 -0.094 0.129 0.774 0.738 -0.022

0.906 0.746 0.998 1.326 0.921 1.797 0.666 1.312 1.079 1.319 1.620 0.204

8.369 -7.379 26.764 -5.235 -5.782

0.111D+08 0.111D+08 0.821D+15 0.111D+08 0.111D+08

0 0 0 0 0

fixed fixed fixed fixed fixed

-1559.31490

Mean coefficients of the utility function Alternative-specific constant Stabilizing soil degradation Reducing soil degradation Irrigable water Purify-able water Same amount of water More water available Stabilizing biodiversity Increasing biodiversity Strong decrease of jobs Same amount of rural jobs Amount tax increase Class membership coefficients Constant Natural Forestry education Land resource management Soil and water conservation Earth observation studies Based on socioeconomic factors Log likelihood function

-1563.58403

Mean coefficients of the utility function Alternative-specific constant Stabilizing soil degradation Reducing soil degradation Irrigable water Purify-able water Same amount of water More water available Stabilizing biodiversity Increasing biodiversity Strong decrease of jobs Same amount of rural jobs Amount tax increase Class membership coefficients Constant Age of the respondent Wealth status of the family Farming families

CLASS 2

CLASS 1

CLASS 2

Coefficient

Standard Error

Coefficient

Standard Error

3.392*** 0.223*** 0.287*** 0.075 -0.182** -0.057 0.163* 0.042 0.102 0.047 0.222*** 0.002

0.283 0.086 0.080 0.085 0.088 0.075 0.095 0.094 0.076 0.073 0.080 0.023

-0.509 0.170 -0.201 -0.004 -0.035 -0.006 0.019 -0.042 0.190 0.592 0.658 -0.027

0.559 0.880 0.915 0.774 0.879 0.533 0.378 0.562 1.019 0.979 1.253 0.129

1.767 -0.001 -0.013 -0.094

2.228 0.021 0.659 0.513

0 0 0 0

fixed fixed fixed fixed

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3.3.3

COMMENTS ON THE VALUATION OF THE ON- AND OFFSITE BENEFITS OF SWC IN TIGRAY

Conducting choice experiments in only two months is a challenging task, since pre-research nor testing can be thoroughly done in this short time period. There are numerous possible reasons for the low significance of the econometric models that are the result of the DCE in this master thesis research. It is imaginable that the respondents did not understand all levels or attributes, making them unable to compare scenarios in a deliberate way. This can be due to insufficient explanations or due to weariness of farmers or students. The sample is quite small, and the difference in levels is too low. At last, it is also possible that some attributes are insignificant since they are not of importance for the respondents in choosing a scenario. Since the choice design contained a lot of percentages and many farmer respondents were illiterate, it is not possible to identify to what extent they really grasped the differences between the scenarios or percentages. This can lead to irrational choices. This possible erroneous answers are not discernible, unless people were explaining why they choose for a certain scenario. No farmer chose the ‘no measures’ option, indicating that they all were willing to work to some extend for SWC. Since the government obliges the people to work at least 40 days, it is possible that farmers could not imagine a scenario where they do not have to work. It is also conceivable that this ‘no measures’ option was not explained well by the translator. The lion’s share of the farmers said they had learned something by thinking about all scenarios. While this is a positive sign in a development-framework, from an analysis-point-of-view this means a lot of respondents were not aware of all effects or possibilities of SWC structures, thus influencing their decisions and consequently the result of the choice experiments. What is clear is that the total economic value of a SWC management scenario goes beyond what is possible to estimate using market and non-market analysis (Kjær 2005). We cannot assure that biases in understanding are excluded. Yet, by asking to rank the different effects of SWC and comparing this result to the values of those effects, a validation can be done. The average rank on cropland look like this: ‘soil loss reduction’ (2.1); ‘soil moisture increase’ (2.2); ‘increased future fertility’ (2.4); ‘decreased risk on flooding’ (4.2); ‘ploughing convenience’ (4.5) and ‘yearly labour days’ (5.7). On rangeland, the average rank is as follows: ‘increase in herb diversity’ (1.9); ‘soil loss reduction’ (2.6); ‘increase in insect diversity’ (2.8); ‘increased future fertility’ (3.8); ‘increased wood production’ (4.3) and ‘yearly labour days’ (5.4). Most striking here is the very low rank of the labour effort. While in discussions this came out as an important characteristic in the choice for constructing SWC structures, in the ranking it appears ignorable. Also Lanckriet et al (2012) concluded that the labour costs are mentioned less than agronomic drawbacks of certain SWC techniques.

