Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
HS1.3.6 | Advancing understanding of greenhouse cultivation across scales: mapping, monitoring and modelling
EDI
Advancing understanding of greenhouse cultivation across scales: mapping, monitoring and modelling
Convener: Daniele la CeciliaECSECS | Co-conveners: Maarten Braakhekke, Xiaoye TongECSECS, Caroline van der Salm
Globally, agriculture uses up to 80% of freshwater, depleting both surface water and groundwater resources. Protected agriculture (plastic tunnels, glasshouses, etc.) reduce irrigation water usage up to 90% with similar yield compared to open field agriculture. With an increasing human population and climate change driving more severe and frequent hazardous meteorological phenomena, protected agriculture can offer technological solutions towards food security and sustainable water resources management.

However, important research gaps exist to comprehensively understand the effects of protected agriculture on water quantity and quality at the plot and catchment scale. Greenhouses alter the water budget terms (e.g., runoff, evapotranspiration, soil moisture storage, infiltration). Agricultural contaminants (e.g., plant protection products and fertilizers) use and fate are driven by the management of the environment inside the greenhouse rather than external meteorological conditions. The current process-understanding is often implemented in decision support systems used to recommend fertigation volumes at the greenhouse scale. But the upscale of such knowledge at the catchment scale is neglected or substantially simplified.

Another issue concerns the lack of large-scale land use and land cover maps indicating the presence of greenhouses, which hinders the capability to analyse sustainability questions as well as to simulate their impacts in realistic scenarios. For the purpose of greenhouse mapping, satellite remote sensing data and suitable machine learning algorithms have been used, although some challenges for large-scale mapping remain. In contrast to the advances in leveraging earth observation and machine learning techniques on the large-scale mapping and monitoring of conventional land uses, there remains significant potential for such applications within greenhouse contexts.

This session invites contributions that improve our quantitative understanding of the hydrological and contaminant transport impacts of protected agriculture, soil-bound and soil-less, through:
1) experimental studies and environmental monitoring;
2) numerical modelling of water use and water-saving strategies as well as fate and risk mitigation of agricultural contaminants (including sustainable alternatives such as organic production)
3) advances and applications of greenhouse mapping and monitoring methods exploiting remote sensing data and deep learning algorithms.