1

Assessment of land health and targeting sustainable land management

interventions in smallholding farming systems in Ethiopia

Ermias Ayenkulu, Miyuki Iiyama, Richard Coe, Keith Shepherd

2

Abstract

Land degradation is recognized as a global environmental and development problem but there

is a lack of location specific evidence to guide actions. The aim of this study is to generate

relevant information on land health to assist planning and targeting site-specific management

interventions. We applied the Land Degradation Surveillance Framework (LDSF) to

characterize two sentinel sites in semi- arid and sub-humid agroecologies in Ethiopia. A

sentinel site is a 10 km x 10 km block stratified into 16 clusters of 100 ha. Each cluster then is

represented by 10 randomly selected plots of 0.1 ha area. Within each sampling, plot there are

4 subplots of 0.01 ha where data on vegetation cover, structure, floristic composition, land

use, topography, visible signs of soil erosion and soil physical characteristics were collected.

The study sites are dominantly under cultivation in which crop lands cover about 70% of the

land. We found 66 and 55 trees per ha in the sub-humid and semi-arid sites, respectively.

Croton macrostachyus (relative density= 16%) and Acacia tortillis (relative density =30%)

are the dominant species in the sub-humid and semi-arid site, respectively. The sub-humid (46

species) was more species rich than the semi-arid (28 species) site. Leguminosae is the

dominant family in both sites. The humid-tropics site has significantly higher carbon and

nitrogen content than the semi-arid site for all soil depth classes (0-20 cm, 20-50 cm, 50-80

cm and 80-110 cm). The mean (standard deviation) for carbon for the top soils (0 – 20 cm) in

the sub humid and semi arid sites were 3.05(0.81) and 1.18(0.49), respectively. Most of he

plots in the semi arid (93%) had low soil carbon content (< 2 %) while small part of the plots

in the sub-humid (8%) had low carbon content which could be attributed to inherent soil

properties and land management practices. The soils in the humid-tropics site, for instance,

are richer in clay content than the soils in the semi-arid site which could contribute for higher

soil carbon in sub-humid site than in the semi-arid site. Soil carbon and nitrogen content also

varies with land use types in which cultivated lands had less carbon andnitrogen than semi-

natural vegetation. Sheet erosion was identified as the major cause of soil erosion in both

sites. However, we found no high risk of inherit soil degradation in both sites. The semi-arid

site has low carbon and nitrogen content that needs an integrated soil management practices to

improve soil quality and prevents further soil degradation.

Keywords: Land degradation, soil fertility, soil variability, land health

Acknowledgements

The research was conducted with the financial support from the Australian Centre for International Agricultural Research (ACIAR).

3

1. Introduction

To meet the increasing global demand for both food and energy, substantial yield increases

are required. In sub-Saharan Africa, farming systems are currently under threat from land

degradation, climate change, occurrence of weeds, pests, diseases and land use change. The

majority of the soils in Africa are characterized by deficiencies of N, P, K and low soil

organic matter content (Grant, 1981) and the world’s lowest mineral fertilizer application

rates (Morris et al., 2007). Further declines in soil fertility have adverse effects on the

livelihoods of many African smallholder farmers, as crop and fodder yields are further limited

(Gitari et al., 1989). However, there are some efforts to enhance food production in

smallholding crop production systems in Sub-Saharan Africa. The result from the 80

Millennium Villages in Africa for instance shows some promises that Africa can increase it

agricultural productivity (Sanchez et al. 2009).

The growing human population and the increasing global demand for food and energy led to

land degradation (Terborgh and van Schaik 1997; Noss 1999). Habitat loss and change, over-

harvesting, pollution, and climate change have been the direct causes of global land

degradation (Wood et al. 2000), while population growth, changes in economic activities,

socio-political factors, cultural factors, and technological change are indirect drivers (MA

2005). Besides these global factors, lack of technical knowledge and awareness, and political

instability have exacerbated the problem in many developing countries (Ayyad 2003). Forest

degradation in Sub-Saharan Africa, for instance, has widely taken place because people gain

immediate economic benefits from the forest-related economic activities (Mogaka et al.

2001). Land use change undermines the capacity of ecosystems to sustain the provision of

ecosystems services (Jonathan et al. 2005).

Similarly, accelerated land degradation that arise largely due to the conversion of forests to

other agricultural land-use types and the over-utilization of land resources to satisfy the food

and energy requirements of the increasing population are major environmental concerns in

Ethiopia (Machado et al. 1998; Dessie and Kleman 2007). According to pollen and charcoal

studies in northern Ethiopia, forest disturbance has a 3000-year history (Darbyshire et al.

