INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue III, March 2025
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Impact of Annual Temperature and Rainfall Anomalies on Maize
Yields in Machakos County: An Analysis from 1993 To 2023
1*
Amos Kinamboge Ombevah,
2
Moses Kathuri Njeru
1
Master of Arts in Geography, Chuka University
2
Lecturer, Chuka University
DOI : https://doi.org/10.51583/IJLTEMAS.2025.140300030
Received: 17 March 2025; Accepted: 27 March 2025; Published: 10 April 2025
Abstract:
Purpose: To investigate the impact of Annual temperature and Rainfall Anomalies on Maize Yields in Machakos County from
1993 to 2023.
Methodology: The study utilized qualitative and quantitative data, collected through structured questionnaires as primary data
and a secondary time series data template for secondary data. The target population included households, agricultural officers,
and administrative officers in Matungulu East, Kaani/Kaewa, Mwala/Makutano, Ikombe, and Kangundo East wards. The sampled
administrative wards contained 36,976 maize farming households selected through purposive sampling. The population was
determined using Yamane's formula, resulting in a sample size of 395 maize farmers (households). Maize farming households
were identified using cluster random sampling.
Results: The study found a statistically significant negative effect of temperature variability on maize yields (Ξ²=-0.054,
p=0.000). This implies that a one-unit increase in temperature variability is associated with a 0.054 tones per hectare decrease in
maize yields, highlighting the sensitivity of maize to temperature extremes. Conversely, rainfall variability showed a negative but
statistically insignificant effect on maize yields (Ξ²=-0.020, p=0.946). This suggests that other factors, possibly adaptation
strategies, may mitigate the impact of inconsistent rainfall on maize yields.
Unique contribution to theory, policy and practice: The study underscores the differential impacts of temperature and rainfall
variability on maize yields, emphasizing the need for targeted adaptation strategies to manage temperature fluctuations.
Recommendations include promoting heat-resistant maize varieties, improving irrigation infrastructure, and enhancing water
management practices. These insights contribute to agricultural planning and policy-making, aiming to enhance the resilience of
farming households in Machakos County to climate variability.
Keywords: Maize Yield (In Tonnes Output (kgs) .Land size(acres).Temperature variability.Rainfall variability .Seasonality
Trends .Climate justice
I. Introduction
Climate variability significantly impacts global crop yields with temperature and precipitation changes becoming apparent over
extended periods (Maitah, 2021). A study by Zhang et.al (2018) highlights that irregular precipitation events due to climate
change are expected to increase. From 1850 to 2012, the global temperature rose by 0.78Β°C, and the International Panel on
Climate Change (IPCC) projects a further rise of 0.5Β°C to 2Β°C by 2100 (Esayas et al., 2019). While natural phenomena like
volcano eruptions and the El Nino Southern Oscillation (ENSO) contribute to climate variability, human activities are primary
drivers of climate change, notably through greenhouse gas emissions (Aggarwal, 2003). Despite the known causes of climate
variability across the world climate justice cannot be achieved unless industrialized countries take full responsibility and reduce
emission of greenhouse gases, provide climate financial support to help the vulnerable developing countries adapt to climate
variability and promote technology transfer and ensure capacity building in vulnerable countries across the world. In Asia,
Mendelsohn (2014) reports that climate change models predict a temperature rise of 1.3 to 1.4Β°C by 2100. Notably, observed
warming between 1960 and 1990 reached up to 3Β°C, with the impact of climate variability dependent on the extent of climate
change. In Africa, Sagero et al. (2018) documented a 0.5Β°C warming trend, with temperatures projected to rise by 3 to 4Β°C by the
21st century's end, affecting rainfall patterns and economic growth. In Ethiopia, Esayas et al. (2019) noted a temperature increase
of 0.2Β°C to 0.28Β°C per decade since 1960, adversely affecting regions south of Ethiopia. Mohamed (2022) found that Sudan's
climate variability, characterized by droughts, floods, and CO2 emissions, negatively impacts food security. In East Africa,
climate variability shows similar trends, with erratic rainfall influenced by factors such as ENSO and the Indian Ocean Dipole
(Sagero et al., 2018). In Kenya, Mairura et al. (2021) observed a shift in rainfall patterns in central Kenya over the last 50 years,
with reduced river water volumes due to climate change (Jegede, 2018; Pielke, 2017). Sagero et al. (2018) linked extreme weather
events like the 2011 and 2014 droughts to climate change, causing significant economic damage. The annual cost of droughts to
Kenya's economy is estimated at 2%–2.8% of GDP (Kilavi et al., 2018; GOK, 2018).
