Agricultural lands occupy 13 percent of the territory of Russia, and a potential increase in the frequency of droughts and dryness, humus reserves exhaustion are the major negative factors of climate change for national agriculture. This means that it is important to be able to simulate all possible variants and reveal the most crucial parameters. In order to avoid time-consuming and costly work., and to increase the level of accuracy, new technologies are required.
In this example of agritech, scientists from Skolkovo Institute of Science and Technology, Moscow, Russia have developed a process aimed at assisting farmers with digitalizing their crop growth.
This is undertaken through processing big data through a supercomputer and then drawing on analytics so that meaningful trends can be ascertained. In the current study, among analyzed soil parameters are sand and clay fraction, carbon to nitrogen ratio, pH, organic carbon concentration, and bulk density. Among crops are sugar beet, soybean, and spring barley.
Some of the results made by the supercomputer include the finding that soil organic carbon content play an essential role in all crops. What is of more significance is the inference that the change in bulk density and soil organic carbon has a more significant impact on spring barley yields than on other indicators. This is to the extent that almost all the difference in soybean yield is due to the change in the soil clay fraction.
Taking a different crop, the analysis showed that the yield of sugar beet depends on the content of soil clay and bulk density, which coincides with real data, since beets are demanding to water nutrition, and clay content and bulk density can affect water regime.
Such findings can aid farmers is making improvements both in real-time and in terms of planning ahead for future years, in order to maximize crop yield.
The associated research paper has been published in the proceedings of the International Conference on Computational Science 2020. The research paper is titled “Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing.”
