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American Journal of   
Environment and Climate (AJEC)

Knowledge, Trust, and Resilience: Factors Shaping Climate Adaptation in Zanzibar’s 
Agricultural Sector

Mohamed Khalfan Mohamed1*

Volume 4 Issue 3, Year 2025
ISSN: 2832-403X (Online) 

DOI: https://doi.org/10.54536/ajec.v4i3.5300
https://journals.e-palli.com/home/index.php/ajec

Article Information ABSTRACT

Received: May 25, 2025

Accepted: June 30, 2025

Published: September 13, 2025

Communities that depend on agriculture are seriously threatened by climate change, 
especially smallholder farmers in low-income countries like Zanzibar. This study examines 
how farmers in Unguja Island’s Central District view climate variability and change, what 
influences their views, and how Climate-Smart Agriculture (CSA) methods are being 
adopted. Data were gathered from 396 households in five villages using a mixed-methods 
approach that included key informant interviews, focus groups, and structured surveys. 
The majority of  farmers reported higher temperatures, less consistent and variable rainfall, 
and shorter growing seasons, according to statistical analyses using logistic regression and 
descriptive methods. These factors all contributed to decreased crop yields and increased 
food insecurity. Education level, farming experience, access to extension services, climate 
information, and participation in CSA training all had a significant impact on how people 
perceived climate change. Adoption of  CSA practices is still uneven despite widespread 
awareness, with high adoption of  pest management and irrigation techniques but low 
engagement in crop diversification and planting calendar adjustments. These findings 
underscore the urgent need for evidence-based, locally-based policies that improve access to 
climate information, strengthen institutional support, and encourage inclusive CSA strategies 
to increase the adaptive capacity of  smallholder farmers in Zanzibar and similar contexts.

Keywords
Adaptation, Agricultural 
Resilience, Climate Change, 
Climate-Smart Agriculture, 
Logistic Regression, Smallholder 
Farmers, Zanzibar  

1 Abdulrahman Al-Sumait University, Zanzibar, Tanzania
* Corresponding author’s e-mail: mohamed.khalfan76@gmail.com

INTRODUCTION 
Climate change continues to pose a huge worldwide 
hazard, harming human well-being, economic activity, 
health systems, livelihoods, and food supply (Dhimalet al., 
2021). Although its detrimental consequences are observed 
worldwide, developing nations, particularly smallholder 
farmers living in poverty, are disproportionately affected 
due to their low capacity to adapt (Hernández-Delgado,  
2015). In many nations across Sub-Saharan Africa (SSA), 
agriculture is prioritized as a strategy to secure accessible 
and sufficient food supply, given its essential role in 
achieving food security. However, agriculture is still quite 
vulnerable to short-term and long-term climate changes. 
Regretfully, many emerging regions still lack the necessary 
and robust steps to strengthen this vital sector (Rashid, 
2019).
Therefore, neglecting to address climatic variability 
and change could result in severe food insecurity, with 
developing countries likely to experience the worst 
effects (Rashid, 2019). For instance, a 1.5°C increase in 
temperature is predicted to exacerbate climate-related 
risks to human safety, livelihoods, food availability, water 
resources, public health, and economic growth in many 
low-income nations (Colombini et al., 2023). Due to their 
heavy reliance on rain-fed crops, smallholder farmers are 
particularly vulnerable to the effects of  climate change. 
Widespread poverty, inadequate infrastructure, and a 
lack of  technical improvements all contribute to their 
vulnerability (Mbuli et al., 2021). Due in large part to 
Tanzania’s extreme sensitivity to variations in rainfall, the 

effects of  climate change have presented significant risks 
to the country’s economy and food security (Gwambene 
& Mung’ong’o, 2023). The agricultural industry, which is 
still essential to Tanzania’s economic growth, has been 
hampered by these climate changes (Kahimba et al., 2015).
With 65–70% of  the workforce employed in the 
agricultural sector (Gupta, 2020) and a GDP contribution 
of  roughly 26–30% (Epaphra & Mwakalasya, 2017), the 
industry is extremely sensitive to the effects of  climate 
change (Mafie, 2022). Since most farming is rain-fed, 
crop yields, food security, and rural livelihoods are 
directly threatened by altered rainfall patterns, protracted 
droughts, and extreme weather events (Mafie, 2022). 
The production of  important export products, including 
coffee, cotton, tobacco, cashew nuts, and cloves, which 
contribute between 30 and 40 percent of  the nation’s 
foreign exchange earnings, is similarly impacted by 
climate change (Epaphra & Mwakalasya, 2017). The 
agricultural sector is especially vulnerable due to its 
reliance on natural weather cycles, which presents threats 
to national development, economic stability, and poverty 
alleviation (Gwambene et al., 2023). Tanzania’s agricultural 
production and food systems must be protected in the 
face of  climate change by bolstering climate-resilient 
farming methods, increasing irrigation, and investing 
in early warning systems and climate-smart agriculture 
(Komba & Muchapondwa, 2018).
Through adaptive strategies, the agriculture sector has a 
great deal of  potential to improve resilience and mitigate 
the effects of  climate change (Liu et al., 2024). Despite 



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being essential to the production of  food, the industry 
contributes significantly to deforestation, which is 
thought to be the cause of  7–14% of  world greenhouse 
gas emissions, and is also accountable for about 3.7% of  
global GHG emissions (Filonchyk et al., 2024). However, 
the industry is extremely vulnerable to the negative 
effects of  climate change, which will especially affect 
low-income and smallholder farmers who frequently lack 
the means and ability to adequately respond to climatic 
shocks (Praveen & Sharma, 2019). Food instability and 
worldwide poverty are made worse by this susceptibility 
(Mulugeta, 2023). Therefore, tackling environmental and 
socioeconomic issues globally requires a dual strategy that 
reduces agricultural emissions while employing adaptive 
measures to maintain crop production (Jellason, 2018).
In many places, ecosystems, economic livelihoods, and 
agricultural output have all suffered as a result of  climate 
change (Praveen & Sharma, 2019). The viability of  
food production and subsistence methods is seriously 
threatened by these changes, especially in vulnerable 
African populations like those in Tanzania (Sieber et al., 
2015). To lessen these negative effects, local farmers’ 
ability to adapt to climate unpredictability must be 
strengthened. This entails putting policies into place that 
address the root causes of  climate change and lessen 
the vulnerability of  crops and subsistence systems to 
climatic stresses (Bedeke, 2023). Communities’ level of  
adaptation and readiness for climate change is directly 
related to how vulnerable they are. Smallholder farmers 
in Sub-Saharan Africa (SSA) account for around 80% of  
all agricultural activities and represent the backbone of  
the food systems in the region (Bahri et al., 2021). Even 
though they have shown remarkable resilience, the speed 
at which climate change is occurring poses a threat to 
surpass their present coping mechanisms. These farming 
systems’ susceptibility is further increased by a lack of  
institutional support, poor market connections, limited 
financial resources, and limited access to technology 
(Petersen-Rockney et al., 2021).
The preservation of  vital natural resources and the 
upkeep of  important ecosystem services are necessary 
to guarantee food security while tackling the problems 
caused by climate change (Telo da Gama, 2023). It 
is imperative that we move toward more sustainable 
farming methods, methods that encourage the efficient 
use of  resources while also increasing productivity. By 
making this change, agricultural outputs become more 
stable, yield variations are reduced, and resilience to 
environmental hazards, shocks, and persistent climate 
variability is strengthened. One of  the most effective 
frameworks for accomplishing these objectives is 
Climate-Smart Agriculture (CSA) (Karri & Nalluri, 2024). 
By concurrently increasing output, improving adaptive 
capacity, and reducing greenhouse gas emissions that fuel 
climate change, it provides a “triple win” (Islam, 2024; 
Guillen-Hanson et al., 2018).
Climate-Smart Agriculture (CSA) is a progressive 
agricultural approach that aims to maximise benefits 

while minimising trade-offs by considering the unique 
social, economic, and environmental conditions of  
each application context. It is intended to improve 
subsistence farming and food security, with a focus on 
supporting smallholder farmers (Amejo et al., 2018). 
Its effectiveness can be greatly increased through the 
adoption of  appropriate technologies and practices 
across the agricultural value chain—including 
production, processing, and market access—as well 
as the sustainable management of  natural resources. 
According to Rodríguez-Barillas et al. (2024), farmers’ 
willingness to adopt CSA practices is influenced by their 
perception of  climate-related risks, and it is generally 
the case that those who perceive greater climatic threats 
are more likely to implement CSA measures. A variety 
of  factors, such as socio-economic status, geographic 
and environmental conditions, institutional support, and 
farm-level characteristics, influence the choice of  specific 
CSA strategies (Thottadi & Singh, 2024).
Smallholder farmers are the primary drivers of  the 
agricultural industry in Zanzibar Island’s Central 
District, making significant contributions to both local 
food security and economic stability. Despite playing a 
crucial role, little empirical research has been done on 
how farmers view climate variability and change, two 
aspects that have a significant impact on the adoption 
of  Climate-Smart Agriculture (CSA) techniques for 
resilience and adaptation. This knowledge gap makes 
it more difficult to develop evidence-based policies, 
make calculated investments, and carry out locally 
customised interventions that would increase the farming 
community’s capacity for adaptation. In light of  this, 
the purpose of  this study is to methodically investigate 
farmers’ awareness, attitudes, and reactions to stresses 
associated with climate change in the Central District. 
It is anticipated that the results would produce empirical 
insights that will help establish institutional policies, 
improve extension services, and pinpoint scalable CSA 
approaches appropriate for Zanzibar’s agro-ecological 
circumstances and similar settings.

MATERIALS AND METHODS
Study Area 
The Central District (Wilaya ya Kati) is one of  six districts 
on Unguja Island in the United Republic of  Tanzania’s 
Zanzibar Archipelago (Figure 1). Approximately between 
latitudes 6°13′ and 6°25′ South and longitudes 39°20′ and 
39°35′ East, the district is situated in the central-eastern 
region of  Unguja Island. It shares boundaries with the 
West District to the west, South District to the south, the 
North A District to the north, and the Indian Ocean to 
the east. The district comprises several shehias (wards) 
that serve as units for administrative, developmental, 
and statistical purposes. The district experiences a 
tropical climate characterized by two rainy seasons: the 
long rains (Masika) from March to May and the short 
rains (Vuli) from October to December. Mean annual 
rainfall ranges between 1,000 mm and 1,600 mm, with 



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average temperatures hovering between 24°C and 32°C 
throughout the year (RGoZ, 2013). However, recent 
years have witnessed a noticeable increase in climate 
variability and extreme weather events, including irregular 
rainfall patterns, prolonged dry spells, and occasional 
flash floods (URT, 2022). The majority of  the population 
in the Central District depends on subsistence agriculture 
as their primary livelihood. Rice, bananas, sweet potatoes, 
cassava, and spices like black pepper, cinnamon, and 
cloves are examples of  common crops (URT, 2022). These 
farming systems are extremely susceptible to drought 
and unpredictable rainfall because they are mostly rain-

fed and have little irrigation infrastructure. Petty trading, 
small-scale fishing, and subsistence animal rearing are 
ways that smallholder farmers augment their incomes. 
Ecologically vulnerable regions, including as portions of  
the Jozani-Chwaka Bay National Park, which is home to 
Zanzibar’s only surviving natural forest, are also located in 
the Central District. In addition to having large mangrove 
forests and wetlands that help store carbon, protect the 
shoreline, and conserve biodiversity, this region is home 
to a variety of  endemic and endangered species, such as 
the Zanzibar red colobus monkey (Piliocolobus kirkii) 
(RGoZ, 2014).

Figure 1: Study area

Sampling Techniques and Data Collection
To guarantee a representative selection of  the target 
household population throughout the study area, a 
stratified multi-stage random sampling approach was 
used. A structured questionnaire that was rigorously 
pre-tested to evaluate its validity, reliability, and internal 
consistency served as the main tool for gathering 
data. To make it easier to gather both quantitative and 
qualitative data, the questionnaire included both open-
ended and dichotomous items. Prior to the primary data 
collection effort, a pre-testing phase was carried out on 
a pilot group of  farmers in the study location in order 
to find and address ambiguities and improve contextual 
relevance. Three focus group discussions (FGDs) were 
arranged in easily accessible locations inside each chosen 
ward in order to supplement the survey data and improve 
triangulation. Eleven people participated in each FGD. 
Key community stakeholders that were chosen for the 
FGDs based on their familiarity with locality agricultural 
practices and institutional dynamics included religious 
leaders, village elders, and members from women’s and 
youth associations.
Semi-structured key informant interviews (KIIs) 
were carried out with 32 employees from pertinent 
governmental and non-governmental organisations in 
order to gather institutional viewpoints and enhance 

comprehension of  adaptive techniques advocated 
to assist local farmers. These included academicians 
from Zanzibar’s higher education institutions as well 
as representatives from the Tanzania Meteorological 
Authority, the Ministry of  Agriculture, and the Zanzibar 
Agriculture Research Institute.  Using Cochran’s sample 
size determination formula (Heinisch & Cochran, 1965), 
396 households were selected from five villages in the 
central district (Chwaka, Binguni, Fuoni, Mwera, and 
Bambi). This allowed for proportionate distribution 
among the three wards in accordance with population 
distribution data. In order to optimise data timeliness and 
relevance, the research’s empirical phase was conducted 
over two months, from February to March 2025, which 
corresponded with the main agricultural season.

Data Analysis 
IBM SPSS Statistics software (version 23), which is well 
known for its resilience when working with social science 
datasets, was used to do quantitative data analysis (Bala, 
2016). The main variables of  interest were summed up 
using descriptive statistical methods, such as frequencies, 
percentages, means, and standard deviations. These 
included the respondents’ socioeconomic characteristics 
(e.g., age, household size, income sources, and education 
level), institutional factors (e.g., membership in 



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agricultural cooperatives or access to extension services), 
farmers’ perspectives on climate change and variability, 
and the particular climate-smart agriculture (CSA) 
practices implemented. A logistic regression model was 
used to investigate the underlying elements impacting 
farmers’ views of  climate change. The likelihood that 
a farmer would perceive climate change in general, an 
increase in temperature, or a decrease in rainfall—each 
represented as a separate dependent (dummy) variable—
was estimated using this model, which is appropriate for 
analysing binary (yes/no) outcomes (Song et al., 2021). A 
variety of  demographic, social, and institutional elements 
that were thought to influence people’s perceptions were 
included as independent variables in the model.
To find out how farmers who used CSA techniques 
differed from those who did not, comparative analyses 
were conducted. For these comparisons, a number of  
inferential statistical tests were used. While the chi-square 
(χ²) test evaluated relationships between categorical 
variables, the analysis of  variance (ANOVA) and the 
F-test were utilised to compare means across groups. 
Mean comparison tests shed more light on the factors 
that influence CSA adoption by elucidating statistically 
significant variations in important variables between 
adopter and non-adopter groups. The Cronbach’s 
alpha test, a statistical indicator of  internal consistency, 
was used to evaluate the questionnaire’s reliability. The 
questionnaire items showed adequate internal coherence 
and were probably assessing the intended constructs 
reliably across the sample, as evidenced by the Cronbach’s 
alpha value of  0.730, which is higher than the generally 
accepted threshold of  0.70.

Ethical Considerations 
Ensuring that every participant understood the goal and 
nature of  the study was crucial before starting the data 
gathering procedure. In order to do this, each respondent 
gave their informed consent. This required giving 
thorough and understandable explanations of  the study’s 
goals, the kinds of  data that would be gathered, and 
the intended uses of  the data. Respondents were given 
the opportunity to ask questions and were guaranteed 
the freedom to choose to participate in the study at 
any time without facing any repercussions. The study’s 

participants’ rights to confidentiality and privacy were 
protected throughout the consent process, which was 
conducted in accordance with ethical research norms. 
The data was collected only after their express agreement 
was obtained.

RESULTS AND DISCUSSION
Results 
Socioeconomic and Demographic Profiles of  
Smallholder Farmers
According to the demographic profile of  the populations 
of  Chwaka, Binguni, Fuoni, Mwera, and Bambi, the 
majority of  the population is Muslim, with percentages 
averaging 98.8% and ranging from 97.3% in Mwera to 
100% in Binguni (Table 1). In comparison, the percentage 
of  Christians is rather low, averaging around 1.2%. The 
proportion of  genders is fairly balanced, with 50.7% 
of  the population being female and 49.3% being male. 
A population of  seasoned farmers is shown by the age 
distribution, with the largest percentage of  people in 
the 50–59 age range (average 54.2%) and a significant 
presence in the 40–49 age range (30.8%). Although still 
present, the 60+ age group makes up only 15% of  the 
population, with Bambi having the lowest number (5.8%). 
Data on marital status reveals a low divorce rate (4.5%) 
and a moderate percentage of  widowed people (4.8%), 
together with high marriage rates (90.7% on average).
Additionally, research shows that only 3.6% of  people 
have finished higher education, indicating inadequate 
access to university education. With an average of  54.0%, 
secondary education is more prevalent, especially in 
Binguni (70.5%). With an average of  42.4%, primary 
education is the most common educational level, with 
Chwaka having the largest percentage (56.4%). According 
to household size data, the majority of  households 
(59.6% on average) are between six and ten people, with 
Chwaka having the largest households (70.3%). With an 
average of  25.5%, households with one to five people 
are less prevalent than those with ten or more people, 
which make up 15% of  the population. These results 
imply that the population is made up of  a well-established 
group of  seasoned farmers who have larger families and 
comparatively little formal education beyond secondary 
school.

Table 1: Socioeconomic and demographic variables of  participants in the study areas
Variable Sub-Category Chwaka (%) Binguni (%) Fuoni (%) Mwera (%) Bambi (%) Average (%)
Religion Muslims 99.1 100 99.6 97.3 98.2 98.8

Christians 0.9 0 0.4 2.7 1.8 1.2
Gender Male 48.4 49.8 47.5 46.7 54.2 49.3

Female 51.6 50.2 52.5 53.3 45.8 50.7
Age 40–49 22.3 34.5 33.2 38.4 25.5 30.8

50–59 56.3 50.6 52.2 43.2 68.7 54.2
60+ 21.4 14.9 14.6 18.4 5.8 15.0

Marital 
Status

Married 90.2 83.5 94.3 92.4 93.3 90.7
Widowed 8.2 5.4 1.1 4.2 5.1 4.8
Divorced 1.6 11.1 4.6 3.4 1.6 4.5



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Agricultural Access and Support Indicators of  
Smallholder Farmers 
The study used Chi-square (χ²) tests to analyse ten 
binary (yes/no) indicators in order to investigate spatial 
differences in farmers’ access to basic agricultural 
services across five communities: Chwaka, Mwera, Fuoni, 
Bambi, and Binguni. There were notable variations in a 
number of  categories (Table 2). For example, farmers in 
Fuoni and Mwera reported having access to agricultural 
advising services at rates of  34.8% and 34.6%, 
respectively, whereas only 24.1% and 19.7% of  farmers in 
Chwaka and Binguni, respectively, reported having such 
services. At the 1% level, significance was shown by the 
comparable χ² value of  16.8. The biggest difference was 
also shown in access to extension officer visits, with only 
2.8% of  farmers in Chwaka reporting visits, compared 
to 31.0% in Fuoni and 29.1% in Mwera. This indicator 
showed a very unequal distribution of  public extension 
services with a χ² value of  35.2, which was also significant 
at the 1% level.
Some services did not exhibit considerable spatial 
variation and showed consistently low access across all 
areas. With a non-significant χ² value of  4.6, access to 
agricultural credit or loans was less than 20% in every 
community, ranging from 11.9% in Binguni to 19.6% 
in Chwaka. Likewise, there was often little access to 
irrigation or dependable water sources; Fuoni reported 
the highest percentage at 32.5%, while Chwaka reported 
the lowest at 12.7% (χ² = 5.8). Even fewer farmers in 
Chwaka and Mwera received early warning systems for 
weather-related hazards, with just 6.0% and 12.9% of  
farmers, respectively, receiving such information (χ2 = 