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3.4 DISCUSSING THE PRESENT-DAY INTEGRATED WATERSHED MANAGEMENT IN THE MAYLEBA CATCHMENT, TIGRAY 3.4.1 SUMMARIZING THE ATTITUDE OF FARMERS TOWARDS GENERAL SWC MANAGEMENT The opinion of the farmers in Mayleba differs concerning the long term soil production. Rainfall is in any case the most crucial influencing factor. While crop yield increases are often explained by human management actions like adding more/better fertiliser or terracing the soil, crop yield decline is mostly attributed to natural causes like erosion, hail, diseases and pest animals. Rangeland endured overexploitation and also water erosion poses a problem, but the appearance of exclosures has given farmers the sign that increasing the biomass presence is possible. This is evidenced by the high percentage of farmers that is aware of the biomass increase over the last 5 years. Three quarter of the farmers opine that the fertility of the rangeland and cropland are (strongly) improved since the implementation of soil and water conservation measures, which signifies that farmers are aware that better agricultural practices and watershed management are needed and can help to improve the general productivity. There has been and are still a lot of extension services in the area (Tillahun, pers.com.), and knowledge about the threat of erosion is higher than in other areas in Tigray (Taye 2006). Respondents mentioning the lack of SWC structures as a cause of erosion evidences the imbedded use of SWC. What is clear, is that most farmers in the Mayleba catchment are open for information and innovation. Proof for this is the fact that farmers never denied a ‘scientific’ interview, that most were interested and that a lot of the farmers asked further questions about the management of their fields. Another evidence is the frequently-heard answer “We only got to know this technique since this year, so we will apply it next year”. Working on the own cropland is seen as profitable by many, supported by quotes as “There is no need for payment in cropland, because the structures are there only for yourself, you are the only one that gets benefit from This master thesis research can conclude that younger people who are more aware of the erosion problem and have a lot of cattle will be more willing to work for free on SWC management. Younger farmers have more energy, if they have more cattle, the ploughing will be done more easily and thus there is labour free for constructing SWC structures. People having larger land are also more likely to (re)construct SWC structures if they are filled, but maybe this can be explained by the fact that they have more structures and thus restoration is more needed. Often this takes place in the form of moving the stone bund a bit or re-digging the trenches.

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CROPLAND

It is demonstrated by the ranking exercise that the efficiency of a SWC structure on soil loss reduction and increasing soil moisture are essential when choosing a certain scenario for cropland. These effects indeed are the highest scorers on the Likert-scales, showing that the stone bunds and stone-faced trenches indeed fulfil the needs of the farmers. On the other hand, future fertility, followed by ‘more difficult ploughing’ and soil loss, seem the most preferred effects in the analysis of the DCE on cropland. While ploughing convenience did not appear to be important in the ranking, there is a very strong aversion against more difficult ploughing according to the DCE. As discussed, there are techniques to overcome this, which can explain the low rank. A clarification for the insignificance of soil moisture in the DCE can maybe be found in variable states and types of the soil. While labour has a significant influence on the choices of farmers for certain scenarios, this influence is dependent on the number of household members and is small, as expected from the ranking. R ANGELAND On rangeland, the increase in presence of herbs and insects and increasing biodiversity indeed jump out as most important attributes to influence farmer’s choice for SWC scenarios. Soil loss reduction completes the top 3. While for ‘erosion protection’ and ‘increase of grass density’, the current structures satisfy the requests of the farmers, this is not the case for enhancing the abundance and diversity in fauna and flora. Also the WTC labour for biodiversity increase is high, but soil loss reduction appears to be not very important in the DCE. An option to interpret this non-correspondence, is the ‘not in my backyard’ phenomenon. It is for people important to stop soil erosion on rangeland, but if they have to weigh it again own, gratuitous, labour time, the opportunity cost is too high or they prefer other things that –maybe- appear to be more beneficial for themselves. This is evidenced by the fact that farmers with more cattle are willing to work more. As expected, the labour days are significant but not large. A high alternativespecific constant indicates also that some important attributes may be missing in the analysis. O FFSITE Considering the off-site effects and the preferences of the communities, there indeed is a certain interest in protecting the environment for degradation. Besides, biodiversity and rural employment are of concern for the interviewed students. While there was a reasonable amount of significant attributes in the DCE about off-site effects, the tax pay variable was not. It shows that the society thinks that SWC are important, but not worth paying for. Again, there is a problem induced by the positive externalities of SWC. One can argue that a productivity decline the fields is harmful for the whole society, and thus current subsidies and incentives by the PSNP are justifiable and may even increase to create resilience and food security in Tigray.

