Authors
1. Nchanji Eileen
FR1.3: Gendered differences in 2. Lutomia Cosmas
accessing and using climate-smart 
agricultural technologies in 3. Mutua Mercy
Tanzania 4. Waswa Boaz
5. Ndunguru Agness
6. Kabungo Catherine
7. Katunzi Adolph
Presenter 
8. Nyamolo Victor 
Eileen Bogweh Nchanji (ABC)
Background 
❏ Agriculture is critical to the economic growth of sub-Saharan African countries but is 
negatively affected by climate change. 
❏ Some of the rampant effects of climate change in SSA include
➔ Prolonged drought
➔ Flooding
➔ Erratic rainfall
➔ Increased pest and diseases, and
➔ Rising temperatures
Background 
❏ Smallholder farmers are the most affected because of their reliance on rain-fed
agriculture.
❏ As a result, climate change results in increased cases of food insecurity and poverty,
affecting local markets, and slowing down economic growth.
❏ The vulnerability of smallholder farmers to climate change is worsened by pre-
existing conditions, including:
➢ Low access to markets,
➢ Weak institutional support and policy,
➢ Low technology adoption, and poverty
Background 
❏ The impacts of climatic shocks are skewed with women being the most vulnerable
group in Tanzania.
❏ Smallholder farmers, especially women cultivate relatively small pieces of land
which often lack access to reliable irrigation.
❏ Their participation in output markets is low largely because of poor harvests.
❏ As a result, climate change has undermined the ability of Tanzania to achieve the
Sustainable Development Goals.
Background 
❏ Few studies have focused on how access and use of these technologies vary by gender in
Tanzania
❏ The potential role of individuals and households in gendered differences in the use of
climate-smart agriculture has not been explored comprehensively in Mbeya and Mbozi
districts.
❏ This study investigated the role of gender in the uptake of climate-smart agricultural
practices in Tanzania.
Conceptual Framework
Financial Capital  (credit, cashable properties 
and regular cash flows)
Physical Capital (farm machinery and 
equipment such as computers and mobile Contributes to 
phones) increased access 
and use of 
Human Capital (knowledge and skills climate smart 
acquired experientially or through education) technologies 
Natural Capital (land) 
Demographic characteristics (age, household 
headship, gender, primary occupation, marital 
status, household type) 
Methodology 
Study Area
❏ The study was conducted in
➢ Mbeya rural
➢ Mbozi districts.
❏ Main occupation
➢ Farming
❏ Seasons
➢ Long rains October to May
➢ Short rain June to September
Sampling Design
❏ Two districts were selected for the study
➢ Mbeya
➢ Mbozi
❏ Six villages were randomly selected from the two districts
❏ Participants were then selected randomly from lists of farmers that were provided by
local extension offices in the different wards.
❏ Farmers randomly selected using the RAND function resulting in 357 respondents
Sampling Design
❏ The two districts were selected for the study because they are among the main bean
production hubs in the Southern Highlands of Tanzania.
❏ They also receive diverse bean value chain interventions.
Data Collection
❏ Data was collected using semi-structured questionnaire.
❏ The tool was co-developed by all stakeholders
❏ The information collected comprised
➢ Demographic characteristics
➢ Land ownership, access, and allocation to bean production
➢ Types of seed and bean varieties planted by farmers
➢ Bean production practices
➢ Production constraints
➢ Farmers access to information on bean production, technologies, and marketing
Data Analysis
❖ Measures of Central Tendency (Mean and standard deviation)
❖ Analysis of proportions (Frequencies and percentaged)
❖ Inferential statistics
❖ Analysis of test of significance (ANOVA and chi-square).
Results
Sociodemographic characteristics of respondents by gender
.
