Policy tangents to Livelihood Security & Climate Change Adaptation

Respublica Literaria Republic of Letters

Lecture series – SD VI-2015

Policy tangents to Livelihood Security & Climate Change Adaptation

Resource Base Carrying Capacity, Climate Change Variability, and Population Growth: A Case Study of four Communities in Wonago Woreda

Southern Ethiopia

Costantinos Berhutesfa Costantinos, PhD President, Lem Ethiopia, the Environment & Development Society, Professor of Public Policy,

School of Graduate Studies, College of Business and Economics, AAU

Background to the lecture to the Second All Africa Youth Conference on Environment and Climate Change:

“Climate change will hit Africa” African Union Hall, April 2015, Addis Ababa

Summary

The farming community is besieged with problems of land shortage and other interrelated envi-ronmental constraints such as climate change, poor agricultural production and poverty. With the current high growth rate (2.9%), the population in the area is estimated to reach about 31,062 by year 2021. Closely linked to this is the reduction in land holding which is also estimated to be re-duced to below 0.2 hectares/ household by the same year. This will likely to result in serious short-age of land and unbearable strain on food security. It can be said that the traditional coping strate-gies that have been under use for generation, have now reached a challenging stage where they might no longer help much to mitigate the problem of land shortage. Therefore, the future for the majority of the households in this area could be disastrous unless appropriate measures are taken to provide viable farm size or other alternatives that will supplement household income.

There are two alternatives: the first is introduction of non-farm activities. In this case, schemes of alternative means of income generations such as cottage industries have to be introduced. How-ever, this proposal is not an easy task to be achieved in a short time. Secondly, in the face of such limited resources, it seems that the most appropriate to relocate those households with smallhold-ings and the landless to sparsely populated areas. Any strategies to control and or reduce the popula-tion pressure have to be supported by appropriate government policies and political commitment if it is to bring a sustainable solution. Further, concerted efforts have to be made to promote improved farming technologies to increase agricultural production and productivity. The effects of climate re-lated problems can be mitigated through introduction of sound land management, improved farm-ing system and the introduction of irrigation farming.

1. Introduction

Ethiopia had originally a rich natural resource base. Nevertheless, this resource has undergone substantial changes because of increasing population pressure, expanding agricultural practices, in-tensive exploitation and a variety of other local causes mostly man-made. For instance, about 88% of the highlands are believed to have been covered with dense high forests. However, with increas-ing population during the past three to four millennia, these forests have been cleared to give way for expanding agricultural activities and to meet the increasing demand of fuel wood and construc-tion materials. With increasing human population, the number of livestock has also been increasing as well, thus putting much grazing pressure on pastureland. This intensive use of land resources without any conservation measures has resulted in increased run-off and soil erosion, which in ag-gregate led to a serious land degradation.

As alternatives to farming are very few, the unchecked population growth and overstocking have forced encroachment to steep slopes, marginal land and to ecologically fragile areas to meet the in-creasing demand for food, fuel wood and livestock feed. Further, the ongoing trend of population dynamics combined with backward agricultural practices, has led to the reduction in land holding per household. Thus, the majority of households in larger parts of the highlands have no sufficient size of land to produce enough food to support their families. In other words, it can be said that un-der the prevailing land holding and land use systems, the optimum carrying capacity of land resource base is already surpassed in most highland areas. Likewise, the consumption of wood has also ex-ceeded the natural regeneration rate of the existing remnant forests.

On the other hand, the deforestation process (clearing and burning for expansion of cultivation and in-tensive grazing) which attributed to human population growth is contributing to climate change. There is increasing evidence that the removal and burning of forest vegetation increases the concentration of Green House Gases (GHGs) in the atmosphere, which in turn causes climate change. These in-clude the increase in temperature, changes in the amount and distribution of rainfall and the increase in the incidence of tropical storms, which again could have serious impact on agricultural production and as a whole on human developments. From the above brief discussion, it can be concluded that there is a direct and indirect linkages between the three factors: population growth, land resource base carrying capacity and climate change/ variability. Against this background, Lem, the Environ-ment & Development Society of Ethiopia has decided to conduct a study on the link between re-source base carrying capacity, climate change variability and population growth in the four Quebelles (Kara Soditi, Tumata Chirecha, Bale Bukisa and Hase Haro) of Wonago Woreda in Gedeo Zone of the Southern Nations, Nationalities and Peoples Regional State (SNNPRS). Accordingly, as per the agreement made on 16/12/2009, Lem Ethiopia has made a contract with ERCAND Consult to car-ry out the mentioned study.

This document therefore presents the result of the study, which has been undertaken, from Jan-uarys to April 2010. The document consists of ten sections. Section 2 provides a brief account on the objective of the study. Section 3 highlights the major approach and the methodologies adopted in accomplishing the study. Section 4 presents a summarized account of the reviewed literature re-garding population growth and resource base degradation both at global and country level. Section 5 gives an overview of the study area. Section 6 deals with the land use land cover of the study area. Section 7 and 8 presents a brief summary of the result of the household survey and population vs. resources base carrying capacity of the study area respectively. Section 9 provides a general back-ground on climate change variability and the results of the assessment of major climatic factors. Sec-tion 10 gives the result of the analysis of land resource base carrying capacity while section 11 sum-marizes data base infrastructures. The last section (12) will provide summary of the conclusion and recommendation.

2 | Wonago Challenges to livelihoods and Climate change Adaptation

1.1. Objective of The Study

As stated in the Terms of Reference (ToR), the objective of this study is to assess “the link Be-tween Resources Base Carrying Capacity, Climate Change Variability and Population Growth” in four Quebelles (Bale Bukisa, Tumata Chirecha, Hase Haro and Kara Soditi) of Wonago Woreda in Gedeo Zone of the SNNPRS. The study is therefore based primarily on the assessment of the exist-ing natural resource base and socioeconomic conditions of the communities residing in the above mentioned four target Quebelles of the Wonago Woreda.

1.2. Approach and Methodology

Different approaches and methodologies were used to collect the required information from the study area. A Participatory Rapid Rural Appraisal (PRRA) was employed to assess the socioeconom-ic situation of the farming communities living in the study area. Thus, about 5% of the population (household heads) were sampled for detailed assessment. Accordingly, through a random sampling, an interview of 244 household heads was carried out, using a carefully prepared household ques-tionnaire. This semi-structured interview was conducted by Development Agents (DAs) from the four Quebelle Associations (KAs), who were given orientation/ training on the procedures and methods of undertaking the survey. The interview focused largely on socioeconomic condition and other basic information. These included

demographic characteristics of the households;

land use system, farming system, land holding and livestock population, agricultural produc-tion and technological inputs, on/off-farming activities, forest resources management (de-velopment and utilization), soil and water conservation activities, access to land resources (land for cultivation, forest and grazing area) and constraints to agricultural production as perceived by the respondents;

available social service/ infrastructure;

In addition to the household survey that was carried out using questionnaire, group discussion was organized upon which three household heads from each QA were invited to participate. The participants were one poor farmer, another well off farmer and a woman farmer from each QA. Thus, 12 farmers participated in the group discussion. The discussion was organized mainly to ob-tain in-depth information of the topical issues relating to the past and current situation as regards to socio-economic conditions and management and utilization of farm land and natural resources. The group members were also made to be organized and selected based on their location and knowledge of their Quebelle’s and/ or community’s past and present trend in resources management.

Further, an interview of the leaders or executives of each QA, using a semi-structured question-naire were also carried out in order to gain important information at QA level, such as human popu-lation, livestock population, land use and natural resource management. In addition, key personnel and senior officials working in different government and non-government organizations, especially those who are directly engaged on agricultural and rural development and natural resources man-agement were consulted. Besides, other stakeholders such as the higher learning institutions, and those individuals who have special knowledge about the project area were consulted to obtain rele-vant information. Major secondary data utilized in this study are as follows:

Climatic data (precipitation and temperature data) of the past 30 years from the nearest available station (in Dila) were obtained from the National Metrological Agency (NMSA),

Satellite data (SPOT 5 images of 2007) with a spatial resolution covering the study area, pur-chased from Ethiopian Mapping Agency,

Available records on population of the Wonago Woreda from the Central Statistical Agency

Available reports including published and unpublished articles and research works on the study area,


As noted above, time series of monthly rainfall, monthly maximum temperature and monthly minimum temperature for the study area from 1988 to 2008 were processed and were used to identi-fy climatic potentials and constrains of the area. Accordingly, the seasonal and annual rainfall varia-bility and reliability was assessed, based on the computation of standardized rainfall anomaly in a time series, coefficient of variability of the seasonal and annual rainfall and the computation of the expected minimum assured rainfall at different probability levels. Moreover, the monthly, seasonal and the annual rainfall trend were assessed. Monthly, seasonal and annual Reference evapo-transpiration were computed using FAO crop-wat8 software and using this result as an assessment of the length of the growing period and its trend were assessed based on the long-term tendency of the rainfall and temperature pattern.

2. Literature Review 2.1. Population Growth And Resource Base Degradation:

The earth’s population was only 2 billion in 1930 and reached 6 billion in 1999. According to the UN population projection, the world’s population is expected to grow and reach somewhere be-tween 8-10.5 billion by 2050 (Jiang and Hardee, 2009). Population growth at countries level varies much. The population of less developed nations is growing annually at approximately 2.4%, while the annual rate of increase for more developed nations is about 0.5 (Daily and Ehrlich, 1992). Ac-cordingly, some of the developing countries are projected to triple their populations in 2050. For example, the population of Africa grew from about 200 million in 1950 to over 600 million in 1993, and it is projected to about 1.6 billion (more than double) by the year 2025 (Ukpolo, 1994). Africa has the highest rate of population growth and the highest level of fertility. Accordingly, Ethiopia’s population which was about 73,918,505 two years ago (CSA, 2008), is estimated to reach 213 million in 2050.

Various social, cultural and economic forces are believed to operate across a broad spectrum of scales (time and spatial) in the process of natural resources and environmental degradation. In par-ticular, the problem of land degradation as a result of deforestation and intensive farming are most often recognized to be closely connected to local social and economic environment (Jirstorm and Rundquist, 1992). Several reports indicate that Africa’s environment is becoming increasingly de-graded primarily because of population growth. These reports indicate that there is a close associa-tion between the rapid population growth and the corresponding increase in demand and consump-tion of the land resources which results in environmental degradation.

However, others argue that the greatest environmental problems because of high consumption of energy, raw materials and manufactured goods prevail among the population of developed world, rather than in developing nations. According to this group, due to their high rate of consumption and waste, industrialized nations have large per capita impact on the environment. For example, U.S. has only 4.7% of the world’s population, but consumes 25% of the world’s resources, and generates 25-30% of the world’s waste (Bryant, 2005). The same source also indicated that one American uses the same amount of energy as 35 Indians, 150 Bangladeshis or 500 Ethiopians and consumes as much grain as 5 Kenyans. The latter group therefore concludes that many environmental problems have less to do with population growth than with technological, economic and institutional elements governing human societies (Swanson and Cervigni, 1996).

Similarly, Lindsay (2005) strongly argues that human population “Population explosion” is not a problem that would lead to famine. Acknowledging the truth that there are famines in some areas, he stated that “the problem usually is not an excess of people but an excess government, which leads to gross misallo-cation and misuse of resources as corrupt bureaucrats or dictators seek power more than the welfare their subjects.” Accordingly, he connects famine with repressive government, rather than a scarcity of resources. In fact, he pointed out that hungry people are suffering due to poverty, but not because of overpopula-tion. In line with the above statements, Chambers (1994) also indicated that blaming the poor peo-ple for having too many children, for not adopting improved agricultural methods and sound man-agement of the natural resource base and for causing environmental degradation has to be seen as hiding the truth by professionals projecting their faults on to others. According to the same re-


searcher, the rich do by far higher environmental damage than the poor. Therefore, we find three main camps of thinking or views on the relationship between population growth and the environ-ment: pessimistic, optimistic and neutral.

Those, like Thomas Malthus, who wrote an essay on the Principle of Population in 1978, and several modern environmentalists have a common view that the growth of populations tend to out-strip the productive capacity of land resources. In other words, the supply of food could not contin-ue to grow with the population, and the growing human population will eventually lead to resources exhaustion, famine and the collapse of the entire economic growth. They argue that population growth would lead to increasing pollution and environmental degradation, and the only way to con-trol the problems is to take measures that can reduce the population (Lockwood, 1995).

Thus, a number of other workers in this group including Maurice (1976) suggest that population in each region should remain within the limits set by that region’s current ability to produce or im-port an adequate food supply. They strongly recommend the necessity of limiting the size of fami-lies. According to this group, if human population is allowed to increase more than the food supply, then the result would be disastrous.

The Optimists from Godwin, Marx or Mao to the modern conservationists however argue that there are no limits to growth, and population growth is far from being a cause for environmental degradation. This group rather considers population growth as a positive stimulus to innovation and problem solving (Sileshi, 2000). Thus, they strongly argue that all the advances in agricultural tech-nologies and the consequences of increased productivity and food production have evolved in rela-tion to population growth (Marquette, 1997). They also believe that people can adapt to the chang-ing circumstances, so that the whole process will lead to sustainable use of land resources or con-trol/ minimize environmental degradation.

For the third group (neutral), population growth, alone is not the driving force for environmen-tal degradation or resources depletion. They rather consider population growth as one factor or a compound element for resources degradation. They argue that resource depletion and environmen-tal problems have more to do with political, economic and social factors than population growth (Serra, 1996). According to this group, the causes for population pressure on resource base could emanate from maladministration and government policies in areas of land use and tenure systems, access to resources, limited market opportunities and policies related to human settlement. Howev-er, in contrary to the debate of particularly the second group (pessimists), the realities on the ground; especially in the third world countries have shown that population growth has rather negative impact on the environment. In most of these countries, increasing population has resulted in excessive pressure on land resources and environmental degradation.

2.2. Resource Base Carrying Capacity:

Carrying capacity is defined by Ecologists as stated by Daily and Ehrilich (1992), as “the maxi-mal population size of a given species that an area can support without reducing its ability to support the same species in the future”. According to Roughgarden (1979) cited in the same source, specifi-cally it is “a measure of the amount of renewable resources in the environment in units of the num-ber of organisms these resources can support”.

Thus, for human population, it is the maximum number of people that a given environment can support without detrimental effects. Resource base carrying capacity is therefore, the optimum size of resource base available that can support maximum size of population. Indicating the difficulty to estimate carrying capacity, the same source pointed that for human beings it varies markedly with culture and level of economic development. Thus, they distinguish between biophysical carrying ca-pacity (the maximal population size that could be sustained biophysically under given technological capabilities) and social carrying capacities (the maxima that could be sustained under various social systems). There is a close linkage between carrying capacity and environmental impact (Davis, 2006). In general, population pressure can significantly pose negative impact on human environment. Ehrilich and Holden (1971) cited in Davis (2006), have suggested that the environmental impact of


human population can be measured by using the formula Impact (I) = PAT; where P is population size, A is its affluence or per capita consumption and T is the technology factor. It is a measure of environmental impact as a result of technologies and economic standards.