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3.4.2 EMPIRICAL SCENARIO ANALYSIS OF REALISTIC SWC MANAGEMENT IN MAYLEBA Some of the hypothetic but realistic SWC scenarios are examined in detail in a small costbenefit analysis, where all perceived benefits will be weighed against the labour and tax costs that they imply. The analysis is based on the PhD research of Taye (2014) and participatory watershed development guideline. The insignificant WTP for offsite effects does not allow to balance costs and benefits quantitatively. S CENARIO 1 – ON PLOT SCALE , ONLY THE FIRST YEAR On a wheat field of 1 tsimidi (50x50m), one starts to convert stone bunds into stone-faced trenches. Since it is a very sloping land (16%) and the skeletic Cambisol provides enough stones, the stone bunds are present every 7m, covering a total length of 300m. Soil moisture is a problem on the field, so it is hoped that trenches provide solution for this. Only the trenches need to be dug out; given their length of 3m and spacing of half a meter, around 86 trenches are necessary, equalling 29 PD of labour. The runoff coefficient decreases from 0.08 for SB to 0.04 for SFT, improving soil moisture with 50%. At the same time, soil loss reduces from 4.3t/ha to 1.1t/ha, thus reducing soil loss with 75% the first year.The wheat crop yield on this field increases slightly since it was moisturerestricted, with 1% if accounted for the space the structures take. The construction decreases the amount of rodents on the field, since the holes between the stones are now filled with sediment. However, turning the oxen during ploughing becomes more difficult. Given the only significant willingness to work for reducing soil loss (0.4PD per percent), ploughing convenience (-13 PD) and future crop yield (11 PD per percent), the average farmer in Mayleba wants to work (75%*0.4+1%*11-13=) 28 days the first year, which is almost same as the maintenance of SB would be (50% of 4m/PD * 250m = 30PD) which equals the needed 29PD. S CENARIO 2 – ON PLOT SCALE , WITH TIME FRAME OF 3 YEARS A flat (5%) teff cropland of 2 tsimidi (two fields of 25 m wide and 100 m long) situated on Vertisol becomes protected by constructing stone bunds every 20 m. One wants to protect the land from flooding but does not want to take in too much space, since it is very fertile soil. Large stones (10-40 cm) are collected from nearby outcrops and a wall of ca. 70 cm high and ca. 80 cm wide is built. Small rock fragments and weeds are used as backfill material. In total, the required stone bund length is 5*25=125 m. This requires a bit more than 30 PD for the family to construct but in exchange, followed by 15 PD the second and third year, since maintenance is estimated to be half the amount of work of constructing SWC structures.

