This document summarizes a study on farmers' perceptions of the effects of climate variability on dairy farming in Masaba North, Nyamira County, Kenya. The study found that temperatures have risen by 0.5 degrees Celsius over the past 30 years, while rainfall amounts have fluctuated. Dairy production levels are declining due to climate impacts. Farmers perceive that climate variability has increased disease and pests affecting cattle. The most common adaptation strategy was crossbreeding cattle to improve heat and drought resistance. The study concludes climate change has likely contributed to lower milk production in the region. It recommends further research and education for farmers on climate impacts and adaptive strategies.
1. FARMERS’ PERCEPTIONS ON THE EFFECTS OF CLIMATE
VARIABILITY ON DAIRY FARMING IN MASABA NORTH, NYAMIRA
COUNTY, KENYA
BY
NGARE OSORO INNOCENT (BSc.C.R.M)
N50/27277/2014
2. Introduction
• Climate change has been termed as the most challenging global phenomenon
of our time( Bulkely and Newell, 2015).
• To the dairy sector, the complexity of climate variability affects animal
health, feeding, disease resistance and fertility (Kolbert, 2015)
• In Africa, for decades it has been dominated by indigenous breeds. However
today crossbreeding is done to improve breeds (Mwai et al., 2015).
• Ngeno et al., (2014), indicate that Sub-Saran Africa already has been hit by
climate variability effects on its livestock systems making dairy farming to be
vulnerable
3. Problem Statement
The destroying effects of climate variability globally are increasing and their
effects are predicted to occur in developing countries such as Kenya (Musema
et al., 2012).
Dairy farming levels of production continues to decline due to climate
variability effects (Moreki and Tsopito, 2013).
Dairy production in Masaba North is vulnerable to climate variability effects,
affecting both the farmers and dairy cattle.
No research has been done in the study area to determine farmers’ perception
on effects of climate variability on dairy farming in the study area.
4. Research Questions
1. What is the trend of climate variability in Masaba North for the past 30
years?
2. How is the level of dairy production in Masaba North and its productivity
trend?
3. How do dairy farmers perceive climate variability effects on dairy cattle?
4. How do dairy farmers mitigate against climate variability on dairy
farming?
5. Research Objectives
1. To determine the trend of climate variability in Masaba North for the past
30 years.
2. To ascertain the level of dairy production in Masaba North and its
productivity trend.
3. To identify the dairy farmers’ perceptions of climate variability effects on
dairy cattle.
4. To determine strategies used by dairy farmers to mitigate climate
variability
8. Methodology
• The study employed a survey design
• The target populations was dairy farmers (12,000)
• The study used purposive sampling and systematic random sampling
techniques
• Nasiuma 2000 formula was used to determine the sample size (100
respondents)-
Where n= Sample size , N= Population , Cv = Coefficient of variation
(take 0.5) & e = Tolerance of desired level of confidence
• collection tools were questionnaires, interviews and Observation
9. Findings
Objective 1 : Climate Variability trend in the past 30 years(1980-2015)
Maximum average temperatures (1980-2015)
Figure 4.8: Maximum average temperature
Temperature in the study area has slightly increased with an average of around +0.50 C. This study agrees with
IPCC and NOAA, 2012 that suggests global average temperature increase of +0.9°C through models.
10. Cont.’…
Annual precipitation (1980-2015)
Figure 4.1: Annual precipitation
Observed noted changes, increase and decrease might have resulted from climate variability. This
findings therefore agrees with that of Gobiet et al., 2014 through climate modes where some parts
will receive variations in rainfall annually.
11. Objective 2: Dairy production trend
Table 4.4
From the findings, the indigenous breed were the the least performers ranking 6th in milk
production where the Friesian ranked 6th as per milk productivity. The findings disagrees
with Buaban et al., 2015 that says exotic breeds are not well performers in milk production
within the tropics.
Breed Rankin Scale
Total (%)
1 2 3 4 5 6
Friesian 75 10 7 8 0 0 100
Guernsey 3 49 27 23 5 0 100
Jersey 6 20 44 20 1 2 100
Ayrshire 5 16 37 38 4 0 100
Crossbreed 7 4 19 31 36 3 100
Indigenous 3 1 1 2 13 80 100
12. Objective 3: Dairy farmers perception on climate variability effects to
dairy cattle
Table 4.10
Through Likert-Scale, farmers felt disease increase was due to climate variability with a
T.W(443.7)
The findings agree with Chevalier et al., 2016 that climate change has contributed to vector-
bone disease increase affecting animal feeding. Thornton and Gerber 2010 had a similar
observation from their study on climate variability impact on livestock.
STATEMENTS Responses
S.A A UD D S.D (Total Weight)
C.V increases disease and 37(52.1) 29(40.9) 4(5.6) 1(1.4) 0(0.0) (443.7)
insect-pest infestation in animals
Dairy cattle feeding behavior has
changed
32(47.1) 21(30.9) 9(13.2) 5(7.4) 1(1.5) (411)
C.V has no effect on livestock
farming
0(0.0) 4(5.9) 8(11.9) 34(50.7) 21(31.3) (192)
C.V causes no animal health risks 0(0.0) 1(1.4) 2(2.8) 33(46.4) 35(49.3) (248.9)
13. Objective 4: Climate variability mitigation strategies by
dairy farmers
Figure 4.10
Crossbreeding had 42% as the leading mitigation strategy in the area with fodder
storage and planting feeds that are climate variability resistant coming 2nd and 3rd
respectively.
14. Hypotheses
• H0 An increase in temperature results to a decrease in milk
production in Masaba North
The critical chi-square was (ᵪ2=0.103). After performing the test, it was
realized that the obtained chi- square was (ᵪ2=0.087,df=2,p=0.001). The
obtained chi-square value is less that the critical chi-square value.
Therefore, the hypothesis was accepted
• H0 Climate variability affects fodder growth and milk production in
Masaba North
The obtained chi-square value is (ᵪ2 =1.213, df=6,p=0.003). From
computation it is less that the critical chi-squire value(ᵪ2 =1.635).
Therefore, the hypothesis was accepted.
15. Summary
• There were changes in temperature and rainfall between (1980-2015).
Averagely temp had rose by +0.5C
• Among the dairy breeds , indigenous breeds were least performers (6th
ranking scale) with exotic breeds leading( 1st ranking scale)
• Majority of the farmers perceived climate variability had affected dairy milk
production Total Weight (443.7)
• Crossbreeding was the popular climate variability mitigation strategy to
better production(42%)
16. Conclusion
• The findings showed that climate had varied in Masaba thus probably
framers’ have experienced low milk production
• Dairy farmers perceived that climate variability was affecting milk
production.
17. Recommendations
• Dairy farmers to be educated on climate variability effects and
mitigation.
• An experimental research to be done to establish whether climate
variability leads to milk production.
• More study on exotic dairy cattle hybrids that are resistant to climate
variability to be done.
