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AGROCLIMATIC CHARACTERIZATION OF MAKUENI COUNTY
USING RAINFALL DATA
BY
CAROLINE LUVANDWA AMUKONO
I10M3/47074/2012
SUPERVISORS:
MS. EMILY BOSIRE
MR CROMWEL LUKORITO
A RESEARCH PROJECT SUBMITED IN PARTIAL FULFILMENT FOR THE AWARD OF
DEGREE OF BACHELOR OF SCIENCE IN METEOROLOGY AT THE UNIVERSITY OF
NAIROBI
20TH MAY 2016.
I
DECLARATION
This research project is my original work and has not been presented to any other university for
the award of a degree
Signature: -------------------------------------- Date: ------------------------------------------
Caroline Luvandwa Amukono
I10M3/47074/2012
This research proposal has been submitted for review with our approval as the University
Supervisors
Signature: -------------------------------------- Date: ------------------------------------------
Ms. Emily Bosire
Department of Meteorology
Signature: -------------------------------------- Date: ------------------------------------------
Mr. Cromwel Lukorito
Department of Meteorology
II
DEDICATION
I dedicate this study to my family for their kind support.
III
ACKNOWLEDGEMENT
I thank God for seeing me through this research project. I would also like to extend my sincere
gratitude to all the people who contributed to making this project a success; my supervisors, Ms
Emily Bosire and Mr Cromwel Lukorito whose advice, guidance and contributions are highly
appreciated. I also thank the Kenya Meteorological Department (KMD) for their assistance which
helped in carrying out this research effectively.
IV
ABSTRACT
Climate change and climate variability are posing major threats to community livelihoods and
socio-economic development of the Sub Saharan Africa (SSA) region. This continues to make the
region a food crisis epicentre of the world. Although Kenya has well-developed agricultural
research systems, use of modern science and technology in agricultural production is hindered by
changing climate and weather variation. Inadequate research–extension–farmer linkages to
facilitate demand-driven research and increased use of improved technologies continue to
constrain efforts to increase agricultural productivity as farmers continue to use out dated and
ineffective technologies.
This study looks at growing season dates in terms of onset, cessation, length of growing season,
wet and dry spell lengths and number of rainy days among others using INSTAT software version
3.37. The study uses daily rainfall data sets for the lower south-eastern lowlands of Kenya
covering Makueni County observed at - meteorological stations running from 1980 to 2014. The
data was obtained from the archives at the Kenya Meteorological Service Headquarters in Nairobi.
Results indicated across the stations that a late start has an early end of rainfall hence a short
growing season. It is also noted that county has very low probability of a wet day with Mbooni
having the highest probability of 0.48 despite the OND season being its best season with 60% of
the total annual rain fall. With regard to the length of the growing season of the short rains (OND)
is between 65-81 days. The study concludes that Makueni County has three main agro climatic
zones. The first zone is the up land which includes Mbooni and the hilly areas in the northern part
of the county, the second zone is the central Makueni which includes Makindu, Dwa estate ltd-
Kibwezi and Ikoyo areas and the third zone is the lower Makueni which includes Divisional
agricultural ext. office - Mtito Andei and Muthingiini areas. There is need for improvement of the
meteorological data collection and archiving methods to enable more detailed research to be
carried out to cover the region using other parameters.
V
VI
Table of Contents
DECLARATION ........................................................................................................................................... I
DEDICATION .............................................................................................................................................. II
ACKNOWLEDGEMENT ........................................................................................................................... III
ABSTRACT ................................................................................................................................................. IV
Table of Contents ..................................................................................................................................... VI
List of tables ............................................................................................................................................... VIII
List of figures ............................................................................................................................................. VIII
CHAPTER ONE ........................................................................................................................................... 1
1.0 INTRODUCTION .................................................................................................................... 1
1.1 Background to the Study ................................................................................................................. 1
1.2 Problem statement ........................................................................................................................... 2
1.3 Objectives of the Study ............................................................................................................. 3
1.3.1 Main Objective ............................................................................................................................. 3
1.3.2 Specific Objectives ...................................................................................................................... 3
1.4 Justification of the Study................................................................................................................. 4
1.5 Study Area ................................................................................................................................ 4
CHAPTER TWO: ......................................................................................................................................... 7
2.0 LITERATURE REVIEW ............................................................................................................... 7
2.1 Introduction to Literature Review ................................................................................................... 7
2.2 Vulnerability of Agriculture to Climate Change ............................................................................. 7
2.3 Impact of Climate Variability and Change on Agriculture ............................................................. 7
2.4 Growing Season Characteristics ............................................................................................... 8
2.5 Water Availability ..................................................................................................................... 9
CHAPTER THREE .................................................................................................................................... 11
3.0 Data and Methodology ................................................................................................................. 11
3.1 Data Types and Sources ................................................................................................................ 11
3.1.1Data quality control ..................................................................................................................... 11
3.1.2 Homogeneity test....................................................................................................................... 12
3.2. Data analysis ................................................................................................................................ 12
