A Metapopulation Approach to Farmer Seed Systems

M. Eric Van Dusen

Ciriacy-Wantrup Post-Doctoral Fellow

UC Berkeley

Metapopulation…a set of local populations which interact via individuals moving among populations… -Hanski and Gilpin (1991)

Farmer Seed System

…a set of farmers whose crop varieties are related through the exchange of seeds…

Seed Systems – In Situ Conservation

• Move from targeting individual farmers to larger spatial scales of communities and regions

• Environmental heterogeneity limits the extent of genetic erosion– landraces survive in niches

• In Situ conservation is dynamic, encompassing evolutionary processes

Zoatecpan, Puebla – infra-subsistence production, small landholdings, contiguous maize plots

Seed Systems – Biosafety

• Escape of transgenes in Mexico– How did it get there?

• Possible Impacts and Containment– Selection Pressures, Selection Practices– Mixing with Local materials

• Biosafety for future releases– How far does material travel?– Document farmer practices

Private Traders – 40 tons of maize direct from the US border at Laredo

Government maize supplies – hundreds of tons per week…

Seed Systems – dissemination of improved materials

• Green Revolution gains limited by low varietal turnover rates

• Farmer-to-Farmer exchange fundamental to dissemination in many areas

• Move towards participatory approaches, especially to reach marginal environments

• Robust approaches to disaster seed relief

Participatory Breeding – new emphasis on techniques to integrate with local practices, focus on local selection behavior, target marginal conditions and marginal farmers

MacArthur and Wilson (1967)

Mainland - Island

Bio-geographic Model

Levins (1969)

Meta-Population

Ecological Model

Evolution of Meta-population theory

Metapopulation characteristics• Patchiness of the environment

– Heterogeneity of landscape creates ecological niches, where certain species dominate

• Local extinction possible– As long as there is some degree of migration, local

extinction in any given patch is possible

– Extinction Debt – present but declining

– Genetic Rescue – add enough variability to make patch viable

• Colonization of empty patches– Distance and distribution of patches matters

– Successful establishment can depend on other factors

Crop meta-populations

• Individual farmers manage local populations, and are linked through seed exchange and gene flow

• Seeds are adapted to local agro-ecological conditions (patchiness)

• Farmers experience loss of seed (local extinction), but this is mitigated through seed exchange (migration)

• Varieties may compete for the same land area for in situ conservation (habitat fragmentation)

Biology matters

• Self pollinated - Wheat, Rice– Exchange seed without loss of quality– Seed remains relatively pure– Geneflow less common

• Open Pollinated – Maize– adapt to local conditions– high diversity within one seed lot– Geneflow through pollen

Case Study : Mexican milpa system

• Survey Sample– 280 HH – 24 villages– 2 ecological zones

• Social – Economic module

• Seed System module

Extinction parameters(i.e. my dissertation)

• Household-Farm model of activity choice– Link diversity outcomes to economic forces– Nest household, agro-ecological and market models

• Major versus Minor Crops– Varieties: blue and yellow maize– Species: intercropped beans and squash

• Land area, agro-ecological conditions drive maize diversity

• Household characteristics, market integration, labor intensity impact secondary crop diversity

Migration parameters to derive from household data

• Geneflow– Pollen – Seed sample size – drift, inbreeding, mutations

• Turnover Rate– Age of Seed Lots– Loss, Change, Replacement

• Exchange– Within community– Within ecological region

Geneflow - Pollen Drift

Field 1 Field 2

Field 3

•Contamination decreases with distance

•Field size determines level of exposure to pollen drift

Geneflow – pollen drift

0%

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30000200001000050002500

Maize area in m^3

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Percent of HH with maizearea

Contaminated Area

Effective Population SizeSelection Behavior

Percent of Farmers

Select in field % 1

Select in storage % 99

Select at harvest % 27

Select during the year % 18

Select at planting % 55

Selected by man % 47

Selected by woman % 20

Selected by both % 33

Effective Population SizeMinimum number of ears selected

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30000200001000050002500

Maize area in m^3

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Percent of land area

Number of Ears

Years White Yellow Blue Total 0-5 42 19% 11 23% 4 21% 57 20%

5-10 32 15% 2 4% 1 5% 35 12% 10-15 17 8% 4 9% 1 5% 22 8% 15-20 12 5% 2 4% 1 5% 15 5% 20-25 2 1% 1 2% 0 0% 3 1% >25 115 52% 27 57% 12 63% 154 54%

Totals 220 47 19 286

Turnover Rate

Age of Maize Seed Lots by type

White Yellow Blue

Father 45% 56% 68%

Village 52% 40% 32%

Other 3% 4% 0%

Father Neighbor Other Totals

Age 0 to5 40 37 2 43 Age 5 to 25 24 36 3 63 Age > 25 71 42 1 114 Totals 99 115 6 220

Crosstabulation: Source versus Age

Source of seed by type

YearsPh

PolyanthusPh

Vulgaris(bush)

PhVulgaris

(vine)

PhCoccineus

Other

0-5 25% 40% 10% 33% 36%5to20 14% 19% 10% 33% 18%>=20 61% 40% 81% 33% 45%

Source of SeedFather 40% 33% 43% 33% 27%

Same Village 47% 45% 48% 50% 36%Other Village 13% 21% 10% 17% 36%

N 104 42 21 6 11

Age and Origin of Bean Seed

Turnover Rate

0 to 5 5 to 25 > 25 TotalsAge 0 to5 33 1 9 43Age 5 to 25 27 23 13 63Age > 25 59 4 51 114Totals 119 28 73 220

Last time seed renewed

How old is a seed lot, really?

Crosstab: Age vs Renewal

Q1- How long have you had the seed you are currently planting?

Q2 – When is the last time you renewed your seed?

Econometrics

• Link seed age to socio-economic factors• Tobit: age of seed lots (censored at >25)

– Nest household, farm, market conditions

• Other specifications on – Logit: who holds seed forever, who replaces frequently

– Duration Model – Semiparametric specs

Summary Statistics

Tobit Regresion Age of Seed Lots

Tobit RegresssionTotal Varieties Planted

Directions for Future Research

• Build simulation model with empirical parameters

• Compare across crops and regions

• Build different scenarios for diffusion, conservation, genetic escape

• Incorporate genetic data

Cases

• Contamination - Spread of Gene into local population– Solve for Rate under a) selection b) no

selection

• Drift – Accumulation of Mutations - – Solve for Effective Population size / Renewal

Rate

• Spread of Improved Materials– Solve for rate of adoption/ diffusion

Three scales of analysisand parameters for model

1) Farmer and FieldContamination RateField Size [0.1-2 ha]Inflow Rate [0.001 – 0.005]Shape of Field [Square, Rectangle…]Rate of Deleterious mutations [0.001 – 0.01?]

2) Group of Farmers in VillageField Size [0.1-2 ha]New Seed renewal Rate [1-2 Farmers/Village/Yr]Seed Age Classes [0-5 yrs, 5-25 yrs, >25 yrs]Spatial Configuration [lattice, hub-spoke, non-scaling]

3) Group of VillagesRate of Exchange between villages [1-5%]Spatial Configuration [lattice, hub-spoke, non-scaling]

How many populations can you see in this picture?