*the corn market price on the harvest day
Agremo took part in quite an interesting project in Eastern Europe which was an experiment on precision agriculture. The idea was to grow corn in both ways- a traditional way and by using precision agriculture technology. The client, who owns a large farm of over 4000ha and has dozens of fields in different locations, wanted to test how digitization in agriculture and Crop Monitoring software can affect the production of the crops and to what extent they can improve the whole process, especially decision making.
Five fields were included in the project, and the average field surface area was approximately 20ha, with corn as the chosen crop. Each field was split into two equal parts. On one part of the field, participants were engaged in conventional agriculture and on the second part, our Crop Monitoring software was used to perform precision farming operations. Both parts of all fields had the same hybrid, and the same type of pre-sowing fertilizer was applied. Fields were irrigated in order to avoid potential drought impacts.
In order to determine the homogeneity of each field, we have first tested the percentage of arable area and field elevation, which has confirmed the uniformity in both aspects on all fields.
A collaborating company has performed Electrical Conductivity (EC) mapping which implies measuring the uniformity of the mechanical composition of the soil and its salinity. This parameter indicates if the soil is healthy as too much Na and Mg can negatively affect the soil health the same way as low levels of nutrients can be detrimental. EC mapping was done in all locations, and the results additionally confirmed the overall uniformity of the soil.
This examination was performed before planting corn. On the parts of the fields where Crop Monitoring software was used to analyze crops and make data-driven decisions, the planting was done using variable-rate seeding, while on the parts with conventional practice seeds were applied using a flat-rate. Also, from the emergence of the crop till the V3 phase, all fields were monitored for weed activity which has shown a 3,96% lower overall weed pressure on parts on which precision ag was applied, as shown in the chart above.
In the cornfields where Crop Monitoring software was used, the accuracy of the crop data was at a much higher level compared to traditional agriculture. It had a direct impact on the yield and profit increase, which were about 228 €/ha and 127 €/ha respectively.
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The overall impression of the corn production was that the growing process was much more precise and efficient in the fields where Agremo’s AI technology was applied. Our client obtained accurate data of the condition of his crops which helped him make in-season and postseason decisions on farming operations. Additionally, this new approach saved our client tons of time because it was possible to scout large fields in a shorter amount of time.
On the field where corn was grown by following conventional agriculture practices, the difference between the counted number of plants and the planned number of plants was from 19% to 33% below the rate, whereas that percentage was much more favorable in the fields where Crop Monitoring, precision agriculture software, was used to collect and analyze crop data. The plant count results presented in the table show that plant productivity was around 15% better on the fields where precision agriculture was applied.
An average plant stress percentage on the fields shows that there was 4 times less plant stress in the fields where Crop Monitoring software was used to analyze corn data. Plants were healthier and the germination rate was higher in the same fields.
All these factors we mentioned above had a direct impact on the yield and profit increase, which were about 228 €/ha and 127 €/ha respectively. In the cornfields where Crop Monitoring was used, the accuracy of the crop data was at a much higher level compared to traditional agriculture. Pinpointing the stress on the field and the exact areas with poor vegetation or low germination, resulted in more accurate crop data, seasonal decision-making reduced expenses for agricultural inputs, and most importantly profit and yield increase.
This was an experiment after which our client completely switched to the use of technology in his fields. Business users understand the language of numbers best, and with our solution, it was confirmed.
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