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Crop Advice & Expertise

How to use yield maps to reduce your costs of production

Using yield maps to drive decisions

  • Map yields
  • Calculate costs (£/ha)
  • Combine yield and cost information to map profitable tonnage
  • Identify reason(s) for loss-making areas
  • Consider whether poor results are a one-off or underlying problem
  • Evaluate all possible solutions
  • Implement changes
  • Analyse yield maps & cost data to evaluate effectiveness of changes.

Yield has a massive impact on production cost per tonne, so combining yield maps with costings data is an excellent way of highlighting spatial variations in crop profitability within fields.

It provides more detailed management information than analysing costs just on a crop or enterprise level and helps pinpoint under-performing areas that may be dragging down average profitability.

Reasons for mapping costs:

  • Highlights underperforming or loss-making areas and allows targeted improvements or other action to increase efficiency
  • Helps evaluate the cost-benefit of using variable-rate inputs (e.g. P, K, N seed)
  • Identifies the most profitable areas, which can be examined to learn from (i.e. what characteristics they have that other areas of the field don’t)
  • Provides a way of evaluating the effectiveness of management or agronomy changes.

There are often good reasons why some parts of the field perform better than others, but deciding what to do with low-yielding areas should be an informed choice.

Options for poor areas include:

  • Rectify issues restricting yields, e.g. compaction, poor drainage, low fertility, pests/weed/disease issues, etc.
  • Use variable rate technology to target inputs more closely to yield/ profit potential
  • Make more fundamental changes to cropping or cultivation strategy that may suit site conditions better
  • Allow poorer-performing areas to be “subsidised” by better parts of the field (particularly if poor performance is due to inherent site characteristics that cannot be easily remedied)
  • Remove areas from crop production altogether - e.g. put headlands or field corners near woodland into stewardship margins
  • Reduce inputs on lower-yielding areas to cut costs?

 

Using multiple years of yield data

 

Given the impact seasonal weather variations have on yields, it is important to avoid knee-jerk reactions and ideally base any major agronomic or business changes on more than one year’s data, says Agrovista’s Steve Butler.

“It is possible to make basic decisions based on one or two years of yield data, together with a sound knowledge of the farm/field.

“But ideally look at multiple years, preferably three to five, to build a more accurate picture of the areas that consistently under-perform.”

Agrovista’s cloud-based Axis crop management system allows such analysis of multiple yield maps and is integrated with the Muddy Boots Greenlight Grower Management system to incorporate relevant crop management information.

Mr Butler encourages growers to retain the original data used to generate yield maps, as it may be required for future analysis.

“It’s no use just keeping the coloured paper copy maps; it always pays to keep hold of the raw data too. We always ensure the original data is returned to growers, as well as any maps or other analysis.”

Other systems to combine multiple years of yield maps into single “field performance maps” are also offered by different providers, including Soyl, Gatekeeper (Farmplan), and Hutchinsons (Omnia).

Mr Butler recommends yield data is “cleansed” to remove any erroneous figures before analysing results. For example, yield monitors can record low yields where combine headers lift up to turn.

Such anomalies need to be checked and corrected with more representative figures.

 

Combining yield maps with CoP data

 

Although access to yield mapping capability is increasing, relatively few growers use it in conjunction with costings data.

However doing so is a “powerful business planning tool” that helps farmers and agronomists identify areas that cost more than the farm’s breakeven price to produce.

Precision application maps can also then be used to help target agronomy to specific areas to tackle variability.

At a basic level, yield maps can be manually combined with an average cost of production figure (£/ha) calculated separately to give an idea of how the cost (£ per tonne) varies with changes in yield.

There are however digital systems that do this automatically, such as the cost of production mapping function within Omnia, from Hutchinsons.

It allows users to combine production cost data, either based on standard industry figures or actual business costings, with a yield map from that season to visually show how cost per tonne varies within individual fields.

Omnia analysis of one winter wheat crop put the average total cost of production at £995/ha, equivalent to a breakeven price of £134/t at an average yield of 7.4t/ha.

Combining this data with actual yield maps revealed that costs ranged from £95/t on the highest yielding areas (hitting around 12t/ha) to £221/t on the worst parts of the field (where yields were as low as 3t/ha).

Although Omnia allows multiple years of yield map data to be analysed, only a single year is used for the cost of production mapping as it has to be representative of that crop and season, notes Hutchinsons Oliver Wood.

“We need to understand why areas are costing more than the cost of production for that crop and make a plan as to how farmers and agronomists can work together to change that.”

As precision farming capability becomes more widely available on farm machinery and data analysis/ computing systems improve, there are likely to be more options for combining the host of crop management, financial and physical crop data (e.g. yield) in future.

“There aren’t many industries where you have little or no control over both input costs and the selling price, so we’ve all got to make the most of the part in the middle,” says Mr Butler.

 

Mr Wood’s top five tips for getting the most out of yield maps are:

1. Use multiple years of data (min three years ideally)

2. Check data for errors before analysing

3. Look for trends as opposed to single year spikes

4. Always think about production costs as £/tonne

5. Don’t assume the only course of action is to take land out of production.

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