アジア・アフリカ地域研究 第 23-1 号 2023 年 9 月
Asian and African Area Studies, 23 (1): 26–64, 2023

A Transition in Soil Fertility Management Practices
in the Semi-Arid Ethiopian Rift Valley:
A Case Study of Agricultural Intensification in Sub-Saharan Africa

Mukai Shiro*

Abstract
Two ethnic groups, the Amhara and Oromo, used distinct soil fertility management
practices until the mid-1970s in the northern semi-arid Ethiopia Rift Valley. The
Amhara carried compost from their house-yards and applied it to crop fields, whereas
the Oromo repeated short-distance transfers (house move) to amend the soil in theirvicinity. Soil fertility decline and the introduction of inorganic fertilisers (or chemicalfertilisers: IFs) techniques were the primary drivers for the Amhara to change their soil
fertility management practices after the mid-1970s. Land constraints caused bypopulation increase, the introduction of IF techniques, and government policy(villagisation) were the primary drivers for the Oromo to change their soil fertility
management practices. Since then, their soil fertility management practices merged
into what the Amhara had established after the mid-1970s, the combined use oforganic fertilisers (OFs) and IFs. Despite the continuous deterioration in the limited
availability of animal dung and increasing commuting distances to the crop fields, theadoption rate of the OF techniques and the proportion of manured fields haveremained unchanged. A hypothesis of population-induced agricultural intensificationin sub-Saharan Africa indicates that future resource constraints can encourage
farmers to use more IFs and improved seeds. To enhance the OF-IF integration, OFstechnique development of thorough utilisation of organic materials in farmers’
vicinity, such as compost techniques, and their dissemination through linkages
between research, extension services, and farmers, are the requirements forsustainable soil fertility management in the northern semi-arid Ethiopia Rift Valley.

1. Introduction
Recent studies indicate that the global agricultural production of 2005 needs to increase by 70–
100% to meet the rising food demand in 2050 [e.g. Bruinsma 2009], with a wide variety ofprospects from pessimism [Mueller et al. 2012; Ray et al. 2013] to a cautious optimism [e.g.

* Independent scholar, 1-2-3 Kakinokizaka, Meguro-ku, Tokyo, Japan 152-0022
Accepted September 30, 2022

26

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

Mauser et al. 2015; many international organisations reviewed by Tsubota 2016]. In contrast,
most researchers are pessimistic about food security in sub-Saharan Africa (SSA) in 2030
[Mason-D’Croz et al. 2019] and 2050 (other researchers). In 2050, SSA should feed the
population increased approx. 2.5-fold compared with 2005/2007, with an increased demand for
cereals approximately triple [van Ittersum et al. 2016]. However, it will be challenging to attainthis goal by closing the gap between current farm yield and yield potential on existing cropland[van Ittersum et al. 2016]. It is because of their small farm sizes and limited market access formost smallholders [Harris and Orr 2014] and intensified soil degradation processes [Pender et al.
2006; FAO 2016]. Major areas in SSA experience nutrient limitation as a significant yield gap
component [Vanlauwe et al. 2014]. Closing maize yield gaps to 50% of attainable yields(approx. 2.3 Mg ha-1) in SSA primarily requires addressing nutrient deficits [Mueller et al. 2012].
To overcome low soil fertility problems, most farmers are constrained by a shortage of cash touse inorganic fertilisers (or chemical fertilisers: IFs) partly because farm-level fertiliser prices in
SSA are among the highest in the world [Morris et al. 2007]. Many medium-term (over five
years and more) soil fertility management experiments in SSA suggested that the treatments thatcombined inorganic and organic inputs showed the best crop yields [e.g. Bedada et al. 2014].
Given these contents, integrated soil fertility management, the combined use of organic fertilisers(OFs) and IFs has been enhanced in SSA since the 1990s [Chivenge et al. 2011].
In highland temperate and maize-mixed farming systems in Eastern and Southern Africa,
including Kenya and Ethiopia, cattle are the most important livestock [Dixon et al. 2001]. Cattle
manure is an important source of nutrients for crops grown by many smallholders there [Paul etal. 2009]. In these parts of SSA, maize is the most or second-most important crop. In SSA,
where mixed subsistence- and cash-crop economies prevail, farmers’ soil fertility management
practices reflect their production choices (subsistence- or cash-crop) [Omamo et al. 2002].
Smallholders in Nakuru [Omamo et al. 2002] and Vihiga [Waithaka et al. 2007] districts, Kenya,use significantly more IFs for cash crops than for subsistence crops (e.g. maize). Part of theEthiopian highlands, called the maize belt, is Ethiopia’s main maize production area, wheremaize has dual roles, household consumption and sources of cash income; 35% of maize
produced was marketed [Gebremedhin et al. 2007]. In the maize belt, the adoption rate of IFs
was 60% for DAP (di-ammonium phosphate) and 59% for urea in 2001 [Gebremedhin et al.
2007], whereas that of OFs was 62% in 2000 [Bacha et al. 2004]. The adoption rate and theintensity of IF used for cash crops, such as tef (Eragrostis tef) and wheat, have been increasing in
Ethiopia. The farmers who applied any IF were 84% and 89% for tef and wheat (averaged over27

Asian and African Area Studies, 23 (1)1995–2004), respectively, across Ethiopia [Endale 2011]. Another survey showed that theintensity of DAP (94% of the total tef farmers used) use increased from 51 kg ha-1 to 88 kg ha-1
from 2002 to 2012, while that for urea (93% used) increased from 35 kg ha-1 to 64 kg ha-1
[Minten et al. 2013].
Maize production and productivity in Ethiopia doubled between 1990 and 2013. Increases in
the adoption rates of improved maize varieties and IFs are the two primary drivers behind this
rapid increase in productivity [Abate et al. 2015]. Besides, an analysis based on panel data
(Ethiopian rural household surveys) collected from 1,520 sample households from the northern
Ethiopian highlands, including the maize belt, reported that the farmers’ adoption rate of manure
increased from 52% in 2000 to 59% in 2002 and 74% in 2005 [Mekonnen and Köhlin 2008].
However, national statistics showed that the maize area covered by OFs declined from 27% in2004 to 18% in 2013 in Ethiopia [Abate et al. 2015]. Several case studies on adopting
innovative soil fertility management practices conducted in different Ethiopia measured the
proportion of the field areas where OFs and IFs were used.1) If the samples are limited to maizeplots: (1) manure and IFs were used at 27% and 67% of the 1,616 maize-legume cropping plots,
respectively, sampled from Amhara, Oromia, and SNNPR regions [Teklewold et al. 2013]; (2)manure and IFs were applied to 49% and 78% of the 148 maize plots sampled from the ArsiNegele district (woreda), part of the central Ethiopian Rift Valley [Ahmed 2015]; (3) manure andIFs were applied to 59% and 38% of the 480 maize plots, respectively, sampled from eastern
Hararge zones, part of the eastern highlands [Ahmed et al. 2017]; and (4) compost and IFs wereapplied to 82% and 2% of the 266 maize plots sampled from the northern semi-arid Ethiopian
Rift Valley [Mukai 2017a]. If the samples contain all the randomly selected crop fields: (1)compost and IFs were used at 17% and 24% of the 348 plots, respectively, from the semi-arid
Tigray region [Kassie, M. et al. 2009]; (2) manure and IFs were applied to 57% and 23% of the489 plots from the East and West Hararge zones, Oromia [Ketema and Bauer 2011]; and (3)manure and IFs were applied to 25% and 53% of the 1,344 plots from the south Tigray [Hassen
2015]. These findings show that the crop fields where OFs and IFs were used, reflecting local
farmers’ soil fertility management strategies, are diverse across Ethiopia.
Recently, nine technology adoption studies on the determinants of the farmers’ soil
1) Organic fertilisers (OFs) include manure and compost. Most manure, or animal manure, consists of animal
faeces. Meanwhile, compost is a mixture of organic residues (manure, animal carcasses, straw, crop residues,
feed refusals) that have been piled, mixed and moistened to undergo thermophilic (high heat) decomposition[Eghball 1997]. However, the case studies picked up here do not necessarily use the definitions of manure andcompost properly.

28

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

amendment techniques have been conducted in different parts of Ethiopia (Table 1). Thesestudies provided that farmers’ soil fertility management strategies are diverse, depending muchon the biophysical characteristics of the crop fields, the farmers’ socioeconomic endowments,and exposure of the local economy to commercialisation.
This paper describes how farmers in the northern semi-arid Ethiopian Rift Valley have adapted
their soil fertility management strategies, part of their indigenous ecological knowledge, to thelimited availability of various natural resources and sociopolitical changes since the 1970s. To
this end, all the aforementioned case studies on the determinants of the farmers’ soil amendment
techniques have two difficulties. First, they targeted the present phenomena. Thus, otherapproaches that compare before and after would be required. Second, they all take only
econometric approaches. However, the econometric models that value the reciprocal
relationships (complementarity or substitutability) between two soil amendment options, e.g. theinstrumental variable or multivariate probit analyses, cannot distinguish the sources of thepotential simultaneity bias of adoption decisions [Belderbos et al. 2004]. This made it difficult tocompare the analyses conducted in different environments or periods and to understand thesources of the complementary or substitute relationships between OF and IF use in the respective
studies [Mukai 2023].
Therefore, this study collected two temporal datasets, (i) before the mid-1970s and (ii) at thetime of the field survey in 2012, from two major maize production zones in the northern semi-arid
Ethiopian Rift Valley using remote sensing, household surveys, and interviews. In the area where
quantitative research methods [Cohen et al. 2007] can use, the econometric models were
formulated for the before-the-mid-1970s and 2012 datasets. The model considered the potential
simultaneity between OF application and IF use. Meanwhile, in the area where quantitative
research methods cannot use, qualitative research methods [Cohen et al. 2007], i.e., fieldobservations and careful interviews with farmers, were used. The qualitative research methods also
help explain the background of the econometric analyses. Mixed methods (quantitative andqualitative) used in this study may have a more persuasive power to compare the differences in the
farmers’ determinants in different environments and periods, therefore, more policy implications.
This study addresses the following research questions: what was the farmers’ indigenous
knowledge of soil fertility management practices? How have farmers changed their soil fertility
management practices? What were the drivers of the changes? What types of soil fertility
management practices will they select against the unfavourable changes in various resources andcommercialisation in crop production?
29

30

+
–
+
+***
–***
–d
–
–***

+
+
–
+**
–**
+***d
–
–***

Manure

+
+
–
+
+
–d
+
–

LPb
IF
+
–
–**
+**
–
+e
n.u.
–**

OF

HPb
+
+**
–
–
+***
–
–*** +***+–+d+e+*** n.u.
–** –***

IF

+**
+***
+**
+*
+***
n.u.
n.u.
n.u.