2.3. Population Growth and Natural Resources Degradation in Ethiopia:

The present population of Ethiopia is estimated at 83.5 million. Based on the census of 1994, the CSA projects the Ethiopian population at 106 million for year 2020, while the World Bank esti-mation for the same year is 135.5 million (EFAP, 1994 and EEA, 1999/2000). The estimation or projections of the World Bank differ significantly from the projections of the CSA. The national census of 1994 estimates the population growth rate at 2.44% per annum from the current year to 2015, and thereafter decline to 2.26 during 2015-2020. The result of the 2007 Population and Hous-ing Census shows that the population of Ethiopia grew at annual average rate of 2.6% between 1994 and 2007; a decrease of 0.2% from the previous period (CSA, 2008). In spite of the declining growth rates, however the population continues to increase at increasing rate. This high growth rate of pop-ulation is driven by high fertility rate of about 7.5 children per women (EFAP, 1994).

The 1994 census also revealed that of the 54 million people, about 86% (46.2 million) were rural based. Similarly, the result of 2007 census shows that about 83.9% (61.95 million people) were found in rural areas. Hence, the distribution of the people has not changed much; and the good ma-jority of the Ethiopian people are rural based with high degree of dependence on natural resources. Thus, population growth combined with other factors such as poverty, lack of other alternative means and absence of appropriate policy frameworks has lead to much pressure on the natural re-sources resulting in environmental degradation: deforestation, soil erosion and desertification.i

Deforestation process is therefore the driving force behind land degradation, though the factors influencing degradation are multiple and mutually reinforcing (EFAP, 1994). However, as shown in Figure 1, it is generally accepted that the degradation process starts with increasing human popula-tion that leads to increasing demand for agricultural land and wood for fuel and construction materi-als. A number of studies including that of McCann (1990), Daniel (1990), Markos (1990) Shiferaw and Holden (1997) to mention a few, have also reported that population growth is a major driving force behind land resources degradation.

Increasing human population

Reduced productivity Increasing demand:agricultural land Increased demand:fuel wood

Increased burning of cattle dung & crop residue

Clearing vegetation for expan-

sion of cultivation

Tree cutting for fuel and construction

Land degradation Increased run-off, soil erosion Increased shortage of fuel wood

Figure 1. Population growth and the process of land degradation In addition to demographic pressure, the causes for land resources degradation in Ethiopia have

also been recognized as multiple and complex, and includes the following factors (Wood 1990, Shiferaw and Holden, 1997): soil type and rainfall intensity, size of holdings, poverty and lack of household resources, insecurity of access to land, excessive surplus extraction and improper policies on crop prices and return to farmers, weak institutional support, poor technological input and ab-sence of appropriate agricultural research and drought and political instability;

From the above, it can be drawn that population growth, by itself may not create the problem. In comparison to many other developed and underdeveloped countries, the population density in Ethiopia is quite low. Nevertheless, the problem of degradation is quite serious in our country com-paring to countries like India and China. Thus, as also noted by Aklilu and Tadesse (1994), popula-tion pressure has been able to cause the degradation process in combination with other factors such as poverty and un proper policy frameworks including absence of improved agricultural support programmes and institutional inadequacy in management of natural resources.


Therefore, in recognition of the impact of population growth, on land resources and the link be-tween population growth and economic development, the Ethiopian government has already taken a measure towards controlling / reducing the high growth of population. Accordingly, the govern-ment has formulated a National Population Policy (NPPE) in 1993. The major objective of this pol-icy includes closing the gap between high population growth and low economic productivity through planned reduction of population growth and increasing economic returns; and maintaining or improving the carrying capacity of the environment by taking appropriate environmental protec-tion/ conservation measures. The specific objectives include reduction of the current fertility rate of 7.7 children per woman to 4.0 children and increasing the prevalence of contraceptive use from 4% to 44% by the year 2015.

3. The Study Area

The study area is located in the northern parts of Wonago Woreda, which is situated in the Gedeo Zone of the Southern Nation, Nationalities and Peoples Regional State (SNNPRS) at about 386 km south of Addis Ababa. Geographically, the study area is located between 6o 29’ to 6o35’ North and 38o20’ to 38o31’ East. The woreda (including the study area) is accessed by an asphalt road running from Awasa (the regional capital) to Moyale. The study area comprises four KAs: Bale Bukisa, Tumata Chirecha, Kara Soditi and Hase Haro.

With its altitude ranging from 1270-2070m, the woreda falls in between the upper moist Kolla and the moist Wiona Dega agro climatic zones of Ethiopia. The rainfall distribution is clearly bimod-al. There are two rainy seasons in a year, the Belg that extends from March to June and the Kiremt season from August to October. The record of 30 years in the nearest metrological station in Dila shows that the mean annual rainfall is about 1311 mm. The actual values in the given source data range from 1560 mm in a wet year like 1988 to as low as 974 mm in the year 2003, which is consid-ered as a drought year for the area. The mean annual temperature is about 20.30C and ranges from 19.50C in the month of December to 21.60C in the month of March. Mean annual maximum tem-perature is about 280C and ranges from 310C in the month of March to 25.30C in the month of July. Mean annual minimum temperature is about 12.50C, ranging from 10.1 in Dec-Jan to 14.10C in July.

The natural vegetation of the study area can be broadly classified into Moist evergreen montane forests, which are widely distributed in the south and south west parts of the country. The major tree species include Croton macrostachys, Albizia schimperiana, Milletia ferruginea, Erythrina abyssinica, Ficus sycomorus, Polyscias fulva, Pouteria adolfi-friderici, Calpurnia, Cordia Africana and Vernonia amygdalina. Check-list of the plant species including herbs in the study area are shown in the Annex. There is no much natural forest left in the study area. However, based on the observation made on the nearest rem-nant natural forest in southern hilly areas, the forest lacks the emergent species such like the Aninge-ria spp., which forms the highest non-continuous stratum of the same type of forest formation in the south west of the country. Similarly, the canopy of medium sized trees and the lower strata of woody plants have no much species. On the other hand, at higher altitude of above 2000 m, this type of forests are increasingly richer in Podocarpus gracilior, which become dominant, and in some area forming a closed canopy of up to 40m high. However, here, Podocarpus is also very frequent and with height reaching only as high as 30m. Climbers are common along forest edges where light is able to penetrate. The herbaceous stratum in the forest floor is rich in species, but mostly discon-tinuous in mature forest.

In the past, the hills around the study area were known to harbour different species of wild ani-mals including Spotted hyenas, Civet, Leopard and Dikdik. With increasing human pressure in the area, the wild animals in the surrounding areas have declined at a faster rate. Today except the various species of birds, these large animals are hardly seen in the area. Nevertheless, according to a farmer in Bale Bukisa QA, baboons and monkeys are causing serious damage on crops in the farm located at a distance from the villages. He also indicated the occurrence of Mole rats and Antelopes in the area. The geology of the study area constitutes volcanic rock of Jima volcanic ignimbrite and pum-ices of the rift floor as well as subordinate lacustirine sediments and some swamp deposits. The topo-graphic feature consists of gently sloppy and undulating areas to hilly terrain with some incised val-


leys with small frequently perennial rivers. Southeastwards, the hilly areas rise up to 2000 m asl. The soil type of the study area can be categorized into farrsole and nithosole as per the classification of FAO. According to SLUF (2006), the Woreda’s soil comprises largely (90%) brown soil, 5% red soil and another 5% black soil.

4. Land Use Land Covers:

As an input to determine the carrying capacity, the land use and land cover of the study area was assessed and information on the spatial locations, characteristics vegetation types, and crop mix and area coverage of existing land units was generated.

4.1. Methods

Description of the study area: As noted above, the study area is located between 6o 29’ to 6o35’ North and 38o20’ to 38o31’ East and includes four Quebelle Administrations, namely Tumata Chirecha, Kara Soditi, Hase Haro and Bale Bukisa of Wonago woreda of the SNNPRS. The study area covers an area of 3,340 hectares; of which Bale Bukisa covers 498 hectares, Tumata Chirecha (734 hectares), Hase Haro (841 hectares) and Kara Soditi consists 1267 hectares. The area is bound-ed by Bilate woreda of Oromia National Regional State in the north and northwest, Dilla zuria wereda (SNNPRS) in the north-east and some other 11 Quebelles of Wonago District, including Wenago town in the southern and south western directions. One of the rift valley Lakes, Abaya, is located 25 km to the northwest of the study area.

Data set & data pre-processing: SPOT 5 images of 2007 with a spatial resolution of 5m were used in determining the existing land cover of the study area. Furthermore, different vector data were integrated in GIS to support the analysis, interpretation and presentation thereof. The SPOT 5 images are acquired from the Ethiopian Mapping Agency and were geo-referenced to the UTM WGS 84 zone 37N projection. Prior to supervised classification, as to improve accuracy, the image was rectified using the GCP's collected during the field work;

Classification:

Unsupervised classification: using the ISODATA algorithm with a minimum of five and a maximum of 10 unique clusters was made with 20 iterations. This preliminary classification depicted groups of spectrally homogenous pixels and mainly assisted ground data collection.

Ground truthing: Field data was collected on representative land cover categories identified through the preliminary unsupervised land cover classification output of SPOT 5 image and sup-plemented with guides from local knowledgeable persons and topographic maps. At each observa-tion plot, data on biophysical characteristic including the vegetation type, species composition, per-cent real cover of the different physiognomic vegetation types and crop mix, intensity of cultivation of the agricultural units and other relevant characteristic information were recorded on the field forms. The geographical locations of these points were registered using GPS (Garmin 12). All col-lected data are entered into a database and were exported to ERDAS for the extraction of training data that served to portray accurately the spectral complexity of the identified land units and the proper execution of the classifier. Furthermore, the validation data collection points were selected by generating random verification points over the classified image. Overall, a total of 96 observation plots distributed over the study area were assessed.

Supervised classification: The Maximum Likelihood Classification is employed to conduct the supervised classification. Land cover signatures are developed by superimposing the coordinate points of collected data from the field on the image, followed by recognition and on screen digitizing of group of pixels. The spectral separability of each train-ing class on the images were tested and modified for lower separability values by categorization of the training signature and re-digitizing as to approach the highest level of separability and ensure the accuracy of classification and the true interpretation of the results.

Accuracy assessment, data integration, processing software and equipments

ERDAS Accuracy assessment utility was used to compare the accuracy of the classification. By comparing, the validation data with the classification result, the overall, producer and user accuracies


and as well, Kappa coefficients were calculated. Ancillary data layers including digital elevation mod-els and administrative boundaries are integrated into the classified image to generate statistical data pertaining to the objective of the study. ERDAS 8.4 was used to process the images, ArcGIS 9.2 to manage and manipulate geographic data, whilst MS Excel 2003 to enter and edit the database. Gar-min 12 GPS was used to identify geographical location during inventory of land units and collection of spatial information.

4.2. Results and Discussion

4.2.1. The identified land cover classes:

Based on the supervised classification of the satellite image and ground verification, ten land cover types are recognized (Table 1). These include wooded shrub grassland, shrub grassland, grass-land, Agroforestry, intensively cultivated land, moderately cultivated land with dense trees, moder-ately cultivated land, sparsely cultivated land and settlements. Of the identified land cover types, agroforestry is the dominant land cover type and is mainly found in the eastern and southern parts.

Moderately cultivated land is the second major cover type (23.1%) and exist mostly in the mid-northern and central parts. Intensively cultivated land (16.44) is the third major category of land cover, typically located in the north western and to some extent in mid northern parts. Moderately cultivated land with dense trees (6.87%) is mostly found dispersed and in patches within all places except eastern and north western parts where its' occurrence is very limited. Of the identified natural vegetation types wooded shrub grassland covers 2.7% and shrub grassland covers 1.2%, whilst grassland have the lowest extent (0.1%) and can be visible at two places at a scale of 1:35,000, oth-erwise is very inconspicuous.

The distribu-tion of the land cover types by Quebelle Admin-istration (Table 2) shows most of the Agroforestry unit is located in Hase Haro QA whilst its other land cover types are proportional less than other studied KAs. Grasslands, hav-ing the lowest area coverage, occur somewhat equally in Tumata Chirecha and Bale Bukisa but none (in reference to the mmu) in Hase Haro. Generally, most anthropogenic origin cover types, with the exception of Agroforestry and significant amount of the natural vegetation occur in Kara Soditi QA.

Table 1. Distribution of land cover types by QA

Quebelle Administration

Mapping unit

Total

Agr

ofo

rest

ry

Inte

nsi

vel

y cu

lti-

vat

ed lan

d

Mo

der

atel

y cu

l-

tivat

ed lan

d w

ith

den

se t

rees

Mo

der

atel

y cu

l-

tivat

ed lan

d

Sp

arse

ly c

ult

i-

vat

ed lan

d

wo

oded

sh

rub

gras

slan

d

Sh

rub

gra

ssla

nd

Gra

ss lan

d

Pla

nta

tio

n

Tumata Chirecha 311.2 103.9 45.0 242.5 1.8 21.0 7.5 0.8 2.6 736.3

Kara Soditi 393.9 385.9 94.2 306.2 8.5 49.9 28.4 1.5 2.9 1271.3

Land cover type Area (hectares) Percent Forests

Wooded shrub grassland 88.66 2.65

Shrub grassland 41.39 1.23

Grassland 3.29 0.10

Human-made forest 7.51 0.22 Agriculture

Agroforestry 1643.62 49.05

intensively cultivated land 550.84 16.44

moderately cultivated land with dense trees 230.32 6.87

moderately cultivated land 774.29 23.11

sparsely cultivated land 11.22 0.33

settlements


Hase Haro 694.1 16.1 27.5 100.1 0.2 3.9 1.1 0.0 1.3 844.4

Bale Bukisa 244.9 45.1 63.7 126.1 0.7 13.9 4.4 0.9 0.7 500.5

Total 1644.1 550.9 230.4 774.9 11.2 88.7 41.4 3.3 7.5 3352.5

Percentage 49.0 16.4 6.9 23.1 0.3 2.6 1.2 0.1 0.2 100.0

4.2.2. Description of the land units:

The characteristic features and distribution of the identified land cover types are described brief-ly in the following sub-sections.

Wooded shrub grassland: This unit covers an area of 88.6 hectares. The real cover is about 60% and percentage composition of trees, shrubs and grasses is 7%, 40-60% and 10-40% respective-ly. Bare and/or rock-outcrops in some places reach up to 10%. Main characteristics species of this unit include Cordia africana, Ficus spp., and Annona spp. The major land use activity is collection of wood; nevertheless, on some places it is highly encroached to expand agriculture activities

Shrub grassland: The shrub grass land unit covers an area of about 41.3 hectare. The propor-tion of tree, shrub and grasses on average is 5, 60 and 31 % respectively. Bare-land / open rock out crop are also observed in the unit (~4%).

