A Research Paper Done by - apps.worldagroforestry.org

Design For Improved Water productivity in Intensification of Agroforestry Systems using Irrigation Water from Ponds in Dry mid Highlands (Kajiado)Agroforestry Systems: Supplementary Irrigation from Farm Ponds in
Semi- Arid Olepolos Highlands in Kajiado District
A Research Paper Done by:
Amos Karanja Mwangi
Supervisors:
ii
ABSTRACT
The project was aimed at determining the best combination of crops and trees for an
intensified Agroforestry system that would maximize land productivity in a sustainable way.
Thereafter, a criteria and procedure for designing the size of pond that would fulfill the
irrigation requirements for the same Agroforestry system (optimizing the pond size) was to
be determined.
The project used supplementary irrigation and irrigation water productivity as the bases of
arguments. An Agroforestry system was modeled for the area, which included cereal, legume,
vegetable and fodder crops and trees. The total water requirement for the system was
computed to be 1400m3 of which 40m3 was to be obtained from supplemental irrigation
Rainfall and catchment characteristics were analyzed for the study area. The total available
catchment area for the pond system (both within the farm and outside) was considered as
approximately 0.5 acres. The design rainfall was based on the monthly rainfall data recoded
for a 10-year period from Kajiado Maasai Rural Trading Centre meteorological station. It was
determined to be 640 mm. Seepage was assumed to be negligible assuming the system had
lined ponds. The volume lost to evaporation was computed to be 47.5m3 from data got from
Narok meteorological station which experiences similar weather with Kajiado. The losses
expected from the drip irrigation system were taken as 9.72 m3
Pond capacity to support the Agroforestry system was computed to be 100m3. The total
rainwater harvest potential was computed 1160m3/yr. The potential rainfall was capable of
providing a continuous supply of water for the system. Water productivity for each
Agroforestry element was then computed on the basis of FAO documented expected yields.
A procedure for determination of water productivity benchmark was then established. A two-
week irrigation schedule was drawn up using water balance for the Agroforestry system. A
procedure for designing of pond capacity using water productivity benchmark was then
established. Shapes of ponds were also proposed and dimensioning procedures outlined.
iii
4 RESULTS AND DISCUSSION ..................................................................... 35 4.1 THE AGROFORESTRY SYSTEM.................................................................................35
iv
7 APPENDICES ........................................................................................... 45 7.1 CALCULATION AND ANALYSIS OF RAINFALL OCCURRENCE OF 67% PROBABILITY 45 7.2 DESIGN OF THE POND .............................................................................................46
7.2.1 Conveyance design ........................................................................................................46 7.2.2 Design of the Pond Reservoir ..........................................................................................48
Table of Tables
TABLE 1: INCREMENTAL WATER PRODUCTIVITY OF SUPPLEMENTAL IRRIGATION (KENYA)....................... 7 TABLE 2: DIFFERENT DEFINITIONS OF WATER PRODUCTIVITY ............................................................... 13 TABLE 3: CROP WATER REQUIREMENTS AND PRODUCE FOR SELECTED AFS CONSTITUENTS. .................. 20 TABLE 4: CROP WATER REQUIREMENT FOR EACH STAGE OF CROP GROWTH. ......................................... 20 TABLE 5: MONTHLY RAINFALL DISTRIBUTION AT KAJIADO MAASAI RURAL TRADING CENTRE ............. 22 TABLE 6: WATER HOLDING CAPACITY {(MM/CM) DEPTH OF SOIL} OF MAIN TEXTURE GROUPS ............... 24 TABLE 7: 10 YEAR EVAPORATION VALUES FOR NAROK METEOROLOGICAL STATION .............................. 25 TABLE 8: AVERAGE MONTHLY EVAPORATION FOR OLEPOLOS AREA ...................................................... 26 TABLE 9: RUN OFF CURVE NUMBERS ................................................................................................... 27 TABLE 10: CURVE NUMBER FOR THE AREA ........................................................................................... 29 TABLE 11: IRRIGATION VOLUMES AND WATER PRODUCTIVITY (WPI) FOR ELEMENTS ............................. 30 TABLE 12: WATER PRODUCTIVITY (WPI); EVAPORATION AND CONVEYANCE LOSSES INCLUDED............. 31 TABLE 13: PLANT AVAILABLE WATER CONTENT (PAWC) FOR A RANGE OF SOIL TYPES ......................... 32 TABLE 14: SOIL COEFFICIENT (KS) AS A FUNCTION OF DEPLETION LEVEL............................................... 32 TABLE 15: TYPICAL IRRIGATION SCHEDULE FOR THE MONTH OF FEBRUARY.......................................... 37 TABLE 16: RANKED ANNUAL RAINFALL DATA, MOGADISHU (SOMALIA) .............................................. 45 TABLE 17: MAXIMUM PERMISSIBLE VELOCITY FOR DIFFERENT TYPES OF CHANNELS .............................. 47
Table of Figures FIGURE 1: HYDROLOGICAL CYCLE......................................................................................................... 3 FIGURE 2: LOCATION OF OLEPOLOS AREA IN KAJIADO DISTRICT (SOURCE: ILRI MODIFIED)................... 4 FIGURE 3: ANNUAL RAINFALL DISTRIBUTION IN OLEPOLOS .................................................................. 23 FIGURE 4: SEASONAL CROP WATER REQUIREMENTS AND CUMULATIVE PRECIPITATION ......................... 24 FIGURE 5: SUGGESTED FARM PLAN...................................................................................................... 35 FIGURE 6: ROPE AND WASHER PUMP .................................................................................................... 37 FIGURE 7: CHANNEL ............................................................................................................................ 46 FIGURE 8: TRAPEZOIDAL SHAPE DRAWING OF THE POND. (PLAN AND SIDE VIEW).................................. 48
vi
CIG Common Interest Group
IR Irrigation requirement
RWH Rainwater Harvesting
SSA Sub-Saharan Africa
vii
ACKNOWLEDGEMENTS
I express my sincere gratitude to my supervisors at The World Agroforestry Centre (ICRAF),
SearNet, Mr. Maimbo Mabanga Malesu and Alex Oduor for their guidance, support and
positive criticism and above all for providing most of my reference material, software that
contributed to the research and preparation of this report. I thank them for their patience in
providing direction and assistance despite their busy schedules.
I am also very grateful to Prof. E. K. Biamah of the University of Nairobi, Environmental and
Biosystems Engineering Department for support in acquisition of much needed data for the
report and also for the incessant goodwill throughout the research project.
I am grateful to other individual lecturers from the Environmental and Biosytems
Engineering department, University of Nairobi who also gave advice on various components
of the project.
I am also sincerely grateful to the experts who agreed to do peer review of the content of this
report. These are among others: Dr. Stephen Ngigi
I am thankful to the personnel of Kenya Meteorological Department, for agreeing to provide
the information that was requested in as much as the institutions policies would allow.
I thank my colleges that I worked closely with while at ICRAF for their support and
encouragement.
In conclusion I extend my sincere thanks to my family, friends, and fellow students for their
constant encouragement and best wishes.
1
1.1 Water productivity
Water productivity is the ratio of the net benefits from crop, forestry, fishery, livestock, and
mixed agricultural systems to the amount of water required to produce those benefits. In its
broadest sense it reflects the objectives of producing more food, income, livelihoods, and
ecological benefits at less social and environmental cost per unit of water used, where water
use means either water delivered to a use or depleted by a use. Put simply, it means growing
more food or gaining more benefits with less water. Physical water productivity is defined as
the ratio of the mass of agricultural output to the amount of water used, and economic
productivity is defined as the value derived per unit of water used. Water productivity is also
sometimes measured specifically for crops (crop water productivity) and livestock (livestock
water productivity). Increasing water productivity holds the key to future water scarcity
challenges. Without further improvements in water productivity or major shifts in production
patterns, the amount of water used for agriculture, industrial and domestic activities will
increase by 60–90 percent by 2050, depending on population, incomes and assumptions about
water requirements for the environment. In agriculture alone, the total volume of water used
in crop production would be 11 000–13 500 km3, almost double the 7 130 km3 of today.
Cotton production, a high-demand agricultural practice, is projected to grow by 1.5 percent
annually, and a further burden is expected to come from increased demand for biofuel. World
energy demand will rise by 50 percent, and two-thirds of this demand will come from
developing countries.
There are important reasons to improve agricultural water productivity. These are among
others:
♦ To meet the rising demand for food from a growing, wealthier, and increasingly
urbanized population, in light of water scarcity;
♦ To respond to pressures to reallocate water from low return ventures to higher return
ones and to ensure that water is available for other environmental uses;
♦ To contribute to poverty reduction and economic growth. For the rural poor more
productive use of water can mean better nutrition for families, more income,
productive employment, and greater equity and
♦ Targeting high water productivity can reduce investment costs by reducing the
amount of water that has to be withdrawn.
2
Water productivity in agriculture has increased steadily in recent decades, largely owing to
increasing crop yields, and the potential exists for further increase. However, the pace of such
increase will vary according to the type of policies and investments put in place, with
substantial variations in the impact on the environment and livelihoods of rural populations
1.2 Intensification of Agroforestry
Intensification in agriculture in essence implies the use of farms, in such a way that maximum
use of resources is achieved. This can be achieved by incorporating animals, trees and crops
in a farming systems. This has an effect of concentrating the use of the land to a lesser
portion, which is more profitable. Intensification of Agroforestry therefore implies the use of
trees livestock and crops in farms that get maximum utility of the space available and
equivalent returns.
1.3 RWH ponds
Rainwater harvesting has its basis on the hydrological cycle (Figure 1). The reason behind
rainwater harvesting is to store rainwater so that it can be available in times when there is no
rain. RWH technologies are diverse and include pans, ponds, tanks, dams, trenches and holes
etc. RWH ponds are open surface reservoirs constructed by communities or individual
farmers. They consist of a catchment area, inlet canal and an appropriate live fencing to break
the wind and protect animals and humans. Communal ponds are estimated to have surface
areas of upto 1000m2 with depths ranging from 2.5 to 6m. Embankment heights range from 2
to 3m. The individual farm ponds can have as much as 130m3 capacities.
Figure 1: Hydrological Cycle
1.4 Description of the Study Area- Kajiado
Kajiado is located in latitude 10 51’ 05” and longitude 360 46’ 60” in the Rift Valley
province of Kenya. The approximate population over a 7km radius is 2100 giving an
approximate population of 508,800. Kajiado has a geographic area of 22000 km2. The land
varies in altitude from about 500 meters around Lake Magadi to about 2,500 meters in the
Ngong Hills area. The diverse physiography of the study area has resulted in a wide range of
soils, most of which are deep and fine-textured. These are mostly sandy clays. On the
volcanic uplands and plains the soils range from stony Cambisols on the upper slopes to dark,
cracking Vertisols in bottomlands and valleys. In the Chyulu Hills the main soils are
Lithosols on lava flows, Andosols on coarse ash deposits and deep Luvisols on the flatter
plains. Soils overlying gneissic basement complex are generally sandy, well drained and
susceptible to erosion. The plains in the central, driest part of Mbirikani feature dark clays
with vertic and saline-sodic properties (Touber,1983).
The weather data that has been collected in Kajiado area over sometime gives an average
rainfall of between 500mm and 1250mm. Nevertheless the district does not have adequate
surface water resources for livestock and human consumption or irrigation. The occurrence of
the ground water in the district is mainly influenced by climate and topography as well as
origin of underlying parent rock. The other alternative source of water for domestic and
livestock are sub-surface resources such as water pans, ponds, dams and shallow wells. The
amount of surface water varies from area to area.
3
The project area, Olepolos, is located in Kisamis sub-location of Keekonyokie-Central
location in Kajiado district (Figure 2).
Figure 2: Location of Olepolos area in Kajiado District (Source: ILRI Modified)
The main ethnic community in the area is Maasai. Semi-nomadic pastoralism has been the
traditional Maasai mode of life, practiced on land that was communally owned. The Maasai
keep cattle, goats, sheep and donkeys. However, this lifestyle has undergone changes due to
on-going land adjudication and sub-division of group ranches leading to individual land
tenure system. This has increased adoption of farming though the advancements in this are
slow.
Sub-division of land, settlement, establishment of National Reserves and population increase
have disrupted Maasai’s traditional and successful nomadic system of grazing. These
constraints have been exacerbated by poor rainfall experienced in the areas of late.
Availability of domestic, farming and livestock water is a major constraint in Olepolos.
Women and children have to trek for several hours to fetch water for domestic use and
herders move cattle long distances in search of pasture and water.
4
UNEP and Regional Land Management Unit have sponsored rainwater harvesting activities
among the nomadic Maasai community in the semi arid Olepolos in Kajiado district. The
projects on RWH were started by UNEP in 2002 with a pilot project known as “Empowering
Women in Rainwater Harvesting” which was highly successful.
The project objectives were as follows:
1. To empower the community through the Common Interest Groups (CIGs) to run the
groups activities which affect them at household level,
2. To improve the economic status of women by provision of the necessary skills and
seed money for the revolving fund in the microfinance systems,
3. To improve the environmental status in the area through installation of soil and water
conservation measures, tree planting, tree nurseries and improved stoves and finally
4. To improve water and sanitation through installation of water storage facilities, water
ponds, rehabilitation of springs.
Later, RELMA in ICRAF and UNEP came together through a Memorandum of
Understanding (MoU) and sponsored the RWH project in Kajiado further in 2005. There has
been notable advancement especially in 2007 when they were able to construct two lined
ponds to deal with seepage out of the four that had been proposed. These were in Agnes
Kimer and Levina’s farms. The two others are in Sarah Setek and Jerusa Lasoi’s farms.
1.6 Zone classification of area
Kajiado can be classified to be in the sub humid and semi arid agro climatic zones. It is
notable that the major needs for farmers in ASAL are firewood, fencing, construction
materials, fodder during the dry season, maintaining soil fertility and income generation.
Therefore any innovations in this area should strive to deal with these needs.
The study used Kajiado as a representative for the semi arid and sub humid highlands agro
climatic zone. Though Kajiado lies in the two agro-climatic zones, the semi arid zone
(Olepolos area) was be considered. This was as documented by Azene Bekele (2007) since
the Agroforestry innovations can be adopted in similar agro-climatic zones across Africa with
5
slight adjustments. The agro-climatic zones are developed using variables of rainfall and
altitude.
1.7 Problem statement
Presently 13% of the world’s population doesn’t have access to enough food to live a healthy
and productive life yet the ability, technology and resources to produce enough food for
everyone exist. Most of this population lives in arid and semi arid areas. In Kenya 50% of the
population lives below the poverty line. Of this population, over 60% lives below the
poverty line (FAOSTATS). Following the continued need for improved agriculture and the
scarcity of rain in arid and semi- arid areas in the country, there is need to construct water
harvesting structures for irrigation e.g. ponds. Kajiado district does not have adequate surface
water resources for livestock and human consumption or irrigation. The occurrence of the
ground water in the district is mainly influenced by climate and topography as well as origin
of underlying parent rock. The other alternative source of water for domestic and livestock
are sub-surface resources such as water pans, ponds, dams and shallow wells. The amount of
surface water varies from area to area.
It is notable that land use in Kajiado is mainly in four areas. These are: livestock herding,
forests, rain fed and irrigated agriculture. Semi-nomadic pastoralism has been the traditional
Maasai mode of life, practiced on land that was communally owned. However, this lifestyle
has undergone changes due to on-going land adjudication and sub-division of group ranches
leading to individual land tenure system. This means that the otherwise pastoralist tendencies
have been suppressed. In spite of the increased adoption in agriculture, the Maasai have not
completely utilized the available options. Adopting Agroforestry systems would help come
up with sustainable land use. It is notable that water productivity in smallholder agriculture
systems in Kenya in 2000/01 was at an unacceptable incremental level of 19.9kg ha-1mm-1
(Table 1)
Fertilizer Application
LR1999 (Kg ha-1 mm-1)
0F 6.0 6.3 -9.3 4.2 19.9
30F 3.5 4.8 32.7 5.5 -17.2
80F 2.8 4. -19.1 7.0 -8.1
Source: UNESCO-IHE Institute for Water Education, Delft, The Netherlands
Land productivity has also been very low. For instance, maize production is less than 1 ton
per hectare and has remained low for over four decades. One way to abate this problem is to
improve land and water productivity through intensification of Agroforestry systems. This
proves a challenge when giving exact sizing, operation & maintenance and efficient water
utilization of the ponds for such Agroforestry systems.
1.8 Problem Justification
Lands in Africa continue to be fragmented. In Kenya, the situation is not different. With this
fragmentation, there is need to apply land use practices which maximize productivity. It has
been documented that the most efficient farming system is an integrated one and thus an
Agroforestry system would be apt to maximize on the use of the land. Since ASAL areas are
fragile environments, there is need to conduct research on improving water productivity and
designing sustainable rain water-harvesting systems. Solutions to this have been innovated
and the on farm runoff harvesting ponds stand out as one of the affordable options for small-
scale farmers.
Success in improving water productivity in intensified Agroforestry systems would go a long
way in dealing with the current increase in food insecurity and raise the living standards of
the otherwise poor farmers. Also, such systems if implemented on a large scale would go a
long way in reclaiming lands under desertification and thus mitigate climate change.
7
1.9 Hypothesis and Objectives
The research hypothesis is ‘determination of a water productivity benchmark for evaluating
intensified Agroforestry systems is possible and can contribute to designing suitable runoff
ponds’.
The following are the research objectives:
1. To determine a combination of crops and trees for an intensified Agroforestry system
that maximizes land productivity in a sustainable way,
2. To come up with a criteria and procedure for designing the size of pond that will
fulfill the irrigation requirements for the same Agroforestry system (optimizing the
pond size) and
1.10 Research Questions
• What is the best Agroforestry system that would be practical in a semi arid zone of
Kenya?
• What is the water productivity of this system?
• Is it possible to harvest that water and what is the size of the pond that will be
required?
• What is the supplemental irrigation that would be required to sustain such a system?
• What is the benchmark for assessing water productivity in such an Agroforestry
system?
• How will the benchmark of water productivity be used in designing run-off ponds?
8
Most countries in Sub-Saharan Africa (SSA) are experiencing profound socio-economic and
political problems the most dramatic being food crises and disruptive conflicts (Ngigi, 2003).
Presently 13% of the world’s population doesn’t have access to enough food to live a healthy
and productive life yet the ability, technology and resources to produce enough food for
everyone exist. The world is also faced by climatic change and efforts are being made
everywhere to come up with ways of mitigating it. Solutions have been sought to eradicate
the food crises. One of these is intensification of crop and livestock production systems.
2.2 Intensification of Agroforestry systems
Various modes of intensification have been employed depending on what the focus is on e.g.
ecological intensification, crop–animal intensification, etc. According to Kenneth G.
