Fertiliser Use Efficiency with AI
Fertiliser Use Efficiency with AI
批准号:
EP/Y008154/1
负责人:
Tiina Roose
金额:
$16.19万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
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英文摘要
One of the biggest challenges facing the agricultural industry is achieving high yields with the use of fertilisers while simultaneously minimising the environmental side effects of the fertiliser application and production. In this highly weather-reliant industry, developing weather-dependent fertilisation strategies will help maintain crop production and reduce the greenhouse gas (GHG) emissions in the face of climate change. This is particularly important for phosphorus and nitrogen (P&N) fertilisers, which have a high environmental footprint in terms of GHG emissions and pollution. One approach to achieving this goal is by harnessing new computational technologies within a precision agriculture framework.In the first six months of 2022, fertiliser prices have significantly increased and in some cases tripled. These prices are likely to remain volatile; the increases are largely driven by disruptions to the production due to climate change (warming, droughts etc) and geopolitics (energy/gas supply, war in Ukraine etc). In addition, the Haber-Bosch process for nitrogen fertiliser production is at best of times a highly energy consuming process responsible for 1.2% of the global energy usage. While there are worldwide efforts to deal with climate change, the climate is expected to remain volatile for the foreseeable future. Computer simulations are a valuable tool for considering climate volatility, as they allow for the testing of different soil/crop management approaches under different climate scenarios. Computer simulations/models for fertiliser use efficiency (FUE) allow for fast and cheap testing of different scenarios as part of the multifaceted decision making process for fertilisation applications. Approaches to enhance FUE address at least one of the 4R's of FUE: right type of fertiliser, right rate, right time and right place of application. My ERC Consolidator Grant (CoG) DIMR-646809 created new mathematical models, which now allow us to start tackling these issues.
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