Soil organic carbon sequestration potential explained by mineralogical and microbiological activity using spectral transfer functions.

The ability of soil to sequester carbon and reduce atmospheric CO concentrations is limited and depends on the soil minerals and their interaction with the microbiota. Microbial activities are closely associated with the types and amounts of soil organic matter (SOM) and clay minerals that have functional groups that interact with energy in Vis NIR-SWIR and Mid-IR wavelengths. The main objective of this research was to determine, based on these spectral ranges, the relation between mineralogical and organic compounds, as their sequestration and specialization in soils from Brazil.

Geotechnologies on the phosphorus stocks determination in tropical soils: General impacts on society.

Phosphorus (P) is a critical nutrient for primary production in terrestrial and aquatic ecosystems. As P mineral reserves are finite and non-renewable, there is an increasing discussion on its sustainable utilization to safeguard food security for future generations. Understanding the spatial distribution of soil P is central in advancing effective phosphorus management and fostering sustainable agricultural practices.

Digital mapping of the soil available water capacity: tool for the resilience of agricultural systems to climate change.

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties.

A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics.

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields.

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging.

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented.