Evidence map›Paper›PMID 41904160›Full record

ArticleScientific data2026

Survey data on Circular Economy practices in Italian farms with a focus on surplus food and food waste.

Niso Randellini, Giovanni Scotti, Giulia Valentini, Paola Garrone

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Niso RandelliniDepartment of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy. niso.randellini@polimi.it.
Giovanni ScottiDepartment of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy.ORCID http://orcid.org/0009-0002-3207-0962
Giulia ValentiniDepartment of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy.ORCID http://orcid.org/0009-0000-9802-5633
Paola GarroneDepartment of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy.ORCID http://orcid.org/0000-0002-0953-5007

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current food systems present several shortcomings that hinder their long-term sustainability leading to depletion of natural resources, land degradation and biodiversity loss. Circular Economy (CE) promises to mitigate these problems, by regenerating the ecosystems, enhancing production efficiency, and alleviating ecological pressures. Farms implement circularity in agriculture through renewable resources use, food waste prevention, biomasses valorization and regenerative practices. In particular, food waste prevention alleviates ecological pressures from production by enhancing output efficiency per unit of input. Farm level data are essential for understanding the mechanisms guiding CE diffusion, yet data availability is limited. This study tries to fill the knowledge gap by providing a new representative dataset on Italian farms. Data were collected through a structured survey administered to 1,200 farms. The dataset comprises 443 variables covering farm characteristics, surplus and waste generation and valorization, regenerative practices, use of circular inputs, and food waste performance providing the first granular overview of CE and food waste management practices in agriculture.

Indexed as

AgricultureFarmsFoodFood Loss and WasteConservation of Natural ResourcesItalyFood Loss and Waste

Identifiers

PMID41904160
PMCPMC13195135

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.