Cassmann yield per unit time and land has increased markedly during the past 30 years in
intensification systems, a result of intensified crop management. Meeting future food demand
while minimizing expansion of cultivated area, will therefore primarily depend on continued
intensification of these same crop production systems already in place. It is notable that
increased yield from intensification of wheat, rice, and maize systems contributed 79–96% of
the total increase in the global supply of wheat, rice, and maize since 1967. In the USA,
although wheat area has remained relatively constant, total maize area increased by 30
million hectares (ha), which is 12% greater than the total USA maize area in 1997
(http://apps.fao.org/).
Continually, tracts of land are becoming smaller, yet the population is increasing and hence
the food demand. It has been established that an additional 446 million ha of land would be
required to achieve 1997 levels of wheat, rice, and maize production at 1967 yield levels,
which represents 3-fold greater more area than the present total area of wheat, rice, and maize
in the USA and China combined. Hence, intensification of crop production (and therefore
Agroforestry) systems has spared expansion of agriculture into natural ecosystems and
marginal land prone to degradation from intensive cropping.
As in many areas of the developing world, Kenya's semi-arid regions are under increasing
ecological pressure due to a growing population and a lack of arable land. In areas of high
population density and small land holdings, Agroforestry plays an important role in many
farmers' economic strategies. Improved Agroforestry techniques help to mitigate the effects
of deforestation, land depletion and rural poverty. Understandably, many farmers are
concerned principally with meeting household needs using tree products (e.g. fuel wood,
building material and fruit), and only secondarily in potential cash benefits from trees. Using
tree products for local consumption has the additional benefit of not having to rely on
uncertain market conditions for cash crops. This strategy minimizes risk and contributes to
the overall diversification of family farms.
As part of a broader effort to address the issues involved in promoting sustainable
Agroforestry in semi-arid areas, Kenya's Embu District was the focus of a study undertaken
by the International Centre for Research in Agroforestry (ICRAF). In this particular study,
the objective was to improve the nutrition, income, and general welfare of low-resource
households in Embu. Crops such as tea, coffee, and a variety of foods are grown in the
District alongside a number of tree species used for firewood, building materials, fruit,
medicine, fencing and fodder, or sold for cash. The study concentrated on developing and
testing a model of inter-institutional collaboration in Agroforestry research. Of particular
interest were the multiple linkages that exist between farmers and the many actors within the
agricultural knowledge and information system. The benefit accrued from such systems was
enormous.
In Uganda, the Agroforestry programme has been solving problems by generating
technologies that enhance integration of trees on farms for increased production and
environmental sustainability. The motivation is that trees can achieve this owing to the
multiplicity of products and services they provide (timber, food, fuel wood, poles fodder,
medicine, spices, gums, raisins, soil fertility improvement, erosion control wind breaks, shade
etc). From this programme, a number of technologies have been generated. These include:
• From a number of exotic and indigenous upper storey trees species that have been
screened, promising ones have been identified for boundary or scattered planting in
cropland for different areas of the country. These include Grevillea robusta, Cedrela
10
serrata, Casuarina spp. and Markhamia lutea (low and mid altitudes) and Alnus
acuminata, Grevillea robusta and Acacia melanoxylon (high altitudes).
• Contour hedgerows of Calliandra calothyrsus have been found to reduce soil erosion
by about 60% in the sloping areas of Kabale.
• Block plantings of nitrogen-fixing shrubs such as Calliandra calothyrsus and
Leucaena diversification on degraded fields in Kabale resulted in restoration of crop
production by about 50%.
• Optimum management of tree crowns to reduce competition to crops and maximize
wood production
• Information has been generated on nursery economics under different nursery
management techniques.
Also the use of boundary tree planting, contour tree planting and tree fodder banks has been
adopted for increased intensification of these systems. In the example of Tanzania,
alternative agroforestry technologies such as rotational woodlots, improved fallows, fodder
banks and relay cropping systems for semi-arid areas have been tested in Morogoro,
Shinyanga and Tabora regions. The World Agroforestry Centre (then ICRAF) through
Hifadhi Ardhi Shinyanga (HASHI) Project have for the past two decades introduced
agroforestry to the semi-arid areas of Tanzania in Shinyanga and Tabora in the Acacia and
miombo woodlands. Agroforestry technologies introduced included: fodder banks, improved
fallows, rotational woodlots and domestication of indigenous fruits and medicinal plants. The
World Agroforestry Centre (ICRAF) in Kenya is now looking at the agroforestry systems in
Kenya and seeking to improve their productivity with the results from what has been obtained
before in other areas.
2.3 Water Productivity
In production of the Agroforestry systems it is important to note that what is required at the
end of the day is the extent of productivity of the systems. World over, the trend is towards
increased productivity. Ways of assessing the productivity of a system therefore become very
important. One of the ways is by use of the crop response to water. Some of the ways the
response is applied are: to evaluate water use efficiency and crop water productivity under
prevailing rain patterns and traditional farm practices and defining with farmers options for
improvement and appropriate strategies to optimize yields and to reduce risks of crop failure.
Water use efficiency is generally understood to be a measure of the output obtainable from a
11
given input. It is mainly looked at in terms of: conveyance, distribution and field application
efficiencies. The water use efficiency concept provides little information on the amount of
food that can be produced with an amount of available water.
It is notable that the overall aim of agricultural water management is to enable farm managers
achieve high levels of irrigation efficiencies, water use efficiencies and crop productivities
that will maximize return on investments in rain fed and irrigated conditions under adequate
or deficit water supply. In this respect, water productivity, defined, as the amount of food
produced per unit volume of water used is more useful. Because the water used may have
various components (evaporation, transpiration, gross inflow, net inflow, etc.), it is important
to specify which components are included when calculating water productivity. To quantify
the water productivity, there are formulae for calculating the physical productivity of crops
that have been recorded. However effects of rain fed and irrigation water shortages on crop
yield and productivity can be predicted with acceptable accuracy provided reliable Ky factors
are available and an accurate quantification of maximum agronomic yield Ym can be made.
It has been established that the recorded values of Ky factors are not as accurate and need to
be revised. In the case of Ym, it is now possible to offer a choice of several standard methods
ranging from a static method, to a semi-dynamic or to a process-based method. Alternatively
it is now possible to predict actual crop yield under deficit water supply, Ya, directly using
process-based simulation models such as CERES or CROPSYST. (Amir Kassam and Martin
Smith, 2001)
According to FAO statistics in 438 projects in 57 countries, there are 172,389 farmers who
have embraced smallholder irrigation resulting in 357,296 ha under sustainable agriculture.
This has resulted in 16.9% increase in crop yields. This shows that the use of water for
production is vital and that the means of introducing the water in the field is of great
importance. With water shortages emerging as a constraint on food production growth, the
world needs an effort to raise water productivity similar to the one that nearly tripled land
productivity during the last half of the twentieth century. Worldwide, average irrigation water
productivity is now roughly 1 kilogram of grain per ton of water used. Since it takes 1,000
tons of water to produce 1 ton of grain, it is not surprising that 70 percent of world water use
is devoted to irrigation. Thus, raising irrigation efficiency is central to raising water
productivity overall. Water productivity can be further defined in several ways according to
the purpose, scale and domain of analysis (Molden et al., 2001; Bastiaanssen et al., 2003).
12
Stakeholder Definition Scale Target
Plant physiologist Dry matter/ transpiration Plant Utilize light and water
resources
Farmer Yield / irrigation Field Maximize income
Irrigation engineer Yield/canal water supply Irrigation
scheme
Policy maker $ / Available water River basin Maximize profits
According to Dang et al. (2001) the water productivity is defined in three different ways. The
water productivity per unit of evapotranspiration (WPET) is the mass of crop production
divided by the total mass of water transpired by the crop and lost from the soil. The water
productivity per unit of irrigation (WPI) is the crop production divided by irrigation flow.
The water productivity per unit of gross inflow (WPG) is the crop production divided by the
rain plus irrigation flow. Water productivity with reference to evapotranspiration WPET takes
into accounts only water evaporated or transpired and is therefore focused on plant behaviour
whereas WPI and WPG include not only ET but also water used in other ways for crop
products and water that is wasted. Ximing Cai et al. (2003) reported that water productivity
of irrigated crops is higher than that of rain fed crops in developing countries, and is lower in
developed countries.
Water productivity can be improved by introducing precision irrigation. This involves
applying precise quantities of water to the root zone when required. This includes, for
example, application of a small amount of water during a dry spell to overcome plant stress at
a critical growth stage. Technologies for achieving high levels of control are already
available. One example is the micro-drip technique for high frequency, low volume
application of water and nutrients to specific crop areas. Precision irrigation reduces
unproductive depletion of water from the soil. Applying water directly to the root zone
increases transpiration—due to improved contact between water and roots—and reduces soil
evaporation and deep percolation. This increases water productivity. Furthermore, improved
control over the timing of application makes it easy to implement supplementary irrigation
strategically to overcome seasonal dry spells. Work has showed that water productivity in
13
rain fed wheat production in Jordan could be increased from 0.33 kg/m3 to 3 kg/m3 by
strategic supplementary irrigation.
To be able to evaluate using water productivity, the source of water for the Agroforestry
system in question must be determined. Semi-arid agro-ecosystems are characterized by
erratic rainfall and high evaporation rates leading to unreliable agricultural production. Total
seasonal rainfall could be enough to sustain crop production, but its distribution and the
occurrence of intra-season dry spells (ISDS) and off-season dry spells (ODS) affect crop
production. Frequency analysis of rainfall reveals that there is 80% probability of occurrence
of dry spells exceeding 10 and 12 days during the long rains and short rains, respectively in
semi arid areas. The occurrence of off-season (after rainfall cessation) dry spells is more
pronounced than intra-seasonal (within the rainy season) dry spells. The length of intra-
seasonal (10-15 days) is less than off-season dry spells (20-30 days). The occurrences of off-
season dry spells coincide with the critical crop growth stage, in particular flowering and
yield formation stages. This therefore necessitates the harvesting of rainfall and/or runoff and
applying the water through irrigation in the otherwise dry area.
