LandCorteva Agrisciences2022

Getting 1.1 million acres of farmland through a data-entry gauntlet

Qualifying for carbon credits meant an enormous amount of manual data entry. I designed the structure, statuses, and bulk actions that got submission rates to 94%.

My role: Product designer — persona research, task flows, interaction design, testing with 12 farmers
Focus: Product design, UX research, Interviews, UI/UX
Outcome: 1.1M acres enrolled, 94% submission rate, 60% less CS time

Granular Insights carbon field details screen showing historical baseline data for a field, over a photograph of a crop field

Background

The Corteva Carbon Initiative launched as a pilot in 2021 and expanded through a joint effort with Indigo Ag to generate rigorous, registry-issued carbon credits for farmers at scale. Participating farmers enter their practice change data into Granular Insights, Corteva’s free digital tool for measuring impact, improving soil health, and earning additional income.

The problem

Qualifying for carbon credits requires an extensive amount of data entry. The goal was to make that process as simple and seamless as possible for the farmer, who is doing this on top of actually farming. We tested and implemented four approaches: clear statuses, breaking the work into steps, bulk actions, and smart defaults.

Primary persona

Many participants adopt practice changes for agronomic reasons first and see carbon payments as a small value-add. Our primary persona — Pathway Patty — represented the farmer most likely to participate.

That ordering mattered to the design. If the payment is not the main motivation, then the tool cannot lean on the payout as the reward for finishing a long form. The reward had to be legible progress and confidence the data was right.

Persona sheet for Carbon Pathway Patty: a South Dakota farmer in her early sixties experimenting with reduced tillage, with motivation ratings for experimentalism, tech propensity, and soil health — Patty's stated motivation is agronomic. Carbon payment is a bonus, which changes what the product has to reward.

Task flows

I mapped the flows for entering and verifying practice change data across fields and seasons, which is where the sheer volume of the task became concrete.

Workshop journey map built from colored sticky notes across three horizontal swimlanes, tracking customer steps, feelings, and opportunity areas — Journey mapping with the customer success team, who were fielding the questions the product should have answered.

Task flow diagram with branching decision diamonds mapping how a farmer enters practice change data across multiple fields and crop seasons

Design iteration

Determining information hierarchy was the central problem, and we tested it internally and externally with our pool of 12 farmers. Farmers organize their files in strikingly different ways, so the goal was finding the structure that made sense to them rather than the one that mirrored our data model.

I sketched the three plausible orderings and put them in front of farmers directly, rather than testing a single polished direction. Cheap sketches are easier to disagree with than a finished screen.

Hand-drawn whiteboard sketch of a data entry table organized field first, then practice change event, then year — Option one: field → event → year.

Hand-drawn whiteboard sketch of the same data organized field first, then year, then practice change event — Option two: field → year → event.

Hand-drawn whiteboard sketch organizing data by year across the top with practice change events listed down the side — Option three: year → event → field. Farmers think in seasons, and this ordering matched how they already keep records.

Carbon field details screen showing crop seasons from 2016 to 2020 as separate cards, each with a completion percentage and an edit button — The resulting structure. Progress is legible per season, so a farmer can stop and resume without losing their place.

Historical baseline data table listing practice change events by date with edit controls, and a green confirmation banner at the bottom

Field data entry form asking whether cover crops were planted during the 2020 crop season, with yes and no options and conditional follow-up questions — Conditional questions keep the form short for farmers who did not adopt a given practice.

After feedback that manual entry became repetitive, we added bulk actions.

Three states of the bulk entry control: applying a value to all fields, to some fields via a multi-select, and a disabled state when no fields remain — The states that mattered were the edge cases — what the control does when some fields are already filled, and when none are left.

Spreadsheet synthesizing feedback from advisor sessions, with participant responses grouped by question across columns

Outcomes

1.1M acres enrolled
31 states enrolled
94% submission rate
60% reduction in customer success time

The 60% CS reduction is the one I point to. It means the guidance in the product replaced a human explaining the same thing over and over — which is the real test of whether the structure was right.