socio economic analysis of farmers potential for...
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SOCIO ECONOMIC ANALYSIS OF FARMERS’ POTENTIAL FOR ADOPTION OF
EVERGREEN AGRICULTURE IN BUGESERA DISTRICT, RWANDA
SEPTEMBER 2012
World Agroforestry Centre (ICRAF)
Nairobi, Kenya
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ABSTRACT
Evergreen agriculture is increasingly gaining importance as a sustainable land management
approach capable of dealing with the challenges of low land productivity and food insecurity
in Africa. However, the concept is still new among farmers and efforts are being made to
promote it on a wide scale.
This study was informed by the need to scale up evergreen agriculture in Eastern Africa, with
particular focus on Rwanda. This research sought to undertake socio economic, farm,
institutional, and demographic analysis of targeted farmers’ characteristics in Bugesera region
of Rwanda as well as examine their potential to adopt evergreen agriculture.
The study found that the potential for adoption of evergreen agriculture exists since some
farmers were already practising some forms of agroforestry (intercropping trees with food
crops) and conservation agriculture (reduced tillage, cover cropping and mulching).
Farmers were also keen on taking up measures to control soil erosion and improve soil
fertility on their farms. Food insecurity as a result of declining soil fertility and low land
productivity was a major problem in the area and this can serve as an entry point for
promotion of evergreen agriculture.
Lack of information, knowledge and skills about the benefits of conservation agriculture and
agroforestry were major hindrances to adoption and more emphasis at equipping farmers
with necessary skills and information sources would boost scaling up efforts.
The potential adoption of evergreen agriculture was found to be significantly and positively
influenced by the age of the household head, access to credit, affordability of seedlings, and
membership in farmer groups.
Efforts at promoting evergreen agriculture ought to look into how farmers who face
constraints related to credit access, affordability of seedlings or membership in groups can be
targeted by catering to their specific farming needs.
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Further, the study finds that farmers choose only certain types of tree for intercropping with
their crops and one or two conservation agriculture practices. Thus, future research should
examine what practices work best in different agro ecological contexts and the typologies of
farmers that would best benefit from specific practices within evergreen agriculture.
Knowledge dissemination and training also need to targeted to wide audiences and
packaged in easy to understand messages to enable farmers make informed decisions.
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ABBREVIATIONS & ACRONYMS
AfDB: African Development Bank
CA: Conservation Agriculture
CFU: Conservation Farming Unit
EC: European Commission
FAO: Food and Agriculture Organisation of the United Nations
GART: Golden Valley Agricultural Research Trust
GIS: Geographical Information Systems
GNP: Gross National Product
IFAD: International Fund for Agriculture Development
KMs: Kilometres
MDGs: Millennium Development Goals
NEPAD: New Partnership for African Development
RoR: Republic of Rwanda
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TABLE OF CONTENTS
Abstract ........................................................................................................................ 2
Abbreviations and Acronyms ........................................................................................... 3
Introduction ................................................................................................................... 6
Materials and Methods ................................................................................................. 13
Site selection and sampling ............................................................................... 13
Data collection and analysis.............................................................................. 13
Results and Discussions ................................................................................................ 15
Description of sample characteristics .............................................................................. 15
Potential for adoption of evergreen agriculture ............................................................... 33
Determinants of adoption of agroforestry practices ............................................. 35
Determinants of adoption of conservation agriculture .......................................... 36
Conclusions ................................................................................................................ 38
References .................................................................................................................. 40
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LIST OF FIGURES
Figure 1: Administrative divisions of Rwanda ---------------------------------------------------- 9
Figure 2: Wall types of farmers’ houses --------------------------------------------------------- 17
Figure 3: Roof types of farmers’ houses --------------------------------------------------------- 17
Figure 4: Farmers’ household food sufficiency ------------------------------------------------- 18
Figure 5: Households’ management of food needs ------------------------------------------- 18
Figure 6: Farmers’ land ownership categories ----------------- Error! Bookmark not defined.
Figure 7: Slope categories of farmers’ land -------------------- Error! Bookmark not defined.
Figure 8: Intercropping of trees with food crops --------------- Error! Bookmark not defined.
Figure 9: Access to credit ------------------------------------------------------------------------- 31
LIST OF TABLES
Table 1: Ownership of household assets ----------------------- Error! Bookmark not defined.6
Table 2: Food shortage coping strategies ------------------------------------------------------ 19
Table 3: Crop production, consumption and sales in a mono cropping system ------- Error!
Bookmark not defined.
Table 4: Distribution of livestock by breed and fodder type -- Error! Bookmark not defined.
Table 5: Main tree species planted by farmers ----------------- Error! Bookmark not defined.
Table 6: Farmers’ income sources --------------------------------------------------------------- 28
Table 7: Hypothesizing determinants of adoption of evergreen agriculture ---------------- 33
Table 8: Determinants of adoption of agroforestry practices --------------------------------- 35
Table 9: Determinants of adoption of conservation agriculture ------------------------------ 36
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1.0. INTRODUCTION
Agriculture is vital for promoting growth and reducing poverty in Africa. Agriculture supports
the livelihoods of 80 per cent of the African population. An estimated 70 per cent of the
population depends on agriculture for full-time employment and many others rely on
agriculture for part of their household income.
However, two hundred million Africans are food insecure. It is estimated that Africa will not be
able to feed more than half its population by 2015. Moreover, agriculture in Africa faces a
host of challenges. The soils of the continent’s vast land surface are typically old and leached.
About 494 million ha of Africa’s soils are degraded and the continent is estimated to be
losing nutrients worth $4 billion per annum. Yet, farmers use fertilizer at a rate of only about
8kg/ha and far less in smallholder farms compared to a target of 50kg/ha.
Rainfall is often unreliable and the effects of drought are aggravated by fragile soils with low
water holding capacity. Deforestation and the associated loss of forest products and
environmental services are some of the other serious challenges facing African countries
today.
Investments are therefore needed to build up assets, introduce new technologies, enforce non
market allocation mechanisms where feasible, and improve risk management capacity (AfDB,
2010).In the early 2000s, when the international community agreed to implement the
Millennium Development Goals (MDGs), agriculture again became an important issue.
There is a link between the goals of reducing extreme poverty and hunger, ensuring
environmental sustainability and agricultural development as key drivers of growth in Africa
(AfDB, 2010). Sustainable agriculture has emerged as an alternative agricultural system that
addresses the many constraints faced by resource-poor farmers and at the same time ensures
environmental sustainability.
It refers to the capacity of agriculture to contribute to overall welfare by providing sufficient
food and other goods and services in ways that are economically efficient and profitable,
environmentally and socially responsible.
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There is growing evidence that sustainable agricultural practices have been able to increase
productivity with minimum damage to the environment compared to conventional agriculture
(Kassie and Zikhali, 2009).
Conservation Agriculture (CA) is considered one of the sustainable agricultural practices. CA
comprises the simultaneous application, through good management, of three key principles:
minimum mechanical soil disturbance, permanent organic soil cover and diversification of
crop species grown in sequence or associations.
Long term increase and stability of yields can be achieved while at the same time stopping
and reversing land degradation (Mazvimavi, 2011). The role of agroforestry in adding fertility
to CA farming systems is recognised and some research in the region has shown benefits
such as increased soil health when some agroforestry tree species are established within CA
systems (GART, 2008).