Over the years, maize production in Kenya has declined due to climate variability. Bosire et al. (2019) found that climate
conditions determine crop choices, with precipitation intensity during planting seasons affecting yields. Temperature variations
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also influence pollen and seed dispersal. In highland areas like Trans Nzoia and Uasin Gishu, maize takes longer to mature
compared to lowland areas like Machakos and Makueni, where warmer climates allow for two planting seasons annually. Arid
and semi-arid lands (ASALs) in Kenya, such as parts of Machakos County, face high crop failure risks due to their semi-arid and
arid nature (Mwanzia, 2020). Maize yields in these regions have been inconsistent over time. Maize yield trends in Machakos
County showed bumper harvests as recorded in 2015 and 2013 while 2017, 2016, 2014 and 2012 recorded meagre yields (Velesi,
2018). It can also be noted that the Size of the land did not affect the production in MT, very little was recorded in some years yet
the land size was big enough. For example, in 2012 and 2017 maize yields were 90926 and 63984 metric tons yet land size was
149388 and 129010 ha (KNBS, 2023). This implies that other than the land size, a number of other factors affect maize yields
climate variability being key. The above statistics is an indication of the irregular maize yields in the county.
Statement of the Problem
ASAL areas face erratic climatic aspects particularly unreliable precipitation patterns and extreme temperature regimes. The
Erratic weather among other factors significantly affect quantity of agricultural yields from the farms. For maximum yields to be
achieved, crops need adequate moisture during the flowering and fruiting period. Therefore, with erratic and extreme weather
patterns, maize yields will be erratic and in most cases, dwindle thus complicating food sufficiency and livelihood stability status
in a good number of households in the County Jeopardizing SDGs goal 1 and 2 on eradicating Poverty and Zero hunger and the
Machakos CIDP 2023-2027 efforts on mitigating climate change and food security efforts (Machakos CIDP, 2023). Efforts have
been made to mitigate climate change through formulation of coping strategies and development of new maize varieties for
different agro-ecological zones. However, it’s not clearly known the extent to which climate variability impacts different maize
verities for dry lands. This research therefore sought to analyse the extent to which climate erraticism is impacting yields in
different maize verities for dry lands and determine the effectiveness of the coping strategies employed by maize farming
households in Machakos County with the aim of helping to develop policy response and help farmers to better on existing
adaptation strategies to climate change.
Objectives
To analyze how extreme temperature and rainfall anomalies has affected maize yields among farming households in Machakos
County for period 1993 to 2023
Research Hypotheses
H
O:
There is no significant relationship between temperature and rainfall variability on maize yields.
Theoretical Review
The current study was based on the theory of production. This theory was initially suggested by Cobb, Charles W., and Paul H.
Douglas in the late 1920s (Cobb & Douglas, 1976). The theory explains the principles in which the producers make decisions on
how to use the factors of production to optimize production. That is the link between the prices of commodities and the costs
(rents/wages) of the production factors that are used to produce them. The theory helps farmers understand the best combination
of factors of production to optimize the production of maize and its products. The decisions on how much of each commodity the
farmers sell and against how much they produce depend on how much they produce given the social, economic and
environmental factors they are predisposed to. Based on the socioeconomic aspects the following aspects come into play; the land
size, crop production systems and yield per annum. Likewise, there are exogenous factors caused by economic disruptions that
affect production and these include the climate/weather variations, and natural catastrophes. Therefore, the theory is found
relevant in explaining the maize yields in Machakos County, Kenya based on the rainfall variability and the crop production
systems they apply.
