This document discusses the need for better monitoring and analysis of farm typologies and transformations to support the Sustainable Development Goals. It notes that current data on family farms is often measured technically and lacks information on functional typologies and dynamics of rural transformation. The document proposes that countries enhance analysis of farm diversity and monitoring of changes through typology-based approaches to inform inclusive policy dialogue and actions at national and local levels. This will help improve the quality of information available and targeting of policies, extension services, and long-term monitoring to support rural livelihoods.

1. WAW
Support countries enhance their analysis and
monitoring of farm typology& their
transformations, in support of SDG

2. Quality of information available on FF is not good enough:
• Unit of measure is usually technical: Production vs. livelihood
• Limited information on functional typologies of FFs => required to understand dynamic of rural
transformation.
• Data available is usually on food production vs. other functions
• functions and overall performance of farms
• HH income portfolio including off-farm employment of family members
• Environmental performance at farm & landscape level
(From Study on Small-scale Agriculture in the NENA region. FAO, CIRAD, CIHEAM. 2015)
Target 2.3 doubling farm
productivity of small holders &
disaggregated monitoring
Target 2.4 sustainable and resilient practices
Why typology studies and monitoring needed ?
SP3 rationale and SDG
Statistics often missing or not “useable’ (lost & forbidden census…)
⇒ Such may require better statistical data but also goes beyond,
requiring diary, discussions, several surveys etc..
⇒ WAW proposes answers via typology based monitoring

3. • Goal: Enhance national capacities to analyze and monitor diversity of farms
and their transformations to inform policy dialogue
SCALE
GLOBAL
NATIONAL
LOCAL
Country
studies
Country
« watches »
Improvement on the basis of field
experience + network exchanges:
adapt to field needs
REGIONAL
Better targeted policy,
extension & long term
monitoring
Inclusive process & Policy dialogue
 Local impacts for long term
WAW national outputs & processes
Nationally driven and adapted
GLOBAL WAW articulated
to SPs and global
initiatives
Proposing flexible
approach
WAW in a nutshell
FF visibility,
voice &
empowerment
SO3
Reduce
Rural
Poverty

4.  Overview of the diversity
of farms
 Translate the complexity
into pertinent information
for appropriate actions
B. SHORT TERM: STUDIES C. LONG TERM: Enhanced MONITORING
WHAT WE DO:
A. Inception assessment, consultations => agreed vision & project formulation
Typology & transformation
studies
consultations
& multi-
stakeholders
governance
Scenarios & policy analysis
Improved
trust &
Permanent
dialogue
&
governanc
e structure
Recommendations for
surveys
Prototype Information
system (e.g. tabulated
database)
Collective vision & project
document to reinforce
monitoring capacity
PRODUCT INCLUSIVE
PROCESS PRODUCT
INCLUSIVE
PROCESS
PRODUCT INCLUSIVE PROCESS
?

5. WAW Secretariat: FAO
Vietnam
Malawi
France
Madagascar
Brésil
Argentine
Sénégal
Tunisie
Niger
South
Africa
El Salvador
US
Philippines
Costa Rica
UE
Uruguay
Cote d’Ivoire
Bulgaria
Diverse network of experts from 9 countries (GREY) and various institutions
Nicaragua
Burundi
7 Country benefiting from current projects (YELLOW) to pilot such work : studies &
monitoring system
Where and how
3 from past support, request (green)
Burundi

6. Household /
Non-
household
Farm Size &/ or
Access to market;
Commodity specialization
A distinction based on the SNA
Ideally, based on a measure of economic size
Relevant to aggregate agricultural production
and market integration
Based on main product
Orientation & commodity diversification
Integrates with existing statistical system
Option: non HH farm distinguished as FF, patronal,
corporations by labor use &/or legal status
Do households allocate labor to nonfarm work
activities?
Nonfarm income can be used to
maintain the farm and family during
times of low production or prices, and
to expand the farm productive capacity
Can distinguish “Hobby farm”/minor
For households, sources of income
An International Typology Classification:
Potential Concepts based on extensive
literature review and expert consultations
Ex. Crop, mixed, animal
Extract GSARS typology presentation ICAS 2016, Neli Georgieva,
Ahearns, Even, Saravia