Agroforestry practices that promote soil cover and crop rotation greatly elevate CA. Similarly,
agro forestry systems have been known to increase crop yields, improve soil fertility and
provide a diverse array of forest products. A combination of the two systems would
undoubtedly provide a resilient, sustainable and vibrant production system (FAO, 2009).
Evergreen agriculture is a relatively new practice that seeks to combine principles of CA and
agroforestry in order to reduce or reverse soil fertility depletion through the use of ‘fertilizer’
trees, improve carbon and moisture retention in the soil and provide shade and tree cover on
cultivated fields in addition to provision of tree products such as fodder, firewood, fruits etc.
Yield improvements have been observed in various empirical studies on agroforestry and
conservation agriculture and the concept of evergreen agriculture portends complementarities
that yield a climate-smart sustainable agriculture when the two are combined.
The African Union’s New Partnership for Africa’s Development (NEPAD) is building a broad
alliance with governments, international and local partners, to establish evergreen agriculture
throughout the region. The World Agroforestry Centre, the African Conservation Tillage
Network, and the Zambian Conservation Farming Unit are working closely with NEPAD, other
research and development partners, and a growing consortium of supportive donors to
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develop the evidence base and the capacity on the ground to ensure that this vision may
become a reality (World Agroforestry Centre, 2009).
The World Agroforestry Centre received funding from the European Commission (EC) through
the International Fund for Agricultural Development (IFAD) to test the potential for increased
adoption of evergreen agriculture practices in East and Southern Africa through a project
entitled: “Creating an Evergreen Agriculture in Africa: Scaling-up Conservation Agriculture
with Trees for Improved Livelihoods and Environmental Resilience in Eastern and Southern
Africa”. The three year pilot project is being implemented in selected sites in Kenya, Tanzania,
Rwanda and Lesotho and has four components as follows: -
1. Establish baseline information on conservation agriculture and agroforestry in target
areas.
2. Establishing sustainable tree seed/seedling supply systems by using the ‘Rural
Resource Centre’ approach
3. Build the capacity of smallholder farmers and partners for effective adoption of
Evergreen Agriculture practices
4. Development of knowledge and information sharing products
This survey was conducted in Rwanda’s Bugesera district as part of the first project component
with the primary objective of assessing the potential for adoption of evergreen agriculture (CA
and agroforestry).
The baseline study aimed at collecting information on the socio-economic, institutional,
farm/land and demographic characteristics of targeted farmers, and on the potential for
adoption of evergreen agriculture technologies.
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2.0. DESCRIPTION OF STUDY AREA
2.1. Rwanda
Rwanda is located in Central Africa between latitudes 1°04’ and 2°51’ south and longitudes
28°45’ and 31°15’ east. The country is small, mountainous and landlocked with a surface
area of about 26,338 km2. The population was 9.2 million in 2006 and is expected to reach
16 million by 2020 unless family planning, education and outreach strategies are intensified.
With a population density of 397 inhabitants per square kilometre, Rwanda is the most
densely populated country in Africa. Eighty three per cent of the population in Rwanda is rural
with 53 per cent of them women Rwanda is administratively divided into five provinces (Kigali
city, Southern, Western, Eastern and Northern), 30 districts and 415 sectors (Figure 1 shows
the administrative divisions of Rwanda) (REMA, 2009).
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The Rwandan relief is hilly and mountainous with an altitude averaging 1700 meters.
Volcanic mountains are found on the northern fringe and undulating hills are in mostly in the
central plateau. However, the eastern part of the country is relatively flat with altitudes well
below 1500 meters.
This relief pattern gives Rwanda a mild and cool climate that is predominantly influenced by
altitude. Average annual temperatures are about 18.5oC and average rainfall is about 1,250
mm per annum. The lowlands of the southwest in Bugarama plain with an altitude of 900m
are part of the tectonic depression of the African Rift Valley (ROR, 2008). The agricultural
sector is very important to the economy of Rwanda.
It provides employment to 86.3 per cent of the country’s working population and is the main
source of income for 87 per cent of the total population. Agriculture contributes 47 per cent
of the Gross National Product (GNP) and accounts for 71 per cent of the country’s export
revenue.
The total arable land is about 1.4 million hectares, which is 52 per cent of the total surface
area of the country. Nonetheless, the actual area cultivated has exceeded 1.6 million ha in
recent years. Another 0.47 million ha is under permanent pasture, so well over 70 per cent of
the country’s total land surface is exploited for agriculture (RoR, 2008).
Generally, landholdings are very small with more than 60 per cent of households cultivating
less than 0.7 ha, 50 per cent cultivating less than 0.5 ha, and more than 25 per cent
cultivating less than 0.2 ha.
The small size of farms emanates from high population pressure on small landholdings. Thus,
the majority of rural populations cannot produce enough food. Furthermore, about 40 per
cent of Rwanda’s land is classified by the FAO as having a very high erosion risk with about
37 per cent requiring soil retention measures before cultivation.
Only 23.4 per cent of the country’s lands are not prone to erosion. Without the adoption of
better farming methods like terracing and contour ridges to stop soil erosion, agriculture will
continue to be unsustainable (REMA, 2009).
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2.1.1. Bugesera district
This survey was conducted in Bugesera district in the Eastern province of Rwanda. Bugesera
District (1333.9 km2) is located in the south eastern plains of Rwanda’s Eastern Province. It is
bordered in the south by the Republic of Burundi (Kirundo Province), Ngoma district to the
east, and Kigali City and Rwamagana District to the north.
The district of Bugesera currently consists of 15 administrative sectors, 72 cells and 579
villages. The region is sandwiched between Rivers Nyabarongo and Akanyaru which converge
at the southern end to form the Akagera River.
Bugesera is characterised by numerous lakes, the biggest of which are Rweru and Cyohoha.
As a low rainfall area, biodiversity in the district is typical of dry lands, but species often found
in humid ecological areas are also present.The region is predominantly vegetated by dry
savannas.
The extensive savannas and their drought resistant shrubs historically provided grazing lands
for pastoralists who were the first inhabitants of the region. With increasing population, most
of the natural vegetation has disappeared due to conversion into agricultural lands (RoR,
2011; JICA, 2006; MINITERE, 2003).
Relief, climate and soil conditions
Bugesera’s relief has a succession of undulating hills, dry valleys and some marshes due to
tectonic collapse. The area is also dominated by some mountains: Mt. Shyara (1,772 m), Mt.
Juru (1667 m), Mt. Maranyundo (1,614 m), and Mt. Mwendo (1575m). Rainfall is bimodal
with the long rains falling between February and May, and the short rains falling between
September and December.
The highest rainfall ever recorded was 1300 mm in 1969 but annual precipitation ranges
between 700‐900 mm. The mean atmospheric temperature varies but is usually between 21º
C and 29º C.
Dry spells are experienced between June and August. Most soils are sandy loams. The
summits of some plateaus located in the centre and the north of the district have ochre clay
soils, whereas the sides and tops of the plateaus are made up of rocks and schist which
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contain gravel, laterite and quartz. In general, the soils are more or less fertile but permeable
and fragile (JICA, 2006, MINITERE, 2003).
