REalistic WATER budgetING in protected agriculture

Projektinformationen

REWATERING

ID Finanzhilfevereinbarung: 101062255

DOI

10.3030/101062255

Projekt abgeschlossen

EK-Unterschriftsdatum 29 Mai 2022

Startdatum 1 Mai 2023

Enddatum 30 April 2025

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 188 590,08

Koordiniert durch

UNIVERSITA DEGLI STUDI DI PADOVA
Italy

Periodic Reporting for period 1 - REWATERING (REalistic WATER budgetING in protected agriculture)

Berichtszeitraum: 2023-05-01 bis 2025-04-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

Protected agriculture makes use of technology to create an optimal environment for crops growth. The project “REalistic WATER budgetING in protected agriculture (REWATERING)” proposes a novel open-access framework to monitor and model water quantity and quality in agricultural catchments covered also by soil-bound leafy crops grown under plastic greenhouses.

The locations of plastic greenhouses and their spatial and temporal evolution is not currently known. The study area has evolved from an open field agricultural catchment in the 1980s to a greenhouse district in 2024. This has caused an increase in imperviousness, and a consequent increase in the flood risk for the rainfall does not infiltrate in the soil. Yet, farmers have increased food production thanks to the development of greenhouse technology despite the management is still mostly driven by experience. Therefore, the specific objectives of REWATERING have been to: (1) Deliver a methodology for catchment-scale models to explicitly account for the role of protected agriculture on water quantity and quality, (2) Measure water quality in the study area with 14 days-average samples for one year, (3) Share knowledge among stakeholders interested in protected agriculture.

REWATERING is an essential project because protected agriculture is a growing reality in Europe and worldwide for it allows for boosting the production of healthy vegetables and fruits, while saving water resources, especially in semi-arid climates. The novel framework proposed in this research expands catchment-scale hydrological science making possible to predict short- and long-term water quantity and quality management in the presence of protected agriculture.

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

For modelling water quantity fluxes, the framework first uses satellite remote sensing data to locate the presence of greenhouses, vegetated areas and bare soils. Second, the greenhouse climate model simulates the greenhouse environment from weather data, and therefore, the crop water demand. At last, the hydrological model quantifies how water inputs, such as irrigation and rainfall, moves in the catchment as surface water and groundwater.
The satellite-based land cover classifier has been further applied to create the map of greenhouses at the pan-European scale.
The greenhouse climate model and the hydrological model have been assessed against measured data inside an experimental greenhouse to corroborate its usefulness for simulating the indoor environment and irrigation water movement.
The full framework has been applied to understand the consequences of the presence of greenhouses on the water levels in the main channel and the groundwater recharge in the catchment.

Regarding water quality, the project enforces the more and more frequently chosen technique of passive sampling, together with the standard grab sampling, and analyses the samples by means of the more informative non-target approach. This latter approach makes use of big chemical data analytics to tentatively identify any measurable water contaminant rather than searching for a target list of chosen contaminants. The non-target analysis identified at the catchment inlet a multitude of contaminants, agricultural and urban, each with a proper seasonality. Nonetheless, the data revealed a good water quality at the outlet indicating a good management of agricultural chemicals in the catchment as well as the capacity of the channel to remove the contaminants.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

It was necessary to map the spatial and temporal presence of greenhouses. In a collaborative effort using Google cloud computing, I have been able to go beyond my previously developed machine learning model for land cover classification working on a personal laptop. The research made it possible to map greenhouses at the European scale and for preparing time series of land covers at any location. The model has been applied in the study area to prepare maps of greenhouses locations and crop evapotranspiration coefficients.
Some misclassification issues still occurred at the European scale given that the model relies on multi-spectral sensors (11 wavelengths from Sentinel-2). Therefore, the collaboration continued to make use of hyperspectral data (>400 wavelengths) acquired from airborne (NASA) and spaceborne (Italian Space Agency) missions on the study area, and elsewhere to develop a novel approach for efficiently and accurately mapping plastic greenhouses globally.

It was necessary to develop a greenhouse climate model for estimating the crop water demand within greenhouses given the lack of open software as well as the lack of collaborative commercial partners. The greenhouse model is undergoing further development and testing as data are being collected inside and outside an industrial greenhouse farm. The greenhouse climate model was used together with the CATHY model to simulate hydrological processes within the experimental greenhouse of CREA, who had valuable unpublished data for validating the models. Then, the modelling framework for upscaling the plot scale to the catchment scale was realized.
The CATHY model corroborated that the increase in imperviousness is causing the increased flood risk. The risk mitigation measures mandated by the local land reclamation authority appear to be effective at the farm scale, and they are applicable only to new building sites. A supportive regulatory framework is needed to promote the adoption of flood risk mitigation measures.
Application of the model to other climates and greenhouse management conditions would certainly highlight different issues and help to address them in an effective and efficient manner.
A collaboration with two Italian universities have started for validating other two applicability of two more hydrological models. I have prepared training materials for running one of these two software (EPA-SWMM).
Within CATHY, we have improved the reactive transport solver so that it is possible to model any number of aqueous species with biological feedbacks (competition, inhibition, biomass growth). Work is in progress for inserting the routine on chemicals uptake by vegetation and absorption and phytodegradation to include the important role of riparian vegetation and nature based solutions in contaminants removal.

The water quality analyses revealed the important role of continuous monitoring rather than periodic grab sampling considering the higher number of identified chemicals in the former approach. Continuous monitoring was realized by means of passive sampling, an innovative alternative to automatic grab water sampling. Results indicated negligible emission of plant protection products from the greenhouse district to surface water. The non-target analysis identified at the catchment inlet a multitude of contaminants, agricultural and urban, each with a proper seasonality. Further research would pinpoint the contamination sources and identify the best available technology for reducing water contamination.