Supporting Next-Generation Agriculture in the Alps: Direct Evapotranspiration Measurements for Smarter Water Management
Bibliographic record
Abstract
Measuring evapotranspiration (ET) is crucial for understanding the complex interactions among the atmosphere, vegetation, and land. In the context of global climate change, distributed quantification of actual ET has become even more important, as alterations in the hydrological cycle affect water availability, ecosystem dynamics, and thus agriculture.In this study we present a network to directly measure ET across different land uses in the Aosta Valley (Western Italian Alps) in the context of the Agile Arvier project. Supported by funding from the European Union’s economic recovery plan, the Agile Arvier project aims to transform the small village of Arvier into a hub for climate change research in the Alps. This activity is part of one of the five work packages (or “Laboratories”), the Green Lab, which includes studies on water use and smart agriculture, among other activities.Typically, ET is a modelled component in irrigation water requirement (IWR) models, with estimates derived from meteorological data or crop coefficients. While these models provide valuable insights, they often lack the accuracy provided by direct measurements. Measuring actual ET, e.g., by means of the eddy covariance technique, is crucial for improving water management strategies, especially in regions characterized by diverse landscapes and land uses.To this end, in 2025, seven LI-710 Evapotranspiration sensors (from LI-COR) will be installed to directly measure ET across different agricultural lands in the Aosta Valley region. We selected seven monitoring sites representative of the typical crop types in the region, including a vineyard, an apple orchard, and five meadows and pastures ranging from 500 to 1950 meters above sea level (m a.s.l.). To enhance the value of the data collected by the LI-710 sensors, we will integrate into the network decadal ET measurements already available from two ICOS (Integrated Carbon Observation System) associated sites located in the same region: an abandoned pasture and a larch forest at 2150 m a.s.l. (IT-Tor, IT-TrF).Data from the ET network will be compared with IWR data available for the entire region to validate and refine the accuracy of IWR estimates using direct ET measurements. The results of this comparison will be used to inform policymakers and provide the Regional Agricultural Department with an enhanced tool for irrigation management.Finally, by the end of the year, we aim to create an online open-access dataset for ET data consultation and download, available for scientists and policymakers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".