Cost per block

Why per-block cost control matters today

Per-block cost control is no longer an accounting luxury. With Chilean export fruit at record volumes —the 2024-2025 season shipped the largest cherry crop to China on record, according to Frutas de Chile— and real labor costs climbing every season, the farm-wide average cost is no longer enough to make decisions. A 10-15% gap in cost per kilo between two neighboring blocks changes which fruit should go to the spot market and which to grower return.

The expensive decisions —renewing a variety, pulling plants, reallocating water or crews— are made at the block level, not the farm level. You need the figure at that same resolution, because that resolution is where you make or lose money.

That figure also has to be available mid-season, not at close. When per-block profitability analysis lands six months after harvest, you have already shipped the fruit, already paid the labor, and it is too late to fix anything. Per-block costing is the operational half of the profitability equation, and today it is the half that exists: the up-to-date cost is contrasted against budget in the planning & budgeting module, not yet against per-block revenue. Without an up-to-date cost you don't even have that half, and the margin per block stays entirely a black box you only open once you can no longer act on what is inside.

The real problem (no marketing)

The cost spreadsheet fails for predictable reasons, not bad luck. In an export operation with several farms and dozens of blocks, a single person usually owns cost control in Excel; when that person goes on holiday, gets sick, or rebuilds the file each season, the data falls behind or breaks. Chile's fruit industry runs on margins that ODEPA has reported under sustained pressure since 2023, and that context does not forgive weeks lost to a data-entry error.

The breaking points are always the same:

The result is a number nobody fully trusts. When a field manager argues that his block can't cost that much, he is often right: the cost loaded machinery hours that actually went to another block, or a batch of inputs split across three different blocks. Without traceability back to the work order that originated each expense, that argument isn't settled with data — it's settled by hierarchy. And meanwhile you choose which block to harvest first or which crew to pay piece-rate with costs that, if you can't see them in real time, you end up estimating from memory.

How this module solves it

The cost-per-block module assigns every expense to the block where it actually occurred and surfaces cost per kilo at any point in the season, not at the annual close. It feeds automatically from the source modules —labor, inputs, machinery, services— and applies configurable rules to distribute shared costs, so that a higher cost per kilo can always be explained with a concrete fact.

When you run a work order in a block, that task already carries the crew that performed it and the inputs it consumed. Labor comes from the workforce module, materials from inventory, and machinery comes in as each trip's fuel cost —the same figure the trip view shows as cost per kilometer— charged to the cost center you confirmed for that trip:

1. 01

   You run the task in the block

   A pruning, a spray application or a tractor pass is recorded with its crew and inputs at the moment it happens.

2. 02

   It is costed automatically

   The module takes that record and assigns it to the right block with no re-entry. Every peso lands where it occurred.

3. 03

   You see cost per kilo, live

   Cost per kilo stays current every 30 minutes (or instantly with 'Recalculate'), and the block table reflects the season in progress, not the last one.

Costs that don't belong to a single block —irrigation from a shared main, the supervisor's salary, farm administration— are split with rules you define once and that stay visible to everyone: per hectare, per kilo, per hour, by a declared percentage or by a fixed amount, whichever makes sense for each cost type. Instead of an improvised allocation each season, you have a stable, debatable criterion.

Integration with your current stack

The module doesn't aim to replace your ERP: it coexists with it. Your ERP remains the accounting system of record; this module is the live operational figure in between, and it doesn't ask you to drop your spreadsheet overnight:

The piece that makes all of this work is shared master data. Blocks, varieties, inputs, and workers are defined once and every module uses them the same way, so the cost you see on the dashboard matches the one you export to the ERP and the one that appears in traceability. Without that common master data, integrating systems comes down to reconciling spreadsheets by hand —which is exactly the work you are trying to eliminate. With it, every system speaks the same language about the same blocks.

Metrics you'll move

The metric the module keeps current, block by block, is cost per kilo. For export fruit it's the one that rules: it's what you compare against the destination market's expected return to decide which fruit justifies the freight to China and which is better sold closer to home. Hectares and kilos are grouping and allocation axes, not separate cost metrics: we don't compute a "cost per hectare" or a "cost per plant" — that would blend a young high-density block with an old one that barely yields, and the resulting figure would describe neither.

Seeing it in-season, rather than at close, is what separates deciding from guessing. The indicator moves from being calculated at year-end to being visible today, and each cut of the data unlocks a concrete decision:

Knowing that a block runs 20% above the average cost per kilo tells you where to look before you ship, not after. For an exporter with multiple farms, that table applied to dozens of blocks is the difference between managing by exception —attacking the five blocks bleeding money— and reacting at close, when there's nothing left to adjust. Operations moving from spreadsheets to automated costing tend to report close-time reductions on the order of 10-20%; the exact figure depends on how manual your prior process was.