3.2.1 Start of rains: .............................................................................................................................. 12
3.2.2 End of the growing period: ........................................................................................................ 13
3.2.4 Dry and wet days........................................................................................................................ 13
VII
CHAPTER FOUR ....................................................................................................................................... 15
4.0 Results and discussion .................................................................................................................. 15
4.1 Data quality control ...................................................................................................................... 15
4.2 Start of rain, end of season and length of season: ................................................................. 15
4.3 Wet and dry spells within the season ..................................................................................... 19
4.4 Probability of a rainy day .............................................................................................................. 22
4.4 The results for dry spell analysis ............................................................................................. 23
CHAPTER FIVE: ....................................................................................................................................... 26
5.0 CONCLUSIONS AND RECOMMENDATION ............................................................................... 26
5.1 Conclusion ..................................................................................................................................... 26
5.3 Recommendations ........................................................................................................................ 26
References ................................................................................................................................................... 28
Annexes ............................................................................................................................................... 31
VIII
List of tables
Table 1: Meteorological Stations used in the study ........................................................................ 5
Table 2: Mean start of rain, end of season and the length of season. ........................................... 16
Table 3: showing the wet and dry spells results. .......................................................................... 19
List of figures
Figure 1; Map of the study area (drawn using GIS) ..................................................................................... 5
Figure 2; Mass curve for Mbooni and Makindu respectively ...................................................................... 15
Figure 3: Start of rain and end of season for Mbooni Forest station. ........................................................ 17
Figure 4: Showing the onset and end of season dates for Makindu station................................................ 17
Figure 5: Onset and end of season dates for Mtito Andei Station. ............................................................. 18
Figure 6: Onset and end of season dates for Dwa estate Ltd- Kibwezi ...................................................... 18
Figure 7: Onset and end of season dates for Ikoyo Mwove’s farm ............................................................ 19
Figure 8: Showing the longest dry spells for Mbooni stations. .................................................................. 20
Figure 9: Showing the longest dry spells for Makindu station. .................................................................. 21
Figure 10: Showing longest dry spell for Dwa estate ltd- Kibwezi station................................................. 21
Figure 11: Longest dry spells for Divisional agricultural ext. office - Mtito Andei .................................... 21
Figure 12: Longest dry spells for Ikoyo Mwove’s farm. ............................................................................. 22
Figure 13: The probability of a rainy day for Mbooni Forest station ........................................................ 22
Figure 14: The probability of a rainy day for Dwa estate ltd- Kibwezi station. ......................................... 23
Figure 15: The probability of a rainy day for Makindu station. ................................................................. 23
Figure 16: Risk of a 14 and 21 day dry spells after the start of rain for Mbooni Forest station................ 24
Figure 17: Probability of a 7day dry spell after the start of rain for Dwa - Kibwezi station. .................... 24
Figure 18: The probabilities of a 14and 21 day dry for Mtito Andei station. ............................................ 24
Figure 19 Probability for a dry spells of 14 and 21 Makindu. ................................................................... 25
Figure 20 Probability of 14 and 21 dry spells for Ikoyo stations ............................................................... 25
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background to the Study
Today, climate change and climate variability are posing major threats to community livelihoods
and socio-economic development of the Sub Saharan Africa (SSA) region. This continues to make
the region a food crisis epicentre of the world (Scholes and Biggs, 2004). The projected climate
change for the region during the first half of the 21st century and beyond is poised to hit SSA the
hardest compared to other regions of world due to the inherent vulnerabilities including low
adaptive capacities arising from high levels of poverty, low level technological capacity, recurrent
extreme climate events and other socioeconomic miseries. Despite climate change, agriculture
constitutes the mainstay of the region’s economy and hence contributes directly to poverty
reduction efforts and provides an indispensable platform for economic growth (DFID, 2005).
Rain-fed agriculture remains the dominant source of staple food production and livelihood for
majority of the rural poor in SSA. However, production uncertainties associated with the frequent
intra- and inter-seasonal rainfall variability is a fundamental constrain for many investors,
especially the smallholder farmers who comprise the largest share of the farming fraternity. There
has been a tendency for investors in the agriculture sector to overestimate the negative impact of
climate-induced uncertainty. The capacity of farming communities and other agricultural
stakeholders in SSA to cope with this constraint has remained quite low. Knowledge of current
climate variability will on this account be instrumental in informing strategies that will enable
them better adapt to the projected future increase in climate variability and climate change
(Melaine et al., 2014).
Tools and proven approaches are now available that foster better understanding, characterization
and mapping of the agricultural implications of climate variability and the development of climate
risk management strategies specifically tailored to stakeholders needs (Cooper et al., 2008). These
tools and practices have wide ranging applications that permit improved dissemination climate
information and weather-based agro-advisories leading to increased economic growth a midst
climate variability.
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Agriculture sector is the leading contributor to economic development, accounting for 25% of the
gross domestic product (GDP) and 65 per cent of Kenya’s total export earnings besides providing
more than 18% of formal employment (Gichuki, 2000).