OF
+*
+*
+*
+**
+**
n.u.
n.u.
n.u.

a

IF

2) Pender & 3) Marenya
Gebremedhin & Barrett[2006][2007]

Note: *p<0.1, **p<0.05, ***p<0.01, n.u.; not used.
Market indicated the distance to the nearest market.
b
LP; Low potential areas, HP; High potential areas.
c
The total value of assets represented the sample farmers’ wealth level.
dDistance to the nearest input supply shop represented.
eDistance to the nearest district (woreda) town represented.

GenderOff-farm
Farm
Livestock
LabourMarketa
Plotsize
Distance

House
hold
wastes1) Benin [2006]

–
n.u.
+
+**
+
+
n.u.
+

OF

OF

IF

IF

OF
n.u.
+
–
–
–*
+
+*
–**

IF

7) Ahmed[2015]

–*
+
n.u.
+
–
–
–c +***c–++*** +**
n.u. n.u.
+–––
n.u. n.u.
+
–
–
–

OF

5) Ketema 6) Teklewold& Bauer et al. [2013][2011]+–+
n.u. n.u.
–
+
+
+
+***
+
+**
–
+*
n.u.
–
n.u.
+
n.u. +*** +***
+**
–*
+

IF

4) Kassie,
M. et al.
[2009]IFOFIF9) Ahmed etal. [2017]

+
+
–
+*
–
+*** n.u. n.u.
+c +***c–+**+*** n.u. +*** –***+***+
n.u. n.u.
+–
n.u. n.u.
–+
n.u. n.u.
–***
–
–
–

OF

8) Hassen[2015]

Table 1. Signs and Statistically Significant Levels of the Independent Variables Used in Recent Technology Adoption Studies in Ethiopia

Asian and African Area Studies, 23 (1)Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

2. Materials and Methods
2.1 Study Area
The uppermost reaches of the Tebo and Geldia seasonal river catchments area (265 km2) are in
the Tebba Gersa mountainous area on the southeastern edge of the central Ethiopian highlands(true highland area; 2,100–2,200 m above sea level, with annual rainfall 1,000–1,200 mm; Fig.
1). Other parts of the catchments area (the study area) are located within the northern semi-arid
Ethiopian Rift Valley. Most of the Tebo-Geldia catchments area is in the Boset district, Oromia
region. Northern semi-arid Ethiopian Rift Valley is categorised into the following two sub-areas
in terms of the major maize production zones in Ethiopia: mid-altitude dry (1,000–1,600 m
above sea level, annual rainfall 650–900 mm) and mid-altitude moist (similarly, 1,700–2,000 m,
1,000–1,200 mm) sub-areas [Abate et al. 2015].
Of the nine major crops of the Boset district, maize, sorghum, wheat, barley, lentils, horsebeans, and field peas are used mainly for house consumption (54–76% of the total usage; Table2),2） while tef and haricot beans are primarily used for income sources (57–78%). The major
crops in the mid-altitude moist sub-area and true highland area in the catchments area are wheat,tef, and maize, whereas those in the mid-altitude dry sub-area are sorghum, tef, and maize [ICRA

Fig. 1. Ethiopia and the Semi-Arid Northern Ethiopian Rift Valley (Left Figure), and the Tebo and Geldia
Seasonal River Catchments, the Study Area (Right Figure)31

Asian and African Area Studies, 23 (1)Table 2. Crop Utilisation of the Major Crops in Boset District, Oromia Region, in 2002
Percent utilised for (%)

Crop

Cultivatedarea (ha)House consumption

Sale

OthersMaize
TefHaricot beans
Sorghum
Wheat
BarleyLentils
Horse beans
Field peas

14,512
10,568
4,779
4,016
952
882
293
66
7

73
27
11
76
62
54
76
70
61

13
57
78
15
7
10
19
14
18

14
16
11
9
31
36
5
16
21

Note: Others include seed, wages in kind, and animal feed.
Source: The author’s calculation based on CSA [2003].

1999]. The nine major crops and rainfed vegetable fields are mixed in both sub-areas.
The primary ethnic group living in the true highland area is the Amhara. The Oromo is theprimary ethnic group in the mid-altitude dry sub-area, except for town areas like Welenchiti.
The Amhara and Oromo live juxtaposed in the mid-altitude moist sub-area between the true
highland and mid-altitude dry sub-area. Mekonnen [2013] portrayed the sedentary lifestyle of
the Amhara; fertile volcanic soil combined with generous rainfall and a cool and brisk climate
provided the Amhara with a stable agricultural and pastoral existence in the Ethiopian highlands.
In contrast, the Oromo are believed to have adhered originally to a pastoralist/nomadic and/or
semi-agriculturalist lifestyle [Mekonnen 2013].
Ethiopia has been described as one of the most severely affected countries by soil erosion[Haregeweyn et al. 2015]. A study on soil nutrient balance at a plot level conducted in thenorthern semi-arid Ethiopian Rift Valley showed nitrogen and phosphorus deficits [Yimer and
Abdelkadir 2010]. Northern semi-arid Ethiopian Rift Valley is the most drought-prone part ofEthiopia, with high rainfall variability during the rainy season [Segele and Lamb 2005] (Fig. 2).
Projections for future climate suggest that annual rainfall will change by -40 to +10%, and theannual temperature is expected to increase in the range of 1.4 to 4.1°C by the 2080s [Kassie etal. 2014a]. Accordingly, simulated water-limited maize potential yield for early maturing
2) A fermented dough of maize, sorghum, and barley is used for cooking injera (a thin, fermented Ethiopian
bread). Maize and sorghum are also used for brewing. Wheat flour is used for cooking dabo (Ethiopian bread).
Beans and peas are used for cooking wot (Ethiopian stew).

32

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

Fig. 2. Cropping Calendar (Upper) and Farmer’s OFs Application Practices (Lower)
Farmers were Going to Carry Compost (Kosi) from a Pile to an Arada Field (Left Photo), and A Housewife
was Going to Dump House Wastes onto an Arada Field (Right Photo)
Source: Monthly mean rainfall data is from the Welenchiti rainfall gauge (1992–2013).

cultivars would decrease by approx. 8% [Kassie et al. 2014b]. The balance between the
population, livestock numbers, and natural resource reserves will likely worsen in the northernsemi-arid Ethiopian Rift Valley [Meshesha et al. 2012]. The soil fertility decline in the semi-arid
Ethiopian Rift Valley is likely to continue further [Yimer and Abdelkadir 2010].
2.2 Narrative Inquiry Interviews
In 2011, narrative inquiry interviews were held with elderly farmers (three individuals for the
mid-altitude moist sub-area and four individuals for the mid-altitude dry sub-area) to ascertain
the research subjects: (i) history of village formation; (ii) land use/cover changes; (iii) changes incropping systems and farming practices; and (iv) changes in soil fertility management practices
by a narrative inquiry method [Clandinin and Huber 2002] (Table 3). The interviewees with rich
33

Asian and African Area Studies, 23 (1)Table 3. Research Subjects and Sample Interview Questions in the Narrative Inquiry Interviews
Research subjects

Sample interview questions

History of village formation

Can you please tell us your paternal family’s history of migration andsettlement?
Who were the pioneer settlers of this village?
Please tell us about the land use/cover changes in your village.
When were the croplands of your ancestors established?
Please tell us if you have experienced any temporal changes in your cropping
system, e.g., the field types (arada, masa, and golba), crops cultivated, andcrop sequences.
Were there any resource constraints or socio-political changes that
influenced changes in the cropping system?
Have you experienced any changes in soil fertility management practices?
Were there any technological advances that impacted soil fertility
management practices?

Land use/cover changes
Changes in cropping systems
and farming practices

Changes in soil fertility
management practices

information on village formation and farming knowledge and experience were selectedpurposefully [Baxter and Eyles 1999]. A snowballing sampling strategy [Patton 2002], in whichinterviewees suggested other potential participants, was used to identify and select informationrich cases. The seven recorded interviews were transcribed into written English. Initial codes
were created based on the emerging patterns in the transcribed texts [Cope 2003], which were
often commonly seen in the texts from both the sub-areas. Moreover, to dig deeper into theprocesses, secondary codes were generated based on the frequency of appearances in the texts,
which were often distinctively different between the two sub-areas.
The narrative inquiry interviews found that farmers categorise crop fields into arada, masa,
and golba (Fig. 3). Farmers dichotomise arada and masa fields regarding their relative soil
fertility levels. Farmers apply OFs (compost and household wastes) to aradas (the plural form ofarada; Fig. 3). It improves soil chemical properties (significantly increases soil organic carbon,
total nitrogen (N), phosphorus (P), cation exchange capacity, K, Na, Mg, electric conductivity,
and NO3-N and significantly lowers pH [Mukai 2019]). It also improves soil physical properties(significantly reduces soil compaction and bulk density and significantly increases porosity)
compared to those in masa fields [Mukai 2019]. With few exceptions, aradas are locatedadjacent to homesteads, whereas masas (the plural form of masa) are away from homesteads (Fig.
3). Most households hold continuously cropped maize fields in aradas because maize yields are
significantly affected by soil fertility levels, particularly N and P [Debelle et al. 2002]. Farmers
harvest the upper half of the maize stalk early for feed. They sometimes harvest it in September,34

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

Cropping systems

Main crops

Field
types

SCCS

Continuous
maize
cultivation

Maize, barley

Arada

Continuoussorghum
cultivation

Sorghum, maize

Golba

Cash crops
andsubsistence
crops
are
rotationallycultivatedCash crops (tef, Masa
haricot bean, rainfed
vegetables),subsistencecrops(sorghum,wheat,
barley, beans, peas)

CCCS

Soil fertility management practices
Before the midIn 2012
1970s
Continuous
OFs [1] Continuous OFs
application
application or [2] no soil
amendment on ona(previous arada)
No soil amendment Same as on the left
because golba soils
are fertile
[1]
No
soil
amendment or [2]occasionalOFsapplication[1] Only IFs use or [2]
IFs
use
and
an
occasional
compostapplication

Fig. 3. Micro-Topography, Land Use, Field Types, Cropping Systems, and Soil Fertility Management
Practices before the Mid-1970s and 2012 in the Northern Semi-Arid Ethiopian Rift Valley
Note: SCCS; subsistence crop-based cropping system, CCCS; cash crop-based cropping system, OF; organic
fertiliser, IF; inorganic fertiliser. Flat and low-lying cropland and golba can be seen only in the midaltitude dry sub-area. Arrows indicate farmland holdings.