Demographics
According to the August 2002 Population and Housing Census, Bugesera had a total
population of 266,775 and was estimated to be 297,168 people in 2006 and 327,561 in
2010. Half of the population is young (between 0 and 17 years).
Among Bugesera’s 59,665 total households in 2004, the average household size in
Bugesera was 5 people (GoR, 2011). Population density has progressively increased from
181 inhabitants/ km2 in 1980, to 200 inhabitants/ km2 in 2002.
Although this was less than the national average of 321 inhabitants/km2, it raises concerns
about the carrying capacity since the region is ecologically fragile (RoR, 2011; JICA, 2006).
Agriculture
Bugesera is a predominantly rural area and the main occupation of the population is
subsistence agriculture. The agricultural year starts in mid September and ends in mid
September of the next year.
Mixed farming is the most common farming system and households rely on family labour.
Farming is usually done using hoes and machetes. Intercropping, crop rotation and use of
some soil and water conservation techniques are typically practiced.
Trees such as Grevellia are intercropped with crops. The main food crops grown in Bugesera
are sorghum, maize, groundnuts, cassava, soy bean, sweet potatoes, beans, and rice.
Arabica coffee is the major cash crop mainly grown in the northern parts.
Though, the agronomic characteristics of Bugesera have been described as favorable for the
production of a variety of crops, adverse climatic conditions hamper the productivity potential
of the district. Domestic animals such as cattle (especially the local Ankole species), goats,
sheep, rabbits, poultry and pigs are raised by farmers (RoR, 2011; JICA, 2006).
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3.0. MATERIALS AND METHODS
3.1. Site selection and sampling
Site selection was based on topography which determined the prevailing farming systems as
well as accessibility. A topographic map overlaid with the district’s administrative units (Juru,
Rweru and Gashora) was developed through the help of the Geographical Information
Systems (GIS) unit at the World Agroforestry Centre.
A transect line representing the highest and lowest point in the district was drawn on the map
and stratified into upper, middle and lower elevations to capture the variety of farming
systems in the district.
Three to six villages were selected in each administrative unit and village heads requested to
provide a list of households in every village. This information was cross checked with that held
by the district agricultural office.
The developed list of households formed the sampling frame for this study. A sample of 495
households was randomly selected from the sampling frame. This sample size was considered
representative of the total population since it represented 10 – 15% of the total number of
households in the selected villages as per the population census statistics.
3.2. Data collection and analysis
Data was collected using semi structured questionnaires that were administered to 495
households. The questionnaires were designed to capture information related to the socio
economic status of the district, potential for adoption of agroforestry and CA (evergreen
agriculture) practices, existing institutional frameworks and land health status.
Data was analysed using the Statistical Software for Social Scientists (SPSS). Descriptive
statistics were done by use of tables, graphs, charts, percentages, modes and means while
inferential statistics were generated using chi-square tests and t-tests at p<0.05.
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Binary logistic regression was utilised to assess the potential of adoption of evergreen
agriculture. For purposes of this study, potential for adoption of evergreen agriculture was
assessed in terms of farmers practising agroforestry (intercropping trees and crops) and those
applying conservation agriculture principles (such as reduced tillage and cover cropping).
Binary logistic regression allows the prediction of a discrete outcome such as group
membership, from a set of variables that may be continuous, discrete, dichotomous, or a mix
of any of these.
One major advantage of logistic regression analysis is that unlike multiple linear regression
methods whose dependent variables are either in interval or ratio scale, the dependent
variable in logistic regression is categorical or dichotomous (binary).
This makes it the most appropriate method for this study since our dependent variables were
dichotomous (the practices of agroforestry and soil erosion control and soil fertility
improvement measures).
The binary variable takes the value 1 with a probability of success , or the value 0 with
probability of failure 1- The relationship between the predictor and response variables is
not a linear function in logistic regression; instead, the logistic regression function is used,
which is the logit transformation of
Where is the constant of the equation and are the coefficients of the predictor
variables.
For this study, the dependent variable depicted the practice of agroforestry (intercropping
trees and crops) by farmers and took the value of 1 if the farmer practised and 0 if otherwise.
Likewise, the dependent variable took the value of 1 if a farmer practised soil erosion control
and soil fertility improvement measures on their farms and 0 if otherwise.
Predictor variables are as presented in Table 7 of section 4.2 of this report.
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4.0. RESULTS AND DISCUSSION
The findings of this survey are presented in two sections. The first section provides a general
description of the sample characteristics while the second one assesses the potential for
adoption of evergreen agriculture (here in referred to as a combination of CA and
agroforestry practices).
4.1. Description of sample characteristics
4.1.1. Household characteristics
Majority of the household heads in the survey regions were aged 45 years, suggesting that
the productive segment of the rural population was engaged in farming activities. Most
households were male headed (76.1%) and married (77.1%).
Household headship may influence decision making on technology adoption especially in
patriarchal societies where men make most decisions regarding the household and/or farm.
The average household had 5 members.
Household size depicts availability of labour to conduct farming activities or to take up newly
introduced innovations such as evergreen agriculture. 51.8% of the farmers had migrated
from other regions within Rwanda or neighbouring countries such as Burundi, Tanzania or
Uganda into the study area.
Reasons given for migration were varied, with majority saying that they came to the area in
search of farming land either from other parts of Rwanda that were experiencing land scarcity
or as returnees who had fled the country during the genocide. The primary occupation of
most households (95.9%) was farming.
Farmers who regard farming as their main occupation are likely to invest more time, energy
and money into farming as a key source of livelihood. Most farmers (58%) had primary level
education while 36.8% had no education at all.
17
Farmers with some formal education are more likely to adopt evergreen agricultural
innovations especially if those innovations require education to understand and implement.
Table 1 provides information on the number and type of assets owned by households.
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Table 1: Household assets ownership
Household assets owned Mean SD
Car 0.00 0.00
Motorcycle 0.00 0.06
Television 0.01 0.09
Radio 0.67 0.52
Water tank 0.01 0.09
Grain mill 0.00 0.06
Granary 0.00 0.45
Ox cart 0.00 0.00
Mobile phone 0.45 0.70
Bicycle 0.43 0.55
Hoe 2.16 1.08
Machete 1.07 0.52
Ox plough 0.00 0.05
Spade 0.16 0.38
Wheel barrow 0.01 0.12
Sprayer 0.02 0.13
Milking can 0.07 0.40 Legend: SD (Standard Deviation)
The results showed that most households possessed at least two hoes and one machete. A
radio was also widely owned by households. Apart from the mobile phone and bicycle, the
rest of the household assets were not popular among the sample population. Low assets
ownership could have been because of the high levels of poverty in the district.
The widespread ownership of hoes and machetes could be attributed to the fact these were
the most common farming implements in the region (JICA, 2006). Ownership of assets is an
indicator of the livelihood status of a household and/or their ability to support a certain
means of living.
Assets ownership may have an effect on adoption of certain agricultural technologies
especially if farmers do not possess certain required implements and cannot afford to
purchase them.
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The most common source of water for cultivation was rain water, with more than 90% of
farmers relying on it. Drinking water was mainly sourced from taps (88.9%) as well as water
used for domestic chores (76.2%) and by livestock (75.4%).
Tap water was mostly provided in public installations within walking distances of about
1kilometer or between 20 and 35 minutes from farmers’ homesteads. High dependence on
rain fed agriculture as seen in this survey is likely to put farmers at risk due to rainfall
unreliability during dry seasons.