2003), and soil erosion following vegetation clearance occurred in the middle Holocene (Bard

et al. 2000). Land degradation in Ethiopia entails several socio-economic and environmental

challenges that have strongly affected the capacity of land to provide ecosystem services

(Badege 2001). Shortage of fuel wood is a serious problem in the Ethiopia Teketay (2001)

and the use of animal dung and agricultural residues as household fuels has increased, which

otherwise could be used as organic fertilizer (Gebreegziabher 2007). Deforestation is also a

major reason for the accelerated soil erosion in the highlands of Ethiopia (Hurni 1988), where

4

the annual soil loss is estimated to be 1493 million tons (Zeleke and Hurni 2001). This again

causes an estimated annual grain yield loss of 1-1.5 million tons (Taddese 2001). Following

the long history of land degradation, many land rehabilitation and conservation programs

have been carried out in Ethiopia. A historical vegetation cover change study by Nyssen et al.

(2009) indicates that the vegetation cover in northern Ethiopia has improved during the last

century through land rehabilitation programs. The major drawbacks in the conservation

efforts are related to policy setting and implementation, which are often criticized for lacking

an active participation of the local people (Hagos et al. 1999).

Renewed interest in increasing agriculture productivity to meet food security needs and

increasing resilience of agricultural systems in developing countries, especially in sub-

Saharan Africa, makes understanding soil fertility constraints and trends ever more important

(Sanchez et al. 2009). Measurement and monitoring of soil quality and land health are

fundamental to developing a sound knowledge of problems and solutions for sustainable crop

production and land management, including agroforestry. Much of the current analysis on

agricultural productivity is hampered by the lack of consistent, good quality data on soil

health and how it is changing under past and current management. This is especially critical in

the face of increased variability in weather conditions brought on by climate change.

ICRAF and partners have proposed a land health surveillance and response framework, which

is modelled on scientific principles in public health surveillance, to increase rigour in land

health measurement and management. The key objectives are to: (i) identify land health

problems, (ii) establish quantitative objectives for land health promotion, (ii) provide

information for the design and planning of land management intervention programmes and

resource allocation priorities, (iv) determine the impact of specific interventions, and (v)

identify research, service and training needs for different stakeholder groups (Shepherd et al.

2014; UNEP, 2012).

Sustainable land management is essential to sustain ecosystem services. Biophysical

characteristics, including soils, are key components to assessment of land health and

sustainable land management (Eswaran, 2003). Soil quality, land quality, soil health and land

health are the common expressions in assessing the land degradation and land productivity

which have sometimes been used interchangeably (Eswaran, 2003). Here we use the term

land heath which can be broadly defined as the quality of land to sustain ecosystem services

to human being (Shepherd et al. 2014).

Land health surveillance is being operationalized by combining accurate ground observations

with satellite imageries to measure and monitor changes and improvements in landscape

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health, closely integrated with statistical methods to form a scientific basis for policy

development, priority setting and management (UNEP, 2012). Soil spectroscopy is a key

technology that makes large area sampling and analysis of soil health feasible (Shepherd et

al., 2007; AfSIS, 2014) and overcomes the current impediments of high special variability of

soil forming process and high analytical costs, which are key challenges in monitoring soil

health at a landscape scale (Conant et al. 2011).

The approach is being applied at sub- Saharan Africa scale in the Africa Soil Information

Service (AfSIS, 2014), at regional (Vågen et al. 2013) and national scale by the Ethiopia Soil

Information System (EthioSIS, 2014), and at landscape scale (Waswa et al. 2013), as well as

being deployed by the CGIAR in sustainable land management projects and sentinel

landscapes. Lack of adequate information on constraints and potentials of land is another

challenge to recommend appropriate land management interventions (Waswa, et al 2013).

The main aim of this study was to evaluate land constraints and potentials as basis for

targeting appropriate agroforestry and other sustainable land management practices in two

selected smallholding farming systems in Ethiopia.

2. Methods

2.1. Study area

The study was conducted at Ano (36o 38' E and 9o 5' N) and Alem Tena (38o 54' E and 8o 14'

N) sentinel sites in Ethiopia (Fig. 1). The two sites were selected as they represent semi-arid

and sub-humid smallholder farming systems. Ano site is located at an elevation of 1860 m

a.s.l. which is characterized by sub-humid climate having a unimodal rainfall pattern

receiving an average annual rainfall of 1280 mm and an average annual temperature of 20 oC

(Yadessa, et al. 2005). Alem Tena site is located at an elevation of 1700 m a.s.l. which is

characterized by semi-arid climate receiving a mean annual rainfall of 748 mm. Both sites are

characterized by plane topography largely used for crop cultivations.

