: EVALUATION OF PHYTONEMATODES IN SOYBEAN ROOTS AND SOIL, MICROBIAL ACTIVITY AND SOYBEAN YIELD IN INTEGRATED CULTIVATION SYSTEM

• Grant number: 25/22571-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: November 01, 2025
• End date: June 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geodesy
• Principal Investigator: Nilton Nobuhiro Imai
• Grantee: Wislayne Romão da Silva
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Associated research grant: 21/06029-7 - High resolution remote sensing for digital agriculture, AP.TEM

Abstract

In agricultural areas with predominantly sandy soils and low organic matter content, studies are needed to evaluate how new cropping systems can increase agricultural sustainability. In this regard, the integrated crop-livestock system (ILPS), with soybean cultivation in the summer and pastures in the off-season, has gained prominence in recent years as a management strategy that achieves improvements in soil properties with greater sustainability. However, some challenges in assessing soil and crop attributes more quickly and in conjunction with other technological tools require further study. The relationship between remote sensing and microbial activity, nematode presence, and crop biomass production deserves attention and should be investigated to assess whether integrated cropping systems are aligned with more sustainable food production. The study will be conducted in a SILP (Plant for Plant Protection), already established for nine years in an experimental area in the municipality of Caiuá, São Paulo. The experiment will be conducted in plots cultivated with Urochloa ruziziensis and soybeans annually. Ninety soil and plant collection points will be established at this site using georeferencing and will be monitored for 12 months using aerial and proximal remote sensing analysis. The relationship between plant parasite occurrence, soil microbial attributes, and soybean and grass yield will be studied using models that integrate remote sensing analysis.