A contribution to the Challenge Program on Water & Food Project PN17
“Integrated Water Resource Management for Improved Rural
Livelihoods: Managing risk, mitigating drought and improving water
productivity in the water scarce Limpopo Basin”
Socio-economic conditions and agricultural
water management practices of smallholders in
Quaternary Catchment B72A,
Olifants River Basin, South Africa
by
Everisto Mapedza, Sylvie Moraradet, Christian Cheron, Manuel Magombeyi
May 2008
Socio-economic conditions and agricultural water management
practices of smallholders in Quaternary catchment B72A,
Olifants River basin, South Africa
Table of contents
1
INTRODUCTION ....................................................................................................................................... 4
2
PRESENTATION OF THE SOUTH AFRICAN STUDY SITE, B72A QUATERNARY
CATCHMENT IN LIMPOPO PROVINCE....................................................................................................... 5
3
RESEARCH METHODS............................................................................................................................ 9
3.1
SUSTAINABLE LIVELIHOOD APPROACH AND TYPOLOGY OF FARMING / HOUSEHOLD SYSTEMS .............. 9
3.2
DATA COLLECTION ..............................................................................................................................10
Primary data.................................................................................................................................................10
Secondary data .............................................................................................................................................14
3.3
AVAILABLE SOCIO-ECONOMIC AND TECHNICAL DATA ON THE SITE .....................................................14
3.4
DATA ANALYSIS METHODS ..................................................................................................................15
4
RESULTS....................................................................................................................................................18
4.1
CHARACTERISTICS OF THE HOUSEHOLDS .............................................................................................18
Household size..............................................................................................................................................18
Age................................................................................................................................................................19
Gender of household head............................................................................................................................20
Marital status ...............................................................................................................................................21
Education level .............................................................................................................................................21
Family structure, HIV/AIDS and poverty .....................................................................................................24
Occupation ...................................................................................................................................................24
Working force ...............................................................................................................................................25
4.2
ASSETS OWNERSHIP .............................................................................................................................26
4.3
LIVELIHOOD ACTIVITIES ......................................................................................................................28
4.4
WATER SOURCES, USES AND MANAGEMENT ........................................................................................31
Global water management context ...............................................................................................................31
Domestic water services ...............................................................................................................................31
Domestic water access .................................................................................................................................33
Small scale irrigation schemes .....................................................................................................................34
Water uses ....................................................................................................................................................35
4.5
AGRICULTURAL PRODUCTION ..............................................................................................................36
Farming purpose and main crops ................................................................................................................36
Farming practices ........................................................................................................................................36
Farming constraints and challenges ............................................................................................................38
Crop yields ...................................................................................................................................................38
Access to markets .........................................................................................................................................39
4.6
FOOD SECURITY STATUS ......................................................................................................................40
4.7
PERCEIVED QUALITY OF LIVELIHOOD AT HOUSEHOLD AND COMMUNITY LEVELS ................................43
4.8
TYPOLOGY OF FARMING HOUSEHOLDS ................................................................................................43
Principal component analysis: .....................................................................................................................43
Cluster analysis ............................................................................................................................................44
5
CONCLUSION ...........................................................................................................................................50
6
REFERENCES ...........................................................................................................................................51
Page 1
List of figures
Figure 1: Location of the study site in Olifants River Basin and South Africa....................................................... 7
Figure 2: Household/farming system functioning..................................................................................................10
Figure 3: Agricultural areas in B72A quarternary catchment ................................................................................12
Figure 4: Main villages in the B72A quaternary catchment...................................................................................12
Figure 5: Distribution of households per size in wards 1-5 of Maruleng municipality in 2001.............................18
Figure 6: Distribution of population of wards 1 to 5 of Maruleng municipality per gender and age class in 2001
...............................................................................................................................................................................19
Figure 7: Distribution of household heads per gender and age class in the study area ..........................................20
Figure 8: Education level attained by people over 20 years old in wards 1 to 5 of Maruleng municipality ..........21
Figure 9: Distribution of households per gender and education level of head in Nyalungu’s and Malatji’s surveys
...............................................................................................................................................................................22
Figure 10: Distribution of households per age and education level of head in Nyalungu’s and Malatji’s surveys23
Figure 11: Household typology - Representation of variables in the PCA factorial plan 1-2 ................................44
Figure 12: Household typology - Dendogram of the cluster analysis operated on factorial coordinates (Ward
method) ..................................................................................................................................................................45
Figure 13: Distribution of household total income per source according to household types................................48
List of tables
Table 1: Main agricultural areas in B72A quaternary catchment...........................................................................11
Table 2: Number of farming households surveyed by village and study ...............................................................13
Table 3: Socio-economic and technical data collected by the 3 surveys................................................................15
Table 4: Variables used for the farming household typology ................................................................................17
Table 5: Age range of respondents per type of farming.........................................................................................19
Table 6: Level of education of the surveyed farmers in Ntsheme’s survey ...........................................................22
Table 7: Distribution of potentially active population per employment status in wards 1-5 and Maruleng
municipality ...........................................................................................................................................................25
Table 8: Distribution of employed people per industrial sector in wards 1 to 5 of Maruleng Municipality ..........26
Table 9: Dwelling types in wards 1-5 and the Maruleng municipality ..................................................................27
Table 10: Number and percentage of households owning some domestic assets in Maruleng municipality and
wards 1 to 5 in 2001...............................................................................................................................................28
Table 11: Asset ownership in the study area..........................................................................................................28
Table 12: Distribution of households per income class in Nyalungu-Malatji surveyed households......................29
Table 13: Distribution of households per level of annual income in wards 1 to 5 and within the Maruleng
municipality ...........................................................................................................................................................29
Table 14: Source of income: number of households per source, average income per household per source.........30
Table 15: Distribution of annual household income across sources per income classes........................................30
Table 16: Sources of Income in Enable and Kodumela areas ................................................................................31
Table 17: Main characteristics of domestic water infrastructures in B72A ...........................................................32
Table 18: Distribution of households per type of access to water in wards 1 to 5 and whole Maruleng
municipality ...........................................................................................................................................................33
Table 19: Distribution of households according to their water access and population ..........................................34
Table 20: Leading childhood diseases below 5 years in Enable and Kodumela ADP ...........................................34
Table 21: Main crops and number of farmers engaged per cropping season .........................................................36
Table 22: Methods of soil conservation used by hillside farmers ..........................................................................37
Table 23: Challenges faced by farmers in different farming systems ....................................................................38
Table 24: Yield of main crops in B72A catchment................................................................................................39
Page 2
Table 25: Number of households, average herd size and average livestock income per livestock production
purpose...................................................................................................................................................................40
Table 26: Indicators of food insecurity in the study area .......................................................................................42
Table 27: Household level of satisfaction with their living conditions ..................................................................43
Table 28: Household typology - Results of Chi-Square test on categorical variables ...........................................45
Table 29: Household typology - Distribution of sampled households per types and villages................................46
Table 30: Household typology - Mean values and standard deviation for the whole sample and mean values for
each cluster, F statistic and test ..............................................................................................................................49
List of boxes
Box 1: History of the Sekororo-Letsoalo area......................................................................................................... 8
Box 2: Land and water management practices observed in B72A catchment (zones 3 and 4) ..............................37
Box 3: Agricultural marketing facilities for small-scale farmers in Sekororo-Letsoalo area.................................40
Box 4: Farming household types............................................................................................................................47
Page 3
1 Introduction
This study is undertaken under the Challenge Program on Water and Food, (CPWF) which is
an international, multi-institutional research initiative to create and disseminate international
public goods (IPGs) that improve the productivity of water in river basins in ways that are
pro-poor, gender equitable and environmentally sustainable.
CPWF practices research for development. Ongoing research work exemplifies this emphasis,
and illustrates the Challenge Program'
s mix of site-specificity, scaling up to the basin level,
and the production of international public goods.
The Challenge Program is working towards achieving:
•
•
•
•
Food security for all at household level
Poverty alleviation through increased sustainable livelihoods in rural and peri-urban
areas
Improved health through better nutrition, lower agriculture–related pollution and
reduced water-related diseases
Environmental security through improved water quality as well as maintenance of
water-related ecosystems and biodiversity
The Limpopo basin, located in South-eastern Africa, covers 1.3% of the continent and spreads
over four countries (Botswana, South Africa, Zimbabwe and Mozambique). The Limpopo
river (1,770 km) flows from Limpopo Province, South Africa in a great arc: first north
(forming part of the South Africa–Botswana border), then east (forming the South Africa–
Zimbabwe border), and finally southeast through Mozambique to the Indian Ocean.
When the rains hit the Limpopo basin, they are intense, but the rainfall is highly unreliable. In
this mainly semi-arid environment, the effects of rain are short-lived. People living by the
major reaches of the Limpopo and its tributaries may see water flowing for only 40 days or
less in a dry year (ARC, LNR, IWMI, 2003 Limpopo Basin Profile). Food security is a
constant problem. Around a million people currently rely on food aid.
The CPWF program is focusing on the areas of greatest poverty and encouraging equitable
allocation of increasingly scarce water resources to improve food security and maximize the
use of available water. If people can overcome their food security problems, they will be
better able to manage their water resources, make decisions regarding land use and help plan
for a sustainable future for the basin as a whole.
The Basin priorities include:
•
•
•
•
•
Promoting sustainable agricultural development for poverty alleviation
Facilitating greater cross-border cooperation and ensuring equitable inter-country and
intersectoral water allocation
Protecting and restoring areas of environmental degradation
Introducing technologies to optimize water use efficiency
Improving natural hazard forecasting, particularly drought and floods.
Page 4
IWMI is a partner in the CPWF project entitled “The Challenge of Integrated Water Resource
Management for Improved Rural Livelihoods: Managing Risk, Mitigating Drought and
Improving Water Productivity in the Water Scarce Limpopo Basin” whose overall essence is
the application of IWRM principles and ideas in improving rural livelihoods of smallholder
farmers (mainly rain-fed) in Mozambican, South African and Zimbabwean portions of the
Limpopo Basin. It includes agricultural and hydrological interventions / techniques /
innovations at farm scale to mitigate risk to rain-fed smallholder farmers (especially that
posed by droughts and dry-spells) and developing the institutional and informational basis to
support this at water management area and basin scales. It also includes establishing the
impact of the interventions at all scales. This project is managed by WaterNet.
It contributes to three CPWF themes, (1) Crop water productivity improvement, (2) Water and
people in catchments, and (3) Integrated Basin Water Management Systems.
The project entails several components:
1. Analysis of the constraints and opportunities of current agricultural practices
2. Upgrading farming systems through improved water productivity, risk mitigation and
integrated land-water management options
3. Exploring appropriate institutional models for water governance
4. Developing guidelines for catchment management strategies across political
boundaries
5. Human capacity building
6. Project management, knowledge base and dissemination
As part of the first component (Output 1: Constraints and opportunities of current agricultural
practices), in each of the three pilot catchments, baseline studies were carried out and yielded
data on their physical and socio-economic features. An analysis of socio-economic conditions
of smallholders, including farming systems and land tenure, was carried out based on the
available data to contribute to Activity 1.5: “survey of socio economic conditions of
stakeholders” and Activity 1.6 “survey of current agricultural water management practices” .
The present report presents the results of activity 1.5 and 1.6 for the South African research
site, the quaternary catchment B72A, in the Olifants River catchment. Section 2 gives a short
presentation of the study site. Methods used to collect and analyse the data are introduced in
section 3. Finally Section 4 includes the presentation of demographic characteristics of
households, their livelihood activities, water sources, uses and management, agricultural
production and farming practices including access to markets, and food security status, and
ends with the presentation of the typology of farming households based on their livelihoods
activities and asset endowments.
2 Presentation of the South African study site, B72A
quaternary catchment in Limpopo Province
The study area is the quaternary catchment B72A (Malomanye and Makhutsi rivers) of the
Olifants river basin, about 60km south of Tzaneen in the Limpopo province. It is located in
the Maruleng local municipality, in Mopani district municipality and is part of the Sekororo
and Letsoalo tribal authorities. B72A is the research site for two IWMI-led projects (Waternet
and Multiple Use Systems) and several research works are currently on going in the area.
Page 5
A large part of the catchment (80%) falls under the former Lebowa homeland. The total
population is estimated around 56,000 inhabitants (for a total municipal population of 93,700
inhabitants – Census 2001). A high population density and a high level of poverty and
unemployment, characterize the area. The main income resource is constituted by pensions
and welfare subsidies from the government, whereas small-scale subsistence agriculture
provides only part of food requirements. Agricultural productivity is hampered by poor soil
quality (low fertility, low organic matter, susceptibility to erosion) and poor water resources
and water infrastructure development, in the context of a semi-arid climate, with high
variability of rainfall. Seven small-scale irrigation schemes were built by the apartheid
government but collapsed after the state withdrawal in the early 1990s. They are currently
earmarked for rehabilitation under the RESIS program of the provincial department of
agriculture. The catchment is also used in its northern part by large commercial farms, which
provide some employment for the local population. These farms have generally developed
good water access and irrigation infrastructure during the apartheid. As in other rural areas in
South Africa, access to water services and sanitation is poor: for almost one third of the
population water access is below Reconstruction and Development Programme standard
(community stand at less than 200m from dwelling) and more than one third of households
have no sanitation device. Even when people are connected to a reticulated system and have
tap water at homestead, water supply is highly unreliable. Domestic water supply schemes
were built from 1975 to 1992, with refurbishment needed on most of them. Illegal
connections and vandalism are some of the problems mentioned. These irrigation schemes has
been transferred by the Department of Water Affairs and Forestry (DWAF) to the District
Municipality. The main source of water for domestic uses is groundwater, with the exception
of villages at the foot of the Drakensberg escarpment, which are supplied from springs or
streams coming from the mountains. The map below shows the location of B72A in Olifants
river basin and Olifants in South Africa and Limpopo basin.