Soils at Ano site are dominated by Eutric Nitosol (FAO/ UNESCO) characterized by deep

and well-drained tropical soils (Piccolo and Huluka, 1986). Soils in both sites are deficient in

phosphorus (Piccolo and Huluka, 1986). Ano site is located within the Central Rift Valley,

which is characterized by small and erratic rainfall and high evapotranspiration which makes

rainfed crop production difficult (Jansen et al. 2007 ). The soils are generally characterized by

low organic carbon content, low moisture holding capacity which leads to high runoff

(Zeleke, et al. 2004).

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Figure. 1. Study site and the sampling sentinel site. Nested sampling where 16 clusters per

sentinel sites and 10 plots per cluster selected randomly.

2.2. Sampling design and field data collection

Field sampling was made based on the Land Degradation Surveillance Framework (LDSF)

protocol (UNEP 2012). LDSF is a hierarchical stratified random sampling approach which

involves sentinel sites of 10 x 10 km in size. Each sentinel site stratified into 16 clusters of 1-

km radius circle (Fig 2a). Each cluster is further stratified into 10 sampling plots of 1000 m2.

Within each sampling plot, there are 4 subplots of 100 m2 each (Fig. 2b). Data on vegetation

cover, structure, floristic composition, and specific tree attributes such as breast height

diameter (dbh) and height as well as land use, topography, visible signs of soil erosion and

soil physical characteristics were compiled at plot level.

Field data on vegetation and soils are collected at sub-plot level. All trees (height > 1.5 m)

(height < 1.5 m) and shrubs within each subplot were counted to obtain density estimates.

Moreover, additional 30 m x 30 m plots were used to collect data on woody species diversity

and structure. All woody species were recorded and data on diameter at breast height (1.3 m

above the ground) and height were collected. We used species accumulation curves to

measure the inventory efficacy and completeness within a given study and compare species

diversity between the two sites.

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Figure 2. (A) Example of sampling distribution of 16 clusters and 160 plots (dots) within 10

km × 10 km sentinel site and (B) A sampling plot (1000 m2) consisting of four 100 m2

subplots. Source: Vågen et al. 2010

Soil samples were collected from the top (0-20 cm) and sub (20-50) samples from all the 160

plots in each sentinel site. The soil samples from each sampling point and depth were mixed

thoroughly in a bucket to form one composite sample for each depth. Soil samples were air-

dried, grounded and sieved through 2-mm sieved prior to analysis. To determine the nutrient

stocks we used the soil mass than bulk density. Soil mass was collected from top and sub soils

for all samples. To study the patterns of nutrient stocks with increasing depth of soil

additional soil mass data was collected from 50-80 cm and 80-110 cm from 48 plots (3 plot

per cluster) from each sentinel site. Soil cumulative samples were collected from the centre

subplot.

2.3. Laboratory analyses

Soils samples were analysed for chemical (carbon and nitrogen) and physical properties

(texture) at the soil-plant spectral lab of the World Agroforestry Centre. All soil samples were

scanned for mid-infrared soil spectroscopy. Eighty five samples were selected from the two

sites as reference samples for infrared spectral calibration and validation. The reference

samples were analyzed for soil organic carbon concentration and Nitrogen using thermal

oxidation method (Skjemstad and Baldock, 2008). To avoid the influence of inorganic carbon

(carbonate) soil organic carbon is determined on acidified samples, i.e. fumigated with

hydrochloric acid to remove inorganic carbon (Harris et al., 2001). Soil texture using laser

diffraction particle size analysis method.

(a)

(b)

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2.3.1. Soil spectroscopy

Infrared spectroscopy (IR) is now routinely used for analyses of a wide range of materials in

laboratory and process control applications in agriculture, food and feed technology, geology

and biomedicine (Shepherd and Walsh, 2007). The mid infrared (MIR, 2.5-25 µm)

wavelength region was investigated for non-destructive analyses of soils and can potentially

be usefully applied to predict a number of important soil properties, including: soil colour,

mineral composition, organic matter and water content (hydration, hygroscopic, and free pore

water), iron form and amount, carbonates, soluble salts, and aggregate and particle size

distribution (Shepherd and Walsh, 2004). Importantly, these properties also largely determine

the capacity of soils to perform various production, environmental and engineering functions.