II. Research Methodology
Figure 1: Study area map
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This map illustrates the study location and regional focus of the research. The target population included households, agricultural
officers, and administrative officers in Machakos County. The sampled administrative wards contained a total of 36,976
households. The population was determined using Yamane's formula, ( n =the number of respondents in the
research study. where N = size of the population in the study, e = the level of data accuracy) resulting in a sample size of 395
maize farmers (households). Maize farming households were identified using cluster random sampling from wards including
Matungulu east, Kaani/Kaewa, Mwala/Makutano, Ikombe, and Kangundo east. The study utilized both qualitative data from oral
interviews, focused group discussions and observations and quantitative data obtained from the Kenya meteorological department
(KMD) and Data on maize yields from the National cereals and produce board (NCPB) and Kenya national bureau of statistics
(KNBS) reports, collected through structured questionnaires as primary data and a secondary time series data template for
secondary data. Quantitative secondary data were analyzed using descriptive and inferential statistics, with descriptive statistics
summarizing data through counts, percentages, and means. Correlation analysis tested relationships between variables, while
regression analysis determined causal effects of independent predictors on the dependent variable. A significance level of 0.05
(95% confidence interval) was used for error variance. Data were coded and analyzed using SPSS and STATA, and results were
presented in tables, diagrams, and charts.
Research Findings, Data Analysis and Presentation
Across the wards the household respondents acknowledged to have experienced temperature variations over the study period. The
temperature anomaly experienced in Machakos County within the period of 30 years. The means was calculated for fifteen year
period from which the anomaly were computed on 15 year period that is 1993 to 2008 (first Quindencennial) and 2008 to 2023
(second Quindencennial). In Figure 2, the first Quindencennial, 1994 recorded zero deviation, 1999, 2000, 2002,
2004,2005,2006,2007 and 2008 recorded temperature above the mean. Lowest temperature above the mean was recorded in 1999,
2000 and 2002 at 0.2
o
C while the highest temperature above the mean was in 2008 at 0.7
o
C. Seven years recorded negative
deviation from the mean, these were; 1993, 1995, 1996, 1997, 1998, 2001 and 2003. The Highest negative deviation was recorded
in 1995 and 1998 at -0.4
o
C while the lowest was in 2001 at -0.1
o
C.
Figure 2: First Quindencennial Temperature Anomaly (1993-2008)
In the second Quindencennial, as captured in (Figure 1.7) below 2013 and 2019 recorded zero temperature variation from the
mean 2009, 2011, 2012, 2014, 2015 and 2023 recorded temperature above the mean. 2011 recorded the highest deviation of 0.6
o
C while 2009 and 2014 recorded the lowest temperature of -0.1
o
C. Negative Temperature deviation was recorded in 2010, 2016,
2017, 2018, 2020, 2021 and 2022. Lowest negative deviation was recorded in 2022 -0.1
o
C while the highest was recorded in
2018 at -0.7
o
C. The second Quindencennial had the highest lower temperature deviation of -0.7
o
C while the first Quindencennial
had the highest temperature above the mean of 0.7
o
C. Secondly the first Quindencennial period recorded higher deviations from
the mean compared with the second Quindencennial.
Figure 3: Second Quindencennial Temperature Anomaly (2009-2023)
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Rainfall Trends in Machakos County
The survey presented the results on changes in rainfall patterns over recent years across various wards highlighting notable
perceptions and experiences within local agricultural communities. The information in (Figure 1.8), which detail the frequencies
of perceived changes in rainfall patterns over the last few years across various wards, across all the wards maize growing
households reported to have experienced changes in rainfall patterns though the percentage varied across the wards.