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The first year, runoff coefficient reduces with 25%. The second year, the difference is even 30% while the third year, effectivity of the SB reduces to only a 20% lower runoff coefficient compared to no structures. Soil loss is only 7 t/ha compared with 12 t/ha on non-conserved plots (-45%) in the year after installing the structure. This increases to 55% over the years. Again, ploughing could become more difficult, but the structures are easy to remove and reconstruct if cattle has to pass. Besides the amount of rodents on the field increases. The new dry shelter place on clayey soil attracts those animals. Without accounting for the space, the crop yield strongly improved. However, the SB take 4% of the space and rodents destroy some crops, and this results in a nett gain of 1% in crop yield the first year, 2% the second year and it because of a wet year, it has no effect the third year. The first year, the willingness to contribute labour equals (45%*0.37+1%*11 * 2si) 28 days per tsimidi, the next year this is (50%*0.37+2%*11) 40 days, in the last year it is only 20 days. However, over the whole period, these 88 days are far more than the 60 days predicted by the Participatory watershed guideline. S CENARIO 3 – TIMEFRAME OF 5 YEARS , SPATIAL SCALE OF 1 HECTARE A medium sloping (12%) rangeland is taken under management, closing it from cattle and protecting it – according to guidelines - to trenches every 12 m. The land is 4 tsimidi (a strip of 125m*800 m) and situated on calcaric Cambisol. Extra vegetation is planted on the earth that is removed by from the trench, in order to improve the sediment retention capacity of the structure. The to protect distance is around 8333m, demanding 2400 trenches (800PD) and 240PD for revegetation. If the management of this exclosures need to be done for free, it requires 26 people each working 40 days in this area the first year. Afterwards, this is half this amount each year. Because of the installed trenches, soil loss reduces with 10%, 30% and 50% over

these

years.

A

guesstimate

about

the

willingness

to

work

for

this

is

10%+30%+50%+50%+50%*0.227 = 43PD. Five years closed areas have significant higher levels of soil nutrients, improving the soil fertility and restoring vegetation. Also biodiversity increases (Mengistu et al. 2005), a strong increase in herbs results in 12 PD extra. Woody species of 0.5m high, grasses with tall weeds and short bushes are present after 5 years (Mekuria et al. 2009), a biomass of around 166kg/ha and 433 trees per hectare are observable. However, the wood regeneration does not allow sustainable harvest yet. Grass biomass goes from 0.5 in year 0 to 1.1, 1.5, 1.9, 2.4 and 2.8 t/ha over 5 years (Balana et al. 2012). The future biomass has a large WTW, and increases with almost 500%. This equals an impossible amount of 600days worth of work if the econometric model in this master thesis research is correct. However, such a large willingness to work for free for exclosures is doubtful if the reactions of the farmers are taken into account.

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S CENARIO 4 – WATERSHED SCALE OVER A LONGER PERIOD The community-based participatory watershed development guide describes a scenario of a community that decides to protect a whole food-insecure sub-watershed with large degraded areas. By summing up the labour requirements needed for moisture conservation systems, gully control, water ponding, spring development and installing exclosures, they total an amount of 90000-11000 PD for the community (Desta, 2005). Within a time span of 3 years for a community with 100 households, this implies at least 20 PDs per household per year. 35 households are supported by the PSNP and receive 3kg wheat per PD, thus yearly 1200kg of food aid is needed (650 euro (World Bank 2015)). Farmers are significantly willing to work for reducing soil loss, increase biomass and biodiversity on rangeland. Besides, the groundwater recharge will make the fields more plough-friendly and there is a willingness to work for easier ploughing. Rodents are a threat but this can be managed in another way. Assuming a soil loss reduction of 50% on watershed scale, a strong increase in insects and herbs because the uplands are closed, a small amount of usable biomass increase (cut and carry system) of 1% and ploughing is more easy, on average farmers indeed willing to work the required 30 days for this. Though, this means one month of work that cannot be used on the fields. The opportunity cost of not to keep up with the structures on their own fields, plus this is all about communal land, which reduces their willingness to contribute own free labour. To finance the food aid with resources coming from the area, the society of Tigray needs to be accosted. The DCE showed that this society values the rehabilitation of degraded land, the recharge of groundwater (providing drinking water) and the biodiversity in general. While in this master thesis research the price variable is not significant, larger and more detailed research will probably find a willingness to contribute taxes for these offsite effects of SWC. The extra taxes could be used to finance the structural poor and pay them for their work on watershed development.