IR enables soil-sampling density (samples per unit area) to be greatly increased with little

increase in analytical costs. The soil organic carbon and nitrogen estimates were made from a

PLS calibration model using spectral data and log transformed carbon and nitrogen reference

data for 85 samples from the two sites, which was cross-validated using leave one out

procedure. The result indicates that MIR is good estimator (R2 = 0.92; RMSE = 0.28, n = 85)

of soil organic carbon and nitrogen (R2 = 0.94; RMSE = 0.28, n = 85) (Fig. 3).

Figure 3. Mid-infrared spectroscopy was superior in predicting soil organic carbon and

nitrogen

2.3.2. Carbon stocks

A soil organic carbon stock is commonly determined using carbon content at different soil

depths, bulk density and course fragments. The result can be expressed in Kg m-2, t ha-2 or Gt

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(Pg) over a specified area and depth (Batjes, 2001). Estimates of soil carbon stocks to a fixed

depth using single depth bulk density are mostly biased (Lee et al., 2009; VandenBygaart and

Angers, 2006). For instance Murty et al (2002) found that the impact of conversion of forests

into cultivated lands on the changes in soil carbon stock is often inflated due to the

confounding influence of changes in bulk density. Therefore, it is necessary to consider

corrections for spatial and temporal variation in bulk density in quantifying SOC stock along

a soil profile (Lee et al., 2009). In this study we used the soil mass instead of bulk density, to

calculate soil carbon stocks. During soil sampling the mass of soil was determined for each

depth. Soil carbon stock for a given soil layer was calculated by multiplying the carbon

concentration by fine soil mass instead of using bulk density (Eq. 1).

𝑆𝑂𝐶  𝑠𝑡𝑜𝑐𝑘   = 𝐶  ×  !"#$  !"##!

 ×100 (1)

Where: SOC stock = soil organic carbon stock (t C ha-1)

C = soil organic carbon concentration of soil fines (fraction < 2 mm) determined in

the laboratory (g kg-1)

Soil mass = the fine mass of soils collected from a given sampling depth

A = area of the hole that the sample is collected calculated using the auger radius

(r=3.8 cm). A=π × r2 in cm2

100 is used to convert the unit to t C ha-1 Statistical analyses Differences among the different soil depths in soil carbon and nitrogen concentrations were

tested using a one-way ANOVA with Kruskal-Wallis test at P ≤ 0.05. All data analyses were

conducted in R (R Development Core Team, 2014).

3. Results and discussions

3.1. Vegetation health

3.1.1. Land use

Crop cultivation was the dominant land use practice in both sentinel sites. Ano and Alem

Tena sites have an estimated total area of 7900 ha (79%) and 6800 ha (68%) area under

cultivation, respectively (Table 1).

Table 1. Estimated area under cultivation or management in each cluster of Ano and Alem

Tena sentinel sites in Ethiopia

10

Cluster Ano Alem Tena

% area

1 80 70

2 100 100

3 70 80

4 60 80

5 60 100

6 40 80

7 50 100

8 60 50

9 90 90

10 70 80

11 60 100

12 60 70

13 70 90

14 90 40

15 90 60

16 40 80

Average1 68 79 1Estiamted  sentinel  site  average  

3.1.1. Vegetation cover and structure

Woody cover Block-level estimate of area under dense woody vegetation (trees and shrubs) based on

woody cover (WC) scores >3 (i.e. ≥ 40 % WC) are shown in Table 2. Both sites have poor

woody vegetation cover.

Table 2. Estimated area under dense woody cover in Ano and Alem Tena sentinel sites in

Ethiopia

Sentinel site Area (ha) Ano 0.4 Alem Tena 1.2

The overall shrub density was higher in Alem Tena site while tree density was higher in Ano

site (Table 3). Alem Tena and Ano sites have tree density of about 50 and 66 trees per ha,

respectively.

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Table 3. Estimated shrub and tree density in each cluster in Ano and Alem Tena sentinel sites

in Ethiopia

Cluster Shrub density (shrubs ha-1) Tree density (trees ha-1)

Ano Alem Tena Ano Alem Tena

1 17.5 272.5 17.5 276.25

2 0 95.0 82.5 0

3 5 245.0 97.5 5

4 55 27.5 40 22.5

5 37.5 23.3 20 0

6 105 72.5 185 37.5

7 0 57.5 45 25

8 130 185.0 35 52.5

9 2.5 160.0 47.5 102.5

10 35 42.5 32.5 25

11 30 57.5 170 12.5

12 52.5 110.0 77.5 87.5

13 0 30.0 25 0

14 0 82.5 35 57.5

15 7.5 95.0 17.5 60

16 57.5 92.5 125 35

Average1 33.41.953.1 10353.8

122.5 65.830.686.3 49.910.2

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Estimated total number of shrubs

334000 1030000 657813 499219 1 Estimated sentinel site averages with 25% (lower) and 75% (upper) quintiles

We found less shrub (Fig. 4a) and tree (Fig. 4b) densities in cultivated (1) than semi—natural

lands in both Alem Tena and Ano sites.

