By Blake Jackson
Healthy soil is crucial for agriculture, clean water, and adapting to climate change. However, understanding what influences soil health has long been a challenge due to the complex interactions between climate, soil characteristics, and land management practices.
Cornell Soil Health researchers have made a breakthrough by applying machine learning to a large dataset of soil health results gathered across New York State.
These advanced models now allow scientists to estimate soil health even in areas that have not been directly sampled. Additionally, the models help predict how different land-use choices may affect soil conditions.
One of the key findings is that land use and management practices account for 42% to 58% of the variation in soil health. This demonstrates that the way we manage land significantly impacts soil quality. The models also integrate climate data and natural soil properties, allowing for more accurate statewide mapping of soil health.
Practices that enhance biomass inputs such as planting cover crops, rotating with perennial species, and applying manure are shown to improve soil health.
With this data-driven approach, farmers, landowners, and policymakers can make better-informed decisions to support sustainable land management and long-term environmental resilience.
Photo Credit: gettyimages-casarsaguru
Categories: New York, Crops