2.9). These low numbers and statistically insignificant 
differences point to systemic impediments that may be 
caused by institutional flaws and inadequate infrastructure 
in all locations assessed.
Other services displayed differing levels of  importance. 
A χ² value of  7.9, which indicates significance at the 
10% level, showed that the percentage of  participants in 
climate-smart agriculture training varied from 29.3% in 
Bambi to 42.6% in Fuoni. This points to slight regional 
differences in the reach of  climate adaption initiatives. 
Bambi had a higher percentage of  members in farmer 
cooperatives or groups (35.2%) than Mwera (19.4%) 
and Chwaka (20.2%). The χ² score in this case was 10.5, 
which is significant at the 5% level and indicates that 
organisational participation may be influenced by external 
support or local community engagement. The percentage 
of  Binguni and Fuoni that had access to inputs, including 
seeds, fertiliser, and tools, varied from 29.6% to 41.0%; 
however, the χ² value of  3.9 showed no discernible spatial 
variations.
Significant variations in market proximity were noted, 
despite the fact that overall market access was high, 
ranging from 70.5% in Binguni to 87.2% in Fuoni (χ² 
= 6.2, not significant). Only 54.0% of  farmers in Bambi 
and 58.2% in Binguni said that marketplaces were within 
tolerable travel time, compared to nearly 80% of  farmers 
in Chwaka, Mwera, and Fuoni. Significant locational 
disadvantages were indicated by this indicator’s χ² value 
of  22.4, which was significant at the 1% level. For farmers 
in more remote locations, these geographic restrictions 
may limit their capacity to participate in markets, weaken 
their negotiating position, and decrease their profitability.

Education 
Level

Tertiary 3.2 4.3 2.7 1.6 6.3 3.6
Secondary 40.4 70.5 50.6 48.2 60.3 54.0
Primary 56.4 25.2 46.7 50.2 33.4 42.4

Household 
Size

1–5 members 15.3 18.4 25.2 38.3 30.1 25.5
6–10 members 70.3 68.5 60.2 43.5 55.4 59.6
10+ members 14.4 13.1 14.6 18.2 14.5 15.0

Table 2: Spatial Distribution of  Farmer Access to Agricultural Services in the study areas
Farmer Access Indicators 
(yes/no questions)

Responses across wards (%) χ² - 
ValueChwaka Mwera Fuoni Bambi Binguni

Yes No Yes No Yes No Yes No Yes No
Access to advisory services 24.1 75.9 34.6 65.4 34.8 65.2 21.7 78.3 19.7 80.3 16.8
Extension officer visit 2.8 97.2 29.1 70.9 31.0 69.0 25.9 74.1 18.4 81.6 35.2
Access to credit/loans 19.6 80.4 17.5 82.5 16.4 83.6 13.1 86.9 11.9 88.1 4.6
Climate-smart training/info 36.9 63.1 34.7 65.3 42.6 57.4 29.3 70.7 30.3 69.7 7.9
Market accessibility 78.5 21.5 81.0 19.0 87.2 12.8 71.6 28.4 70.5 29.5 6.2
Proximity to market 81.3 18.7 84.3 15.7 83.7 16.3 54.0 46.0 58.2 41.8 22.4
Access to irrigation/water 12.7 87.3 27.8 72.2 32.5 67.5 23.6 76.4 26.1 73.9 5.8
Membership in a farmer group 20.2 79.8 19.4 80.6 21.6 78.4 35.2 64.8 21.8 78.2 10.5
Access to inputs 34.6 65.4 38.2 61.8 41.0 59.0 31.8 68.2 29.6 70.4 3.9
Weather early warning access 6.0 94.0 12.9 87.1 11.4 88.6 9.5 90.5 8.3 91.7 2.9

Note: χ² ≥ 13.28 → Significant at 1%, χ² ≥ 9.49 → Significant at 5%, χ² ≥ 7.78 → Significant at 10%, χ² < 7.78 → Not significant



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Perceptions of  Climate Variability and Change 
Among Smallholder Farmers
Small-scale farmers from five Zanzibari villages—
Chwaka, Mwera, Fuoni, Bambi, and Binguni—perceive 
climate change indicators and their effects on agricultural 
practices and productivity, according to the data given 
(Figure 2). In order to find indications of  climatic changes, 
farmers compared the weather patterns of  today to those 
of  20 to 30 years ago using local indicators that could 
be observed. Perceived decreases in total rainfall amounts 
were indicated by a large percentage of  respondents in 
all five wards, with Fuoni (75.6%) and Chwaka (70.4%) 
expressing the greatest worry. While Bambi (45.2%) and 
Binguni (52.5%) indicated lesser perceptions, Chwaka 
(80.1%) and Fuoni (78.4%) reported the most prevalent 
perceptions of  the delayed commencement of  the rainy 
season. Traditional planting calendars are upset by these 
delays, which frequently result in inconsistencies between 
crop growth phases and water availability.
Many people also thought that the rainy season ended early 
and that the amount of  rainfall was shorter, particularly in 
Chwaka (65.5% and 85.9%, respectively) and Fuoni (60.7% 
and 82.2%). According to these impressions, farmers are 
having shorter growing seasons, which can impede crop 
growth and lower yields. One of  the most strongly perceived 
indicators, especially in Binguni (94.2%), Mwera (88.1%), 
and Bambi (86.8%), was the increased frequency of  dry 
periods during the wet season. Since sporadic dry spells 
harm crops and reduce soil moisture, this unpredictable 

intra-seasonal rainfall variability probably presents one of  
the biggest obstacles to maintaining consistent agricultural 
production. More than half  of  respondents reported 
more intense rainfall episodes in most wards, with Mwera 
(65.0%) reporting the greatest, despite a comparatively 
lesser percentage perceiving such incidents. This points 
to a trend towards more intense and erratic precipitation, 
which can exacerbate soil erosion and flooding. At the 
same period, farmers noticed higher average daytime and 
nighttime temperatures, especially in Chwaka (85.2% and 
78.4%) and Fuoni (88.3% and 80.2%). 
Farmers have observed an increase in the frequency of  
floods and droughts, which reflects the dual nature of  
climate extremes: both excess and scarcity of  water are 
becoming more prevalent. Mwera (72.4%) and Fuoni 
(75.0%) showed the most evidence of  this. The biggest 
percentage of  respondents expressed significant concern 
about the perceived changes in planting and harvesting 
dates brought on by weather variability in Binguni (62.0% 
planting; 49.1% harvesting). Although the degree of  
adaptation may be constrained by information or resource 
availability, these developments show that farmers are 
modifying their agronomic calendars in response to 
changing meteorological conditions. Changes in climate 
patterns were blamed by respondents for lower crop 
yields, particularly in Chwaka (72.6%) and Fuoni (70.7%). 
Food security is at risk due to the combined effects of  the 
previously stated climatic indicators, which work together 
to produce unfavourable growth conditions.

Figure 2: Smallholder farmers’ perceptions of  climate variability in agriculture

Determinants Shaping Smallholder Farmers’ 
Perceptions of  Climate Variability and Change
Important information about the variables affecting 
smallholder farmers’ views of  climate variability 
and change in Zanzibar is revealed by the regression 
analysis. The results show that farmers’ awareness and 
interpretation of  climatic dynamics are significantly 

shaped by a variety of  sociodemographic, institutional, 
informational, and cognitive factors (Table 3). The 
findings indicate that education level is a significant driver 
of  whether or not people perceive that the climate has 
changed. Higher educated farmers were substantially 
more likely to believe that climate change is happening 
(Odds Ratio = 1.82, 95% CI: [1.10, 3.01]). Furthermore, 



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this perception was positively impacted by years of  
farming experience (OR = 1.07), indicating that farmers 
who have been involved in agricultural operations for 
a longer period of  time are better able to detect subtle 
changes in the climate.  Institutional backing is also very 
important. Climate change was more likely to be reported 
by farmers who had access to extension services (OR 
= 2.13), received climate information (OR = 3.45), or 
participated in climate-related workshops (OR = 2.50). 
Furthermore, the likelihood of  recognising climate 
change was considerably raised by dependence on 
traditional knowledge systems (OR = 2.01), membership 
in farmer cooperatives (OR = 1.90), and strong trust in 
the accuracy of  climatic information (OR = 1.85).
Education continues to be a major influence in 
perceptions of  decreased rainfall (OR = 1.76), 
demonstrating the ongoing significance of  formal 
education in understanding climate change. This view 
was also impacted by farm size (OR = 1.58), indicating 
that farmers overseeing larger plots might be more aware 
of  and subject to variations in rainfall. Significantly, 
there was a negative correlation (OR = 0.52) between 
livelihood diversification and rainfall, suggesting that 
farmers who have several sources of  income may be less 
reliant on rain-fed agriculture and, thus, less sensitive to 

variations in rainfall. Perceiving a decrease in rainfall was 
also strongly influenced by information-related factors, 
including participation in workshops (OR = 2.12), access 
to extension services (OR = 1.92), and availability of  
climatic information (OR = 2.98). Furthermore, radio 
transmission of  climate information (OR = 1.81) was 
found to be a significant influence, highlighting the role 
of  mass media in raising awareness in rural areas.
Education (OR = 1.50) and agricultural experience (OR 
= 1.09) were also significant when it came to experiencing 
extreme weather occurrences, supporting the idea that 
knowledgeable and experienced farmers are more likely to 
identify and report climatic impacts. Cooperative-affiliated 
farmers were also more likely to report these experiences 
(OR = 2.01), possibly as a result of  peer conversations and 
shared learning. Direct exposure to organised information 
significantly improves farmers’ capacity to identify and 
describe extreme weather events, as evidenced by the highest 
impacts of  access to climate information (OR = 3.71) and 
workshop participation (OR = 2.88). Additionally, farmers 
were more likely to report experiencing extreme weather if  
they had high trust in government climate actions (OR = 
1.88), and if  they trusted the accuracy of  climate information 
(OR = 2.06), indicating that trust in institutions reinforces 
the perceived relevance of  climate events.