(b) (a)

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Figure 4. (a) estimates of shrub and (b) tree densities in cultivated (1) and semi-natural (0)

lands in Ano and Alem Tena sentinel sites in Ethiopia

Herbaceous cover Herbs in both sites were annual. The herb cover is slightly higher at Alem Tena than Ano site

(Fig. 5). However, we found no significant difference in herb cover between cultivated and

semi- natural lands in both sentinel sites.

Figure 5. Site level predicted herbaceous cover scores in cultivated (1) and semi-natural (0)

lands in Ano and Alem Tena sentinel sites in Ethiopia

3.1.1. Species diversity

We found 28 woody species in Alem Tena (Appendix 1) and 46 species in Ano (Appendix 2)

sentinel sites. Acacia tortillis and Croton macrostachyus have the highest relative density in

Alem Tena (Fig. 6a) and Ano (Fig. 6b) sites, respectively. Most of the species at Alem Tena

are naturally grown while Ano site had some planted trees (e.g. Eucalyptus camaldulensis).

The dominant species at Alem Tena are nitrogen fixing trees that can be used as fertilizer

trees to improve soil health

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Figure 6. (a) Relative density of the most common woody species at Alem Tena and (b) Ano

sentinel sites in Ethiopia

The species accumulation curve (Fig. 7) shows that Ano site has more accumulation rate of

new species over the sampled area than Alem Tena site. Both species accumulation curves

indicate more species could be captured with increasing sampling efforts in the studied

landscapes.

Figure 7. Species-accumulation curve for Alem Tena and Ano sites in Ethiopia

0 10 20 30 40

Other species Faidherbia albida

Dichrostachys cinerea Acacia senegal

Croton macrostachyus Acacia tortilis

Species relative density (%)

(a)  

0.0   10.0   20.0   30.0   40.0   50.0   60.0  

Others Markhamia lutea Rosa abyssinica

Eucalyptus camaldulensis Cordia africana

Croton macrostachyus

Species relative density (%)

(b)  

Number of samples

Cum

ulat

ive

num

ber o

f spe

cies

14

3.2. Soil health

3.2.2. Physical constraints Root depth restrictions We found no root depth restrictions up to 50 cm depth in both sties (Table 4).

Table 4. Estimated cluster frequency of root depth restriction within 0-20 cm and 20-50 cm

soil depths in Ano and Alem Tena sentinel sites in Ethiopia

Cluster 0 – 20 20 – 50

Ano Alem Tena Ano Alem Tena

%

%

1 0 0 0 0

2 0 0 0 0

3 0 0 0 0

4 0 0 0 0

5 0 0 0 0

6 0 0 0 0

7 0 0 0 0

8 0 0 0 0

9 0 0 0 0

10 0 0 0 0

11 0 0 0 0

12 0 0 0 0

13 0 0 0 0

14 0 0 0 0

15 0 0 0 0

16 0 0 0 0

Average 0 0 0 0

The probability of facing root depth restriction in cultivated and semi-natural lands between

0-20 cm (Fig. 8a) and 20- 50 cm (Fig. 8b) in  semi-natural and cultivated lands in both sites

was almost zero.

 

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Figure  8.   (a)  Predicted  probability  of  root  depth  restriction  within  0-­‐20  cm  (RDR20)  and  (b)  

20-­‐50  cm  (RDR50)  soil  depths   in  cultivated  (1)  and  semi-­‐natural   (0)   lands   in Ano and Alem

Tena sentinel sites in Ethiopia

Soil inherent soil degradation risk Shallow root depth, steep slopes and textural discontinuity between top and sub soils were

used as proxy to inherent soil degradation risks (UNEP 2012). Accordingly, except for two

clusters we found no sign of inherent soil degradation risk in both sentinel sites (Table 5).

Table 5: Estimated cluster- and block-level proportion of areas predicted to have high

inherent soil degradation risk.

Cluster   Alem   Ano  

 %  

 1   0   0  

2   0   0  

3   0   0  

4   0   0  

5   0   0  

6   0   0  

7   0   0  

8   0   10  

9   0   0  

10   10   0  

11   0   0  

12   0   0  

13   0   0  

14   0   0  

15   0   0  

16   0   0  

Average1   0.63   0.63  1Estiamted  sentinel  site  average  

Both sites are found in plain terrain with no root depth and hence less prone to inherent soil

degradation risk.