Grassland: Grassland covers an area of only about 3.3 hectare. Herbaceous species are the ma-jor component of the unit (>90%). The proportion of tree and shrub is below 3 and 5 % respec-tively. Wide units of this cover type are mostly communal lands and cut and carry, mainly for roof hatching, is the only type of use permitted.

Agroforestry: This unit is characterized with an upper storey of trees, density ranging between 25 to 40%, including trees such as Millettia ferruginea and Cordia africana and as well fruit trees such as Mango (Mangiferea indica) and Avocado (Persea americana). Coffee (Coffea arabica) is the dominant cash crop cultivated (65%) whilst Enset (Ensete ventricosum) covering some 10% and banana (Musa spp.)(<2%) are also found. A variety of crops such as sweet potato (Ipomoea batatas L.), boyna, godere (Colacasia esculanta L.) and beans (Phaseolus spp.) are also grown in the lower stratum. Small pockets of grasslands (<1%) also exist in patches. Exotic tree species, mainly Eucalyptus sp. and Grevillea ro-busta, are found on some places planted mostly on the peripheries of farm boundaries. Of the identi-fied land cover units agroforestry has by far the highest spatial extent, covering 1643.6 hectare.

Intensively cultivated land:

This unit ccovers an area of 550.8 hectares and is mostly. Almost all cultivated crops are rain-fed and main crops planted include maize, Teff, barley and beans. Coverage of cultivated land is above 72 %. Trees on farm (~6%), shrub (4-8%) and grass (4-10%) are also found scattered over the unit.

Moderately cultivated land with dense trees: The unit is characterized by cultivated land (60-71%) with scattered indigenous and exotic trees scattered on farm (10-20%), shrubs (2-6%) and grasses (6-14%) and covers an area of 230.3 hectare. Major crops grown include cereals like maize (Zea mays) and Teff (Eragrostis tef), pulses like haricot bean (Phaseoules sp), root crops such as Sweet potato (I. batatas) and as well sugar cane. Fruit trees like avocado (P. americana), banana and as well root crops like Enset are also found mostly localized to home gardens.

Moderately cultivated land: Covering an area of 774.3 hectare, the unit is characterized by ag-ricultural land (60-71%) with scattered trees (<7 %), shrubs (2-6%) and grasses (6-14%). The crop mix is same as with C2-1 (Moderately cultivated land with dense trees) described above.

Sparsely cultivated land: Covering an area of 11.2 hectare, this unit is characterized by a culti-vated land covering some 40-59%, trees scattered on farm (6-8%). shrub (10%), and degraded grass-land (16-25%). Other minor units found within this category include wooded shrub grassland (up to 20%).

Human-made forest: Plantation forests are composed of tree species like Cupressus lusitanica and different Eucalyptus spp. However, most of these areas are highly encroached. Enrichment planting is done on places. The unit covers a total of 7.51 hectare.


Settlements: This unit is not mapped and is described only based on data gathered from field work. The unit are found either localized in selected areas, mostly further away from roads or dis-persed along the main asphalt road. In the latter case, the houses are observed on the image but de-lineation or buffering of road as to discriminate would have the risk of increasing area coverage.

4.3. Accuracy Assessment:

Result of the accuracy assessment of the final land cover map has produced an overall accuracy of 68.82 and a kappa coefficient of 0.54.

5. Result Of Household Survey

5.1. Population Characteristics:

According to the information from KAs office, the population of the study area totals about 26787. Details of the population and the number of households of the four KAs are shown in Table 3. As shown in this Table, Hase Haro QA contains large population followed by Kara Soditi. Bale Bukisa QA has a relatively less population. Accordingly, the population density (crude) in the study area is estimated to be 802 people per Km2. This figure is higher than the estimate reported by WBISPP (2001) for the entire Woreda, which were 593 persons per Km2. There are 4842 households in the study area. A household in this case is a group of related people living together as a family sharing expenses and revenues. The number of person per household in each QA is shown in Table 4. Ac-cordingly, the average household size in the project area is 7.1. This figure is higher than the 1994 census based estimation for the national average, which was estimated at 5.

The average family size of the sampled households is about 7.1. The majority of the households (about 71%) have 6-10 members. About 21% of the sampled house-holds have 1-5 persons while the remaining 8% have more than 10 persons. On the other hand, the percentage of larger family (6-10 persons/ household) is relatively higher in Hase Haro than the other KAs.

According to the census of 1994, the rural population of Wonago woreda was reported to be 146,882 (CSA, 1994). However, after this census, the previous Wonago woreda was restructured, and some areas with 24 KAs from the northern parts were seceded to form the present

Dila Zuria woreda. Thus, by summing up the census result of 2007 for the two woredas, the present population is estimated to be about 205,761. From this, the average annual growth for Wonago woreda is estimated to be 2.8%. This figure is close to the reported estimate for the region (2.9%) by CSA (2008).

The growth in population of the KAs in the study area can be observed from available data which has been recorded during 1984, 1994 and the current year (Table 5). The population data for 1984 and 1994 is obtained from CSA (1989) and CSA (1994) respectively. The data for 2007 is also obtained from CSA (personal comm.).

Note that the population data of 2007 for the Quebelles were not yet released for public use by the CSA during the prepara-tion of this report. However, data for the current year is obtained from the QA of-fices. As shown in the table, below there is a difference between the population data from the two sources. If the population of

Table 3. Details - size of study area population Quebelle Association Population Households Bale Bukisa 5416 801 Tumata Chirecha 6000 1275 Hase Haro 9173 1505 Kara Soditi 6198 1261 TOTAL 26787 4842

Table 4: Number of persons per households in each QA

Pers

on

s/

HH

Bale

Bu

kis

a

Tu

mata

Ch

irech

a

Hase

Haro

Kara

So

dit

i

To

tal

1-5 8 15 16 13 52 6-10 29 46 52 45 172 10+ 3 3 8 6 20 Total 40 64 76 64 244

Table 5: Population by QA Quebelle 1984 1994 2007 2010

Bale Bukisa 2498 2697 2981 5416 Tumata Chirecha 3238 3556 4558 6000

Hase Haro 4834 5545 7397 9173 Kara Soditi 3064 3918 5761 6198

Total 13,634 15,716 20,697 26,787


2007 is projected to the current year using the regional annual population growth rate (2.9%) during 1994-2007 (CSA, 2008), still there will be a difference of about 4230 people.

5.2. Population Composition:

Headship in the area is characterized by patriarchal family structure. Thus, the number of male heads is higher than the female headed households. Female headed families’ accounts only 3.7% of the total sampled households. However, this figure is quite lower than the estimation made by the leaders of the four KAs, which rises to about 14.5%. According to the sample household survey, the majority of the population (about 82%) belongs to the ethnic group of Gedeo. The Oromos and Amharas consist of 13.9% and 3.9% respectively. The remaining small portion (0.2%) belonged to other ethnic group. Almost all the sampled households are Christian, out of which about 89% are Protestants, while 10% are belonged to the Orthodox Church. Very few individuals (about 1%) are belonged to Catholic Church. The age distribution of the households covered by the survey shows a non- balanced, pyramidal form with larger proportion of young people. About 54.6% of the popula-tion is young with the age class of 0-15.

The population in the working class: >15 years is 52%. The age structure is characterized by higher proportion of young age and low proportion of old age group. In other words, it can be said that the economically active and mature section constitutes the minority of the proportion of the population of the sampled households. The data also shows that for each 100 persons in the pro-ductive age group (>15-59) there are about 126 dependants to be supported.

As shown in Table 6, women are propor-tionally more than the men in the younger age groups. The sex composition of the population generally indicates that males are less than females.

5.3. Education Status:

Almost half of the individuals of the total sampled population have no education or are illiterate. A very small proportion have a basic education (could write and read), accounting only 4.6% of the total. Members of the households with primary and secondary education combined contribute to 45% of the total population. However, a negligible number of individuals (0.3%) out of the total population have managed to reach a college level education. In most cases, the education level of the sampled population shows remarkable differentiation by gender. Thus, the number of female indi-viduals who have primary and secondary education is lower than the male individuals are.

Therefore, a characteristic feature of the household heads with regard to education re-flects the high illiteracy rate in the study area. About 62% of the household heads are illit-erate, while only 18% and 8.6% of them have reached to primary and secondary levels respec-tively. The remaining 11.5% of the household heads have basic education. In view of the ex-isting number of school in the locality as well as in the nearby Wonago and Dila towns, the community in the study area enjoys a relatively better access to educational institutions.

5.4. Social Services:

There is one health post with 2-3 health extension workers in each Quebelle. The health posts have been developed in the past two years. There is also an additional Health Centre with one health

Table 6: Percentage distribution of population by age group & sex

Age group Total Male Female Average

per HH No % No % No % <5 151 8.7 66 3.8 85 4.9 0.62

5-15 792 45.9 360 20.9 432 25.0 3.25 16-39 676 39.2 287 16.6 389 22.5 2.77 40-59 88 5.1 84 4.9 4 0.2 0.36

>59 19 1.1 18 1.0 1 0.1 0.08 1726 815 911 7.08

Table 7: Education status - survey population

Sex

Illi

tera

te

Basi

c e

du

-

cati

on

P

rim

ary

Sch

oo

l

Seco

nd

ary

Sch

oo

l C

oll

eg

e

leve

l

Un

der

ag

e

To

tal

Male 280 37 301 125 6 66 815 Female 432 43 276 77 - 83 911 712 80 577 202 6 149 1726


officer and two nurses in Hase Haro QA. In line with this, over 81% of the household heads indi-cated that they have also access to reproductive health care services, which is provided by the health extension agents. There are two primary schools (grade 1 - 4) in Tumata Chirecha QA, one Junior Secondary School in each of Hase Haro and Kara Soditi QA, while Bale Bukisa has two Junior Sec-ondary Schools. The most prevalent diseases identified by the respondents are malaria, diarrhoea and typhoid. All the household heads indicated that they use hand dug toilets. About 44% of the household heads indicated that they use water tap which are located within the distance ranging from 100 meter to 1.5 km. Almost equal proportion of the respondents use spring water that is lo-cated within the distance ranging from 100 meter to one km, while the remaining few respondents said that they are using rivers and ponds.

5.5. Land holding and Use:

All the respondents indicated that they do own the land they cultivate or use as farmland. On the condition of possession of the land, over 90% of the household heads indicated that they inherited from their parents. In other words, they acquired their land through inher-itance. Very few (about 3.7%) have said that they purchased some small plots from the neighbouring or surrounding areas, while only two household heads indicated that they got their land on rental basis. No respondents have got land through the government land reform or redistribution programme. The average size of land holding of the surveyed households is about 0.63 hectares, with minimum of 0.014 hectares and maximum of 2 hectares and above. The average figure however differs significantly from the average calculated based on the estimated mean by the QA leaders, which comes around 0.4 hectares.

Nevertheless, the frequency of distribution of holding (Table 8), shows that over 55% of the re-spondents have an average holding ranging from 0-0.5 hectares, while the prevalence of holding of up to 1 hectares reaches over 78%. Only 33 households (13.5%) of the sampled households pos-sessed more than 2 hectares of land. Surprisingly, there is a household that owns 8 hectares in the study area. Therefore, the majority of the households have a serious shortage of land, owning 1 hec-tares or less; out of which over 55% have 0.5 hectares or less. Thus, shortage of land is greatly felt by the majority of the household heads (about 82%), who indicated that they have no sufficient land for cultivation and feed their family. In contrast, only 16.8% of the respondents have the feeling that they have sufficient size of land to support their family. According to the response of the former group, at present capacity they can cultivate and efficiently utilize an additional land of 1.7 hectares. The issue of acute scarcity of grazing area for livestock has also been felt by the good majority (87%) of the respondents. Only 10% of the sampled households indicated that they have no problem of grazing land. However, the data on livestock holding revealed that the later group are mainly com-prised of the households with no livestock. Land utilization in the study area is dominated by per-manent cultivation of both perennial and annual crops. According to the information from the household survey, over 84% of their land is used for cultivation, and an average of 5.4% is utilized as home gardens. In all cases, the main crops cultivated by the respondents in priority order include coffee, annual crops, Enset and fruit trees. In addition, an average of 4% of their land is used for grazing purpose, while land left as fallow and woodlot accounts 3.5% and 3.1% respectively.

In line with land holding, the respondents were also asked for reasons behind the scarcity of land in their locality. Most of the household heads indicated that population increases (increase in family size) is the main factor for scarcity of farmland. Obviously, a large number of people need a large size of land to cultivate. Each family is expected to allocate land to their children/ descendants when they reached maturity or they are ready to form their own separate households. This process will continue and the land possessed by each family (household) has to be passed to successive gen-

Table 8. land holding in the study area

Lan

d s

ize

(hec

tare

s)

Bal

e

Bukis

a T

um

ata

Ch

irec

ha

Has

e

Har

o

Kar

a

So

dit

i

To

tal

%

0 - 0.25 5 11 25 5 46 18.9 >0.25 - 0.5 19 17 36 17 89 36.5 >0.5 – 1.0 8 18 8 22 56 23.0

>1.0 - < 2.0 5 12 4 11 32 13.0 >2.0 3 6 3 9 21 8.6 Total 40 64 76 64 244 100.0


erations in a progressively fragmented fashion, thus leading to declining farm size. The presence of agricultural crops including perennial plants which covered the entire land of the study area, has also been identified as the other main factor contributing particularly to the shortage of grazing land.

5.6. Occupation, Income Source And Migration:

Almost all the household heads covered by this study are farmers. Nevertheless, about 17.6% of this group indicated that they are also engaged on off-farm activities. A number of households in this group are involved in activities of safety net programme, while some 28 households said that they are engaged on coffee trade during August to February mainly to earn additional income. Two respondents also indicated they are employed as civil servant all the year round, while another two said that they are earning additional income from carpentry work. Others also indicated that they are engaged on retail trade, café business, salvage trade and cloth sewing (local tailor). The result of the household survey is more or less the same with the estimation of the QA leaders as regards to the occupation, with the exception that the later group has the estimation that about 1.5% of the house-holds are also involved in poetry. SLUF (2006) also reported that local people also work as daily la-bourer in farming activities such as slashing, fattening of oxen taken from rich farmers and share of cropping from widow women.

In general, the result from the assessment of the household income may not be always absolute, especially when the study has to depend on the response of the households. In most cases, the re-spondents are used to withhold information on the exact amount of their earnings. For example, there is a variation on cash income earned by different households from the same product sold in the same local market. Similarly, there is also a variation between the price rate of the same agricul-tural products collected by the study team from the local market and the selling rate obtained from the household survey. The assessment on income may even be affected more when the studies are carried out over a short period, and thus may not allow a further crosschecking and follow up of relevant factors such as production and market situation. Hence, the data on household income should not be regarded as actual figure; it is rather an approximate to the real amount.

