SOURCE-LINKED INTELLIGENCE

Optimizing fertilizer use in global crop systems: unveiling historical trends, current patterns, and future projections

CORDIS · observation · Publication date unknown

ough three key work packages. First, the N(P)UEdiff method will be used to analyze historical NUE and PUE trends from 1961 to 2020 for major crops worldwide, identifying critical breakpoints. Second, machine learning models will predict the spatial distribution of NUE and PUE from 2000 to 2020, factoring in environmental and management variables to reveal driving factors. Lastly, future climate change scenarios will be integrated into these models to project NUE and PUE changes through 2100 and propose optimal agronomic strategies. The innovative combination of the N(P)UEdiff method with dynamic data across 205 countries allows for the first comprehensive analysis of NUE and PUE trends and projections. This project will offer valuable insights for optimizing fertilizer use, increasing yields, reducing costs, and mitigating environmental harm, supporting global food security. The hosts expertise in global change ecology, coupled with the fellow's agroecology background, ensures successful implementation, while the interdisciplinary approach and open science practices will maximiz

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recordType

award

status

SIGNED

region

EU

value

194074.56

unit

EUR