From the Next Farmhand to the Next AI Agent: Turning Generational Knowledge Into Training Data

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For 150 years, farmers wrote logbooks so the next person on the job would know what to do. That same text — written for a human successor — turns out to be exactly what an AI agent needs to learn the job instead.
Author

Jan Scholtes

Published

November 21, 2026

Somewhere in a Dutch cherry orchard there’s a logbook from 2013. A page reads, roughly: 8 April — copper fertilizer oxychloride, half a liter on 200 liters of water, 7:30 in the morning, about 7°C, wind from the northeast, a long cold spring with a month of wind and no rain. Whoever wrote that note wasn’t writing for a machine. They were writing for the next person who’d stand in that orchard — maybe themselves next year, maybe a successor after they retired — so that person would know what was done, when, under what conditions, and, implicitly, why.

That’s the quiet shift worth naming: text written for decades to train the next human on the job can now train the next agent on the job instead. This post walks through what that actually looks like in practice, using a real project built around exactly this kind of data.

The Knowledge Was Always Meant to Be Passed On

Modern agriculture — fruit farming especially — is losing something it can’t easily get back: an aging workforce is retiring, and with it goes decades of empirical knowledge about micro-climates, pest pressure, and crop cycles that was never formalized anywhere except in handwritten logbooks and oral tradition. That knowledge was always meant to transfer to a successor. The logbook was the transfer mechanism.

At the same time, farmers now face a genuinely hard balancing act: mounting pressure toward organic methods and reduced chemical use, set against the economic reality of needing to protect yield and quality. A general-purpose chatbot can’t responsibly help here — a wrong pesticide recommendation can mean crop failure, environmental damage, or the loss of organic certification, and generic models trained on the open internet have no access to what actually happened, and worked, in this orchard, under these conditions.

The knowledge that would actually help already exists. It’s just sitting in a form built for a human reader, not a machine.

The Same Data, Read a Different Way

Here’s the reframe that makes this possible: every entry in a farming logbook is already, structurally, an observation → action → consequence record. Cloudy, no wind, post-bloom (observation) → applied ureum, borium, zinc (action) → maximal leaf absorption without evaporation (intended consequence). That’s not a coincidence — it’s how anyone documents a decision they want a successor to be able to trust and repeat. And an observation-action-consequence triple is, not coincidentally, exactly the data structure reinforcement learning is built around: state, action, reward, next state. The format a farmer used to teach a human apprentice turns out to be the format needed to teach an AI agent.

The practical pipeline looks like this:

  1. Digitize. OCR and multimodal models convert handwritten pages — dates, dosages, weather, outcomes, all in a farmer’s own shorthand — into machine-readable text.
  2. Structure into JSON. Free-form, tabular handwriting gets serialized into a consistent schema: date, weather conditions, substance applied, quantity, timing, and (where recorded) the outcome. This structured layer is what every downstream training pipeline builds on.
  3. Extract component-specific training data. This is the part worth walking through in detail, because a single logbook entry can be turned into several different kinds of training signal, each feeding a different part of an agentic architecture:

One Logbook Entry, Five Different Kinds of Training Data

Take that same April entry and see what it becomes for each component of an agent:

For retrieval (RAG): the entry becomes a semantically chunked fact the agent can look up later — “on this orchard, copper oxychloride at this dose was applied under these specific weather conditions” — embedded and stored so a future query can retrieve it directly, cited to its source.

For reranking: paired with the question a farmer might actually ask (“how do I treat fruit rot risk during a wet cold spring”), the same entry becomes a query-answer pair used to train a reranker to recognize this historical intervention as the relevant one, rather than a generically similar but wrong result — solving exactly the “lost in the middle” problem where an agent drowns in twenty broadly similar retrieved passages and misses the one that actually matters.