This in turn may lead to food insecurity. Firewood was ranked the most preferred energy
source for cooking followed by crop residues and charcoal respectively. Kerosene was very
popular for lighting purposes. The extensive use of firewood for cooking raises concerns
about environmental degradation since farmers may cut down trees or other vegetation to
generate firewood.
On average, one house was found in each farming homestead. Mud walled and iron sheet
roofed houses were the most common (80%) among farmers in Bugesera (see Figures 2 and
3).
Figure 2: Wall types of farmers’ houses Figure 3: Roof types of farmers’ houses
80.4%
14.5% 5.1%
Wall types of farmers' houses
Mud
Wooden
Brick/cement
17.8%
81.8%
0.4%
Roof types of farmers' houses
Grass thatch
Iron sheets
Tiles
20
In Africa, the type of housing a family has indicates wealth status especially in rural areas. A
stone walled house symbolises a ‘rich‘ household, an wooden or iron sheet walled house
illustrates ‘average‘ wealth, while a mud house symbolises a ‘poor‘ household.
We therefore infer that majority of households in the study sites were poor. Farmers also
reported that the distance to the nearest market was 7.92Kms while that to the nearest
agrochemicals shop was 7.85Kms.
Distance to the markets and agrochemicals shops is an important index to determine
accessibility. However, other factors such as road networks and availability of transport may
compound or facilitate accessibility to markets agricultural inputs.
4.1.2. Family food security and nutrition
Most households reported that they were not food sufficient (Figure 4). A further 76.6% of
these households noted that they had difficulties managing their food needs (Figure 5).
Figure 4: Farmers’ household food sufficiency
87.8%
12.2% 0
10
20
30
40
50
60
70
80
90
100
No Yes
Household food sufficiency
21
Figure 5: Households’ management of food needs
On average, households experienced food deficiency 5 months in a year. The most food
deficient months were reported to be between August and December of every year. The three
main reasons given to explain the lack of food sufficiency in some months of the year were
poor agricultural water supply, low land productivity and limited land.
This situation may have been partly attributable to the dry spell experienced in the district
between June and August annually (MINITERE, 2003). Farmers ranked their four major
coping strategies in order of importance as shown in Table 2.
Table 2: Farmers’ food shortage coping strategies
Food shortage coping strategy Rank
Buying food from markets 1
Eating fewer meals 2
Eating different foods 3
Migrating to towns in search of work 4
Yes
76.6%
No
23.4%
Household has difficulties managing food needs
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Household food security is a key indicator of livelihoods that are sustainable. Households that
are not food secure may be willing to adopt technologies that could boost their farm
productivity.
If reasons given for insufficient food production include low land productivity and shortage of
agricultural water as seen in this survey, CA and agroforestry practices (evergreen agriculture)
that ameliorate these conditions may offer solutions to farmers.
4.1.3. Land health
The average farm size in the study area was 0.72 hectares. Majority of farmers (95.4%)
indicated that they owned land. Land ownership was through inheritance, buying, borrowing,
renting and other forms of ownership (Figure 6).
Figure 6: Farmers’ land ownership categories
Farmers noted that loamy soil was the most common soil type (58.9%) followed by sandy soil
(20%) and loamy clay soil (9.5%). Black cotton was reported by 4.5% of the respondents. The
rest of the farmers reported having various combinations of loamy, sandy, clay or black
cotton soils.
29.7% 31.8%
6.3%
12.4%
19.4%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
Inheritance Buying Borrowing Renting Others
Forms of land ownership
23
When asked about the fertility of their soils, 2.7% reported that their soils were very fertile,
58.9% categorised their soils as fertile while 38.3% thought their soils were not fertile.
However, it was not clear what indicators farmers used to rank their soil fertility status.
Most respondents (50.8%) indicated that the fertility of their soils had decreased over time
while 33.3% had noticed no change in their soil fertility over the years. Only 15.9% said that
their soil fertility had improved.
Among other goals, evergreen agriculture seeks to promote soil fertility enhancing practices
in farms through fertilizer trees or advocating for practices such as cover cropping and
minimum tillage. Farmers who perceive low soil fertility as an impediment to land productivity
and food security are likely to adopt technologies that have the potential to improve soil
fertility.
53.8% of the respondents reported that their slopes were gentle while very few had steep or
very steep slopes (4.6%). Figure 7 shows farmers’ responses about the slope of their land.
Figure 7: Slope categories of farmers’ land
0
10
20
30
40
50
60
Flat Gentle Steep Very steep
Slope categories of farmers' land
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The slope is indicative of the extent of soil erosion in an area. Land that is very steep is likely
to experience high erosion levels than land that has a gradual slope while flat land may
experience flooding.
Erosion takes away essential nutrients and organic matter from the top layers of the soil,
leaving it bare and unproductive. A large number of farmers classified the severity of soil
erosion on their farms as either moderate (43.7%) or non-existent (44.1%). 11.6% indicated
they experienced serious erosion while only 0.7% thought their farms experienced very serious
erosion.
Rain was reported to be the major soil erosion agent. The study further found that 98.8% of
farmers did not irrigate their land, implying that soil water lost through erosion was not
replaced. 68.1% of farmers indicated that they applied measures to conserve soil erosion and
improve soil fertility.
The most commonly applied measures were digging of trenches, planting cover crops and
applying manure. Those who did not apply any soil conservation and fertility improvement
measures (31.9%) noted lack of knowledge and skills, lack of funds and limited land as the
main constraining factors.
Water scarcity was experienced by majority of farmers (93%), usually for a period of five
months each year. Most farmers (77.6%) applied water conservation measures to deal with
water scarcity. Digging trenches, mulching and planting trees were the most commonly
preferred measures.
Those who did not apply any water conservation measures (22.4%) cited lack of knowledge
and skills as well as lack of funds as the main impediments. Evergreen agriculture offers
solutions that can control erosion and conserve soil water such as cover cropping,
diversification of crop species, agroforestry trees and minimum tillage among others. Farmers
who appreciate the need to control erosion and conserve water on their farms are likely to
adopt evergreen agriculture.
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4.1.4. Common farming practices
Information was sought on the farming practices that were carried out on farms in order to
assess the extent of adoption of practices related to evergreen agriculture. About 40% of
farmers practised intercropping of trees with subsistence crops while the rest did not (Figure
8).
Figure 8: Intercropping of trees with food crops
The reasons given for intercropping of trees with subsistence crops were varied but the main
ones were to increase income, conserve the soil, to control soil erosion and to obtain
firewood and fruits from trees.
Those who were not intercropping trees and food crops cited many reasons but the most
common ones were: lack of knowledge and skills, lack of seedlings, limited farming land,
drought and insecure land tenure.
Majority of farmers (96.2%) reported that they did not intercrop subsistence crops with
commercial crops. Key reasons given included lack of seedlings, lack of knowledge and skills,
unfavourable climatic conditions, limited land and prohibitive government policies (for
instance, farmers were not allowed to intercrop coffee with other crops as this would affect
the quality of coffee and prices).
40.4%
59.6%
Intercropping of trees with food crops
Yes No
26
Only 12.8% of farmers practised reduced tillage (a core pillar of conservation agriculture)
while 87.2% did not. Those who practised reduced tillage said that it saved time and costs,
and reduced soil disturbance thereby improving soil fertility and productivity.
Those not practicing said they preferred to prepare their land before planting to control
weeds and to break the hardpan. Others reported lack of knowledge and skills, lack of
technology, and the fear of reduced yields as the main reasons.