According to the result of the household survey, the average total annual income of the household is about Birr 4,151. This annual in-come is lower than the country’s real GDP per capita (US$ 510) for 1998, which was reported by UNDP (1999). However, if the revenue from off-farm activities and the income from sales of hon-ey (that is extracted from the data given by respondents for the week-ly sale of the product under different occasion), then the average total income will rise to about Birr 4,960. It should be noted that there is a considerable inter household variation in annual income with minimum of Birr 50 in Bale Bukisa QA and a maximum of Birr 90,000 in Kara Soditi QA. However, the majority of the respondents (about 75%) earned an annual income of Birr 5,000 and below. The highest source of cash income is the sale of agricultural crops, followed by the sale from livestock. As shown in Table 9, the sale of agricultural crops accounts about 58.3% of the cash in-come for the sampled households. Based on the estimation from the information given for weekly marketing supply, the income from honey stands superior to that from fruit and vegetables. Howev-er, DAs disagree with this, indicating that the income from fruit and vegetables is by far greater than that of honey. Some farmers also earn a considerable size of income from off-farm activities and sale of tree poles.

Two forms of migration are recorded. Internal migrants within the QA and the second are mi-gration outside the QA boundaries. The household survey reveals that about 3.7% of the population

Table 9: Cash income from sales of agricultural products Income source % Average annual income

Agricultural crops 58.3 705,782 705,782 Fruit and vegetable 4.3 51,720 51,720

Sale of Honey 0.6 2,560 128,208* Sale of Trees 2.2 26,630 26,630

Sale of livestock 18.4 223,234 223,234 Others 6.2 2,950 74,750* Total 1,012,876 1,210,324

Average 4,151.1 4,960.3 *Based on data from weekly marketing activities


of the sampled households is moving both within and outside their KAs in search of additional in-come in both on and off-farm activities. According to the estimation of QA leaders, about 2.7 % of the population of the four KAs are moving outside their QA territories to be engaged on other em-ployment activities. From this, it can be said that due to unknown reason large-scale migration is not a common practice among the people residing in the study area. One should expect that a larger number of people migrating outside their village area either to towns or other KAs in search of work, especially in the study area where access to land and employment in nonfarm sector are diffi-cult to find. Households with shortage of land and even the landless are not migrating as a coping strategy.

5.7. Energy Source and Consumption:

Wood is the main source of energy, providing over 88% of the domestic energy requirement of the community residing in the study area. The second important source of energy is kerosene, sup-plying 10.3% of domestic energy required for cooking and lighting. Cow dung, crop residue and electric power supply only a small fraction (around 1.5%) of the domestic energy requirement (see also Table 10). In particular, the use of electric power is limited mainly to a few households residing close to Wonago town.

Table 10: Details of domestic energy source and consumption Energy Source Amount Kcal Unit Qty Converted quantity Kcal % Wood 3.5kg 4708 Bundle 549 18117kg 24369953 88.1 Cow dung 12.3kg 2092 “ 30.0 1260kg 178585 0.6 Crop residue “ 37.0 1050kg 258013 0.9 Electricity 4.7 Kw 860 Watt 10.3 10.3 watt 1885 - Kerosene 1.0 litre 8600 Litre 330.6 330.6 lit. 2843160 10.3 Total 27651596 100

According to most households, tree species, which are used as fuel, include Milletia ferugenea, Cro-ton macrostachys, Vernonia amygdalina, Eucalyptus spp., Ficus spp. Cordia Africana and Prunus African (Tikur inchet). Some of the households indicated that in the case of eucalyptus and Cordia Africana, they only use the branches, leaves and twigs; rather than the whole stem, since the later is usually sold as pole for construction purpose. Asked whether there is forest to meet their wood demand, over 67% of the respondents pointed out that there is no sufficient forest resources in the surrounding areas. Again, asked for the reasons behind the absence of plantation and natural forests in their locality, about 48% of the respondents indicated the shortage of land partly due to agricultural crops as the main barrier for existence of forest in the area, while about 17% blamed deforestation to be the cause for the absence of natural forest. Some households (about 10%) also suggested that lack of desire or interest to plant trees as the reason for the absence of plantation forests.

The majority of the respondents (over 78%) indicated that they collect fuel wood mainly from their private holding, while only few (about 15.6%) said that they fetch from natural stands. Thus, the most important source of fuel wood is the trees in the homestead and in the farm area, which are owned privately. A few households indicated that they obtain firewood from both private hold-ing and community forests. It seems that in the later case they mean some remnant public forests, because according to the information from DAs there is no as such community plantations in the area. The data on the type of cooking stove tells that the majority of the households (over 93%) use traditional stoves, while only very few (1.6%) use improved (energy saving) stoves, which are made from cement. As to why the former group are not using improved stoves, over half of them indicat-ed that the price is too high, about 20% said that they have no idea about it, while the remaining 19% think that improved stove do not bring much change on energy consumption.

5.8. Reforestation and Soil Conservation:

There is one forest nursery and a coffee nursery run by the Woreda Agricultural and Rural De-velopment Office (WARDO). The forest nursery with capacity of 65,000-90,000 seedlings is located in Tumata Chirecha QA. Seedlings raised in this nursery are distributed to interested households free


of charge. The major tree species raised in the forest nursery include Milletia ferruginea, Eucalyptus spp., Cordia africana, Grevillea robusta, Cuperessus lusitanica, Casuarina equisetifolea and Erythrina abysinica. As noted earlier, farmers carry out tree planting in the form of farm forestry (private tree production on farms), largely as homestead, field tree and farm boundary plantings. Due to shortage of land, they are unable to establish block plantation. The objectives of tree planting are mainly for production of fuel wood, construction poles, fodder, improving soil fertility and shade for coffee plantation.ii

5.9. Agricultural Activities

5.9.1. Farming system:

According to the information collected from the household survey, the major crops cultivated in the four KAs are coffee, Enset, banana and root crops such as sweet potato and godere. The larger portion of farmland is occupied by coffee plantation. This is followed by maize, Enset and banana plantation. Enset (Enset ventricosum) also known as false banana is a carbohydrate rich crop with a strong pseudo stem and edible bulbs and corn. Equally important are the annual crops raised in the area are teff, beans and barley. Thus, the coffee plantation has covered a relatively larger farm area of about 51.3% with about 37.1% share of total yield. Maize is the second in both total area coverage (13.5%) and contribute to total yield that comes to about 21.5%.

Table 11: Details of crop produced in the study area Type Of Crops Area sown (ha) % Yield (Kg.) % Average/ha

Annual crop

Maize 51.41 13.54 60200.10 21.52 11.71 Teff 25.47 6.71 18100.25 6.48 7.12 Sorghum 2.11 0.56 1700.50 0.63 8.29 Barley 9.87 2.60 4800.50 1.73 4.91 Soya bean 13.68 3.60 10500.40 3.77 7.70

Sub-total 102.54 27.00 95400.75 34.12

Perennial crops

Coffee 194.7 51.27 103800.7 37.12 5.33 Enset 35.60 9.37 28300.50 10.13 7.96 Banana 23.03 6.06 22400.00 8.01 9.73

Sub-total 253.33 66.71 154600.20 55.26

Root Crops

Sweet potato 19.54 5.15 25000.80 8.96 12.84 Godere 1.17 0.31 1300.20 0.47 11.28 Boyena 3.17 0.83 3300.00 1.18 10.41

Sub-total 23.88 6.29 297.00 10.61 Total 379.75 2797.95 100.00

The farming system is based on traditional agroforestry system, which is considered as sound system of land use. In other words, the system is based on a combination of three components: farming, animal husbandry and forestry on the same land to increase the overall production and re-duce production risks. The term agroforestry is defined by ICRAF as “Land use systems and practices where woody perennials are deliberately integrated with crops and/or animals on the same land management unit”. In contrary to the common horizontally single storied cultivation and the common agroforestry system, this is a relatively advanced multi-storey land use system with more or less three strata: The highest stratum of the farm is dominated by fruit trees and forest trees. The most commonly grown fruit tree is Avocado (Persia Americana). Abuka or Kazamora (Casimiroa edulis) trees, which seem to be in-troduced recently are also seen in the upper storey of some farms. The main tree species in this layer are Milletia, Cordia, Croton, Prunus, Ficus and Eucalyptus. In this case, both forest trees and fruit trees are playing a great role in maintaining soil fertility for crops in the lower stratum. This is achieved through shedding leaves and barks as well as the litters that replenish the organic matter in the soil. Farmers in the study area believe that especially leaves of Milletia feruginea decomposes faster than that of other species. Trees also improve the chemical and physical condition of the soil while they also keep the soil intact and therefore prevent from erosion hazards. The middle layer is occupied by perennial crops mainly coffee, Enset and some fruit trees. Coffee plants are grown largely under the shade of forest and fruit trees. In most cases, farmers prefer to grow coffee plants under the shade


of the major tree species such as Milletia, Cordia and Erythrina. With this are also annual crops like maize and sorghum. The ground layer is dominated by tuber crops like Godere, Boyena, sweet potato and other horticultural crops like pepper. The lower layer is also used to cultivate annual crops/ pulses such as beans. The farmers’ choice of crop depends mainly on consumption pattern followed by market demand. Some respondents also indicated that their choice is based on combination of both their consumption, market demand and suitability of the land. In general, cultivation is based on traditional or cultural system. As condition doesn’t permit ox plough, the majority of respondents (81%) use hand tools (hoe) to cultivate their farm area. About 16% of the household heads indicated that they use both hand and ox plough. Only one respondent practice ox plough while none of them use mechanized (tractor) cultivation. Surprisingly, none of the surveyed households also prac-tice irrigation farming. A smaller proportion of the respondents (28%) are using inorganic fertilizer (DAP and Urea) largely in maize and Teff cultivation. This group is using a total of 220 kg of DAP and 500 kg of urea. A very few individual in this group also said that they use 60 kg of DAP in cof-fee and Enset cultivation. A much less number of household heads (6.6%) indicated that they used a total of 25 litre of insecticide (2 4D) in maize and Teff farm. Further, about 24% of the respondents said that they are using in aggregate 492.2 kg of pioneer and 75 kg Awas quality seed of maize and 50 kg of Awas quality seed of Teff. According to the response of KAs leaders, the major constraints are land shortages, population, erratic rains, poor soil fertility, know how or farming technology and inputs.

5.9.2. Livestock production:

Livestock production is another important economic activity in the study area. Livestock plays an important role in the economic and social wel-fare of the farming communities. The major bene-fits from livestock are cash income from sales of live animals (partly based on livestock fattening), pack animals for transportation and in some cases oxen for farm work.

According to the information from the house-hold survey, sales from livestock during the last five years by the sampled households alone totals about Birr 590,000. Livestock ownership also determines status and prestige in the community. De-tails of the size of livestock holding are shown in Table 12. Over 80% of the sampled households are engaged in some form of livestock production, while almost all have poultry. However, unlike other areas, there are relatively few pack animals.

This is probably due to the occurrence of tsetse fly, which are the vectors of livestock dis-ease: trypanosomiasis. The data from the sam-pled households tells that there are about 746 cattle (oxen, cows, heifers, bulls, calves), 288 sheep, 133 goats and 17 pack animals (horses, donkeys and mules). The farming system and the prevailing land shortage in the area may not al-low open grazing system. Thus, most households practice stall-feeding, cut and feed system. Open grazing is therefore practiced in rare occasion. Free grazing in this area will obviously result in trampling and damage to both agricultural crops and tree seedlings. According to the household survey, the most important source of livestock feed is agricultural by products such as leaves of En-set and banana in all seasons. Leaves of some tree species such as Milletia, Erythrina, Vernonia and

Table 12: Details of the size of livestock owned by the households

Liv

esto

ck

typ

e

Num

ber

No

/h

ous

eho

ld

Liv

esto

ck

typ

e

Num

ber

No

/h

ous

eho

ld

Ox 191 0.8 Sheep 288 1.2 Young oxen 98 0.4 Poultry 918 3.8 Cows 252 1.0 Horse 3 Heifer 83 0.3 Donkey 14 Calf 122 0.5 Beehives 468 1.9 Goat 133 0.5

Table 13: Weekly animal product marketing

Typ

e

Un

it

HH

co

n-

sum

pti

on

Su

rplu

s

Sell

ing

pri

ce

An

nu

al

in-

co

me

Milk Lt 168 32 314.4 15,091 Butter kg 58 23 671 32208 Hide No - - - NA Skin No - - - NA Egg No 967 762 673.5 32328 Honey kg 33 149 2671 128,208 1,226 966 7159


Choe are also used as fodder (livestock feed). Straw/ hay comes the next source especially in the dry seasons while natural grazing might serve as supplementary source in wet seasons Thus, the average size of livestock per household is less than elsewhere in the country.

The major constraints to livestock produc-tion are shortage of feed, diseases and lack of vet services. There is no as such a spare land to be used as communal grazing area. Therefore, due to shortage of land, farmers have no grazing areas or open space to graze their cattle. Ac-cording to the sampled household heads, cattle are attacked by Anthrax, Black leg and foot & mouth disease, mostly in dry seasons. Tripano-somiasis and Alkit are also posing livestock health problem in the area. In some of the sam-pled households subsistence milk production is

practiced, and the surplus is sold both unprocessed and largely as butter. A number of animals are annually sold at local market. Weekly animal products and marketing are shown in Table 13. Annual household consumption and marketing of livestock during the past five years are in Table 14.

5.10. Socioeconomic Problems:

According to the QA leaders, shortage of arable land, which emanated from population pres-sure, is the leading socioeconomic problem in the project area. The scarcity of land for both cultiva-tion and grazing was bitterly felt by the majority of the sampled household heads who believe that their farm land holding are too small to produce sufficient grain to meet their family’s consumption requirement. Asked what additional size of land they could be able to cultivate with their present capacity, more than half of the respondents in this group demanded an additional land of more than one hectare. The average demand of the group is about 1.73 hectares/ household. The result of the household survey also shows that the community has to spend a significant size of their income to buy additional grains to fill the deficits. Table 15 shows details of the amount they spend to purchase different crops. Thus, on average a household spends about Birr 1074 annually to buy grains.

Unlike other pre-urban areas, there are neither factories nor investments in agricul-tural sectors that could absorb the landless young group. Thus, opportunities for em-ployment in non-farm sectors are limited. As noted earlier, only 8.4% of the total sampled populations can be engaged on off-farm ac-tivities such as cafe business, employment, carpentry, retail trade and coffee trade. Thus, declining farm size, combined with lack of other alternatives or non-farm sector to ab-sorb the growing human population is among the leading causes of poverty in the area. Further, climatic variability (erratic rain), poor soil fertility, lack of education (know how) and lack of agricultural inputs are among the most pressing constraints to agri-cultural production identified by the re-spondent household heads.

