We've updated our Privacy Policy to make it clearer how we use your personal data.

We use cookies to provide you with a better experience. You can read our Cookie Policy here.

Advertisement

Most Influential Parameters for Crop Yield Revealed by Supercomputer

Credit: Pixabay.

Want a FREE PDF version of This News Story?

Complete the form below and we will email you a PDF version of "Most Influential Parameters for Crop Yield Revealed by Supercomputer"

Technology Networks Ltd. needs the contact information you provide to us to contact you about our products and services. You may unsubscribe from these communications at any time. For information on how to unsubscribe, as well as our privacy practices and commitment to protecting your privacy, check out our Privacy Policy

Read time:
 

Nowadays, agriculture is going to become AI-native: Skoltech researchers have used the Zhores supercomputer to perform a very precise sensitivity analysis to reveal crucial parameters for different crop yield in the chernozem region. Their paper was published in the proceedings of the International Conference on Computational Science 2020.

Farmers all over the world use digital crop models to predict crop yields; these models describe soil processes, climate and crop properties and require environmental and agricultural management input data to calibrate them and improve the forecasts. In some countries, however, agrochemical data is not freely available for users of these models, and this calibration can become expensive and time-consuming.


A Skoltech team led by full professor Ivan Oseledets and assistant professor Maria Pukalchik used one of the popular open-source process-based model called MONICA and figured out a way to reveal only the most important parameters for crop yield based on historical data and process-modelling. Moreover, they sped up computational efficiency from one simulation per day to half a million model simulations per hour using Zhores, the flagship Skoltech supercomputer.


This stunning amount of simulations is necessary to perform high-quality sensitivity analysis that helps determine how the changes in certain input factors (such as soil parameters or fertilizer) influenced the output crop yield prediction.


The research team used field data from an experiment in the Russian chernozem region, with seasonal crop-rotation of sugar beet (Beta vulgaris), spring barley (Hordeum vulgare) and soybean (Glycine max) observed from 2011 to 2017. They picked six main soil parameters for sensitivity analysis and performed what’s called Sobol sensitivity analysis (named after Ilya Sobol, a Russian mathematician who proposed it in 2001).


“Soil is a very complicated issue in this country. Unfortunately, the data about soil properties and crop yield are not published. We have found an opportunity to overcome this barrier and set up the Zhores supercomputer to solve this issue. Now we can simulate all possible variants and reveal the most crucial parameters without time-consuming and costly work. We hope that our achievements will help farmers digitalize their crop growth,” said Maria Pukalchik.

Reference
Mikhail Gasanov et al. Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing. International Conference on Computational Science 2020At: Amsterdam June 2020, pp 731-741, DOI: 10.1007/978-3-030-50436-6_54

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

Advertisement