Majority of farmers (89.3%) did not practise cover cropping or mulching while few did
(10.3%). Those who cover cropped or mulched said they did so to conserve soil moisture and
to enhance soil productivity. Those that did not cover crop or mulch noted that most of the
crops they planted did not need cover cropping or mulch.
Some reported they lacked cover cropping or mulching material whereas others lacked
knowledge and information about the importance of the practice. Fertilizer application was
practised by more than half of the farmers (51.1%) mainly to increase productivity and soil
fertility. Farmers who did not apply fertilizers (48.9%) regarded them as expensive and difficult
to access.
Pest management was done by most farmers (80.7%) to protect their crops from damage by
pests and diseases. Farmers who abstained (19.3%) from pest management said it was either
due to cost implications, lack of access or zero incidences of pests and diseases in their
farms. 98.3% of farmers practised weed control to avoid competition for water and nutrients
between crops and weeds, as well as to increase yields.
Majority of the respondents (55.2%) practiced marketing in order to increase income and
cater for domestic and farm expenses. Those that did not market their farm produce (44.8%)
reported lack of surplus due to low productivity.
Overall, based on the above mentioned observations, evergreen agriculture practices were
more likely to be adopted if they increased productivity and farm incomes and less likely to be
adopted if they were expensive to implement and inaccessible.
27
Moreover, for adoption of evergreen to be realised, farmers will require to be imparted with
relevant knowledge and necessary skills as the lack of these has been highlighted severally as
key reasons why farmers did not conduct certain practices in their farms.
4.1.5. Labour analysis
Labour scarcity was faced by 43.4% of the respondents while 56.6% did not experience the
problem. Operations that were considered most labour scarce ranked in order of priority
were land preparation, weeding and harvesting.
March and February were the most labour scarce months of the year. Hired labour was the
most preferred intervention to deal with labour scarcity followed by the use of family labour
and working for more hours. The promotion of evergreen practices and tree species ought to
be done after consideration of labour implications since farmers may not support labour
intensive practices.
4.1.6. Crop production
Farmers planted various types of crops for both consumption and sale. Based on Table 3,
beans were the most commonly planted crops and cassava came in second. The larger
portions of the harvests were consumed and smaller portions sold, with the exception of
coffee, peanuts, sorghum, soybeans, tobacco and tomatoes. Tomatoes provided the most
income, followed by bananas and Irish potatoes.
28
Table 3: Crop production, consumption and sales in a mono cropping system
Crop % Average production cost
Yield (Kg)
Average consumption
Average sold Price/Kg Total income
Profit/ Loss
Kg % Kg %
Bananas 2.72 275,308.56 3,430.40 2,289.61 66.74 1,140.79 33.26 180 617,472.00 Profit
Beans 44.2 75,608.34 363.77 235.04 64.61 128.73 35.39 450 163,696.50 Profit
Cassava 29.38 68,160.78 1,476.98 1,200.74 81.29 276.24 18.71 200 295,396.00 Profit
Coffee 1.23 230,025.84 440.41 0 0 440.41 100 - - -
Maize 4.44 74,443.32 360.55 224.94 62.39 135.60 37.61 - - -
Peanuts 1.48 99,996 94.44 38.89 41.18 55.56 58.82 900 84,996.00 Loss
Irish potatoes
0.49 32,608.50 2,391.29 2,391.29 100 0 0 170 406,519.30 Profit
Sorghum 9.63 59,604.89 640.69 219.66 34.29 421.02 65.71 - - -
Soybeans 1.73 111,800.59 509.31 144.10 28.29 365.22 71.71 - - -
Sweet potatoes
3.95 74,368.50 1,777.23 1,472.69 82.86 304.54 17.14 200 355,446.00 Profit
Tobacco 0.25 399,996 222.22 0 0 222.22 100 - - -
Tomatoes 0.49 167,500 7,500 50 0.67 7,450 99.33 400 3,000,000.0
0 Profit
It is important to note that total income was calculated based on total yield regardless of
whether or not the yield was consumed. Most crops were sold at a profit, with the assumption
being that if what was consumed was sold, it would still fetch the same market price or that
post harvest costs were not high.
With regards to intercropping, the most common intercrops were maize and beans (15.6%),
cassava and beans (9.27%), cassava and sweet potatoes (8.29%) and beans, cassava and
sorghum (8.29%). Most intercropped crops were mainly for consumption.
For adoption of evergreen agriculture to be successful, it is important to understand the types
of crops planted by farmers and whether or not they are for subsistence or commercial use as
well as the cropping systems preferred. This would assist in planning and advising on the best
evergreen practices for specific circumstances.
29
4.1.7. Livestock production
Majority of farmers (72.8%) owned between 1 and 3 animals with 93.1% of the animal
breeds being local (Table 4).
Table 4: Distribution of livestock by breed and type of fodder
Type of animal
Breed (%) Type of feed (%)
Exotic Mix Local Crop residue
Fodder trees
Napier grass
Bulls 8.3 8.3 83.3 0 0 72.7
Chicken 0.0 0 100 0 0 0
Cows 6.4 12.8 80.7 0 4.7 63.6
Goats 0.9 0.5 98.6 0 3.2 48.1
Rabbit 0 0 100 0 0 100
Pigs 12.5 6.3 81.3 5.3 26.3 21.1
Sheep 0 0 100 0 6.7 66.7
Most respondents (88.9%) sourced fodder from their own farms while the rest either
purchased it in the market or obtained it from the neighbours. Cows and goats were the most
popular livestock in many households. Napier grass was a very common fodder crop
especially for cows. Products obtained from reared animals included milk, meat, eggs and
manure.
4.1.8. Tree production and management
Farmers reported that the five most common tree species on their farms were: Grevillea
robusta (26.1%), Senna spectabilis (16.5%), Persea americana (15.5%), Mangifera indica
(14.5%) and Eucalyptus spp. (9.9%).
The tree species were found in different elevations. In areas of higher elevation (Gashora
sector), the most common trees were Grevillea robusta and Mangifera indica. Grevillea
robusta and Senna spectabilis were very common in mid elevation (Juru sector).
30
In the lower elevation (Rweru sector), the main species were Senna spectabilis and Eucalyptus
spp. 58.4% of farmers said that they mainly sourced seeds/seedlings from group nurseries
and 23.1% indicated natural regeneration in their farms as the main source. 7.4% of the
respondents collected seeds and raised their own nurseries.
Table 5: Main tree species planted by farmers
Tree species n %
Acacia spp. 3 1
Carapa procera 1 0.3
Carica papaya 5 1.7
Citrus limon 11 3.6
Citrus sinensis 5 1.7
Eucalyptus spp. 30 9.9
Grevillea robusta 79 26.1
Grewia similes 6 2
Hougols 3 1
Hypericum revolutum 2 0.7
Mangifera indica 44 14.5
Markhamia lutea 4 1.3
Passiflora edulis 2 0.7
Persea americana 47 15.5
Psidium guajava 2 0.7
Senna spectabilis 50 16.5
Tukolgotis 3 1
Vangueria infausta 1 0.3
Vepris nobilis 2 0.7
When asked about where they planted their trees, 34% of the respondents said that they
planted them around the home compound while 24.4% and 22.8% planted along the
external boundary and scattered them in crop farms respectively.
