Global black soil distribution

Black soils, characterized by their thick, dark horizons enriched with organic matter, epitomize highly fertile soils. However, their fertility precipitates intense land use, engendering challenges such as soil erosion, nutrient depletion, pollution, compaction, salinization, and acidification. Notably, these soils are significant contributors to global greenhouse gas emissions, primarily due to substantial losses in soil organic carbon. Despite these challenges, black soils are pivotal for global food production. This paper delineates the implementation of digital soil mapping for the global cartography of black soils and human interference on these soils. Predominantly distributed in Eastern Europe, Central and Eastern Asia, and North and South America, black soils cover an approximate area of 725 Mha, with the Russian Federation,Kazakhstan, and China collectively have over half of this area. Agriculturally,these soils underpin significant proportions of global crop yields, producing 66% of sunflower, 30% of wheat, and 26% of potato outputs. The organic carbon content in the upper 30 cm of these soils is estimated at 56 Gt.Sustainable management of black soils is imperative for ensuring food security and addressing climate change on a global scale.

Guest editorial-soil erosion assessment, tools and data: A special issue from the Global Symposium on soil Erosion 2019

This special issue on soil erosion assessment,tools and data creation,consolidation and harmonization presents advances in soil erosion research with a focus on new tools that are being used to assess soil erosion rates.This publication includes eleven selected contributions presented at the Global Symposium on Soil Erosion(GSER,15-17 May 2019,Rome,Italy)dealing with erosion indicators'improvement,the use of remote sensing,nuclear techniques and geochemical fingerprinting as promising methods to assess soil losses,management practices that reduce soil erosion in vineyards and olive groves plantations and their modelling,and national and regional erosion assessments.

Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning:From prediction to explainability

The ability to characterize rapidly and repeatedly exchangeable potassium(Kex)content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture.In this paper,we show how this can be now achieved using a Convolutional Neural Network(CNN)model trained on a large Mid-Infrared(MIR)soil spectral library(40,000 samples with Kex determined with 1 M NH4OAc,pH 7),compiled by the National Soil Survey Center of the United States Department of Agriculture.Using Partial Least Squares Regression as a base-line,we found that our implemented CNN leads to a significantly higher prediction performance of Kex when a large amount of data is available(10000),increasing the coefficient of determination from 0.64 to 0.79,and reducing the Mean Absolute Percentage Error from 135%to 31%.Furthermore,in order to provide end-users with required interpretive keys,we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting Kex.Used in the context of the implemented CNN on various Soil Taxonomy Orders,it allowed(i)to relate the important spectral features to domain knowledge and(ii)to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different,sometimes underrepresented orders.