not waiting for its maturing stage from October to November, to eat raw (Fig. 2). For poor
farmers, maize is significant as emergency food to weather a food shortage in the slack season.
Farmers cultivate barley when the soil fertility of an arada is declined. In contrast, othersubsistence crops (sorghum, wheat, barley, beans, and peas) and cash crops (tef, haricot beans,
and rainfed vegetables) are rotationally cultivated in relatively unfertile masas. Some crop fields
are situated in the flat and low-lying cropland areas, and their soil fertility is maintained by the
inflow of fertile soil from the upper-reach fields during the rainy season. This field is referred toas golba. Golba is only observed in the mid-altitude dry sub-area, located at the lower reaches of
the Tebo and Geldia seasonal river catchments (Fig. 1). Farmers cultivate maize and sorghum in35

Asian and African Area Studies, 23 (1)a fertile golba (Fig. 3).
Compost (kosi) is made from various locally available organic materials, such as animal dung,
kitchen ash, crop residues, and feed refusals (Fig. 2). These compost materials are piled up in thecorners of house-yards for several months to a few years for decomposition [Mukai and Oyanagi
2019]. A housewife mainly collects these organic materials through house-yard sweeping anddumps them onto a compost pile. Farmers carry compost from the compost piles to fields from
April to June before crop seeding and scatter it on the ground. The compost is incorporated into
the soil by subsequent ploughings [Mukai 2018]. Household wastes are of substantially various
compost feedstock materials [Mukai and Oyanagi 2021], collected through house-yard sweeping
and dumped directly onto a backyard maize field every few days (Fig. 2).
The narrative inquiry interviews also found that, around the 1870s, the Amhara and Oromo
pioneers migrated from the Ethiopian highlands to the sub-areas. They exploited the forest tosettle there. The interviews also showed that the practice of OFs (compost or household wastes)
application was popular even before the mid-1970s in the mid-altitude moist sub-area among theAmhara. Even before the mid-1970s, they had distinct relationships between the field types(aradas and masas) and cropping sequences, similar to those observed now. In the 1950s, grainmarketing was well-developed, even in rural areas in Ethiopia [Holmberg 1977]. Tef has beencultivated mainly for selling in both the sub-areas since the 1950s at the latest. The government
introduced IF techniques during the latter half of the 1970s. Since then, farmers in the northernsemi-arid Ethiopia Rift Valley began using IFs on masas mainly for cash crop cultivation. In
contrast, the interviews showed a significant change in the relationships between the field types,
cropping sequences, and soil fertility management practices that had occurred in the mid-altitude
dry sub-area among the Oromo after the mid-1970s. It was a transition period from the Haile
Selassie Imperial era (1930–1974) to the Derg socialist regime (1974–1991).
Thus, in the mid-altitude moist sub-area, this study focused on comparing (i) the determinantsof soil fertility management practices (OFs application and IFs use) in the fields and (ii) some key
resource indices (e.g., household farm size, livestock ownership, adoption rates of OF techniques,
areas of OFs application) before the mid-1970s and 2012. Towards those ends, quantitative
research methods were mainly used. In contrast, in the mid-altitude dry sub-area, this study
examined how the Oromo adapted their indigenous soil fertility management practices toresource constraints and sociopolitical changes since the mid-1970s. For this purpose, qualitative
research methods were mainly used.

36

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

2.3 Quantitative Research Survey
A structured questionnaire survey was conducted in the mid-altitude moist sub-area in 2012.
The sub-area consists of two villages (kebele). Sample plots and household heads who held thesample plots were selected randomly from the two villages. For the dataset before the mid1970s, 199 plot data were collected from 103 households, while for the 2012 dataset, 282 plot
data were collected from 142 households. These sampled household numbers in the mid-1970s
and 2012 datasets and sampled plot numbers in the 2012 dataset statistically correspond torespective confidence intervals of 8.5%, 7.6%, and 5.7% under a 95% confidence level,compared to the estimated household and plot numbers in the two villages at the correspondent
times.3)
When asking about the farmers’ soil fertility management practices before the mid-1970s,
consideration was given to the interviewees’ ages and home villages. If the interviewees were in
their late 50s at the interview, i.e. they had reached 20 years of age in the mid-1970s, their own
experience was ascertained. In contrast, if the interviewees had not reached that age yet, they
were interviewed about their parents’ soil fertility management practices. If their parents
migrated from other maize production zones, their parents’ experiences in farming after
migrating to the present area were assessed.
The questionnaire survey dataset before the mid-1970s was analysed using binomial logit
models. Adoption studies of soil amendment options (e.g. OF or IF use and OF and IF use)
appeared in literature in the early 2000s, and case studies on SSA dominate it [Mukai 2017a].
Among those, econometric models that are usually used to predict a binary dependent variable(i.e. OFs were adopted or not adopted, or IFs were adopted or not adopted) were binomial logit[Somda et al. 2002; Mkhabela and Materechera 2003] or binomial probit [Abdoulaye andSanders 2005]. Because OFs use was only soil management practice in the study area before themid-1970s, the dependent variable (man) used for the binomial logit models represented whether
OFs were applied (man=1) or not (man=0; Table 4). Independent variables (Table 4) were
determined by referring to previous adoption studies on soil fertility management practices in3) In Ethiopia, population censuses were conducted in 1984, 1994, and 2007. District (woreda) level population
data is available over the three years, and village-level population and household numbers are available fromthe 1994 and 2007 censuses [CSA 1984; 1994–1997; 2010]. Using the rural population data in 1984, 1994,
2005, 2007, and 2014 (the government projected those in 2005 and 2014 [CSA 2005; 2013]), the total
household numbers in the two villages in 1975 and 2012 were estimated. Using the mean plot number held by ahousehold in the northern semi-arid Ethiopian Rift Valley in 2012, 5.7 [Mukai 2023] and the estimated total
household number, the total plot number in the two villages in 2012 was estimated.

37

Asian and African Area Studies, 23 (1)Table 4. Dependent and Independent Variables for Econometric Analyses and Summary Statistics of the2012 Dataset

Dependent
variables

Man (binary): 1=Organic fertilisers(OFs) were applied, 0=not applied
Fer (binary): 1=Inorganic fertilisers(IFs) were used, 0=not used

OFs were applied or not(man)
Applied Not applied

Independent [1] HH (household head) gender avariables(binary): 1=male, 0=female(1)[2] HH ethnic group (binary): 1=the
Household Oromo, 0=the Amhara
attributes[3] HH tenancy (binary): 1=landlord,
0=tenant[4] Education expresses the numberof years the HH spent in a school(year)b[5] Farm is the total cropland size
held by the HH (ha)[6] Labour is the total family andregular labour force converted to an
adult (from 16 to 65 years old) labour
force equivalent (persons)[7] Livestock represents: livestocko w n e r s h i p l e v e l ( T L U ) c= C a t t l eownership level (TLU) × (1–Fuel(%)/100)+other livestock ownershiplevel (TLU)[8] Donkey represents the ownershiplevel of donkey and camel (heads)[9] Fuel means the percentage of thehousehold’s cattle dung consumed forfuel (%)(2) Plot[10] Crop means the main cropping
attributes
system to which the sample plot
belonged: 1=CCCS, 0=SCCS[11] Plotsize represents the size of thesample plot (ha)[12] D i s t a n c e i s t h e c o m m u t i n g
distance to the sample plot (m)IFs were used or not
(fer)

p

Used

Not used

p

0.86

0.79

ns

0.84

0.84

ns

0.09

0.17

*

0.13

0.11

ns

No dataNote: a Italics are variable names.
b
Adult education was counted to be 0.5.
c
TLU, Tropical livestock unit.

38

No data

1.2

0.7

**

1.0

1.0

ns

2.2

1.8

**

2.0

2.0

ns

3.9

3.4

**

3.7

3.7

ns

3.7

2.4

***

3.2

3.3

ns

1.4

1.1

***

1.3

1.3

ns

32

34

ns

33

32

ns

0.31

0.84

***

1.00

0.00

***

0.22

0.25

***

0.26

0.19

***

203

857

***

726

145

***

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

SSA [Mukai 2017a]. All the independent variables were grouped according to whether the OFs
were applied. For binary and numerical variables, t-tests and Pearson’s chi-square tests wereused to detect differences in the means of the variables between the binary choices in man,respectively.
Among the independent variables selected, the land tenancy of the household head (tenancy) isused only for the before-the-mid-1970s dataset. During the Haile Selassie Imperial regime(1930–1974), the land was owned by tiny feudal landlords, and most farmers were their tenants.
The Derg socialist regime (1974–1991) that overthrew the imperial regime adopted a socialist
mode of government. They abolished landlord-tenant relations, nationalised all rural land, andredistributed it to farmers.4) The Derg regime also implemented the villagisation policy, whichstarted in 1985. The villagisation aimed at an agglomerated rural settlement, permanently
removing villagers from their ancestral lands.5)
Because OFs application and/or IFs use were standard soil amendment options after the mid1970s, the 2012 questionnaire survey dataset was analysed using the bivariate probit model.
Several adoption studies have recently analysed a reciprocal relationship (complementarity or
substitutability) between OFs and IFs use in SSA. They used instrumental variable analysis[Benin 2006; Pender and Gebremedhin 2006; Ketema and Bauer 2011] or multivariate probit
analysis [Marenya and Barrett 2007; Kassie et al. 2009; Teklewold et al. 2013; Ahmed 2015;
Hassen 2015; Kassie et al. 2015; Ahmed et al. 2017]. A variable man represents household
heads’ binary choices of OF adoption (man=1) or no adoption (man=0) in the plot (Table 4). In
contrast, variable fer denotes IF adoption (fer=1) or no adoption (fer=0).
Mukai [2023] analysed the farmers’ determinants of soil fertility management practices using
524 plot data collected from the semi-arid northern Ethiopian Rift Valley. He hypothesised thatthe plot data were categorised into two groups representing the subsistence-crop-based cropping
system (SCCS; 266 plot data) and the cash-crop-based cropping system (CCCS; 258 plot data).
A data segmentation approach and a dummy variable method were used to incorporate thestructure of a local farming system into econometric models. Based on the K-means cluster
4) The landholding structures did not change much before and after the implementation [Fassil 1993:132–133].
Since then, the Ethiopian government has advocated state ownership of land whereby only usufruct rights are
bestowed upon landholders. The usufruct rights exclude selling or mortgaging the land [Crewett et al. 2008].
5) The villagisation program was intended to facilitate the delivery of social services like health and education to
the people by clustering the settlement of villagers [Geda 2018]. In the mid-1980s, deep tubewells wereconstructed with the help of international donors at the village level in the Boset district. This also helped
cluster the settlement of villagers because people were relieved from going to communal ponds or the Tebo
River to fetch water.

39

Asian and African Area Studies, 23 (1)analyses (K=2), the two subdatasets representing SCCS (250 plot data) and CCCS (274 plotdata) were created. The following four bivariate probit models were formulated: (i) model 1
used the pooled dataset with independent variable crop (cropping system to which the sample
plot belonged; 1=SCCS, 0=CCCS), (ii) model 2 used the pooled dataset without crop, (iii) model
3 used the SCCS subdataset, and (iv) model 4 used the CCCS subdataset. Models 3 and 4 arebased on the data segmentation approach, while model 1 represents the dummy variable method.
Model 2 is with the conventional approach, not considering the structure of the pooled dataset.
Several goodness-of-fit (i.e., how well the model fits into a set of observations) tests validated
that it is better to analyse each subdataset separately using models 3 and 4 than analysing thepooled dataset. Exogeneity tests showed that the household heads considered their OF and IF
use independently. This study considered employing these approaches. Stata 13.0 (StataCorp
LP) was used to perform the binomial logit and bivariate probit analyses [Cappellari and Jenkins
2003], while SPSS ver. 20 (IBM) was used for the other statistical analyses.
2.4 Qualitative Research Methods
A field database was created targeting a 27.6 ha area near Merko hamlet, Merko Odalega
village, Boset district, to ascertain the changes that occurred for the Oromo in the mid-altitude
dry sub-area. This target area included homesteads for 20 households (6.4 ha in total) and 79
plots (4 aradas and 75 masas; 21.2 ha in total) held by 33 household heads at the time of thesurvey. Information on (i) the establishment periods of the plot and (ii) land use/cover, (iii)
landholder, cultivator, field type, cropping sequence, and soil fertility management practiceduring each period of the Haile Sellasie Imperial regime (1930–1974), the Derg socialist regime(1974–1991), and 2012 were fed into the field database. A structured interview was conducted
in 2012 with 43 individuals to create the database. These individuals were descendants of thehousehold heads who had held land or currently had land in the 27.6 ha target area since the1950s.
Information on land use/cover was obtained from the interview and aerial photos taken in
1957, 1972, and 2005. Orthophotographs (1:50,000) were created from the 2005 aerial photos.
The geometric rectification of the 1957 and 1972 aerial photographs was performed by coregistration with the 2005 orthophotograph [Mukai 2017b]. Using ArcGIS 10.1 (ESRI), land
use/cover changes between three periods (1957, 1972, and 2005) were digitally mapped.
Enlarged aerial photos from 1957 (representing the mid-Imperial period) and 1972 (representing
the late-Imperial period and a transition period from the Imperial to the Derg periods) and achronology were prepared for the interview. During the interview, these were shown to the40Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

informants as reference materials. They helped ascertain the establishment periods of the plots
and the starting years of their OF and IF use. It was because, on the 1:50,000 aerial photos, plot
boundaries with the downslope ones, which often form soil or stone bunds, are seen. When
ascertaining the positions of crop fields (plots), grazing lands, and homesteads in the past, an
accurate global positioning system (GPS; Trimble GEO XT 2008 series) with planimetric andaltimetric accuracies at the sub-meter level was used. The location data acquired from the GPS
were positioned on the digital map using ArcGIS.
A semi-structured interview and field measurement were also conducted in the mid-altitude
dry sub-area with the informants who participated in the structured interview. Interview
questions asked in the semi-structured interviews were based on the secondary codes generated in
the narrative inquiry interview and the literature on traditional knowledge (TK; Table 5).6) For
less-structured interview questions asked in the semi-structured interviews (e.g. questions 1-1-2,
2-1-1, and 2-1-2 in Table 5), unrecorded interviews were noted down in field notes, which weretranscribed. The same coding process taken in the narrative inquiry interviews was adopted togenerate key themes.
A structured interview and field measurement were conducted in 2012 with one male
household head, Teshome Yadete (aged 68 at the interview), to visualise the changes in soil
fertility management practices in the mid-altitude moist sub-area. A field database was created,
into which information on the boundaries with surrounding fields, cropping sequence, and soil
fertility management practice of each crop field, held by Teshome Yadete in the mid-1950s, at the
beginning of the 1970s, and in 2012 was input.

3. Results and Discussion
3.1 Changes in Determinants of Soil Fertility Management Practices in the Mid-Altitude Moist
Sub-Area
For the quantitative research survey, 199 plot data and 282 plot data were collected from the6) TK is the “cumulative body of knowledge, practice, and values acquired through experience and observations
on the land and handed down from generation to generation” [Pearce et al. 2015]. Many studies examined therole of TK in adaptation to diverse and changing socioeconomic and environmental conditions, including
climate change [e.g. Anderson et al. 2018]. Reviewing previous TK studies, Pearce et al. [2015] conceptualised
the role of Inuit (indigenous communities in Canadaʼs subarctic) TK in adaptation to climate change as
exposure sensitivities, adaptive capacity (or resilience), and adaptations. Exposure sensitivity is a joint propertyof the TK systemʼs characteristics and socioeconomic and environmental stimuli characteristics. Adaptive
capacity refers to the underlying socioeconomic and political drivers influencing how an individual orcommunity experiences change and their capacity to adapt. TK contributes to adaptive capacity in changing
socioeconomic and environmental conditions [Pearce et al. 2015].

41

Asian and African Area Studies, 23 (1)Table 5. Secondary Codes and Key Themes Emerged from the Narrative Inquiry Interviews and Sample
Questions in the Semi-Structured Interview in the Mid-Altitude Dry Sub-Area
Secondary codes and key themes

Sample interview questions (examples)1) O romo traditional soil fertility management
practices (Oromo TK)1-1) House move1-2) Household wastes input
1-3) Kraal move1-4) Crop rotation in the field

1-1-1) H istory of your or your father’s/mother’s
homestead moves (location and area).
1-1-2) Why and how did you move the homestead?
1-2-1) Where did you apply household wastes?
1-3-1) Was kraal move a popular soil fertility
management practice for you?
1-4-1) Did you cultivate crops rotationally?

2) C hanging conditions that affected the Oromoindigenous soil fertility management practices(exposure sensitivity)2-1) Scarcity of the land suitable for the housemove2-2) Derg villagisation policy2-3) Introduction of IF and compost techniques

2-1-1) W hen and why did you or your father/mother stop the homestead move?
2-1-2) When and why did you stop the kraal move?
2-3-1) Have you perceived soil fertility decline in thefield since the mid-1970s? If yes, where did
you perceive it?
2-3-2) When did you or your father/mother begin
compost making and application?

3) Adaptation to the changing conditions (adaptive
capacity and adaptations)3-1) Long-term settlement and sedentary life3-2) A ssimilation into the Amhara cropping
systems and soil fertility management
practices

3-1-1) H ow did the homestead structure differ
before the mid-1970s and the present?
3-2-1) When did the distinction of the fields between
arada and masa begin?
3-2-2) Do you apply compost rotationally to somemasa fields?

Note: Secondary codes and key themes corresponded to the components of traditional knowledge (TK).

mid-altitude moist sub-area for the before-the-mid-1970s dataset and 2012 dataset, respectively.
The narrative inquiry interviews found that both datasets could be divided into two cropping
systems in terms of the relationship between field types (arada or masa, which represents soil
types, soil fertility levels and slopes), crops cultivated and crop sequences, farmers’ tendency touse crops (for house consumption or selling), and soil fertility management practices (Fig. 3).
One can be called a subsistence-crop-based cropping system (SCCS), where maize (andsometimes barley) is continuously cultivated in a fertile arada. Both crops are mainly used forhouse consumption (i.e., subsistence crops). The SCCS contains 96 samples in the before-themid-1970s dataset and 140 samples in the 2012 dataset (Table 6). Another can be called a cashcrop-based cropping system (CCCS). In the CCCS field, cash crops (i.e., crops mainly used for
selling), such as tef, haricot bean, and rainfed vegetables, and subsistence crops, such as barley,
wheat, beans, and peas, are rotationally cultivated in masas. The CCCS contains 103 sample
42

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

Table 6. S ummary Statistics for the before-the-Mid-1970s and 2012 Datasets in the Mid-Altitude Moist SubAreaa
Before the mid-1970s (n=199b)

Periods
Cropping
systemsc

All plots(n=199)[1]Soil fertility
management
practicesd
GenderEthnicTenancy
Education
Farm
LabourLivestock
DonkeyFuel
Plotsize
Distance0e0<≤100e100<≤1000e
1000<eSCCS(n=96)[2]
OnlyOFs
96 (100)

0.90
0.08
0.08
0.2±0.6
3.1±1.6
4.0±2.2
10.5±9.0
2.1±1.22±7
0.4±0.2
169±442
15 (8)
122 (61)54 (27)
8 (4)

0.91
0.08
0.33
0.2±0.6
3.1±1.6
4.1±2.2
10.7±3.1
2.2±1.2
1±5
0.4±0.2
28±52
5 (5)82 (85)
9 (9)
0 (0)

CCCS (n=103)[3]Pseudo-SCCS(n=7)[4]p: [2] p: [2] p: [5]
and
and
and
[6]
[4]f
[3]

Only
OFs
43 (42)[5]No soilamendment
60 (58)[6]No soilamendment 7

0.79
0.02
0.44
0.3±0.6
3.3±1.5
4.3±2.6
13.0±11.9
2.3±1.42±7
0.4±0.3
113±175
5 (12)
20 (46)18 (42)
0 (0)

0.98
0.12
0.23
0.2±0.5
2.9±1.6
3.8±1.9
8.4±5.4
1.9±1.02±10
0.4±0.2433±7235 (8)
20 (33)27 (45)8 (13)

0.86
0.00
0.14
0.1±0.2
2.3±0.9
3.6±1.9
5.6±5.5
1.4± 0.811±30
0.4± 0.11189±337
0 (0)
0 (0)
0 (0)
7 (100)

ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
***

ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
***

***
*
**
ns
ns
ns
**
*
ns
ns
***

p:
[8]
and
[9]

p:
[10]
and
[11]

p:
[12]
and
[13]

p: [1]
and
[7]

ns
ns

ns
ns

ns
*

***
***

ns
ns
ns
ns
ns
ns
***
***

ns
ns
ns
ns
ns
ns
ns
***

**
*
**
***
**
ns
ns
**

***
***
ns
***
***
***
***
***

In 2012 (n=282)
All plots(n=282)[7]