31
Majority of the respondents (73.5%) had between 1 and 10 trees in their farms. Farmers cited
their main tree uses as: for clean air or to control global warming (20.1%), fuel wood (18.5
%), other uses (15.2%) and shade (13.9%). In particular, Grevillea robusta, Senna spectabilis
and Eucalyptus spp were mainly planted for fuel wood.
Meanwhile, Persea americana was planted for shade (34%) and Mangifera indica for fruits
(34.1%) and clean air/global warming (34.1%). More than half of the trees planted (59.1%)
had no observed effect on crops while 21.8% of the trees were reported to improve crop
growth.
Farmers were then asked if they were aware of any trees on their farms that enhanced soil
fertility. 88.2% of them said that they were not aware of trees in their farms that improved soil
fertility. 29.8% of them mentioned Senna spectabilis while 26.3% mentioned Grevillea spp as
soil fertility enhancing trees.
Most of the tree species mentioned were said to improve soil fertility when their leaves fell and
decomposed to form manure. Farmers’ awareness about soil fertility improving trees is a plus
in promoting evergreen agriculture since agroforestry is a key component of evergreen
agriculture.
Furthermore, farmers’ indigenous knowledge about trees can complement existing scientific
knowledge and assist in prescribing tree species that are sensitive to specific farmer needs
and agro ecological conditions.
4.1.9. Nursery information
Majority of the respondents (84.3%) did not buy seeds/seedlings for use in their farms. Many
of them received their seedlings from group nurseries or obtained them through natural
regeneration on farm.
Farmers had about 29 tree nurseries from which to source seedlings from but the two most
preferred ones were Gashonga (35.7%) and Mirayi (21.6%) problem due to the distance
from their homes.
32
Nearly half of the respondents said that the distance from the nursery to their homesteads was
between 1.1 and 5km while 24.3% and 20% of the respondents said that the distance was
between 5.1 and 10km and less than 1km respectively.
Most respondents reported last visiting a tree nursery in November (23.8%) and December
(22.7%) 2010. The most sought after species from the tree nurseries were: Grevillea robusta
(23.3%), Persea americana (16.6%), Eucalyptus spp. (15.1%) and Mangifera indica (12.9%).
The top three desired but unavailable tree species were fruit trees i.e. Persea Americana
(16.3%), Mangifera indica (13.6%) and Citrus sinensis (12.7%). This could be explained by
the fact that farmers mainly intercropped of trees with crops for fruit and fuel wood
production.
4.1.10. Main production and marketing problems
Water scarcity was the main production problem experienced by farmers (58.7%) while
16.3% said it was limited land. Lack of fertilizers, low fertility and poor quality seeds were also
cited by about 15.1% of the respondents.
The most common marketing problems were low yields (57.7%), low market prices (12.5%)
and distance from farms to markets (10.9. high transport costs was a major problem for 7.6%
of the respondents.
33
4.1.11. Sources of income
Farmers had both on farm and off farm sources of income. Table 6 indicates the various
sources of income and the amount of money generated annually.
Table 6: Farmers’ income sources
Income sources Annual total income (RWF)
Employment 2,738,000
Business income 4,793,600
Wage labour 3,352,500
Remittances 1,171,000
Leasing land 245,000
Honey sales 72,600
Firewood sales 150,800
Timber sales 95,000
Fodder sales 6,000
Fish sales 1,180,000
Charcoal sales 480,000
Tree nurseries 10,000
Others 135,000
Business income was found to generate the most money followed by wage labour and
employment. Fodder sales generated the least amount of money. However, it is worth noting
that the generated figures (Table 6) were only based on available data. Most farmers did not
answer income related questions.
It was not clear if this was because they made no extra income from the mentioned sources or
they preferred not to share income information with enumerators. This issue requires further
probing.
34
4.1.12. Training and extension
The study found that 74.4% of farmers attended training sessions while 25.6% did not. The
training sessions were focused on agriculture, environment and capacity building issues.
No gender disparities were noted in involvement in training sessions, with men and women
attending equally. In most cases, at least 73.1% of both gender attended training events.
93.2% of farmers did not perceive a gender bias in training opportunities.
Moreover, 91.8% reported that training provision was equal and available for both wealthy
and poor farmers. Non Governmental Organisations (NGOs) (42.2%) and government
(26.6%) were the most common providers of training.
The most preferred training topics were crop production (14.9%) and cooperation in
cooperatives (11.9%). Most farmers (97%) said that they applied what they learnt from the
training in their farms. Although majority of farmers had not attended training in the previous
three years, they were willing to attend future training sessions.
4.1.13. Access to and dissemination of information
Respondents were asked about important sources of information on agricultural production.
Farmers primarily relied on government extension workers, own experience for information
and other farmers/neighbours.
Farmers’ organisations were the least preferred information sources. The most common
strategies used to promote agroforestry or conservation agriculture were seminar/training
(22%), farmer to farmer knowledge sharing 21%), farmer exchange visits (19.4%) and
demonstration farms (19%).
The least common promotion strategy was printed material (1.9%). Information is a powerful
tool in scaling up evergreen agriculture in Africa. A thorough understanding of farmers’
information needs and the means to communicate with them is vital in the promotion and
adoption of evergreen agriculture. This is because informed farmers are in a better position to
35
make decisions concerning improving their farming practices than farmers with no access to
information.
4.1.14. Collective action
There were 39 farmer groups in the study area, implying that farmers attached value to
collective action. 78.8% of the respondents said that both males and females participated in
the groups.
The three main activities practiced in the groups were: cassava production (13.8%), maize
farming (13.3%) and vegetable farming (10.1%). Most respondents performed these activities
with community members (58.5%) and neighbours (30.8%).
The primary benefits from collective action were access to agricultural information (21%),
access to health/sanitation (19%) and access to credit and finance (15%). Collective action is
necessary for the successful implementation of evergreen technologies especially in regions
where individual farmers are not able to solely take up the initiative due to constraints such as
finance or lack of labour. However, there is need to determine the extent to which collective
action is beneficial to promotion of new agricultural innovations.
36
4.1.15. Credit availability/access
Majority of farmers (Figure 9) had not accessed credit in the past 12 months. Those who did
(38%) said that banks followed by farmer associations (15%) were their most important
sources of credit. Credit SACCOs (13%), relatives and friends (13%) and other local initiatives
(10.8%) were also important sources of credit.
Figure 9: Farmers’ access to credit
Farmers who did not access credit cited difficulties in repayment (44%), lack of security (15%)
and lack of interest (15%). Most farmers used the borrowed money for farming (38.5%) and
off-farm activities (22%). Household heads who were businessmen, civil servants, local
entrepreneurs and teachers invested the money in off-farm activities, farming, consumption
and housing respectively.
Access to credit is likely to be a key determinant of adoption of evergreen agriculture.
Farmers who have access to credit are able to purchase inputs, hire labour and invest in
improving their farming methods.
No
75.8%
Yes
24.2%
Access to credit
37
4.1.16. Natural resource management policies/laws
Generally, farmers were aware of natural resource management policies related to farming.
Crop production (89%), water management (85.7%) and conservation agriculture (82%) had
the highest awareness levels. In relation to conservation agriculture, farmers were aware of
some of its principles.
32.8% cited application of fertilizers and manure to improve soil fertility, crop rotation
(29.5%) and planting cover crops (21.3%). Farmers were also aware of principles of crop
production such as land consolidation (46.1%), planting crops in lines (22.6%) and mono
cropping (10.5%).