For instruction-following (SFT): the raw entry gets rewritten as a structured instruction-response pair — “It’s late May, light rain, and the orchard is at risk of fruit rot. What’s the historical intervention?” → “Historically, Signum (0.25 kg) combined with a leaf mix was applied to prevent fruit rot during light rain.” This is what teaches a model to adopt the terminology, tone, and reasoning style of an actual agronomist rather than a generic assistant.

For preference learning (DPO): the same scenario, paired with a rejected alternative — a heavy chemical spray that would technically work but violates current organic certification — teaches the model that not every factually correct answer is the preferred one. This is where an agent learns to default toward the sustainable, compliant option rather than whatever seems most immediately effective.

For planning and multi-hop reasoning: a multi-week sequence from the logbook — evaluate blossom readiness, order bees, place hives on a specific date, remove hives before spraying three weeks later — becomes a task-decomposition example: how a single seasonal goal (“manage pollination”) breaks down into an ordered sequence of dependent sub-tasks, each with its own timing constraint.

One page of handwriting, five different downstream capabilities — and none of it required the original farmer to think about AI at all. They were just doing what farmers have always done: writing down what happened so someone else could learn from it.

Why the Oldest Text Might Be the Most Valuable

Here’s a genuinely counter-intuitive point buried in this approach: the most useful historical documents for building a modern, organic-focused agricultural agent might be agricultural texts from 1850 to 1930 — before synthetic pesticides existed at all. These archives, digitized in collections like Cornell’s Core Historical Literature of Agriculture and the USDA’s National Agricultural Library, describe ecological pest management and soil techniques that were simply forgotten once cheap synthetic chemicals took over for most of the 20th century. As farming now transitions back toward organic methods, that “obsolete” pre-chemical knowledge is suddenly directly relevant again — and it exists in exactly the same form as the personal logbook: text written by one generation of practitioners for the benefit of the next, that happened to skip a generation or two before finding its intended reader.

The Guardrails Have to Come From the Same Place

None of this works safely without an equally serious investment in what not to do, and — fittingly — that data comes from the same kind of source. A “critic agent” checking a draft recommendation needs concrete negative examples: a 2020 logbook entry mentioning a chemical (say, Calypso) that was legal then but is now restricted, paired with the corrected, currently compliant alternative, and — crucially — a stated rationale (“rejected: violates EU Organic Regulation 2018/848, Article 9”). That rationale is what teaches a critic model not just that something is wrong, but why, in a form it can generalize from. Historical logbooks and current legislation, cross-referenced against each other, become the raw material for the safety layer just as much as they do for the advice-giving layer.

The Bigger Pattern

Step back from the orchard specifically and the pattern generalizes cleanly, and it connects directly to something this site has covered before: any organization’s internal manuals, standard operating procedures, incident logs, and handover notes were always written with an implicit reader in mind — the next person doing the job. That framing has quietly been the whole point of workplace documentation for as long as workplaces have existed. What’s changed is who that “next person” can now be. The same discipline that made a good logbook entry useful to a human successor — being concrete about conditions, actions, and outcomes, rather than vague — is precisely what makes it usable as training data for an agent. Good documentation was never really about compliance or record-keeping for its own sake. It was about making tacit expertise transferable. It just turns out the recipient doesn’t have to be human anymore.

The Takeaway

A logbook entry from 2013, or a farming manual from 1890, was never written with a language model in mind — and that’s exactly why it works so well as training data. It’s honest, concrete, and grounded in what actually happened, because it was written for someone who’d need to trust and repeat it. Turning that text into structured retrieval data, reranker pairs, instruction examples, preference pairs, and planning templates isn’t a reinterpretation of what the knowledge was for — it’s the same transfer of expertise from one practitioner to the next, just with a new kind of successor on the receiving end.

Key papers

  • Lewis et al. (2020), Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — arXiv:2005.11401
  • Rafailov et al. (2023), Direct Preference Optimization — arXiv:2305.18290
  • Yao et al. (2022), ReAct: Synergizing Reasoning and Acting in Language Models — arXiv:2210.03629

Further reading on this site