Page 6
Figure 1: Location of the study site in Olifants River Basin and South Africa
The history of the area is summarized in Box 1. As all the rural territories in South Africa, the
quaternary catchment B72A reflects the inequitable distribution of land and water resources
inherited from the colonisation process and the apartheid policy.
Page 7
Box 1: History of the Sekororo-Letsoalo area
• Before the arrival of European settlers in the 1830s, people lived on the higher plateau and mountains slopes
which enjoyed better soils and rainfalls than the lower plain which, moreover,was infested tith tse tse fly and
malaria. The elevation also allowed for watching enemies, like the Sawzus, coming. The traditional farming
system was agro-pastoral with a clear distribution of roles between men (preparation of land for cropping,
breeding of livestock) and women (in charge of the crops and domestic tasks). The plots were close to the
rivers were occupied first. Cattle grazed on communal lands and were protected at night in the family kraals.
• 1830: Louis Trichard one of the leaders of the Great Trek stayed in Trichardsdal on his way to find a trading
route between the Highveld and Delagoa Bay (now Maputo). These first contacts with Africans were friendly.
• Gradually relations turned sour with increasing competition for fertile and well watered land. From 1850s, start
of the European colonisation.European farms (mainly cattle breeding) expanded on the more fertile and better
watered lands included the Sekororo area. Black population was increasingly forced into taxation and labor
provision.
st
• After the 1 world war, in 1920, a number of British settlers formed the Officers Colonial Land Company
(OFCOLACO) and started farming. After several attempts of livestock farming and various crops and the
collapse of the company, land was purchased by individual farmers who opted for tropical fruit production
(mangoes, paw-paw). They built a first irrigation canal from the Selati River with the support of government.
The irrigation system was run successfully until severe droughts of the 1960s. The basis of inequitable
economic relations between African population and white settlers, which later become the core of the
economic and political power of the apartheid system, was put in place: easy access to land and water
resources for white farmers, abundant and cheap labor provided by black populations. However, contrary to
other regions like Sekhukhune land and countries outside South Africa, this area did not provide many migrant
workers to the mining industry in the Highveld.
• The Land Acts of 1913 and 1936 further reinforced the exclusion of black Africans from access to land, as they
nd
were assigned to a limited portion of the country, the reserves. After 2 world war, war veterans were given
land in the area and started orchards of mangos, bananas, and vegetables. Black inhabitants provided
agricultural labor organised through black foremen, under harsh conditions.
• In 1948, the new government of apartheid introduced the department of agriculture and water affairs as well as
the conservation department, under which the “betterment policy” demarcated land into residence, cultivation
and pasture zones for Africans. Hillside farming was forbidden, and so did the cutting down of trees and
cultivation near river beds. Some white farmers were chased out and forced removal of people from different
tribes and origins (Sotho and Shangaan speaking people) started, resulting in the weakening and even
destruction of the social fabric. Increasing population on a limited land quickly resulted in over exploitation of
natural resources (overgrazing, depletion of water). Land was allocated by traditional authorities (and
sometimes formalized inwriting through a Permission to Occupy)
• In the mid 1950s, impoundments were constructed across rivers and streams to hold water for irrigation and
domestic use in the adjacent white areas. This marked the intensification of irrigation schemes in the area.
With the powers of allocating water in the government‘s hands, water provision to black farmers was
considerably limited and often frequently interrupted.
• 1960s: creation of the Selati Irrigation board, which was assigned powers and functions in accordance with the
1956 Water Law. A new canal was built with government support and water allocation made to 12 white
properties (total scheduled area of 998ha).
• 1970s: Creation of the Lebowa homeland, which was the most important one in Transvaal. Farms in the Selati
catchment were bought by the South African Development Trust for the purpose of consolidating the Lebowa
homeland, some of them being leased back to white farmers in the meantime. New black immigrants joined
existing villages and Shangaan-speaking people were forced to move to a new homeland, Gazankulu. All
these displacements caused major tensions among the population.
• In 1984, proclamation of the Lekgalameetse Conservation Area on the Drakensberg escarpment. In 1986,
extension of Lebowa homeland, eviction of some white farmers. The Lebowa government started to build
electricity and domestic water supply infrastructures.
• The election of a democratic government in 1994 brought about considerable relaxation of the rules inimical to
the development of the black population. Control over land and water management loosened with withdrawal
of people previously employed to manage land and water. As a result, cultivation on the mountain slopes for
short term food crops; tree felling and cultivation near riverbanks increased. Irrigation schemes were no longer
maintained. Land claim and restitution process were and are still translated into the settlement of new
emerging farmers, supported by the new agricultural policy.
(Sources: Van Koppen, 2007a; 2007b; Liebrand, 2006; Ramay and Beullier, 2005)
Page 8
3 Research methods
3.1 Sustainable livelihood approach and typology of farming /
household systems
Our conception of farming / household systems functioning is based on the Sustainable
Livelihood approach used by DFID (DFID, 1999) (Figure 2). This framework assumes that
people may have access to five categories of assets (human, financial, physical, social, and
natural) and combine them to achieve their objectives through livelihood strategies. The
social, institutional, organizational and natural context and the vulnerability context in which
they are operating influence these strategies. The livelihood outcomes they can achieve
contribute in return to the development of their assets. At household level, we assume that the
household has priorities and goals, and takes decisions about the use of some of its assets in
certain activities to reach these goals. The household can take these decisions as a unit, or
more likely, different members within the household may pursue different goals, accessing
different assets and be engaging in different activities.
The Sustainable Livelihood framework acknowledges the huge diversity of livelihood
systems within rural communities (Ellis, 2000; Coomes et al., 2004). Rural households may
differ by the combination of assets they have access to, the socio-economic conditions in
which they take their decisions and the system of activities they perform, in particular
cropping, livestock and natural resource use activities (Bergeret and Dufumier, 2002).
Household typology appears to be an appropriate tool to describe this diversity and analyse its
determining factors. Historically, farm typologies have been developed in order to better
design extension interventions and farm development projects (Landais, 1998; Perret, 1999).
In our project the objective of the household typology is to illustrate the diversity of
household water resource use activities and the contribution these uses to their livelihood
strategies. It may also be used later to formulate recommendations on water use and
management practices adapted to each category of users.
A wide range of elements drives the behaviour of households in terms of water resource use,
and therefore each farm or household typology is specific to the local context and the
objectives of the research or development project into which it fits. Nevertheless, experience
shows that the following factors are most likely to influence choice of livelihood activities
(Bergeret and Dufumier, 2002; Coomes et al., 2004):
- The importance and composition of the different categories of assets;
-
The household demographics (i.e., age, size and composition of the households)
-
Their socio-economic conditions, i.e., their relationships with other categories of
actors (other farmers, land owners, traders, credit institutions, industries and small
businesses, …);
-
The local availability of environmental resources;
-
And the vulnerability context (the risks and shocks they are exposed to and their
ways of coping with them).
Page 9
Policies,
processes,
institutions
VULNERABILITY
CONTEXT
Household members
define
Household/individual
goals
To fulfill
Have
access
to
And
combine
them
into
natural
physical
LIVELIHOOD
STRATEGIES
Crop
production
ASSETS
financial
ACTIVITIES or
Livestock
breeding
human
Costs/benefits
Arts and crafts
Paid jobs
social
…
Develop/exhaust
Figure 2: Household/farming system functioning
3.2 Data collection
Primary data
Socio-economic and agronomic data were collected at household / farm level through various
surveys in 2005.
- survey of land and water management practices by Osten Ntsheme (Ntsheme, 2005)
-
survey of impacts of socio-economic conditions and water management on food
security of small holders by Musa Nyalungu (Nyalungu, forthcoming)
-
survey of small scale irrigation farmers by Sylvia Malatji (Malatji, forthcoming).
Five zones can be distinguished within the catchment based on natural characteristics
(topography, rainfall and sources of water, geology and soils) and land use and type of
farming (Figure 3). The main features of these five zones and corresponding villages are
summarized in Table 1 (see Figure 4 for location of villages).
Page 10
Table 1: Main agricultural areas in B72A quaternary catchment
Main areas
Rainfall and water
sources
1. Drakensberg
mountains
Rainfall > 700mm
lots of springs
Geology and soils
(altitude >600m)
2. Central plain,
north of Makhutsi
river
500-700 mm
Alluvium
Boreholes (6080m)
Deep sandy loam
soils
Makhutsi river
3. Central plain,
between Makhutsi
and Malomanye
rivers
500-700 mm in
the West and
<400 mm in the
East
Makhutsi gneiss
Deep clay and
sandy soils
Wells, rivers
Land use and
type of farming
Villages
Nature reserve on
the top
hillside farming on
lowest slopes
[Sofaya]*
[Madeira]*
[Turkey]*
[Ga-Sekororo]*
Commercial
farming (tropical
fruits, vegetables)
Trichardsdal
Emerging farming
Small-scale
irrigation farming
(maize in rainy
season,
vegetables in dry
season)
Cattle grazing
Dense
settlements
4. Central plain,
south of
Malomanye river
<400 mm
Harmony granite
Wells, no
permanent rivers
Draining sandy
soils
Small scale
dryland farming
(maize in rainy
season)
Nasionaal
Calais
Balloon
Sofaya
Ga-Sekororo
Lorraine
Tickyline
Madeira
Metz
Makgaung
Turkey
Enable
Ha-Fanie
Human
settlements
5. Eastern plain
<400mm
Harmony granite
game farming or
extensive cattle
grazing, nature
reserve
Sparse farms
* The villages are not located in this area but people from these villages have access to mountain
slopes for farming
Page 11
Drakensberg
mountains
Central plains:
commercial farming
Central plains: smallscale irrigation farming
Central plains:
rainfed farming
Eastern plain:
game farming and
natural reserve
Figure 3: Agricultural areas in B72A quarternary catchment
Figure 4: Main villages in the B72A quaternary catchment
Page 12
In Ntsheme’s study 12 villages were chosen to represent the various areas and main types of
farming (Table 2). The farmers surveyed were selected after interviews with stakeholder
representatives in the village. Their landholdings vary according to size and distribution of
fields and encompass the range of land ownership conditions such as self land allocation and
inheritance from past generations. Temporarily assistants were hired to administer the
questionnaires and much of the work was done in collaboration with Agricultural Extension
Officers found in the respective villages. In addition, key informants from DWAF,
Department of Agriculture, Maruleng municipality, NGOs operating in the area, Traditional
Authorities (chiefs and headmen) were interviewed.
Table 2: Number of farming households surveyed by village and study
Main
areas
Villages
Ntsheme’s survey
4
Enable
4
Ha-Fanie
4
Turkey
3
Makgaung
3
Sofaya
12
3
Madeira
12
3
Metz
3
Lorraine
3
Tickyline
15
3
Balloon
10
2
Trichardsdal
and
Nasionaal
2
Calais
Dryland
farmers
Total
Hillside
farmers
Irrigation
farmers
Commercial
farmers
Emerging
farmers
10
Nyalungu’s
survey
Malatji’s
survey
7
6
11
12
15
7
3
15
17
37
15
21
21
15
21
8
84
75
15
6
8
50
36
60
6
8
In Nyalungu’s study also, the purpose was not to select a statistically representative sample
but to cover the diversity of farming systems. Five farming systems were previously identified
as follows: 1) Commercial farmers; 2) Emerging farmers; 3) Communal gardens; 4)
Subsistence farmers on the mountain; and 5) Homestead gardens. However, the study focused
on subgroups 2 to 5. Five villages were selected to represent the different farming areas.
Initially 20 farming households per village were planned to be interviewed, but due to the
difficulty to identify farmers in some villages, the final sample size was 84 (Table 2).
Malatji’s study focused on small scale irrigation farmers. Out of the 6 small scale irrigation
schemes present in the area only four (Sofaya, Madeira, Metz and Makgaung) are active. The
other two (Lorraine and Jelle) have been abandoned in the past few years due to the poor state
of the infrastructure (canals, fence) and lack of water. The number of farmers interviewed in
each scheme was proportional to the total number of farmers using the scheme, totalling 75
farmers (Table 2). In each scheme farmers were selected randomly. Sofaya and Madeira are
Page 13
the most active schemes. In Makgaung, the canals have been destroyed by floods and they are
filled with sand preventing flowing of water. The destruction of fences is also a problem for
the few remaining farmers as livestock is roaming freely and may destroy the crops. In Metz
scheme most of the plot owners have passed away and young farmers are not interested in
farming. The scheme is currently under rehabilitation which hinders farming activities.