Overall, Mwala/Makutano stands out as the Ward with the highest frequency of perceived changes in rainfall patterns, suggesting
the most significant impact or awareness of rainfall variability. Conversely, Kangundo East and Kaani/Kaewa report the lowest
frequencies, indicating a comparatively lower perception of changes in rainfall. The results highlight the varying degrees of
impact that changes in rainfall patterns have across different wards, reflecting diverse local experiences and potentially differing
environmental or climatic conditions.
Figure 4: Frequencies of Changes in Rainfall Pattern
Precipitation just like temperature is a determinant of climate, Slight changes in amounts received is key to understanding climate
variability. This study purposed to establish rainfall trends in Machakos County for the period 1993 to 2023. It can be concluded
that annual rainfall has significantly varied showing a declining trend.
In the first Quindencennial as captured in (Figure 1.9), 1994, 1997, 1998, 2002, 2005, 2006 recorded a positive deviation. 2006
had the highest rainfall deviation above the mean of 34.5mm while 2002 had the lowest at 4.2mm. The years 1993, 1995, 1996,
1999, 2000, 2001, 2003, 2004, 2007 and 2008 recorded negative rainfall deviation. The year 2001 recorded the highest negative
deviation of 24.3mm while 1993 recorded the lowest of 4.1mm. These low rainfall amounts were occasioned by the La NiΓ±a
Modoki events of 2000-2001, 2008-2009 while rainfall above the annual means were recorded in 1994 (81.5mm), 1997 (68mm),
1998 (92.5mm), 2002 (65.9mm), 2005 (79.7mm), 2006 (96.2mm) the Record high amounts are due to the El NiΓ±o events
experienced in 1997-1998, 2002-2003, 2006-2007.
Figure 5: First Quindencennial Rainfall Anomaly (1992-2008)
In the second Quindencennial as shown in (Figure 2.0), 2010, 2012, 2013, 2015, 2018, 2019, 2020 and 2023 had positive
deviation from the mean. The highest negative deviation was 34.9 mm recorded in 2018 while the lowest positive deviation was
1.3mm recorded in 2012. Negative deviations from the mean were recorded in 2009, 2011, 2014, 2016, 2017, 2021 and 2022. The
highest negative deviation was recorded in 2022 at 36.6 mm while the lowest negative deviation was recorded in 2011 a rainfall
of 4.7mm. Rainfall below the annual/Annual mean were recorded in 1996 (42.1mm), 2000 (40.3mm), 2001 (37.4mm), 2009
(40.7mm), 2016 (23.4mm), 2021 (37.6mm) and 2022 (17.9mm). These low rainfall amounts were occasioned by the La NiΓ±a
Modoki events of 2008-2009, 2010-2011, 2016-2017 and the 2020-2021 while rainfall above the annual means were recorded in
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2010 (66.6mm), 2015 (72.2mm), 2018 (89.2mm), 2019 (66.6mm), 2020 (77.6mm) and 2023 (64.7mm). Record high amounts are
due to the El NiΓ±o events experienced in 2009-2010 and the October 2023 to January 2024.
Figure 6: Second Quindencennial Rainfall Anomaly (2009-2023)
Generally the first Quindencennial had many years recording negative deviations from the mean implying the rainfall received
was lower than the mean. The second Quindencennial had the least negative deviation from the mean implying the period
received high amount of rainfall.
Testing Hypothesis
Based on the analysis of temperature results for Machakos County from 1993 to 2023, (Table 2.2) the following conclusions can
be drawn regarding Hypothesis One, H0
1:
There is no significant variation in the long-term mean annual temperature and rainfall
of Machakos County for period 1993 to 2023. Thus, from the results from the trend analysis, and variability test, the study
rejected the null hypothesis that there is no significant variation in the long-term mean annual temperature of Machakos County
from 1993 to 2023. The findings confirm that there has been a significant variation in mean annual temperatures, with a notable
increasing trend and substantial fluctuations throughout the study period.