Majority of the respondents (93.1%) mentioned zero grazing as the local policy for livestock
production. Farmers were aware of terracing (42%) and digging trenches to avoid soil
erosion (27.4%) as soil management policies.
Tree farming polices such as planting many trees to obtain many benefits (45.7%) and
agroforestry (19.2%) were also known. For water management, farmers were aware of the
need to construct water storage facilities such as dams, wells, and tanks (62.3%) and digging
trenches to avoid soil erosion (30.6%).
Majority of the respondents (98.5%) said they were not involved in the formulation of these
policies but 79% benefited from them. Access to new farming technologies (40.9%) and
improved farming systems (28.9%) were mentioned as the main benefits.
Half of the respondents perceived the policies as good while 24.7% thought they were
excellent. Meanwhile, 96.2% of the respondents said that environmental policies were needed
to adopt/promote the integration of trees, livestock and crops in farming systems, among
many other sustainable farming practices.
The two policies ranked highest by the respondents as necessary to promote sustainable
farming practices were the promotion of sustainable farming technologies and enhancement
of marketing systems.
38
Scaling up of evergreen agriculture requires policy support at the local level. Policies that are
already operational and known to farmers are easier to promote than new policies. If those
policies support evergreen agriculture, scaling up efforts are highly likely to be accepted by
farmers.
4.1.17. Farmers’ aspirations
The respondents’ top three aspirations with respect to desired quality of life were soil fertility
improvement and soil erosion prevention (24.7%), availability of inputs (10.6%) and
diversification of farm products (9.5%).
4.2. Potential for adoption of evergreen agriculture
For purposes of this study, the potential for adoption of evergreen agriculture was determined
by the assessment of determinants of adoption of agroforestry (intercropping trees and
subsistence crops) and determinants of adoption of soil erosion control and soil fertility
improvement measures.
It was assumed that farmers who were concerned about trees and conservation agriculture
principles (such as reduced tillage and cover cropping) were likely to be interested in
innovations that would help boost these practices, in this case, evergreen agriculture.
Table 7 provides a list of predictor (independent) variables against which the potential for
adoption was assessed. The variables were selected and hypothesized based on existing
literature and author discretion.
Table 7: Hypothesizing determinants of adoption of evergreen agriculture
Variable Measure Expected sign Rationale
Gender of HHH 1=Male
0 = Female
+/- Male headed households are likely
to adopt CA due to less labour and
financial constraints. Alternatively,
Female headed households could
adopt CA to avoid land preparation
constraints such as ploughing
Household size Number +/- Households with more active
members are likely to adopt labour
intensive technologies while those
with less active members are likely to
prefer labour saving innovations
Age of HHH Number + / - Younger farmers are likely to adopt
because they are risk takers while
older farmers may adopt because
40
they have more farming experience
Access to formal
education by HHH
1=Yes, 0=No
+ Educated farmers are more likely to
embrace and understand new ideas
and technical innovations unlike
uneducated ones
Labour availability 1=Yes, 0=No +/- Households with available are likely
to adopt labour intensive
technologies while those with labour
scarcity are likely to adopt labour
saving innovations
Main occupation of
HHH
1= Farming
0 = Otherwise
+ Farmers whose main career is
farming are likely to invest time and
money in adoption of evergreen
innovations
Farm size Area in
hectares
+/- Farmers with small parcels of land
are likely to engage in intensive
farming innovations such as
intercropping while those with large
parcels may invest in extensive
innovations such as agroforestry
woodlots
Access to credit 1 = Yes, 0 =
No
+ Access to credit facilitates purchase
of inputs and labour thus increasing
the likelihood of adoption
Willingness to attend
training
1 = Yes, 0 =
No
+ Training enhances adoption of new
innovations through information
dissemination
41
Group membership 1 = Yes, 0 =
No
+ Membership in a farmer group
enables access to information and
collective action related benefits
which is likely to promote adoption
of evergreen agriculture
Food sufficiency 1 = Yes, 0 =
No
- Farmers who are food insecure are
likely to adopt innovations such as
evergreen agriculture that could
improve their food security
Seedlings affordability 1 = Yes, 0 =
No
+ Farmers who can afford seedlings
are more likely to adopt innovations
that require the regeneration or
planting of trees and certain crops
Awareness about natural
resource management
policies
1 = Yes, 0 =
No
+ Awareness about natural resource
management policies is likely to
promote adoption of innovations that
those policies advocate for
Legend: HHH: Household head; + (positive effect on adoption of evergreen agriculture) ; -(negative effect on adoption of evergreen agriculture)
These hypothesized independent variables (Table 7) were then subjected to logistic regression
analysis and the results were as shown in Tables 8 and 9.
42
Table 8: Determinants of adoption of agroforestry practices by farmers
Variables B S.E. Wald Sig. Exp (B)
Age of HHH 0.025 0.010 6.910 0.009*** 1.026
Gender of HHH -0.560 0.341 2.688 0.101 0.571
Main occupation of HHH -0.344 0.668 0.265 0.607 0.709
Formal education -0.286 0.285 1.009 0.315 0.751
Household size -0.089 0.063 2.001 0.157 0.915
Food sufficiency 0.119 0.388 0.094 0.759 1.126
Access to training 1.172 0.747 2.460 0.117 3.228
Labour availability -0.149 0.282 0.281 0.596 0.861
Group membership 0.523 0.288 3.301 0.069* 1.688
Access to credit 0.295 0.326 0.817 0.366 1.343
Awareness of conservation
policies 0.296 0.363 0.666 0.415 1.344
Affordability of seedlings 0.799 0.360 4.932 0.026** 2.222
Farm size 0.222 0.176 1.593 0.207 1.249
Constant -1.665 1.201 1.921 0.166 0.189
Legend: HHH: Household head; Significant at *=10%; **=5%, ***=1%
The model correctly predicted 66% of all responses from farmers. The exponential beta (β) or
odds ratio indicated the proportion with which adoption of agroforestry could occur, while the
beta (β) sign predicted whether the variable influenced adoption positively (+) or negatively (-
). The model predicted that adoption of agroforestry practices was significantly and positively
affected by: age of the household head, affordability of tree seedlings and farmer group
membership.
A unit increase in the age of the household head increased the probability for adoption by a
factor of 1. Age is an indicator of farming experience and the older a farmer is, the more
experienced and more likely he or she is to adopt new technologies (Mazvimavi, 2011).
43
Farmers who could afford to purchase seedlings were likely to adopt agroforestry tree
practices by a factor of 2. During the survey, the cost of seedlings was cited as a hindrance to
intercropping of trees and food crops in farms.
Membership in farmer groups increased adoption by about two times. The coming together
of farmers to create groups that were mutually beneficial to them was a form of collective
action. Farmer groups were formed to provide credit to members, to start up tree nurseries
and to promote other agricultural and social activities.
Farmers who were members of groups were able to access seedlings and this boosted their
adoption of agroforestry trees. Kariuki and Place (2005) found that a farmers’ membership in
groups increased adoption, as farmers exchanged information, labour and obtained
resources through groups.
Groups allowed farmers to obtain new technologies, benefit from economies of scale, enter
into stable relationships with suppliers and set rules for natural resource management.