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S CENARIO 5 – WATERSHED SCALE , TIMEFRAME OF ONE SEASON A large gully crossing many fields (length 700m; width at maximum: 6m) is protected by constructing 6 loose stone check dams along its slope. On average, they are 2m high, 0.5m thick and 5m wide, which implies 6*2*5*0.5= 30m³ of loose stones. 10 young people need work together for one month to get this job done. They are part of a CFW program thus need to be compensate for this by support of the PSNP, totalling 10 times 750ETB. Biodiversity increase in gullies is a rarely studied phenomenon but is visible in the fields. The cost of gully erosion (per gully) appears to be 2750ETB (Yitbarek et all), this is calculated by monetizing the estimated lost nutrient mass and the prices of commercial fertilizers; the costs of reservoir sedimentation are not included. The opportunity costs for the society appears to be higher than the benefits. However, it is seen in the choice experiments that students value the stabilisation and reduction of soil degradation, the amount of rural jobs and the increasing biodiversity. Thus with subsidies this management plan would maybe still be feasible.

PICTURE 1: MINIMAL 'STONE BUND' PRESENT ON RANGELAND. PROBABLY DONE DURING THE OBLIGED FREE LABOUR. MAYLEBA, 08/2015. AUTHOR

The scenario analysis above is based on hypostatical scenarios and an estimated CBA but is able to give an idea about the current satisfaction of farmers and the society of the present SWC management around Mayleba. It appears that the willingness to work for the present SWC management plans exists. This is seen in the field as every farmer does have it on the fields and most of them also helped on rangeland. The analysis gives insight in the equity of SWC management: one can weigh the profitability for farmers with the positive effects beneficial for the community and conclude if the investment- and conservation costs must be private or should be seen as social costs.

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3.4.3 EXAMINING THE LIMITING FACTORS IN AGRICULTURAL DEVELOPMENT T HE NEED FOR EXTRA LABOUR RESOURCES Many see it as a labour-intensive job to protect the fields against degradation, and speak about the non-option for fields to exist without SWC structures. They recognise the hard work of protecting all fields and have the belief that there is money needed to pay for extra labour resources. From the interviews and discussions afterwards, labour shortage appears to be a major constraint to the adoption of SWC structures. The same conclusion can be found in the research of (Bewket 2007) in Dilgil, who found that 92% of the responding farmers thinks that the current structures in the catchment requires too much labour, and 81% of the farmers speaks about an overall labour shortage for SWC management. “It is becoming difficult to face the problem from water erosion (coming from the cliff) alone, we need to protect the land in group. Only if we work in organised group the erosion problem can improve.” T HE NEED FOR EXTRA FINANCIAL RESOURCES Despite the fact that most farmers acknowledge the usefulness of SWC structures, only 53% have the opinion that this work has to be done for free (“There is no payment needed, because SWC are beneficial for farmers and the whole community here”). This is far less than the 80% of farmers in (Esser & Haile 2002) or the 98% of farmers in the North-Western Highlands that are convinced about the profitability of SWC structures on cropland. (Teshome et al 2015,). However, their request for extra money to help finance the work on SB, SFT and TR is based on the idea of hiring in extra labour. Some farmers have the opinion that it is logical that the government should financially help SWC building because they should recognize the positive externalities. “SWC structures in rangeland and gullies are not compensated by extra crop yield, so government should fund it” Teshome et al. (2015) indeed points to the long pay-back period of conservation investments. A reduced planting area, high investment costs and only a small yield increase reduces the short-term household budget. Shiferaw and Holden (2001) find a solution for this problem in subsidizing the initial investment costs in order to improve farmer’s incentive to adopt SWC techniques. Another idea could be the link with tax payment and installation of SWC structures. Others voice that supporting SWC management is good for the food security of the farmers (via the food for work programme) and for the whole community, since the crop yields will increase if SWC is applied, strongly stated by one farmer as: “If there is food shortage or lower income, the Safety Net should help providing money or food for working at SWC”.

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T HE NEED FOR EXTRA MOTIVATIONS While only a few people recognise this work on rangeland and gullies as beneficial for themselves, most people need extra motivation for this work. They also prefer to work here in group. This results in quotes as “Working in gullies is hard and takes time, nobody is willing to do that for free. Only if the government or we do it together (in development groups), gully erosion can be stopped” “Nobody helps maintaining the rangeland SWC structures because there is no direct benefit for the farmer and there is no control.”

FIGURE 68: : THE START OF A GULLY IS PROTECTED BY THE FARMERS HIMSELF. HE STATES TO NEED MORE HELP TO PROTECT HIS FIELDS MORE EFFICIENT. 08/2015 MAYLEBA.