Secondary data
When possible, survey data were compared with other sources of information on the study site
or neighbouring areas. The most important sources of information on the area are:
- The 2001 Census 2001 from Statistics South Africa at ward and municipal level: it
gives exhaustive demographic and socio-economic information on the local
population;
- Baseline reports from World Vision South Africa, an NGO operating on the site
(World Vision, 2005b, a). World Vision objective is to alleviate poverty, create
employment and improve health, education and living conditions of the people. It
had initially two Area Development Programmes (now merged into one programme)
in the study site: Kodumela and Enable areas, which covers respectively Sofaya,
Makgaung, Moshate, Madeira and Metz; and Enable, Turkey, Worcester and
Butswana. The baseline surveys (done in 2005) used in the present report were
meant to obtain key information on the children, household members and social
services availability in the area and used them to design development programs.
They address questions related to health, agriculture, education, nutrition,
HIV/AIDS, water and sanitation.
- ICRISAT and ARC report presents the results of a baseline survey carried out by
ICRISAT (Zimbabwe) and Agricultural Research Council (South Africa) for the
project number 1, under the Challenge Programme on Water and Food (ICRISAT,
2007). The project Number 1 goal is to improve food security, incomes and livelihoods of
smallholder farmers in the Limpopo Basin. To achieve this goal, the project is building on
past and current collaborative research by national programs and the CGIAR on crop-water
productivity in drought-prone areas, innovative approaches to participatory technology
development and extension, and new institutional arrangements that link the public and
private sector with the smallholder farmer in appropriate market chains. The objective of
-
the baseline study was to provide quantitative and qualitative information that could
be used to characterize the farming systems of the Limpopo river basin before the
implementation of project activities. The study in South Africa was carried out in
Capricorn, Mopani and Sekhukhune Districts.
Merrey and van Koppen (2007) synthesized research from IWMI and other
institutions done on water, equity, productivity and sustainability within the Olifants
River Basin (Merrey and van Koppen, 2007). Their report also includes the
historical trajectory of water use and allocation dating back to the beginning of the
colonial period in South Africa. It also looks at the political, institutional, economic
and social challenges facing the water sector in the catchment The synthesis
followed the framework proposed by the Comprehensive Assessment of Water
Management in Agriculture.
3.3 Available socio-economic and technical data on the site
Data collected through the three surveys mentioned in section 3.2 is summarized in Table 3.
Page 14
Table 3: Socio-economic and technical data collected by the 3 surveys
Nyalungu’s
survey
Ntsheme’s
survey
Malatji’s survey
ECONOMIC DATA
"
!
#
#
!
$
!
$
%
$
&
'
!
$
&
(
%
%
)
SOCIAL DATA
*
+
,
&
%
OTHER TECHNICAL DATA
'
(1)
(2)
(3)
(x)
not detailed by type of land
we only know whether the household has access to in-house piped water
secondary information at village/catchment level is also available
partial information
3.4 Data analysis methods
Descriptive statistics (average, standard deviation, frequency table) and bivariate analyses
(cross tables, linear regression) were used in all three studies to describe the main
characteristics of the farming households interviewed.
Malatji first built a typology of small scale irrigation farmers based on five criteria: type of
farm labour, diversification of crops, purpose of farming, production assets and source of
income. At farm level, gross margins of each crop cultivated were calculated and then
aggregated to evaluate the total gross margin of the farm. Gross margins of each farmer were
then compared to assess economic viability.
The present report only deals with the descriptive statistics as Nyalungu and Malatji have not
finished their analyses. The main characteristics of farming household systems are described
variable per variable.
Page 15
In addition, a farming household typology was built to represent the household functional
diversity and prepare the modelling of the farming systems.
Two main types of methods can be used to build a farm typology: (i) use of multivariate
analysis techniques (such as principal component analysis, correspondence analysis and
cluster analysis) applied to a large set of factual data collected through a survey of a sample of
households so as to identify the most discriminating combinations of variables and the
statistical relationships among them; and (ii) direct search of cause-effect relationships
between variables based on key informants interviews (Perret, 1999; Bergeret and Dufumier,
2002). What ever the method used, it must be emphasised that a typology is always a
simplified representation of the reality designed for a particular purpose and relative to a
specific point in time. Each farm type remains heterogeneous and the limits between types
may be blur and overlapping. Finally diversity of livelihood systems is a dynamic process:
each farm type has its own evolution over time and consequently the typology cannot be
fixed.
In the case of the B72A a consolidated database was built using data collected by Nyalungu
and Malatji. It comprised 159 households1. The main information in the database and used for
the typology building is summarized in Table 4. A principle component analysis (PCA) was
conducted on the table of 159 households × 24 variables. Then a cluster analysis using Ward
method was run on the basis of the first 8 factorial coordinates of the households in the PCA.
This allowed distinguishing 8 types of households.
1
It should be noted that due to the mix origin of the sample (Nyalungu’s and Malatji’s studies) and the different
sampling strategies used in both studies, the sample is not statistically representative of the whole population.
For example Malatji’s sample was biased towards irrigation farmers. However we can assume that it gives a
good image of the diversity of farming household systems in the research site.
Page 16
Table 4: Variables used for the farming household typology
Topic
Family characteristics
Manpower
Household income
Assets
Agricultural practices
Access to services
Variables
age of household head (AGE)
gender of household head (GENDER)
education level of household head (EDUC)
number of family members working on farm (FAMBF)
number of hired workers (HIRWO)
total income (TOTINC)
percentage of income from employment (EMPINC)
percentage of income from irregular off farm activities
(OFFINC)
percentage of income from livestock (LIVINC)
percentage of income from cropping (CROPINC)
percentage of income from remittances (REMT)
percentage of income from pensions (PENS)
percentage of income from irrigated crops in crop income
(IRRINC)
livestock number (LIVNB)
land area (LAND)
domestic assets (HHASST)
total seed costs for all crops (SEED)
total quantity of fertilizers used (FERTZ)
market costs (MARK)
access to credit (CREDT)
availability of savings (SAVNG)
Agricultural strategies
reason for rearing livestock (LIVREA)
diversification of crops (number of vegetable crops: VEGET)
Food security
food security (FOSEC)
Page 17
4 Results
4.1 Characteristics of the households
The Limpopo Province, jointly with the Eastern Cape Province, is one of the poorest
provinces in South Africa. The Limpopo Province has a total population of 5 273 642 (2001
Census). With 89 % of its population being rural, the Limpopo Province has the highest
percentage of rural population in South Africa.
The total population of the study site is estimated around 56000 people, mainly composed of
Sepedi people. As in many other rural areas in South Africa, households in the study site are
characterized by a high level of poverty, a high percentage of female headed households, and
high HIV/AIDS prevalence.
The research site is constituted of parts or totality of wards 1, 2, 3, 4 and 5 (demarcation of
2001) of Maruleng municipality. Most of demographical data given in the following sections
are derived from the 2001 Census and Ntsheme’s, Malatji’s and Nyalungu’s surveys.
Household size
The distribution of households per size in the study area is given in Figure 5.
Figure 5: Distribution of households per size in wards 1-5 of Maruleng municipality in 2001
number of households
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1
2
3
4
5
6
7
8
9
10 and
Over
household size
(Source: Statistic South Africa, Census 2001)
The census results for wards 1 to 5 show a household size of 4.96 people which is slightly
more than for the whole Maruleng municipality (4.8). World Vision South Africa study
indicates an average size of households of 3.8 people for Enable area (Enable, Turkey,
Worcester and Butswana) and 4.9 people in Kodumela area (Sofaya, Turkey, Makgaung,
Moshate, Madeira and Metz) (World Vision, 2005b, a). The ICRISAT study of Capricorn,
Mopani and Sekhukhune districts had an even higher average household size of 6 people.
Page 18
Age
The distribution of population per age class in wards 1 to 5 is given in Figure 6. One can
notice the highest proportion of women being above 15 years old. This situation is prevalent
in rural areas in South Africa, due to out migration of male adults to urban or industrial areas.
As in the rest of the province the percentage of people under 15 is quite high (43% against
39% in the whole Limpopo province).
Figure 6: Distribution of population of wards 1 to 5 of Maruleng municipality per gender and
age class in 2001
Over 65
Age classes
35 to 64
Females
15 to 34
Males
5 to 14
0 to 4
0
2000
4000
6000
8000
10000 12000
Number of people
(Source: Statistic South Africa, Census 2001)
Available data from Census do not give information of age of household head. The combined
database of Nyalungu’s and Malatji’s studies gives an average age of 54.4 years old for the
household heads. Table 5 shows the age distribution of farmers per type of farming in
Ntsheme’s survey. All three surveys seem to give consistent figures.
Table 5: Age range of respondents per type of farming
Hill side farming
Number
of
farmers
%
0
0%
7
19%
23
64%
25-34
35-44
45-54
55 and
over
6
Total
36
(Source :Ntsheme 2005)
17%
100%
Rainfed farming
Number
of
farmers
%
9
18%
11
22%
13
26%
17
50
34%
100%
Page 19
Irrigation farming
Number
of
farmers
%
11
18%
9
15%
24
40%
16
60
27%
100%
Total
Number
of
farmers
%
20
14%
27
18%
60
41%
39
146
27%
100%
Gender of household head
Nationally 42% of the households are female headed (Statistics South Africa, 2001; Aliber,
2003; Panesar, 2006). In the research site female-headed households account for 64% of the
surveyed sample according to Nyalungu’s and Malatji’s survey data (combined dataset). Age
and gender distribution of household heads in our sample is given in Figure 7. Ntsheme’s
2005 survey showed that 68% of the small scale communal farmers were females and the
remaining 32% were males. This is in line with statistics which show that women make 7080% of the agricultural sector in the Limpopo Province (Department of Agriculture, 2001).
Contrary to these figures which describe a common situation in rural areas in South Africa,
World Vision study found out 26.4% and 45.5% of female headed households in Enable and
Kodumela development areas respectively (World Vision, 2005b, a).
Age class
Figure 7: Distribution of household heads per gender and age class in the study area
Female
Male
0
5
10
15
20
25
30
35
Number of households
(Source: Nyalungu’s and Malatji’s combined surveys)
Generally, both gender and age of farmers could be considered as not being balanced with
virtually no youth involved in farming. The majority of farmers across all households are
middle aged with a considerable number of old people taking part in farming. Ntsheme’s
survey also shows that the youths are not interested in agricultural activities and are looking
forward to pursuing non-agricultural employment opportunities elsewhere. Women form a
large part of the farmers in the study area with husbands migrating to urban centres and farms
looking for work (as it is the case in the whole Olifants River basin - see Merrey and van
Koppen, 2007).
Page 20
Female headed households experience poverty more often than male headed ones. About 60%
of the female headed households, in South Africa at large, experience chronic poverty which
calls for targeted interventions in such households (May et al., 2000, cited by Panesar, 2006).
‘A household headed by a resident male has a 28% probability of being poor, whereas a
household with a de jure female head has a 48% chance of being poor and a household with a
de facto female head (because the nominal male head is absent) has a 53% chance of being
poor’ (Woolard, 2002 cited by Nyalungu, forthcoming). Nyalungu noted that there are several
factors which result in female headed households being poor: ‘female-headed households are
more likely to be in the rural areas where poverty is concentrated, female-headed households
tend to have fewer adults of working age, female unemployment rates are higher and the
wage gap between male and female earnings persists’ (Woolard, 2002, p.3). Female headed
households tend to have fewer assets and financial resources at their disposal. Such assets
include physical, human, financial, natural and social capital (FANRPAN, 2007).
Marital status
The Ntsheme’s study further demonstrated that only 42% of the respondents involved in
agricultural activities were married with the majority being old age widows. The levels of
education and skills were much lower for female farmers than for the males according to
Ntsheme’s study. This seems to be the trend across a number of countries in Africa (FAO,
2005).
Education level
The education level of the total population (people over 20 years old) in the study area
according to Census 2001 is given by Figure 8.
Figure 8: Education level attained by people over 20 years old in wards 1 to 5 of Maruleng
municipality
10000
9000
Number of people
8000
7000
6000
5000
4000
3000
2000
1000
0
No
Schooling
Some
Primary
Complete Secondary Grade 12
Primary
Education level
(Source: Statistics South Africa, Census 2001)
Page 21
Higher
36% of the population has no formal education at all. If 27% have reached secondary school
only 10 % have completed their secondary education and only 5% have a higher level. Better
educated people are most likely to move to urban areas where they can find jobs in relation
with their education level. In the sample surveyed by Nyalungu and Malatiji distribution of
household heads across education levels is similar as 40% have no formal education and 42%
have reached secondary education as shown in Figure 9.
Figure 9: Distribution of households per gender and education level of head in Nyalungu’s and
Malatji’s surveys
120
Number of households
100
80
60
40
20
0
Male
Female
no formal education
primary
child
secondary
tertiary
(Source: Nyalungu’s and Malatji’s surveys 2005)
Ntsheme’s survey gives on average similar figures with differences according to type of
farming as illustrated in Table 6.
