For growers using our mapping technology, we work with two types of biomass imagery – Normalised Difference Vegetation Index (NDVI) and Leaf Area Index (LAI).

With NDVI typically considered the most commonly used model, in this blog I’m going to explain the difference between the two and delve a little deeper into the value of LAI insights in particular.

Firstly, what is biomass?

In agriculture, biomass typically refers to the above-ground area taken up by organic materials such as plants, trees, hedgerows, crops, animals and even features like crop residue after harvest.

Measuring and monitoring the biomass of a growing crop in the field can give useful indicators at vital points as to how well it will yield, helping growers to make informed management decisions across the growing season.

What is LAI?

While NDVI is a standard way of calculating relative biomass in a field directly from satellite raw data, LAI is a measurement that quantifies the actual amount of biomass or leaf area. It is defined as the ratio of the area of leaf to the unit of ground area, with LAI maps and the way they are created unique to Frontier’s precision service.

As an example of how LAI works, if I was going to do a very manual measurement on wheat, I would mark out 1m² on the ground and then cut all the leaves in that area, laying them out across the square to see how much of it they covered. If they managed to cover 0.5m² then the LAI would be 0.5, or if it was 1.4 m² then the LAI would be 1.4 and so on.

LAI maps – how are they generated and how do they work?

Our precision specialists work with growers to create LAI maps by combining satellite imagery with in-field measurements.

Each week during the season, we have a team of three working across the country with an LAI meter. During this time you can imagine we collate hundreds of measurements – and these equate to thousands over the 10 years we have been doing this work!

These LAI results are eventually compared against raw satellite values. From these we’re able to generate a plot or calibration curve on a graph, from which we can then convert the raw satellite reflectance values to an LAI value.

The unique part of this process is that it is based on real-time data, calculated from actual field measurements which means that a greater level of accuracy can be achieved.

At this point, you’re probably now wondering why we’re going to all this effort to produce LAI maps.

What are the advantages over the standard NDVI approach?

A huge benefit is that LAI can be directly linked to nitrogen uptake; with an LAI map essentially giving us a picture of nitrogen availability to the crop.

The ability for a plant to access nitrogen has a direct impact on crop canopy expansion. This is because the amount of nitrogen required for each unit of LAI growth is constant, at 36kg/ha. Rather than simply deducing that one part of a field has a higher or lower nitrogen supply, we can determine what the actual difference is.

The allows a more precise adjustment of nitrogen rates based on this information so that we can help growers to optimise applications and tailor inputs to where they are needed most.

Optimisation of the crop canopy

All crops have an ‘ideal’ canopy size, with the optimum biomass needed to maximise access to sunlight and therefore improve yields.

If a canopy is too small, the risk is that not enough sunlight can be intercepted. As a result, yield can be negatively affected, and the crop may not be competitive against weeds. Conversely though, if a canopy is too big then the shading of leaves can reduce sunlight capture. Large canopies are typically more costly to produce too and can be at a greater risk from foliar disease and lodging.

So, what is the optimum canopy size? Research included in the Agriculture and Horticulture Development Bord’s (AHDB) wheat growth guide states that a maximum LAI of 6.9 is required for wheat (red line in the graph below).

We can see that to be able to benchmark our growing crop against the ‘ideal’, we need to be using LAI to measure biomass.

To reach the maximum, optimum figure, the growth of the crop needs careful management and control through the season. Benchmarks are provided at all growth stages, for example, optimum LAIs at tillering, stem extension and flag leaf.

The graph shows that at tillering in February/March, we should aim to be at an LAI of around 0.5 and at first node (GS31) at the beginning of stem extension, we should be targeting an LAI of 2.0 (green line).

Once we know the LAI, we can use this model with our growers to check how we are progressing through the year.

Nitrogen recommendations

With the understanding of how we can benchmark crop canopies, the next consideration is how best to adjust nitrogen rates to respond to any differences.

As an example, if the benchmark at GS31 is an LAI of 2.0 but the actual, physical measurement of the crop shows the LAI is at 1.0, we know that the canopy is smaller than it ideally should be. We also know that, as it is one whole unit of LAI below the target, nitrogen uptake is going to be 36kg/ha less than that of a crop that is at the optimum size.

With this information, we’re able to help growers adapt their nitrogen rate, increasing it by the right amount to fill that gap. If, however, the crop was at an LAI of 3.0, this would signal that nitrogen uptake was actually too high and applications should be reduced.

Using our LAI maps, we can measure the LAI in each part of the field and adjust nitrogen rates to account for the variability of indices. This produces a much more precise application map, with growers able to simply increase or decrease their rates by a percentage.

Similar LAI benchmarks are also available for oilseed rape, which has an optimum LAI of 3.5 at flowering.

What are the broader benefits of LAI when it comes to overall crop performance?

Ultimately, the insights gleaned from our LAI method means we can support growers to be more precise with their crop management decisions and nutrition strategies.

With more targeted nitrogen applications using our variable rate technology, growers often see a 3-4% yield increase on average, greater consistency when it comes to grain quality and protein specification, and much improved nitrogen use efficiency (NUE). Understanding the size of a crop and being able to compare it to optimum indices to support it’s development can also support other management decisions, essentially helping growers to reduce the risk of other pressures such as lodging or disease.

WATCH: View a tutorial from applications team leader, James Wyllie on how to view and interpret biomass imagery:

If you’ve other questions about how our precision services could help you to optimise crop production and reduce risk on your farm, you can also speak to your Frontier advisor or get in touch at info@frontierag.co.uk or by calling 0800 227445.