From these limitations, one can draw the conclusion that most farmers see it as a private benefit to protect their own land, making them willing to work more for free in cropland areas. Degradation in communal land like rangeland is seen as an environmental concern and the society benefits from the protection. Therefore, many farmers are willing to contribute labour but prefer if it is compensated by food or cash. The work in gullies is seen as a difficult and hard task. Gullies pose a serious threat to their fields, so they are willing to protect it. But with extra resources, this can be done on a larger more sustainable scale.

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CHAPTER 4: CONCLUSIONS

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4.1 SUMMARY OF MAIN RESULTS S OIL AND WATER CONSERVATION IN M AYLEBA Since the 1970s, intensive efforts are done to protect the Highlands of Tigray from soil degradation. Watershed committees provide integrated watershed management (IWM) plans, development groups work together to implement these plans. In Ethiopia, people are required to work 40 days for free on community work. Besides, the Productive Safety Net programme (PSNP) provides food-for-work and cash-for-work for the resource-limited rural population, supported by the view that rural poverty, ecosystem supplies and natural resource protection are issues of global concern. In the Mayleba sub-watershed, gullies are being protected by loose stones check dams and gabions. Since 2012, around 100 000 m³ loose stones check dams and 30 000 m³ gabion check dams are built. Rangeland is actively converted to exclosures, revegetation and reforestation schemes realised and conservation trenches, stone bunds and stone-faced trenches are installed in these areas. On cropland, farmers create stone bunds on the fields together and they can reinforce these structures by converting them to stone-faced trenches on their own. In the last five years, more than 1000 km trenches, 350 km stone bunds and 300 km stone-faced trenches are constructed in the study area. Guidelines provide information about the setup of SWC structures but often farmers adapt them to own local preferences. A N INVENTORY OF THE ON - AND OFF - SITE EFFECTS OF THE CURRENT SWC IN M AYLEBA Institutional, socioeconomic and biophysical factors play a role in the perceived profitability of this SWC management. From this research, it is clear that mainly on-site benefits and drawbacks of SWC structures influence the perception of farmers. Reduced runoff leads to an increase in vegetation, with an improved soil moisture and less flooding but also water logging as consequences. Reduced erosion leads to a decrease in soil loss and nutrient leaching, less crop burial but also sedimentation of ‘foreign soil’ as results. The interviews point out that there appears no explicit trend in crop yield and biomass production. On-site productivity is influenced by the crop type, slope, rainfall depth and initial state of the soil. The construction of SWC structures reduces the productive area and can increase the damage by rodents. However, the structures enhance biomass growth too, which can improve the flora and consequent fauna diversity. Recharge of the groundwater table can induce new springs. The risk on gullies can be lowered and the siltation of water and irrigation reservoirs can be halted. Hence, the assessed soil and water conservation structures have many benefits but limited profitable effects for farmers in the short run.

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I NTERESTS AND PERCEPTIONS OF FARMERS IN M AYLEBA CONCERNING SWC MANAGEMENT Structures on cropland preferably are soil loss reducing, soil moisture increasing and crop yield increasing. Stone bunds are by far the most widely spread structure on this land use in Mayleba. While most farmers think stone-faced trenches are the more effective in soil loss reduction, soil moisture improvement, crop yield increase, flood protection and ploughing convenience, their space-filling and labour-intensive character impedes farmers from installing them. Both structures are perceived to improve the state of the soil (strongly). Farmers with fields on higher elevation sandstones or on limestone found a higher soil improvement by the installation of SWC structures compared with others. Around two thirds of the respondents think the SWC structures on cropland are profitable and worth working for. The random parameter logit model found that a scenario is more likely to be preferred if it is more effective in reducing soil loss and increasing future fertility of the soil while it does not disturb the ploughing convenience. Besides, there is a significant negative influence of the amount of labour days (per tsimidi per year) in choosing for a certain SWC scenario. However, larger families perceive the amount of labour as less negative. Stone bunds, trenches and by stone-faced trenches in rangeland improve the state of the soil moderately in Mayleba. Stone-faced trenches again appears to be the most effective in soil loss reduction. This is followed by the soil moisture improvement, the increase in (fire)wood production, the diversity of fauna and flora and the intensification of grass density. Households with a lot of cattle that have seen an increase in biomass over the years, have a better perception about the effectivity of SWC on rangeland than households who did not. The random parameter logit model found a negative alternative specific constant, indicating that there is a general dislike towards SWC on rangeland. Nevertheless, the effects of SWC on rangeland, namely a large influence on soil loss reduction, an improvement in flora and fauna diversity and an increase the wood production and future biomass, appears to have a significant encouraging effect on the preferences of farmers. Only if structures require more work, they are less likely be chosen. A general lower aversion to the amount of labour can be assigned to farmers with more animals or poorer households. In gullies, both loose stone check dams as gabion check dams are effective, the last one is stronger, more difficult to construct and more expensive. According to the farmer respondents, loose stones are more effective in stopping gully growth, gabions reduce reservoir sedimentation more.