Table 6: Level of education of the surveyed farmers in Ntsheme’s survey
Hill side farming
Number
of
farmers
%
No
22
education
61%
Primary
11
education
31%
Secondary
3
education
8%
Total
36
100%
(Source: Ntsheme 2005)
Rainfed farming
Number
of
farmers
%
17
34%
29
58%
4
8%
50
100%
Page 22
Irrigation farming
Number
of
farmers
%
Total
Number
of
farmers
%
24
40%
63
43%
23
38%
63
43%
13
60
22%
20
146
14%
100%
Figure 10: Distribution of households per age and education level of head in Nyalungu’s and
Malatji’s surveys
45
40
Number of households
35
30
25
20
15
10
5
0
<35
35-44
45-54
55-64
65 and over
Age class
no formal education
primary
secondary
tertiary
(Source: Nyalungu’s and Malatji’s surveys 2005)
Education level varies with gender and age of household head as shown in Figure 9 and
Figure 10: female heads tend more often than male to have no formal education and young
heads are better educated than older ones. This is in relation with the general improvement of
education services since the end of the apartheid with the generalization of secondary school
in poor rural areas. An ICRISAT survey in Sekhukhune district showed that 39.7% of the
male headed households had never been to school with a correspondingly higher percentage
of 61.4% of female household heads with no education. This is largely attributed to the fact
that in times of financial hardships, the girl child would be withdrawn from school before the
boy is withdrawn (ICRISAT, 2007).
Higher education levels tend to correlate to better living standards. According to the 2008
draft World Development Report, low education levels are a major contributor to low
agricultural productivity and poverty. The draft 2008 Report acknowledges that whilst land is
a key asset in agricultural production, education is often the most valuable asset to enable
rural communities to make productive use of the land. In the study area, Nyalungu’s research
indicated that 58 % of adults without formal education were poor, 53 % of adults with
primary education or less were poor and 34 % of adults with incomplete secondary schooling
are poor. Poverty rates drop significantly with the attainment of matriculation2 and further
qualifications. The study showed that 15 % of those who completed high school were poor
compared to only 5% of those with tertiary education who were classified as poor (Nyalungu
forthcoming). Within the broader Olifants Basin, illiteracy rate was 50% within the black
population in 2005 (Merrey and van Koppen, 2007). Education levels are some of the
important indicators which determine adoption of technologies which save water and increase
agricultural productivity which can lead to poverty alleviation.
2
Matriculation is attained after 12 years of education in the South African education system.
Page 23
World Vision Reports shows that 92.5% of those aged between 7 and 18 have been to school.
Only 27.6% of the orphans and vulnerable children were able to go to school. The rest of the
orphans did not attend school for various reasons ranging from lack of uniforms to looking
after ill parents (World Vision, 2005b, a).
Family structure, HIV/AIDS and poverty
The demographic structure of the local population is also being reconfigured due to the
impact of the Acquired Immune Deficiency Syndrome (AIDS). At national level the
prevalence of AIDS for the 15-49 age groups is 18.8% (Statistics South Africa, 2006; UNDP,
2006). AIDS has greatly affected the Limpopo Province. A detailed study in Capricorn
District, one of the four districts in the Limpopo Province showed that AIDS affected
households are more often female-headed as compared to non affected households (53%
compared to 46%) (FANRPAN 2006. Only 43 % of the households had both father and
mother resident in the rural area, with 25.4 % of households being led by widows compared to
4.5% which were led by widowers. Only 1% of the households were child headed largely due
to the incorporation of orphaned children into the extended family, especially into their
grandparents’ households which is common in rural settings in South Africa (FANRPAN
2006, 2007). The World Vision study also found that the demographic structure has been
altered by HIV/AIDS with increasingly grandparents looking after their grandchildren after
the loss of one or both of their parents due to AIDS. 37% of the surveyed households were
looking after orphaned or vulnerable children in the Enable area (World Vision, 2005b, a). Of
this total, 33% are taking care of orphans who lost one parent, 4% lost both parents, 38% of
the parents are not working and 25% are staying with chronically ill parents. Chronically ill
household heads cultivated areas which were half of those cultivated by households with a
healthy household head (World Vision, 2005b, a). Finally, an ICRISAT survey found out that
at least 20% of the surveyed households, in the Capricorn District at large, had at least one
household member who was chronically ill (ICRISAT, 2007).
HIV and AIDS have resulted in the increasing burden on women who have to fend for their
families. The situation is even worsened by the fact that ill persons who reside in urban areas
usually go back to their community when they are no more able to work.
Occupation
According to World Vision study about 3.8% of the people in Enable are employed with the
majority of them working on large scale commercial farms (World Vision, 2005b, a). The
2001 Census figures put unemployment within Maruleng Municipality at 40% in the 15-65
age group as shown in Table 7 below. The unemployment in the case study wards was even
higher at 65%.
These figures are above the 47% reported by Merrey and van Koppen for the whole Olifants
River basin (Census 2001cited by Magagula et al., 2006; Merrey and van Koppen, 2007), and
42% unemployment rate in the Limpopo Province at large (FANRPAN 2006; Nesamvuni et
al. 2003). Commenting more generally on former homelands in the Olifants Basin, Merrey
and van Koppen (2007) point out that these are underdeveloped, overcrowded with under- and
unemployed people (Merrey and van Koppen, 2007).
Page 24
Table 7: Distribution of potentially active population per employment status in wards 1-5 and
Maruleng municipality
Wards 1-5
% of people
Number
aged 15 to
of people
64
employed
4116
unemployed
7596
labour force
11712
not economically active
19410
unemployment rate
(Source : Statistics South Africa, Census 2001)
13%
24%
38%
62%
65%
Maruleng municipality
Number of
people
14895
9970
24865
28463
% of people aged
15 to 65
28%
19%
80%
53%
40%
Working force
Agriculture and community/personal services were the two largest sources of employment in
both the Maruleng Municipality and in the 5 wards included in the case study, according to
the 2001 Census as shown in Table 8. However, the percentage of people employed in
agriculture is much lower in our case study than in the whole Maruleng municipality.
Agricultural working force is mainly family as shown by the Nyalungu’s and Malatji’s
combined surveys of 159 households, which found out that only 20% of households have no
family members working on their fields and 53% of the surveyed households did not hire
people to work on their fields. One of the themes which emerged from qualitative interviews
carried out by Berumen (2006) and the World Vision study indicated that dependency on rain
fed agriculture and lack of access to suitable land and water resulted in the labour force within
the agricultural sector not realizing its full potential through practices such as year round
irrigation. This is in line with ICRISAT survey for Capricorn, Mopani and Sekhukhune
districts which showed that very few households were working full time in agriculture
(ICRISAT, 2007), with male headed households generally having more people working full
time in agriculture than female headed households. This has implications in terms of coping
with labour intensive technologies (ICRISAT, 2007). The health and the quality of the labour
are also important. In the neighbouring district of Sekhukhune, for instance, 37.1% of the
household heads were chronically ill. This has serious implications for food security and
quantity and quality of labour (ICRISAT, 2007).
Non-farm employment opportunities are limited to government jobs (teachers, police,
community services), permanent and temporary jobs in commercial farms locally or in
Tzaneen, mining industry in Phalaborwa, and some job opportunities in the tourist industry
(Hoedspruit, Kruger National Park). They are largely restricted by the lack of education and
skills required in the non-farm employment sector. The study by World Vision indicated that
52% of those not involved in economic activities failed to do so due to lack of skills. The
skills which were available were largely basic skills in baking, fence making, carpentry,
sewing and brick making.
Page 25
Table 8: Distribution of employed people per industrial sector in wards 1 to 5 of Maruleng
Municipality
Wards 1-5
Number
% of
of
employed
people
people
Maruleng municipality
1152
1407
276
30
28%
34%
7%
1%
Number of
people
6122
2539
561
85
117
126
48
0
312
162
195
351
4176
(Source: Statistics South Africa, Census 2001)
3%
3%
1%
0%
7%
4%
5%
8%
100%
431
475
129
0
1140
1133
1103
1179
14897
Agriculture/Forestry/Fishing
Community/Social/Personal
Construction
Electricity/Gas/Water
Financial/Insurance/Real
Estate/Business
Manufacturing
Mining/Quarrying
Other
Private Households
Transport/Storage/Communication
Undetermined
Wholesale/Retail
% of employed
people
41%
17%
4%
1%
3%
3%
1%
0%
8%
8%
7%
8%
100%
4.2 Assets ownership
Access to assets is a key attribute in the configuration of rural household production.
Ownership of assets enables rural communities to overcome unexpected events such as deaths
or droughts. The more assets a household has, the more chances it has of successfully
absorbing the shocks without falling into the poverty trap (UNDP, 2006).
In the case study area, access to land was a key attribute which would help determine whether
a household will be able to produce food for both subsistence and marketing (Nyalungu
forthcoming). Generally former homeland area such as the study site, tended to have limited
access to land of poor quality. Nationally, the average commercial farm size is 1300 hectares
whereas in the former homelands over 70% of the farmers cultivate less that 2 hectares of
land (Orkin and Njobe, 2000). This is also the case in the B72A catchment with an average
land area of 1.3 ha (with a standard deviation of 1.5) (Nyalungu and Malatji combined dataset
2005). In irrigation schemes plots were allocated to farming households at time of
construction in the 1950s (Permit To Occupy) and then passed on to descendants. In drylands,
plots are generally allocated by traditional authorities, although some cases of self-allocated
plots were mentioned during Ntscheme’s survey. Most hillside farmers cleared their plots just
after the end of apartheid without authorization and title deed. The number of hillside farmers
has increased recently due to lack of suitable land and recurrent droughts in the plain. Land
reform was a key issue raised in the research area, with a number of people seeing land
reform as a first step towards increased agricultural productivity. Agricultural production is
further limited by lack of general agricultural equipment as noted from Nyalungu’s survey
(Table 11).
Ownership of livestock such as cattle is instrumental in enabling timely cultivation of crops
especially within the context of rain fed agriculture. Farmers need enough cattle to form a
span for draught power. Cattle also offer food and can be a source of income if sold during
years of poor harvests. Tragic events such as funerals would also need the slaughtering of
livestock which puts further strain on poor households. In our case study area, Nyalungu’s
Page 26
and Malatji’s surveys show that the average livestock ownership is equal to 2.6 Tropical
Livestock Units (standard deviation: 3.6) with 20% of households with no livestock and 27%
with only poultry. Cattle are the dominant kind of livestock kept by the farmers surveyed by
Nyalungu. The herd sizes were below fifteen per farmer and only five farmers had cattle
exceeding twenty. Other kinds of livestock are kept in numbers as low as three and only four
farmers had donkeys. Livestock ownership is central for agricultural production as shown by
Cousins, 1989 and Ferguson, 1990 in a number of African countries (e.g., Zimbabwe and
Lesotho).
The proportion of households owning cattle in our sample differs from the ICRISAT survey,
which found out that 75% of the surveyed households did not own cattle. This latter study
also showed that cattle ownership was highly skewed in favour of male headed households
(ICRISAT, 2007). Less than 10% of the households surveyed in the ICRISAT survey owned
an ox-drawn plough, and therefore there was a high dependence on hiring of mechanised
ploughing means such as tractors (ICRISAT, 2007). Asset ownership was tilted in favour of
male-headed households. Women tended to have ownership and control of smaller livestock
such as goats and chicken. Such small livestock can be important in terms of providing
income for schooling and other household requirements (ICRISAT, 2007).
The type of dwelling is generally a good indicator of the wealth of a household. Census 2001
results show that in the case study wards 78% of the households had formal dwelling units
which favourably compares with 81% for the Maruleng Municipality as a whole. Only 20%
of the households in the case study wards were classified as being traditional (Table 9).
Table 9: Dwelling types in wards 1-5 and the Maruleng municipality
Wards 1-5
Number of
% of total
households households
Formal
9294
78%
Informal
282
2%
Traditional
2364
20%
Other
27
0%
Total Households
11967
100%
(Source: Statistics South Africa, Census 2001)
Maruleng municipality
Number of
% of total
households
households
15906
81%
581
3%
3115
16%
70
0%
19672
100%
Ownership of other domestic and productive assets is reported in Table 10 for ward 1-5 and
Maruleng municipality (Census 2001) and in Table 11 for households interviewed by
Nyalungu.
Television and radio are key assets for dissemination of water and agricultural information
and water saving technologies (ICRISAT, 2007). In B72A, Census data show that if radio was
quite widespread across the population in 2001, the number of households owning a television
set was still limited (Table 10). It seems that the situation has improved recently with 68% of
households in Nyalungu’s survey having a television set (Table 11). This can be compared
with the results of ICRISAT survey showing that over 78 % of respondents owned radios with
over 50% owning televisions.
Page 27
Table 10: Number and percentage of households owning some domestic assets in Maruleng
municipality and wards 1 to 5 in 2001
Wards 1-5
% of total
Number of
households households
Telephone in dwelling yes
174
1%
Cell-Phone yes
2466
21%
Radio yes
(*)
(*)
Television yes
(*)
(*)
Computer yes
(*)
(*)
Refrigerator yes
(*)
(*)
(Source: Statistics South Africa, Census 2001)
Maruleng municipality
Number of
% of total
households
households
790
4%
4139
21%
12656
64%
4622
23%
507
3%
5475
28%
One can also note from Table 11 that ownership of means of transport is very limited with
only 13% of the surveyed households having a car.