2.4 Rainwater Harvesting (RWH)
According to Oduor and Gadain (2007), Rain Water Harvesting systems (RWH) for Arid and
Semi Arid Lands (ASAL) can be divided into crop production, livestock production and
domestic water & conservation systems. RWH systems can be classified into in-situ water
conservation practices and run-off based systems, Ngigi (2003). It has been established that
the technologies that benefit from direct runoff and that are used for full or supplementary
irrigation of crop production systems are earth dams, ponds, and flood/spate water among
others.
GIS maps have been done in Africa and a determination of the RWH potent areas has been
established through thematic maps. According to Mati et al. (2007), surface runoff harvesting
is a site-specific intervention’ and therefore potentially possible almost anywhere so long as it
rains. Also Kenya is a relatively dry country with population concentrated around the wetter
central highlands and the Rift Valley. Development domains for RWH in Kenya reveal a
pattern, especially for roof top and runoff harvesting. Sand dams are predicted to be
potentially applicable in the dry areas. Bearing this in mind, it is understood that the
population that lives in the ASAL is majorly low income and would thus find it hard to
14
very successful. However, small scale, land user oriented innovations and interventions seem
to offer the much needed sustainable solutions to food insecurity if used for irrigation (Ngigi,
2003). A low cost technique that is more suited to the conditions, that has been adopted and
that would meet the needs of the inhabitants of these areas is the pond.
Ponds are an easy and cheap way of temporarily storing rainwater. These can be made by
digging into the ground or by creating an embankment on the ground into which rainwater
runoff collects. This water can be used for livestock or for irrigation. (Oduor and Gadain,
2007) RWH and storage in small ponds and pans draws the water from collection of runoff
from open surfaces, such as roads, home compounds, hillsides, open-pasture lands and may
also include runoff from watercourses and gullies. Therefore this is an intervention that could
be implemented almost anywhere, so long as local site conditions permit.
Rainwater harvesting (RWH) and management, especially on-farm storage ponds for
supplemental irrigation offers an opportunity to mitigate the recurrent dry spells. Farm ponds
being small runoff storage structures have capacities ranging from 30 m3 to 130 m3 used
mainly for supplemental irrigation of kitchen gardens, and sometimes for domestic and
livestock water supply (Ngigi, 2004).
Ngigi’s findings on the Laikipia area where rainwater-harvesting ponds have been adopted
cite the use of such ponds in the cultivation of crops, specifically cabbages in kitchen gardens
alongside livestock farming. In another of the studies in Kajiado, Sang & Wambui (2006)
established that the ponds have been used for planting of trees alongside livestock farming.
The benefits gained from planting the trees by use of the RWH ponds have led to improved
living conditions of the people in the area. The Kenya intensified Social Forestry Project in
Semi-Arid Areas Impact Assessment Report of 2007 cited the financial and economic
analysis of wood products, fruit orchards and food crops. The conclusion from this study
showed a great financial and environmental gain in the growth of food crops and fruit trees
separately. From this, it is therefore apparent that Agroforestry systems would be of higher
financial gain assuming that the input of crops and trees is as if they were grown individually.
On another part, although very useful, many ponds often fail because they lose water to
seepage and evaporation. Some tend to silt. (Oduor and Gadain, 2007) According to GIS
15
studies in Africa in general, runoff harvesting into ponds depends not only on run-off
generated but also on soil type and geology, especially to avoid seepage problems. As there
has not been continent-wide spatial data in Africa showing the relevant soil properties, e.g.
low permeability, the aspect of soil types could not be incorporated in the thematic maps that
have been developed. The thematic maps that have been developed should be used bearing in
mind that seepage can be controlled in water pans/pond through different interventions; a fact
that has been left out in the development of the maps.
Taking a case study in Kenya; the Lare experience, some hindrances to adoption of rainwater
harvesting have been identified. These include high seepage rate, tank sizing, cost of
constructing tanks and ponds, unequalled output for the water harvested in the ponds and
excessive evapotranspiration losses. Significant water losses through seepage and
evaporation, have accounted on average for 30-50% of the stored runoff. Such shortcomings
of some rainwater harvesting systems have discouraged further adoption in the locality. On
the other hand, success of the design would hasten adoption by slow-adopters. Also notable is
the issue raised by Ngigi, (2004) of the ineffectiveness of ponds due to seepage and
evaporation losses in the ponds constructed in Laikipia, Kenya. There is need therefore to
introduce cheap means of reducing seepage and evaporation losses. Some of the inputs given
by key informants said that there was need to plant trees, which provide shade and reduce
wind influence thus reduce these losses. Therefore appropriate Agroforestry trees should be
identified, which have direct economic benefit to the farmers (Malesu et al., 2006).
Ponds have been used in Kenya in areas like Laikipia, Lare, Kajiado, Kitui, among others.
This means that there is no problem in terms of introduction of new technology and
community adoption. Field evaluations have revealed that on-farm rainwater harvesting
ponds range from 30 m3 to 120 m3 and catchment areas vary from 0.3 to 2 ha in Kenya. In
other places however e.g. in Tanzania, Ethiopia and Rwanda, the ponds have been designed
to hold larger volumes. The success of ponds has been experienced in Kajiado where two
lined ponds are already functional in Agnes’ and Levinar’s farms. The ponds are used for
farming in spite of the pastoralist background of the community living there. There is concern
on the much that will be achieved in terms of productivity.
There is need therefore to have a benchmark or evaluation of a pond in terms of how much
water is harvested and the corresponding water productivity attained form the Agroforestry
16
system elements. It is with this in mind that this paper seeks to bring to light “ Improved
Water Productivity and Intensification of Agroforestry Systems using Irrigation Water from
Ponds in Dry mid highlands (Kajiado)”. Also of interest will be to seek and recommend
cheap and environmentally sound ways of reducing losses form ponds and an irrigation
system that would best utilize the harvested water. This will be achieved by seeking to come
up with a criterion and procedure for determining the size of pond that will fulfill the
irrigation requirements for a particular area (optimizing the pond size), modeling an
Agroforestry system and determining water productivity in intensified Agroforestry systems
that best utilize water from the ponds.
17
3.1 Site Selection
There is a wide range of soils in the Kajiado area most of which are deep and fine textured.
On the uplands and plains the soils are cambisols and vertisols. In other places soils overlying
gneissic basement complex are generally sandy, well drained and susceptible to erosion. The
driest parts feature dark clays with vertic and saline-sodic properties (Touber). Mainly the
soils in this area can be classified as sandy clays. Open grasslands predominate in the Athi-
Kapiti Plains and many parts of the Amboseli ecozone. Bush and woodland are found mostly
in the Central Hills and in the western part of the Amboseli ecozone. Forest is rare and mostly
confined to isolated remnants on hill crests and on the lava flows in the Chyulu range.
The district does not have adequate surface water resources for livestock and human
consumption or irrigation. The occurrence of the ground water in the district is mainly
influenced by climate and topography as well as origin of underlying parent rock. The other
alternative source of water for domestic and livestock are sub-surface resources such as water
pans, ponds, dams and shallow wells. The amount of surface water varies from area to area.
The area has on average farms size ranging from 0.47 - 1.34ha. Olepolos was chosen to
represent the dry semi arid areas of Kajiado. In spite of the relatively large sizes of the farms
in Kajiado and Olepolos in particular, only a very small fraction is used for agriculture. These
fractions are about 0.5 acres. Therefore, the size of the farm in this study was assumed to be
0.5.acres (approximately 2000 m2) for convenience.
3.2 Determine Best combination for an irrigated Agroforestry system
The Agroforestry systems may be classified as crop-tree, tree-livestock or a combination of
tree-crop-livestock. The AFS chosen in this study was a tree-crop AFS which would utilize
pond water for supplemental irrigation. The element selection was done to ensure that the
components of this system would ensure maximization of the land available and giving of
products that would benefit the farmers who embraced the system (i.e. intensification). The
criterion used for selecting these constituents was quantitative. The elements selected for use
in this system were:
• Tree nursery for fruit trees (Olive)
• Fruit trees (olives)
• Fodder crops (Napier)
Benefits from the elements would be:
Olive seedlings
This may be used by the farmers who adapt this system for expansion in other areas and/or
selling them to other farmers who adapt this system.
Olive trees
They may be used to supplement the diets of the community living there and also as income
earners. Fruits have been recorded to have high profit returns. The trees would also serve as
environmental conditioners and would be used as windbreakers to the pond, which would be
close by.
Napier
These will be used as fodder to feed the livestock of the community in this place, which is
predominantly a pastoralist community. This will translate to increased produce from the
livestock, which may turn into great income earners.
Vegetables (tomatoes)
This would be used majorly to supplement the diet of the community.
Legumes (beans)
These would be used as part of the diet of the community and also as nitrogen fixers in the
soil.
Cereals (sorghum)
Would be used as part of the diet and also, if the produce is improved, then they could be sold
to generate income.
19
Values of yield of crop and crop water requirement were obtained from already documented
values by FAO (Table 3). Other AFS elements may be selected depending on their
appropriateness.
Table 3: Crop water requirements and produce for selected AFS constituents.
Product Annual crop water requirements (mm)
Crop water Requirement (m3)
Produce quantity (kg/ha)
Fruit Tree (olive) inclusive of tree nursery
800 640
Fodder (Napier)
700 200
Vegetables (tomatoes)
Legumes (beans)
Cereals (sorghum)
Totals 0.5
There was need to look at the CWR for the crops according to the growth stage so as to know
how much water would be required at certain times and to help in the irrigation scheduling
(Table 4). Table 4: Crop water Requirement for each stage of crop growth.