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V ALUE OF THE ALLEGED BENEFITS VIA THE WILLINGNESS TO CONTRIBUTE (WTC) LABOUR The willingness to work for reducing soil loss with 60% is valued the same as increasing future crop yields with a realistic 2%, being +22.5 days per year for each. Hindering the ploughing convenience reduces (-13 days per year per tsimidi) the willingness to work while facilitating this ploughing habits increases (+5 days per year per tsimidi) the willingness to work. Again, significant differences in willingness to work for most attributes exist. The willingness to work for a strong increase in herbs and insects on rangeland is 20 days per year per tsimidi, when the grass biomass increases with 2% this is another 20 days extra per year per tsimidi. A strong (5%) improved production of woody species or a 60% soil loss reduction are both valued another 13 days of work per year per tsimidi. These values are quite high and seem unrealistic but need to be reduced with the effect of the alternative-specific constant. One has to remark that the influence of the translator has a significant effect on the result of the discrete choice experiments on rangeland, as proven in the model. INSIGHT IN THE INTERESTS OF THE SOCIETY OF T IGRAY CONCERNING

SWC MANAGEMENT

Choice experiments with respondents from Mekelle University representing ‘the society’ show that there is an insignificant willingness to pay extra taxes for off-site benefits of SWC management. A negative alternative-specific constant indicates a general tendency to nonpreference for SWC scenarios. However, if scenarios succeed in stabilizing or even reducing the soil degradation and increasing the biodiversity while not reducing the amount of rural jobs, the likelihood to be a preferred scenario rises. There is an inexplicable negative opinion about the purification of water and a wide standard deviation for all off-site effects of SWC management. Factors negatively influencing the attitude towards paying extra taxes are the wealth status and the fact that the parents of the respondent are farmers. A latent class analysis found that respondents studying land resource management and younger respondents coming from farming families have a positive alternative-specific constant and also value the benefit of an increased water availability. It is clear that environmental rehabilitation is valued a lot but not worth paying for, signifying a ‘commons dilemma’.

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E CONOMIC EFFICIENCY OF CONSERVATION SCENARIOS WITH IN M AYLEBA “Is the implementation of stone bunds, check dams and trenches economic efficient considering the perception of farmers and the society of Tigray concerning both off-site and on-site effects of these soil and water conservation measures?” Finally, the profitability of the currently implemented SWC structures is be reviewed by examining all results in an exploratory scenario analysis. Many farmers see SWC management as a necessity to have productive land, which is proven by the fact that no farmer chose for the ‘no measures’ scenario in the experiments. A scenario analysis - assuming perfect knowledge and no financial or time limits - indeed shows that the willingness to work mostly exceeds the required amount of work. It is interesting to look at the characteristics of farmers influencing their confidence concerning the effect of SWC structures on soil fertility. Farmers that have been using SWC structures for a longer period and who see the crop yield or grass biomass starting to increase, are generally more positive about the effectivity of SWC structures If it is asked to farmers if they think financial support is needed to protect cropland, more than half of them answered that it results in private benefits. This wide adoption can be assigned to the obliged work days, opposed by the government. Nevertheless the interviews point out that more than two third works also on own initiative, pointing to perceived profitability. Controversy, while most farmers prefer stone-faced trenches over stone bunds, the labour- and productive land- constraints prevents them from installing this first one. On rangeland and in gullies, the benefits and costs are not private at all; Positive externalities play a role in the perception of farmers. Despite high willingness to work for different effects of protecting communal land, three quarter of the respondents finds it crucial to receive support for the hard work on rangeland and in gullies. Farmers indeed value biodiversity, fodder production for their animals and an increase in bees to improve pollination. On the other hand, the current non-maintenance of the structures in communal land evidences that this profit is not high enough. The negative alternative-specific-constant on rangeland indeed points to a less likely adoption on this communal land. Protecting gullies and rangeland with exclosures introduces externalities for the whole society of Tigray. The offsite benefits are clear but the willingness to pay taxes – modelled via discrete choice experiments about offsite effects of SWC - for it is absent; Again the ‘commons dilemma’ is visible.