0.84
0.12
No data1.0±1.7
2.0±1.6
3.7±1.8
3.3±2.5
1.3±1.033±20
0.2±0.1437±71274 (26)72 (26)80 (28)56 (20)SCCS (n=140)[8]Only OFs
124 (89)[10]No soilamendment
16 (11)[11]

0.85
0.10
No data1.1±1.8
2.1±1.6
3.8±1.8
3.5±2.6
1.3±1.032±190.2±0.2
27±80
67 (54)47 (38)11 (9)
0 (0)

0.75
0.19
No data0.5±1.2
1.4±0.8
3.3±1.7
2.4±1.5
1.1±0.937±30
0.2±0.1
1061±527
0 (0)
1 (6)
4 (25)
11 (69)CCCS (n=142)[9]Only IFs
85 (60)[12]

OF andIFs 57 (40)[13]

0.80
0.16
No data0.7±1.3
1.8±1.1
3.4±1.7
2.4±2.0
1.1±0.834±220.3±0.1819±9035 (6)13 (15)37 (44)30 (35)0.89
0.07
No data1.4±2.2
2.3±2.1
4.2±1.8
4.3±2.8
1.5±1.232±16
0.3±0.1
587±7102 (4)
12 (21)28 (49)15 (26)

Note: a Figures are means or mean±standard deviation.
b
The total number of the plot data before the mid-1970s does not include pseudo-SCCS plots.
c
SCCS; subsistence crop-based cropping system, CCCS; cash crop-based cropping system.
dOFs; organic fertilisers were applied, IFs; inorganic fertilisers were used. Numbers are frequency,
and numbers in brackets are %.
e
Segments of distance (m). Numbers are frequency, and numbers in brackets are %.
f
e.g. “p: [2] and [4]” represents Pearson’s chi-square tests or t-tests between [2] SCCS data before themid-1970s and [4] pseudo-SCCS data. ns; not significant, * p<0.10, ** p<0.05, *** p<0.01.
43

Asian and African Area Studies, 23 (1)plots in the before-the-mid-1970s dataset and 142 sample plots in the 2012 dataset (Table 6).
K-means cluster analysis (K=2) for the before-the-mid-1970s dataset did not show any
consistency between the data categorised in Cluster 1 (196 data) and Cluster 2 (3 data) and thedata categorised in the SCCS (96 data) and CCCS (103 data). Therefore, binomial logit analyses
were performed for the pooled dataset.
K-means cluster analysis for the 2012 dataset found that the plot data categorised into Cluster
1 was the same as those in the SCCS subdataset. Those categorised into Cluster 2 were the sameas those in the CCCS subdataset. Moreover, all the SCCS plots did not use IFs (fer=0), whereas
all the CCCS plots used IFs (fer=1). Under these conditions, the bivariate probit models 3 and 4
were not formulated. The exogeneity tests showed contrasting results: The bivariate probit
model 1 (the pooled dataset with crop) indicated the household heads considered their choices ofOF and IF use independently (signs of atanh ρ and ρ were positive, suggesting a complementary
relationship between OFs and IFs; however, the z-scores of atanh ρ indicated that H0: ρ=0 were
not rejected, and the likelihood ratio test also showed H0: ρ=0 were not rejected). In contrast,
the bivariate probit model 2 (the pooled dataset without crop) indicated that the two soil
amendment choices were significant substitutes; those were the same results as Mukai [2023].
The bivariate probit analyses showed that the squared residual obtained from model 1 was 38.5
and that from model 2 was 97.3. The per cent correctly estimated values were 82% for model 1
and 60% for model 2. The BIC (Bayesian information criterion) was 368.2 for model 1 and671.7 for model 2. These tests consistently showed that model 1 had a better goodness-of-fit
than model 2. However, the bivariate probit analysis with model 1 did not show any meaningful
results for farmers’ IFs use. It was probably because of the collinearity between fer and crop (feris always 0 when crop=0, whereas fer is always 1 when crop=1).
In these conditions, it can be better to analyse the 2012 pooled dataset using a model that does
not consider a reciprocal relationship between OFs and IFs use because the household heads canconsider the two soil amendment choices independently. The 2012 pooled dataset can also be
analysed separately, representing the Cluster 1 subdataset (=SCCS subdataset) and Cluster 2
subdataset (=CCCS subdataset). Thus, four binomial logit models were formulated: (i) model 1
used the pooled dataset with the independent variable crop (shown as model C in Table 7), (ii)model 2 used the pooled dataset without crop (model D in Table 7), (iii) model 3 used the SCCS
subdataset (model E in Table 7), and (iv) model 4 used the CCCS subdataset (model F in Table 7).
Models 1, 3 and 4 assessed only the determinants of OFs application because of the collinearity
between fer and crop. The bivariate probit analysis using model 2 (the pooled dataset without
44

Before the mid1970sb
A
Binomial logit(pooled)201245

Note: a Figures are coefficients (standard errors). * p<0.10, ** p<0.05, *** p<0.01.
b
The analysis for the before-the-mid-1970s dataset did not include the pseudo-SCCS plot data.
cThe variable crop was omitted from the binomial logit analysis for the before-the-mid-1970s dataset because of the collinearity between man and crop(man is always 1 when crop=0; Table 6).

C
DEF
Binomial logit
Binomial logit (pooled)
Binomial logit Binomial logit(pooled) withwithout crop
(SCCS)(CCCS)crop
IFs (fer)OFs (man)OFs (man)IFs (fer)OFs (man)OFs (man)-0.03 (0.24)
0.18 (0.46)
0.19 (0.42)-0.11 (0.41)-0.27 (2.98)
0.14 (0.58)
0.11 (0.26)-0.69 (0.50)-0.63 (0.45)
0.12 (0.45)
1.26 (2.73)-0.61 (0.67)
0.01 (0.06)
0.26**(0.11) 0.20**(0.10)
0.02 (0.08)
1.30 (1.47) 0.25**(0.12)
Not usedNot usedNot usedNot usedNot usedNot used
0.01 (0.06)
0.10 (0.13)
0.08 (0.12)
0.01 (0.11)
0.03 (0.59)
0.06 (0.14)-0.02 (0.05)
0.14 (0.11)
0.13 (0.10)-0.05 (0.09)
0.79 (0.94)
0.13 (0.12)
0.01 (0.04)
0.20**(0.10) 0.19**(0.10)
0.01 (0.07)
0.13 (0.59) 0.24**(0.11)-0.04 (0.10)
0.04 (0.22)
0.04 (0.21)-0.04 (0.18)-0.53 (1.43)
0.05 (0.24)
Not used -2.10*** (0.37)
Not usedNot usedNot usedNot used
2.76***(0.64) -2.94**(1.37) -4.51***(1.20) 4.58***(1.10) -17.50 (13.23)-1.75 (1.66)
0.00***(0.00) -0.00***(0.00) -0.00***(0.00) 0.00***(0.00) -0.02**(0.01) -0.01** (0.00)
282
282
282
282
282
142
104.91***(18) 129.43***(10)92.49***(9) 84.48***(10) 129.43***(10) 28.21***(9)-276.59-119.25-137.72-153.22-119.25-81.54
60
82
79
72
82
73
Not available
0.35
0.25
0.22
0.88
0.15
671.7
300.6
331.9
362.9
300.6
212.6

B
Bivariate probit (pooled)without cropOFs (man)OFs (man)-2.70**(1.29)
0.10 (0.25)-1.09* (0.65)-0.41 (0.26)
0.04 (0.37)
0.12**(0.06)
0.65 (0.47)
Not used
0.02 (0.17)
0.05 (0.07)-0.01 (0.11)
0.07 (0.06)
0.09**(0.05)
0.10**(0.05)-0.09 (0.29)
0.03 (0.12)
Not used
Omittedc-0.77 (1.01) -2.58***(0.70)-0.01***(0.00) -0.00***(0.00)
199
72.05***(10)Log likelihood-85.79% correctly predicted (%)
79
Pseudo R2
0.30
BIC
229.8

Dependent variables
GenderEthnicEducation
Tenancy
Farm
LabourLivestock
DonkeyCrop
Plotsize
Distance
Sample no.
Wald χ2

Model

PeriodTable 7. Binomial Logit (before the Mid-1970s and 2012) and Bivariate Probit (2012) Analysesa

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

Asian and African Area Studies, 23 (1)Fig. 4. Cropping Sequences and Soil Fertility Management Practices of Teshome Yadete (A Male Farmer
Aged 68 at the Time of the Interview) in the Mid-Altitude Moist Sub-Area before the Mid-1970s (Left
Figure) and 2012 (Right Figure)Note: S and C denote SCCS and CCCS plots, respectively. Cropping sequences were described as the crop
cultivated in the 1st year → that in the 2nd year (for the right figure, crop cultivated in 2011→ that in2012). Sorg; sorghum, BB; broad beans, HB; haricot beans. Soil fertility management practices wererepresented in the third row. NA; no soil amendment, HW; household wastes application, Comp;
compost application, IF; IFs use.

crop) was also shown (model B in Table 7).
Soil fertility management practices at the SCCS plots mostly stayed the same before the mid1970s and 2012 (Table 6 and Fig. 4). Before the mid-1970s, OFs (compost or household wastes)
were applied continuously to all the SCCS plots. In 2012, OFs were applied continuously to
89% of the SCCS plots, while 11% of the SCCS plots had no soil amendment practices (Table 6).
All the SCCS plots that took no soil amendment option in 2012 were ona fields. Onas (theplural form of ona) were previous aradas, where soil fertility that a long-term OFs application
had induced before remained somehow even after their latest homestead move.
Soil fertility management practices at the CCCS plots changed much from before the mid1970s to 2012 (Table 6 and Fig. 4). Before the mid-1970s, 58% of the CCCS plots had no soil
amendment practice, while OFs (compost or household wastes) were occasionally applied to the46

