how can gender-disaggregated data inform local adaptation planning? examples from uganda and...

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Jennifer Twyman, Juliana Muriel, Wendy Okolo, and Kelvin Shikuku ICAE 2015, Milan, August 9 - 14, 2015 How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia. Photo credit: Neil Palmer (CIAT)

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Page 1: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Jennifer Twyman, Juliana Muriel, Wendy Okolo, and Kelvin Shikuku

ICAE 2015, Milan, August 9 - 14, 2015

How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia.

Photo credit: Neil Palmer (CIAT)

Page 2: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Project Objectives & Research Questions

• Support community efforts to create local adaptation plans that decrease gender inequalities.

• What are current gender inequalities?

• How can such inequalities be decreased through local adaptation planning?

Photo credit: Jennifer Twyman (CIAT)

Page 3: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Background: Lessons from Previous Gender and Agricultural Research

• Need to consider the gender division of labor and all roles that women and men play on the farm and in the household.

• In general, women…

Have less access and control of productive assets and inputs.

Have less access to information and technical assistance.

• Agricultural production decisions are made within households…

Male dominated;

Female dominated; or

Joint.

May depend on crop, activity, and/or parcel (among other factors).

• Men and women may have different information and perceptions; thus, they may give different responses to survey questions. We need to hear from both men and women.

Page 4: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Data: CCAFS Gender Survey

Colombia Uganda

Section 1: Couple interviewed together

Household Roster and

Demographic InformationYes Yes

Labor 2 adultsAll HH

members

Housing Characteristics Yes Yes

Assets Yes Yes

Land & Agricultural Production Yes Yes

Food Security No Yes

Credit and Insurance Yes Yes

Section 2: Couple interviewed separately

Group Membership Yes Yes

Decision-Making Yes Yes

(CSA) Practices Yes Yes

Information (sources and

types)Yes Yes

Climate shocks Yes Yes

Perceptions of climate change Yes Yes

Adaptation Yes Yes

Gender Roles and Personal

ValuesYes Yes

Labor Preferences Yes No

Questionnaires available on DATAVERSE - Gender Household

Survey, Phase 2: http://dx.doi.org/10.7910/DVN/28324

Photo credit: Wendy Okolo (CIAT)

Page 5: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Sites in Colombia & Uganda

Cauca,

Colombia Nwoya,

Uganda

• 14 communities in

Cauca

• HH size (4)

• Land sizes (1.3

ha)

• Main crops:• Coffee

• Sugar cane (for

local panela

production)

• Representative of Nwoya

district

• HH size (7 - 8)

• Land sizes (2 - 3 acres)

• Mixed crop-livestock

systems:Maize, beans, cassava,

sesame, rice, groundnuts

Cows, goats, & poultry

Country Dates of Data

Collection

Total

Households

Partnered Households

where both spouses

interviewed

Colombia Oct. – Dec. 2014 198 125

Uganda Nov. 2014 – Jan. 2015 585 474

Household Survey Sampling

Page 6: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Gender Division of Labor, Home and Land Ownership, Access to Information, and Decision-Making

Preliminary Results

Photo credit: Manon Koningstein (CIAT)

Page 7: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Background: Lessons from Previous Gender and Agricultural Research

• Need to consider the gender division of labor and all roles that women and men play on the farm and in the household.

• In general, women…

Have less access and control of productive assets and inputs.

Have less access to information and technical assistance.

• Agricultural production decisions are made within households…

Male dominated;

Female dominated; or

Joint.

May depend on crop, activity, and/or parcel (among other factors).

• Men and women may have different information and perceptions; thus, they may give different responses to survey questions. We need to hear from both men and women.

Page 8: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Women are not typically recognized as farmers but they play significant roles in agricultural production.

92%

0%8%

Primary Occupation of Men

Ag

HH chores

Other

7%

90%

3%

Primary Occupation of Women

Time spent on agricultural production activities:

Men: 48 hours per week on average;

Women: 45 hours per week on average.

Data from Colombia site

Page 9: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Background: Lessons from Previous Gender and Agricultural Research

• Need to consider the gender division of labor and all roles that women and men play on the farm and in the household.

• In general, women…

Have less access and control of productive assets and inputs.

Have less access to information and technical assistance.

• Agricultural production decisions are made within households…

Male dominated;

Female dominated; or

Joint.

May depend on crop, activity, and/or parcel (among other factors).

• Men and women may have different information and perceptions; thus, they may give different responses to survey questions. We need to hear from both men and women.