Item Cumulative length in days per stage Crop water requirements (mm)
Olive 0 0
20
Item Cumulative length in days per stage Crop water requirements (mm)
60 117
25 53
55 116
100 172
130 124
3.4 Determine the rainfall amount and its distribution in the area
The Kajiado district has a bimodal rainfall pattern. The short rains fall between October and
December while the long rains fall between March and May. Annual rainfall in Kajiado
District is strongly influenced by altitude. Loitokitok, which has a high elevation, has the
highest average rainfall of 1,250mm while Magadi and Lake Amboseli with the lowest
elevations have the lowest annual average rainfall of about 500mm. Heavy rains also occur
around Ngong Hills, Chyulu Hills and Nguruman Escarpment. The rainfall pattern on the
slopes of Mt. Kilimanjaro (Loitokitok region) is, however different in that, the October to
December rainfall is more than the March to May rainfall.
The average rainfall of 875mm would have been used to determine the volume of potential
harvestable water. However, due to the inaccuracy of this average rainfall (875mm), monthly
rainfall data for Olepolos over 30 years was sought from the Kenya Meteorological
Department headquarters (KMT). Owing to the revised policies of the institution, only 10
years data was obtained. Also it was appropriate to get data from several stations in Kajiado
but there was only one functional station i.e. Kajiado Maasai Rural Trading Centre. Therefore
21
the calculations made only include one station and bear some errors as regards accuracy of
predictions. Data from this station is as shown in Table 5.
Table 5: Monthly Rainfall distribution at Kajiado Maasai Rural Trading Centre
Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
1997 1.5 0 12 96 0 0 53.3 0 178 178 158
1999 7 13.2 189 27.7 0 0 0 10 0 38 131 59.5
2000 2 0 2.9 42.6 6.6 3.4 0 9.5 0 21 64.9 71.5
2001 282 11.4 246 116 12 11.2 21 0 0 2.6 222 2.9
2002 73 24.6 67 159 234 0 0 4 12 99 78 106
2003 3 79 17.9 83.1 179 0 2.6 20.3 7.2 28 75.7
2004 73.1 81.8 105 51 0 0 0 18.3 43 74.8 60.2
2005 93.1 21.2 127 208 74 0 1.5 1.4 0 41 40.9 0
2006 0 32.3 109 141 48 0 17.9 1.8 19 170 212
Mean 59 27 95 110 78 2 3 13 4 52 115 84
Source: Kenya Meteorological Department.
The total of these monthly averages is 637.9958mm approximately 640 mm. It is important to
note that 67% probability of occurrence of rainfall was not used since the data obtained was
only for 10 years. If more data was available, the design rainfall would have been calculated
using the procedure in Appendix 1.
Therefore the total volume over an area = Area of farm x rainfall experienced in the area
= 2000 x 0.64
Hence the rainfall volume on the farm area is 1280m3
22
3.5 Determine the volume of supplementary water from the pond.
This was obtained by subtracting the rainfall from the crop water requirement since it is
considered as the supplemental irrigation requirement. Therefore
SIR Volume = Total crop water requirement, Σ (CWR) – rainfall volume
Σ CWR = (640+200+100+160+220) = 1320 m3
Supplementary Irrigation Volume (SIR is therefore 40 m3.
40m3 is expected to be harvested from the excess of what will be taken up by the plants.
Figure 4 shows how the cumulative rain and CWR for distinct growth stages relate.
23
-100
0
100
200
300
400
500
600
700
Growth duration in days
3.6 Establish seepage losses.
Table 6: Water holding capacity {(mm/cm) depth of soil} of main texture groups
Figures are averages and vary with structure and organic matter differences Texture Field capacity Wilting point Available water Coarse sand 0.6 0.2 0.4 Fine sand 1.0 0.4 0.6 Loamy sand 1.4 0.6 0.8 Sandy loam 2.0 0.8 1.2 Light sandy clay loam 2.3 1.0 1.3 Loam 2.7 1.2 1.5 Sandy clay loam 2.8 1.3 1.5 Clay loam 3.2 1.4 1.8 Clay 4.0 2.5 1.5 self mulching clay 4.5 2.5 2.0 Source: dept of agriculture bulletin 462, 1960(better soils 2005)
The soils in the area are sandy clays. From Table 7, the soil water holding capacity is 2.8
mm/cm at field capacity, 1.3 mm/cm at wilting point and the available water is 1.5 mm/cm.
Also typical infiltration figures for sandy loams have been set to 25mm/hr. In the study area,
some of the existing ponds showed a lot of seepage and thus necessity to line them. However
some others showed substantial ponding and it isn’t necessarily important to line them.
Needless be, in order to maximize on the water harvested, it was advocated to line the ponds
where financial implications were not straining.
24
For this study, seepage losses were assumed to be negligible since the construction of the
pond would involve the application of 0.8mm plastic lining that would prevent seepage.
3.7 Establish losses via evaporation.
There was no available data on evaporation from the area from Kenya Met Dept. (KMT).
However, counsel sought from the personnel at KMT clarified that Kajiado climatic detail in
terms of evaporation wasn’t different from Narok. Therefore data on Narok district would be
reasonably accurate. The data thus used here is from Narok Met. Station. Also from this
station, only 10-year data was obtainable. Table 7: 10 year Evaporation values for Narok Meteorological station
Station name Element_ Name Year Jan Feb Mar Apr May Jun Jul Aug
NAROK MET STATION
Evap. ‘97 183.1 209.8 270.9 143 121.5 113.6 114.6 152.5
NAROK MET STATION
Evap. ‘98 138.3 135.6 177.2 162.2 106.8 93 81.3 105.5
NAROK MET.STATION
Evap. ‘99 177.4 204.2 153.3 128.1 140.9 123.7 134.1 132
NAROK MET.STATION
Evap. ‘00 197.6 209 201.5 177.3 165.2 145.9 148.1 168.9
NAROK MET. STATION
Evap. ‘01 96.5 165.5 161.6 111 120.5 116.6 116.1 137.2
NAROK MET. STATION
NAROK MET. STATION
Evap. ‘03 161.1 160 193.9 147 118.9 103.2 121.5 54.3
NAROK MET. STATION
Evap. ‘06 218.4 195.5 168.4 130.4 119.1 88.6 123.6 142.5
Table 7 (continued)
NAROK MET STATION
NAROK MET STATION
NAROK MET.STATION
NAROK MET.STATION
NAROK MET. STATION
NAROK MET. STATION
NAROK MET. STATION
NAROK MET. STATION
(Source: Kenya Met Dept)
Table 8: Average monthly evaporation for Olepolos area
Month Average monthly evaporation (mm) January 189.75 February 177.75 March 181.15 April 138.7 May 119 June 95.9 July 122.55 August 98.4 September 159.05 October 194.7 November 114.15 December 110.7 Total 1701
These observed values of pan evaporation were multiplied by a pan factor of 0.7 (Chow et.
al., 1988) to convert them to equivalent open water evaporation values from the pond. The
annual equivalent open water evaporation values were multiplied by the pond surface area to
give a figure for annual loss from evaporation.
Pond evaporation thus was calculated to be
0.7 x 1701=1190mm
Therefore the evaporation volume is 47.5m3
3.8 Establish the capacity volume required in the pond.
The water balance equation produced:
Pond volume = volume used for irrigation + volume lost through evaporation +
Volume lost through seepage + Losses via drip irrigation system
Pond volume = 40 + 47.5 + 0 + 9.72
= 97.22m3
The pond volume thus could be designed to be 100 m3 providing for freeboard.
26
3.9 Determine the catchment area and its characteristics
The catchment is where runoff is generated from before it is conveyed for storage. The runoff
was to be generated from the cropped area and if insufficient, then from outside catchments.
The farm area was used as the catchment area. Since the area was assumed to have employed
conservation farming, the description of land use was arrived at as ‘cultivated agricultural
land with conservation treatment’.
The catchment very uneven but generally sloping terrain and thus the runoff was expected to
flow without much hindrance. The mean catchment slopes of the areas where ponds have
been constructed are assessed to be approximately 1: 4.
3.10 Estimate run-off potential and comparing to expected pond size.
The values in the table below were used to determine the run-off numbers for the
characteristics of the area Table 9: Run Off Curve Numbers
Hydrologic soil groups Description of land use A B C D
Paved parking lots, roofs, driveways 98 98 98 98
Streets and Roads 98 98 98 98
Paved with curbs and storm sewers
Gravel 98 98 98 98
Dirt 76 85 89 91
Cultivated (agricultural crop) land
Pasture or range land
Poor (<50% ground cover or heavily grazed) 68 79 86 89
Good (50-75% ground cover; not heavily grazed) 39 61 78 80
Meadow (grass, no grazing, mowed for hay) 30 58 71 78
Brush (good, >75% ground cover) 30 48 65 73
Woods and forests
or burning)
45 66 77 83
Fair (grazing but not burned; some brush) 36 60 73 79
27
Hydrologic soil groups Description of land use A B C D
Good (no grazing; brush covers ground) 30 55 70 77
Open spaces (lawns, parks, Golf courses, cemeteries, etc)
(Grass covers 50-75% of area) 49 69 79 84
Good (grass covers >75% of area) 39 61 74 80
Commercial and business districts (85%
impervious)
Residential areas
0.05 ha plots, about 65% impervious 77 85 90 92
Source: Chow et al., 1998
The hydrologic soil groups A to D in the tables refers to infiltration potential of the soil after
prolonged wetting and are:
Group A soils: High infiltration (low runoff) Sand, loamy sand, or sandy Loam.