According to the information from the staff of WARDO, attempt has been made by the region-al government to counteract the constraints related to land shortage, through a resettlement pro-gramme. Thus, 419 household heads were taken on voluntary basis to Gura Ferda area in Bench Maji

Table 14:Household consumption and mar-keting of livestock in the past 5 years

Liv

est

ock

Un

it

HH

co

n-

sum

pti

on

Su

rplu

s

for

sale

Sell

ing

rice (

Bir

r)

Cattle No 9 263 549,685 Small Ruminants No 156 124 28,432 Equines No - 2 2,450 Poultry/Hen No 116.7 533 9,430

Total 1332 922 589,997

Table 15: Details of crops purchased by the households to fill deficits

Cro

p t

yp

e

Qu

an

tity

pu

rch

ase

d

(Q.)

Un

it p

rice

(Bir

r/Q

.)

To

tal

pri

ce

(Bir

r)

Ave

rag

e p

ur-

ch

ase

/H

H

(Kg

)

Maize 121.4 250.0 30,350 49.8 Wheat 13.4 400.0 5,340 5.5 Barley 12.6 400.0 5,020 5.2 Teff 18.9 700.0 13,210 7.8 Enset *862.2 *130.0 149,440 353.3 Beans & peas 61.4 700.0 42,960 25.2 Cabbage *56.3 *60.0 4,500 23.1 Sweet potato 7.3 150.0 1,100 3.0 Total 261,920

*horse load (70-80 Kg)


Zone of the same region. Some participants in the group discussion indicated that in the process of this settlement, only the household heads, leaving their family behind, were taken to the new settle-ment area. Nevertheless, since they find it difficult to acclimatize themselves to the new area, they rather returned to Wonago. After this, however no any other measures (such as the land redistribu-tion programme undertaken in other regions) have been attempted to alleviate the problem of land shortage in the area.

6. Population Vs Resource Base Carrying Capacity

Resource base in this case refers mainly to land, water and forest resources upon which the peo-ple in the project area heavily relay for their livelihoods. Based on the estimate for the Woreda, the population of the study area is increasing at an average rate of 2.8% per year. Closely linked to this process is the reduction in land holding per household. With the growing population, the new gen-erations are demanding more land for cultivation, or survival. In the past, such new land for newly married couples are normally obtained either from parents of either spouse or by clearing forests (in unoccupied land/ marginal lands). However, this is no more in the heavily populated areas like the Wonago Woreda. In fact, like in many other parts of the country, there is no spare land to be seen in the study area.

As one elderly farmer explained during the group discussion, the process of land fragmentation because of population dynamics has continued for generations; and now reached a point where the good majority of families could no more be able to transfer a reasonable size of land to their chil-dren. As also noted by SLUF (2006), the carrying capacity of land in some areas in the woreda has already reached a state of climax with over 1000 persons per Km2. Therefore, it is likely that room for further intensification or improve agricultural productivity is much limited due to soaring popu-lation pressure in the study area. Obviously, the population in this area will continue to increase in the coming years, and with the available resource base, it will be even more difficult to absorb the additional people.

As pointed out earlier, the average land holding in the project area is about 0.63 hectares per family, and over 78% of the sampled households own a small size of land ranging from 0-1ha. This figure is below the extent of land required to sustaining both human and livestock. In general, the minimum economic holding size per typical household under low and moderate input levels of technologies is estimated to range from 6.5 hectares to 5.4 hectares respectively (MoA, 1989 cited by Gizachew, 1994). Based on the study of the impact of the size of holding on farm performance, Bir-hanu et al. (2002) came to a general conclusion that with minor exceptions, households with larger size of holdings perform better than the smaller holdings, with regard to food production and in-come generation. Thus, the current land holding in the project area is too small for further intensifi-cation to support the livelihood of the majority of the households residing in the study area.

In fact, the effect of population pressure on the resource base in the study area is masked by ag-ricultural system being practiced by the local people. Thus, the ongoing multi-storied farming system has helped much to reduce the impact of population growth on the existing natural resource base. This system is probably the most efficient and sustainable utilization of land in the country. Appar-ently, it can be said that, the high population in the study area has not resulted in a total environ-mental collapse due to traditional farming system and different forms of coping mechanisms. In line with this, a number of sources including SLUF (2006) noted that the population factor is the original driving force or motivation for the gradual shift from a horizontally single storied cultivation to the present vertical, multi-storied cultivation system.

7. Climate Change Variability

7.1. General:

It is well known that recurrent drought, famine and the recent prevailing floods are the major climate related natural hazards that affect millions of Ethiopian people every year. In particular, drought has caused a much disastrous damage that resulted in huge loss of life and property. For example, one million people died and many livestock were lost during the 1983-84 drought/ famine


(NAPA, 2007). The major causes of drought as stated by NMSA (1996)2 is the quasi-periodic oscillation of rain-producing weather systems, triggered by the combined effects of the so-called El Nino / Southern oscillation (ENSO) events. The same source indicated that the ENSO events have great impacts on the recur-rence of droughts in the country. Other climate related hazards include heavy rains, frost, high tem-perature and strong wind. Many scientists believe that droughts, floods and the other natural hazards are the result of climate changes that occurred due to increase in GHG levels, which include carbon dioxide (CO2), water vapour (H2O), methane (CH4) and nitrous oxide (N2O). As stated by Hough-ton (1991) in FAO (1995) “As agriculture and animal husbandry developed, the world’s population increased and human society became more industrialized, the levels of some of these gases increased significantly.” Further, more specifically, IPCC in PAI (2009) stated, “higher population growth projects generally result in more GHG emissions.”

As noted above, population growth is a key leading factor in accelerating the degradation of nat-ural resource base (deforestation and associated burning for expansion of farm land and intensive grazing), which in turn affect the climate directly by increasing the level of GHGs. In fact, removal and burning of forest vegetation for preparation of farmland or for livestock grazing is the main contributor to increases in the level of GHGs (FAO, 1995). The same source also reported that for-ests could mitigate the effect of climate changes because of their ability to absorb CO2 and store in their woody tissue. Hence, their removal and burning results in a massive and rapid release of car-bon, primarily as CO2 in to the atmosphere. In addition, loss of forest vegetation can also alter cli-mate through increasing reflectivity (albedo) and decreasing evapo-transpiration. With the later event, the amount of moisture that should be recycled into the atmosphere decrease significantly. It is re-ported that evaporation accounts for one-third to two thirds of rain formation in the semi arid re-gions of the Sahel (Brown, cited in Dejene, 1990).

From the above discussion, the linkages among the three factors (population growth, natural re-source base and climate change/variability) are obvious. In the first step, there is clear connection between population growth and shrinking land holding that leads to land hunger. In the absence of other livelihood alternatives (other economic sectors) that can absorb the growing rural population, the problem of “land hunger” leads to extensive deforestation (reduction of vegetation cover) for agricultural purposes, which in turn significantly contribute to increases in the level of GHGs. The increase in the levels of GHGs in the atmosphere causes climate changes, which include increase in average temperature, changes in precipitation and climate variability.

7.2. Local Community’s Perception and Coping Methods:

Climate variability has already posed significant effect on agricultural production, resulting in failure of crop production and productivity in the study area. The problem related to the climate change is recognized very well by most of the sampled household heads. Thus, the majority of the respondents (over 96%) do recognize the occurrence of climate change in their locality. The signals for climate change, according to these group includes erratic rain, dry air, frost, shortage of rain and cold night. As regards to their source of information, surprisingly more than half of the group indi-cated that it is based on their own observation (self observation), while about 38% have said that they got the information from the DAs.

Asked for the impact that the change in climate brought upon them; more than a quarter of the group indicated the occurrence of drought and famine as well as crop disease, while nearly the same size of respondents rather indicated decrease in agricultural production. The other 20% of this group blamed climate change as causing poverty. According to the sampled household heads, a number of traditional methods of coping with these changes are practiced by local people. These include changes in type of crops cultivated, planting trees, undertaking of off-farm activities (em-ployment), praying to god and irrigation farming. Most (about 77%) agree that family size affects the ability to cope with the problems emanated from climate change. The group pointed out that the increase in family size results in much deforestation and shortage of agricultural products and in-creased demand on products; thus decreasing the capacity to cope with the problems caused by cli-mate change. The majority of the respondents (over 87%) think that there will be even more risks to


be brought by climate change in the future, and they indicated that these risks or climate change re-lated hazards could be famine, drought, wide spread of crop and livestock diseases as well as migra-tion. Asked on their suggestion as to what should be done to counteract or reduce these risks, the good majority suggested the following

Planting trees or reforestation;

Introduction of irrigation, “modern” farming, meaning improved farming, family planning;

Provision of training and awareness creation by government;

For some individuals praying to god will also help in reducing the risk. Though it is not clear, some respondents also indicated that controlling of illegal merchants might help in the efforts to avert the risk that comes from climate change. Similarly, climate change related problems are also recognized very well by respondents who participated in the group discussion. For instance, one par-ticipant has expressed bitterly the losses or failure of crop she encountered due to climate change

Last year, I bought improved seed of maize or pioneer maize from Quebelle. After having sown in the farm in the right time, it germinated well. However, the rain stopped and all the young seedlings doomed to de-struction. Again, I bought another 15 Kg of Teff and inorganic fertilizer for Birr 600 from Quebelle, and sown during the Belg season. Unfortunately, the same disaster occurred, which I think due to climate change. The rain that normally comes in April has shifted towards July, thus complicating the entire situation.

Another participant also indicated that Croton macrostachys (a tree species) used to flower in May is now started flowering in December. He further said, “This clearly signals the change in climate.” An elderly farmer also summarized the overall situation as follows:

Our fathers used to tell us that coffee and Enset plants have no diseases, but now there is widespread dis-ease on these plants. Apparently, the yield has decreased much. Due to unknown reason, the yield from Godere and Boyena is also decreasing from year to year. In the past, we can buy a bundle of Kocho at local market for Birr 60, now you have to pay Birr 500 for the same bundle. Some people in my village are starving. In the past, there was no one who begs others. Today, you can find a number of people begging in the street. I think population growth and climate change are the root causes of the entire problem in our area.

7.3. Results of climatic factors assessment

Rainfall: Like many other tropical countries, rainfall is the most important climatic influence on agricultural production and productivity in Ethiopia. It is also one of the major essential climatic re-sources required for drinking water and hydroelectric power generation. Likewise, the amount and distribution of rainfall also determines the success in forestry development, as these factors affect the choice of species, growth rates, quality of wood produced and the purpose of growing trees. As noted earlier, the study area is categorized by NMSA (1996)1 within the Bimodal Type – 1 rainfall regime. According to this source, the study area is characterized with double maxima rainfall pattern with peaks during April and October.

Rainfall characteristics: The mean rainfall pattern of the study area is characterized with two peaks, in April in the first season and in September–October in the second season. There is a short break in the month of June and the second seasonal rainfall starts from the month of July and reaches the second peak in the month of October. Computation of seasonal percentage of the an-nual rainfall indicates that the February to May rainfall season comprises about 41% of the total an-nual rainfall and the June to October rainfall comprises about 49% of the total annual rainfall. It is important to note that due to the peak in the month of October, November also normally experi-ences some rainfall activity and the seasonal rainfall cessation occurs during the month of Novem-ber-December.

Pattern for the study area:

Mean Annual rainfall is about 1311 mm, and actual values in the given source data range from 1560.5 mm of rainfall in a wet year like 1988 to 974 mm of rainfall in the year 2003, which is consid-ered as a drought year for the area. Mean February-May seasonal rainfall is about 539mm of rainfall, and this value ranges from 327 mm of rainfall in 2003 (a drought year for the area) to about 687 mm


of rainfall in 1990 Feb-May season). Mean Kiremt (June-Sept) rainfall is about 645 mm of rainfall and this value ranges from about 936.3 mm of rainfall in 1988 June to October rainfall reason to 383 mm of rainfall in the June to October rainfall season of 2004.

Rainfall variability:

The standardized rainfall anomaly analy-sis indicates that the Belg rainfall season is susceptible to drought occurrences over the area in six out of 21 years. The most notable years include 1989, 2003, and the consecu-tive years of 2007 and 2008. The standard-ized rainfall anomaly of the June to October rainfall analysis indicates that the Kiremt rainfall season is susceptible to drought occurrences over the area in 8 out of 21 years, once in 2 to 3 years. The years 1990, 1999 and 2004 are the most notable drought years.

Rainfall Trend:

Rainfall trend analysis for the February to May rainfall season indicates that the February to May rainfall season shows the tendency of decreasing every year close to by 4 mm every year.

However, correlation coefficient for the trend computation is about 0.24. In the case of the se-cond season, the characteristics of the rainfall trend for the June-September, which is about 6 mm rainfall decrease differs greatly if we consider the June-October rainfall season, which is about a de-crease of only about 1 mm of rainfall every year. The correlation coefficient for the June to Septem-ber rainfall trend is 0.33 while the correlation coefficient for the June-October rainfall trend is only about 0.1, which is not statistically as significant as `that of the June to September rainfall trend. Thus, the seasonal June-September rainfall-decreasing trend has more statistical significance than that of the decreasing trend in the Feb-May season. On the other hand, the annual rainfall trend analysis indicates that the annual rainfall is decreasing by close to 6 mm every year with the correla-tion coefficient of 0.23. Here, it is important to note two months, February and October, which gave correlation coefficient of statistical significance. The maximum decrease of monthly rainfall is exhibited in the month of February with a trend of about 3.55 mm of rainfall decrease per year with correlation coefficient equal to 0.48, which is statistically significant.

July also shows about 2.7mm of rainfall decrease per year. June also shows about 1.85 mm of rainfall per year and the month of March shows about 1.7 mm of rainfall per year. However the sta-tistical significance of the trend is less for these months, since the correlation coefficient for the trend for the month of March is 0.2, for the month of June is 0.22 and for the month of July is 0.26 (Table 17). The maximum increase of monthly rainfall with a statistically significant result (Correla-tion Coefficient = 0.39, Table 17) is exhibited in the month of October (about 5 mm of rainfall in-crease every year), followed by the month of April (An increase of about 1 mm of rainfall every year, with a correlation coefficient of 0.3).

Table 17: Trend of monthly rainfall and coefficient of determination Yearly

trend (mm) J F M A M J J A S O N D

0.32 -3.56 -1.7 1.05 -0.7 -1.85 -2.7 -1.5 -0.72 4.97 0.49 -1.4 R2 .004 .23 0.04 0.1 .004 .05 .07 .05 .025 .15 .0047 .05 N 21 21 21 21 21 21 21 21 21 21 21 21

Rainfall reliability & Coefficient of Variability of the rainfall:

The analysis of inter-annual rainfall variability can be used to assess the reliability of seasonal rainfall at a given place. Accordingly, we have used two approaches: that of the computation of the coefficient of variability of the rainfall, and the computation of the minimum assured rainfall values at different probabilities. The coefficient of variability of the annual rainfall is about 12.7%, which can be considered associated with low variability of the inter-annual rainfall when compared with that of the central and eastern half of the country. The C.V. of Kiremt season is 21.1%, which can

Table 16: Trend of seasonal rainfall and coeffi-cient of determination

Yearly trend (mm)

Feb

-May

Jun

e -

Oct

ob

er

Jun

e-Sep

t

An

nual

Rai

nfa

ll

-3.94 -1.3 -6.3 -5.96 R2 0.06 0.003 0.11 0.05 N 21 21 21 21


be considered as of high variability, and the C.V. of the Belg season is 19.3%, which can be, charac-terizes as relatively medium variable.