Table 1Effect of Temperature Variability on Maize Yields
Source
SS
df
MS Number of obs =
144
F(1, 142) =
65.18
Model
3.672615
1
3.67261462 Prob > F =
0
Residual
8.001312
142
.056347271 R-squared =
0.3146
Adj R-squared =
0.3098
Total
11.67393
143
.081635854 Root MSE =
0.23738
Yield (MTHA)
Coef.
Std. Err.
T
P>t
[95% Conf.
Interval
Av temp
-0.054
0.007
8.070
0.000
0.041
0.067
cons
-0.477
0.123
-3.870
0.000
-0.720
-0.233
With an F-statistic of 65.18 and a p-value of 0.000, the model demonstrates that temperature variability is a statistically
significant predictor of maize yields. The R-squared value of 0.3146 indicates that approximately 31.46% of the variability in
maize yields can be explained by temperature variability. The study showed a negative and significant effect of temperature
variability on maize yields among farming households in Machakos County (Ξ²=-0.054, p=0.000). This implies that, holding other
factors constant, a one-unit increase in temperature variability is associated with a 0.054 decrease in maize yields (measured in
metric tons per hectare). Thus, it can be implied that extreme temperature fluctuations can disrupt the critical growth stages of
maize, such as pollination and grain filling, leading to reduced yields.
Effect of Extreme Temperature and Rainfall Anomalies on Maize Yields among Farming Households in Machakos
County for Period 1993 To 2023
The impact of extreme temperature and rainfall anomalies on maize yields is a crucial area of study, particularly for regions like
Machakos County, where maize is a staple crop. This section will explore the relationship between climatic extremes and maize
production over the past three decades, from 1993 to 2023. By analyzing the effects of these anomalies on maize yields, we aim
to understand the vulnerabilities of farming households to climate variability. This analysis highlights the extent to which extreme
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weather events have influenced maize productivity, offering insights into the broader implications for food security and
agricultural sustainability in Machakos County.
Annual Maize Output in Machakos County
The annual maize output in Machakos County, Kenya, is influenced by various factors including climate variability, agricultural
practices, soil fertility, pest management, and socio-economic conditions. Maize is a staple crop in Kenya and plays a crucial role
in food security and livelihoods, particularly in rural areas like Machakos County. By studying annual maize output, Machakos
County can strengthen its agricultural sector, improve food security, and contribute to the overall development goals of Kenya.
Maize Varieties Grown in Machakos County
The respondents were also asked to indicate which major maize varieties they have been cultivating. The survey results provide
valuable insights into the preferences and practices of farmers in each region. The data highlights the diversity in maize variety
adoption and sheds light on the predominant varieties grown by farmers. In Ikombe sub-county, the majority of farmers primarily
cultivate Duma 43, which accounts for 74% of the responses. This is followed by a combination of Kikamba and Duma 43 (6.8%)
and other varieties like Dk 47 and Kikamba, each representing smaller percentages. Kaani/Kaewa sub-county also shows a
significant preference for Duma 43, with 60% of farmers indicating its cultivation. Pioneer holds a notable share at 7.3%,
demonstrating a diverse selection of varieties but with a clear preference towards Duma 43. In Kangundo East, Duma 43 is the
most popular, reported by 56.9% of respondents. Pioneer is also significant, with 27.5% of farmers opting for this variety. This
suggests a two-fold preference in this sub-county for both established and newer maize varieties. Matungulu exhibits a more
diversified pattern in maize cultivation, with combinations like Duma 43/Dana, Duma 43/Sungura, and Katumani/Duma 43 each
accounting for 19% of responses. This indicates a preference for hybrid varieties catering to specific farming needs and
environmental conditions. Dk 47/Duma 43 and Sungura varieties also hold substantial shares, reflecting a mix of traditional and
newer maize varieties among farmers. In Mwala/Makutano, Duma 43 is the dominant variety, chosen by 25.6% of respondents,
followed by Dk 47 with 8.9%. This sub-county shows a preference for established varieties like Duma 43, though the presence of
Dk 47 indicates some diversity in variety adoption among farmers. The predominance of Duma 43 across multiple sub-counties
suggests its popularity due to its adaptability, yield potential, and possibly other agronomic traits valued by farmers.