In view of the above highlights, it is imperative that actors and stakeholders within the agricultural
sector undertake to shift their technologies and practices in order to adapt to the eminent climate
change impacts (DFID, 2005; World Bank, 2006) and better cope with the current climatic
variability (Cooper et al.,2013).
1.2 Problem statement
Although Kenya has well-developed agricultural research systems, optimum use of modern
science and technology in agricultural production is often constrained by changing climate and
weather variability. The effects of climate change are felt mostly by the farmers practising rain-
fed agriculture. The associated changing and unpredictable rainy seasons has greatly affected their
ability to plan their farming activities. Areas which received adequate rainfall, now receive
insufficient rainfall reducing agricultural land productivity. This calls for transformation of current
farming practices to explore irrigation farming especially in the Arid and Semiarid Lands
(ASALs). However, inadequate research–extension–farmer linkages to facilitate demand-driven
research and increased use of improved technologies continue to constrain efforts to increase
agricultural productivity as farmers continue to use out dated and ineffective technologies. This
further calls for transformation of agricultural extension services that integrate climate research
products and information services in their extension messages to farmers.
The growing challenges of the adverse impacts of climate variability and change are increasing
pressure on land and other production resources. Notably, climate variability and change are
manifesting in increased frequency and severity of extreme climate events such as drought, floods,
heat waves, frost, hail, dry spells, and erratic rainfall among others. Other manifestation of climatic
variability and change include shifting growing season dates in terms of onset, cessation, length
of growing season, wet and dry spell lengths and number of rainy days among others. This has
served to increase the uncertainty in agricultural planning and decision making at farm level.
Over time, through sensitization of farming communities on the usefulness and availability of
climate information services, farmers have become aware of the seasonal climate forecasts and
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most of them are keen to know when the seasonal rains would start but fail to know how long the
season would last and the variation within the season. In addition, interpretation of the seasonal
forecasts disseminated by the National Meteorological and Hydrological Services remains a big
challenge for the non-climate experts’ end users. There is therefore increasing need for simplified
climate information and communication approaches to inform and alert the vulnerable
communities for better preparedness in anticipation of impending climatic risks.
Although rainfall variability both in space and time has been extensively studied in the greater East
Africa region (Bowden and Semazzi, 2007; Camberlin and Okoola, 2003) and Kenya (Funk et al.,
2010; Ogallo, 2010; Omenyi et al., 2012), an in depth analysis of the intra-seasonal characteristics
of the rainfall season that is more meaningful for agriculture application still remains a major gap.
Characterization of the within the season behaviour of rainfall will provide farmers with
information to guide agricultural decisions on enterprise selection, crop choices, planting dates
and other farm management practices .
This study was therefore designed to characterize the intra-seasonal climate behaviour of Makueni
County in terms of the onset, end of growing season and lengths of the growing season, dry and wet
spells using historical daily rainfall data from Meteorological Stations distributed across the entire
Makueni County.
1.3 Objectives of the Study
1.3.1 Main Objective
The overall objective of this study was to characterize the agro-climate of Makueni County with a
view to determining the intra-seasonal behaviour of rainfall in order to inform planning and
decision making for improved agriculture productivity of the smallholder farmers.
1.3.2 Specific Objectives
To achieve the main objective, the following specific objectives were carried out:
i. To determine the onset, end of growing season and length of growing period
ii. To determine durations of the wet and dry spells within the seasons.
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1.4 Justification of the Study
With the adverse effects of climate change continuing to be a reality especially among Africa’s
smallholder farming systems, predominantly causing shifting of agro-ecological zones and inter-
and intra-seasonal climate characteristics, it is becoming increasingly necessary to re-characterize
the agricultural climates of farming systems across agricultural ecosystems to capture the current
realities that could spur sustainable agricultural production (IPCC, 2013). The information
generated in this study will be crucial in assisting smallholder farmers in Makueni County to gain
better understanding and planning to better manage the increasing climate related risks during their
cropping seasons.
The determination of start, end and length of the growing season and the patterns of dry and wet
spells during the season is useful information for the agricultural planning and farm management
operations including land preparation, crop planting, weeding, fertilizer and other agro-chemical
applications, harvesting and post-harvest handling activities. The length of the growing period in
any region indicates yield potential, determines management practices and maturity length of the
crops.
The characteristics determined for the growing season will assist the farmers, agricultural
extension agents and other actors along the agricultural value chain to determine sowing dates,
enterprise selection, type and variety of crops to plant depending on the length of the season. This
study considered rainfall as a basis for characterizing the intra-seasonal pattern of climate in
Makueni County since it is the most highly variable climatic element and hence major determinant
of success or failure of agriculture.
Therefore, determining the length of the growing season /period would help the Makueni farmers
to cope better with the current seasonal rainfall variability. In addition, knowledge thereof will
also enable farmers to adopt and invest more in other extreme climate resilient land management
practices such as water conservation, mulching, and trenching among others.
