DS-07 Agriculture

Smallholder Farmer Behaviour & Decision Dataset

20K+ longitudinal household survey records tracking smallholder farmer decision-making across crop selection, input adoption, market channel preference, and climate-adaptation strategy — the behavioural foundation for personalised agri-advisory and extension AI across Nigeria, Ghana, Tanzania, and Ethiopia.

This is a synthetic dataset generated from high-quality expert-labelled seed data. All records are algorithmically derived — statistical distributions, inter-field correlations, and annotation characteristics faithfully replicate real-world patterns from the source data, while ensuring no real individual, organisation, or transaction can be identified or reconstructed.

The African Smallholder Farmer Behaviour & Decision Dataset captures the decision-making patterns of 20K+ farm households across Nigeria, Ghana, Tanzania, and Ethiopia through repeated household surveys conducted over three consecutive growing seasons (2021–2024). Each record encodes the farmer's crop-portfolio choices, input usage decisions (fertiliser, improved seed, pesticide), market channel preference (farmgate, local market, cooperative, digital platform), and perceived climate-risk adaptation strategies.

Survey instruments were designed in close collaboration with national agricultural extension services and draw on established behavioural frameworks including the Technology Acceptance Model, Prospect Theory, and the Sustainable Livelihoods Framework. Enumerators were trained to administer the questionnaire in eight local languages; translated response values are harmonised to English-language enums in the dataset. Household wealth proxy scores are computed from asset indices validated against DHS-comparable survey rounds.

The dataset is structured for panel analysis — each household carries a persistent anonymised ID, enabling longitudinal modelling of adoption curves, yield-behaviour correlations, and the effect of weather shocks on decision-making. Advisory AI teams have used it to build farmer-segment classifiers, personalised input-recommendation engines, and extension-service targeting models that outperform blanket-broadcast advisory approaches by 30–40 % on adoption rate metrics in pilot evaluations.

Key Use Cases

Personalised agri-advisory content recommendation
Farmer segmentation for extension service targeting
Input adoption propensity modelling
Climate-risk perception and adaptation strategy analysis
Market channel preference prediction
Longitudinal panel models of technology adoption curves
Wealth proxy scoring and financial inclusion assessment
Gender-disaggregated analysis of farming decision authority

Data Quality Indicators

Survey Completion Rate 94 %
Panel Attrition (3 seasons) 11 %
Local-Language Translation Coverage 100 %
Wealth Index Validation (DHS) 86 %

Geographic Coverage

Primary Coverage
Other Regions

Dataset Schema

Each record represents one household survey response for a single growing season. Fields cover household identity, farm characteristics, crop decisions, input use, market behaviour, climate perception, and annotated segment labels.

Field NameTypeDescriptionNullableExample
household_id STRING Anonymised persistent household identifier No HH-NGA-TG-08814
country_code STRING ISO 3166-1 alpha-2 country code No NG
admin1 STRING First-level administrative division No Taraba
survey_season STRING Survey growing season (YYYY-S1 or YYYY-S2) No 2023-S1
farm_size_ha FLOAT Total cultivated farm area in hectares Yes 1.5
primary_crop ENUM Main crop grown this season: MAIZE, CASSAVA, SORGHUM, RICE, YAM, OTHER No MAIZE
num_crops_grown INTEGER Total number of distinct crops grown this season No 3
improved_seed_used BOOLEAN True if certified / improved seed variety was used No true
fertiliser_used BOOLEAN True if chemical or organic fertiliser was applied No false
pesticide_used BOOLEAN True if pesticide or herbicide was applied Yes false
market_channel ENUM Primary sales channel: FARMGATE, LOCAL_MARKET, COOPERATIVE, DIGITAL No LOCAL_MARKET
has_smartphone BOOLEAN True if the farmer owns or has regular access to a smartphone No true
uses_agri_app BOOLEAN True if the farmer uses any mobile agri-advisory application Yes false
extension_contact_frequency ENUM How often the farmer contacts extension services: NEVER, ANNUAL, SEASONAL, MONTHLY No SEASONAL
climate_risk_perception ENUM Perceived climate risk level: LOW, MODERATE, HIGH, VERY_HIGH No HIGH
adaptation_strategy ENUM Primary adaptation: NONE, CROP_SWITCH, IRRIGATION, EARLY_PLANTING, DIVERSIFICATION Yes EARLY_PLANTING
wealth_index_score FLOAT Composite household wealth index (0–100, DHS-validated) Yes 41.2
household_head_gender ENUM Gender of household head: MALE, FEMALE No MALE
years_farming INTEGER Years the household head has been farming No 14
farmer_segment ENUM Annotated segment: SUBSISTENCE, TRANSITIONAL, COMMERCIAL No TRANSITIONAL

Sample Records

Four representative household survey records spanning countries, farmer segments, and climate adaptation strategies.

farmer_behaviour_sample.json
[ { "household_id": "HH-NGA-TG-08814", "country_code": "NG", "admin1": "Taraba", "survey_season": "2023-S1", "farm_size_ha": 1.5, "primary_crop": "MAIZE", "num_crops_grown": 3, "improved_seed_used": true, "fertiliser_used": false, "pesticide_used": false, "market_channel": "LOCAL_MARKET", "has_smartphone": true, "uses_agri_app": false, "extension_contact_frequency": "SEASONAL", "climate_risk_perception": "HIGH", "adaptation_strategy": "EARLY_PLANTING", "wealth_index_score": 41.2, "household_head_gender": "MALE", "years_farming": 14, "farmer_segment": "TRANSITIONAL" }, { "household_id": "HH-GHA-UE-03021", "country_code": "GH", "admin1": "Upper East", "survey_season": "2023-S1", "farm_size_ha": 0.8, "primary_crop": "SORGHUM", "num_crops_grown": 2, "improved_seed_used": false, "fertiliser_used": false, "pesticide_used": null, "market_channel": "FARMGATE", "has_smartphone": false, "uses_agri_app": false, "extension_contact_frequency": "ANNUAL", "climate_risk_perception": "VERY_HIGH", "adaptation_strategy": "CROP_SWITCH", "wealth_index_score": 22.7, "household_head_gender": "FEMALE", "years_farming": 22, "farmer_segment": "SUBSISTENCE" }, { "household_id": "HH-TZA-MO-11540", "country_code": "TZ", "admin1": "Morogoro", "survey_season": "2022-S2", "farm_size_ha": 3.2, "primary_crop": "RICE", "num_crops_grown": 4, "improved_seed_used": true, "fertiliser_used": true, "pesticide_used": true, "market_channel": "COOPERATIVE", "has_smartphone": true, "uses_agri_app": true, "extension_contact_frequency": "MONTHLY", "climate_risk_perception": "MODERATE", "adaptation_strategy": "IRRIGATION", "wealth_index_score": 63.5, "household_head_gender": "MALE", "years_farming": 9, "farmer_segment": "COMMERCIAL" }, { "household_id": "HH-ETH-OR-07382", "country_code": "ET", "admin1": "Oromia", "survey_season": "2023-S1", "farm_size_ha": 2.1, "primary_crop": "MAIZE", "num_crops_grown": 3, "improved_seed_used": true, "fertiliser_used": true, "pesticide_used": false, "market_channel": "LOCAL_MARKET", "has_smartphone": true, "uses_agri_app": false, "extension_contact_frequency": "SEASONAL", "climate_risk_perception": "HIGH", "adaptation_strategy": "DIVERSIFICATION", "wealth_index_score": 54.8, "household_head_gender": "MALE", "years_farming": 18, "farmer_segment": "TRANSITIONAL" } ]
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