❖ Most households were men Variable Total Youths Women Men p-value
(N=357) (n=132) (n=110) (n=115
headed. )
❖ Women had lower education Gender of respondent (%) 36.97 30.81 32.21
qualification compared to men Age of respondent (years) 41.41 29.34 47.82 49.15 0.000
(11.69) (3.63) (8.48) (8.60)
and the youth. Relation of respondent to HHH 63.59 52.27 39.09 100.00 0.000
❖ 31% of the women were either (%)
Education level respondent (%)
widowed, separated are 
No formal education 10.36 9.85 19.09 2.61 0.000
divorced. Primary 73.11 59.09 74.55 87.83
Secondary or higher 16.53 31.06 6.36 9.57
Farming as the main occupation 83.19 81.06 84.55 84.35 0.711
(%)
Marital status - Married (%) 86.83 92.42 69.09 97.39 0.000
Household type (%)
Dual type 87.96 93.18 71.82 97.39 0.000
Woman only 9.52 4.55 25.45 0.00
Man only 1.68 1.52 0.91 2.61
Woman with absentee husband 0.84 0.76 1.82 0
Farm and bean farming characteristics 
Men
Women
Youths
Total
0 10 20 30 40 50 60 70 80 90
Both man & woman Woman Man
Decision-making on the purpose of growing beans was made jointly between man and 
woman in a household but secondly, by women, even though men said otherwise
Farm and bean farming characteristics 
7
5.83
6
5.33
5
4 3.63 3.78
3.18
3
2
1
0
0
Youths Women Men
Average acres of land owned Average acres of land accessed
❖Men owned more land (5 acres) than women (3 acres) and youth (0 acres).
❖Men also accessed more land (6acres) than women  (4acres) and youth (4 acres)
Farm and bean farming characteristics 
Men
Women
Youths
Total
0 10 20 30 40 50 60 70 80 90 100
Income Food
❖Beans were mainly grown for income (76%), while only 24% grew for food. It is 
important to note that beans are now more commercialized and this might have 
implications for women in relation to income from sale of beans
Bean Production Constraints
Constraint Tota Youth Wome Men
l s n
❖ Pest and diseases was a common Production constraint (%)
production constraint affecting Pests and diseases 56.0 65.15 51.82 49.5
majority of the responds, 2 7
especially the youth (65%). Access to production finance 11.4 12.88 9.09 12.1
8 7
❖ More women (5%) than men (3%) 
Drought 7.00 7.58 8.18 5.22
and youth (2%) were affected by 
Floods 8.68 6.82 8.18 11.3
lack of access to fertilizer.  
Access to fertilizers 3.36 1.52 5.45 3.48
❖ This result indicates that 
Access to quality seed 2.52 0.76 1.82 5.22
vulnerability to adverse effects of 
Other 1.12 0.76 0 2.61
climate change varies by gender Poor soils 1.4 0.00 0.91 3.48
possibly due to disparities in Access to knowledge & 0.56 0.00 0.91 0.87
resource endowment, awareness, information 
knowledge, and access to Labour constraints 0.28 0.00 0 0.87
agricultural support services Increase the input prices 2.24 0.00 5.45 1.74
None 5.32 4.55 8.18 3.48
Post-harvest constraints
Constraint Tot Youth Wom Men p
❖ More women (21%) al s en
mentioned excessive rains Post-harvest constraints (%) 0.69
during harvesting to be a 7
major post harvest Excessive rain during postharvest 19.8 18.94 20.91 20
9
constraint. 
❖ Lack of knowledge on post Lack of knowledge on post- 3.08 2.27 3.64 3.48
harvest handling
harvest handling was 
mentioned by more women Storage pests 2.52 3.03 0.91 3.48
(4%) than men (3%) and Lack/access to PHH 1.68 1.52 1.82 1.74
facilities/equipment
the youth (2%).
Labour constraints 1.12 0.76 0.91 1.74
❖ By contrast more men 
storage space 0.56 0 0.91 0.87
(3%), and youth (3%) 
Other 0.84 0 0 2.61
mentioned storage pests as 
None 70.3 73.48 70.91 66.0
a post harvest constraint 1 9
Marketing constraints
Constraint Total Youths Women Men p
❖ More women (10%) 
reported that distance to 
market was a marketing Marketing constraints 0.160
constraint. 
Price fluctuation 34.17 32.58 27.27 42.61
❖ Additionally, more women 
(4%) than men (3%) and Distant market 7.56 4.55 10 8.7
youth (2%) mentioned poor Poor means of transport 2.8 2.27 3.64 2.61
means of transport as a Unstandardized weighing 1.4 2.27 0 1.74
constraint. scale
Bad roads 0.56 1.52 0 0
❖ By contrast more men 
(42%), and youth (33%) Other 0.56 0.76 0 0.87
mentioned price None 52.94 56.06 59.09 43.48
fluctuations as a marketing 
constraint 
Changes made in response to production constraints by gender
Change made Total Youths Women Men
Pesticide 49.39 50.00 52.73 49.09
Use fertilizer 21.34 25.00 16.36 18.18
Change variety/improved 16.46 13.89 14.55 18.18
Early/timely planting 6.1 5.56 7.27 7.27
Conservation agriculture 3.66 4.17 5.45 3.64
Sell assets 0.61 1.82
Timely harvesting 0.61 1.82
None 1.83 1.39 3.64
❖ Majority of the women (53%) used pesticides. Majority of the youth (25%) used 
fertilizer, while majority of the men (18%) preferred improved varieties. 
