Managing variability for performance and profit!

Pome fruit Nov. 21, 2021

*In orchards we often find ourselves comparing the relative performance of our orchard blocks (management units) against each other. This block-to-block variability can have significant impacts on each business’s bottom line, often with a relatively minor percentage of high-performing blocks contributing to the majority of the orchard’s profitability.

Poor performing blocks are managed in various ways, with additional expenditure (better pruning, thinning, nutrition etc), grafting or complete removal being the common outcomes.

Whilst this block-to-block variability is relatively easy to identify, taking a closer look at high, average, and low performing blocks can begin to highlight where additional gains can be made.

The simplest example of this ‘within block’ variability (tree-to-tree variability) is biennial bearing in some apple varieties such as Fuji. Whilst a premium can be achieved for this variety, its tendency for biennial bearing, where crops are heavy one year and light the next, is (or was) the bane of many growers.

Take for example two Fuji blocks.

· Block 1’s yield in 2019 was 63t/ha, 2020 was 4t/ha and in 2021 was 67t/ha. Looking at these figures, this block appears to be in a fairly significant biennial bearing pattern with significant implications to block profitability, fruit quality, fruit maturity and marketability (here one year, gone the next)

· Block 2’s yield in 2019 was 34t/ha, 2020 was 32t/ha and in 2021 was 33t/ha. Looking at these figures, this block is a relatively consistent performer, however, it’s yield is relatively low for a mature block, and storage issues due to mixed quality and size are a persistent problem.

On further investigation it quickly became apparent that this block was almost exactly the same as block 1 except for one crucial difference. Rather than all trees having a full crop one year and none the next, this block has half the trees ‘on’ and the other ‘off’ within a given year, resulting in a relatively low average yield with fruit of mixed quality.

So what are the fixes?

Block 1 is likely to benefit from aggressive chemical and hand thinning in the ‘on’ year, coupled with excellent vigour control, techniques like girdling and different winter pruning regimes, depending on which stage of the biennial bearing cycle the trees are in.

Block 2 will benefit from similar practices however, the two different ‘types’ of trees need to be managed accordingly. To facilitate this, tree butts can be painted (Figure 1) to identify which trees are on/off when it is not necessarily that obvious (i.e. in winter) or where clear instructions are needed for workers e.g. ‘girdle all trees with a red dot on their base at flowering’.

How can we reduce tree-to-tree variability easily?

In short, there is no silver bullet to reduce tree-to-tree variability. Hard work and ideally timed and performed corrective actions are all crucial to ensuring consistency.

Whilst modern canopy design is tending toward simpler tree structures that can be easier to achieve consistency on than older, larger trees, active management of tree-to-tree variability is as important as ever.

A clear understanding of suitable crop loads for each tree, tasks required in each block and the optimal timings to undertake those tasks are all critical to ensuring consistency in the block.

New tools to measure tree-to-tree variability

Whilst walking through blocks and observing what each individual tree is doing sounds great on a small scale, the reality is that having to walk 2–3.5km per hectare to go up and down every row taking notes or counting each tree would be more than a full-time job in itself. As a result, growers typically employ sentinel trees or random subsampling within the block to gauge their whole block decisions on.

With the advent of new data capturing and handling technologies, some aspects of tree-to-tree variability can now be collected through a variety of means whether that is from aerial imagery (satellite, drone, plane etc) or ground-captured systems (vehicle, handheld device etc), with maps and data outputs able to be generated to provide snapshots of key orchard indices.

This objective mapped data can then be used to develop variability management plans, quantify the extent of issues within blocks, target use of difficult or expensive management techniques and enable crop estimation that takes full variability into account, rather than just counting a few trees per block.

Looking towards the future, this type of data will be important as variable rate spreaders and spray controllers become more common.

This year Fruit Help has partnered with Green Atlas to operate a Cartographer unit to assist growers to both identify and quantify tree-to-tree variability within their blocks across a range of objective data points including tree crop load, canopy area, density, and height.

Shifting to objective, whole block, data capture rather than more subjective measures (e.g. ‘strong area’, ‘short trees’) will enable more precise/targeted orchard management, and ultimately, greater potential profitability.

Fruit Help will be operating a GreenAtlas Cartographer during the 2021/22 season across Victoria. If you are interested in having a block or your orchard scanned to develop variability management strategies, contact Nic Finger on 0492 334 066 or visit: www.fruit.help

*Written by Nic Finger a director at Fruit Help, a Victorian-based independent horticultural consulting group specialising in apple/pear production and independent research services. Contact nic@fruit.help

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