Table 3: Logistic Regression of  Factors Influencing Smallholder Farmers’ Perceptions of  Climate Change
Variables (1) Climate has 

changed
(2) Rainfall 
decreased

(3) Experience with 
extreme weather events

Odds 
Ratio

95% CI Odds 
Ratio

95% CI Odds 
Ratio

95% CI

Age of  farmer (proxy for long-term observation) 1.04 [0.98, 1.11] 1.02 [0.95, 1.10] 1.01 [0.95, 1.09]
Education level (primary, secondary, tertiary) 1.82* [1.10, 3.01] 1.76* [1.02, 3.03] 1.50* [1.01, 2.45]
Years of  farming experience 1.07* [1.01, 1.14] 1.03 [0.97, 1.10] 1.09 [1.02, 1.17]
Farm size (acres, log transformed) 1.2 [0.82, 1.77] 1.58* [1.05, 2.39] 1.44 [0.91, 2.27]
Household size 0.91 [0.71, 1.18] 1 [0.78, 1.30] 0.95 [0.75, 1.21]
Livelihood diversification (1 = yes) 0.68 [0.36, 1.29] 0.52* [0.28, 0.97] 0.6 [0.31, 1.17]
Member of  farmer cooperative (1 = yes) 1.90* [1.01, 3.58] 1.45 [0.78, 2.67] 2.01* [1.02, 3.95]
Access to extension services (1 = yes) 2.13* [1.15, 3.94] 1.92* [1.00, 3.66] 1.88 [0.97, 3.65]
Access to climate information (1 = yes) 3.45* [1.92, 6.20] 2.98* [1.68, 5.30] 3.71* [2.02, 6.80]
Source of  information: Radio (1 = yes) 1.66 [0.89, 3.11] 1.81* [1.02, 3.20] 1.73 [0.93, 3.24]
Attended climate/environment workshops 
(1 = yes)

2.50* [1.40, 4.46] 2.12* [1.18, 3.83] 2.88* [1.52, 5.46]

Perceived reliability of  climate info (1 = high 
trust)

1.85* [1.08, 3.18] 1.44 [0.81, 2.56] 2.06* [1.15, 3.70]

Trust in government climate action (1 = high) 1.42 [0.76, 2.67] 0.95 [0.50, 1.79] 1.88* [1.01, 3.50]
Trust in local/traditional knowledge (1 = high) 2.01* [1.10, 3.68] 1.28 [0.70, 2.35] 1.91 [0.98, 3.73]
Religious/cultural beliefs about nature (1 = yes) 0.72 [0.40, 1.28] 0.84 [0.48, 1.50] 0.69 [0.36, 1.31]
Number of  observations 396  396  396  

Remarks: *significant at the 5% level, significant at the 10% level, and ***significant at the 1% level. The reference category for education 
is secondary education; for livelihood diversification, it is non-diversified; for cooperative membership, it is non-member; for extension 
services, it is not available; for climate information, it is not available; for information sources, it is not radio; for workshops, it is not present; 
for trust variables, it is low; and for religious/cultural beliefs, it is not. Dependent variables are dummy variables that measure how people 
perceive climate change: (1) an overall shift in the climate, (2) a decrease in rainfall, and (3) exposure to extreme weather occurrences. The 
95% Confidence Intervals (CI) for the odds ratios are provided in parenthesis. 396 observations were made.



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Smallholder Engagement with Climate-Smart 
Agriculture Practices
Improving farmers’ ability to adapt is still essential to 
mitigating the negative consequences of  climate change 
and variability. Smallholder farmers’ adoption of  climate-
smart agricultural (CSA) techniques, which seek to increase 
resilience and stabilise yields, is heavily influenced by their 
perceptions of  climate-related shifts (Figure 3). There is 
ample evidence that changing climatic circumstances are 
having an effect on agricultural production, since many 
farmers in the research reported decreasing crop yields. In 
particular, 82.1% of  those surveyed reported fewer food 
crops were collected in the previous seasons.  Only a small 
percentage of  farmers are unaffected or have successfully 
adapted, as estimated by 10.4% of  farmers who reported 
higher yields and 7.5% who saw no discernible change. 
More than half  of  the respondents (53.8%) described 
their losses as moderately severe, indicating consistent 
but not catastrophic disruptions in food production. 
The quantity of  losses further supports this tendency. 
In the meantime, 30.6% of  farmers suffered extremely 
severe crop losses, indicating that a sizable portion of  the 

farming population is highly vulnerable. Relatively few 
households have been able to protect themselves from 
climatic stressors, since only 15.6% of  them categorised 
their losses as not severe.
Focus Group Discussion (FGD) participants stated that 
tackling the effects of  climate change and variability 
on agriculture requires enhancing farmers’ capacity for 
adaptation. About 81.3% of  respondents reported that 
they had harvested fewer food crops in the most recent 
seasons, which they attributed to unpredictable weather 
and protracted dry spells. Just 9.4% of  responders said 
their yields had improved, while 9.2% said their harvests 
had not changed much. 52.7% of  individuals classified 
their crop losses as somewhat serious when asked how 
terrible they were, indicating common but manageable 
difficulties. Another 31.6% said their losses were very bad, 
which frequently led to almost complete crop failures. 
However, just 16.9% of  participants said their losses were 
little or no severe, indicating that only a tiny percentage 
of  farming households had successfully adjusted to or 
were less impacted by the difficulties associated with 
climate change.

Figure 3: Perceived extent of  food crop losses and changes in crop quantity

The evaluation of  Zanzibari farmers’ adoption of  Climate 
Smart Agriculture (CSA) shows a wide range in the use 
of  these practices, indicating both positive advancements 
and enduring challenges in the transition to sustainable, 
climate-resilient agriculture (Table 4). While certain 
CSA programs have seen significant adoption among 
the farming community, others are still underutilised. 
For example, just 16.3% of  respondents reported crop 
diversity, a crucial tactic for protecting against climate-
related shocks. This suggests that monoculture systems, 
which increase vulnerability to droughts, pests, and 
other climatic stresses, are still preferred. Similarly, just 
20.9% of  farmers changed their planting schedules, and 
22.4% used crop rotation or mixed crops, even though 
these strategies have been shown to be cost-effective in 
increasing resilience to climatic variability. On the other 
hand, some practices have become increasingly popular. 
35.7% of  farmers had adopted improved crop varieties, 
and 31.1% of  farmers reported applying organic manure, 

indicating growing knowledge of  the benefits of  these 
crop varieties for climate adaptation and productivity.
Additionally, a number of  other practices showed a 
moderate level of  adoption: 43.9% of  respondents 
practiced soil conservation techniques, such as mulching 
and terracing, indicating a growing appreciation for 
managing soil health; 48.0% of  farmers had diversified 
their livelihoods, such as by farming seaweed; 41.6% 
reported using climate and weather data to inform 
farming decisions; and 56.9% of  respondents adopted 
agroforestry, which involves integrating trees into 
agricultural systems, indicating a moderate level of  uptake 
of  sustainable land-use practices. A number of  CSA 
tactics showed comparatively high adoption rates. 51.0% 
of  the farmers reported better livestock management, 
which included using better breeds and zero-grazing 
methods. Perhaps in reaction to diminishing yields and 
changing climate conditions, a significant percentage 
(63.8%) had increased the area of  their farmed land. 



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The notably high acceptance rate of  75.0% for Integrated 
Pest Management (IPM) could be attributed to either 
reliance on traditional ecological knowledge or competent 
extension services. With 81.6% of  farmers using irrigation 
or rainwater harvesting techniques, water-related solutions 
were particularly prevalent, highlighting the critical role 
that water management plays in climate adaptation.
A sizable majority (88.8%) stated that they had reduced 
the amount of  land under cultivation, which could be 
attributed to changing land use patterns, salinisation, 
or soil degradation. However, this finding might also 
be a reflection of  more general adaptive strategies. The 
transition of  29.4% of  farmers from crop to livestock 
farming was one CSA practice with varying adoption 
rates. A lack of  infrastructure to support livestock-
based systems, restricted access to essential inputs, or 
sociocultural norms could all have an impact on this 
comparatively low rate.
According to key informants (KI), smallholder farmers 
in Zanzibar have different views on Climate Smart 

Agriculture methods. Of  the farmers who responded to 
the survey, 14.2% believe that crop diversification is the 
most important strategy, followed by 11.7% who value 
adjusting planting timings to deal with unpredictable 
rains, 10.3% who prioritize crop rotation and mixed 
cropping to increase soil productivity, 9.6% who believe 
that using manure is essential, 8.4% who believe that 
using improved crop varieties is essential, and 7.9% who 
emphasize the importance of  soil protection techniques. 
Among adaptation strategies, 3.2% of  KI perceive less 
land under cultivation, whereas only 4.3% report more; 
4.8% of  KI perceive integrated pest management to be 
practiced in the district; 5.6% believe irrigation and water 
harvesting to be practiced in the district; 3.6% believe 
farmers have switched from crop cultivation to livestock 
farming; 1.0% believe farmers actively use climate and 
weather data to inform their decisions; and 2.8% value 
agroforestry approaches. Diversification of  livelihoods 
accounts for 6.5% of  KI, while 6.1% consider improving 
animal raising as a crucial adaptation.

