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Carbon Farming and Digital Soil Mapping

Organic Carbon Predicted by AI vs Measured

Understanding Soil Variability

Soil the only layer we need to know everything about and instead we do not know its composition. Knowing soil composition is essential for proper agronomic management. Soil influences all management from crop choice, rootstock, tillage, seeding depth and density, fertilization and irrigation. Knowing it is also essential to be able to carry out agronomic practices that allow for excellent yields but at the same time be able to improve its fertility.

But soil does not have a uniform distribution, it has a very pronounced spatial variability. Let's take look to the following figure which show the spatial variability of organic carbon.

Soil organic carbon variability

If we make just one soil sample in our field, it's just waste of time.

So making a single soil sample is like knowing only a portion of your field and nothing more. Today there are sampling strategies that allow you to be able to know your field and its spatial variability. And today soil is under the magnifying glass, both for agricultural policies but also for the growing carbon farming market.

Introduction to Carbon Farming

What is carbon farming?

May you wonder, what is carbon farming and why agribusiness should care about it? Carbon farming is a strategy created to combat climate change, meaning that a company through its daily operations can have an environmental impact or instead reduce the environmental impact of others. In this view, agriculture is seen as a protagonist if crop operations are carried out to sequester more carbon, and the soil and crops have great absorptive capacities.

Therefore, agriculture can be one of the main players for carbon credits having great absorption capacity. But the problem now is, how do we quantify the amount of co2 absorbed? How do we determine that there is an increase in organic carbon in the soil if the fields have their own spatial variability and therefore sampling at random would bring problems?

The solution is smart soil sampling and Artificial intelligence.

Soil Sampling Strategies

We were taught that soil sampling should be conducted randomly in the field to be analyzed, and then homogenize the samples to form the composite sample to be taken to the laboratory. Nothing could be more wrong, because this strategy does not take into account the spatial variability of the soil.

Random Soil Sampling

There is another, very expensive strategy that allows soil analysis to be conducted as best as it can be done-that is, the grid soil sampling strategy. An example is given below.

Grid Soil Sampling

This strategy requires the use of GPS, and to be able to conduct this sampling requires a lot of time and cada one of the samples must be analyzed separately, and this has a very high cost. Obviously, this type of sampling is the best that can be done and allows the highest accuracy to be achieved.

But isn't there an intermediate point between random and gridded soil sampling? Actually yes.

A much more consonant strategy for doing soil tests properly is to plan a priori-that is, “before going into the field” the locations on which to sample. The next section will show you what we can use some technologies to be able to properly plan soil sampling points before going into the field and without ever having made a field visit.

Smart Soil Sampling

We present smart soil sampling, a strategy to keep sampling costs low but at the same time obtain data of the highest accuracy. In order to perform smart soil sampling, several technologies must be combined including:

It all starts with satellite imagery that allows us to know the variability of your crop that can be attributable to soil variability if it has been managed all uniformly. Below is a satellite image acquired on a sunflower field.

Satellite Images

Based on the spatial variability of the satellite image we can plan directly the soil samples to understand very well the soil spatial variability and contain the cost of soil analysis. We below there is the example of the planned soil samples.

Soil Sampling strategy based on satellite images

The next step is to take the drill and go into the field so that you can acquire the samples. Smart, simple and painless. With this data we can then proceed to do agronomic balances so that we can determine the correct dose of fertilizer to give to your crops but not only that we can also start a carbon farming project. This is precisely where artificial intelligence and digital soil mapping comes in.

Digital Soil Mapping & Artificial Intelligence

From the combination of various data sources including satellite data, one can calibrate within-field models that can go on to generate soil variability maps with high accuracy from very few smartly acquired soil samples.

To do this, the use of specific artificial intelligence algorithms is essential, which allow the soil variability to be estimated with very high accuracy. An example is shown below where you can find the predicted soil variability of the soil and the actual measured variability.

Organic Carbon Predicted by AI vs Measured

It's quite amazing, don't you think?

In our application you can do the same directly to your field. check it out.

Benefits of Digital Soil Mapping

The advantages of the smart soil sampling and digital soil mapping are multiple:

  • Low cost laboratory soil analysis, Avoid doing unnecessary soil sampling with the composite sample or spending too much money to be able to do grid sampling

  • Have a very high accuracy soil variability map, Good soil sampling planning combined with artificial intelligence allows the generation of very high accuracy soil variability maps

  • Applying precise agronomic balances both for fertilization but also for irrigation, With soil variability maps it is then possible to apply a spatial agronomic balance across the entire field to determine the right dose of fertilizer and water

  • Monitoring the value of organic carbon in the soil over time and carbon credit generation, By calibrating a specific model on your field, we can monitor the evolution of organic carbon over time and therefore generate carbon credits

  • Application of the precision agriculture techniques, With these data, precision agriculture is within reach

  • Reducing Environmental Impact, by tailoring the crop nutrition, you will have higher carbon footprint of your agricultural activities.

  • Long-term Farm Sustainability, By applying eco-friendly agricultural activities you will be able to achieve stability or improvement in the fertility of your fields

  • Differentiate the rootstocks , You'll be able to determine which rootstocks you need and differentiate based on management areas

Don't you think this analysis can be useful to you? Do you want to get to know us? Contact us go below and send us a message.

Explore the Carbon Farming & Digital Soil Mapping combination

We at Automatic Farm Solution have developed a solution that allows you to do each of these procedures. contact us and try the application.

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