Table 11: Asset ownership in the study area
Number of
households with % of total households
Agricultural_equipment
4
5%
Fridge
56
67%
TV set
57
68%
Hi-Fi
39
46%
Stove
33
39%
Sewing machine
21
25%
Car
11
13%
Cellphone
53
63%
(Source: Nyalungu’s survey, 2005)
4.3 Livelihood activities
IWMI surveys (Nyalungu and Malatji combined data set 2005) show that the average annual
income per household amounts to R17,320 (standard deviation R20,600), which is equivalent
to a daily individual income of USD1.5 (with an average household size of 4.9 persons and
average exchange rate in 2005 of R6.37 for US$1). However the distribution of income is
highly unequal as shown in Table 12: 37% of people in the surveyed sample live on less than
US$0.84 per day (less than R4800 per annum and household) and another third earn between
US$0.84 and US$1.68 per day (between R4800 and R9600 per annum and household). Over
75% of children in the Limpopo Province lived in households earning less than R800 or less
in 2005 (Merrey and van Koppen, 2007). The Maruleng IDP Review for 2005 to 2006 points
out that about 75% of its residents earned less than R800 per month with 325% of them
without any income at all (Maruleng Municipality, 2005). About 46% of the province’s
economically active population is unemployed and the HDI (Human Development Index) at
provincial level is 0.47 and its rate of poverty is close to 60% (Nyalungu forthcoming). In
2003, over a million people in the broader Limpopo Province depended on food hand outs
(Nyalungu forthcoming).
The comparison of IWMI survey with Census data in wards 1 to 5 and in Maruleng
municipality as a whole (Table 13) shows that the surveyed sample comprised slightly less
Page 28
poor people than the whole population: in particular there was no household without any
income in our sample. This can be due either to the bias in our sampling procedure or to the
general improvement of income between 2001 and 2005. However our sample reflects the
diversity of income level found in the total population in the area.
Table 12: Distribution of households per income class in Nyalungu-Malatji surveyed
households
Number of
Annual income class
households
%
R1 - 4800
33
21%
R4801 - 9600
26
16%
R9601 - 19200
54
34%
R19201 - 38400
37
23%
R38401 - 76800
7
4%
over R76801
2
1%
Total
159
100%
(Source: Malatji’s and Nyalungu’s surveys, 2005)
Table 13: Distribution of households per level of annual income in wards 1 to 5 and within the
Maruleng municipality
Wards 1-5
Number of
% of total
households households
None
R1 - 4800
R4801 - 9600
R9601 - 19200
R19201 - 38400
R38401 - 76800
R76801 - 153600
R153601 - 307200
R307201 - 614400
R614401 - 1228800
R1228801 - 2457600
Over R2457600
(Source: Statistics South Africa, 2001 Census)
4944
1323
2859
1503
708
387
174
33
18
3
3
6
41%
11%
24%
13%
6%
3%
1%
0%
0%
0%
0%
0%
Maruleng municipality
Number of
% of total
households
households
7269
2610
4842
2403
1101
693
462
174
54
30
18
15
37%
13%
25%
12%
6%
4%
2%
1%
0%
0%
0%
0%
Households in the area generally have several sources of income. For example, in the small
scale irrigation schemes, farmers who depend on income from farming only were found to be
35%, 38% on income from farming and pension, 18% from farming and child grants, and
finally 9% depend on income from farming and remittances (Malatji 2005). Although almost
all households are engaged in cropping activities, only 20% of the total income of the
surveyed household is derived from cropping. This is because most of the agricultural
production is for subsistence purposes only with very little being sold. Employment provides
the largest part of the income in the area, but this concerns only 36% of the surveyed
households. Half of the households receive pensions and/or child grant from the state welfare
system, which is the second most important source of income (Table 14).
Page 29
Table 14: Source of income: number of households per source, average income per household
per source
Source of income
employment
off farm activities
crop
livestock
pensions
remittances
number of % of total
households households
58
36%
26
16%
154
97%
31
19%
81
51%
23
14%
average
income per
year
per
household
19252
3570
3568
2048
9828
5896
17321
total
income
from
this
source
% of total income
1116600
41%
92820
3%
549433
20%
63500
2%
796080
29%
135600
5%
2754033
100%
(Source: Malatji’s and Nyalungu’s surveys, 2005)
Table 15 gives further information about the distribution of income across sources and income
class. The study showed that low income classes derive most of their income from crops (69%
and 50% for the first and second income classes respectively), households in middle income
classes derive more than a third of their income from pensions and the richest classes derive
most of their income from employment. Targeted agricultural improvements therefore have
the potential to improve the livelihoods of the poorest households.
Table 15: Distribution of annual household income across sources per income classes
% of annual income
R4801 R9601 - R19201 - R38401 –
from
R1 - 4800
9600
19200
38400
76800
Employment
12%
7%
20%
37%
69%
Off farm Activity
7%
5%
1%
2%
13%
Livestock
2%
3%
2%
4%
0%
Crops
69%
50%
26%
17%
5%
Remittances
&
grants
0%
7%
6%
7%
3%
Pension
10%
30%
45%
34%
10%
100%
100%
100%
100%
100%
(Source: Malatji’s and Nyalungu’s surveys, 2005)
over
R76801
98%
0%
1%
1%
Total
41%
3%
2%
20%
0%
0%
100%
5%
29%
100%
The distribution of households per source of income in our survey is similar to the one
reported by World Vision in Kodumela Area Development programme, except for the portion
of household earning income from farming, but quite different from the one in Enable Area
Development Programme (Table 16). The difference in the importance of farming income is
mainly due to the bias towards irrigators in our sample. It should also be noted that World
Vision reported only one main source of income per household whereas families in the area
generally have several sources of income. Variation between the two Area Development
Programmes is due to the fact that Kodumela area includes bigger villages (such as Metz)
where most of the government employees are living whilst Enable area comprises only small
villages with fewer job opportunities.
Page 30
Table 16: Sources of Income in Enable and Kodumela areas
!
" #
%
!
#
$
&
"'
( '
)
+
& &
&
!
!
!
*
** *
! !
** *
(Source: World Vision 2005a and b)
4.4 Water sources, uses and management
Global water management context
Water management in the Olifants River Basin is a contested issue with an estimated water
deficit of 196Mm3 (Sally et al., 2003) and a projected shortfall of 243Mm3 by 2025 as a result
of increasing needs by all sectors and setup of the environmental reserve (Lévite and Sally,
2002). Such ‘water scarcity’ debates, according to some analysts, may be used to stall the
ongoing implementation of water allocation reforms in rural South Africa. In SekororoLetsoalo area, water rights were held by the irrigation boards such as the Selati Irrigation
Board which cater for large scale commercial farmers. The river passes through the former
homelands, but small-scale black farmers had no legal right to use water for productive
purposes. The promulgation of the Water Act in 1998 (NWA) is one of the legal mechanisms
aimed at re-allocating water to the previously disadvantaged black majority. In particular the
NWA promotes the constitution of Water Users Associations which regroup water users from
all sectors (commercial, emerging and subsistence farmers as well as domestic users and
industries) to manage water resources at local level, as one of the institutional innovations to
redress some of these past inequities (DWAF, 1999). However this process is far from being
achieved in the B72A quaternary catchment. In addition to the slow implementation of the
water reform process spearheaded by DWAF, one has to regret the lack of synergy between
this process, the Land Reform Process driven by the Department of Land Affairs and also the
rehabilitation of irrigation by the Limpopo Department of Agriculture (Revitalization of Small
Scale Irrigation Schemes - RESIS).
In B72A catchment, it is clear that over commitment of the little available water resources is
likely to have a serious impact on the lives of people in the near future. This is highlighted by
the fact that most rivers remain dry during low rains (DWAF, 1991,Faysse, 2004) showing
that there will be severe shortage of water in 2010 (Ntsheme 2005).
Domestic water services
Present domestic water infrastructures are rudimentary. Village domestic networks are
generally composed of one or more boreholes or a weir diverting water from a stream, one to
three reservoirs and a small reticulation system supplying public stand pipes. Most of these
networks were built in the 1980’s during apartheid by the government of Lebowa homeland,
Page 31
some improvements (weirs, reservoirs) and extensions being added after 1994 (see Table 17).
In recent years, some households paid for a private water connection in the yard or in the
house. However, as the schemes were not designed for this type of service, water supply is
unreliable and quantity supplied inadequate. After a period of transition during which DWAF
was in charge of managing the water services, networks have been handed over to the Mopani
District municipality, which lacks the required capacity.
The water schemes are not working properly and households often resort to several water
sources to meet their needs, including collecting water from the nearby rivers and streams or
from neighbours with a private tap or borehole. In addition, all households have invested in
storage capacity to cope with unreliability of water supply, the number and size of containers
depending on the family wealth. Problems reported by water users during focus groups
discussions conducted by Ma-Edward Motoboli and Phillipa Kanyoka in 2007 include:
- the lack of water in the local streams during winter
- damaged water infrastructures (broken pump or stand pipes) not taken care of by
responsible institutions
- low frequency and unreliability of water supply: in many villages water is not
available every day, even for households that have a private connection
- low pressure
- quality of water: salty groundwater and surface water polluted by animals
In some villages such as Enable water is sometimes supplied by municipal trucks when the
collective network is not working. Problems of communication between water users and the
institutions in charge of managing water systems (DWAF, municipality, water committee)
and difficulty for water users to organize themselves and solve their problems are also
reported.
Table 17: Main characteristics of domestic water infrastructures in B72A
Zones
Villages
Population
Water
source
Date
reticulation
Total
reservoir
capacity
Reservoir
capacity per
person (l)
Date
reservoir
(m3)
3
4
Balloon
3453
groundwater
1987
150
43
1987
GaSekororo
6895
groundwater
1980
483
70
1978-19831990
Lorraine
6829
groundwater
1983
930
136
1983-1998
Sofaya
3055
surface
1982
749
Ticky Line
7555
groundwater
1986
300
Madeira
3677
surface
1983
690
Makgaung
3752
surface
1980
115
Metz
7451
groundwater
1984
1415
190
1982-19842001
Bismarck
2400
groundwater
1992
230
96
1992
Turkey
8208
groundwater
1984
115
14
1984
Enable
2419
groundwater
1987
150
62
1987
(Source: DWAF, 2003)
Page 32
99
108
1979-1982
1986
1982
1980
Domestic water access
In the broader Olifants Basin, 45% of the population has no access to water sources that meets
RDP standards3 (Merrey and van Koppen, 2007). Table 18 shows the distribution of
households per type of water access in the case study wards and for Maruleng municipality.
About 35% of the households had access to water within their yard, with 2.5% having access
to water within their dwelling, according to the 2001 Census. 11% of the households in wards
1-5 used water from rivers and streams for their domestic uses.
Table 18: Distribution of households per type of access to water in wards 1 to 5 and whole
Maruleng municipality
Wards 1-5
Maruleng municipality
Number of
% of total
Number of
% of total
households households households households
Dwelling
306
3%
1110
6%
InsideYard
4188
35%
7439
38%
Community Stand
3099
26%
4232
22%
Community stand over 200m
2415
20%
3844
20%
Borehole
51
0%
132
1%
Spring
222
2%
234
1%
RainTank
12
0%
19
0%
Dam/Pool/Stagnant Water
57
0%
386
2%
River/Stream
1263
11%
1737
9%
Water Vendor
15
0%
19
0%
Other
336
3%
513
3%
11964
100%
19665
100%
(Source: Statistics South Africa, Census 2001)
Water access varies across villages as demonstrated by World Vision reports and interviews
conducted by Motoboli and Kanyoka (Motoboli, forthcoming; Kanyoka, 2008). Villages
located closer to the Drakensberg Mountain (zone 3) have generally a better access than
villages in central plain south of Malomanye river (zone 4). In Enable ADP, only 23% of the
survey respondents had access to potable water. Half of the respondents (51%) used water
from rivers for domestic purposes, with 7% using boreholes, 14% using springs and 5% using
rainwater. In Kodumela ADP, on the other hand 95% of the surveyed households accessed
water through pipes into yard, a tap in the house or a public tap outside (World Vision, 2005b,
a).
In some villages such as Enable, World Vision had financed rainwater harvesting tanks,
which are used for both domestic and productive purposes. A limited number of wealthiest
households are able to drill their own borehole and have probably the better water access in
the area but at a very high cost. Higher financial resources also allow households to acquire
increased storage capacity to cope with low reliability of water services (Motoboli’s
interviews, 2007).
Water access still remains much skewed along racial lines as shown in Table 19. Per capita
water consumption averages 47 liters per day in the former homelands and 183 liters per day
in areas where the majority of the white population live (Nyalungu forthcoming).
3
RDP standards for water services have been defined as 25 litres of clean water per person and per day available
at less than 200m from the house and interruption of service not exceeding 7 days per year.