Table 4: Climate Smart Agriculture Practices among smallholder farmers in Zanzibar
CSA Practice % Practicing % Not Practicing
Crop Diversification 16.3% 83.7%
Change of  Planting Time 20.9% 79.1%
Crop Rotation and Mixed Cropping 22.4% 77.6%
Use of  Manure 68.9% 31.1%
Change of  Crop Varieties 64.3% 35.7%
Soil Conservation Measures 43.9% 56.1%
Livelihood Diversification 48.0% 52.0%
Enhancing Animal Rearing Practices 51.0% 49.0%
Increase in Land under Cultivation 63.8% 36.2%
Use of  Integrated Pest Management (IPM) 75.0% 25.0%
Irrigation/Water Harvesting 81.6% 18.4%
Reduction in Land under Cultivation (as adaptation) 88.8% 11.2%
Switch from Crop Farming to Livestock 29.4% 70.6%
Agroforestry (e.g., planting trees with crops) 56.9% 43.1%
Use of  Weather and Climate Information 41.6% 58.4%

Discussion
Socioeconomic and Demographic Profiles of  Small-
Scale Farmers 
The Central District of  Zanzibar’s smallholding farmers’ 
ability to adjust to Climate Smart Agriculture (CSA) 
is significantly influenced by the demographics of  the 
communities in Chwaka, Binguni, Fuoni, Mwera, and 
Bambi. The majority of  the population is Muslim, and 
Islam is represented almost everywhere in the region. 
This represents a strong cultural and theological 
framework that may have an impact on sustainable 
agriculture practices and attitudes. Cultural norms, 
particularly religious beliefs, have a substantial impact 
on how communities adapt to climate change (Murphy 
et al., 2016). In this situation, the common religious 
identity may encourage group efforts and community-

based projects to apply CSA techniques, which frequently 
depend on ecological knowledge and social cohesiveness 
(Guragain, 2024). An ageing farming population that 
may possess a lot of  traditional knowledge but may find 
it difficult to accept modern technology is indicated by 
the age distribution of  farmers, with a significant part 
(average of  54.2%) in the 50–59 age range. In contrast 
to younger generations, older farmers are generally less 
willing to adopt innovations and more risk-averse (Dadzie 
et al., 2022). This is especially true for CSA operations, 
which frequently call for farmers to implement cutting-
edge methods like new pest control techniques or crop 
varieties that are climate resilient. However, research 
by Wang et al. (2024) emphasises the importance of  
intergenerational knowledge transfer in fostering adaptive 
capacity, so involving both older and younger farmers in 



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CSA programs may increase the effectiveness of  these 
initiatives (Zakaria et al., 2020). The presence of  younger 
farmers in the 40–49 age group (averaging 30.8%) could be 
viewed as an opportunity to bridge the generational gap.
Although education is essential for adopting sustainable 
agricultural practices because it gives farmers the skills 
and knowledge to implement creative solutions, the 
relatively low level of  formal education, 3.6% with tertiary 
education and 54% with secondary education, presents 
both a challenge and an opportunity for CSA adaptation. 
However, the limited access to higher education may 
make it more difficult for farmers to engage with more 
technical CSA practices, like precision agriculture or 
climate modelling (Sisay et al., 2023). However, Gemtou 
et al. (2024), who stress the need for accessible, context-
specific educational interventions in rural farming 
communities, suggest that secondary education, which is 
more common in areas like Binguni (70.5%), provides a 
basis on which training programs could be constructed. 
With an average of  6–10 people (59.6%), the large 
household sizes have a big impact on the adoption of  CSA 
practices. The labor needed to execute labor-intensive 
activities, such managing soil fertility or conserving water, 
can be supplied by larger households (Mosissa, 2019).
However, the availability of  resources, including capital 
and land, must be weighed against the labor supply. The 
economic sustainability of  CSA can also be impacted 
by household size, especially if  resources are distributed 
among several family members, according to Muriithi 
et al. (2023). Large households may therefore have the 
workforce to implement CSA, but they may also be limited 
in their ability to invest in CSA technologies without 
outside assistance due to land and financial constraints. 

Agricultural Access and Support Indicators of  
Smallholder F.armers 
Significant differences were found when the spatial 
variations in farmers’ access to agricultural services among 
the five communities (Chwaka, Mwera, Fuoni, Bambi, 
and Binguni) were analyzed. These differences were 
especially evident in services pertaining to agricultural 
advice and visits from extension officers. For example, 
Chwaka had significantly lower outreach from extension 
officers (2.8%) than Fuoni (31.0%) and Mwera (29.1%), a 
difference that was corroborated by statistically significant 
Chi-square values. These tendencies are consistent with 
more general patterns seen throughout sub-Saharan 
Africa, where regions with better institutional capacities 
and infrastructure are typically given preference when 
it comes to the distribution of  extension services. 
Similar results were observed by Madan and Maredia 
(2021), who emphasized that administrative and 
logistical limitations frequently contribute to the unequal 
distribution of  extension staff. According to Imran et al. 
(2024), extension programs are often restricted to more 
accessible locations due to a lack of  funding and human 
resources, underserving outlying communities.
On the other hand, there were no appreciable statistical 

disparities in the accessibility of  services like early 
warning systems, irrigation infrastructure, and agricultural 
financing across all study areas. For example, credit access 
was still less than 20% in all communities, highlighting 
a pervasive issue that is not region-specific. These 
consistently low access levels most likely indicate flaws 
in the system. According to Fanadzo and Ncube (2018), 
these limitations are caused by structural problems, such 
as weak rural banking institutions and a lack of  funding 
for irrigation and other agricultural infrastructure. Early 
warning systems’ restricted reach is also a reflection of  
a larger lack of  climate communication networks, which 
is becoming a more pressing problem for adaptive 
responses to climate variability (Islam et al., 2025).
There were moderate spatial disparities in a few 
agricultural services. Climate-smart agriculture (CSA) 
training participation was statistically significant at the 
10% level and varied from 29.3% in Bambi to 42.6% 
in Fuoni. Similarly, there was a wide range in farmer 
cooperative membership, with a high of  35.2% in Bambi 
and a low of  20.2% in Chwaka. These results are in line 
with those of  Bullock et al. (2020), who pointed out that 
locations where non-governmental organizations or 
development agencies are actively involved tend to have 
higher levels of  participation in CSA projects and farmer 
groups. Additionally, Hartmann et al. (2023) contend that 
institutional networks and local social capital have a major 
impact on program participation.
Although there was a reasonably modest amount of  
availability to basic inputs including seeds, fertilizer, 
and equipment (between 29.6% and 41.0%), the spatial 
disparities were not statistically significant, suggesting 
a more equal distribution or shared difficulties 
throughout groups. Nonetheless, there were notable 
and statistically significant differences in the physical 
closeness to marketplaces (χ² = 22.4). In contrast to 
more over 80% in Chwaka, Mwera, and Fuoni, farmers 
in Bambi (54.0%) and Binguni (58.2%) reported having 
limited access to local markets. Farmers’ capacity to 
sell produce effectively and profitably is impacted by 
this regional difference in market proximity, which 
makes it crucial. According to Ma et al. (2024), remote 
farmers frequently face major disadvantages because 
of  the substantial impact that distance to markets has 
on transaction costs, market participation, and income 
potential.
Overall, this study’s conclusions about spatial dynamics 
are consistent with those found in other African contexts, 
including Ghana and Nigeria. Agricultural services 
frequently concentrate in more economically established 
or politically linked locations, marginalizing periphery 
populations, according to studies by Rotz et al. (2024) and 
Thomas et al. (2019). These findings imply that spatially 
sophisticated policy actions are necessary. In order to 
address locational inequities, particular investments and 
customized service delivery plans that take into account 
each community’s unique institutional and infrastructure 
constraints are needed.



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Smallholder Farmers’ Observations of  Climatic 
Shifts and Variability
In terms of  smallholder farmers’ reactions to climate 
change, the research findings from the five Zanzibari 
villages—Chwaka, Mwera, Fuoni, Bambi, and Binguni—
reflect an expanding corpus of  empirical and perception-
based studies carried out throughout sub-Saharan Africa. 
According to studies conducted in rural Ethiopia, Kenya, 
Tanzania, and Ghana, farmers use decades of  accumulated 
ecological memory to assess long-term changes in 
weather patterns. This approach is consistent with the use 
of  local climatic indicators and experiential knowledge 
(Gezie, 2019; Vaughan et al., 2019; Gebremariyam, 
2021). The apparent drop in overall rainfall in Zanzibar, 
especially in Fuoni and Chwaka, is consistent with a study 
conducted in Tanzania by Makame & Shackleton (2021), 
which discovered a correlation between decreased maize 
yields in semi-arid and sub-humid regions and decreasing 
rainfall trends. According to similar findings, traditional 
agricultural cycles in Malawi and Uganda have been 
significantly interrupted by decreased rainfall (Lunyolo 
et al., 2021; Amare et al., 2023). These views, which 
emphasize the extensive effects of  changing rainfall 
regimes, are corroborated by climatological data as well 
as farmer testimony in several African contexts (Sinore 
& Wang, 2025).
Widely held beliefs in Chwaka and Fuoni regarding the 
rainy season’s delayed start and early end are consistent 
with research conducted in Ethiopia by Alemu & 
Dioha (2020), which found that uncertain rainfall onset 
shortened growing seasons and raised farming hazards. 
Similar experiences were documented by farmers in 
Senegal and Nigeria, who reported more unpredictable 
rainfall initiation and termination, which resulted in crop 
failure and decreased food supply (Onwutuebe, 2019). 
Compressed growing seasons are the result of  these 
changes along with shorter rainfall durations, which 
are particularly noticeable in Chwaka and Fuoni. This 
significantly limits farmers’ capacity to control planting 
schedules and leads to subpar crop performance, as 
Zhang & Swaminathan (2020) point out.
Another important signal that has been frequently noted 
in research throughout East Africa is the increased 
frequency of  dry spells during rainy seasons, which 
are most strongly recorded in Binguni and Mwera. 
Onwutuebe (2019) asserts that intra-seasonal dry 
spells, especially in maize-based systems, might result 
in complete crop loss, especially during crucial phases 
of  crop development (such as blooming). Chronic 
food insecurity is exacerbated by longer and more 
unpredictable dry spells, according to farmers in Malawi 
and Zimbabwe (Chimimba et al., 2023). Their prevalence, 
particularly in Mwera (65.0%), indicates knowledge of  
the growing frequency of  extreme weather occurrences, 
a trend supported by multiple climate model projections, 
even though fewer respondents reported experiencing 
more heavy rainfall episodes. Studies conducted in Kenya 
and Rwanda, for instance, have shown an increase in 

