Lucas Carvalho Gomesa, b, 1, Cássio Marques Moquedacea, c, d, 1, Ivan F. Souzaa, Ben Bond-Lambertye, Rodrigo Vargasf, Lars Vesterdalg, Gustavo V. Velosoa, Marcio Rocha Francelinoa, Carlos E.G.R. Schaefera, Kendalynn A. Morrise, Elpídio I. Fernandes-Filhoa
a Laboratório de Pedometria e Geoprocessamento (LabGeo), Department of Soil Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
b Department of Agroecology, Aarhus University, Tjele, Denmark
c Geotechnologies in Soil Science Group (GeoCiS), Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, 13416-900, Brazil
d Center for Carbon Research in Tropical Agriculture (CCARBON), Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, 13416-900, Brazil
e Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD, USA
f School of Life Sciences, Arizona State University, Tempe, AZ, USA
g Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg, Denmark
Highlights
- Machine learning revealed soil texture as a key control on 21st-century soil respiration
- Global soil respiration rates are projected to increase at high latitudes and decrease at low latitudes
- Ignoring soil texture leads to higher projections of future heterotrophic respiration
- A positive soil carbon-climate feedback is projected for tundra regions over the coming decades