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

remaining 42% (Table 6). In 2012, 60% of the CCCS plots used only IFs, while, on theremaining 40%, IFs are regularly used and compost is occasionally applied. This change wasprobably because of a soil fertility decline in masa fields. Masa soils in both the mid-altitude
moist and mid-altitude dry sub-areas have the mean available phosphorus (P) contents of 1.9–
2.6 mg kg-1. That is categorised as a very low Olsen P level for crop cultivation [Mukai 2019],
while that in aradas was 10–11 mg kg-1, categorised as a medium level. To obtain good yields
from cash crops cultivated in masa, DAP application for supplementing P deficiency has recently
become a must for farmers.
Before the mid-1970s, OFs were applied to all the 96 SCCS plots (Table 6). However, thedeterminants of OFs application to their maize fields are unclear in this situation. Therefore,
additional plot data were collected from 7 household heads who had not had SCCS plots, i.e.
who had not continuously cultivated maize on aradas, before the mid-1970s. These household
heads were selected from those other than the before-the-mid-1970s dataset. The following
questions were asked: (i) did they (or their parents) want to cultivate maize before the mid1970s? If the answer was “yes,” another additional question of “if you (your parents had)cultivated maize, what plot it would be (would have been)?” was asked, and then the attributedata of the corresponding plots and household head were collected. These additionally collected
seven plots were referred to as pseudo-SCCS plots.7) T-tests or Pearson’s chi-square tests wereapplied to detect differences in the means of the variables between the plot data of manured
SCCS (n=96; “manured” referred to as any OFs were applied) and non-manured pseudo-SCCS(n=7; Table 6).
The binomial logit analyses for the before-the-mid-1970s dataset (model A in Table 7)
indicated that the household heads who (i) were females (gender), (ii) owned a larger quantity oflivestock (livestock), and (iii) had the plots that were closer to their houses (distance) significantly
tended to apply OFs to their plots (p<0.05). The summary statistics showed that distance was
the only variable that significantly differed between the manured SCCS plots and non-manured
pseudo-SCCS plots (commuting distance; Table 6). All the household heads who did not holdany SCCS (pseudo-SCCS households) had commuting distances of over 1,000 m to their nearest
fields. In contrast, 87% of the manured SCCS households had maize plots at a commuting
distance of lesser than 100 m, with a 400-m distance as the maximum case. It was difficult forhousehold heads with no field within approx. 400 m from their homesteads to apply OFs to their
7) We can consider that the holders of these 7 plots preferred cultivating maize on the plot, but they gave up for a
specific reason. These 7 pseudo-SCCS data were not used for the binomial logit analyses.

47

Asian and African Area Studies, 23 (1)fields. Farmers apply as much as 6.3 Mg ha-1 of compost to their SCCS fields every 1.1 years[Mukai 2018], thus, giving up continuous maize cultivation. Summary statistics also showedthat the household heads who (i) were females (gender), (ii) were landlords (tenancy), (iii) owneda larger quantity of livestock (livestock), and (iv) had plots with shorter commuting distancesfrom their houses (distance) were significantly more likely to apply OFs to their CCCS plots
before the mid-1970s (Table 6). A significant difference in variable distance between the optionsof no soil amendment and OFs application can be explained by the aforementioned nature of theOFs application techniques observed in the SCCS plots. Farmers divided the manured CCCS
fields into some blocks and applied compost rotationally to each block once every several years.
During the imperial era, many farmers cultivated crop fields as tenants. The odds ratio of thebinomial logit analysis indicated that landlord households had a 3.2-fold higher probability ofapplying OFs than tenant households. Thus, the binomial logit analyses for the pooled dataset
were consistent with the findings from the summary statistics.
The bivariate probit analysis for the 2012 dataset (model B in Table 7) demonstrated that thehousehold heads who (i) spent long years in schools (education), (ii) own a larger quantity oflivestock (livestock), (iii) had a smaller plot in size (plotsize), and (iv) had a short commuting
distance plots (distance) were significantly more likely to apply OFs to their plots. The twobinominal logit analyses for the pooled dataset (models C and D) also showed similar results tothe bivariate probit analysis for the determinants of OFs application.8) These results reflect thesummary statistics for the 2012 pooled dataset (Table 4).
However, the two binomial logit analyses for the manured plots (models E and F in Table 7)
showed contrasting results between the SCCS and CCCS subdatasets. The significant
determinant was the only distance for the SCCS (model E), whereas those were (i) education, (ii)livestock, and (iii) distance for the CCCS (model F). Summary statistics showed that distance8) The two binomial logit models for the pooled dataset with crop (model C) and without crop (model D) and twobinomial logit models for the SCCS (model E) and CCCS (model F) subdatasets are compared in goodness-offit. The indicators used to assess it were (i) the squared residuals obtained from the pooled dataset (SSR1 formodel C and SSR2 for model D) and the sum of the squared residuals obtained from the two subdatasets(SSR3+SSR4=SSR3 for model E+SSR4 for model F); (ii) the per cent correctly estimated values and (iii) the BIC[Mukai 2023]. For (i), SSR 1=38.2, SSR2=43.8, SSR3+SSR4=29.1. For (ii), the per cent correctly estimatedvalues=82% for model C, 79% for model D, 99% for model E, and 73% for model E. For (iii), the BIC=300.6
for model C, 331.9 for model D, 61.7 for model E, and 212.6 for model E. Thus, except model E showed alower per cent correctly estimated value, other indicators demonstrated that the model with a dummy variable
method (model C) showed better goodness-of-fit than the conventional model (model D). This agrees with thebivariate probit analyses and Mukai [2023]. Besides, the models with the data segmentation approach (models
E and F) showed the best goodness-of-fit among the models compared. This was also the same finding as Mukai[2023].

48

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

was the only significant independent variable for the SCCS (P: [10] and [11] in Table 6). In
contrast, the CCCS had several significant independent variables, such as education, labour,
livestock, donkey, and distance (p<0.05; P: [12] and [13]), both of which generally support thebinomial logit analyses with the two subdatasets (models E and F).
The bivariate probit and binomial logit analyses for the pooled dataset (models B, C, and D)
appear to combine the two binomial logit analyses for the SCCS and CCCS subdatasets (models E
and F). However, the analyses for the pooled dataset (models B, C, and D) and the summary statistics(Table 4) commonly showed (i) plotsize as a significant variable and (ii) the plots where IFs wereused had a significantly longer commuting distance (distance). In contrast, the analyses with thetwo subdatasets (models E and F) and the summary statistics (Table 6) showed different results.
All the non-manured SCCS plots were in ona fields (previous SCCS plots in aradas) in 2012.
Thus, it can be natural that neither differences in biophysical features, except distance, norsocioeconomic endowments of the household heads were observed between manured and nonmanured plots. All the CCCS plots were situated in masa fields, where no difference in plot size
was observed between manured plots (i.e. the plots that took the “OFs and IFs” option) andnon-manured plots (i.e. the plots that took the “only IFs”) (P: [12] and [13] in Table 6). The
important factor for farmers to successfully grow cash crops is employing all available soil
amendment options (OFs and IFs). However, their practices depend on their resources
endowments (e.g. education, labour, livestock, donkey) and the plot position (e.g. distance). In
terms of commuting distances (distance) to the CCCS plots, considering the nature of compost
application techniques observed over time, it is highly probable that farmers applied compost to
the shorter commuting distance CCCS plots. Therefore, the binomial logit analyses andsummary statistics for the SCCS and CCCS subdatasets (models E and F and [12] and [13] inTable 6) appear natural, representing field reality.
In the mid-altitude moist sub-area, the household heads did not use IFs at any SCCS plot andused IFs at all CCCS plots.9) The summary statistics and the econometric analyses for the pooled
dataset most probably did not represent farmers’ determinants of IFs use. These represented thedifferences between the plots where IFs were used (the CCCS plots) and those where IFs were notused (the SCCS plots).
Summary statistics (Table 6) showed that female household heads significantly applied OFs to9) In another proximate way of putting it, the household heads did not use IFs for subsistence crops but used IFs
for cash crops.

49Asian and African Area Studies, 23 (1)the CCCS plots before the mid-1970s.10) In contrast, female household heads became less likely
to apply OFs to the SCCS and CCCS plots in 2012.11) When the distance to the manured CCCS
plot was relatively short (113 m in the mean; Table 6) before the mid-1970s, female household
heads had more advantages over male household heads in applying household wastes to the
CCCS plot. However, in 2012, the mean distance to the manured CCCS plot increased to 587 m(Table 6). In addition, female household heads had significantly a lesser number of total family
members and regular labour force (labour; 3.0 and 3.9 persons for the female and male,respectively), significantly lesser donkey and camel ownership levels (donkey; similarly, 1.0 and
1.3 heads), and significantly lesser livestock ownership level (livestock; 1.9 and 3.5 TLU) in
2012; these disadvantaged the female household heads to apply OFs to the CCCS plot. Of the
124 manured SCCS plots in 2012, 82 were situated at a distance shorter than 5 m from thehousehold heads’ houses, and the mean plot size (0.15 ha) was significantly smaller than that of
the other 40 manured SCCS plots (0.31 ha). These are backyard maize plots, a new maize
cultivation method in the early 1990s. Farmers established continuous maize cultivation fields ofseveral hundred m2 in their house-yards, where they dumped household wastes on the ground.
These backyard maize plots decreased the plot size of the manured plots in the pooled dataset,
which probably made plotsize a significant independent variable in the bivariate probit andbinomial logit analyses with the pooled dataset (models B and D).
Thus, overall, the econometric analyses and the narrative inquiry interviews in the mid-altitude
moist sub-area showed that the models with the data segmentation approach (models E and F)
reflect the field reality. This was the same finding as Mukai [2023]. Second, the determinants of
OFs application to the SCCS plots did not change much between the two periods. Over time, theavailable volume of animal dung and the commuting distance to the fields have been the major
determinants of OFs application to the SCCS fields. Third, in contrast to the SCCS, farmers’
determinants of OFs application to the CCCS plots appear to change much over the periods.
Tenancy disappeared after the regime change; however, other resource endowments, such as
education, labour, and donkey, were added.

10) In Table 6, the mean gender value, 0.79 when the soil fertility management option was “only OFs” at the CCCS
plots before the mid-1970s was significantly (p<0.01) lower than the value, 0.98 when the option was “no soil
amendment” (P: [5] and [6]).
11) The mean gender value, 0.85, in the “only OFs” option was higher than that in the “no soil amendment,” 0.75,
for the SCCS. The mean gender value, 0.89, in the “OF and IFs” option was higher than that in the “Only IFs,”
0.80, for the CCCS (both were insignificant).