periods of  heavy precipitation that cause flash floods, 
topsoil erosion, and infrastructure devastation (Lydie, 
2022). These alterations reveal a twofold vulnerability in 
which farmers must deal with unexpected surpluses as 
well as water constraints, making management choices 
more difficult.
Perceptions from respondents provide ample evidence of  
the observed increase in average daytime and nighttime 
temperatures, particularly in Fuoni and Chwaka. Rising 
temperatures in East and Southern Africa were identified 
by Bakala et al. (2024) and Ayal (2021) as one of  the 
most frequently mentioned climatic stressors, especially 
for heat-sensitive crops like maize and beans. A larger 
tendency of  climate extremes noted in assessments by 
Allan et al. (2023) is reflected in the increased frequency 
of  droughts and floods, which are especially noticeable 
in Fuoni and Mwera. According to studies conducted in 
Madagascar and Mozambique, the same communities 
are quickly suffering from both flood-related devastation 
and drought-induced crop failures, which weakens their 
ability to bounce back and recover (Holleman et al., 2020). 
Farmers in Ghana and Burkina Faso report modifying 
crop calendars in response to local weather signals, 
which is consistent with moderate levels of  perception 
in Zanzibar regarding changes in planting and harvesting 
dates (Yang et al., 2019). But much like in Zanzibar, a lot 
of  farmers do not have access to trustworthy seasonal 
forecasts, technologies, or extension services, which 
limits their ability to make the best agronomic choices 
(Salum et al., 2021). According to modeled forecasts, 
agricultural yields in East Africa are predicted to decrease 
by 10–20% by 2050 under current warming trajectories. 
This decrease is especially evident in Chwaka and Fuoni. 
(Guido et al., 2020).

Factors Influencing Smallholder Farmers’ 
Understanding of  Climate Variability and Change
Complex connections between sociodemographic, 
institutional, and cognitive elements and smallholder 
farmers’ perspectives on climate change in Zanzibar are 
revealed by the regression analysis. Education level was 
found to be a significant predictor of  perceptions of  
climate change (OR = 1.82, CI: [1.10, 3.01]), supporting 
findings from research in Nigeria (Azeez et al., 2024) 
and Kenya (Yvonne et al., 2020), where a higher level 
of  education increased awareness of  long-term climate 
changes. Evidence from Moutouama et al. (2022) in 
Africa, who contend that farmers acquire “environmental 
memory” through years of  interacting with their land and 
weather patterns, was also supported by the finding that 
farming experience (OR = 1.07) positively influenced 
perceptions of  climate change. The likelihood of  noticing 
climate change was considerably raised by institutional 
support, which was provided through extension services 
(OR = 2.13), access to climate information (OR = 3.45), 
and workshop participation (OR = 2.50). These results 
are in line with Antwi-Agyei & Stringer (2021) in Ghana, 
who emphasize that farmers who receive information 



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through organized channels are more likely to perceive 
and adapt to climate risks; farmers in cooperatives (OR 
= 1.90) benefited from shared learning, which is in line 
with findings from Bwalya et al. (2023) in Zambia, where 
collective membership improved knowledge exchange; 
and farmers who trusted traditional/local knowledge (OR 
= 2.01) and thought climate information was reliable were 
more likely to perceive climate change. These findings 
highlight the dual value of  scientific and indigenous 
knowledge systems, which is in line with Cebrián-Piqueras 
et al. (2020), who stress the complementarity of  local and 
formal knowledge in forming climate perceptions.
Additionally, education (OR = 1.76) and farm size (OR 
= 1.58) had a significant impact on how people perceived 
decreasing rainfall. According to research by Abid et al. 
(2019) in Pakistan, larger landholders might be more 
dependent on and aware of  variations in precipitation 
patterns. Interestingly, there was a negative correlation 
(OR = 0.52) between livelihood diversification and 
the perception of  a drop in rainfall. This shows that 
farmers who have other sources of  income might be less 
vulnerable to climate-related agricultural disturbances, 
as Ricart et al. (2025) observed. Perception was again 
strongly influenced by access to climate information (OR 
= 2.98), extension services (OR = 1.92), and workshops 
(OR = 2.12), highlighting the significance of  formal 
information channels. The importance of  radio (OR = 
1.81) as a medium is consistent with research conducted 
in Cameroon by Elvis (2024), which emphasized the 
effectiveness of  radio in rural climate communication.
The results of  the research demonstrated that farmers’ 
detection of  extreme occurrences was significantly 
influenced by their level of  education (OR = 1.50) 
and agricultural experience (OR = 1.09), confirming 
that knowledge and familiarity with the environment 
improve the ability to identify and understand climatic 
anomalies. These findings are consistent with those of  
Ricart et al. (2025) in Italy. The reported experiences were 
significantly shaped by cooperative participation (OR = 
2.01), information availability (OR = 3.71), and training/
workshops (OR = 2.88). These results corroborate 
Rajesh’s (2024) assertion that organized and group 
information-sharing systems can raise awareness of  
extreme climatic occurrences. In line with Arjomandi et 
al. (2023), who contend that institutional trust is a crucial 
determinant of  risk perception and adaptive response, 
perceptions of  extreme weather were significantly 
influenced by trust in government initiatives (OR = 1.88), 
as well as trust in the reliability of  climate information 
(OR = 2.06). Farmers are more likely to internalize and 
act upon messages about climate risks when they have 
faith in government and scientific institutions.

Adoption of  Climate-Smart Agriculture by 
Smallholder Farmers
Research from sub-Saharan Africa, Asia, and Latin 
America consistently shows how important smallholder 

farmers’ perceptions of  climate change are in 
determining their adaptive behavior. The findings from 
Zanzibar, which show varying adoption of  Climate 
Smart Agriculture (CSA) practices and notable crop yield 
declines attributed to climate variability, are consistent 
with an increasing body of  global evidence highlighting 
the importance of  farmer perceptions and adaptive 
capacity in the face of  climate change. For instance, in 
a cross-country study conducted in Africa, Paul et al. 
(2023) discovered that farmers were far more likely to 
implement adaptive methods if  they sensed long-term 
changes in rainfall and temperature. In a similar vein, 
Bedo et al. (2024) found a direct correlation between the 
likelihood of  implementing conservation agriculture and 
other resilience-enhancing activities and the perceived 
severity of  climate impacts in Ethiopia.
Evidence from areas like the Sahel in West Africa, where 
periodic droughts have resulted in comparable yield 
losses, is strongly consistent with data from Zanzibar, 
where 82.1% of  farmers reported a decrease in food 
crop yields (Sultan et al., 2019). Only 10.4% of  Zanzibari 
farmers reported higher yields, and only 7.5% reported 
no change. This reflects trends in smallholder systems 
around the world, where adaptation success is still only 
achieved by a select few farmers who have greater access 
to resources, information, and institutional support 
(Azadi et al., 2021). The magnitude of  Zanzibar’s reported 
crop losses also mirrors global trends. In Tanzania, more 
than 60% of  smallholder farmers reported moderate 
to severe yield losses as a result of  climate stressors, 
specifically unpredictable rainfall and pest outbreaks, 
according to a study by Gwambene et al. (2023). This is 
also true in Kenya (Kalia, 2024). The study found that 
30.6% of  farmers experienced very severe impacts and 
53.8% of  farmers reported moderately severe losses. 
These numbers are nearly identical to these regional 
trends, suggesting that rain-fed agricultural communities 
are generally vulnerable.
Although Zanzibar has moderate to high adoption rates 
for agroforestry and water harvesting (56.9% and 81.6%, 
respectively), adoption rates elsewhere in the world 
differ significantly. For example, land fragmentation 
and institutional limitations contribute to the limited 
adoption of  water harvesting in South Asia, despite 
the region’s considerable sensitivity to water-related 
stress (Rani, 2025). Agroforestry, on the other hand, 
has gained traction in Rwanda due to strong extension 
assistance and farmer cooperatives that encourage tree-
based systems (Rwaburindi, 2024). Another difference is 
Zanzibar’s comparatively low adoption rate of  agricultural 
diversification (16.3%). Diversification is more prevalent 
in areas like Southeast Asia and the Andean highlands, 
where it is frequently motivated by both market incentives 
and objectives related to climate resilience (Beltrán-
Tolosa et al., 2022). The prevalent monocultural customs, 
lack of  extension services, and socioeconomic obstacles 
in Zanzibar may be the cause of  this discrepancy.



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CONCLUSION 
This study emphasizes how vulnerable smallholder 
farmers in the Central District of  Zanzibar are to 
climate variability, such as decreasing rainfall, increasing 
temperatures, and changing weather patterns, which 
have resulted in lower crop yields and altered agricultural 
methods. Due to restricted access to credit, extension 
services, and accurate climate information, some 
Climate-Smart Agriculture (CSA) practices—including 
crop diversification and modified planting calendars—
remain underutilized, even if  others, like water harvesting 
and agroforestry, have gained popularity. Education, 
experience, institutional support, and confidence in both 
traditional and scientific knowledge all have a significant 
impact on farmers’ perspectives of  climate change. . In 
order to provide more resilient and sustainable agricultural 
systems throughout Zanzibar, strengthening adaptive 
ability will necessitate focused policy interventions that 
increase Community-Based Adaptation training, enhance 
infrastructure, and encourage equitable access to climate 
resources.

REFERENCES 
Abid, M., Scheffran, J., Schneider, U. A., & Elahi, E. 

(2019). Farmer perceptions of  climate change, 
observed trends and adaptation of  agriculture in 
Pakistan. Environmental management, 63, 110-123.

Alemu, Z. A., & Dioha, M. O. (2020). Climate change 
and trend analysis of  temperature: the case of  Addis 
Ababa, Ethiopia. Environmental Systems Research, 9, 1-15.