1.5 Study Area
The study was conducted in Makueni County. The county is bounded by several counties;
Kajiado to the west, Taveta to the south, Kitui to the east and Machakos to the north. It lies
5
between latitude 10 35’ and 30 00’ south and longitude 370 10’ and 380 30’ east. It has 8
administrative constituencies namely, Mbooni, Makueni, Kaiti, Kibwezi east Kibwezi west and
Kilome. Makueni County is characterized by a rapidly growing population, water scarcity,
falling food production, and low resilience to climate change (Makueni County’s Integrated
Development Plan (CIDP), 2013-2017)). The combined effects of climate change and rapid
population growth are increasing food insecurity, environmental degradation, and poverty levels
in the county. According to Kenya National Bureau of Statistic census report, the population of
the County stands at 884,527 persons spread in an area of approximately 8,009 square
kilometres. Figure 1 shows the map of the study area. Rapid population growth places enormous
pressure on natural and environmental resources such as forests, water, and land.
Figure 1; Map of the study area (drawn using GIS)
The climatology of the region is generally dry exhibiting semi-arid conditions. The climate is
influenced by the seasonal shifts and the intensity of the ITCZ. The average annual rainfall,
evaporation and temperatures are 600mm, 2000mm and 230c respectively and rainfall is
characterized by small total amounts, strong seasonal and bimodal distribution. (Nyagito et al.
6
2008). The county experiences erratic rainfall distributed in two rainy seasons, known as the long
and short rainy seasons. Long rains occur from March to May and the short rains from
November to December, with the short rains being more reliable than the long rains. According
to Gichuki (2000), 60% of the annual rainfall is received during the short rains while the long
rains and the dry season contribute 37% and 3% of the annual rainfall respectively. Agricultural
production is mainly undertaken under rain-fed conditions on soils with low fertility potential.
The yields are therefore low with high yield variability strongly tied to climatic factors especially
rainfall (Mc Cown et al., 1990). Smallholder farmers are often exposed to the high risk of
unreliable rainfall. Cropping seasons are characterized by high frequency and intensity of
extreme climate events, especially droughts, which often lead to low agricultural productivity
and high crop failure rates. The yield per season depends on the onset, end and duration of rains
among other factors (Carberry and Abrecht, 1990). Open-air markets and trading concept is
another major economic activity with days set aside as ‘market days’ where large amounts of
produce are traded.
7
CHAPTER TWO:
2.0 LITERATURE REVIEW
2.1 Introduction to Literature Review
This chapter presents reviews of past works relating to a wide range of topics that crucial for
underpinning this study. Mainly, the chapter addresses aspects of the associated vulnerability of
Africa’s agriculture to climate variability and change, expounding on the potential impacts of
climate change on agriculture with particular emphasis on changing agro-ecological zones, shifting
of cropping seasons, water scarcity and prevalence of crop pests and diseases among other
biophysical and socioeconomic impacts. Finally, the chapter presents various approaches for
determining the intra-seasonal characteristics of a given agricultural ecosystems.
2.2 Vulnerability of Agriculture to Climate Change
In the developing world, the focus has is on increasing agricultural production with emphasis on
sustainable agricultural production and reducing the impact of diseases and pests such as desert
locusts. The productivity of a region may be increased by the reduction of loss resulting from
unfavourable climate and weather conditions, and also by more rational use of labour and
equipment. Research by FAO, WMO and the united nations educational scientific and cultural
organization (UNESCO), has found that the most important practical applications of macro-
climate survey include: choosing crops, varieties and domestic animals; determining favourable
periods for sowing, haymaking and harvesting; establishing areas where dry land farming is
possible and where irrigation has to be applied; finding the optimum range of climatic variables
for increasing yields and agricultural production in general.
An agro climatology survey of the humid tropics of South-East Asia (WMO, 1982) shows the role
of agro climatology in determining strategies to increase food production in the humid tropics. In
West Africa the length of the growing season is strictly associated with the rainy season (WMO,
1967) while in East Africa the focus has been on the crop water requirements in the various
localities in the area (WMO, 1973).
2.3 Impact of Climate Variability and Change on Agriculture
The start and end of the growing seasons vary annually and within crops. These variations are not
consistent, therefore early, late, or false starts of rains while the ending could be abrupt. This affects
the length of the growing season (Cooper et al., 2013) While the occurrence of the dry spells within
8
the growing period impact on the effective rainfall available for the plants (Mupangwa et al., 2011),
the effects of natural climate oscillations such as El-Niño and La-Nina affects the start of the rain
season. Obviously, this variability makes selection of crop type and variety very difficult (Cooper
et al., 2013).
Studies conducted in semi-arid West Africa countries indicate a relationship between the start, end
and length of the growing period with crop production (Sivakumar, 1988). Oladipo and Kyari,
1993 found the length of the growing period to be more sensitive to the start than end of the rains.
In Zimbabwe, length of the growing period increased with the early start of the rains Chiduza
1995), while in Southern Africa there is established a relationship between the start and the length
of the growing season in the semi-arid regions, (Kanemasu et. al, 1990).