Page 33
Table 19: Distribution of households according to their water access and population
Source of Water
Black African
Coloured
White
Piped water inside dwelling
590 (3%)
Piped water inside yard
7223 (38%)
6 (67%)
210 (27%)
Piped water on community stand: distance less
than 200m from dwelling
Piped water on community stand: distance
greater than 200m from dwelling
Borehole
Spring
Rain-water tank
4216 (22%)
3 (33%)
13 (1.7%)
3811 (20%)
33 (4.2%)
129 (1%)
234 (1%)
19 (0%)
3 (0.4%)
Dam/pool/stagnant water
386 (2%)
River/stream
Water vendor
1731 (9%)
19 (0%)
Other
513 (3%)
520 (66%)
(Source: Statistics South-Africa, Census 2001)
This poor access to water in terms of quantity, quality and reliability results in a high
frequency of water related diseases as reported by World Vision baseline surveys (Table 20).
Diarrhoea was ranked as the second highest (23%) most dangerous disease for the under five
children in Enable ADP and the first one in Kodumela ADP. Cases of cholera are also
mentioned (World Vision, 2005b, a).
Table 20: Leading childhood diseases below 5 years in Enable and Kodumela ADP
Diarrhoea
AIDS related
Pneumonia
Malnutrition
Cholera
Malaria
Coughing
Scabies
Other
Total
Enable ADP
Frequency
Percent
90
23.0
16
4.1
31
7.9
75
19.1
16
4.1
13
3.3
123
31.4
28
7.1
392
100.0
Kodumela ADP
Frequency
Percent
116
29.6
5
1.3
9
2.3
76
19.4
24
6.1
24
6.1
115
29.3
19
4.8
4
1.0
392
100.0
(Source: World Vision 2005a and b)
Small scale irrigation schemes
Six small-scale irrigation schemes were built during the apartheid in the 1950s to support
subsistence agriculture: Lorraine scheme on Makhutsi river, Jele scheme (Ticky Line) and
Madeira scheme on Moungwane river, Sofaya scheme on Morola river, Metz scheme and
Makgaung scheme on Moetladimo river. Each irrigation scheme was divided in 100 plots of
one hectare each and each farmer endowed with a Permit to Occupy (PTO). An Extension
Officer paid by the homeland government managed the irrigation scheme and farmers’
responsibility was restricted to producing crops as stipulated by the Extension Officer. The
Page 34
government also provided farm inputs (fertilizers, seeds, tractors), and organized fencing and
marketing of products. After the withdrawal of state support to agriculture in early 1990s, the
schemes were handed over to farmers but most of them collapsed due to poor management.
Today only part of the schemes is cultivated and water productivity has decreased due to the
poor maintenance of the canals and uncontrolled livestock grazing caused by fence
destruction (Ramay and Beullier, 2005). World Vision has been trying to improve small-scale
irrigation farming through a number of strategies such as low-cost drip irrigation and seedling
production for backyard gardens (Ramay and Beullier 2005). Some of these schemes are
earmarked for rehabilitation under the RESIS programme of the Limpopo provincial
government. Metz irrigation scheme is the first one to be rehabilitated. Physical rehabilitation
is associated with a reform of governance of the schemes: according to the National Water
Act of 1998 irrigation committees composed of farmer representatives, who used to manage
the schemes since the end of apartheid, in particular the water distribution, are in the process
of being incorporated into a water users association at quaternary catchment level. This
process is a major challenge for small scale irrigating farmers as it is the case in other regions
in South Africa (Faysse, 2004). Panesar’s study on Sofaya irrigation scheme further stresses
the difficulty for women farmers to participate in the decision-making process since the
irrigation management institutions favor the male irrigators (Panesar, 2006).
Water uses
For productive purposes, small and large-scale farmers together consume 50% of the total
water supplied by the Olifants river basin. This share is further disaggregated into 45% being
consumed by the large-scale farmers with 5% going to the small-scale farmers in the former
homelands. The remaining 50% of water supplied is consumed by mining companies. Largescale farmers occupy about 95% of the irrigated area and 30% to 50% of the initial allocation
of water is used by these farmers (Lévite and Sally, 2002). Studies by IWMI in the Olifants
Basin at large have highlighted inequities in access to water for both domestic and productive
uses within the rural contexts. The studies included the Equity coefficient4 (Prasad et al.,
2006), Gini Coefficient5 (Cullis and van Koppen, 2007) and the Water Poverty Index
(Magagula et al., 2006).6 Cullis and van Koppen (2007) study shows that 95% of all water is
used by 0.5% of the users. The Olifants Basin is said to be closer to the closed river basin
stage than the open river basin (Merrey and van Koppen, 2007). The current water reform
process is marginally attempting to change water allocation but redressing past inequalities
calls for going beyond tinkering at the margins and demands an integrated reform process
which encompasses all resources including land (Merrey and van Koppen, 2007).
At household level in the study area, water use ranges from 10 liters per person per day to
more than 100 liters per person per day, depending on the type of access, the size and wealth
of the family and the village. Water is used for domestic purposes (drinking, cooking, bathing,
and washing) but also for a wide range of productive purposes: irrigation of backyard garden,
4
The Equity coefficient measures the “skewness,” degree of diversion from total equity. The equity coefficient
ranges in value from 0 to 1, with 1 being most equitable and zero the least equitable (Merrey and van Koppen
2007).
5
The Gini coefficient is a measure of equality or inequality and has largely been used to measure distribution of
income. The result from the Gini coefficient are shown on a Lorenz-curve with a straight line denoting perfect
equality. Any divergence from the perfect equality line demonstrates inequality (Merrey and van Koppen 2007
or Cullis and van Koppen 2007).
6
Water Poverty Index measures the impact of water scarcity and water provision on human populations using a
scale from 0 to 100, where a low score indicates high water poverty. It is comprised of five component indices:
resources, access, capacity, use, and environment, each with various sub-indices (Merrey and van Koppen 2007).
Page 35
watering of livestock, brick making, beer brewing, and other small businesses. Access to
water for productive purposes emerged as one of the major issues in the focus group
discussions conducted by Motoboli in Worcester, Mohlomelong and Metz. However due to
limited water sources and inadequate water services, use of water for productive purposes
such as gardening is restricted, either by community rules or by the municipality.
4.5 Agricultural production
Farming purpose and main crops
Both arable and pastoral agriculture are practiced in the area. About 80% of the surveyed
farmers keep livestock as well as grow crops, while 20% of the farmers grow crops only
(Nyalungu’s and Malatji’s combined dataset). Most of the crops are grown for consumption
purposes. Some farmers also sold part of their produce but, as it was mentioned before income
from cropping is usually very limited (50% of the population earns less than R2370 per year
from cropping).
The main crop grown in the study area during summer is maize, often interspersed with
groundnuts or bambara groundnuts. Only farmers who have access to irrigation plots can farm
during winter. Main crops grown in dry season are sugar bean, cabbage, onion, beetroot, and
spinach (Table 21). This is in line with findings from the ICRISAT survey, which showed that
in the 2004-05 planting season in Capricorn district 95 % of the land was under maize
cultivation, with the remainder under bambara groundnuts, groundnuts and cowpeas.
Table 21: Main crops and number of farmers engaged per cropping season
Season
Summer
Winter
Winter and summer
Crops
Maize
Groundnut
Bambara nuts
Butternut
Sugar bean
Cabbage
Onion
Beetroot
Green bean
Spinach
Green pepper
Tomato
Sweet potato
Peri peri
Total number of farmers
in combined surveys (in
Malatji’s survey)
(*) data available only in Malatji’s survey
(Source: Nyalungu’s and Malatji’s surveys 2005)
Number of farmers
116
57 (*)
44 (*)
7 (*)
99
66
30 (*)
61
18(*)
54
3(*)
95
34(*)
5(*)
159 (75)
%
73
76 (*)
59 (*)
9 (*)
62
42
40(*)
38
24(*)
34
4(*)
60
45(*)
7(*)
Farming practices
Various land and water management practices identified in B72A catchment are summarized
in Box 2. Farming practices differ with the type of farming. Cutting of trees and bushes is
done with axes and pangas in hillside and rainfed farming. Hillside farmers only use hand
hoes for cultivation whereas irrigation farmers all used hired tractors to prepare their plots.
Page 36
Land preparation practices of rainfed farmers vary from hand hoes to donkey drawn plough to
tractors (Ntsheme 2005).
Use of inputs also varies across farming types: none of the surveyed hillside farmers used
mineral or organic fertilizers. 42% of rainfed farmers used both mineral fertilizers and
manure, 24% used only manure and 16% only mineral fertilizers. In irrigation farming, the
use of inputs is even higher with 95% of the farmers using mineral fertilisers and 5% using
only manure. In all farming systems, crop residues are ploughed back before the next
cropping season. Grazing of crop residue mainly occurred when the plots are not cultivated
during the dry season. All farmers indicated that they practice crop rotations (Ntsheme 2005).
Only irrigation farmers used mulching (with grass, tree leaves or small artificial nets) to keep
soil moisture and suppress weeds. Other farmers have no specific water management practices
on their plots, and none of the farmers practice rainwater harvesting7. In hillside and rainfed
farming, plots remain fallow during the dry season. In irrigation schemes 85% of farmers
indicated that they left part of their farm fallow either because of lack of manpower or
because they thought it is a good practice. Most hillside farmers (55%) used methods of soil
conservation such as contouring, log and stone barriers to reduce erosion (Table 22).
Ploughing across slopes is also observed in rainfed farms on undulating terrain.
Table 22: Methods of soil conservation used by hillside farmers
Method adopted
Number of farmers
% of farmers
Contouring
7
19.4
Log barriers
8
22.2
Stone barriers
5
14
None
16
44.4
Total
36
100
(Source: Ntsheme 2005)
Box 2: Land and water management practices observed in B72A catchment (zones 3 and 4)
- Application of organic manure
- Application of inorganic fertilizers
- Ploughing across slopes
- Use of log barriers
- Use of stone barriers
- Ploughing back of crop residues
- Extend crop rotations where land is available, particularly with the inclusion of legumes.
- Strict water rationing in irrigation schemes
- Mulching and net shading
- Augment water supply with borehole water
- Greenhouse planting of seedlings
- Use of low water consuming technologies (drip irrigation)
(Source: Ntsheme 2005)
7
Rainwater harvesting refers to different technologies used to harness rainwater so as to be used for productive
and domestic water purposes.
Page 37
Farming constraints and challenges
Table 23 sums up the major challenges faced by the different types of farmers surveyed by
Ntsheme (excluding commercial farmers who are not presented in this report). The problems
faced by dryland and hillside subsistence farmers entailed very basic requirements although
emerging farmers mentioned more sophisticated requirements. Most farmers reported the lack
of resources to buy artificial fertilizers and hire agricultural equipment. With low livestock
ownership and the livestock management practices of free grazing, manure is not easily
available. The IWMI survey found out that only 11% of the respondents had access to credit
facilities. Most of the credit is not from commercial banks. The Department of Agriculture in
the Limpopo Province indicated that lack of formal credit facilities has resulted in a
significant number of people resorting to loan sharks. Most households are not keen to reveal
these as their main source of credit.
Table 23: Challenges faced by farmers in different farming systems
Farming system
Emerging farmers
Irrigation schemes
Hillside farmers
Dry land farmers
(Source: Ntsheme 2005)
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Challenges met by farmers
Inadequate knowledge to operate mechanised systems
Shortage of capital hence of farm equipment
Poor marketing arrangements
Shortage of water
Dilapidating fences around schemes
Uncontrolled movements of livestock
Shortage of farm inputs (manure and seeds)
Lack of draft power
Difficulties in marketing products
Rodents and guinea fowls
Lack of access routes to the farms
Destruction of crops by livestock
Reduction of crop yields by cutworms, (stalk borer)
Low and erratic rainfall
Destruction of crops by livestock
Shortage of draft power
These constraints do not differ from farming constraints reported by other studies in similar
areas in South Africa. For example, 51% of the ICRISAT surveyed respondents in Capricorn
district used artificial fertilisers. The main reason for the non-use of fertiliser was the high
prices. Poor access to land, poor or no access to inputs and credit, lack of assets, lack of
farming skills and drought and intra-season dry spells were some of the problems experienced
by farmers (ICRISAT, 2007).
Crop yields
Crop yields calculated from Malatji’s survey in irrigation schemes are summarized in Table
24. One can notice the high variability of yields that can be attributed to a diversity of farming
practices and agricultural knowledge. Part of the variability may also be due to the fact that
farmers do not know very well their level of production as they directly consume most of their
crops, hence the yields could not be estimated with accuracy. According to Ntsheme (2005)
maize yields vary across farming types: the proportion of farmers with yield less than 2t/ha
tends to be higher among dryland farmers than hillside farmers. A majority of farmers thought
that harvested outputs were decreasing over years in all farming types. They attributed this
trend to droughts and shortage of labour (Ntsheme 2005).
Page 38
Table 24: Yield of main crops in B72A catchment
Bambara
groundnut Groundnut Sugarbean
Yield (kg/ha)
Maize
Average
1514
869
1646
880
Standard deviation
1444
756
1930
1015
Median
1119
720
960
480
Number of farmers
64
44
56
51
(Source: Malajti’s survey 2005)
Access to markets
Box 3 summarizes the marketing facilities available for small-scale farmers in the study area.