Dependable rainfall:

Dependable rainfall is the minimum assured rainfall expected at a given place at a given proba-bility level, which in this case is the 80% probability level. We have computed the minimum amount of rainfall that is expected in four out of five years. This is especially important for various types of environmental and agricultural planning and implementation. The computation shows that about 490 mm of rainfall (as opposed to the mean seasonal rainfall of 644 mm) can be expected in the Kiremt season and about 475 mm of rainfall is expected during the Belg (as opposed to the mean sea-sonal rainfall of 539 mm in the season) in four out of five years. Therefore, in general different types of agricultural and environmental planning’s can best be implemented which can require about 965 mm of rainfall for the two seasons.

7.4. Temperature characteristics:

Temperature is another most important climatic parameter that determines agricultural produc-tion (NMSA, 1996)1. In general, the growth of plants is affected much by the temperature of the air and soil. Different species of plants in their different stages of growth have their minimum, opti-mum and maximum temperature limits. Hence, the “amplitude of variation” in temperature is more important for plant growth than the mean value. As noted earlier, mean annual temperature of the study area is about 20.3 degree Celsius and ranges from 21.6 degree Celsius in the Month of March to 19.5 degree Celsius in the month of December. Mean Annual maximum temperature is about 28 degree Celsius and ranges from 31degree Celsius in the month of March to 25.3 degree Celsius in the month of July. Mean Annual minimum temperature is about 12.5 degree Celsius, ranging from 10.1 degree Celsius in December-January to 14.1degree Celsius in the month of July.

Average temperature trend:

There is a general trend of increase in the average temperature. Thus, mean annual aver-age temperature shows a general increase of about 3 degree Celsius in hun-dred years, with a statisti-cal correlation of degree and that is roughly about an increase of 0.6 degree Celsius for the twenty years data period. If we consider seasonal trends, February to May seasonal average temperature trend analysis shows about 2.4 degree Celsius increase in hundred years with a lesser statistical significance, correlation coeffi-cient = 0.25, (Table 18) that is about an increase of 0.48 degree Celsius for the last twenty years data available year. On the other hand, if we consider seasonal trends for the June to October aver-age temperature, then we consider increase of 3.2 degree Celsius for a hundred year period, with a statistical significance of correlation coefficient of 0.43, which will be about 0.64 degree Celsius in-crease for the last twenty-year data period.

Average maximum & minimum temperature trend:

Annual average maximum temperature trend analysis shows an increase of about 4.9 degree Cel-sius increase every hundred years, with a statistical correlation of 0.42 (Table 18). June to October maximum temperature trend analysis shows an increase of about 4.5 degree Celsius increase every hundred years, with a statistical correlation of 0.33 (Table 18). February to May maximum tempera-ture trend analysis shows an increase of about 7.9 degree Celsius every hundred years, with a statisti-cal correlation of 0.38 (Table 18). Annual average minimum temperature trend analysis shows an increase of about 1.06 degree Celsius every hundred years, with a correlation coefficient of 0.17 (Ta-ble 18). June to October average minimum temperature trend analysis shows an increase of 2.2 de-

Table 18: Temperature trend and coefficient of determination

Rat

e/ye

ar

An

nual

Aver

tren

d

An

nual

Max

tren

d

An

nual

min

Tre

nd

Feb

-May

Aver

Tre

nd

Feb

-May

Max

tre

nd

Feb

-May

Min

Tre

nd

Jun

-Oct

Aver

Tre

nd

Jun

-Oct

Max

tre

nd

Jun

-Oct

Min

Tre

nd

R2 .032 .049 .015 .024 .0798 -.009 .0319 .0448 .022 R .2027 .1779 .0557 .06 .1445 .0035 .1888 .1109 .079


gree Celsius every hundred years, with a correlation coefficient of 0.28 (Table 18). There is no clear trend in the Feb to May average minimum temperature and the statistical correlation is very low.

Evapo-transpiration:

The term evapo-transpiration is simply the process of the transfer of water from the earth to atmosphere through evaporation (from surface water and soil) and transpiration (from vegetation). The two processes occur simultaneously and there is no easy way of distinguishing between the two processes. The agricultural and the water resources potential of a given area is based on the relation between the rainfall and the evapo-transpiration at the given area. There are various ways to estimate the evapo-transpiration. In this case, we used the Cropwat8 software to compute the Reference Evapo-transpiration. Reference evapo-transpiration (RfETo) is a climatic parameter expressing the power of evaporation of the atmosphere (FAO, 2009)). According to the same source, the concept of RfETo is used to study the evaporative demand of the atmosphere independently of crop type, crop development and management practices. The computed mean annual value is 1758 mm (4.8 mm/day) and the pattern of the Monthly RfETo is that it attains peak value in the month of March and reaches minimum value during the month of July. Comparison of the mean rainfall and the mean RfETo can be used to identify humid periods important for flowering (phonological period) and reproduction time, and planting time and length of growing period.

The result of the analysis of increasing trend in the temperature is one major factor that indicates that there would be an increasing trend also in the Reference Evapo-transpiration. Thus, scenario of evapo-transpiration was assessed at ten years interval based on the maximum and minimum temper-ature trend using the Cropwat8 software and the result shows that in average, we can expect an in-crease of about 4.3 mm of evapo-transpiration per year. This indicates that there can be an impact on the water resource of the area, from the long-term point of view, considering the decreasing trend of the annual and the seasonal rainfall of the area. Moreover, this can also have a negative im-pact on the length of the growing period for rain-fed agriculture.

Length of Growing Period (LGP) & scenario of LGP after ten and twenty years

FAO’s methodology of the assessment of length of growing period was used in assessing length of growing period for the study area. The starting of the growing period was identified by compu-ting the ratio of the rainfall to Reference Evapo-transpiration, where a value of .5 is considered as an identifier value of the growing period and a value greater or equal to a value of 1 is used to indicate the mid season flowering and reproductive period. This analysis indicates that the area has a poten-tial of growing period, which includes the month of March, April , May, June ,July, August, Sep-tember and October; i.e. 8 months of growing period. There are two possibilities: the cropping pat-tern Inter/double cropping is advisable in the first season and a short maturing single crop in the second season. Planting time in the first season is during March and after July in the second season. Recommended crops in the first season include sorghum, millet, groundnut, oats, beans and vegeta-bles. The second season crops are potato, sugarcane, banana, maize and vegetables.

There is a tendency of the planting period to shift from March to April in the first season and the chance of moisture stress for long cycle crops planted in the month of April to face moisture stress during the month of June, this is compensated by the extension of the growing period to No-vember. Scenario of LGP after twenty years based on the rainfall, temperature and evapo-transpiration trend indicates that the shift of the planting month from Match to April is clearly ex-hibited here. The second season planting period is also clearly shifted towards the month of August. For the first time the rainfall peak in the second season (October) is larger than the rainfall peak in the first season (April). Moreover, the break between the two seasons is more distinct than that ex-isted with current conditions.

7.5. Concluding remarks:

The major result of the assessment shows that there is going to be a shift of the planting month from March to April, for the next ten to twenty years. In general, the crop growing period shift is from March-October to April-November. Further, there is a tendency of a distinct break during the


month of June between the two seasons, which indicates the probable occurrences of moisture stress for long cycle crops planted in the month of March/April and maturing in the months of Oc-tober/November. Scenario assessment of crop growing period after thirty years shows that the growing season would comprise of only two months in the first season and four months in the se-cond season, which indicates that, in the long term, it would be worth consideration to introduce supplementary irrigation system in the study area.

The rainfall, temperature, evapo-transpiration and crop growing period trends over the Wonago area indicates that projects of conservation of environment, would be very important to reduce the negative impacts of rainfall and evapo-transpiration trend over the agricultural and water resources of the area. `Thus, the most important problem that can be identified from this investigation can be the risk of agro-climatic shift which can be more compounded with the impacts of the great increase in the population density of the area in the imbalance between the available natural resources base. These can have devastating result of land degradation; hence, projects aimed at increased renewable energy resources can be very important in the future. Furthermore, supplementary irrigation for in-tensive agricultural undertakings aimed at income generating agricultural products can be very im-portant, as the area has a good agro-climatic potential with a length of crop growing period for about 8 months and average temperature, which is favourable for the cultivation of various types of tropical perennial crops.

8. Land Resource Base Carrying Capacity Analysis

As noted above, the high growth of population has led to increasing pressure on land resources. Thus, with the present capacity, the size of land possessed by nearly 78% of the sampled households is far below the minimum recommended standard required to sustain both human and livestock needs. It can be concluded that the optimum carrying capacity of the land resource base is already surpassed. In this section, the population support capacity of the land will be analyzed to determine the degree of population pressure on land resources. The analysis is based primarily on assessment of the minimum size of land required to produce sufficient grain to support an average household in the study area. In other words, the analysis is based on only on-farm crop production to estimate land supply. Grain purchased to fill deficit and off-farm incomes will be considered later in the anal-ysis. Methodologies of MoA/FAO (1987) and WBISPP (2001) were partly adopted, in undertaking the analysis. Therefore, the minimum size of land (crop area) required by an average household is estimated through the compilation of the followings: annual energy requirement per household, the total energy supply from on-farm crop production and the balances of energy supply vs. consump-tion from existing farm land.

8.1. Annual Energy Requirement/ Household:

To compute the annual energy requirement per household, the average family composition (adults, chil-dren and infants) was converted to adult equivalents using the estimation given by WBISPP (2001) as shown in the following Table 19. Thus, with the assumption that the daily energy requirement for an adult is about 2,000 Kcal, the annual energy requirement for an aver-age household in the study area is estimated to be 4,380,000 Kcal.

8.2. Energy Supply from Crop Production:

The energy supply from crop production will be computed through estimation of crop yields and the gross and net production per household and energy supply of the crop net production. The data collected from the sampled households were used in establishing the gross production for crop yields but less the post harvest loss.

The net production was calculated by subtracting the seed requirements for next year’s cultiva-tion and losses related to food preparation and eating from the gross production. Seed requirement

Table 19: Average family composition and conversion to adult equivalents

Ad

ult

s= 1

.0

Ch

il-

dre

n=

.75

Infa

nts

=.5

Ad

ult

eq

uiv

ale

nt

Age (years) > 15 5 - 15 < 5 No 738 794 149 Mean 3.2 3.3 .6 3.2 2.5 .3 6.0


or the sowing rate for different crops was extracted from NSIA (2001). The assumption on the next year’s seed requirement and losses during food preparation and eating are shown in Table 20 below. Accordingly, the calculated gross production per household, which are computed by multiplying the yields per hectares by an average crop areas cultivated, by a household is presented in the 4th column of Table 20, and while the net production per household estimated from the gross production is shown in the 7th column of the same table. Therefore, using conversion factor of the Ethiopian Nu-trition Institute (Table 21), the energy supply value of each crop type was calculated, and the result is shown in the last column of Table 2.

Table 2: Yield per hectares, gross production and net production per household

Crop

Yield/hectares (Kg)

Area sown (hec-tares)

Gross product

(Kg)

Seed re-quirement

(Kg)

Waste of edible por-

tion (%)

Net pro-duction Kg/ HH

Annual Kcal energy sup-

ply/HH

Maize 1171 .21 245.91 25 8 221.36 785,828 Teff 712 .10 71.20 35 5 64.31 218,654 Sorghum 829 .01 8.29 8 5 7.78 26,452 Barley 491 .04 19.64 125 15 12.44 41,487 Beans 770 .06 46.20 90 5 38.76 141,474 Coffee 533 .8 426.40 0 0 426.4 Enset 796 .15 119.40 0 0 119.40 238,800 Banana 973 .09 87.57 0 0 87.57 175,140 S. potato 1284 .08 102.72 0 15 87.31 109,138 Godere 1128 .01 11.28 0 15 9.59 11,988 Boyena 1041 .01 1.41 0 15 8.85 11,063 Total 1.56 1,760,024

8.3. Energy Supply/ Consumption Balances:

The annual energy requirement per household is already calculated to be 4,380,000 Kcal, indicat-ing that the consumption exceed the supply significantly. Nevertheless, the annual energy supply from the present on-farm production is only 1,760,024 Kcal or about 40% of the requirement. Hence, the daily deficit per capita is about 1192.5 Kcal. However, as noted earlier, the surveyed households have indicated that they usually buy additional grains every year to fill the deficits. The list of grains /crops and the average size of grains purchased per family is shown in Table 22.

As shown in this Table, if the pur-chased crops are included, the total ener-gy supply will raise to 3,337,371 Kcal. This comes to about 77% of the annual energy requirement. It should be noted that in addition, a household could pro-duce an average of 645.32 Kg of coffee annually. According to the information from the household survey, an average family can consume a total of about 79 Kg of coffee per annum. Thus, a family can remain with a total of 347.4 Kg of coffee for marketing, with additional cash ranging from Birr 3109.6 to Birr 6948. Note that the for-mer figure is based on the selling price (Birr 8.95 per Kg) obtained from the household survey, while the later data is based on the selling price (Birr 20 per Kg), obtained from the local market in Won-ago town. If we take the average, it comes to Birr 5028.8. As noted earlier, the average annual in-come from farm (including sales of crops, livestock, fruit and vegetables, honey and wood/ poles) and off-farm activities totals Birr 4457.5 per household. With the current farming system and level of technologies, a minimum of 3.9 hectares/ household is required to meet the optimum annual en-

Table 21: List of crops and corresponding energy values in Kcals/Kg net edible portion

Coffee 0 Lowland maize 3,550 Enset 2,000 Finger millet 3,335 Pulses 3,650 White potato 870 Wheat 3,400 Sweet potato 1,250 Barley 3,335 Yam 1,250

Teff 3,400 Oil crops 0 Highland sorghum 3,400 Peppers 0 Lowland sorghum 3,400 Chat 0

Highland maize 3,550 Gesho 0


ergy requirement. In other words, an additional farmland of about 2.3 hectares per household is re-quired in order to eliminate the above indicated energy deficits.

Based on the result of household survey, with the assumption that 93% of the potential arable land is suitable for cropping, the potential number of households that the study area can support is only about 797 households. In other words, with the current land use system the study area can sup-port 797 households, with an average of 7.1 persons per family. From this, it can be said that the status of the land resource base of the study area has achieved about 600% of its population support capacity, and therefore, it is under critical conditions. However, as also noted above, the community has the potential to purchase additional grains to fill their deficits, and this potential (means of earn-ings) comes largely from the sale of cash crops like coffee. Hence, if this additional earning is includ-ed, then a minimum of 2 hectares/ household is sufficient to meet the optimum annual energy re-quirement. In this case, with the current farming system and level of technologies the study area can support 1553 families. From this, it can be roughly concluded that the land resources base of the study area has already achieved about 312% of its population support capacity.