The smallholder farmers in eastern Kenya are exposed to the risk of unreliable rainfall. The season
type depends on the onset of rainfall. Thornton et al (2008), indicates that a good season is
associated with early onset of rainfall, similarly a poor season is associated with late start of rain
.Research is intended to assist smallholders optimise crop and livestock yields given the
unavoidable nature of the region’s climatic risks.
2.4 Growing Season Characteristics
The growing season is the period of time each year during which perennial crops such as pasture
and forages and annual crops on the whole can grow to maturity. The growing season is different
for different crop varieties. It depends on water, temperature and radiation conditions (Hakanson
and Boulion, 2001). White et al. (2001) described the length of the growing season as the time
when water and temperature permit plat growth based on estimates of available soil water. It is
necessary to determine the growing season of an area or station so as to investigate whether it
can match optimum growing period of a particular crop. The growing season is sometimes
referred to as the rainy season, since rainfall is the main constraint for agriculture. The start and
duration of the rain season has been previously investigated for agricultural, botanical and
ecological purposes, to define the effective time to plant, to estimate the growing season length,
germination and seedling emergence, (Benoit, 1977).
When using the rainfall to define the start of rains, a long dry spell after the start may lead to a
false start and so different definitions are given for different regions. Stern et al (2003) provides
a condition of no dry spell of more than 10 consecutive days in the following 30 days for semi-
9
arid regions. A period of 30 days is the average length that the initial growth stage of most crops
will have emerged and be established, (Allen et.al., 1998).
In this study we define the start of rainfall as the first day of the month when the rainfall
collected is more than 20mm totalled over 2 days and not followed by a dry spell of more than
10 consecutive days in the following 30 days for semi-arid regions.(Stern et.al 2003). This
definition shall be used for both the early start and for the late start. This is to be able to assist
both the risk tolerant farmer (is the farmer who is willing to sow early) and the risk averse farmer
(is the farmer who is not willing to sow early).
2.5 Water Availability
Soil water stress occurs when there is no balance between the atmospheric demand for water and
supply of water available in the soil (Shaw, 1977) atmospheric demand is a function of the
energy available (solar radiation), movement of water away from the evaporating surface (wind),
dryness of the air (humidity), and air temperature or sensible heat levels (Shaw and Newman,
1991). For crop water to be adequate, the available soil water must be more than sufficient to
meet the atmospheric evaporative demand to avoid water stress for the crop.
The amount of water available depends on the effectiveness of precipitation or irrigation, and on
the soils physical properties and depth. The rate of water loss depends on the climate, soil type
and the root system of the plants.
The amount of available soil water can therefore be used to determine the end of the growing
season. As the growing season approaches the end the plant water requirement reduces but if the
there is water shortage before plant maturity, then the growth of the crops becomes affected and
may result in poor yields depending on the stage of plant development.
The length of a wet spell is defined as the consecutive number of days with a significant amount
of rainfall. The minimum length of a wet spell is taken as one day (Herath and Ratnayake, 2004).
Sharma (1996) defined a wet day as a day with rainfall of more than zero millimetres and the
probability of occurrence of a wet day depends on the climate system of a place or region. Wet
spells are an inherent property of climate and depending on their duration and the rainfall
associated with them, they can have significant advantages as well as disadvantages(Mwangala,
2003).for example in agriculture, wet spells of relatively short duration, typically not exceeding 3
10
days with light to moderate rainfall, can be very conducive to crop growth. However, if the spells
are longer, crop damage can easily occur due to water logging or even flooding.
Dry spell occurrences in the tropical regions, particularly in West Africa, have been studied
extensively due to their effects on rain-fed agriculture. Long dry spells can result into high cost
of production and even poor yields. Ghana studies have shown a significant decrease in the
number of rainy days the first weeks of the planting season. The report also shows that drought
and climate change drive the needs of the farmers to adopt the climate change to reduce
agricultural and non-agricultural risks.
Semi-arid regions including Makueni are characterized by dry weather spells, which may reach
exceptional proportions. This greatly affects agricultural activities and requires special
agricultural planning strategies and management decisions (De Jeger et al, 1998). Although the
definition of dry spells may vary depending on the aim and methodology used in each study, it
generally refers to the number of days without appreciable precipitation. The important aspect in
the definition is the significance of the rainfall threshold of a dry day.
11
CHAPTER THREE
3.0 Data and Methodology
This chapter details the sources and types of data used to analyse the specific objectives of this
study, and goes on to describe the methodologies that were employed for this study.
3.1 Data Types and Sources
The study used daily rainfall data sets for the lower south-eastern lowlands of Kenya covering
Makueni County observed at meteorological stations (Table1) running from 1980 to 2014.
Table 1: Meteorological Stations used in the study
Station
number
Station name Latitude Longitude Elevation in
meters a.m.s.l
Data
From To
9237000 Makindu Met station
20 17’S 370 50’E 1000 1980 2014
9237062 Ikoyo Mwove’s farm
20 14’S 370 47’E 987 1985 2013
9137068 Brothers of St. Peter
Claver
1044’S 37034’E 1158 2000 2013
9237068 Dwa estate ltd- Kibwezi
20 24’S 370 59’E 914 1980 2012
9137099 Mbooni forest station
10 38’S 37027’E 853 1980 2008
9238039 Divisional agricultural
ext. office - Mtito Andei
20 36’S 38005’E 884 2000 2013
The data was obtained from the archives at the Kenya Meteorological Service Headquarters in
Nairobi.