44
Table 9: Determinants of adoption of conservation agriculture by farmers
Variables B S.E. Wald Sig. Exp (B)
Age of HHH -0.001 0.012 0.005 0.942 0.999
Gender of HHH 0.262 0.403 0.423 0.516 1.300
Main occupation of HHH 0.643 1.106 0.338 0.561 1.902
Formal education -0.052 0.354 0.022 0.882 0.949
Household size 0.031 0.077 0.157 0.692 1.031
Food sufficiency 0.082 0.482 0.029 0.865 1.085
Access to training -0.056 0.832 0.005 0.946 0.945
Labour availability -0.172 0.351 0.242 0.623 0.842
Group membership 0.383 0.344 1.243 0.265 1.467
Access to credit 0.628 0.363 2.991 0.084* 1.873
Awareness of conservation
policies 0.439 0.491 0.801 0.371 1.551
Affordability of seedlings 0.721 0.398 3.281 0.070* 2.057
Farm size -0.051 0.211 0.059 0.808 0.950
Constant -3.135 1.616 3.762 0.052 0.043
Legend: HHH: Household head; Significant at *=10%; **=5%, ***=1%
The model correctly predicted 81.2% of all responses. Adoption of conservation agriculture
principles (cover cropping and mulching, and reduced tillage) was significantly and positively
influenced by: access to credit and affordability of seedlings.
Farmers who had access to credit were likely to adopt conservation agriculture by a factor of
about 2. Access to credit enabled farmers meet the cost of inputs required to facilitate
implementation of improved farming practices.
Ouma et al (2002) found that farmers who had access to credit had more options to acquire
costly new technologies such as improved seeds or fertilizer. The lack of cash and access to
credit was noted to be central to a farmer’s use of a technology.
45
Farmers who could afford seedlings were twice likely to adopt conservation agriculture than
those who could not afford. Farmers with financial constraints could not afford seedlings such
as those required for cover cropping or for trees whose leaves eventually provided mulch,
thus limiting their adoption potential.
5.0. CONCLUSIONS
This baseline survey set out to conduct a socio economic analysis of targeted farmers in
Bugesera district of Rwanda as well as examine their potential to adopt evergreen agriculture.
The findings indicate that the ‘evergreen’ concept is new to Rwandan farmers although some
of its practices are well known and have been practised over time. The potential for adoption
of evergreen also exists as revealed by farmers’ responses during interviews.
As noted earlier, evergreen agriculture encompasses agroforestry and conservation
agriuclutre practices. Available data indicates that farming was the primary occupation of
majority of households in the study area and that most farmers were within the productive
age.
This implies that farmers would be willing to embrace evergreen agriculture if they deemed it
beneficial since their livelihoods were dependent on their farms and they were energic
enough to conduct farming activities. Furthermore, the high levels of food insecurity in the
area call for different farming approaches.
Farming methods such as evergreen agriculture that offer solutions to declining soil fertility
and soil erosion would be considered by farmers since low soil fertility and soil erosion were
mentioned as impediments to high productivity and food security.
In addition to other soil conservation and fertility enhancing measures that farmers were
undertaking in their farms, some practised agroforestry, reduced tillage, cover cropping and
mulching (evergreen agriculture).
46
However, these practices were taken up by few farmers who were aware of their benefits. The
rest cited lack of knowledge and skills as key reasons for the low uptake.
Promotion of evergreen agriculture requires that farmers are imparted with knowledge and
skills about its benefits and trained on how to implement it. Of importance too is that
scientists understand farmers’ needs and link them to the specific evergreen agriculture
practices they recommend.
For instance, most farmers planted trees on their farms primarily for firewood and fruits. They
also preferred to source their seeedlings from group nurseries or through natural
regeneration.
Their most preferred modes of training were through seminars, farmer to farmer knowledge
sharing and demonstration farms. An understanding of such farming dynamics would enable
scientists utilise scaling up approaches that complement rather than antagonise exisitng
systems.
Gender was not found to have a negative effect on the potential adoption of evergreen
practices since both men and women equally participated in farming related activities such as
training and tree nursery groups.
Therefore, promotion of evergreen agriculture ought to be equally targeted at both men and
women, although it is important to determine if there are certain practices that would appeal
more to female headed households than to male headed ones and vice versa.
Labour scarcity was identified as a constraining factor by several farmers, implying that
evergreen practices that save on labour maybe a welcome relief if targeted to this segment of
farmers. Access to credit was also said to be very low for majority of farmers.
This too provides an opportunity to promote evergreen agriculture since some of its practices
such as minimum tillage and mulching are known to save on costs. Nevertheless, there is
need for a comprehensive understanding of the different typologies of farmers, their
agroecological settings, their needs, and how best their farming can be improved through
evergreen agriculture.
47
This study further analysed the determinants of adoption of evergreen practices by asssessing
the adoption of agroforestry and conservation agriculture. Adoption of agroforestry was
found to be significantly influenced by age of the household head, farmer group membership
and ability to purchase seedlings.
Adoption of conservation agriculture was significantly affected by affordability of seedlings
and access to credit. As such, promotion of evergreen agriculture technologies ought to put
these factors into consideration in order to ensure that in addition to targeted farmers, those
farmers who would be unable to take up evergreen agriculture due to constraints arising out
of any of these factors are put into consideration during the design and dissemination of best
practices.
Moreover, there is need for further studies on which typologies of farmers and farms would
stand to benefit from evergreen agriculture and how best to ensure that the practices are
promoted and implemented in a manner that meets their farming aspirations.
48
REFERENCES
1. GOR (Government of Rwanda) 2011. Impacts assessment and evaluation of the pilot
project for introduction of rain water harvesting and utilisation techniques in Bugesera
district. Report prepared for the Government of Rwanda.
2. JICA (Japan International Cooperation Agency) (2006). Sustainable rural and
agricultural development in Bugesera district, Eastern province of Rwanda. Progress
report one, ministry of agriculture and animal resources of Rwanda.
3. Kariuki, G. and Place, F. (2005). Initiatives for rural development through collective
action: The case of household participation in group activities in The Highlands of
Central Kenya CAPRi Working Paper # 43
4. Kassie, M. and Zikhali, P. (2009). Sustainable agriculture. Brief prepared for the
expert group meeting on “sustainable land management and agricultural practices in
Africa: bridging the gap between research and farmers.” University of Gothenburg,
Germany.
5. Mazvimavi, K. (2011). Socio economic analysis of conservation agriculture in
Southern Africa. FAO regional emergency office for Southern Africa.
6. MINITERE (The Ministry of Lands, Environment, Forestry, Water and Mines)
(2003): Environment Policy
7. Ouma N., Muriithi F., Mwangi W., Verkuijl H., Gethi M., Groote H. (2002). Adoption
of Maize Seed and fertilizer in Embu District, Kenya. CIMMYT, Nairobi.
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8. REMA (Rwanda Environment Management Authority) (2009). State of environment
outlook – our envorionment for economic development. Report prepared for the
Rwanda Environment Management Authority.
9. ROR (Republic of Rwanda) (2008). Strategic Plan for the Transformation of Agriculture
in Rwanda – Phase II (PSTA II). Final Report. Ministry of Agriculture and Animal
Resources, Republic of Rwanda (ROR), Kigali.
10. World Agroforestry Centre (2009). Creating an evergreen agriculture in Africa.
Working paper series for World Agroforestry Centre, Nairobi, Kenya.