Project Details
Description
Changing climate, variable yields, and resource constraints are challenging UK agriculture and global food security. Our goal is to enhance sustainable and efficient production for two crops of major importance in the UK, wheat and potatoes. We will work with farmers and end users in the application and deployment of novel crop sensing and diagnostic technologies. We will develop a tool that can predict and diagnose crop response to water and nutrient related limits. In turn, this knowledge will guide crop management and help to inform stakeholders from across the arable supply chain about the best approaches for land management towards more sustainable and efficient production, using precision agriculture.
We will test remote sensing technologies and couple them with methods for analysing crop and soil processes. We will deploy sensors at farm sites on fixed towers and unmanned aerial vehicles (UAV), and compare these against satellite sensors with global coverage. We will evaluate whether changes in leaf temperature, fluorescence and reflectance are related to yield reductions, comparing sensor data against field measurements of plant growth, yield and ecophysiology, and plant and soil temperature and moisture. We will understand how, and under what circumstances, sensors and platforms can be employed to determine and diagnose crop yield limits. Links to simulation process modelling will provide a rich set of diagnostics related to the plant-soil system, and forecast its sensitivity to management changes. Data assimilation approaches will allow model updates and improvements based on field observations and sensor output, to generate more reliable, near real-time and robust analyses.
Our technologies will underpin a crop diagnostic system, indicating crop water and nutrient status, quantifying reductions to yield, that can be used at sub-field to farm scale, with a clear quantification of reliability. Working with farmers, our technologies will be combined to generate a decision support tool, with capacity for (i) immediate (near-real time) mapping of crop stress, and its likely impact on crop yield and (ii) providing detailed spatial information on optimal management interventions to support decision making for sustainable high yield.
This work directly addresses a priority of the UK research councils to support UK farming with high quality and practical research to support consistent high returns from crop production against a background of changing climate and increasingly competitive global markets. Our deliverables will provide advanced diagnostics for farmers, and guide cost effective strategies for water and nutrient management for consistent yield.
We will test remote sensing technologies and couple them with methods for analysing crop and soil processes. We will deploy sensors at farm sites on fixed towers and unmanned aerial vehicles (UAV), and compare these against satellite sensors with global coverage. We will evaluate whether changes in leaf temperature, fluorescence and reflectance are related to yield reductions, comparing sensor data against field measurements of plant growth, yield and ecophysiology, and plant and soil temperature and moisture. We will understand how, and under what circumstances, sensors and platforms can be employed to determine and diagnose crop yield limits. Links to simulation process modelling will provide a rich set of diagnostics related to the plant-soil system, and forecast its sensitivity to management changes. Data assimilation approaches will allow model updates and improvements based on field observations and sensor output, to generate more reliable, near real-time and robust analyses.
Our technologies will underpin a crop diagnostic system, indicating crop water and nutrient status, quantifying reductions to yield, that can be used at sub-field to farm scale, with a clear quantification of reliability. Working with farmers, our technologies will be combined to generate a decision support tool, with capacity for (i) immediate (near-real time) mapping of crop stress, and its likely impact on crop yield and (ii) providing detailed spatial information on optimal management interventions to support decision making for sustainable high yield.
This work directly addresses a priority of the UK research councils to support UK farming with high quality and practical research to support consistent high returns from crop production against a background of changing climate and increasingly competitive global markets. Our deliverables will provide advanced diagnostics for farmers, and guide cost effective strategies for water and nutrient management for consistent yield.
| Status | Finished |
|---|---|
| Effective start/end date | 1/12/16 → 30/11/20 |
Funding
- Biotechnology and Biological Sciences Research Council
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
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SDG 2 Zero Hunger
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SDG 13 Climate Action
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Combining process modelling and LAI observations to diagnose winter wheat nitrogen status and forecast yield
Revill, A., Myrgiotis, V., Florence, A., Hoad, S., Rees, R., MacArthur, A. & Williams, M., Feb 2021, In: Agronomy. 11, 2, 314.Research output: Contribution to journal › Article › peer-review
Open AccessFile14 Link opens in a new tab Citations (Scopus)156 Downloads (Pure) -
The effect of antecedence on empirical model forecasts of crop yield from observations of canopy properties
Florence, A., Revill, A., Hoad, S., Rees, R. & Williams, M., 18 Mar 2021, (First published) In: Agriculture. 11, 3, 258.Research output: Contribution to journal › Article › peer-review
Open AccessFile6 Link opens in a new tab Citations (Scopus)153 Downloads (Pure) -
UAV-based approaches for crop parameter retrievals
Florence, A., Hoad, S., Rees, R., Revill, A., MacArthur, A. & Williams, M., 5 Nov 2018, (First published).Research output: Contribution to conference › Paper
Datasets
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ATEC manuscript 3 - supporting data: "Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield"
Revill, A. (Creator), Florence, A. (Data Collector), Hoad, S. (Contributor), Rees, B. (Contributor), Rees, B. (Creator) & Williams, M. (Contributor), MDPI Multidisciplinary Digital Publishing Institute, 26 Feb 2021
Dataset