Probability of RDR50

(b)

Probability of RDR20

(a)

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Soil chemical and physical properties

Ano has significantly higher soil carbon content than Alem Tena site (Fig. 9a). Soil carbon

content significantly decreses with increasing depth in both sites (Fig. 9b).

Figure 9. (a) Ano site has higher soil organic carbon in the top (0-20 cm) than Alem Tena

site. (b) Soil carbon decreses with increaseing soil depth in both sites

We found no significant difference in soil carbon (Fig. 10) and nitrogen (Fig. 10) contents

between cultivated and semi-natural lands in both sentinel sites. Soils in Ano site are richer in

clay than Alem Tena site but no difference in clay content between cultivated and semi-

natural lands were found in both sites (Fig. 10d). The higher soil carbon and nitrogen contents

compared to Alem Tena could be attributed to the higher clay content in Ano site than Alem

Tena site (Fig. 10d).

(a) (b)

(a)_

(b)

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Figure 10. (a) Soil carbon, (b) soil carbon stock, (c) nitrogen, and (d) clay content are higher

at Ano than Alem Tena sites for both cultivated and semi- natural lands.

Infiltration The high clay content in Ano site could give a more stable moisture infiltration capacity than

the Alem Tena site which has low clay content (Fig. 11a). We found higher infilitration rates

in semi-natural than cultivated lands in both sites (Fig. 11 b).

(c) (d)

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Figure 11. (a) Infiltration rates in Alem Tena and Ano sites and (b) cultivated and semi-natual

aland uses Infilitration rate in cultivated (1) and semi-natural lands (0) in Alem Tena and Ano

sites in Ethiopia

3.3. Identification of priority intervention areas

3.3.1. Cultivated lands

(a)

(b)

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Although we do not have the “optimum” tree density in such ecosystem in Ethiopia, we

recommend management interventions consider increasing tree density using appropriate tree

planting technology like farmers assisted natural regeneration. Using the mean and highest

tree densities per cluster as a local reference, the number additional trees to be planted in the

cultivated lands are summarized in table (4). The average and maximum tree densities per

cluster for the Ano site were 35 and 134, respectively. The average and maximum tree

densities per cluster for the Alem Tena site were 31 and 123, respectively. Accordingly,

planting of at least between 10 and 99 trees per hectare for Ano site and between 15 and 99

trees per hectare for Alem Tena site are recommended (Table 4).

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Table 4. Estimated number of trees/ha to be planted as enrichment planting in cultivated

lands in Alem Tena and Ano sentinel sites in Ethiopia Site Cluster Mean Min Max Target based on mean cluster

values Target based on mean max cluster value

Ano 1 3.1 0 25 32 131

2 82.5 0 475 0 52

3 39.3 0 225 0 95

4 0 0 0 35 134

5 33.3 0 100 2 101

6 87.5 0 125 0 47

7 10 0 25 25 124

8 20.8 0 75 14 114

9 27.8 0 175 7 107

10 10.7 0 50 24 124

11 37.5 0 150 0 97

12 83.3 0 275 0 51

13 28.6 0 175 6 106

14 38.9 0 125 0 95

15 19.4 0 75 16 115

16 37.5 0 75 0 97

Mean 35 0 134 10 99

Estimated total number of trees to be planted in the sentinel site 100000 990000

Alem Tena 1 219.6 0 725 0 0

2 0 0 0 31 123

3 6.2 0 25 25 117

4 21.9 0 50 9 102

5 0 0 0 31 123

6 28.1 0 75 3 95

7 25 0 250 6 98

8 10 0 50 21 113

9 63.9 0 250 0 60

10 3.1 0 25 28 120

11 12.5 0 75 18 111

12 39.3 0 175 0 84

13 0 0 0 31 123

14 18.8 0 75 12 105

15 37.5 0 150 0 86

16 6.2 0 50 25 117

Mean 31 0 123 15 99 Estimated total number of trees to be planted in the sentinel site 150000 990000

According to IFPRI (2010), Alem Tena site categorized as low potential cereal zone that

requires careful land management. Soils at the Alem Tena site are poor in terms of carbon (<

2%) and nitrogen (< 0.12 %) content for the top-soil (0- 20 cm) that management

21

interventions should consider soil organic carbon building using appropriate interventions. On

the other hand, Ano site had reasonably acceptable levels of soil carbon (> 2%) and nitrogen

(> 0.2) that management interventions should target increasing soil organic carbon or at least

maintain the existing level by avoiding any potential soil degradation.

