The practical objective of applying the technology included in the precision farming is increase efficiency in the use of inputs, be it seed or fertilizerand an aspect that emerges from this agriculture and that is very useful for agricultural production is the characterization of environments.
For this it is important to use different sources of information and not be left alone with one variable when generating a recommendation for planting density or fertilization dose.
Among those sources of information applied in the development of each recommendation are: determinations to field of fertility and of nitrogentrials of density from sowing and of dosage from fertilizer at rate variable O fixed.
This includes the sampling O survey from flooring with Veris harrow. And the collection of data and its analysis, is applied to the characterization of each environment. This was explained by the Agricultural Engineer Juan Ramponi of the Clarion firm of 9 de Julio, Buenos Aires, within the framework of a field meeting organized by the KWS nursery.
The more information, the higher the quality of the recommendation.
To give an example of environmental characterizationRamponi mentioned different sources of information that can be applied in the lot, and made it clear that the greater number of layers of information, the more quality and safety of the resulting recommendation.
Among the tools to obtain information, the survey with elevation using a GPS navigator with RTKthe Veris harrow for measuring apparent electrical conductivitythe measurement of napa and the depth effective soil, the sampling of ground in grid, the Map from yield to harvest, with satellite images and analysis of images taken from drones.
Different tools for environmental characterization: GPS with RTK, Ratra Veris, satellite and drone images, yield-to-harvest maps.
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Another point to consider is that the difference between a satellite image and a drone image is the scale and the ability of work, since with the satellite image there is a greater capacity for work than with a drone, but the resolution of the drone will be higher, allowing greater detail to be achieved in each observation.
Map of apparent electrical conductivity green areas associated with sandy hills, transition yellow, red low with higher content of organic matter
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Work control
Another service of this technology is the labor control that can be had in the image on the PC screen relieved by a drone, which detects, for example, sowing failuresfaults in fertilization, passes that were left without input due to speedingor because it did not enter the header well and they were left unworked fringesor because part of the equipment did not work due to nozzles or dispensers O clogged downspouts.
Another case of application of this technology can occur in a batch of research where two different materials were planted, and you can see the image taken by a drone and by a satellite and how both sources of information can complement each other.
Another example can be in nitrogen management, which is well studied for corn and wheat for grain production and can also be applied to the production of corn silage. The objective is to show that the technology is available and comment on how it helps to make a diagnosis, Ramponi contextualized.
Another way to make a diagnosis is with the foot crop using the plant as an indicator and that she herself “tell us what the nitrogenous state in this case, and we do this through sensors that measure greenness which is related to biomass.
Nutritional monitoring of nitrogen in corn.
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In this type of sensors, depending on the crop we have shandheld devices, such as a Greenseeker or a Spad. We have found the first to be more useful in wheat at the macollage stage and beginning of rooting, or in corn in the case of spad, said the Engineer.
Both sensors are easy-to-use measuring instruments, which allow assess the health or vigor of a crop in seconds, in order to make fertilization decisions. There is a clear correlation between this measurement and the nitrogen content (high mobility makes its deterioration difficult) in the leaf. In general, the color is related to its chlorophyll content and the amount of nitrogen in the leaf.
“We can also do with a drontake an image from which we will have those same readings, but what we do is multiply the N and the number of samples by the density of information that we have with an image, or we can also do it with satellite images, always depending on the scale and of the objective, and which is the source that allows to achieve a better diagnosis”.
All measurements are relative to a sufficiency condition, and can be in a strip or in a plot, with a saturation dose that does not limit cultivation (in this case the N) against which we can compare the measurement that we make in our batch. For which we want to see its nutritional status and if it is necessary to adjust with some practice. And to gain certainty in this it is very important to have local calibrations.
It works with indices like NDVI in corn and wheat trials, and making the relationship between the index and the N doses to know where we have the saturation, which will lead to the optimal dose that we would have to aim for, and see if we can predict with this index where we should aim to have the maximum yield and not limit the crop.
The values are presented in calibration curves, which are graphs of practical use that allow knowing the relationships between the availability of a nutrient and the state of the crop. “Working with a curve of N is much easier and more efficient to make a recommendation and we can also develop curves in crops for silage,” said Ramponi.
Prescription optimal seeding density as it changes according to environment.
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Finally, the specialist said “the crop yield maps of the micro plots allow validating the certainty of the applied treatment”.
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