How to sow not to regret later?

On May 17, the Ukrainian Agribusiness Club Association held their first Agricultural Technology Day. As part of the event, precision seed drills from different manufacturers were tested. The event was held on the dedicated demo-field belonging to the Agro-Region company which actively introduces innovations in the production processes.

8 machinery units became the participants of the tests:

  • Challenger 9108 VE Precision Planting
  • KUHN Maxima TRX 16R
  • Horsch Maestro 24.70 SW
  • Great Plains YPE–825 A
  • Lemken Azurit 9
  • Maschio Gaspardo Julia 24
  • Monosem NG Plus 8 pull type
  • Väderstad Tempo 8 F

The same requirements toward seeding were raised to all seed drills participating. In addition, to assess the correlation of the number of sprouts to the machinery speed, the seeding was performed at two vehicle velocities – 8 km/h and 14 km/h.

We will know which seed drills managed to cope with the task best in September – then, actually, harvesting operations will be held as part of the autumn Agricultural Technology Days.

However, for any agribusiness, be it small or large, it is important to comprehend the prospects for the possible results of the season as soon as possible before autumn comes. Therefore, to make a yield forecast, the SmartFarming Company has decided to provide the event participants with its analysis of the sowing operation conducted.

Sprouts analysis

On June 4, the company experts flew around the demo plots. Unfortunately, it was not possible to automatically identify the sprouts as they were in their early development stage. Consequently, the company conducted a subsequent flight on June 25 – DroneePlane. was flying and photographing the field. The pictures taken by the drone were processed in DroneDeploy. Based on them, an orthophoto map and a vegetation index map were constructed. The flight data was more informative.

The orthophoto map

The orthophoto map received as a result of drone flight, dated June 25, 2018

The images obtained show that some parts of the field have zones with practically no sprouts at all – this plant emergence level will obviously affect the season outcome.

The NDVI index map

The NDVI index map, dated June 25, 2018

However, to make a correct forecast it is necessary to do more work than just once to fly a field around with a drone.

Plant counting

Further on, the SmartFarming experts carried out plant counting. The company is developing this direction in cooperation with Hummingbird, as well as uses its own algorithms.

First, the calculation was made using the Hummingbird service. The data, obtained during the flight, is usually enough to do so, and experts do not waste time on the “manual” calculations.

The plant count map

The plant count map, dated June 25, 2013, constructed using the Hummingbird service

In each of the plots, each plant was identified and counted. In addition, for each of the seed drills, the averaged value of sprouts obtained per 1 hectare was calculated. The analysis showed low plant emergence rates. However, there are several catches here again.

On August 23, SmartFarming conducted another drone flight. Below, you can see the orthophoto map of the demo field, obtained as a result.

orthophoto map The demo-field orthophoto map obtained after the second drone flight, dated August 23, 2018

Within two months, the situation drastically changed. If you look at the NDVI index map, obtained as a result of drone flight, as well as using satellite images, you can clearly see that the vegetation cover increased significantly.

The NDVI index map The NDVI index map received from the satellite, dated August 23, 2018

This time, in addition to drone flight, the experts also counted the plants “manually”.

The complete results analysis of the two plant counting operations is provided in the table below.

 Demo-field plant emergence results analytics

Demo-field plant emergence results analytics

Taking into account that 80 thousand seeds per 1 hectare were initially sown, the first drone flight and plant counting showed that the results were low in all the plots with no exception. However, the second plant counting indicates a significant increase in the number, and we can talk even about oversowing.

The NDVI index map The NDVI index map, dated June 25, 2018

Why do the numbers differ so much?

Let us recall, the time interval between the first and second plant counting equaled 2 months. Within the period between sowing and the first drone flight, there was practically no precipitation, as evidenced by meteorological data. These weather conditions slowed down the plant growth rates.

Precipitation amount diagram Precipitation amount diagram within the period from May 1 to August 25, 2018

Late June and early July were rainy. Consequently, the plant growth rates increased, and as of August 23, 2013, the results of plant counting on most plots showed oversowing.

A large plant stand density adversely affects yields, as plants compete with each other for nutrients, moisture, and sunlight. Due to the shortage of these elements, plants cannot yield a good harvest. Therefore, the correct adjustment of seed drills will allow both avoiding oversowing and reaping more harvest per hectare due to the optimum sowing rate.

The differences in plant emergence rates obtained from the two drone flights once again prove the importance of crops condition monitoring conducted throughout the growing season to find out the cause-effect relationship and the actual factors affecting the yield amounts.

Therefore, we absolutely cannot draw conclusions that the seed drills failed the operational objectives. After all, machinery is just one factor among many, affecting the yield volumes.

The complex analysis of conditions and operation monitoring are the techniques which allow predicting the season results correctly.

You should sow wisely

Soil analysis

If the demo field soil analysis had been carried out prior to test sowing, it would provide data on areas where a low plant emergence rate is possible due to the soil structure and its chemical elements saturation degree.

Let us talk not about the R & D event held for seed drills testing but about the agribusiness. Having this data at their disposal, agronomists comprehend: where the sprouts will initially be worse because of the soil structure (sand, clay), where it is worth conducting deep tillage operations, and where it would be good to apply a different fertilizer amount to energize plants and increase the future crop yields. The data on soil characteristics can be used to construct maps for differentiated fertilizer application.

Sowing monitoring

The next step for agricultural production efficiency improvement is sowing monitoring. Installing seed control sensors on the machinery will allow agrarians to monitor seed drill operation in real time: get data on the operation of each coulter and readjust seed drills if the sowing is incorrect.

 Sowing map with fixed sowing rate

Sowing map with fixed sowing rate

The obtained sowing monitoring map is superimposed on the orthophoto map, so, it becomes possible to see the correlation – the dependence of the number of sprouts on the seed drill coulters quality. If in the areas where the seed drill operated within normal parameters the sprouts are bad, the problem is either in the seed quality or in worse soil characteristics.

Differential sowing will be more efficient. It is performed based on a relief matrix and soil structure map. For different areas, a dedicated sowing rate is set. For example, on hills or on sand, it is reduced, and in valleys or in areas with a high content of organic matter, it is increased.

 Differential sowing map

Differential sowing map

After sowing, it is necessary to count the number of sprouts, of what it was mentioned above. Besides, we have to note that the final results of the season – the yields amount – will also vary depending on the correctness of the subsequent operations or their absence: top dressing and PPP application. At the final step, yield mapping is performed and the factors influencing the results are determined.

To predict the most accurate season results, the condition analysis, planning, and operation implementation should be comprehensively performed.

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