3.1.1Data quality control
Data quality checks were performed rainfall data for all the two levels. The first stage involved
filling in of missing data, while the second entailed test of homogeneity of the data sets used for
analysis. These activities are discussed in the following subsections.
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3.1.2 Estimation of missing data
Rainfall data for all the stations were examined to ensure its quality in terms of homogeneity and
missing values. The missing data was estimated using the normal ratio method. This method is
used if any surrounding gauges have the normal annual precipitation exceeding 10% of the
considered gauge. This weighs the effect of each surrounding station. The missing data are
estimated by
𝑃𝑥 = 1
𝑚∑ ⌈
𝑁𝑥
𝑁𝑖⌉ 𝑃𝑖
𝑚𝑖=1 ……………….………………… (1)
𝑃𝑥 = Estimate for the missing station
𝑃𝑖= Rainfall values of rain gauges used for estimation
𝑁𝑥= Normal annual precipitation of X station
𝑁𝑥= Normal annual precipitation of surrounding stations
m= No. of surrounding station
3.1.2 Homogeneity test
Single mass curve was used to check for the homogeneity of the data set after working out the
missing values.
3.2. Data analysis
The data was analysed in the following steps
3.2.1 Start of rains:
The start of rain for the short rains was defined as:
i. The first day from 1st Oct when the rainfall collected is more than 20mm totalled over 2
days.
ii. The first day from 1st Oct when the rainfall collected is more than 20mm totalled over 2
days and not followed by a dry spell of more than 10 consecutive days in the following 30
days.
iii. The first day from 1st Nov when the rainfall collected is more than 20mm totalled over 2
days.
13
iv. The first day from 1st Nov month when the rainfall collected is more than 20mm totalled
over 2 days and not followed by a dry spell of more than 10 consecutive days in the
following 30 days.
This was done so as to capture both the early start and the late start. The condition that there should
be no 10 day dry period is to ensure there is no false start. This criteria was executed on the rainfall
data to extract onset dates using the INSTAT analysis software Version 3.37
3.2.2 End of the growing period:
This is calculated using the water balance equation (3). INSTAT defines the end of rains differently
from the end of season. In this study the end of rains is the last day before 16th Mar that accumulates
10mm or more. End of the season is the first occasion when the water balance drops to zero after
the end of the rains. Water balance expression used is shown in equation 3.
𝑊𝐵𝑇𝑜𝑑𝑎𝑦 = 𝑊𝐵𝑌𝑒𝑠𝑡𝑒𝑟𝑑𝑎𝑦 + 𝑅𝑇𝑜𝑑𝑎𝑦 − 𝐸. ………………………………….. (3)
Where WB is the water balance today, R is the water balance yesterday and E is the potential
evaporation. This study adopts definitions where evaporation cannot be zero and cannot be more
than 100mm. That is, if water balance today is < 0 then water balance today = 0mm and if water
balance today is > 100 then water balance today = 100mm. Length of the growing season is
obtained by subtracting dates for end of season and start of rains as in equation 4 .
3.2.3 Determination of length of growing period
Length of the growing season for either season was obtained by determining the number of days
between the rainfall onset and cessation dates according to equation 4.
Length of season= end of season- start of rain ………………. (4)
3.2.4 Dry and wet days
A day has been considered wet, when it receives rainfall of more than 0.85mm accumulated in 1
or 2 days (WMO, 2012). However, for semi-arid climatic conditions, rainfall of 0.85mm is
inadequate to influence crop growth. Consequently, this study adopted the definition by Stern et
al 2003 who defined a wet day as a day with at least 4.95mm accumulated in 1 day. Conversely, a
dry day was defined as a day that received less than 4.95mm in one day. Daily rainfall data from
each study station was fitted to the simple Markov chain model as outlined by Stern et al. (2003)
14
and inbuilt into the INSTAT analysis Software. In the first-order Markov chain, the current state
is dependent solely on the state of the immediate previous period and the chance that a process is
in state j at time 𝜏 given that it was in state i at time 𝜏 − 1 is represented by transitional probability
𝑃𝑖𝑗 which is expressed as in equation 5
𝑃𝑖𝑗,𝜏 = 𝑃𝑟(𝑋𝜏 = (𝑗|𝑋𝜏−1 = 𝑖))…………………………………………….. (5)
This model was run by INSTAT to get the probability of dry spells within 30 days following a wet
day using the July to June calendar. The probability of a dry spell for 14 and 21 days after the
planting date was also calculated.
15
CHAPTER FOUR
4.0 Results and discussion
This chapter presents the results of the analysis of data based on the methodologies described in
Chapter Three in accordance with the specific objectives of this study.