❖ Timely planting was preferred equally by men and women (7%) than by the youth 
(6%). 
Decision maker about changes to protect bean production against production 
constraints by gender
More men farmers (79%) than women (74%) and youths (73%) said that both man and 
woman in the household made the decision to make changes in response to the 
production constraints. 
Use of climate-smart agricultural technologies/practices by gender
80
70 67.27
62.46 61.36
59.13
60
50
41.74
39.78 39.39 38.18
40
29.97 31.06
29.09 29.57 30.43
30
23.53
20.45 20
20
10
0
Total Youths Women Men
Conservation agriculture Pesticide use Fertilizer use Use of improved seed
Conservation agriculture (62%) and pesticide use (40%) were the most frequently used 
climate-smart agricultural practices, followed by the use of fertilizer (30%) and 
improved seed (24%).
Farmers access to institutional, technical, and social support services
Variable Total Youths Women Men p-value
Average distance to agro-dealer (km) 17.52 18.33 17.65 16.47 0.541
Percent owning mobile phone 90.76 91.67 84.55 95.65 0.014
Percent receiving information on mobile phone 39.78 34.85 37.27 47.83 0.094
Presence on agriculture social media platform (%) 3.36 6.06 0.91 2.61 0.074
Percent received bean production information 28.01 23.48 27.27 33.91 0.187
Percent received agricultural training 26.61 21.21 26.36 33.04 0.110
Membership to local groups/associations (%) 42.86 40.15 40.00 48.70 0.307
❖ Access to production information and agricultural training was low with only 28% and 
26% of the respondents reporting that they received the support, respectively.
❖ Women had marginally lower access to information via social media and mobile 
phones than men and youths.
❖ Low access to information via mobile phones was reported despite 91% of the 
respondents indicating that they owned mobile phones.
Determinants of use of climate-smart technologies and practices
Seed Pesticides Fertilizer CA
Variable Coeff. Coeff. Coeff. Coeff.
Youths 0.557** -0.096 0.592** -0.114
Age 0.026*** -0.007 0.016 -0.001
Marital status (married) -0.546* -0.152 -0.623** -0.580***
Relation to HH head (=head) -0.125 -0.133 -0.536** -0.205
Education level 0.393** 0.417*** 0.459*** 0.111
Occupation of HH head (=Farming) 1.013*** 0.666*** 0.727*** 1.709***
land size accessed 0.031 0.031* 0.048*** 0.024
Manager of bean plot 0.216 -0.024 0.067 0.605***
Distance to agro-dealer -0.017*** -0.019*** -0.022*** -0.024***
Cell phone ownership 1.222*** 0.443* 0.569** -0.244
Group membership 0.766*** 0.345** 0.488*** 0.261
Region (Songwe) 0.008 0.002 0.022 -0.488***
Constant -4.402 -1.069 -2.304*** 0.413
❖ Younger farmers were more likely to use improved bean seed and fertilizer than women farmers
❖ Education had a significant positive influence on the use of improved seeds, fertilizer-, pesticides, and
conservation agriculture.
❖ Married farmers were less likely to use improves seed, fertilizer, and conservation agriculture.
Conclusion   
❖ Gender and intersectional categories influence adaptation to changing climate
conditions
❖ There were gender differences in ownership and access to land, with men owning
and having higher access to land than women and youths
❖ Joint decision-making dominated bean farming decisions.
❖ There were also gender disparities in bean production constraints with women and
young farmers being more vulnerable than men
Conclusion   
❖ There was also low institutional, technical, and social support to enable farmers
to adopt climate-smart technologies and practices
❖ There were systematic differences in terms of factors that conditioned the use of
agricultural technologies and practices
❖ Addressing gender disparities in land access, access to digital technology,
encouraging women’s literacy through higher education, and collective action
are likely to enhance the resilience dimension in farming
Acknowledgement