8.4. Data Base Infrastructure:

Different spatial and non-spatial data that directly or indirectly affects the distri-bution of population over the study area are created, collated and derived making use of GIS. The major variables / param-eters considered are demographic and bi-ophysical data including climate, land use and land cover, soil, topography and in-frastructure. Table 23 provides a summary of these data. The database provides basic information to characterize the study area and analyze the relationships of the popu-lation and the natural resource. Further-more, it will allow further investigation and help develop future scenarios for pos-sible interventions. Despite the availability of tremendous data, difficulty of access, scale inconsistency, type of formats in which they exist and use of different pro-jection parameters were the major chal-lenges faced to maintain a complete data set. GIS has the capability for storing, in-

tegration and analysis of spatially related diverse information and enable develop better understand-ing of environmental relationship and enhance the evaluation of scenarios derived from the interac-tion of biophysical and social processes (Huddleston et al., 2003, Mccloy, 1995).

Table 23: List of input spatial and non-spatial data

Category Data type Source Format Digital data SPOT 5 EMA Raster

Thematic land use land cover image interpretation vector-polygon Soils FAO vector –polygon Geomorphology LUPRD vector –Point

Climate LGP LUPRD vector Isohyets LUPRD vector –line Temperature regime LUPRD vector Rainfall pattern LUPRD vector –polygon Agro-Ecological zones LUPRD vector Drought risk WBISPP vector

Topography Rivers WBISPP vector –line

Table 22: Details of energy supply from farm pro-duced and purchased grains

Crop

Ow

n p

roduc-

tio

n (

net

) K

g/

HH

Purc

has

ed

Kg/

HH

To

tal K

g/H

H

(net

)

An

nual

en

ergy

sup

ply

/H

H

(Kca

l)

Maize 221.36 49.80 271.16 962,618 Teff 64.31 7.80 72.11 245,174

Sorghum 7.78 7.78 26,452 Barley 12.44 5.20 17.64 588,294 Beans 38.76 25.20 63.96 233,454

Coffee 426.4 Enset 119.40 353.30 472.7 945,400

Banana 87.57 87.57 175,140 S. potato 87.31 3.00 9.31 112,888

Godere 9.59 9.59 11,988 Boyena 8.85 8.85 11,063 Wheat 5.50 5.50 18,700

Cabbage 23.10 23.10 46,200 Total 3,377,371


Lakes WBISPP vector –polygon DTM USGS / ILRI Raster

Statistical/ Demographic population census CSA statistical data Land holdings Field survey statistical data Livestock count

infrastructure Roads EMA vector –Line administrative units BoF&ED vector –polygon Towns WBISPP vector –point

9. Characteristics of the Data Set:

A brief description of the major spatial and non-spatial data, which are either collected or gener-ated, is made in relation to the study area and objective of the study. Derived data sets, moreover, will be dealt with in the next sub-sections.

Population growth: According to estimates of CSA, in year 1984 there were about 13, 634 people living in the study area (Table 24). This number has growth to 15,716 in year 1994 (CSA, 1994) and 20,697 in year 2007 (CSA pers. Commu.). Thus, based on the estimate of CSA, the major-ity (36%) lives in Hase Haro Quebelle Administration, some 28 percent in Kara-Soditi QA, another 22 percent in Tumata-Chirecha QA and the remaining 14 percent live in the Bale Bukisa QA. With an estimated regional population growth of 2.9% (CSA, 2008), the projected population in the study area would reach 2535 and 31062 in year 2014 and 2021 respectively. However, it should be noted that if the population data obtained from KAs office were projected to 2021, then the population would reach 36,687.

Land use/cover: Agro-forestry constitutes 49.1 percent of the study area and is by far the dominating land cover, followed by moderately cultivated land covering some 23.1 percent, inten-sively cultivated land 16.4 percent and other anthropogenic origins 7.4%. Wooded shrub grassland, shrub grassland, and grass-land areas cover about 4 percent. Details of these were presented earlier in section 6.

Isohyets: Study of isohyets reveals 71% of the study area is located within isohyets values of 1200 mm and 1500 mm. 45% of Kara Soditi, 31% of Tumata Chirecha, 15% of Hase Haro and 9% of Bale Bukisa have their area in this category. Isohyets value of 1500-1700 engulfs 29% of the study area. Most of which, 50%, are in Hase Haro.

Rainfall Pattern: Seasonal distribution of rainfall in southwestern part of the study area is of one comparatively long rainy season with rainfall peaks in spring and autumn separated by a season with less but still considerable rainfall. The northeastern part has small rains in spring, big rains in autumn merging.

Thermal zone: The mean daily temperature during the growing season for the study area is ex-tracted from the generalized thermal zone of Ethiopia. Most of the southern part of the study area (89%) is characterized with a tepid (17.5 – 2.0oC) and some of the extreme northern (11%) with a warm (20 - 22.5oC). The entire area (100%) of Bale Bukisa, 96% of Kara-Soditi, 93% of Hase Haro and 65% of Tumata-Chirecha QA have a mean daily temperature of 17.5-2.0oC during the growing seasons and the remaining parts have a mean daily temperature ranging between 20-22.5 C.

Length of growing period: Three different lengths of growing period occur in the area. Most part of the study area (72.6%) fall in the per-humid zone, with a LGP between 300 and 330 days. This includes all area of Tumata-Chirecha and Bale-Bukisa, 98% of Hase Haro and 11.1% of south-ern part of Kara Soditi KAs, 26.9 percent of the study area is confined to the western part of Kara Soditi falls in the humid zone with LGP in days between 270 and 300 days. The southwestern tip of

Table 24: Actual and projected study area human population

KAs Actual Projected

1984 1994 2007 2014 2021 Bale Bukisa 2498 2697 2981 3652 4474 Tumata Chirecha 3238 3556 4558 5584 6841 Hase Haro 4834 5545 7397 9062 11101 Kara Soditi 3064 3918 5761 7058 8646 Total 13,634 15,716 20,697 25,356 31,062


Hase Haro QA, constituting 2% of the Quebelle falls in the per-humid zone with a LGP between 330 and 365 days.

Geomorphology: Soils &Digital Elevation Model: The southern parts of the study area are categorized under the high to mountainous relief hills. The south eastern are of residual landforms (i.e. 34.4% of Hase Haro) whilst the south and south western (100% of Bale Bukisa, 52.2 % of Kara Soditi, 39.1% of Tumata Chirecha and 23.7% of Hase Haro) are of structural landforms with major river gorges, canyons and escarpments (which also may include Rmv, Rhv etc.). The north and northeastern part of the study area (6.9% of Tumata Chirecha, 47.8 % of Kara Soditi, and 41.9% of Hase Haro QA areas) are of residual landforms. Characteristics soil types of the study area are Eutric Leptosols (56%), Eutric Vertisols (37%) and Lithic Leptosols (7%). Eutric Leptosols is found in all studied KAs, making up 100% of Bale Bukisa, 35% of Hase Haro, 57% of Kara Soditi and 49% of Tumata Chirecha. Eutric vertisols is found in Hase Haro (35%), Kara-soditi (43%) and Tumata Chirecha (51%). Lith-ic Leptosols occurs only in Hase Haro making up 30% of its area. The present land use / cover map was draped on top of the 90m digital elevation model to provide a three dimensional view of the landscape and the relief is exaggerated five times. The provided perspective view help examine the land cover in relation with the topographic features of the study area.

Data Model: GIS techniques that make use of the different existing and derived data layers are used to characterize the natural resource base of the study area. Furthermore, statistical data on population and other socio-economic information were integrated with the spatial data set.

Agro-Climate zone: The geomorphology, rainfall pattern, temperature regime and length of growing period of the study area were overlaid and unique mapping units that have homogenous agro-climatic characteristics at a reconnaissance level and with fairly similar response for treatments are derived. The results are also summarized by QA. The characteristics of each unique mapping unit are described in Table 25. To enable a spatial association with the derived data sets and further characterize each QA, information on migration, livestock ownership, land holding, income and ex-penditure, non-farm employment, family planning and nutrition that were collected / measured dur-ing the household survey were also integrated as an attribute data.

Table 25: Description of the Agro-climatic units

Un

it

Th

erm

al

zo

ne

Rain

fall

patt

ern

Geo

mo

r-

mo

r-

ph

olo

gy

LG

P

Administrative areas

Are

a

(hec-

tare

s)

%

RtWPL Warm rains in spring, summer and au-tumn merging together

Rt1v Per-humid Hase Haro (1.65%) and Tumata Chirecha (1.27%)

99 2.92

RgTPL Tepid Rgv Per-humid Hase Haro 289 8.52

RtTPL Tepid Rt1v Per-humid Hase Haro (8.72%) and Tumata Chirecha (3.57%)

417 12.29

ShTPL Tepid Sh1v Per-humid

Kara Soditi (.12%), Bale Bukisa (8.87%) Hase Haro (5.86%) and Tumata Chirecha (4.95%)

672 19.80

RtWHS Warm small rains in spring, big rains in autumn merging together

Rt1v Humid Kara Soditi 53 1.56 RtWPS Warm Rt1v Per-humid Tumata Chirecha 229 6.75 RtTHS Tepid Rt1v Humid Kara Soditi 553 16.29 ShTHS Tepid Sh1v Humid Kara Soditi 291 8.57 RtTPS Tepid Rt1v Per-humid Tumata Chirecha 111 3.27

ShTPS Tepid Sh1v Per-humid Kara Soditi (1.75%), Bale Bukisa (5.80%) and Tumata Chirecha (3.48%)

680 2.04

Figure 57. Three-D view of the study area


Population Density: Population density of the study area is calculated using year 2007 popula-tion census data (CSA, Pers. Comm.) and the area size of currently arable lands of each QA. The later was a preferred method than assuming equal density (crude). Number of people living per QA is divided by the respective arable lands, extracted from the land use land cover map, in order to ob-tain an estimate of the population density. The population density map shows that there are 882.70 persons per km2 in Hase Haro QA, the highest in the study area, followed by Tumata Chirecha (647.08 persons per km2), Bale Bukisa (62.4 persons per km2) and Kara soditi (484.65 persons per km2). However, it should also be noted that as shown in the last column of Table 26, the density would be even higher than this, if the calculation is done using the population data obtained from the KAs office.

10. Conclusion and Recommendations

As discussed earlier, population expansion in the study area has already resulted in diminishing land size, scarcity of grazing land, shortage of fuel and construction wood, the use of cattle dung and crop residue as fuel, over cultivation and absence of fallowing. The community residing in this area is therefore besieged with problems of land shortage and other interrelated environmental problems such as climate change, poor agricultural production and poverty. The situation in this area is quite different from the problems in the northern and central highlands, where poverty and famine occurs due to drought as a result of land degradation. Here, the critical problem is not land degradation, nor land holding system. It is land shortage. It will be meaningless to talk of improved management or intensification, in situation where farming is based on piece of plots of below economic size. Hence, shortage of land to cultivate is the main impediment to increase agricultural production and achieve food security for the majority of rural communities in the study area. In fact, the scarcity of farmland by itself may not lead to shortage of food or poverty if the non-farm sector is well developed to provide employment or means of income for the growing farm community; but this is also not the case in Wonago.

Because of shortage of land, farmers are hardly to let land lie fallow to maintain fertility. Availability of fuel wood is also on the decline, because farmers are tending to utilize the small plots they have for cultivating crops rather than planting trees. On the other hand, shortage of grazing land has forced the households in this area to keep a relatively small numbers of livestock than elsewhere in the country. Apparently, a household possess an average of .2 head of cattle. As a whole, due to scarcity of land the community is unable to diversify their agricultural products.

Here, a considerable size of labour / manpower is wasted or becomes unproductive due to shortage of land, while enormous size of arable land are lying unproductive (un cultivated) in other parts of the region and the country. It could have been quite difficult for the farming community to sustain in this area without the ongoing practice of indige-nous agroforestry system that has been developed and undertaken for generation. However, now it seems that the tradi-tional coping strategy that prevailed for generation has now reached a challenging stage where it might no longer help much to mitigate the problem of land shortage. With the present high growth rate (2.9% per annum), the population in the study area will be estimated to reach 31,062 by year 2021. Similarly, if the present average land holding (.63 hectares) is shared among five children, then the average holding will be reduced to below .2 hectares per household in the same year. This will likely to result in serious shortage of land and unbearable strain on food security. In short, the situation may bring about a total collapse of the whole environment.

Therefore, the future for the majority of the households in this area could be disastrous unless appropriate measures are taken to provide viable farm size or other alternatives that will supplement household income. This might again be

Table 26: Population density of the study area KAs

To

tal A

rea

(km

2)

Cult

ivat

ed lan

d

( km

2)

Po

pula

tio

n

(yea

r 2007)

Den

sity

Per

son

/ k

m2

Po

pula

tio

n

2010*

Den

sity

Per

-

son

/km

2*

Bale Bukisa 5.01 4.81 2981 62.40 5416 1126.0 Tumata Chirechta 7.36 7.04 4558 647.08 6000 852.3

Hase Haro 8.44 8.38 7397 882.70 9173 1094.6 Kara Soditi 12.71 11.89 5761 484.65 6193 521.3

* Based on population data from QA offices


difficult to fulfil since there is no spare land for distribution in the locality. Hence, there are two alternatives. The first is introduction of non-farm activities. In this case, schemes of alternative means of income generations such as cottage industries have to be introduced. Priorities in provision of alternative means of income generation and poverty reduction should mainly be directed to the families/ households with small size of holding. In fact, those households with holdings below .5 hectares are relatively the most eligible group to be assisted. Nevertheless, this proposal too is not an easy task to be achieved in a short time. Hence, in the face of such limited resources, it seems that the most appropriate alterna-tive is relocation. In other words, those households with smallholdings and the landless have to be relocated to sparsely populated areas within the region or in the country.

The strategies to control/ reduce the problem of population pressure also require appropriate supporting policies and political commitment of the government. As` noted by several reports in-cluding EFA (1999/2000) and SLUF (2006), the prevailing boundary demarcation that has been es-tablished on the basis of ethnic group is the major barrier for free movement of the landless group from areas of land scarcity (overpopulated areas) to less populated areas in the country. Thus, the government has to revise some of the existing policies and further devise strategies that can possibly encourage a free mobility of the rural population from areas of land shortage to areas of abundance. In fact, this way is quite easier to bring the solution to land shortage than undertaking the previous type of relocation/ resettlement programmes, which end-up in vain.

The danger of population growth to the natural resources of the country, and the need to con-trol this problem has been repeatedly articulated in a number of the country’s conservation pro-grammes and action plans such as EFAP, ENCS, Biodiversity Strategy Action Plan (BSAP), and Plan for Accelerated and Sustainable Development to end Poverty (PASDEP) and NAPA. As not-ed above, growing human population is the major factor behind the diminishing land holding in the area. Hence, there should be concerted effort to control effectively the high growth rate of human population in the study area. This can be achieved among others, through introduction of voluntary family planning programme, which provide information on and access to contraceptives. In all cases, there is a strong need to promote improved farming technologies to increase agricultural production and productivity. Livestock production can also be improved through introduction of superior breeds. With this, the shortage of livestock feed can also be reduced by introducing improved fodder species such as Sesbania, Leucaena and tree lucern. It is also worth suggestion to introduce vocational training programme so as to train the landless or near-landless households including the youngsters on skills or technical knowhow that they could easily use to generate income.