Page 10: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Home Ownership

• At a household level…

• Form of home ownership:

Owned jointly by couple

Owned by principal woman alone

Owned by principal man alone

Owned by others

• Households in which women are owners (either individually or jointly)

0

10

20

30

40

50

60

70

80

90

100

Colombia (n=125) Uganda (n=474)

Pe

rcen

t o

f h

ou

se

ho

lds

couple principal woman principal man others

28% 32%

Page 11: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Land Ownership

46.5

28.06

0

10

20

30

40

50

60

70

80

90

100

Colombia(n=113)

Uganda(n=474)

Pe

rcen

t o

f h

ou

se

ho

lds

Principal women as landowner (jointly or individually)

Photo credit: Jennifer Twyman (CIAT)

Page 12: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Agro-climatic Information

Colombia Uganda

0

20

40

60

80

100

short termrain forecast

medium andlong term

rainforecasta

cropproduction

andmanagement

post-harvestmanagement

productionand

managementof livestock

Pe

rce

nt o

f h

ou

seh

old

s

both only woman only man

0

20

40

60

80

100

short termrain forecast

medium andlong term

rainforecasta

cropproduction

andmanagement

post-harvestmanagement

productionand

managementof livestock

Perc

ent

of

household

s

both only woman only man

Page 13: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Background: Lessons from Previous Gender and Agricultural Research

• Need to consider the gender division of labor and all roles that women and men play on the farm and in the household.

• In general, women…

Have less access and control of productive assets and inputs.

Have less access to information and technical assistance.

• Agricultural production decisions are made within households…

Male dominated;

Female dominated; or

Joint.

May depend on crop, activity, and/or parcel (among other factors).

• Men and women may have different information and perceptions; thus, they may give different responses to survey questions. We need to hear from both men and women.

Page 14: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Decision-Making

0

10

20

30

40

50

60

70

80

90

100

Husba

nd

Wife

Jo

int

- co

up

le

Oth

ers

Husba

nd

Wife

Jo

int

- co

up

le

Oth

ers

Husba

nd

Wife

Jo

int

- co

up

le

Oth

ers

Husba

nd

Wife

Jo

int

- co

up

le

Oth

ers

Improved Varieties Inter-cropping Mulching No Burning

Form of Decision-Making as Reported by Men and Women in Colombia

Reported by Women

Reported by Men

Page 15: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Background: Lessons from Previous Gender and Agricultural Research

• Need to consider the gender division of labor and all roles that women and men play on the farm and in the household.

• In general, women…

Have less access and control of productive assets and inputs.

Have less access to information and technical assistance.

• Agricultural production decisions are made within households…

Male dominated;

Female dominated; or

Joint.

May depend on crop, activity, and/or parcel (among other factors).

• Men and women may have different information and perceptions; thus, they may give different responses to survey questions. We need to hear from both men and women.

Page 16: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Perceptions of Climate Change

0 10 20 30 40 50 60 70

warmer days

colder days

increase of rainfall

less predictable rainfall

extended drought periods

droughts occur more frequently

heat waves occur more frequently

less predictable seasons

other

Percentage of people

Women(n=149)

Men(n=156)

Page 17: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Awareness of Practices

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Colombia(n=125)

Uganda(n=474)

Colombia(n=125)

Uganda(n=474)

Colombia(n=125)

Uganda(n=474)

Colombia(n=125)

Uganda(n=474)

Improved varieties Intercropping Mulching No burning

Who Reports Awareness of Practice?

both only woman only man

Page 18: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Adoption of Practices

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Colombia(n=124)

Uganda(n=447)

Colombia(n=100)

Uganda(n=474)

Colombia(n=109)

Uganda(n=435)

Colombia(n=111)

Uganda(n=431)

Improved varieties Intercropping Mulching No burning

Who reports that the practice is used on the household farm?

both only woman only man

Page 19: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Conclusions & Next Steps

Photo credit: Wendy Okolo (CIAT)

Page 20: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Creating Local Adaptation Plans with a Gender Focus

• We have identified key gender inequalities…now what?

• What practices/strategies do the households in the community want to use for adaptation?

Different groups within the community (by gender, age, ethnicity, etc.).

• Participatory methodology

Researchers share information about potential costs and benefits (and trade-offs) of different practices.

Use participatory methods to evaluate selected practices in terms of gender and other social factors.

− What are the costs in terms of labor and who will do the different labor activities involved?

− Who will benefit and how?

− Are there ways to implement the selected practices in a more equitable way?

− Should other practices be chosen?

Page 21: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Main Take Home Message

• To make agricultural research matter for women farmers, we must include women.

Recognize their roles in agriculture production.

Interview women (and men).

Encourage active participation in local planning.

− What are their priorities and why?

− How will selected strategies influence labor of men and women?

− Who will benefit?

Page 22: How can Gender-Disaggregated Data Inform Local Adaptation Planning? Examples from Uganda and Colombia

Thank you

Photo credit: Manon Koningstein (CIAT)