4.1 Data quality control
Homogeneity test was done for all the stations and the results obtained were straight lines. Straight
lines indicated that the data was consistent and homogeneous. Representative results for Mbooni
forest station and Makindu are presented in fig 2.
Figure 2; Mass curve for Mbooni and Makindu respectively
4.2 Start of rain, end of season and length of season:
Table 2 shows Mbooni Forest station has the earliest mean start date on 9th November while
Makindu has the latest 17th November. Mbooni has the latest cessation date of 29th January and
also the longest season length of 81 days while Makindu has the shortest season length of 64 days
but Dwa estate ltd- Kibwezi station has the earliest cessation date on 15th January.
0
5000
10000
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20000
25000
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35000
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80
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83
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86
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89
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92
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95
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98
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04
20
07
cum
ula
tive
rai
nfa
ll in
mm
Years from 1980-2007
0
5000
10000
15000
20000
25000
30000
35000
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80
19
83
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86
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89
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92
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01
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cum
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Years from 1980-2007
16
Table 1: Mean start of rain, end of season and the length of season.
Station Mean start of
rain date
Mean end of
season date
Mean length of
season
Mbooni Forest 9th Nov 29th Jan 81
Makindu 17th Nov 20th Jan 64
Dwa estate ltd- Kibwezi 10th Nov 15th Jan 66
Divisional agricultural ext. office -
Mtito Andei
10th Nov 21st Jan 72
Ikoyo Mwove’s farm 12th Nov 17th Jan 66
Figure 3, 4, 5, 6 and 7 shows the analysis for the onset and cessation of rainfall. It is observed
across the stations that a late start has a short growing season. The onset for all the stations lies
within the 2nd week o November except for Makindu while the end of season is the third week of
January except for Mbooni which is in the 4th week with length of growing season having a mean
of about 69days. However, there is an exception for Mtitu Andei station which had variable series
dates for end of season (Fig 7).
17
Figure 3: Start of rain and end of season for Mbooni Forest station.
Figure 4: Showing the onset and end of season dates for Makindu station.
18
Figure 5: Onset and end of season dates for Mtito Andei Station.
Figure 6: Onset and end of season dates for Dwa estate Ltd- Kibwezi
19
Figure 7: Onset and end of season dates for Ikoyo Mwove’s farm
4.3 Wet and dry spells within the season
Mbooni Forest station had the highest probability of having a rainy day of 0.48 while the others
had a probability of <0.3 chance of a rainy day during the OND season.
Mbooni Forest station is the only station with lowest probability of experiencing a dry spell of 14
days after planting and also the least chance of having long dry spell of 40days or more. Dwa estate
ltd- Kibwezi and Ikoyo Mwove’s farm had the highest chance of having long dry spells of more
than 40days of 47% and 46% respectively. Therefore the chance of having a failed season is low
for Mbooni and high for Dwa estate ltd- Kibwezi and Ikoyo Mwove’s farm. The results are as
shown in table 3 below.
20
Table 2: showing the wet and dry spells results.
Station Probability of a rainy
day
Chance of a long dry
spell
Mbooni Forest 0.48 2%
Makindu 0.24 27%
Dwa estate ltd- Kibwezi 0.3 24%
Divisional agricultural ext. office - Mtito
Andei
0.25 47%
Ikoyo Mwove’s farm 0.2 46%
The longest dry spells for the OND season were analysed as shown in fig 8 to fig 12. Mbooni had
the least number of dry years as compared to the other stations in the region
Figure 8: Showing the longest dry spells for Mbooni stations.
21
Figure 9: Showing the longest dry spells for Makindu station.
Figure 10: Showing longest dry spell for Dwa estate ltd- Kibwezi station.
Figure 11: Longest dry spells for Divisional agricultural ext. office - Mtito Andei
22
Figure 12: Longest dry spells for Ikoyo Mwove’s farm.
4.4 Probability of a rainy day
Markov Chain Model was run for the probability of a rainy day for all the selected stations. The
results obtained are shown in fig 13 to fig 18. A rainy day was defined by a threshold of 4.95mm
and above while below this threshold amount is considered a dry day.
Result indicates the probability of rain is high during the short rains. Figures 13 -18 show the
probabilities obtained. For instance in figure 13 shows a time series for the probabilities. Over
MAM period the probability ranges from 0.22 - 0.28 while for the OND the probability ranges
from 0.25 to 0.45 indicating that OND has higher probability of a rainy day.
Figure 13: The probability of a rainy day for Mbooni Forest station
23
Figure 14: The probability of a rainy day for Dwa estate ltd- Kibwezi station.
Figure 15: The probability of a rainy day for Makindu station.
4.4 The results for dry spell analysis
Figures 16 to 17 show the results of the Markov Chain Model indicating the probability of a 14
and 21 day dry spell for all the study stations based on the criteria that a rainy day was defined by
a threshold of at least 4.95mm while below this threshold, the day was considered to be dry. The
14 day dry spell is more frequent then the 21day dry spell. Mtito Andei station has a high chance
of dry spells of 14 and 21days within the season which increases towards the in the 3rd week of
December. However for the other stations the chance of dry spells increases from the end of the
4th week of December. Mbooni had the least chance of having a 14 day dry spell with the chance
of a 21 day dry spell starting at around 31st December
24
Figure 16: Risk of a 14 and 21 day dry spells after the start of rain for Mbooni Forest station.