50

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

3.2 Changes in the Adoption Rate of Organic Fertiliser Techniques and Organic Fertiliser
Application Areas in the Mid-Altitude Moist Sub-Area
In the mid-altitude moist sub-area, the variable distance showed different pictures between the
SCCS and CCCS plots over time (Table 8). The mean commuting distances in 2012 did not
differ significantly from before the mid-1970s for the manured SCCS plots (from 28 m to 27 m;
Table 8).12) In contrast, the mean commuting distance to all the CCCS plots more than doubled
from 300 m to 726 m between the two periods, and the mean distance to the manured CCCS
plots increased by approximately six-fold (from 113 m to 587 m).
For the other key variables, consistent results were observed between the SCCS and CCCS for
all the plots and manured plots (Table 8). Variable labour did not show any significant
difference between the two periods. In contrast, the remaining variables, farm, livestock, fuel(percentage of cattle dung used for fuel), and plotsize, showed significant differences. The
livestock and donkey (donkey and camel ownership) levels in 2012 were reduced to 30–40% andapprox. 60% of those before the mid-1970s, respectively. The increase in fuel can partly help
reduce the volume of animal dung applied to the plots.
Table 8. Comparisons of Several Key Variables before the Mid-1970s and 2012 in the Mid-Altitude
Moist Sub-Areaa
SCCS subdataset
All plots
1970s
(96)c
Farm
LabourLivestock
DonkeyFuel
Plotsize
Distance

3.1
4.1
10.7
2.2
1
0.4
28

b

CCCS subdataset

Manured plots

All plots

Manured plots

2012
(140)

p

1970s
(96)

2012
(124)

p

1970s
(103)

2012
(142)

p

1970s
(43)

2012
(57)

p

2.0
3.7
3.3
1.3
32
0.2
145

**
ns
**
**
**
**
**

3.1
4.1
10.7
2.2
1
0.4
28

2.1
3.8
3.5
1.3
32
0.2
27

**
ns
**
**
**
**
ns

3.0
4.0
10.3
2.1
2
0.4
300

2.0
3.7
3.2
1.3
33
0.3
726

**
ns
**
**
**
**
**

3.3
4.3
13.0
2.3
2
0.4
113

2.3
4.2
4.3
1.5
32
0.3
587

*
ns
**
**
**
*
**

Note: a Pearson’s chi-square tests were applied to detect differences in the means of the variables
between the two periods. ns; not significant, * p<0.05, ** p<0.01.
b1970s; before the mid-1970s.
c
Sample size.
12) This might be because the commuting distance to the household waste application fields became shorter, from
31 m before the mid-1970s (n=37) to 8 m in 2012 (n=62; P=0.06), mainly because of the establishment of thebackyard maize fields. Conversely, the distance to the compost application SCCS fields became longer, from
27 m before the mid-1970s (n=59) to 45 m in 2012 (n=62; not significant). Both offset each other, representing
the phenomena for the SCCS plots.

51

Asian and African Area Studies, 23 (1)How did these changes in the biophysical characteristics of the plots and the socioeconomic
attributes of the household heads affect the soil fertility management practices? The proportionsof the household heads who applied OFs (i.e., the adoption rate of the OF techniques) did notchange much between the two periods, either for the SCCS or CCCS (Table 9). The total
manured plot areas per household significantly declined to approximately half between the two
periods for both the SCCS and CCCS. This probably was a response to the reduction in the total
cropland size per household (farm) between the two periods (Table 8). The manured plot area
per year per household also became nearly half for the SCCS, whereas it was almost identical for
the CCCS. This may imply that the OFs application frequency to the SCCS plot stayed mostly
the same between the two periods. In contrast, household heads applied OF more frequently to
the CCCS fields in 2012 than before the mid-1970s. It was difficult to assess an accurate change
in the OFs application dose between the two periods based on interviews with the household
heads and a single-shot field measurement survey. However, because the rate of livestock
reduction between the two periods was higher than the area reduction rate to which OFs wereapplied per year, the farmers might have coped with the decline in the available volume of animal
dung by decreasing the application dose from the prior level.
3.3 Oromo Indigenous Soil Fertility Management Practices in the Mid-Altitude Dry Sub-Area
Before the Mid-1970s
The target area (27.6 ha) was held by 18 and 19 Oromo household heads for the mid- and lateImperial periods, respectively. All the informants interviewed said they had applied household
wastes to some crop fields close to their houses (Fig. 5). They also said crop rotation had been a
Table 9. Changes in Organic Fertilisers Application before the Mid-1970s and 2012 in the Mid-Altitude
Moist Sub-Areaa
SCCS subdataset

No. of the household heads who applied OFsb
Total manured plot area per household (ha)cManured plot area per year per household (ha)c

CCCS subdataset

Mid1970s(n=96)

2012(n=142)p

Mid1970s(n=103)2012(n=142)p

96 (100)
0.4±0.2
0.4±0.2

125 (88)0.2±0.2
0.2±0.2ns****

43 (42)
1.1±1.4
0.4±0.5

58 (41)
0.6±0.4
0.4±0.2ns**
ns

Note: a ns; not significant, * p<0.05, ** p<0.01.
bFigures are frequency, and figures in brackets are %. T-tests were applied to detect differences
between the two periods.
cMean±standard deviation. Pearson’s chi-square tests were applied to detect differences between thetwo periods.
52

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

Fig. 5. Cropping Sequences and Soil Fertility Management Practices of Bedada Boru (A Male Farmer Aged
60 at the Time of the Interview) in the Mid-Altitude Dry Sub-Area before the Mid-1970s (Left Figure)and 2012 (Right Figure)Note: Remarks are the same as those in Fig. 4. Cropping sequences before the mid-1970s are the crop
cultivated in the 1st year → that in the 2nd year → that in the 3rd year. S1, S2, ---, C5, C6, --correspond to the former house positions of Bedada Boru, indicated in Fig. 6. Before the mid-1970s,
farmers regularly applied household wastes to a crop field or grazing land close to their homestead.
However, those manured fields were usually not amended after their homestead moved to a different
masa. That was why the soil fertility management practices in most fields before the mid-1970s were
described as “HW or NA.”

widespread practice.
Until the mid-1970s, the Oromo in the study area frequently changed their homestead
positions (Fig. 6). Whenever they moved, they established a new house on a relatively unfertile
field (masa), and grazing land (referred to as thithisa) was placed in an immediate downslope
area from their house. Farmers piled up household wastes at a specific place in their house-yards
and sometimes scattered them on their house-yards and grazing land. They applied 4–5 Mg ha-1of household wastes to their vicinity every year. When a kraal in their house-yard became too
wet during the rainy season, they built a simple kraal on their grazing land and moved theirlivestock there. All the informants also said that farmers had intentionally shifted the position ofthis temporary kraal year by year. Using these methods of applying household wastes andlivestock excrement over several to over ten years, a former unfertile field (masa) gained soil
fertility and became fertile (arada). Farmers then moved to the next unfertile field (masa) toboost the soil fertility of that field. Whenever a homestead transfer was performed, a group ofpaternal relatives moved together (Fig. 6).
These homestead transfers were performed basically from a downslope to an upslope direction
53

Asian and African Area Studies, 23 (1)Fig. 6. Two Examples of Homestead Transfers of the Oromo Paternal Relatives Headed by Wandowan
Barushe and Chirmo Bedasa
Note: S1, S2, ---, C5, C6, and C10 correspond to those shown in Fig. 4. The homestead transfers andtransferred years were described for Wandowan Barushe’s paternal relatives. Wandowan Barushe was
Bedada Boru’s paternal grandfather. Land use/cover was the state in 2012.

(Fig. 6). Farmers first amended the fields situated around the boundary between the gentle-slope
cropland (Fig. 3) and gentle-slope homestead and finally settled at areas around the boundary
between the gentle-slope homestead and steep-slope grazing lands. Most of the steep-slope
grazing land area was rock outcropping land. By settling in the unfavourable lands for crop
cultivation, they attempted to maximise the land productivity of the other arable cropland. To
perform this practice, the homestead composition of the Oromo observed until the mid-1970s
was simple. Their homesteads comprised only a house, granary, and kraal. Because theirhomesteads were structured on the presumption that they would move shortly, they were advised
not to surround the homestead with stone fences or plant tree seedlings in their house-yards.
3.4 Changes in Soil Fertility Management Practices in the Mid-Altitude Dry Sub-Area after theMid-1970s
After the mid-1970s, the Oromo indigenous cropping systems/sequences and soil fertility
54

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

management practices changed markedly. Their indigenous soil fertility management practices,
i.e. turning unfertile masas into fertile aradas by changing their homesteads, ended. This was
because: first, farmers had moved towards the rock outcropping land. Currently, mosthomesteads are situated between the gentle-slope homestead and steep-slope grazing land areas(Fig. 6). Second, because the population increased and land tenure relations around the gentleslope homestead area became complicated, the space and scope for individual households tochange their homestead positions became scarcer. Third, the indigenous practice of establishing
the grazing land immediately downslope from a homestead ended. This occurred primarily when
the homesteads and grazing land areas diminished and became too narrow to place new grazing
land. In the 27.6 ha target area, the mean homesteads and grazing lands held by a household
significantly reduced from 0.80±0.27 ha in 1957 and 1972 to 0.12±0.06 ha in 2005 (p<0.01;
Pearson’s chi-square test). Fourth, the Derg regime implemented the villagisation programme in1986 in the Boset district [Geda 2018] (Fig. 6). The land policy of the socialist Derg regime,
agglomerated rural settlement, affected the Oromo indigenous soil fertility management
practices. Around this period, the Oromo began surrounding their homesteads with hedges(primarily Euphorbia leucodendron) and stone fences. Seedling plantations within theirhomesteads became a popular practice. Thus, their homestead structure and compositionchanged from those aimed at a future move to a long-term settlement, similar to the Amhara.
The cropping sequences and soil fertility management practices observed in 2012 in the midaltitude dry sub-area (Fig. 5) are: first. The Oromo farmers began compost and household
wastes applications to turn unfertile fields (masas) into fertile fields (aradas), where they
continuously cultivated maize, i.e. SCCS maize fields. Second, farmers continued the rotational
cropping of cash crops and subsistence crops other than maize in masas, where IFs are usedwhenever cash crops are cultivated, and compost is occasionally applied rotationally, i.e. CCCS
fields. Thus, after the Oromo in the mid-altitude dry sub-area lost their indigenous soil fertility
management practices, the new soil fertility management practices that the Oromo adopted werewhat the Amhara had established in the mid-altitude moist sub-area.
3.5 Resource Constraints and Integrated Soil Fertility Management
In the mid-altitude moist sub-area, most household heads (100% of the sample household heads
before the mid-1970s and 89% in 2012) held continuously cultivated maize-SCCS fields inaradas. Between the two periods, the proportions of manured plots did not change much forboth SCCS (89–100%) and CCCS (approx. 40%; Table 6). The adoption rate of the OF
techniques did not change much over time. Soil fertility decline in masa fields and the55Asian and African Area Studies, 23 (1)introduction of IF techniques were the primary drivers for farmers in the mid-altitude moist subarea to change their soil fertility management practices in the CCCS field. In the CCCS field, IFs
are used whenever cash crops are cultivated, and compost was applied to 40% of the plots in2012 (Table 6). The bivariate probit analysis with the variable crop for the 2012 pooled dataset
found that farmers’ choice of the OFs and IFs use was independent, at least, not substitutability.
In the mid-altitude dry sub-area, the cropping systems and soil fertility management practices
taken by the Oromo after the mid-1970s have become similar to those established by the Amharain the mid-altitude moist sub-area. All the evidence supports that the soil fertility management
practices observed in the study area follow the concept of integrated soil fertility management,
the combined use of OFs and IFs. Thus, for sustainable agricultural intensification in thenorthern semi-arid Ethiopian Rift Valley, how to intensify the present continuous trend in the
future should be considered.
In the mid-altitude moist sub-area, the available volume of animal dung and the commuting
distance to the plots have been the major determinants of OFs application to both the SCCS and
CCCS plots over time. These determinants align with the nine recent technology adoption
studies (Table 1) and Mukai [2023] from the northern semi-arid Ethiopian Rift Valley. Of thenine studies, (i) all studies found livestock ownership levels positively affected OFs application,and seven found the relationship was significant (p<0.1). (ii) eight studies took a commuting
distance to the sample plots as a dependent variable, of which seven studies found commuting
distance negatively affected OFs application, and four found the relationship was significant(p<0.1). These two factors also are likely to affect IF use (Table 1). Greater livestock ownershipis associated with greater use of seeds and IFs, probably because the income generated fromcattle products helps farmers afford to buy these inputs [Pender and Gebremedhin 2006]. In
association with a generational change, new households were transferred lands in relatively
distant places or distributed unfavourably located fields from their parents or peasantassociations in their villages. That was quantitatively proven in Southern Ethiopia [Bezu andHolden 2014], one of the densely populated areas in Ethiopia. The mean household farm size
was 0.86 ha [Bezu and Holden 2014], much smaller than that in the mid-altitude moist sub-area,
2.0 ha (Table 7). Based on the household survey data collected from 93 Highland districts ofEthiopia (densely populated areas), Headey et al. [2014] found that the mean household farm
size (0.96 ha), which is very small by international standards, is declining over time. He also
found that young rural households particularly faced severe land constraints. Thus, land
fragmentation and the increasing commuting distance to the SCCS and CCCS fields caused by56

Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

population increase are critical constraints in determining farmers’ soil fertility management
strategies in the study area and densely populated areas in Ethiopia.
The second point Headey et al. [2014] found was that land-constrained farmers use
significantly more purchased input costs per hectare, such as IFs and improved seeds. Thisagrees with Pender and Gebremedhin [2006] from the semi-arid highlands, Tigray, and partly
agrees with Benin [2006] from the sub-humid highlands, Amhara, and Josephson et al. [2014]
with 1,293 household data (Ethiopian rural household surveys) collected from diversified
farming systems in the country. Furthermore, these agree with the primary drivers of a recent
rapid increase in the nationwide maize productivity [Abate et al. 2015] and other cereals, such astef, wheat, and barley [Bachewe et al. 2018]. Josephson et al. [2014] found that the intensity of
IF use increases as rural population density rises until around 2.0 persons ha-1 in Ethiopia. Thesefindings agree with the recent trend of IFs use in the study area. IFs were used for all the CCCS
plots whenever cash crops were cultivated. Mukai [2023] also found this finding in the northernsemi-arid Ethiopian Rift Valley.
The third point found by Headey et al. [2014] was that land-constrained farmers use morefamily labour and, therefore, achieve higher maize and tef yields and higher gross income perhectare. However, Pender and Gebremedhin [2006], Benin [2006], and Josephson et al. [2014]
found that higher population density was not associated with an increase in crop yields.
Population pressure is associated with more intensive use of labour, improved seeds, and IFs, thefindings of which are consistent with the predictions of population-induced intensification, as
hypothesised by Boserup [1965] and her followers [Pender and Gebremedhin 2006]. These agreewith the findings obtained from the qualitative and quantitative analyses for the OFs application
to the CCCS plots in the mid-altitude moist sub-area. Recently, the household heads’ resources
endowments, such as education, labour, and donkey, have become the major determinants of
OFs application to the cash crops at the CCCS plots.
3.6 Integrated Soil Fertility Management toward Agricultural Intensification
Nin-Pratt [2015] divided SSA countries into four groups in terms of population density: G1 (themean population density of 0.12 person ha-1), G2 (0.30 person ha-1), G3 (0.54 person ha-1), andG4 (1.85 person ha -1). He found that sparsely populated countries, such as G1 and G2,
increased output following a land-abundant path that included (a) more land incorporated intocrop production and (b) increased cropping intensity through reducing fallow periods. In
contrast, in the countries with over 0.5 person ha-1, i.e. G3 and G4, the contribution of the (c)yield increase substantially gained. Within G1, G2, and G3 countries, high IF use levels were57

Asian and African Area Studies, 23 (1)related to the above (a) but not to (c). In contrast, in the case of G4 countries, IF use was notcorrelated with (a), but high IF use levels were expected with higher (c). Nin-Pratt [2015] further
suggested that his hypotheses could be more applicable to highland temperate and maize-mixed
systems in Eastern and Southern Africa.
Population densities of Boset district were 0.39 in the mid-1970s, 0.57 in 1984, and 1.02
person ha-1 in 2012 (estimated by CSA [1984–2017]). In the 1957 aerial photographs, roughly
half of the cultivated fields in the Tebo-Geldia catchments in 2012 had not been exploited yet.
Considering the population density of the catchments area in 1957, the catchments werepositioned in G1 or G2 since the pioneer migrants had begun forest exploitation. The area
surely had a land-abundant path similar to the G1 and G2 countries at the time. The 1972 aerial
photographs showed that most cultivated fields in 2012 had already become croplands. Since
then, the phase of agricultural intensification in the catchments changed to yield-increase- andcommercialisation-oriented ones with integrated soil fertility management practices. This
agricultural intensification process appears to agree with the one typically seen in G3 countries.
Thus, the historical agricultural intensification processes observed in the study area support the
hypotheses suggested by Nin-Pratt [2015]. These findings suggest that the unfavourable
continuous resource constraints in the northern semi-arid Ethiopian Rift Valley do not necessarilyhinder future agricultural intensification. Farmers have recently become more interested ininvesting limited organic resources into cash crop cultivation. The future resource constraints
probably encourage farmers to use more purchased costs per hectare for IFs and improved seeds
in the due intensification course from G3 to G4.
Thus, to maintain the present trend of integrated soil fertility management practices in the
study area, encouraging OFs use under aggravating conditions is critical. Since the beginning of
the 2000s, the district agriculture office began giving fast compost training to farmers [Mukai
and Oyanagi 2019]. The district agriculture office has planned this training, technically
instructed by Melkassa agricultural research centre (a regional agricultural research institute) andfinancially supported by World Vision (an international NGO). Mukai [2023] surveyed twoneighbouring districts, Adama and Boset. He found that: (i) approximately one-third of compost
application farmers to their CCCS plots replied either “I acquired the compost techniques by fastcompost training” or “I knew it previously, but the training facilitated its use.” Moreover, (ii)the training could help significantly more farmers practice the combined use of compost and IFs
on their CCCS plots. The indigenous compost (kosi) and introduced compost (fast compost)contain approximately one-third of animal dung and the remaining two-thirds of other organic
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Mukai: A Transition in Soil Fertility Management Practices in the Semi-Arid Ethiopian Rift Valley

materials in volume (maize and sorghum stalks, crop residue, feed refusals, kitchen ash) collectedfrom their house-yards and fields. It effectively utilises locally available organic materials besides
livestock dung. Mukai and Oyanagi [2019] found that because kosi and fast compost released alimited amount of nitrogen in the soil in an application year, both compost showed low
substitutability for IFs as quick-acting fertilisers. Instead, they cultivate the soil in their mediumor long-term application and gain crop yields by increasing total carbon and nitrogen in the
manured soil; thus, OFs application and IFs use can be supplementary soil fertility management
practices. Compost preparation and application techniques is a countermeasure against theincreasingly unavailable volume of animal dung and a good example of strengthening linkages
between research, extension services, and farmers, one of the requirements for sustainable soil
fertility management in SSA [Stewart et al. 2020].13)

4. Conclusions
Since the 1870s, the Oromo repeated short-distance transfers to amend the field soil. Because ofland constraints and the enforcement of the villagisation policy, they began a sedentary life
similar to the Amhara. Currently, the cropping systems taken by both Amhara and Oromo canbe categorised into subsistence-crop-based and cash-crop-based ones. After the soil fertility
decline in masa fields and the introduction of the IF techniques, their soil amendment strategies
have been characterised as integrated soil fertility management. Despite the continuous
deterioration in the limited availability of animal dung and increasing commuting distances tothe fields, primary determinants of OFs application, the adoption rate of the OF techniques andthe proportion of manured fields have not changed much over time in the northern semi-arid
Ethiopian Rift Valley. Farmers have recently been more inclined to invest limited organic
resources in cash crop cultivation by partly sacrificing their share for subsistence-based crops.
These historical paths of the agricultural intensification in the northern semi-arid Ethiopian Rift
Valley support the hypothesis of population-induced agricultural intensification deduced from
13) Stewart et al. [2020] conducted questionnaire surveys and summit meetings in 2017. The questionnaire surveys
collected their opinions from 491 multi-disciplinary actors working in SSA soil fertility, including international
agricultural centres, national agricultural research and extension systems, agronomic/soil researchers, extension
and development agencies, the private sector, social scientists, and farmer organisations in 32 SSA countries.
They provided evidence-based recommendations that local research institutes conduct quality soil testing,establish regionally specific fertiliser response recommendations, and recommend improved/updated cropping
systems to farmers through strengthened linkages between research, extension services, and farmers. Increasing
access to and use of quality organic materials was consistently identified and prioritised by all regions for thegoal of improving soil fertility.

59

Asian and African Area Studies, 23 (1)spatial and temporal evidence in SSA. Part of Eastern and Southern Africa and West Africa
Sahelian countries, where the crop-livestock system prevails, the future resource constraints canbe counteracted by more intensified soil fertility management strategies of OF-IF integration.
How to effectively utilise available organic resources in farmers’ vicinity should be considered in
linkages between research, extension services, and farmers to maintain sustainable soil fertility
management in the local contexts.
Acknowledgements
This study was financially supported by the Ministry of Agriculture, Forestry and Fisheries, Japan. The
author would like to thank Merko Odalega and Koka Gifawasen villagers for cooperating with this study.

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