Infiltration rate >0.75 cm/hr when wet
Group B soils: Moderate infiltration (moderate run-off). Silt loam or loam
Infiltration rate (0.375-0.75 cm/hr) when wet
Group c soils: low infiltration (moderate to high runoff) Sandy clay loam.
Infiltration rate 0.125 to 0375 cm/hr when wet
Group D soils: Very low infiltration (high runoff). Clay loam, silt clay loam,
Sandy clay, silt clay, or clay
Infiltration rate 0 to 0.125 cm/hr when wet
3.11 Run off volume determination
The soil in the area is mainly sandy loam. Thus it is classified under the soil group C. land
use was considered as cultivated with conservation treatment for the 0.5 ha farm and paved
with dirt for the roads, footpaths and homestead.
28
Soil classification
72 0.4 28.8
Paved with dirt 89 0.1 8.9 Total 0.5 39.7 CN weighted Total (CN) x
Area)/ total area 79.4
Q = (P - Ia )2
(P –I a + S)
S = 25400 - 254 CN
Q= Run-off (mm)
Ia = Initial moisture abstraction (assumed as 0.2)
CN= Dimensionless curve number shown in Table 10
S = 25400 - 254 79.4
705.7
29
By the supply based method of sizing the pond, the pond volume
V= (Q x A)* 10-3 m3
Where:
= 1160.2 m3
The supply of the harvested water is greater than the pond capacity desired (i.e. 100m3) and
thus metering should be done to see that the excess harvested runoff is diverted to other
similar ponds elsewhere downstream.
3.12 Establish Water Productivity ratio for water used from pond.
In determining the water productivity, irrigation water productivity (WPI) was used.
WPI = Yield / irrigation water supply
With this evaluation, the pond volume was used as the irrigation water supply since the pond
is meant to satisfy supplemental irrigation.
Table 11: Irrigation volumes and water productivity (WPI) for Elements
AFS element Area cropped (Ha)
CWR (mm) Irrigation Volume (m3)
Yield Kg/Ha
Water Productivity (WPI) Kg/m3
Cereal (sorghum) 0.1 550 50 900 1.8 Legume (Beans) 0.1 400 70 700 1.0 Vegetable (Tomatoes) 0.05 500 40 5500 6.875 Fodder (Napier grass) 0.05 700 60 2250 1.875 Trees (Olive) 0.2 800 500 19500 7.8 Totals 0.5 700
Average farm water productivity of this system is 6.35kg/m3
30
3.13 Establish a benchmark for water productivity.
In establishing the benchmark for WP, there was need to put some controls to ensure that the
benchmark is reasonable. To this effect, evaporation and conveyance losses were factored in
the irrigation water flow i.e. evaporation losses and irrigation efficiency (90% for drip
irrigation). This was assumed as a safe check for the amount of water that was to be harvested
and the corresponding use for it. Table 12 below shows computed WP for the system
elements.
Any values below these could be used as an indicator that a system is functioning below
average and thus having poor water productivity.
Table 12: Water Productivity (WPI); evaporation and conveyance losses included
AFS element Area cropped (acres)
CWR (mm) Irrigation Volume (m3)
Yield Kg/Ha
Water Productivity (WPI) Kg/m3
Cereal (sorghum) 0.1 550 74.4 900 1.21 Legume (Beans) 0.1 400 104.2 700 0.67 Vegetable (Tomatoes) 0.05 500 59.6 5500 4.61 Fodder (Napier grass) 0.05 700 89.3 2250 1.26 Trees (Olive) 0.2 800 714.7 19500 5.46 Totals 0.5 1042.2
Average water productivity for the system that covering the losses was gotten to be 4.27
kg/m3
3.14 Match the pond with the area.
The pond volume is 100m3 and the area is 0.5 acres/ 2000m2. For similar farming systems in
the area a simplistic design specification for the pond could use 4.76m2/m3 as the area: water
volume ratio. For other systems with the same Agroforestry elements and drip irrigation
installation, the simple relation can be used to do a quick volume design of the pond if it is
possible to harvest the volume of the runoff from the catchment.
31
3.15 Irrigation scheduling
The water content in the effective root zone is estimated by using the water balance equation:
WCt = WCt -1 + IRR + RAIN - AET – DP Equation 1
Where:
RAIN = Rain since yesterday (inches),
AET = Actual ET (inches), and
DP = Deep percolation (inches).
Deep percolation was assumed to be negligible since the method of irrigation used was drip
irrigation. Table 13: Plant Available Water content (PAWC) for a range of soil types
Soil type PAWC (mm/m) Coarse sand 35-60 Sand 60-75 Loamy sand 75-110 Sandy loam 100-160 Fine sandy loam 145-185 Loam 150-220 Silt loam 170-250 Clay loam and silt clay loam 170-220 Silt clay and clay 150-200 Table 14: Soil coefficient (KS) as a function of depletion level
Depletion % KS 0 1.0 5 0.98 10 0.97 15 0.96 20 0.95 25 0.94 30 0.92 35 0.90 40 0.89 45 0.87 50 0.85 60 0.80 70 0.74 (Source: Colorado State University Extension agricultural engineer and associate professor,
chemical and bio-resource engineering. 11/93. Reviewed 2/05)
32
The values used in the tables above were supposed to be used for irrigation scheduling. The
actual daily rainfall values from the area were not obtainable from the Kenya Meteorological
department because of policy issues.
Calculation of KS by hand is very tedious. Computer based programs have been established
e.g. SCHED which is used to calculate Ks as a function of depletion from field capacity
expressed as a percentage. Many of the programs allow the user to choose the method of ET
calculation. The data required are weather, soil and crop information. By using the
computerized method, the irrigation decision can be made quickly. The only labor and time
involved is that needed to collect the data and input it into the computer.
The figures used below were based on approximations since there was no available software
and data required to perform the calculations
It is assumed that all the crops were planted on the 8th day of February.
Balance sheet example for sorghum
Date Etp (cm)
Crop Coeff. (Kc)
Soil Coeff. (KS)
ETa cm
Irrig. (cm)
Rain (cm)
Ad (cm)
Depletion (cm)
09/02 0.25 0.146 0 10/02 0.2 0.7 1.0 0.14 - 0.146 0.14 11/02 0.2 0.7 0.98 0.1372 0.25 0.146 0.0272 12/02 0.2 0.7 0.97 0.1358 0.146 0.163 13/02 0.2 0.7 0.95 0.133 0.25 0.146 0.046 14/02 0.2 0.7 0.94 0.1316 0.146 0.1776 15/02 0.2 0.7 0.92 0.1288 0.25 0.146 0.0564 16/02 0.2 0.7 0.90 0.126 0.146 0.1824 17/02 0.2 0.7 0.89 0.1246 0.25 0.146 0.057
Balance sheet example for beans
Date Etp (cm)
Crop coefficient (Kc)
Soil coefficient (KS)
ETa (cm
Irrig. (cm)
Rain (cm)
Ad (cm)
Depletion (cm)
09/02 0.2 0.146 0 10/02 0.2 0.4 1.0 0.08 0.146 0.08 11/02 0.2 0.4 0.98 0.0784 0.146 0.1584 12/02 0.2 0.4 0.97 0.0776 0.2 0.146 0.36 13/02 0.2 0.4 0.95 0.076 0.146 0.112 14/02 0.2 0.4 0.94 0.0752 0.146 0.1872 15/02 0.2 0.4 0.92 0.0736 0.2 0.146 0.0608 16/02 0.2 0.4 0.90 0.072 0.146 0.1328 17/02 0.2 0.4 0.89 0.0712 0.146 0.204
33
Date ETp (cm)
Soil coefficient KS)
ETa (cm
Irrig. (cm)
Rain (cm)
Ad(cm) Depletion(cm)
09/02 0.24 0.146 0 10/02 0.2 0.6 1.0 0.12 0.146 0.12 11/02 0.2 0.6 0.98 0.1176 0.146 0.2376 12/02 0.2 0.6 0.97 0.1164 0.24 0.146 0.114 13/02 0.2 0.6 0.95 0.114 0.146 0.228 14/02 0.2 0.6 0.94 0.1128 0.24 0.146 0.1008 15/02 0.2 0.6 0.92 0.1104 0.146 0.2112 16/02 0.2 0.6 0.90 0.108 0.24 0.146 0.0792 17/02 0.2 0.6 0.89 0.1.68 0.146 0..2472
Balance sheet example for Fodder
Date ETp (cm)
Eta (cm
Irrig. (cm)
Rain (cm)
Ad (cm)
Depletion (cm)
09/02 0.2 0 10/02 0.2 0.4 1.0 0.08 0.146 0.08 11/02 0.2 0.4 0.98 0.784 0.146 0.1584 12/02 0.2 0.4 0.97 0.776 0.2 0.146 0.36 13/02 0.2 0.4 0.95 0.76 0.146 0.112 14/02 0.2 0.4 0.94 0.752 0.146 0.1872 15/02 0.2 0.4 0.92 0.736 0.2 0.146 0.0608 16/02 0.2 0.4 0.90 0.72 0.146 0.1328 17/02 0.2 0.4 0.89 0.712 0.146 0.204
Balance sheet example for Olive
Date Etp (cm)
ETa (cm
Irrig. (cm)
Rain (cm)
Ad (cm)
Depletion (cm)
09/02 0.25 0.146 0 10/02 0.2 0.65 1.0 0.13 0.146 0.13 11/02 0.2 0.65 0.98 0.1274 0.146 0. 2574 12/02 0.2 0.65 0.97 0.1261 0.25 0.146 0.1309 13/02 0.2 0.65 0.95 0.1235 0.146 0.2544 14/02 0.2 0.65 0.94 0.1222 0.25 0.146 0.1266 15/02 0.2 0.65 0.92 0.1196 0.146 0.2462 16/02 0.2 0.65 0.90 0.117 0.25 0.146 0.1132 17/02 0.2 0.65 0.89 0.1157 0.146 0.2289
34
4 RESULTS AND DISCUSSION
4.1 The Agroforestry System
The system arrived at was an integrated livestock and crop Agroforestry system. The study
took note that the livestock would be considered only through the fodder that was to be
included in the system and not the drinking water requirement for the livestock themselves.
