Evaluation of soil health due to the adoption of cover crops in a soybean production system in the state of Mato Grosso.

• Grant number: 25/05940-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: June 01, 2025
• End date: May 31, 2026
• Field of knowledge: Agronomical Sciences - Agronomy - Soil Science
• Principal Investigator: Carlos Eduardo Pellegrino Cerri
• Grantee: Caroline Andreatto Zanoli
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

The evaluation and monitoring of the impacts of agricultural systems on soil quality are essential for establishing best management practices and ensuring sustainable land use to mitigate climate change, conserve biodiversity, and achieve food and energy security. The Soil Management Assessment Framework (SMAF) has been successfully tested as an objective tool to quantify the effects of land use and management on soil health, even under Brazilian edaphic conditions. Therefore, this study aims to evaluate soil health due to the adoption of cover crops in a soybean production system in Mato Grosso. Five treatments with three replications in a randomized complete block design, combining cover crops in the soybean off-season, will be evaluated in a three-year experiment at the Centro de Aprendizagem e Difusão (CAD-Norte), Sorriso - MT. The treatments are: 1) Corn; 2) Corn + Urochloa (Brachiaria); 3) Corn + Urochloa (Brachiaria) + Sunn hemp (Crotalaria); 4) Urochloa (Brachiaria); 5) Cover crop mix. Disturbed and undisturbed soil samples will be collected at depths of 0-10, 10-20, and 20-30 cm at one point per experimental unit to assess chemical (pH, P, K), physical (porosity, bulk density, and penetration resistance), and biological indicators (organic carbon and enzymatic activity). SMAF scoring curves will be used, measured values will be transformed (range 0 to 1), and an overall Soil Quality Index (SQI) will be calculated. The data will be subjected to an analysis of variance (ANOVA), and if significant differences are found (p < 0.05), means will be compared using Tukey's test (p < 0.05).