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

Descripción del proyecto

Mejor uso de los fertilizantes para una agricultura sostenible

El sector agrícola mundial se enfrenta a un reto acuciante: la disminución de la eficacia de los fertilizantes nitrogenados (N) y fosforados (P). A pesar del uso generalizado de la síntesis de amoníaco y la extracción de fosfatos, la eficiencia de esos insumos ha disminuido con el tiempo, contribuyendo a problemas medioambientales como la acidificación del suelo, la eutrofización y el agotamiento de los fosfatos. El uso de fertilizantes ha superado los límites sostenibles, pero los métodos actuales para medir la eficiencia a menudo no tienen en cuenta las diferencias regionales. Con el apoyo de las acciones Marie Skłodowska-Curie, el proyecto OFUC analiza las tendencias en la eficiencia del uso de N y P (NUE y PUE) desde 1961 hasta 2020. Mediante la integración del aprendizaje automático y los escenarios de cambio climático, pretende optimizar el uso de fertilizantes, mejorar el rendimiento de los cultivos y reducir los daños medioambientales.

Objetivo

This research aims to enhance global agricultural sustainability by improving nitrogen (N) and phosphorus (P) use efficiency (NUE and PUE) in major crops. While the widespread adoption of ammonia synthesis and industrial phosphate mining has significantly boosted the use of inorganic N and P fertilizers, their efficiency has declined over time. From 1961 to 2018, the dramatic increase in fertilizer application surpassed sustainable thresholds, leading to environmental challenges such as phosphate rock depletion, eutrophication, and soil acidification. Existing knowledges for assessing NUE and PUE often overlooks regional differences and specific agricultural practices, limiting their effectiveness.
This project addresses these gaps through 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 maximize the researchs impact and dissemination.