Allan, R. P., Arias, P. A., Berger, S., Canadell, J. G., 
Cassou, C., Chen, D., ... & Zickfeld, K. (2023). 
Intergovernmental panel on climate change (IPCC). 
Summary for policymakers. In Climate change 2021: 
The physical science basis. Contribution of  working group 
I to the sixth assessment report of  the intergovernmental panel 
on climate change (pp. 3-32). Cambridge University Press.

Amare, M., Parvathi, P., & Nguyen, T. T. (2023). Micro 
insights on the pathways to agricultural transformation: 
Comparative evidence from Southeast Asia and 
Sub‐Saharan Africa. Canadian Journal of  Agricultural 
Economics/Revue canadienne d’agroeconomie, 71(1), 69-87.

Amejo, A. G., Gebere, Y. M., & Kassa, H. (2018). 
Integrating crop and livestock in smallholder 
production systems for food security and poverty 
reduction in sub-Saharan Africa. African Journal of  
Agricultural Research, 13(25), 1272-1282.

Antwi-Agyei, P., & Stringer, L. C. (2021). Improving 
the effectiveness of  agricultural extension services 
in supporting farmers to adapt to climate change: 
Insights from northeastern Ghana. Climate Risk 
Management, 32, 100304.

Arjomandi A, P., Yazdanpanah, M., Shirzad, A., 
Komendantova, N., Kameli, E., Hosseinzadeh, M., 
& Razavi, E. (2023). Institutional trust and cognitive 
motivation toward water conservation in the face of  
an environmental disaster. Sustainability, 15(2), 900.

Ayal, D. Y. (2021). Climate change and human heat stress 

exposure in sub-Saharan Africa. CABI Reviews, (2021).
Azadi, H., Moghaddam, S. M., Burkart, S., Mahmoudi, H., 

Van Passel, S., Kurban, A., & Lopez-Carr, D. (2021). 
Rethinking resilient agriculture: From climate-smart 
agriculture to vulnerable-smart agriculture. Journal of  
Cleaner Production, 319, 128602.

Azeez, R. O., Rampedi, I. T., Ifegbesan, A. P., & 
Ogunyemi, B. (2024). Geo-demographics and source 
of  information as determinants of  climate change 
consciousness among citizens in African countries. 
Heliyon, 10(7).

Bakala, H. S., Devi, J., Singh, G., & Singh, I. (2024). 
Drought and heat stress: insights into tolerance 
mechanisms and breeding strategies for pigeonpea 
improvement. Planta, 259(5), 123.

Bahri, T., Vasconcellos, M., Welch, D. J., Johnson, J., Perry, 
R. I., Ma, X., & Sharma, R. (Eds.). (2021). Adaptive 
management of  fisheries in response to climate change (FAO 
Fisheries and Aquaculture Technical Paper No. 667). 
Food and Agriculture Organization of  the United 
Nations.

Bala, J. (2016). Contribution of  SPSS in Social Sciences 
Research. International Journal of  Advanced Research in 
Computer Science, 7(6).

Bedeke, S. B. (2023). Climate change vulnerability and 
adaptation of  crop producers in sub-Saharan Africa: 
a review on concepts, approaches and methods. 
Environment, development and sustainability, 25(2), 1017-1051.

Bedo, D., Mekuriaw, A., & Bantider, A. (2024). Adaptive 
responses and determinants of  adaptation decisions 
to climate change: evidence from rainfed-dependent 
farmers in the Central Rift Valley of  Ethiopia. Cogent 
Food & Agriculture, 10(1), 2430404.

Beltrán-Tolosa, L. M., Cruz-Garcia, G. S., Ocampo, J., 
Pradhan, P., & Quintero, M. (2022). Rural livelihood 
diversification is associated with lower vulnerability to 
climate change in the Andean-Amazon foothills. Plos 
Climate, 1(11), e0000051.

Bwalya, B., Mutandwa, E., & Chiluba, B. C. (2023). 
Awareness and use of  sustainable land management 
practices in smallholder farming systems. Sustainability, 
15(20), 14660.

Bullock, R., Huyer, S., Shai, T., & Nyasimi, M. (2020). The 
CCAFS youth and climate-smart agriculture (CSA) strategy.

Cebrián-Piqueras, M. A., Filyushkina, A., Johnson, D. 
N., Lo, V. B., López-Rodríguez, M. D., March, H.  & 
Plieninger, T. (2020). Scientific and local ecological 
knowledge, shaping perceptions towards protected 
areas and related ecosystem services. Landscape Ecology, 
35(11), 2549-2567.

Chimimba, E. G., Ngongondo, C., Li, C., Minoungou, B., 
Monjerezi, M., & Eneya, L. (2023). Characterisation 
of  dry spells for agricultural applications in Malawi. 
SN Applied Sciences, 5(7), 199.

Colombini, S., Graziosi, A. R., Galassi, G., Gislon, G., 
Crovetto, G. M., Enriquez-Hidalgo, D., & Rapetti, 
L. (2023). Evaluation of  Intergovernmental Panel 
on Climate Change (IPCC) equations to predict 



Pa
ge

 
56

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 4(3) 43-58, 2025

enteric methane emission from lactating cows fed 
Mediterranean diets. JDS communications, 4(3), 181-185.

Dhimal, M., Bhandari, D., Dhimal, M. L., Kafle, N., 
Pyakurel, P., Mahotra, N., ... & Müller, R. (2021). 
Impact of  climate change on health and well-being of  
people in Hindu Kush Himalayan region: A narrative 
review. Frontiers in Physiology, 12, 651189.

Elvis Muse, M. (2024). Assessing the Role of  Radio on 
Climate Change Communication in the Bamenda 
Municipality, Cameroon. Assessing the Role of  Radio on 
Climate Change Communication in the Bamenda Municipality, 
Cameroon (February 12, 2024).

Epaphra, M., & Mwakalasya, A. H. (2017). Analysis of  
foreign direct investment, agricultural sector and 
economic growth in Tanzania. Modern Economy, 8(1), 
111-140.

Fanadzo, M., & Ncube, B. (2018). Challenges and 
opportunities for revitalising smallholder irrigation 
schemes in South Africa. Water Sa, 44(3), 436-447

Filonchyk, M., Peterson, M. P., Yan, H., Gusev, A., 
Zhang, L., He, Y., & Yang, S. (2024). Greenhouse 
gas emissions and reduction strategies for the world’s 
largest greenhouse gas emitters. Science of  The Total 
Environment, 944, 173895.

Gebremariyam, D. E. (2021). Livelihood Adaptation to 
Climate Change and Variability among Smallholder Farmers 
in Central Ethiopia: Dynamics and Effectiveness.

Gemtou, M., Kakkavou, K., Anastasiou, E., Fountas, S., 
Pedersen, S. M., Isakhanyan, G., ... & Pazos-Vidal, S. 
(2024). Farmers’ transition to climate-smart agriculture: 
A systematic review of  the decision-making factors 
affecting adoption. Sustainability, 16(7), 2828.

Gezie, M. (2019). Farmer’s response to climate change 
and variability in Ethiopia: A review. Cogent Food & 
Agriculture, 5(1), 1613770.

Guido, Z., Zimmer, A., Lopus, S., Hannah, C., Gower, 
D., Waldman, K., ... & Evans, T. (2020). Farmer 
forecasts: Impacts of  seasonal rainfall expectations 
on agricultural decision-making in Sub-Saharan 
Africa. Climate Risk Management, 30, 100247.

Guillen-Hanson, G., Strube, R., & Xhelili, A. (2018). 
Reaching the triple-win. Four Future Scenarios of  a 
Healthier, More Equitable and Sustainable Europe in, 2040.

Guragain, R. (2024). Linking ecological and social resilience: 
The role of  Indigenous knowledge in building ecological and 
social resilience: A comparative analysis (Bachelor’s thesis, 
Norwegian University of  Life Sciences). Norwegian 
University of  Life Sciences.

Gwambene, B., Liwenga, E., & Mung’ong’o, C. (2023). 
Climate change and variability impacts on agricultural 
production and food security for the smallholder 
farmers in Rungwe, Tanzania. Environmental 
Management, 71(1), 3-14.

Hartmann, D., Arata, A., Bezerra, M., & Pinheiro, F. 
L. (2023). The network effects of  NGOs on social 
capital and innovation among smallholder farmers: a 
case study in Peru. The Annals of  Regional Science, 70(3), 
633-658.

Hernández-Delgado, E. A. (2015). The emerging threats 
of  climate change on tropical coastal ecosystem 
services, public health, local economies and livelihood 
sustainability of  small islands: Cumulative impacts 
and synergies. Marine Pollution Bulletin, 101(1), 5-28.

Holleman, C., Rembold, F., Crespo, O., & Conti, V. 
(2020). The impact of  climate variability and extremes on 
agriculture and food security: An analysis of  the evidence and 
case studies. Food and Agriculture Organization of  the 
United Nations.

Imran, M. A., Zennaro, M., Popoola, O. R., Chiaraviglio, 
L., Zhang, H., Manzoni, P., ... & Pietrosemoli, E. 
(2024). Exploring the boundaries of  connected 
systems: communications for hard-to-reach areas and 
extreme conditions. Proceedings of  the IEEE.

Islam, M. M., Hasan, M., Mia, M. S., Al Masud, A., & Islam, 
A. R. M. T. (2025). Early Warning Systems in Climate 
Risk Management: Roles and Implementations in 
Eradicating Barriers and Overcoming Challenges. 
Natural Hazards Research.

Islam, M. M. (2024). Addressing Greenhouse Gas 
Intensive Agricultural Trajectories in Developing 
Nations: Exploring Better Approaches for Achieving 
Sustainable and Low-Emission Agricultural Practices. 
Journal of  Social Science, 7(13).

Jellason, P. N. (2018). Environmental Challenges and Linkages 
to Smallholder Agriculture in the Nigerian Drylands: 
Implications for Food Security (Doctoral dissertation, 
Coventry University).

