What can AI do for forest managers and TIMOs?

AI helps with the two things investors judge a manager on. The first is inventory: better stand data from remote sensing and growth models feeds the appraisals that have driven most timberland returns in recent years. The second is plan against actual for every property: harvest volume, stumpage realised, operating costs and accruals against the pro-forma budget, with each difference explained. The second is usually left off the list. It depends on reading and matching contracts, settlements, invoices and ledgers across many properties, contractors and buyers, which is work AI does well.

What the usual answer says

Descriptions of AI for forest managers and TIMOs list remote sensing inventory, growth and yield modelling, harvest scheduling, carbon measurement, fire, pest and storm monitoring, and investor reporting. Each is real and some are mature. The list puts reporting last, as an output. For an investment manager, much of it is the product.

What drives the return

Chung-Hong Fu of Timberland Investment Resources, a timberland investment manager, reported in June 2025 that capital appreciation was "the main driver for total return over the last five years". Index returns peaked at 12.90% for the four quarters to the end of 2022 and had fallen to 5.60% for the four quarters to March 2025. Appreciation in an appraisal-based index rests on the appraisals, and the appraisals rest on inventory. That is where remote sensing and growth models earn their place, and it is a large one.

What investors judge managers on

Brooks Mendell of Forisk, a forest research and consulting firm, set out recommendations for benchmarking timberland investments in March 2026. He advises using annualised returns, and against using the main timberland index "as a stand-alone measure of timberland manager performance", because it summarises returns across cycles better than it compares "manager skills, cost effectiveness, or local timber markets". Instead he recommends supplementing it with "annual variance analysis of pro-forma budgets" and third-party benchmarking of management costs. His test is plain: whether a manager did "what they said they were going to do when they said they were going to do it".

Why variance is a document problem

A property's actuals come from paper and PDFs as much as from systems. Contracts, settlements, scale summaries, invoices and levy statements each come from a different party in its own format. In our experience that matching is where the time goes, and where errors sit unnoticed.

From Quarri's own work with a forestry operation: a levy rate was being accrued at a fraction of the rate actually invoiced, a gap of 500%+.

An accrual like that looks reasonable in the ledger until someone sets it beside the invoices, which is the comparison a variance built from documents makes.

Where AI fits

AI reads the documents into records, with the property, contract and period attached, and matches settlements to contracts and accruals to what was billed. Differences against the pro-forma can then be listed with the documents behind them, for a person to explain. The same records serve due diligence, valuation support and audit.

They also join the two halves of the job. Settlements record what each stand actually yielded, by product. Set beside the cruise or remote-sensing estimate for the same stand, they show how far the inventory was off, in which direction and for which products. That comparison, repeated each harvest, is the most direct test of the inventory the appraisals rest on. It needs stand keys that match between the harvest records and the inventory, which is often the hardest part to get right.

Forisk's advice is for investors, and it is annual. For the manager running the properties, we would build the variance quarterly or monthly, so each difference is explained while the people involved still remember why, and roll it up into the annual analysis investors see. The cadence is ours; the annual view is what investors are advised to use.

When it doesn't apply

A single owner managing their own land, with no investor to report to, may need little variance reporting. Managers whose operations already run on integrated systems, with contracts and settlements captured as data, have much of this done. And index figures remain the right benchmark for the asset class as a whole.

Quarri for forest management is built around how a forest operation runs, from the cruise to the settled account.

Quarri is an AI-native data platform for the timber supply chain. It connects buying, production, sales and inventory for forest management, sawmill, wood products and pulp, paper and packaging operators.