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4.2

POLICY IMPLICATIONS

The conclusion reveals that there are still restraining factors in agricultural development in Mayleba. It is evident that farmers don’t always have the optimal or most preferred type of SWC technique. •

It takes too much space . There is a productive area limit.



It is too much work. There is a labour shortage.



A lack of motivation to work on communal land in combination with bad monitoring causes a free riders problem.



The society values the protection of the environment against degradation but there is no willingness to pay extra taxes

Capital resources in the form of food or cash can improve the willingness to work and create a labour market. Moreover they reduce the problem of a small productive area by increasing food security for poor households and enabling farmers to buy fodder/hay. Mostly for communal rangeland and gullies, both having an enormous share in the catchment sediment yield, extra resources are welcome. Farmers are less willing to work on these areas since the benefits are not only for themselves and the work is hard. Tragedy of the commons not plays a role in the willingness to work on rangeland and in gullies but also reduces the willingness to pay taxes of the society. Positive externalities as a result of SWC affect both the rural and the urban population of Tigray. The Relief Society in Tigray (REST), together with international NGO’s and the Ethiopian Government, are on a good trail to tackle structural poverty, permanent food insecurity and watershed rehabilitation at once. By levelling the costs of environmental rehabilitation and securing a continuous participatory decision making, a the launched sustainable development in Tigray can decouple the trends of population growth, agricultural intensification and land degradation.

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4.3

RESEARCH IMPLICATIONS

L IMITATIONS OF THE STUDY To round up, some comments on the applied methodology can be given. The use of DCE to value bio-physical effects of SWC in order to investigate the on- and offsite profitability of SWC management is the first of its kind. It proved to be hard to value the different biophysical effects in monetary terms. Many effects are only visible in the long-term, are beneficial for the whole society and most importantly, depend on the scale one is looking at. Large variability in preferences is found in the DCE. Hence, the output of the econometric models analysing the discrete choice experiments with the farmers and students appears to be less accurate as hoped. Elevated AIC values indicate a high information loss in the models and only few characteristics are valued significant. To evaluate the onsite economic efficiency of the current integrated watershed management, quantitative interviews with the farmers appear bring as much information as a contingent valuation method, this last one limited by large uncertainties and assumptions. A potential shortcoming in valuing the offsite profitability is the limited sample. Students are assumed to be representative for the society in order to asses social benefits. However, they are young people, only half of them already had a job. This introduces a bias and can only be solved by questioning a larger and more heterogeneous sample of the society. S COPE FOR FURTHER RESEARCH The discrete choice experiments can eventually be interesting and content-rich if conducted on a larger scale. It is interesting to include the preferences of the society, since a large share of the implementation of SWC structures in Tigray is funded by the PSNP programme, financed by NOG’s but also by government. Additional research about the willingness to pay extra taxes to improve the subsidies can be of critical importance in the further evolution of the communitybased watershed development. An option to value on-site biophysical effects of SWC structures with farmers is using a game approach (Speelman et al. 2014). A game –constructed for a certain area and a certain goal – allows farmers to make several choices over a hypothetical amount of years. By simulation real-life conditions like rainfall, crop yield, diseases, labour and so on, farmers have more feeling with the experiment. In the game, they directly see the result (outcome; crop yield, fertility) of their choice and get the opportunity to change their preference in the next ‘level’ of the game. By doing so, quantities of effects are visualised better and the bias of knowledge is avoided. Further research can investigate this method of contingent valuation.

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