Research

Soilytix Publishes Study Linking Soil Microbiome Data & Global Crop Yield Predictions

Soilytix's study demonstrates how machine learning and soil microbiome data can predict crop yield variability globally.
Photo by Nora Jane Long on Unsplash

Key Takeaways:

  • Soilytix’s study demonstrates how machine learning and soil microbiome data can predict crop yield variability globally.
  • A machine learning model based on 26 soil bacterial genera predicted up to 37% of global vegetation variability.
  • Key bacterial genera such as Hyphomicrobium, Luedemannella, and Reyranella were identified as consistent predictors of plant growth.
  • The findings suggest a scalable approach for improving agricultural productivity and sustainability worldwide.

Study Overview

Soilytix GmbH (Profile), a leader in soil microbiome analytics, has announced the results of a pioneering study published in Science of the Total Environment. Titled Local Microbial Yield-Associating Signatures Largely Extend to Global Differences in Plant Growth, the research explores the potential of applying machine learning to soil microbiome data to predict crop yields on both local and global scales.

The study, conducted on a maize field in Germany, demonstrated that a machine learning model could predict approximately 65% of local maize yield variability using soil microbiome data. When applied to global datasets across regions such as North and South America and Asia, the model effectively predicted plant growth metrics, highlighting the universality of soil bacterial associations with crop productivity.


Key Findings

The study revealed several critical insights:

  • Soil microbiomes serve as reliable indicators for predicting crop yields beyond their immediate local context.
  • A machine learning model based on 26 bacterial genera explained up to 37% of global vegetation variability.
  • Specific bacterial genera, including Hyphomicrobium, Luedemannella, and Reyranella, emerged as globally consistent predictors of plant productivity.

These results highlight the potential for leveraging microbiome data and artificial intelligence to address key agricultural challenges, such as crop yield variations and resource optimization.


Expert Insights

Dr. Matthias Schaks, lead researcher at Soilytix, emphasized the study’s significance: “Our findings indicate a globally conserved set of soil bacteria that are useful in predicting plant growth between sites. This work sets the stage for a future where AI and microbiome data could transform agriculture.”

Dr. Bruno Steinkraus, CEO of Soilytix, underscored the broader implications for the agricultural sector: “This study represents an important step for the agricultural sector. At Soilytix, we are committed to equipping farmers and land managers as well as agricultural input providers with cutting-edge tools that not only increase crop yields but also promote sustainability. Our research reaffirms the vital connection between the soil microbiome and global food production.”

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