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3.3.2. Semi-natural lands Amall proportion of the Ano (32%) and Alem Tena (22%) site are support semi-natural

vegetation (Table 1). We found low tree and shrub densities in semi-natural lands in both

Alem Tena and Ano sites that may require enrichment planting. Taking the average and

highest tree density within each cluster as a local reference, the number additional trees to be

planted in the cultivated lands are summarized in table (5). The average and highest tree

densities per cluster for the Ano site were 116 and 360, respectively. The average and

maximum tree densities per cluster for the Alem Tena site were 141 and 256, respectively.

Accordingly, planting of at least between 45 and 245 trees per hectare for Ano site and

between 54 and 144 trees per hectare for Alem Tena site are recommended (Table 4).

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Table 5. Estimated number of trees/ha to be planted as enrichment planting in semi-natural

lands in Alem Tena and Ano sentinel sites in Ethiopia Site Cluster   Mean   Min   Max   Target based on mean

cluster values  Target based on mean max cluster value  

Ano   1   75   0   150   41   285     2  

            3   233.3   0   650   0   127     4   100   0   275   16   260     5   0   0   0   116   360     6   250   0   925   0   110     7   80   0   400   36   280     8   56.2   0   125   60   304     9   225   225   225   0   135     10   83.3   0   200   33   277     11   368.8   0   1425   0   0     12   68.8   0   250   47   291     13   16.7   0   50   99   343     14   0   0   0   116   360     15   0   0   0   116   360     16   183.3   25   725   0   177  

  Mean   116   17   360   45   245  Estimated total number of trees to be planted in the sentinel site   450000   2450000  Alem  Tena   1   408.3   75   1025   0   0     2  

            3   0   0   0   141   256     4   25   0   50   116   231     5  

            6   75   50   100   66   181     7  

            8   95   0   225   46   161     9   450   450   450   0   0     10   112.5   50   175   29   144     11  

            12   200   0   375   0   56     13   0   0   0   141   256     14   83.3   0   175   58   173     15   93.8   0   225   47   162     16   150   25   275   0   106     Mean   141   54   256   54   144     Estimated total number of trees to be planted

in the sentinel site    540000   1440000  

 

24

Conclusions

Both Alem Tena and Ano sites were mainly used for agricultural crop production. Although

we found no sever inherent soil degradation risks in both study sites, soil erosion and

moisture stress are the major risks for sustainable crop production. Removal of crop residues

from cultivated lands for animal feed was another risk making the land vulnerable to soil

degradation. Soils at Alem Tena site were poor in soil carbon and nitrogen contents that

require appropriate interventions like crop residue incorporation, increasing tree cover to

build soil physical, chemical and biological properties to sustain agricultural productivity and

environmental integrity in the study area. Moreover soils at Alem Tena have poor infiltration

and moisture holding capacities that can benefit from retaining crop residues that can improve

soil structure, water use efficiency enable chemical fertilizer to be more effective.

25

Appendix

Appendix 1. Woody plant species identified at Alem Tena sentinel site, Ethiopia No Species Family No of

individuals

Relative

density (%)

1 Acacia tortilis Hayne Leguminosae 119 32.96

2 Croton macrostachyus Hochst. ex Delile Euphorbiaceae 64 17.73

3 Acacia senegal Willd. Leguminosae 55 15.24

4 Dichrostachys cinerea (L.) Wight & Arn. Leguminosae 43 11.91

5 Faidherbia albida (Delile) A.Chev. Leguminosae 15 4.16

6 Balanites aegyptiaca Delile Balanitaceae 13 3.60

7 Acokanthera schimperi (A.DC.) Benth. & Hook.f. ex

Schweinf.