From Malatji’s survey it appears that 77% of irrigation farmers engage themselves in farming
for consumption and market purposes (i.e. they consume the produce and then sell surplus),
8% of farmers engage in farming for market purposes only, and finally 15% for consumption
only (i.e. they consume the produce and maybe give relatives and neighbours the surpluses)
(Malatji forthcoming). Generally summer crops (maize, bambara groundnuts and groundnuts)
are mainly intended for self-consumption, whereas winter crops are more often sold. Local
people and hawkers are the most frequent market outlets and the use of shops and markets
remains very limited.
In the case of vegetable production, farmers produce very small quantities and try to sell them
as soon as possible as they do not have storage facilities. They also harvest progressively over
a period of 2 to 3 months according to demand. Sales take place at home or in the close
neighbourhood, which does not involve high marketing costs. It is only when they have a
higher volume of production that they need to resort to other market outlets. The choice of a
specific outlet depends on their resources: mean of transport, contacts and information on
local town markets. Hawkers generally come to buy locally and do not require transport.
However, as pointed by respondents of Agathe Fabre’s survey, it is only economically
interesting to use hawkers for specific vegetables such as chillies or green peppers, because
hawkers deduct the cost of transport from the buying price (Fabre, 2006).
Most households (53%) are breeding livestock for their own consumption, 13% are keeping
animals both for consumption and market, whereas 10% are mainly market oriented, finally
20% of households do not own livestock (Nyalungu and Malatji surveys 2005). Table 25
gives the average herd size, number of animals per category and livestock income per type of
livestock production purpose.
Although, transport infrastructure has developed recently in the study area with the surfacing
of the main access roads, small-scale farmers have limited access to transport means (see
section on assets) and are not organised to market their produce as groups; consequently their
volume of production is not sufficient to obtain good deals with operators and market prices
remains very low. In addition, they suffer from a harsh competition from supermarkets in
neighbouring towns (Tzaneen, Hoedspruit), that sell agricultural production from large scale
farms at very low price compared to local production costs. The inexperience of small-scale
farmers in marketing is in large part inherited from the apartheid policies as during this period
marketing of surpluses from homelands was organised by the government. This has not
improved because of the lack of advice on marketing issues and market information and the
lack of farmer organization.
Page 39
Table 25: Number of households, average herd size and average livestock income per livestock
production purpose
Number of farmers (%)
Average herd size (TLU)
Average number of cattle
Average number of goats
Average number of
donkey
Average number of poultry
Average annual livestock
income (R/year)
Average % of livestock
income on total income
family
consumption
84
2.1
1.9
0.6
consumption
and market
21
6.5
5.9
4.0
0.1
11.2
market
no livestock
Total
16
6.0
5.4
5.6
38
0
0
0
159
2.6
2.3
1.4
0.2
8.5
0.0
3.9
0
0
0.1
7.5
36
1733
1506
0
410
0%
10%
8%
0%
2%
(Source: Nyalungu’s and Malatji’s surveys)
The commercial farming sector, on the other hand, produces primarily for the market.
Infrastructural developments and transport networks were largely developed to link
commercial farming and mining interests going back to the early 1900s (van Koppen 2007a).
For the former homelands access to markets is one of the important issues that need to be
addressed to enable the formerly disadvantaged communities to move out of poverty.
Box 3: Agricultural marketing facilities for small-scale farmers in Sekororo-Letsoalo area
These villages (Enable, Turkey, Butswana, Worcester) are quite isolated from agribusiness firms. Indeed, there
are neither commercial millers nor inputs seller close to the villages. But there are some local swap millers in
surrounding villages, as well as retailers inside villages for household basic consumption (“spaza” shops and
Indians retailer). For inputs supply, most farmers using inputs purchase them in NTK shops in Ofcolaco a complex
situated at nearly 15km, or in neighbouring towns (Tzaneen, Trichardtsdal or even Hoedspruit). Few farmers buy
seeds locally in small shops in surrounding villages or at the social pay point. This seems to be a strategic point to
make business, as people have just got their money. Many people sell part of their vegetables production or even
cereal production in small quantities at this occasion. Another important place locally is the auctions place in
Turkey zone 3, where buyers and sellers of livestock can realize their transaction once a month.
Regarding cereal production, the majority of farmers has very low level of production, and uses service of local
miller or process themselves their grain production. Few farmers are working with commercial millers, NTK and
Progress Milling, mainly for storage facility. Indeed, they do not send their whole production, but just the part they
need to store, and keep at home what they can consume before it goes bad. None of these farmers have a
transport for their production, and NTK as well as Progress Milling depots are situated in neighbouring towns. In
most cases maize is collected at home and farmers pay extra fees for transport through local suppliers. Some
prefer to hire a bakkie with other farmers to transport their harvest. Indeed, transport appears as an additional
constraint to agricultural marketing, which explains why farmers just do it when they cannot consume their
production before it rots, and thus necessitate a storage facility.
(Source: Fabre 2006)
4.6 Food security status
According to FAO food security ‘exists when all people, at all times, have physical and
economic access to sufficient, safe and nutritious food to meet their dietary needs and food
preferences for an active and healthy life’ (FAO, 1996). Within the Southern Africa
Development Community (SADC) and the New Partnership for Africa’s Development
(NEPAD) all aim at improving agricultural production and food security in Africa and
achieving the first Millennium Development Goal, which proposes to halve the number of
people living on less than US$1 per day and people suffering from hunger by 2015. In South
Page 40
Africa, the 1996 Constitution and the Reconstruction and Development Programmes (RDP)
aim at addressing issues of poverty and food security. The South African government has
tried to address these issues through multilateral, regional and national policies, such as social
grants, provision of free basic services, land reform and water allocation reform, to improve
the access of poor people to these essential resources.
South Africa produces enough food for its domestic requirements but because of huge
inequalities in the country, food security remains a key issue for the poor. The Millennium
Development Goals Mid-Term country report for South Africa shows that the percentage of
people living below R3000 per annum (in 2000 constant Rand) remained stable between 1994
and 2000 around 50% but has decreased since (Anonymous, 2007). However, in 2006 people
living below this poverty line still represent a large part of the total population at 43.2%8. The
report further indicates strong overall income growth, especially since 2002, resulting in the
rise of the income of the poorest 10 and 20 percent of the population. However, of concern is
that income inequality (as measured by Gini-coefficient) has increased from 0.665 in 1994 to
0.682 in 2000 and 0.685 in 2006. With regard to the target of eradicating hunger whose
progress is measured using severe malnutrition amongst children under-5 years of age, the
MDG mid-term country report observes a decline from 88 971 cases in 2001 to 30 082 in
2005. About 35% of South Africans experience food insecurity and vulnerability (Rule et al.,
2005).
Rural areas compare unfavourably to urban areas in terms of poverty and food insecurity.
Rural South Africa has a 14% children under weight (9% urban) and 30.1% children are
stunted compared to 19.8% in urban areas (Resnick, 2004). Food insecurity is strongly
associated with climate variability. Unpredictable rainfall patterns leave the farmers
vulnerable to droughts and starvation. Vulnerability of rural farmers is broadly defined as ‘a
function of exposure to risk and inability to cope’ (World Food Programme, 1999; Kureya
and Majele-Sibanda, 2007).
In the Limpopo Province, where 89% of the population is rural, food insecurity is a result of
lack of fertile land and water resources in order to engage in productive agricultural activities
(Berumen, 2006). About 40% of the Limpopo population suffers from food insecurity
(FANRPAN, 2007). Agricultural development is thus a potential driver for development of
the province (FANRPAN, 2007).
The study area is quite representative of other rural areas in Limpopo province from this
respect. Despite agriculture being the main activity, food security is of major concern in the
area. Reasons for low production include lack of land, no access to water, poor agricultural
support and lack of markets. About 70% of the population in Enable and Kodumela ADPs
rely on rain fed agriculture for their food (World Vision, 2005b, a). World Vision reports also
showed that 19.1% of the under five children in Enable suffered from malnutrition. In the
total population proportion of vulnerable people is estimated at 31.3 % in Enable ADP and
29.3% in Kodumela ADP.
Other serious indicators of food insecurity within the study area are provided by Nyalungu’s
survey. Several questions pertaining to food security were asked to surveyed households.
Table 26 summarizes the results.
8
Based on data from the All Media and Products Survey data; The R3000 per annum threshold is slightly above
US$1 per day in 2000 constant Rand.
Page 41
Table 26: Indicators of food insecurity in the study area
In the last 12 months, did you run short of foods that
you needed to make a meal and did not have money to
buy more?
In the last 30 days, did you ever run out of the foods
that you needed to make a meal and did not have
money to get more?
In the last 12 months, did you ever get food or borrow
money for food from friends or relatives?
In the last 12 months, did you ever send children to the
homes of friends or relatives for a meal because you
were running out of food?
In the last 12 months, did you ever serve only a few
kinds of low cost foods for several days in a row
because you could not afford anything else?
In the last 30 days, did you ever cut the size of your
meals or skip meals because there was not enough
money for food
In the last 12 months, did you ever not eat for a whole
day because there was not enough money for food?
In the last 12 months, did you ever eat less than you
should because there was not enough money to buy
food?
Did this happen in the last 30 days?
In the last 12 months, were you hungry but did not eat
because you could not afford enough food?
In the last 12 months, did you or any member of your
household lose weight because there was not enough
food to eat?
often true
I worry whether my food will run out before I got
money to buy more?
The food that we bought just did not last?
I couldn'
t afford to eat balanced meals?
no
no answer
Total
n=84
50%
32%
18%
100%
40%
44%
15%
100%
34%
27%
40%
100%
24%
30%
46%
100%
36%
14%
50%
100%
27%
19%
54%
100%
24%
17%
60%
100%
25%
8%
67%
100%
17%
13%
70%
100%
19%
8%
73%
100%
17%
10%
74%
100%
sometimes
true
never true
no answer
Total
n=84
50%
27%
12%
11%
100%
44%
56%
29%
19%
17%
14%
11%
11%
100%
100%
How often did this happen that you did not for a whole
day?
In the last 30 days how many days did it
happen that you have to cut size of meal or
skip meals?
yes
<7
almost
every
months
some
months
1-2
months
no
answer
Total
n=84
17%
5%
2%
76%
100%
8-14
15-21
>21
no
answer
Total
n=84
25%
Page 42
15%
6%
1%
52%
100%
4.7 Perceived quality of livelihood at household and community
levels
World Vision baseline survey showed that most respondents in the communal areas perceived
their livelihoods quality as being poor (World Vision, 2005b, a). This is confirmed by
Nyalungu’s survey results reported in Table 27. This contrasts with responses from the
commercial farming areas that had access to better facilities and their livelihood was
comparable to that prevailing in developed countries (Berumen, 2006). Despite the duality,
both communal and commercial farming respondents felt that there was room for
improvement in their livelihoods. The perception that the quality of livelihoods was lower in
rural areas than urban areas, resulted in most youths aspiring to migrate to urban areas in
anticipation of better living conditions.
Table 27: Household level of satisfaction with their living conditions
Number of
households
Very satisfied
1
Satisfied
29
Dissatisfied
44
Very dissatisfied
10
Total
84
(Source: Nyalungu’s survey 2005)
%
1%
35%
52%
12%
100%
4.8 Typology of farming households
The results of multivariate data analysis conducted on the consolidated database from
Nyalugu’s and Malatji’s surveys are as follows.
Principal component analysis:
The correlation matrix shows few strong correlations among variables:
- Positive relation between percentage of livestock income and livestock reason
- Negative relation between percentage of crop income and percentage of pension in
total income
- Negative relation between percentage of crop income and percentage of employment
income in total income
- Negative relation between percentage of crop income and total income
- Positive relation between savings availability, number of households assets and
livestock number
- Positive relation between livestock number and reason for keeping livestock.
The first 8 factors represent 61% of the total inertia of the data set and the first 4, 39%. The
first factor (14.2% of inertia) opposes households with savings and high total income on one
hand, and households with a large part of income from cropping on the other hand. The
second factor (10.3% of inertia) distinguishes households that diversify their crops (high
number of vegetables) and that use a high quantity of fertilisers. The third factor (7.4% of
inertia) opposes livestock-market orientated households with a high percentage of income
from livestock, from households with a high total income and high percentage of income from
employment. Finally the fourth factor (7.2% of the inertia) opposes households whose main
source of income is pension, from those that get their income mainly from formal
employment.
Page 43
Figure 11 below gives a representation of the variables in the first factorial plan (factor 1 on
the horizontal axis, factor 2 on the vertical axis).
Figure 11: Household typology - Representation of variables in the PCA factorial plan 1-2
VEGET
FERTZ
EDUC
0.62
-0.63 0.62
-0.38
LIVNB
HHASST
SEED
CREDT
CROPINC
SAVNG
IRRINC
LIVREA
HIRWO
TOTINC
LIVINC
FAMBF
OFFINC
REMT
MARK
FOSEC
EMPINC
LAND
GENDER
PENS
AGE
Cluster analysis
The dendogram of the cluster analysis performed on the first 8 factorial coordinates is
represented in Figure 12 below. Several clustering are possible: with 2 clusters (between
nodes 317 and 316), 6 clusters (between nodes 313 and 312) and with 8 clusters (between
nodes 311 and 310). We chose to keep a classification in 8 types (see Box 4). The main
characteristics of the 8 types are described in Table 28 and Table 30. Statistical differences
between types are assessed through analysis of variance (Table 30) and Chi-Square test (Table
28). As our sample is biased towards irrigation farmers, the distribution of households across
types in the sample is not representative of the distribution in the whole population. However,
we are confident that the typology gives a trustful representation of the diversity of household
types in the area.