The system was structured to include: trees (olive), legumes (beans), cereals (sorghum),
vegetables (tomatoes) and fodder (Napier). It is notable that olive trees haven’t been planted
in the area before but since the climate and soil conditions are suitable, then a project should
be carried out in the area to confirm this. Also notable was the fact that there might be
acceptance challenges from the community.
The farm plan suggested was as shown in the Figure 5. The fodder strips are proposed to be
along the contours.
Sorghum
Intercropped
Croton Macrostychus boundary
The compounded crop water requirements for the system added up to 1320m3
35
4.2 Pond requirement
Rainfall data used was of a 10-year period due to unavailability of 30-year data. Rainfall
volume falling on the farm was calculated to be 1280m3. Comparing it to the CWR showed a
deficit of 40m3, to be obtained from harvesting runoff. The pond thus had to supply 40m3 all
of which was to be harvested from the farm.
Evaporation data for the area was unavailable and therefore data from an area with similar
climatological characteristics (Narok) was used. Calculations gave evaporation volume
expected to be 47.5m3. Seepage was neglected as the pond was to be lined and thus accounted
as negligible. It was however noted that some areas might have sufficient ponding capacity
and thus might not need the lining. Accounting for irrigation efficiency of 90% for drip
irrigation, the volume of the pond was calculated to be 97.22m3, which was approximated to
100m3.
Irrigation water productivity was used to relate the system with the pond where the volume of
the pond was assumed to be the irrigation water flow. This assumption was found appropriate
to relate the ponds use with actual yield of the AFS elements. The water productivities of
olive, sorghum, beans, vegetables and fodder were respectively established as 5.46, 1.21,
0.67, 6.875 and 1.875kg/m3. These water productivities were assumed to be adequate
benchmarks for such a system since inclusion of evaporation volume in their calculation
overestimates the water used in production and the planting of all elements at the same time
means less productivity. The average water productivity for the whole system was found to
be 4.27 kg/m3.
Observations from the study area showed that the mode of drawing water was crude and was
leading to the tearing away of the lining. Consequently the irrigation water was to be drawn
from the pond using the rope and washer pump (Figure 6).
36
(Source: Lambert, 1990)
An irrigation schedule was made for the month of February with the assumption that all the
AFS elements were planted at the same time. It is as shown in Table 14(Taking that one
irrigation event takes volumes as shown,
Olive tree 11.5m3 after 14 days
Beans 2.7m3 after 4 days
Sorghum 2.72m3 after 7 days
Fodder 7.55m3 after 8 days
Tomatoes 1.36m3 after 3 days
Table 15: Typical Irrigation schedule for the month of February
Day 1-15 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 AFS elements
Tomatoes ↓ ↓ ↓ ↓ ↓ Benas ↓ ↓ ↓ ↓ Sorghum ↓ ↓ ↓ Fodder ↓ ↓ Tree ↓ ↓ Volumes 25.83 - - 1.36 2.7 - 1.36 2.72 10.25 1.36 - - 4.06 - 14.22
37
↓
↓
↓
↓
↓
↓ Irrigation days
5 CONCLUSIONS AND RECOMMENDATIONS
5.1 Validation of Hypothesis
The study showed that it is possible to determine water productivity benchmark for
evaluating intensified Agroforestry systems and it can contribute to designing suitable runoff
ponds.
5.2 Conclusion
A procedure/ criterion for establishing a benchmark for water productivity was laid out. This
encompassed:
1. Getting the CWR for all the AFS elements
2. Subtracting the rainfall volume that falls in the area(acquired from the weather data of
the area)
3. Computing evaporation volume by assuming a surface area for the pond to be
constructed and multiplying by it the depth of evaporation (acquired from the weather
data of the area).
4. Apportioning the evaporation volume to the individual AFS elements occupancy on
the area
5. Adding the portion of evaporation to the CWR volume that is not satisfied by rainfall
for each element.
6. Correcting the volume for efficiency of the irrigation system to get the irrigation
water flow
WPI = Yield of AFS element Irrigation water flow
It was also established that it is possible to use the benchmark of water productivity in
designing a pond. The design specifications were summarized as:
1. Using the optimum yield and relating it to the water productivity (WPI) benchmark
established above, get the volumes required for irrigation for all AFS elements.
2. (Step 1 is equivalent to determining the pond volume by adding the CWR not fulfilled
by rainfall to evaporation losses and the correction for irrigation system losses.)
39
3. Test the catchment area to see if the area has a enough rainwater harvesting potential
to fulfill the harvest volume using the NRCS curve number method as shown in
section 3.10
4. Choose the pond site preferably in the lowest side of the farm to maximize the farm
catchment area. This may be aided by the use of contour maps or by laying out the
contours where the maps don’t exist.
5. Decide on the shape of the pond e.g. trapezoidal, ellipsoidal etc. An ellipsoidal pond
is preferred since it allows for easy withdrawal of the water even when the water line
is receding in dry seasons.
6. Allow for conveyance channels, which should have silt traps at a distance of, say 5m
from each other depending on the extent of siltation. The conveyance channels may
be grassed made out of concrete or compacted earth.
7. Calculate the surface area of the bottom of the pond to be lined to aid in giving the
specifications of the plastic lining.
( Suggested design drawings can be seen in Appendix 3.)
5.3 Recommendations
A study should be done to establish the benchmarks for different combinations of AFS
elements. These, if documented will simplify the process of designing ponds in this and other
similar areas.
Olive trees should be planted in the area as a pilot project to see how viable they are before
the implementation of the study is done.
Increasing the value of water used in Agroforestry should increase water productivity
through:
♦ Increasing yield per unit of supply or depletion,
♦ Changing from low- to high-value crops for example genetically modified crops and
grafted trees,
♦ Lowering the costs of inputs e.g. irrigation technologies,
♦ Obtaining multiple benefits per unit of water (for example, using water for drinking
and agriculture),
♦ Achieving more livelihood support per unit of water (more jobs, nutrition, and income
for the same amount of water) and
♦ Applying an integrated approach to increase the value per unit of water (e.g using the
ponds for fish farming alongside irrigation).
40
Increasing water productivity should also be done by minimization of water use or water
conservation measure i.e. in:
♦ Proper land leveling and grading as a prerequisite for efficient water application,
♦ Reducing unproductive water outflows through, minimizing idle periods during land
preparation, soil management to increase resistance to water flows and water
management to reduce hydrostatic pressure and
♦ Laying pipes for water conveyance in farms wherever feasible to cut the water
conveyance losses.
domestication of indigenous fruits and medicinal plants and relay cropping systems for semi-
arid areas should be adopted in the Agroforestry systems to improve on their sustainability.
Other studies should be done to come up with similar information as developed in this study
to suit other agro-climatic zones.
Studies should be done on other designs of ponds to see how possible it is to cut on the
evaporation losses.
Actual data should be gotten on the ground to be used in software that aid in making an
irrigation schedule that is realistic.
41
6 REFERENCES
Agroforestry: The next 25 years, Annual Report, 2004; Restoring hope restoring the
environment, The World Agroforestry Centre
Amir Kassam and Martin Smith, 2001; FAO Methodologies on Crop Water Use and Crop
Water Productivity Expert meeting on crop water productivity Rome, 3 to 5 December 2001
B. Mati, Tanguy De Bock, M. Malesu, E. Khaka, A. Oduor, M. Nyabenge & V. Oduor,
2007; Mapping the Potential of Rainwater Harvesting Technologies in Africa, Page15
Bekele-Tesemma, Azene, ed., 2007; Profitable Agroforestry innovations for eastern Africa:
experience from 10 agro climatic zones of Ethiopia, India, Kenya, Tanzania and Uganda.
World Agroforestry Centre (ICRAF), Eastern Africa Region.
Elizabeth Edna Wangui, 2003. Links between gendered division of labor and land use in
Kajiado district, Kenya: LUCID working paper series number: 23
James H. Lenhart Methods of sizing water quality facilities, The journal of surface water
Professionals.( http://www.forester.net/sw_0407_methods.html)
John Rockström, Jennie Barron and Patrick Fox, 2003; Water Productivity in Rain-fed
Agriculture: Challenges and Opportunities for Smallholder Farmers in Drought-prone
Tropical Agro ecosystems, UNESCO-IHE Institute for Water Education, Delft, The
Netherlands; Department of Systems Ecology, Stockholm University, Stockholm, Sweden
Joseph Sang & Caroline Wambui, (2006); Rain water harvesting among the Maasai
Community in, Olepolos, Kajiado District in Kenya, Technical evaluation and documentation
report
Lambert, R.A., 1990; How to Make a Rope Washer Pump. Intermediate Technology
Publications, London.
Lester R. Brown, 2008. Raising Water Productivity, Plan B 3.0: Mobilizing to Save
Civilization
Lawrence Mwambo, 2004; Status of arid and semi arid lands of Tanzania, Tanzania
Forestry Research Institute (TAFORI), working paper presented at the Drylands Agroforestry
Workshop 1st-3rd September 2004. ICRAF Headquarters, Nairobi- Kenya.