Effect of Temperature Variability on Maize Yields among farming households in Machakos County
The regression analysis investigating the effect of temperature variability on maize yields among farming households in
Machakos County presents significant findings.
Effects of Temperature anomalies on Maize yields in Machakos County
The analysis of the correlation between temperature anomalies and maize yields in Machakos County reveals several insights into
how deviations in temperature might impact crop performance. Firstly, the correlation between minimum temperature
deviations and maize yields is moderately positive, with a coefficient of r = 0.2546. In contrast, the correlation between
maximum temperature deviations and maize yields is very weak and negative, with a coefficient of r=βˆ’0.0233. The
relationship between mean temperature deviations and maize yields is also very weak and positive, with a correlation
coefficient of r=0.0661. The correlation between temperature deviations from the mean and maize yields is weakly positive,
with a coefficient of r=0.1016. This suggests that deviations in temperature from the average have a slight positive impact on
maize yields.
Effect of Rainfall Variability on Maize Yields among farming households in Machakos County
The study investigated the impact of rainfall variability on maize yields among farming households in Machakos County. This is
done by performing a regression analysis to determine the relationship between rainfall variability and maize yield.
Table 3: Regression Analyses between Rainfall Variability on Maize Yields
Source
SS
MS Number of obs =
144
F(1, 142) =
0.000
Model
0.000373
.000373211 Prob > F =
0.946
Residual
11.67355
.082208126 R-squared =
0.000
Adj R-squared =
-0.007
Total
11.67393
.081635854 Root MSE =
0.28672
Yield (MTHA)
Std. Err.
t
[95% Conf.
Interval]
RAINFALL
0.000
-0.070
-0.001
0.001
_cons
0.029
17.240
0.448
0.564
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The results of the regression analysis indicated that the model's Sum of Squares (SS) for rainfall variability was 0.000373 with 1
degree of freedom (df), resulting in a Mean Square (MS) of 0.000373211. The statistical insignificance of the model was evident
from the F-statistic value of 0.000 and a corresponding probability (Prob > F) of 0.946, indicating no meaningful relationship
between rainfall variability and maize yields. Further, the regression results showed an R-squared value of 0.000, signifying that
the model explained none of the variability in maize yields. The adjusted R-squared value was -0.007, reinforcing that the
inclusion of rainfall variability as a predictor did not enhance the model's explanatory power. The study showed a negative but
insignificant effect of rainfall variability on maize yields among farming households in Machakos County (Ξ²=-0.020, p=0.946).
This implies that, holding other factors constant, a one-unit increase in rainfall is associated with a 0.020 decrease in maize yields
(measured in metric tons per hectare). Thus, rainfall variability has severe implications on the yields of maize.
