Most investment decisions in climate agriculture are made on anecdote. This work replaced anecdote with a fully standardized household data platform designed to capture the true cost structure of climate-smart practices — before a single dollar is committed.

Using SurveyCTO and ODK, production cost databases were built that integrated labor, inputs, environmental indicators, and seasonal variability at household level — then extrapolated to national investment estimates including externalities.

When a donor wants cross-country evidence, the biggest risk is incomparable data. This was solved by engineering a single unified household survey instrument deployed across 11 countries in Africa and Asia — capturing financial, behavioral, and climate-risk variables in a structure designed for cross-country econometric analysis from day one.

The datasets were then paired with Monte Carlo simulation in R and @Risk to model investment decisions under uncertainty — revealing that structural market constraints, not risk perception, are the binding barrier to CSA adoption.

Why does the same technology succeed in one village and fail in the next? A geo-referenced household survey system was designed to link farmer adoption decisions to profitability, soil type, and landscape-level resilience variables — making invisible spatial patterns legible to researchers and investors.

The system powered econometric adoption models that identified the precise socio-economic conditions under which soil-carbon technologies become viable — evidence that directly informed scaling decisions across East Africa.

A donor with limited resources faces a hard question: which climate adaptation strategy deserves the next $10 million? This work contributed to large-scale household survey platforms built across multiple Sub-Saharan Africa value chains to generate harmonized datasets required for cost-benefit ranking of adaptation strategies.

The methodology combined rigorous sampling design with standardized instruments, enabling apples-to-apples comparison of adaptation options across crop types, agro-ecological zones, and national contexts.

How do you measure the economic value of turning waste into wealth? When DANIDA funded Kenya’s circular dairy bioeconomy program, this work contributed to the data system capturing financial viability and systemic impact across the whey, cheese, and yogurt valorization pathway — at the level of each individual value-chain agent.

This agent-level CBA framework required purpose-built survey instruments combining structured interviews with financial modelling modules embedded in the survey instrument itself.

Not every evidence gap needs new data — sometimes it needs someone to make sense of what already exists. Contributed to a systematic review consolidating global evidence on sandy soil reclamation technologies, synthesizing findings on water retention, crop productivity, and soil carbon outcomes across multiple studies to identify what actually works at scale.

Knowing when existing evidence is sufficient — and when a donor genuinely needs new primary data collection instead — is itself a core judgment call in designing the right data system for a program.