Maimbo M. Malesu, Joseph K. Sang, Alex R. Oduor, Orodi J. Odhiambo & Meshack
Nyabenge, 2006. Hydrologic impacts of ponds on land cover change: Runoff water
harvesting in Lare, Kenya. Technical Report No. 32 Nairobi, Kenya: Regional Land
Management Unit (RELMA-in-ICRAF), Netherlands Ministry of Foreign Affairs and
Swedish International Development Cooperation Agency (Sida)
Maimbo M. Malesu, Joseph K. Sang, Alex R. Oduor, Orodi J. Odhiambo & Meshack
Nyabenge, Rainwater Harvesting Innovations in Response to Water Scarcity: The Lare
Experience 2006 Technical Report No.32 Nairobi, Kenya
Molden et al., 2003, D. Molden, H. Murray-Rust, R. Sakthivadivel and I. Makin, A water-
productivity framework for understanding and action CABI. In: J.W. Kijne, R. Barker and D.
Molden, Editors, Water Productivity in Agriculture: Limits and Opportunities for
Improvement. Chapter 1. (2003), pp. 1–18.
Ngigi, S.N. 2003. Rainwater harvesting for improved food security: promising technologies
in the Greater Horn of Africa,Greater Horn of Africa Rainwater Partnership (GHARP), Lino
Printers, Nairobi, Kenya (2003).
Stephen N. Ngigi, Hubert H. G. Savenije, and Francis N. Gichuki., 2004 Hydrological
Impacts of Flood Storage and Management on Irrigation Water Abstraction in Upper Ewaso
Ng’iro River Basin, Kenya
Ngigi S.N. (2006) Hydrologic impacts of land use changes on water resources management
and socio-economic development of upper Ewaso Nyiro basin in Kenya. UNESCO, Institute
for water education and Delft University of Technology. PhD thesis, Delft University of
Technology, the Netherlands
Design, Implementation and monitoring Framework, project Report N0 W-09, October 2007,
Nairobi, Kenya
Pacey A. and Cullis A., 1991. Rainwater Harvesting. The Collection of Rainfall and Runoff
in Rural Areas. Intermediate Technology Publications, Londra, UK.
Temu AB, Chamshama SAO, Kung’u J, Kaboggoza J, Chikamai B and Kiwia A (eds.)
2008. New Perspectives in Forestry Education. Peer reviewed papers presented at the First
Global Workshop on Forestry Education, September 2007. ICRAF, Nairobi Kenya.
Touber, L. (1983); Soils and vegetation of Amboseli- Kibwezi area. Kenya soil surveys
report No. R 6 Nairobi In Pouw et al. ed. 1983. pp. 29-138.
The Kenya intensified Social Forestry Project In Semi-Arid Areas Impact Assessment
Report (FAO website article)
Ximing Cai, Claudia Ringler, 2003. Substitutions between Water and other Agricultural
Inputs – A Modeling Analysis
Young Anthony, 1997; Agroforestry for soil management/ Anthony young -2nd ed.CABI
7.1 Calculation And Analysis of Rainfall Occurrence of 67% Probability
Obtain annual rainfall totals for the cropping season from the area of concern. In locations
where rainfall records do not exist, figures from stations nearby may be used with caution. It
is important to obtain long-term records. An analysis of only 5 or 6 years of observations is
inadequate as these 5 or 6 values may belong to a particularly dry or wet period and hence
may not be representative for the long-term rainfall pattern.
Rank the annual totals with m == 1 for the largest and m = 32 for the lowest value and
rearrange the data accordingly e.g. (Table 16).
The probability of occurrence P (%) for each of the ranked observations can be calculated
(columns 4, 8, 12, 16, Table 14) from the equation:
Where:
P = probability in % of the observation of the rank m
m = the rank of the observation
N = total number of observations used
The above equation is recommended for N = 10 to 100 (Reining et al. 1989). Table 16: Ranked Annual Rainfall Data, Mogadishu (Somalia)
Year R m P Year R m P Year R m P Year R m P
mm % mm % mm % mm %
1961 960 1 1.9 1988 531 11 32.9 1966 395 21 64.0 1986 251 31 95.0
1967 890 2 5.0 1958 529 12 36.0 1973 371 22 67.1 1978 216 32 98.1
1968 680 3 8.1 1982 526 13 39.1 1976 339 23 70.2
1977 660 4 11.2 1965 498 14 42.2 1969 317 24 73.3
1972 655 5 14.3 1964 489 15 45.3 1959 302 25 76.4
1963 633 6 17.4 1957 484 16 48.4 1970 300 26 79.5
1979 594 7 20.5 1962 453 17 51.6 1983 273 27 82.6
1981 563 8 23.6 1985 423 18 54.7 1971 271 28 85.7
45
1980 544 9 26.7 1975 411 19 57.8 1984 270 29 88.8
1987 533 10 29.8 1960 403 20 60.9 1974 255 30 91.1
Plot the ranked observations (columns 2, 6,10, 14, Table í) against the corresponding
probabilities (columns 4, 8,12,16, Table í)
Finally fit a curve to the plotted observations in such a way that the distance of observations
above or below the curve should be as close as possible to the curve. The curve may be a
straight line.
From this curve it is now possible to obtain the probability of occurrence or exceedance of a
rainfall value of a specific magnitude. Inversely, it is also possible to obtain the magnitude of
the rain corresponding to a given probability (in this case P 67%).
The return period T (in years) can easily be derived once the exceedance probability P (%) is
known from the equations.
7.2.1 Conveyance design
The channel is meant to be trapezoidal assuming that they will be grassed, and then they
approximate a semi-circular shape. (Figure 7)
2R
R
Table 17: Maximum permissible velocity for different types of channels
TYPE OF CHANNEL MATERIAL MAXIMUM PERMISSIBLE VELOCITY
(m/s)
Rubble and fine sandy soils 0.46 - 0.61
Sandy soil 0.61 - 0.76
Consolidated loam and clay soil 0.91 - 1.14
Gravel soil 1.23 - 1.52
Densely grassed 1.50 - 2.50
Conglomerate, hardpan, soft sedimentary
(Source: MIT- civil engineering lecture notes)
The conveyance channels are to be grassed and assuming that initially they will be bare
(sandy loam surface) then the dimensions will be calculated using the figures in table by use
of the relation.
Q= A*V
V = velocity (as shown in Table 17)
The velocities in the table are averaged depending on the type of channel surface
Calculations should be made to determine how much the channel will hold initially and how
much it will hold when densely grassed.
47
7.2.2 Design of the Pond Reservoir
The shapes that have been tested in the area have been majorly trapezoidal. In other places
there have been ellipsoidal shapes with the inlet gently sloping to the deep end. Partially full
sphere shapes may also be used.
Figure 8: Trapezoidal shape Drawing of the pond. (plan and side view)
Dimensioning may be done by use of the equation below
V = [a1*b1 + a2*b2 + (a1*b2 + a2*b1)/2] * h/3
Where the bottom is a1 x b1, and the top is a2 x b2, with the a's
parallel and the b's parallel.
48
The ellipsoidal pond approximates a partially full cylinder and volume calculation would be:
V= LD2 / 8 [θ- sin (θ)] T= 2√y(D-y) = D sin (θ/2)
Where:
y = Liquid depth in cylinder
Ø = Angle representing how full the cylinder is [radians or degrees]. An empty cylinder has
Ø=0o, a cylinder with Ø=180o is half full, and a cylinder with Ø=360o is completely full.
Half sphere pond
Volume = π/3 {y2 (1.5D-y)}
49
8 GLOSSARY
Agroforestry is an agricultural approach of using the interactive benefits from combining
trees and shrubs with crops and/or livestock. It combines agriculture and forestry
technologies to create more integrated, diverse, productive, profitable, healthy and
sustainable land-use systems.
Intensification of Agroforestry is a method of increasing the yield of trees and crops in an
Agroforestry system as produced in a certain area.
Irrigation is the artificial application of water to the soil usually for assisting in growing
crops. In crop production it is mainly used in dry areas and in periods of rainfall shortfalls,
but also to protect plants against frost.
The irrigation requirement (IR) for crop production is the amount of water, in addition to
rainfall, that must be applied to meet a crop's evapotranspiration needs without significant
reduction in yield.
Water productivity is the ratio of the amount of water required to produce a unit of yield
and is a vital parameter in assessing the performance of irrigated and rain fed agriculture. It
varies greatly according to the specific conditions under which the crop is grown.
A Rainwater Harvesting (RWH) and Management system is broadly defined as the
collection, concentration and management of runoff water from roof or ground catchments
for production of crop,, pasture/ fodder , trees, livestock and/ or domestic water(Ngigi 2006).
Rainwater harvesting can be viable in areas with as low as 300mm of annual rainfall (Kutch
1982). However, Paccey and Cullis (1986) gave a more conservative range of annual rainfall,
500-600mm.
Arid and Semi-Arid Lands. Arid lands are the areas, which receive 350-500 mm annual
rainfall whereas semiarid areas receive 500-800 mm annually. (Government of Kenya (1979).
The total area receiving less than 500 mm rainfall in Kenya is 379,000 km².
50
RWH Ponds one of the small scale RWH technologies that generates water from runoff
from open surfaces, such as roads, home compounds, hillsides, open-pasture lands and may
also include runoff from watercourses and gullies.
51
1.6 Zone classification of area
1.7 Problem statement
1.8 Problem Justification
3.2 Determine Best combination for an irrigated Agroforestry system
3.3 Determine the water requirements and the corresponding p

A Research Paper Done by - apps.worldagroforestry.org

Documents

Transcript of A Research Paper Done by - apps.worldagroforestry.org