Kahimba, F. C., Sife, A. S., Maliondo, S. M. S., Mpeta, E. J., 
& Olson, J. (2015). Climate change and food security in 
Tanzania: Analysis of  current knowledge and research 
gaps. Tanzania Journal of  Agricultural Sciences, 14(1).

Kalia, D. M. (2024). Climate change perception, vulnerability and 
adaptation among smallholder farmers in Machakos County, 
Kenya (Doctoral dissertation).

Karri, V., & Nalluri, N. (2024). Enhancing resilience to 
climate change through prospective strategies for 
climate-resilient agriculture to improve crop yield and 
food security. Plant Science Today, 11(1), 21-33.

Liu, L., Gao, M., & Cao, E. (2024). Agricultural measures 
to address climate change: Enhancing adaptability 
and sustainable development strategies. Geographical 
Research Bulletin, 3, 88-95.

Lunyolo, L. D., Khalifa, M., & Ribbe, L. (2021). Assessing 
the interaction of  land cover/land use dynamics, 
climate extremes and food systems in Uganda. Science 
of  the Total Environment, 753, 142549.

Lydie, M. (2022). Droughts and floodings implications 
in agriculture sector in Rwanda: Consequences of  
Global Warming. The Nature, Causes, Effects and 
Mitigation of  Climate Change on the Environment.

Ma, W., Sonobe, T., & Gong, B. (2024). Linking farmers 
to markets: Barriers, solutions, and policy options. 
Economic Analysis and Policy, 82, 1102-1112.

Madan, S., & Maredia, K. (2021). Global experiences in 
agricultural extension, community outreach & advisory 
services. Innovations in agricultural extension, 1-16.



Pa
ge

 
57

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 4(3) 43-58, 2025

Mafie, G. K. (2022). The impact of  climate change on 
agricultural productivity in Tanzania. International 
Economic Journal, 36(1), 129-145.

Makame, M. O., & Shackleton, S. (2020). Perceptions of  
climate variability and change in relation to observed 
data among two east coast communities in Zanzibar, 
East Africa. Climate and Development, 12(9), 801-813.

Mbuli, C. S., Fonjong, L. N., & Fletcher, A. J. (2021). 
Climate change and small farmers’ vulnerability to 
food insecurity in Cameroon. Sustainability, 13(3), 1523.

Mosissa, D. (2019). Soil and water conservation practices 
and its contribution to small holder farmers livelihoods 
in northwest Ethiopia: a shifting syndrome from 
natural resources rich areas. Mod Concep Dev Agrono, 
3(5), 362-371.

Moutouama, F. T., Tepa-Yotto, G. T., Agboton, C., 
Gbaguidi, B., Sekabira, H., & Tamò, M. (2022). 
Farmers’ perception of  climate change and climate-
smart agriculture in Northern Benin, West Africa. 
Agronomy, 12(6), 1348.

Mulugeta, S. B. (2023). Drought, vulnerability and adaptation: 
Risk of  food and livelihoods insecurity for pastoralists and agro-
pastoralists in Borana Zone, Southern Ethiopia (Doctoral 
dissertation, University of  Nairobi). University of  
Nairobi.

Murphy, C., Tembo, M., Phiri, A., Yerokun, O., & Grummell, 
B. (2016). Adapting to climate change in shifting 
landscapes of  belief. Climatic change, 134, 101-114.

Onwutuebe, C. J. (2019). Patriarchy and women 
vulnerability to adverse climate change in Nigeria. 
Sage Open, 9(1), 2158244019825914.

Pandey, S. C., Modi, P., Pereira, V., & Fosso Wamba, S. 
(2024). Empowering small farmers for sustainable 
agriculture: a human resource approach to SDG-
driven training and innovation. International Journal of  
Manpower.

Paul Jr, M., Aihounton, G. B., & Lokossou, J. C. 
(2023). Climate-smart agriculture and food security: 
Cross-country evidence from West Africa. Global 
Environmental Change, 81, 102697.

Petersen-Rockney, M., Baur, P., Guzman, A., Bender, 
S. F., Calo, A., Castillo, F., ... & Bowles, T. (2021). 
Narrow and brittle or broad and nimble? Comparing 
adaptive capacity in simplifying and diversifying 
farming systems. Frontiers in Sustainable Food Systems, 
5, 564900.

Praveen, B., & Sharma, P. (2019). A review of  literature 
on climate change and its impacts on agriculture 
productivity. Journal of  Public Affairs, 19(4), e1960.

Rwaburindi, J. C. (2024). Determinants of  small holder farmers’ 
decision to practice agroforestry in Rulindo District, Rwanda 
(Doctoral dissertation, JKUAT-COANRE). Jomo 
Kenyatta University of  Agriculture and Technology.

Rajesh, M. (2024). Inter-disciplinary Approaches to Risk 
Management and Information Sharing for Resilience 
to the Changing Climate. Remote Sensing in Earth 
Systems Sciences, 7(2), 45-54.

Rani, S. (2025). Land and Water Nexus: Exploring 

the Interplay of  Resources in South Asia: An 
Introduction. In Land and Water Nexus in South Asia 
(pp. 1-48). Springer, Cham.

Rashid, L. (2019). Entrepreneurship education and 
sustainable development goals: A literature review 
and a closer look at fragile states and technology-
enabled approaches. Sustainability, 11(19), 5343.

RGoZ. (2013). Zanzibar Environmental Policy. Second 
Zanzibar Environmental Policy. Department of  
Environment, Zanzibar.

RGoZ. (2014). Zanzibar forest resources assessment report. 
Ministry of  Natural Resources and Tourism, Zanzibar.

Ricart, S., Gandolfi, C., & Castelletti, A. (2025). What 
drives farmers’ behavior under climate change? 
Decoding risk awareness, perceived impacts, and 
adaptive capacity in northern Italy. Heliyon, 11(1).

Ricart, S., Gandolfi, C., & Castelletti, A. (2025). What 
drives farmers’ behavior under climate change? 
Decoding risk awareness, perceived impacts, and 
adaptive capacity in northern Italy. Heliyon, 11(1).

Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., 
Horgan, M. & Fraser, E. (2019). Automated pastures 
and the digital divide: How agricultural technologies 
are shaping labour and rural communities. Journal of  
Rural Studies, 68, 112-122.

Rodríguez-Barillas, M., Poortvliet, P. M., & Klerkx, L. 
(2024). Unraveling farmers’ interrelated adaptation and 
mitigation adoption decisions under perceived climate 
change risks. Journal of  Rural Studies, 109, 103329.

Salum, L., Majule, A. E., & Shaghude, Y. W. (2021). 
Perceptions of  Smallholder Rice Farmers on 
Traditional and Conventional Weather Forecasting 
in Zanzibar, Tanzania. Journal of  the Geographical 
Association of  Tanzania, 41(2).

Sinore, T., & Wang, F. (2025). Climate change impact and 
adaptation options in Sub-Saharan Africa: a systematic 
review. Environment, Development and Sustainability, 1-29

Sisay, T., Tesfaye, K., Ketema, M., Dechassa, N., & Getnet, 
M. (2023). Climate-smart agriculture technologies and 
determinants of  farmers’ adoption decisions in the 
Great Rift Valley of  Ethiopia. Sustainability, 15(4), 3471.

Song, X., Liu, X., Liu, F., & Wang, C. (2021). Comparison 
of  machine learning and logistic regression models 
in predicting acute kidney injury: A systematic review 
and meta-analysis. International journal of  medical 
informatics, 151, 104484.

Sultan, B., Defrance, D., & Iizumi, T. (2019). Evidence 
of  crop production losses in West Africa due to 
historical global warming in two crop models. Scientific 
reports, 9(1), 12834.

Telo da Gama, J. (2023). The role of  soils in sustainability, 
climate change, and ecosystem services: Challenges 
and opportunities. Ecologies, 4(3), 552-567.

Thomas, K., Hardy, R. D., Lazrus, H., Mendez, M., 
Orlove, B., Rivera‐Collazo, I, & Winthrop, R. (2019). 
Explaining differential vulnerability to climate change: 
A social science review. Wiley Interdisciplinary Reviews: 
Climate Change, 10(2), e565.



Pa
ge

 
58

https://journals.e-palli.com/home/index.php/ajec

Am. J. Environ. Clim. 4(3) 43-58, 2025

URT. (2022). Climate change adaptation in Zanzibar and the 
implications for evaluation. Commissioner for Monitoring 
and Evaluation, Zanzibar Planning Commission, 
United Republic of  Tanzania.  

Vaughan, C., Hansen, J., Roudier, P., Watkiss, P., & Carr, 
E. (2019). Evaluating agricultural weather and climate 
services in Africa: Evidence, methods, and a learning 
agenda. Wiley Interdisciplinary Reviews: Climate Change, 
10(4), e586.

Wang, T., Liu, J., Zhu, H., & Jiang, Y. (2024). The Impact 
of  Risk Aversion and Migrant Work Experience on 
Farmers’ Entrepreneurship: Evidence from China. 
Agriculture, 14(2), 209.

Yang, H., Ranjitkar, S., Zhai, D., Zhong, M., Goldberg, S. 
D., Salim, M. A., ... & Xu, J. (2019). Role of  traditional 
ecological knowledge and seasonal calendars in the 

context of  climate change: A case study from China. 
Sustainability, 11(12), 3243.

Yvonne, M., Ouma, G., Olago, D., & Opondo, M. (2020). 
Trends in climate variables (temperature and rainfall) 
and local perceptions of  climate change in Lamu, 
Kenya. Geography, Environment, Sustainability, 13(3), 
102-109.

Zakaria, A., Azumah, S. B., Appiah-Twumasi, M., & 
Dagunga, G. (2020). Adoption of  climate-smart 
agricultural practices among farm households in 
Ghana: The role of  farmer participation in training 
programmes. Technology in Society, 63, 101338.

Zhang, Y., & Swaminathan, J. M. (2020). Improved crop 
productivity through optimized planting schedules. 
Manufacturing & Service Operations Management, 22(6), 
1165-1180.