7. Objectives: Operational method to represent diversity & transformation trends,
articulating statistics and qualitative data to face data issues
Territories
Production systems
Farm management,
labor & market
orientation
NATIONAL TYPOLOGY APPROACH
Common
criteria &
POVERTY
Who? Where ? How many?
Trends
Strategies, performance,
transformations
Local dialogue & action points for
policy & interventions
Work and validation with local actors, inclusive governance: inclusive policy dialogue & social legitimacy / validity
SYSTEMIC &
DYNAMIC
Type 1
Type
2
Type 3
A
1
A
2
A
3
A
..
A
4
A
1
A
2 A
3
A
..
A
1 A
2
A
3A
4A
5
A
..
TERRITORIES
2. STATISTICS 3. SURVEY REFERENCE FARMS
National
1. METHODS
QUANTITATIVE
Farm income
Off farm income
Comparable to FADN typology based system

8. A B C D E F
Tables, maps, portraits to assess characteristics,& initiate dialogue to prioritize local assessment with actors
Niger Typology from
Rulis data base
Agric income (green)
Total income (blue)
CFA
% 75% 5 5 2 4 8
NEXT: national policy dialogue and Local dialogue & assessment of reference farms in specific territory

9. Example of a National Typology in
Salvador
Inclusive process & dialogue led to consensual Distinguishing Criteria and Thresholds for
El Salvador Typology as well as agreed recommendations on data to capture farm types

10. Example of a National Typology
Distinguishing Criteria and Thresholds for
El Salvador Typology – ctd.

11. Data base adapted &
connection with Rulis
GLOBAL
NATIONAL
REGIONAL
LOCAL
Statistics : data allow comparison
between countries
Local survey, national statistic
Nationally representative data
Locally representative data

12. Ex: Niger RULIS
Ex: Tunisie ; Salvador ;
Madagascar:
Nationally owned
database
RULIS
More
countries
Concrete recommendations for qualifiers / typology domains
Base for the typology
National cleaned database
Technical suport
- Farm management and labor use: who is operating and working the farm? Is there a
status (FF & SNA distinction)
- Production system and « OTEX »
- Link with the market, % sales
- Share between off/on farm income
- Income classes: add more « absolute » comparable, compared to nat. Or int. poverty line,
minimal income etc.
- …
- Be able to combine qualifiers & more friendly display
Need to involve national policy makers incl. PO and
smallholder representatives!
Some recommendations to articulate rulis with country work

13. Thank you
El Salvador, typology
training
Tunisia, launching seminar Senegal, launching seminar
Study tour in France with all the country

14. FADN: Exemple of typology based
monitoring
• Farms from sample and from
the observation field (structural
survey) are stratified similarly
through the typology
• Individual ponderation for each
farm of the sample to calculate
average results

15. Ex: Niger
Pertinent indicators missing for the WAW typology
• % of the household where a member participate in a cooperative or a farmer's group
(RLM & dataportraits)
• % of farms using superficial/deep/other water irrigation (dataportraits)
• % of household using motorized equipment (RLM)
• % of household using building (RLM & dataportraits)
• Average annual amount of private transfer/credit/international remittance/social
assistance per capita (dataportraits)
• Age of the holder (RLM & dataportraits)
• Agriculture training background of the household headed (RLM & dataportraits)
• Agricultural own-account and contributing family workers/hired labor/seasonal
workers , share of total agricultural employment (%) (RLM & dataportraits)
• Average distance from the nearrest daily/weekly market AND health facilities
(dataportraits)
• % of the production sold (RLM & dataportraits)
• Area of temporary crops harvested (RLM & dataportraits)
• Crop farm households using chemicals/organic/inorganic fertilizer (RLM &
dataportraits)
• Value of food produced/worker (RLM & dataportraits)
• Mean yield of each main crop (RLM & dataportraits)
• Worker / household size (RLM & dataportraits)

16. Need of Farm Typology at International &
National levels: GSARS rationale
• Rapid structural changes
• Need for policy makers to carefully consider the diversity of
holdings and their transformations in the context in which they
operate
• Multi-faceted policy challenges, incl. challenges exogenous to
agriculture => multidimensional typology needed:
• Which contribute the most to aggregate production?
• Which are the most food insecure?
• Contemporary issues such as sustainability, food safety and
nutrition, gender equity, and food insecurity, SDG
Extract GSARS typology presentation ICAS 2017, Neli Georgieva, Ahearns, Even,
Saravia
Why typology studies and monitoring needed ?