Apocynaceae 7 1.94

8 Calpurnia subdecandra (L'Hér. ) Schweick. Leguminosae 6 1.66

9 Olea europaea L. subsp. cuspidata (Wall ex G. Don.) Oleaceae 5 1.39

10 Carissa edulis Vahl Apocynaceae 4 1.11

11 Rhus natalensis Bernh. ex Krauss Anacardiaceae 4 1.11

12 Commiphora sp. Burseraceae 3 0.83

13 Ziziphus mucronata Willd. Rhamnaceae 3 0.83

14 Unidentified sp.1 2 0.55

15 Ehretia cymosa Thonn. Boraginaceae 2 0.55

16 Euclea schimperi (A.DC. ) Dandy Ebenaceae 2 0.55

17 Grewia villosa Willd. Tiliaceae 2 0.55

18 Teclea nobilis Delile Rutaceae 2 0.55

19 Acacia seyal Delile Leguminosae 1 0.28

20 Celtis africana Burm.f. Ulmaceae 1 0.28

21 Combretum molle Engl. & Diels Combretaceae 1 0.28

22 Dodonaea angustifolia L.f. Sapindaceae 1 0.28

23 Unidentified sp. 2 1 0.28

24 Unidentified sp. 3 1 0.28

25 Maytenus senegalensis (Lam. ) Exell Celastraceae 1 0.28

26 Rhus vulgaris Meikle Anacardiaceae 1 0.28

27 Schinus molle hort. ex Engl. Anacardiaceae 1 0.28

28 Unidentified sp. 4 1 0.28

26

Appendix 2. Woody plant species identified at Ano sentinel site, Ethiopia No Species Family No of

individuals

Relative

density (%)

1 Croton macrostachyus Hochst. ex Delile Euphorbiaceae 98 16.09

2 Cordia africana Lam. Boraginaceae 74 12.15

3 Eucalyptus camaldulensis Dehnh. Myrtaceae 57 9.36

4 Rosa abyssinica R.Br. ex Lindl. Rosaceae 38 6.24

5 Markhamia lutea K.Schum. Bignoniaceae 27 4.43

6 Blighia unijugata Baker Sapindaceae 25 4.11

7 Terminalia schimperiana Hochst. ex Delile Combretaceae 21 3.45

8 Acacia abyssinica Hochst. ex Benth. Leguminosae 19 3.12

9 Balanites aegyptiaca Delile Balanitaceae 19 3.12

10 Calpurnia subdecandra (L'Hér. ) Schweick. Leguminosae 17 2.79

11 Gardenia ternifolia Schumach. Rubiaceae 17 2.79

12 Albizia schimperiana Oliv. Leguminosae 16 2.63

13 Combretum molle R.Br. ex G.Don Combretaceae 14 2.30

14 Ficus elastica Roxb. Moraceae 14 2.30

15 Mangifera indica L. Anacardiaceae 13 2.13

16 Carissa edulis Vahl Apocynaceae 11 1.81

17 Maytenus undata (Thunb. ) Blakelock Celastraceae 11 1.81

18 Piliostigma thonningii ( Schumach. ) Milne-Redh. Leguminosae 11 1.81

19 Acacia tortilis Hayne Leguminosae 10 1.64

20 Manilkara butugi Chiov. Sapotaceae 10 1.64

21 Ficus vasta Forssk. Moraceae 9 1.48

22 Ficus sycomorus L. Moraceae 8 1.31

23 Rhus glutinosa Hochst. ex A.Rich. Anacardiaceae 7 1.15

24 Vernonia amygdalina Delile Asteraceae 7 1.15

25 Bersama abyssinica Fresen. Melianthaceae 6 0.99

26 Bridelia micrantha Baill. Euphorbiaceae 6 0.99

27 Premna schimperi Engl. Lamiaceae 6 0.99

28 Maesa lanceolata Forssk. Myrsinaceae 5 0.82

29 Trilepisium madagascariense DC. Moraceae 5 0.82

30 Millettia ferruginea Hochst. Leguminosae 4 0.66

31 Berchemia discolor Hemsl. Rhamnaceae 3 0.49

32 Syzygium guineense DC. Myrtaceae 3 0.49

33 Deinbollia kilimandscharica Taub. Sapindaceae 2 0.33

34 Ficus thonningii Blume Moraceae 2 0.33

35 Rhus natalensis Bernh. ex Krauss Anacardiaceae 2 0.33

36 Ximenia americana L. Olacaceae 2 0.33

37 Acacia seyal Delile Leguminosae 1 0.16

38 Cassia alexandrina ( Mill. ) Spreng. Caesalpiniaceae 1 0.16

39 Ceiba pentandra ( L. ) Gaertn. Bombacaceae 1 0.16

40 Ekebergia capensis Sparrm. Meliaceae 1 0.16

41 Erica cymosa E.Mey. ex Benth. Ericaceae 1 0.16

27

42 Grewia bicolor Juss. Tiliaceae 1 0.16

43 Grewia ferruginea Hochst. Tiliaceae 1 0.16

44 Rhamnus prinoides L'Hér. Rhamnaceae 1 0.16

45 Ricinus communis L. Euphorbiaceae 1 0.16

46 Unindentified sp. 1 - 1 0.16

28

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