|
Information Collection (farmer knowledge, empirical), Validation,
Integration, and Analysis (within and across thematic groups and
sites)
IVb. Information Integration and Analysis within thematic groups (based on global
meeting1999) |
|
Key limiting factors for farmer management of local crop
diversity
(based on farmer knowledge and empirical measurements) |
Burkina Faso |
Mexico |
Morocco |
Nepal |
Vietnam |
Others (Ethiopia, Hungary, Turkey, Peru) |
|
Socioeconomic
|
|
hh size |
market access, mobility, hh size, wealth status |
market access, mobility, hh size, wealth status
gender, land tenure |
|
|
Agro-ecological (natural factors) |
precipitation |
precipitation,
length of drought,
stoniness of the soil |
precipitation, seasonality of precipitation |
soil, temperature, irregular rain, hail storms |
saline soils, flooding |
|
|
Agro-ecological (farmer managed factors) |
|
effect of slash and burn vs. no burn soils |
fertiliser treatment,
irrigation |
use of external inputs |
irrigation system |
|
|
Agromorphological
(key descriptive and selection criteria) |
|
landrace description, user preferences, adaptation to soil types |
|
total no. of varieties per hh
when was the variety obtained |
|
|
|
Population structure and breeding systems (key measurement
information needed) |
|
|
numbers, distribution and population size, comparing many and few
farms with large and small field sizes, fragmentation vs. size of
holding |
|
|
|
Seed supply systems |
|
|
|
how obtained,
from self, within village, outside village market,
amount used, retained, distributed |
|
|
Information Collection (farmer knowledge, empirical), Validation,
Integration, and Analysis (within and across thematic groups and
sites
IVb. Information Integration and Analysis within thematic groups (based on global
meeting1999) |
|
Key points of synthesis within thematic areas
|
|
Area
|
Key Points and Conclusions by partners
|
|
Socioeconomic
|
Two levels of analysis –
- Decision making: the effect sociocultural and environmental
factors have on farmer decisions makes on how they manage
genetic diversity,
- Management of Diversity: incorporating socioeconomic factors
such as gender, ethnic, and age groups to disaggregate
information on farmer preferences, descriptions and management
methods
The determination of the appropriate sample size in relation to
the number of variables being considered is a complex matter
Hypothesis testing may require random sampling of households
selected within villages. Ethnic and gender differences need to be
considered |
|
Agro-ecological |
Agrobiodiversity is a natural resource, and thus management of
agrobiodiversity fits under natural resource management
Two levels of consideration
- site consideration
- plot/farmer field information
Need to collect plot/field information across countries
Need for continuous data to accommodate climate change
Farmer knowledge by PRA methods plus measured data collected by
plot/field collect continuous data on climate, historical data also
when available |
|
Agromorphological |
Greater attention be paid to gender differences and disaggregated
information during data collection
Agromorphological preferences are one basis for choosing traits
for PPB or for improving seed networks
Need for field trial descriptors to taking into account farmer
descriptors for comparison and enhancement PRA + structured
interviews for preferences and description criteria.
Limit criteria/variables to key descriptors (see Mexico example
of 10 key traits that cover farmer descriptors and most heritable
diversity) |
|
Population structure and breeding systems |
Needed for the identification of farmer varieties, to have a
picture of the diversity under farmer management, to enable to
classify farmer varieties for analysis with other factors, to
clarify priorities for intervention
Readily permits comparisons between years, between ecosites,
between countries and between crop species.
Can be scaled up in the technical sense, e.g., specify landrace
names in classes for comparison between years
See table for synthesis of information collection and analysis
methodology |
|
Seed supply systems |
Interaction between the formal and inform systems
Importance of understanding seed flow mechanisms, for
understanding the vulnerability of the system, for making policy
recommendations
study of seed supply systems addresses both genetic diversity
studies and for policy and development activities. Both PRA and
structured surveys needed. |
|
Information Collection (farmer knowledge, empirical), Validation,
Integration, and Analysis (across thematic groups and sites)
Va. Information Integration and Analysis across thematic groups and sites
(based on
global meeting1999) |
|
Achievements |
|
Outputs 1a-e:
( some examples) |
Agro-ecological (natural and farmer managed environmental factors,
e.g., pest/diseases, soil fertility) |
Agromorphological
(descriptive and selection criteria) |
Population structure and breeding systems (size of the
population, density, distance between fields) |
Seed supply systems (access to seeds) |
|
Socioeconomic
(e.g, access to markets, hh size, labour availability, cultural
uses, other socioeconomic factors)
|
Nepal (Rice, Barley)
Mexico (Maize) |
Nepal (Rice, Barley)
Morocco (Alfalfa)
Mexico (Maize) |
Mexico (Maize, Chilli)
Nepal (Barley, Taro, Rice, Pigeon Pea)
Morocco (Durum wheat)
Burkina Faso (Sorghum) |
Nepal (Rice)
Vietnam (Rice)
Morocco (Alfalfa) |
|
Agro-ecological (natural and farmer managed environmental factors,
e.g., pest/diseases, soil fertility) |
|
Nepal (Rice – no tillers vs cold)
Mexico (Maize – drought tolerant, strong stalk for rain
resistance when doubling)
|
Nepal (Rice – cold planting density) |
Ethiopia (Pest and seed storage)
Burkina Faso (drought) |
|
Agromorphological
(descriptive and selection criteria) |
|
|
Mexico (Maize – flowering time – against introgression) |
|
|
Population structure and breeding systems (size of the
population, distance between fields) |
|
|
|
|
|
Seed supply systems (access to seeds) |
|
|
|
|
|
Information Collection (farmer knowledge, empirical), Validation,
Integration, and Analysis (across thematic groups and sites
Vb. Information Integration and Analysis across thematic groups and sites
(based on
global meeting1999) |
|
Taking farmer varieties as the focal point:
working through the process of integrating data
|
|
CROP
|
Insights
|
Problems
|
Good Practices for integration |
|
Sorghum
(Group A)
|
- Data needs to be integrated to understand diversity on farm
- Integration reduces duplication of work
- Start with validation of named varieties
- Use of multivariate analysis such as CA, and ANOVA
|
Preliminary stages with Sorghum information collection |
Consultations and teams collection and analysis to avoid
duplication of work |
|
Faba bean
(Group B)
|
- Different scales/levels of data for integration: plot, HH,
variety
- Diagram with different scales/units/data sources with the
farmer variety as the meeting point
- Need plot level data
|
varied scales and units, difficult to work across themes,
standardisation of data, possessiveness of data |
team approach, integration, feedback from farmers
plot level data for all components |
|
Maize
(Group C)
|
- used adding value example to work through data integration to
data use
- integrated issues together with information collected
|
lack of economic data collection
need gender disaggregation |
working in multi-disciplinary teams
farmer feedback
detection of good yields, additional characterisation |
|
Taro
(Group D)
|
- diagram (tree analysis of crop production/use and thematic
areas
- suggested data be collected at the farmer variety level by all
thematic groups
|
spatial limit of activities |
establishing a close relationship between farmers and scientists |
|
Rice
(Group E)
|
- Integration of population structure information with data done
on farmer variety basis
- Integration of population structure and seed supply
information requires data on sources and sizes (amounts) of
seeds being exchanges. and farmer selection practices
- Population structure can be integrated to agro-ecological
variables, such as: available moisture, soil fertility,
incidence of pest or diseases
|
too many variables, need to limit to key ones |
basing integration on farmers classification system
limit variables measured |
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Validation, Integration, and Analysis
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