The foregoing discussion pointed that lack of sufficient rainfall combined with variability and duration of rains are among the major climate related constraints to agricultural production in the study area. It might not be possible to control the climate related problems such as variability of rainfall and increase in temperature at small scale or local level. However, the effects of climate change can be mitigated through introduction of sound land management and improved farming system (such as changes in cropping and planting practices), combined with proper policy frame-work. Furthermore, one of the prominent strategies to cope with the climate related problems is the introduction of irrigation farming. Though all farmers in the study area practice rain fed farming, the land and water resources in the area are quite suitable for developing irrigation farming.

The ongoing tree planting by local people is quite encouraging, but it will be advisable if some improvement is made as regards to species selection. There are a number of other multi-purpose tree species that can be introduced into the area. For example, Bamboo (Arundinaria spp) is a high yield and fast growing wood plant that can fit in the agroforestry system practiced in the study area. Bamboo is the fastest growing woody plant species that can be harvested at rotational period of 3 years. It is reported that this species has more than 1500 uses. In addition to its wood value and wide range of environmental services, it can provide food and nutritional security for human being as well as serve as fodder for livestock. However, the introduction of this and other species should be un-dertaken with precaution, to avoid any disruption to this stable indigenous farming system.


List of Acronyms and Abbreviations BSAP Biodiversity Strategy Action Plan C.V. Coefficient of Variability CSA Central Statistical Agency DAs Development Agents EFAP Ethiopian Forestry Action Programme EMA Ethiopian Mapping Agency ENCS Ethiopian National Conservation Strategy ENSO EL Nino Southern Oscillation FAO Food and Agricultural Organization GHGs Green House Gases HH Household ICRAF International Council for Research in agro forestry IPCC Intergovernmental Panel on Climate Change KAs Kebele Associations Kcal Kilo calories LGP Length of Growing Period LUPARD Land Use Planning and Regulatory Department NAPA National Adaptation Programme of Action NMSA National Meteorological Services Agency NPPE National Population Policy of Ethiopia PAI Population Action International PASDEP Plan for Accelerated and Sustainable Development to end Poverty PRRA Participatory Rapid Rural Appraisal Rf ETo Reference evapo-transpiration SNNPRS Southern Nation Nationalities Peoples Regional State ToR Terms of Reference WARDO Woreda Agricultural and Rural Development Office WBISPP Woody Biomass Inventory and Strategic planning Project


Checklist of plant species in the home gardens of the study area Species Family Genetic status Gedeo name

Acacia pentagona Fabaceae Climber Indigenous Gora Acantus pubescens Achantahceae Herb Indigenous Qosoro

Achyranthes aspera Amaranthaceae Herb Indigenous Acmelia caulirhiza Asteraceae Herb

Agave sisalana Agavaceae Herb Exotic Ageratum conyzoides Asteraceae Herb

Ajuga alba Lamiaceae Herb Indigenous Allium cepa Alliaceae Herb Exotic

Allium sativum Alliaceae Herb Exotic Amaranthus hybridus Amaranthaceae Herb Indigenous

Ananas comosus Bromeliaceae Herb Exotic Ananasa Argemone mexicana Papaveraceae Herb Exotic Artemisia abyssinica Asteraceae Herb Indigenous Chegugn Asparagus racemosus Asparagaceae Herb Seritii

Beta vulgaris Chenopodiaceae Herb Exotic Bidens pilosa Asteraceae Herb Indigenous

Bougainvillea spectablis Nyctaginaceae Climber Exotic Brassica carinata Brassicaeae Herb Indigenous Shanna Brassica oleracea Brassicaeae Herb Indigenous Shanna

Cajanus cajan Fabaceae Shrub Exotic Capsicum annum Solanaceae Herb Exotic Gundo Berbero

Carica papaya Caricaceae Shrub Exotic Carum copticum Apiaceae Herb Exotic Qimemee

Casuarina cunninghamiana Casuarinaceae Tree Exotic Catha edulis Celastraceae Shrub Indigenous

Clerodendrum myricoides Lamiaceae Shrub Indigenous Coffea arabica Rubiaceae Shrub Indigenous

Colocasia esculenta Araceae Herb Exotic Godere Commelina africana Commelinaceae Herb Indigenous

Cordia africana Boraginaceae Tree Indigenous Wedessa Coriandrum sativum Apiaceae Herb Exotic Jebee

Crotalaria incana Fabaceae Herb Indigenous Wendi Croton macrostachyus Euphorbiaceae Tree Indigenous Mekenisa

Cucurbita pepo Cucurbitaceae Climber Baqula Cynoglossum lanceolatum Bignoniaceae Herb Indigenous

Datura inoxia Solanaceae Herb Exotic Datura stramonium Soloanceae Herb Indigenous

Daucus carota Apiaceae Herb Exotic Delonix regia Fabaceae Tree Exotic

Dioscorea cayenensis Dioscoraceae Climber Indigenous Dovyalis caffra Flacourtiaceae Shrub Exotic

Dracaena fragrans Dracaenaceae Shrub Indigenous Dracaena steudneri Dracaenaceae Tree Indigenous Ekebergia capensis Meliaceae Tree Indigenous Ensete ventricousm Musaceae Shrub Indigenous

Erythrina abyssinica Fabaceae Tree Indigenous Wellena Eucaluptus spp. Myrtaceae Tree Exotic Euphorbia spp. Euphorbiaceae Tree Indigenous Ficus sycomorus Moraceae Tree Indigenous Odae

Galinsoga parviflora Asteraceae Herb Indigenous Girardinia diversifolia Urticaceae Herb Indigenous

Grevillea robusta Proteaceae Tree Exotic Hibiscus rosa-sinensis Malvaceae Shrub Exotic

Hibiscus sabdarifa Malvaceae Shrub


Impatiens ethiopica Balsaminaceae Herb Indigenous Ipomoea batatas Convolvulaceae Herb Exotic

Jacaranda mimosifolia Bignoniaceae Tree Exotic Juniperus procecera Cupressaceae Tree Indigenous Hindhessa

Lantana camara Verbenaceae Shrub Exotic Lucopersicum esculentus Solanaceae Herb Exotic

Mangifera indica Anacardiaceae Tree Exotic Melia azadarichata Meliaceae Tree Exotic Millettia ferruginea Fabaceae Tree Indigenous Tarto Momordica foetida Cucurbitaceae Climber Kiphee

Moringa stenopetala Moringaceae Tree Indigenous Shiferaw Musa x paradisca Musaceae Shrub Exotic

Nicandra physaloides Solanaceae Herb Exotic Ocimum basilicum Lamiaceae Herb

Ocimum lamiifolium Lamiaceae Shrub Indigenous Ocimum urticifolium Lamiaceae Shrub Indigenous

Passiflora edulis Passifloraceae Climber Exotic Percea americana Lauraceae Tree Exotic

Phaseolus coccineus Fabaceae Climber Exotic Phaseolus lunatus Fabaceae Climber Exotic Phaseolus vulgaris Fabaceae Herb Exotic Phoenix reclinata Arecaceae Tree Indigenous

Physalis peruviana Solanaceae Herb Exotic Plantago lanceolata Plantaginaceae Herb Indigenous Podocarpus falcatus Podocarpaceae Tree Indigenous

Polyscias fulva Araliaceae Tree Indigenous Tella Pouteria adolfi-friderici Sapotaceae Tree Indigenous Guduba

Psidium guajava Myrtaceae Shrub Exotic Pterolobium stellatum Fabaceae Climber Indigenous

Pycnostachys abyssinica Lamiaceae Herb Indigenous Ranunculus multifidus Ranunculaceae Herb Indigenous

Rhamnus prinoides Rhamnaceae Shrub Indigenous Geshe Ricinus communis Euphorbiaceae Shrub Indigenous Rumex natalansis Polygonaceae Herb Indigenous

Saccharum officinarum Poaceae Shrub Exotic Salvia lucanta Lamiaceae Herb

Satureja paradoxa Lamiaceae Herb Indigenous Schainus molle Anacardiaceae Tree Exotic

Senna occidentalis Fabaceae Shrub Indigenous Senna petersiana Fabaceae Shrub Indigenous Sesbania sesben Fabaceae Shrub

Solanum americanum Solanaceae Herb Tuttnayee Solanum incanum Solanaceae Herb Indigenous Hiddee Solanum nigrum Solanaceae Herb Indigenous

Solanum tuberosum Solanaceae Herb Exotic Sorgum bicolor Poaceae Herb Indigenous Tagetes minuta Asteraceae Herb Exotic Tagetes patula Asteraceae Herb Exotic

Triumffetta annua Tiliaceae Herb Indigenous Verbenia officinalis Verbenaceae Herb Indigenous

Vernonia amygdalina Asteraceae Shrub Indigenous Vernonia hochstetteri Asteraceae Shrub Indigenous

Xanthosoma sagittifolium Araceae Herb Exotic Godere Zea mays Poaceae Herb Exotic Bedella


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Biological Sciences, University of California, Irvine, CA 92697, U.S.A. CSA, 1989. Population and Housing Census 1984. Population and Housing Census Commission. Addis Ababa.. CSA, 1995. The 1994 Population and Housing Census of Ethiopia. FDRE. Addis Ababa. CSA, 2008. Summary and Statistical Report of the 2007 Population and Housing Census. Population Census

Commission. December 2008, Addis Ababa. Daily G. C and Ehrlich P R, 1992. Population, Sustainability and Earth’s Carrying Capacity: A framework for

estimating population sizes and lifestyles that could be sustained without undermining future generations. American Institute of Biological Science

Daniel G., 1992. The Natural Resources Base of the Northeastern Shoa Region. In Svein Ege (ed), Ethiopian Development No 5: 225-236. University of Trondheim, Centre for Development Study.

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i EFAP (1994) asserts that there are close linkages between population growth, poverty, econom-

ic growth and the state of environment in Ethiopia. According to the same source, economic growth is constrained by the country’s deteriorating natural resource base and environment. It is further stated that “If present trends in population growth continue, this deterioration will be even more rapid in the future.” The high growth in population is a reflection of the incidence of poverty and a key factor in the ac-celerating deterioration of the natural resource base. On the other hand, environmental degradation constrains production and productivity of agricultural sector, and therefore the growth of the overall economy. As noted earlier, 88% of the Ethiopian highlands (areas above 1500m.a.s.l) which occupy 46% of the country were originally covered with high forest (mainly broadleaved deciduous and co-niferous forests). With its area of about 522,000 km2, the highland also supports 88% of the popula-tion and produces the majority of the national product. Nevertheless, with increasing population, the forest vegetation has been cleared for expansion of cultivation and to supply the growing demand for fuel wood and construction materials. In particular, the situation became critical as the process

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of deforestation for settlement and cultivation purpose have gradually expanded from gently sloping land on to the hill sides and steep slopes of the mountains. The removal of forest cover (deforesta-tion process) in the steep slope areas led to an increase in run-off, soil erosion and in aggregate to declining soil fertility. Deforestation process in the highland also led to removal of the humus layer, the soil was unable to retain moisture, and the important role played by forests (vegetation cover) in protecting bodies of water was sharply reduced. As noted by Wood (1990), about 70% of the high-lands have slopes of more than 30%. Thus, if the vegetation is removed, with intense rainfall and the dissected nature of the terrain together with the high slopes, the highland is susceptible to soil ero-sion and could not support sustained cultivation without proper conservation. In particular in the northern and central highlands, the loose and friable nature of the soil on steep slopes and undulat-ing terrain, coupled with traditional farming practices have aggravated the problem so much that over 2 billion tons of soil are claimed to be eroded annually. Apparently, today substantial areas in the northern highlands are out of agricultural production as a result of severe degradation. EFAP (1994) estimated that if this trend of degradation process is allowed to continue, it could destroy the farmlands of some 10 millions farmers in the highland by the current year. The process of deforesta-tion, backward agricultural practices and soil erosion in combination increase surface run-off and reduce the amount of rainfall infiltrating the soil and eventually percolating into ground aquifers (EFAP, 1994). Such low levels of infiltration and water storage in soils also affect the availability of water for human consumption throughout the year, while this situation again leads to higher peak flows in rivers causing flood damage. Another aspect of land degradation is the deterioration of range lands, which in turn led to low quality of animals due to shortage of animal feed. The other most apparent cause of deforestation is the growing scarcity of fuel and construction wood. As wood supplies for fuel have dwindled, the consumption of cattle dung and crop residue which oth-erwise would have been used to improve soil fertility has increased much. The shortage of fuel wood is especially severe in the northern and central highlands where fuel wood has increasingly replaced by crop residue and dung which in aggregate provide up to 60% of the domestic energy. The burn-ing of dung and crop residues is estimated to reduce the country’s crop production potential by up to 20%. The process of deforestation has thus developed into a vicious circle whereby the land deg-radation resulting from vegetation clearing and intensive use of land resources base has led to fur-ther environmental degradation, as the land hungry farmers are forced to convert marginal land and remnant forests on steep slopes into agricultural land, but only accelerating degradation.

ii According to DAs, farmers also use to take advantage of the naturally regenerated seedlings of both forest and fruit trees species such as Milletia, Cordia, Erythrina, Setamo, Ekebergia and Persia Amer-icana (Avocado). Depending on the preference of the farmers, these seedlings are retained and pro-tected until they reach a sapling stages or transplanted to other open space in the farm, where they are allowed to grow. The coffee nursery with annual capacity of about 35,000 is located in Hase Haro QA. According to the information from DAs, the management of this nursery has been under WARDO. Recently, it is handed over to a private person. About 92% of the respondents indicated that they have planted trees in their farms. The main species they plant are Milletia, Cordia, Eucalyptus, Erythrina, Vernonia, Cupressus, Gravellia and Prunus. The majority of the households plant trees for production of fuel wood, construction materials, maintenance of soil fertility and as a source of fod-der for livestock. As a whole, both the economic and ecological functions of trees/ forests are well recognized by the majority of respondents. Over 63% of the respondents also indicated that the shortage of land is the main barrier-hindering tree planting in their locality. However, few house-holds (about 5%) have also pointed that lack of seeds and seedlings as constraints to tree planting. Some (about 16%) household heads practice soil and water conservation activities, while 59% do not practice this conservation activities. The majority (85%) of the later group indicated that their farmland is not affected by erosion, while the remaining respondents pointed out that their farmland is covered by perennial plants and therefore are not worried of run-off and erosion hazards. The former group who practice soil and water conservation indicated that they carry out the activities through construction of terraces and tree planting.

Documents

Policy tangents to Livelihood Security & Climate Change Adaptation