The main factors of differentiation between types are the total income and the composition of
income (Figure 13). The richest households usually are those for which permanent job is an
important source of income (types 1, 2, and 3). Households who derive a large part of their
income from cropping are amongst the poorest types (types 7 and 8). Households with large
proportion of their income coming from pensions or remittances get an intermediate level of
income. Households with highest agricultural input use can be found in type 7 (highest
percentage of irrigation, highest seed costs and highest fertilizer use), and to a lesser extent in
types 6 and 1. Type 8 households, which are the poorest have an average level of input use.
Household head characteristics (age, education and gender) are significantly different across
types (highly significant for age and education level, less significant for gender). Distribution
Page 44
of sampled households per types and villages is given in Table 29. No significant
relationships appear between location and type of households.
Figure 12: Household typology - Dendogram of the cluster analysis operated on factorial
coordinates (Ward method)
4.8
0
170
-0.44
2 clusters
316
315
314
313
6 clusters
312
311
8 clusters
310
309
308
305
307
304
303
306
302
301
300
298
276
297 275 296 299
269
293
295
288
294
292
291
285 287
282
286
262 274
289283290
281278
284
280
279 260
261272
277 265
273258
259
271
268
270
251
267
256
266
249
250
257
240
248
239
207
244
238
245
264
254
263
235
247
236
255
2
46
237
223
204
253
206
222
205
234
230
232
243
229
231
233
228
217
220
198
202
252
242
200
221
219
203
218
197
216
201
199
227
191
193
196
195
224
241
186
210
189
188
213
190
212
226
215
225
185
214
184
187
194
192
211
175
180
208
166
168
179
181
170
172
209
169
176
171
182
164
173
167
174
177
165
183
178
162
161
163
160
Table 28: Household typology - Results of Chi-Square test on categorical variables
Variables
Credit accessibility
Savings availability
Food security
Livestock reason
Education level
Gender of head
Chi-square
65.95
55.84
19.39
103.78
102.47
29.83
Degrees of
freedom
7
7
7
21
21
21
Page 45
Test
***
***
***
***
***
*
Types
7 and (6) (yes)
3 and 5 (yes), 4 (no)
8 and 6 (no), 1 (yes)
3 (market), 5 (consumption)
2, 6 and 1 (high), 4 (low)
4 and 3 (more female)
Table 29: Household typology - Distribution of sampled households per types and villages
Types
Type 1 - very rich
households with
permanent job
Type 2 - rich
households with
permanent jobs
Type 3 - rich
households with
diversified sources of
income
Type 4 - pensioners,
medium income
Type 5 - medium
income with
remittances
Type 6 - young
families, with
pensioners, lowmedium income
Type 7 - croppers, high
level of input use,
medium-low income
Type 8 - poor croppers
without other sources
of income
Total
Sofaya
Metz
Enable
- World
Vision
project
Enable
1
2
12
4
9
8
7
3
1
Makgaung
Ha-Fanie
Madeira
Total
3
1
1
1
2
1
5
4
1
3
1
1
16
54
7
29
1
7
1
3
3
24
1
2
11
31
7
21
1
4
9
2
6
21
2
3
10
1
11
8
42
40
159
2
3
4
6
(Source: Nyalungu’s and Malatji’s combined database 2005)
3
10
This analysis shows that the diversity of sources of livelihood at household level is
generalized in the area. It also confirms that if agriculture provides and important source of
food and sometimes income to most households in the area, the least vulnerable households
complement their income with permanent jobs and social transfers (pensions, remittances,
social grants). It was not possible to include in the typology variables related to the type of
farming (rainfed, irrigation, hillside), except for the percentage of irrigation income in total
crop income.
Page 46
Box 4: Farming household types
Type 1 (node 288 - 3 households) Very rich households with permanent jobs
highest level of income, almost exclusively from employment; highest number of assets and livestock;
large land area , most of them have savings
old head, all male, high level of education
number of farming workers below average, 2/3 from family
lowest % of income from crops, average % of irrigation income, diversified crops, average level of inputs
Type 2 (node 308 - 24 households) Rich households with permanent jobs
total income above average, mostly from employment, average number of assets, low number of
livestock, small land area,
relatively young head, relatively high level of education,
large manpower, 2/3 family
only 12% of income from crops, low level of inputs, 44% of irrigation income, little diversified crops
Type 3 (node 310 - 31 households) Rich households with diversified sources of income
total income above average, diversified sources, highest % of livestock income, high number of assets
and livestock, average land area, rear livestock for market only or for market and consumption
average aged head, average level of education,
average total manpower, lower number of hired workers
only 15% of income from crops, low level of inputs, 43% of irrigation income, little diversified crops, low
level of inputs, average marketing costs
Type 4 (node 304 - 21 households) Pensioners, medium income
average total income, mostly from pensions, few assets and livestock, highest land area,
oldest head, highest % of female head, low level of education,
low total manpower
no savings
only 15% of income from crops, low to average level of inputs, 60% of irrigation income, little diversified
crops
Type 5 (node 294 - 9 households) medium income with remittances
average total income, mostly from remittances and grants, few assets but above average livestock
number, average land area,
old head, average level of education,
large family manpower, very few hired workers
only 16% of income from crops, average seed costs bur low fertilisers, 45% of irrigation income,
diversified crops
Type 6 (node 309 - 21 households) Young families, with pensioners, low to medium income
total income below average, mostly from pensions, few assets and livestock, lowest land area, most of
them have savings
young head, mostly male, high level of education,
low total manpower
only 27% of income from crops, high level of inputs, 67% of irrigation income, diversified crops
Type 7 (node 306 - 10 households) Croppers, medium-low income
below average total income, high % from crop (67%), above average number of assets and livestock,
average land area, most of them have savings
average aged head, relatively high level of education,
large total manpower, more hired workers than family workers
access to credit facilities
low to average level of inputs, 67% of irrigation income, little diversified crops
Type 8 (node 307 - 40 households) Poor croppers without other sources of income
lowest total income, mostly from crops, few assets and livestock, low land area, most of them have no
savings
average aged head, relatively high % of female head, average level of education,
average total manpower
only 88% of income from crops, average level of inputs, 49% of irrigation income, average diversification
of crops
Page 47
Figure 13: Distribution of household total income per source according to household types
Distribution of income per source
140 000
annual income (Rands)
120 000
100 000
80 000
60 000
40 000
20 000
0
Type 1
Type 2
Type 3
Type 4
Type 5
Type 6
Type 7
Type 8
Clusters
Employment Income/yr
Off farm Income
Livestock Income/yr
Crops Income/yr
Remit & grants/yr
Pension Income/yr
Distribution of income per source
annual income (Rands)
30 000
25 000
20 000
15 000
10 000
5 000
0
Type 2
Type 3
Type 4
Type 5
Type 6
Type 7
Type 8
Clusters
Employment Income/yr
Off farm Income
Livestock Income/yr
Crops Income/yr
Remit & grants/yr
Pension Income/yr
Page 48
Table 30: Household typology - Mean values and standard deviation for the whole sample and mean values for each cluster, F statistic and test
Average
Stdev
Type 1
Type 2
Type 3
Number of households
3
24
2,02
1,99
1,33
2,67
Family members working on-farm
0,81
1,12
0,67
1,17
Number of hired workers
1,30
1,54
2,03
0,94
Land area (ha)
56
82
33
19
Total seed cost
73
85
83
41
Quantity of fertilizer Kg
7
9
7
12
marketing cost
17322
20590
127220
25223
Total family income (Rands/year)
21%
32%
91%
73%
% Employment Income
3%
14%
0%
1%
% Off farm Income
2%
6%
0%
0%
% Livestock Income
38%
37%
4%
12%
% Crops Income
5%
14%
0%
3%
% Remit & grants
31%
35%
5%
12%
% Pensions income
54%
38%
59%
44%
Irrigation income / crop income
1,27
1,11
3,33
1,25
Household assets
2,61
3,59
12,1
1,2
Livestock No
2,25
1,67
3,7
1,3
Vegetables
54,4
14,1
67,3
49,4
Age
*** F test significant at 99%, ** : F test significant at 95%, no: not significant at 90%
Type 4
Type 5
Type 6
Type 7
Type 8
F statistic F test
31
21
9
21
10
40
2,48
1,05
3,44
1,38
1,80
1,90
2,53
**
0,48
0,48
0,22
0,62
2,40
0,85
5,15
***
1,56
2,34
1,22
0,86
1,21
1,01
2,38
**
19
48
66
112
121
64
4,76
***
40
44
19
89
230
96
10,78
***
7
8
6
7
1
6
1,70
no
20878
15518
14558
12023
13977
6771
39,74
***
24%
15%
0%
7%
0%
4%
39,36
***
4%
1%
2%
0%
21%
4%
2,97
***
10%
0%
0%
1%
1%
0%
18,73
***
15%
15%
16%
27%
67%
88%
60,29
***
5%
1%
53%
2%
0%
0%
50,41
***
41%
67%
29%
64%
11%
4%
21,22
***
43%
60%
45%
81%
67%
49%
2,71
**
2,06
0,71
0,89
1,00
2,20
0,80
9,37
***
6,0
0,3
4,6
1,4
4,0
1,1
19,57
***
1,5
1,6
3,6
3,2
3,9
2,4
7,81
***
55,8
67,9
60,8
47,2
50,3
51,7
6,10
***
Page 49
5 Conclusion
A better access to and better management of, water resources and services can positively
contribute towards poverty alleviation in rural South Africa especially for the poorest
households. Findings from the study area also demonstrate that water needs to be integrated
with land and other land-based resources in order to meaningfully contribute towards the
Limpopo Province’s food security goal. Investments in the former homelands are important as
these areas have been marginalised for a long period of time since they were seen as reserves
for cheap labour for the mines and farms. The current attempts by the Department of Water
Affairs and Forestry (DWAF) to develop policy on water for growth and economic
development is likely going to contribute towards such ends. However, a lot will depend on
whether water allocation process will meet the requirements of the poor majority in the
country (Cullis and van Koppen, 2007; van Koppen 2007a).
This synergy would ‘contribute to the possible expansion of small-scale irrigation to meet
equity objectives, although this may affect water availability for other sectors’ (Ntsheme
2005). Further ‘improvement on land-water management strategies through efficient farming
methods coupled with water demand management can narrow the gaps in water shortages’
(Ntsheme 2005). Water management goes further than just water re-allocation. Bad soil
management through erosion, compaction and loss of organic materials is directly linked to
low crop yields (Ntsheme 2005; cf. Rockström et al., 2003) Evidence through studies
conducted on land and water management in the semi-arid tropics indicate that there is an
opportunity to make more water available to crops through proper tillage methods (Ntsheme
2005; Rockström et al., 2003; Barron et al., 2003).
The gendered nature of agriculture and poverty in South Africa also calls for policies which
are gender sensitive. Ownership of assets such as cattle which are important for land
preparation and potential sources of information through radios and television are all tilted in
favor of male headed households who have more education. On the other hand women
comprise the majority of people working in agriculture. Agricultural policies need to be
gender sensitive in order to address the myriad of setbacks that female headed households
face in their engagement in agricultural farming in the Limpopo Province.
Drought and intra-season dry spells are some of the constraints faced by farmers in the study
area. Water harvesting technologies could contribute towards alleviating the impact of intraseasonal dry spell. Such technologies have proven to increase soil moisture and can result in
increased crop yields. There was little information on water harvesting technologies within
the Limpopo Province (ICRISAT, 2007).
Due to the impact of HIV and AIDS in rural South Africa most of the elderly households are
also looking after orphans whose parents died or are chronically ill. This seriously undermines
labour availability for agricultural production. Investments into agriculture have to take into
account the existing labour so that the elderly and female headed households are not overburdened due to increased labour requirements.
Poverty alleviation in rural South Africa also has to be much more holistic in that the study
demonstrated that a significant number of people in the study area and the Limpopo Province
at large did not have agriculture as their main source of income. Most of the people also did
not have requisite skills for absorption into the urban economies. Nationally, unemployment
Page 50
is at 25%. How can water and land based investments help contribute towards employment
creation through improving access to the market for agricultural produce coming from the
Limpopo Province for instance? What are the other economic opportunities that can be
created for those who would like to move out of farming? This study demonstrates that well
targeted land and water policies have the potential to improve the livelihoods of the poor
farmers in the Limpopo Province and South Africa at large.
These, coupled with increasing risks of land and water pollution by urban, industrial and
agricultural activities (Fox and Rockström, 2000), raise alarm for holistic land and water
management practices for sustainable utilization of these resources in order to improve rural
livelihoods since rain-fed agriculture remains in future the critical food security valve for rural
people (Rockström et al., 2003; Ntsheme 2005).
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