Effects of Rainfall anomalies on Maize yields in Machakos County
Rainfall mean data shows considerable variability over the years, with fluctuations ranging from a low of 17.9 mm in 2022 to a
high of 96.2 mm in 2006. The mean rainfall over the entire period is 59.9 mm. Maize production has varied significantly, with the
highest production was 143,825 MT in 1994, and the lowest was 55,300 MT in 2005. There appears to be a general increase in
production in recent years, with notable peaks in 2009 and 2015. In years with higher rainfall (e.g., 1994, 2006, 2015), maize
production also tended to be higher, suggesting that adequate rainfall supports better crop yields. Conversely, years with lower
rainfall (e.g., 2000, 2005, and 2020) often show lower maize production, indicating that insufficient rainfall adversely impacts
yields. Periods of excessive rainfall can lead to flooding, for instance, the high rainfall in 2006 coincided with substantial maize
production but can also be attributed to potential risks of over-saturation. Insufficient rainfall often leads to drought conditions,
which significantly impact maize yields. For instance, the low rainfall in 2000 and 2005 aligns with reduced maize production,
highlighting the adverse effects of drought on crop yields. When considering rainfall anomalies, both the mean rainfall (r=0.0540)
and deviations in rainfall from the mean (r=0.0619) show very weak positive correlations with maize yields. These low
correlations suggest that variations in rainfall have a minimal effect on maize yields. This could imply that other factors may have
a more pronounced impact on maize yields, or that the crop is relatively resilient to variations in rainfall within the observed
range. Generally, moderate to high rainfall correlates positively with maize yields, provided the rainfall is well-distributed and
occurs at critical growth stages. Both excessive and deficient rainfall can lead to anomalies in maize yields. Excessive rainfall
may result in waterlogged fields, while deficient rainfall can cause drought stress, both leading to lower yields. In 1994, higher
rainfall was associated with high maize production (143,825 MT). In contrast, 2005 experienced low rainfall and correspondingly
lower maize production (55,300 MT), highlighting the detrimental effect of drought conditions.
Figure 7: Rainfall mean vs Production in MT
III. Conclusions and Recommendations
The study on the effect of extreme temperature and rainfall anomalies on maize yields among farming households in Machakos
County from 1993 to 2023 has yielded several key conclusions. The relationship between cultivated area and production has been
volatile, with production showing significant variability despite the cultivated area remaining within a certain range. For instance,
in 2000, despite a cultivated area of 162,000 hectares, production plummeted to 58,320 metric tonnes. Similarly, in 2015, the
cultivated area was 125,652 hectares, yet production surged to 121,682 metric tonnes. Rainfall variability has a significant impact
on agricultural yields across the sub-counties. The years 1993, 1995, 1996, 1999, 2000, 2001, 2003, 2004, 2007, 2008, 2009,
2011, 2012, 2013, 2014, 2016, 2017, 2021, and 2022 experienced positive deviations from the mean rainfall, indicating higher-
than-average rainfall. Conversely, the years 1994, 1997, 1998, 2002, 2005, 2006, 2010, 2015, 2018, 2019, 2020, and 2023
witnessed negative deviations from the mean, indicating lower-than-average rainfall. The study found a statistically significant
negative effect of temperature variability on maize yields, with a coefficient (Ξ²) of -0.054 and a p-value of 0.000. This suggests
that as temperature becomes more unpredictable, maize yields suffer considerably. In contrast, the study found a negative but
statistically insignificant effect of rainfall variability on maize yields, with a coefficient (Ξ²) of -0.020 and a p-value of 0.946. This
implies that, holding other factors constant, a one-unit increase in rainfall variability is associated with a 0.020 decrease in maize
yields.
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Recommendations of the study
Based on the findings of this study, the following recommendations are proposed to enhance maize yields and address the
challenges faced by farming households in Machakos County. Firstly, Climate justice through promoting sustainable agricultural
practices and providing access to resources can significantly support small-scale farmers in improving their maize yields. This
includes initiatives such as land consolidation, access to improved seeds, and targeted extension services. Secondly, promoting
more heat-tolerant maize varieties, improving water harvesting and storage techniques, and encouraging the adoption of irrigation
systems are key components of these strategies. These measures will help mitigate the adverse effects of unpredictable rainfall
and ensure consistent maize production. Thirdly, strengthening integrated pest management programs is essential for effectively
controlling pests like armyworms and stock borers that threaten maize crops. This can be achieved by promoting the use of
biological control agents, encouraging the adoption of resistant maize varieties, and providing training on proper pest
management practices. Lastly, implementing soil fertility management programs is necessary to address soil exhaustion and
maintain the long-term productivity of agricultural lands. Promoting the use of organic matter, encouraging crop rotation, and
providing access to affordable and sustainable fertilizers are effective ways to enhance soil health.
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2. Government of Kenya. (2018). National Climate Change Action Plan 2018–2022 Volume II: Adaptation Technical
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