Figure 17: Probability of a 7day dry spell after the start of rain for Dwa - Kibwezi station.
Figure 18: The probabilities of a 14and 21 day dry for Mtito Andei station.
25
Figure 19 Probability for a dry spells of 14 and 21 Makindu.
Figure 20 Probability of 14 and 21 dry spells for Ikoyo stations
26
CHAPTER FIVE:
5.0 CONCLUSIONS AND RECOMMENDATION
5.1 Conclusion
Makueni County has three main agro climatic zones.
The first zone is the up land which includes Mbooni and the hilly areas in the northern part of the
county. It has the onset in the second week of November with the end of the growing season
occurring in the last week of January. The area has a growing season of about 81days and the
lowest chance of having a 21day dry spell within the season. The area therefore has the potential
of practicing rain fed agriculture given the right crop variety such as Makueni composite, sorghum
and finger millet which are drought resistant. This region can grow crops that take between 55-76
days to reach dry silk stage in order for them to optimise on the yields.
The second zone is the central Makueni which includes Makindu, Dwa estate ltd- Kibwezi and
Ikoyo areas. This zone has the mean start date within the 2nd week of November and the end of the
growing season within the third week of January. The zone has fairly the same length of growing
season of about 65days. It has a high probability of dry spells and high chance of crop failure with
an approximate 30%. The farmers in this region should not rely completely on rain fed agriculture
due to the high chance of dry spells. Crops with shorter growing length such as green grams
improved (N26) pigeon pea improved etc. Animal husbandry is also a viable practice since the
farmers can plan to store fodder for the animals during the driest seasons.
The third zone is the lower Makueni which includes Divisional agricultural ext. office - Mtito
Andei and Muthingiini areas. The mean start date for this zone is in the second week of November
and the mean end of the growing season is in the second week of January. This zone despite having
a fairly long growing season of about 73days it has a very high probability of dry spells with a
46% chance of crop failure. Rain fed agriculture is not economical for this area the famers should
consider irrigation or cash crops such as cotton and sisal planting or raring of animals mainly goats
which are drought resistant.
5.2 Recommendations
Short length of growing season and high frequency of dry spells is the main course of food
insecurity in the region. Although drought resistant varieties like Katumani maize have been
27
developed and grown, efforts should be made in trying to develop crops that yield well despite the
drought conditions in that they can quickly so that by the time the rains end they should have
completed the most sensitive stages that would suffer due to water stress.
In regions with very high chance of long dry spells such as central and Sothern regions the
government should consider investing in other practices such as irrigation and mulching in order
to benefit those farmers that cannot afford the facility.
Improvement of the meteorological data collection and archiving methods should be improved to
enable more detailed research to be carried out to cover the region full including other
meteorological parameters.
28
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31
Annexes
Table 4: Display of daily data
No Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr May Jun
1 1 32 63 93 124 154 185 216 245 275 306 337
2 2 33 64 94 125 155 186 217 245 276 307 338
3 3 34 65 95 126 156 187 218 247 278 308 339
4 4 35 66 96 127 157 188 219 248 279 309 340
5 5 36 67 97 128 158 189 220 249 280 310 341
6 6 37 68 98 129 159 190 221 250 281 311 342
7 7 38 69 99 130 160 191 222 251 282 312 343
8 8 39 70 100 131 161 192 223 252 283 313 344
9 9 40 71 101 132 162 193 224 253 284 314 345
10 10 41 72 102 133 163 194 225 254 285 315 346
11 11 42 73 103 134 164 195 226 255 286 316 347
12 12 43 74 104 135 165 196 227 256 287 317 348
13 13 44 75 105 136 166 197 228 257 288 318 349
14 14 45 76 106 137 167 198 229 258 289 319 350
15 15 46 77 107 138 168 199 230 259 290 320 351
16 16 47 78 108 139 169 200 231 260 291 321 352
17 17 48 79 109 140 170 201 232 261 292 322 353
18 18 49 80 110 141 171 202 233 262 293 323 354
19 19 50 81 111 142 172 203 234 263 294 324 355
20 20 51 82 112 143 173 204 235 264 295 325 356
21 21 52 83 113 144 174 205 236 265 296 326 357
22 22 53 84 114 145 175 206 237 266 297 327 358
23 23 54 85 115 146 176 207 238 267 298 328 359
24 24 55 86 116 147 177 208 239 268 299 329 360
25 25 56 87 117 148 178 209 240 269 300 330 361
26 26 57 88 118 149 179 210 241 270 301 331 362
27 27 58 89 119 150 180 211 242 271 302 332 363
28 28 59 90 120 151 181 212 243 272 303 333 364
29 29 60 91 121 152 182 213 244 273 304 334 365
30 30 61 92 122 153 183 214 274 305 335 366
31 31 62 123 184 215 275 336
