Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications

Resce, Giuliano; Vaquero‐Piñeiro, Cristina

doi:10.1016/j.foodpol.2022.102345

Cited by 14 publications

(3 citation statements)

References 56 publications

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“…According to the large literature on GIs' socio‐economic impacts, the GIs policy represents an opportunity for local actors to valorize their products and territories, and for consumers to reduce information asymmetry (Sgroi, 2021). However, as pointed out by some papers such as Vaquero‐Piñeiro (2021), Resce and Vaquero‐Piñeiro (2022), Torok et al (2020) and Cei et al (2018), there is heterogeneity in the success of these productions from an economic perspective, especially at the micro level (farmers and products).…”

Section: Literature Reviewmentioning

confidence: 99%

From local to global, and return: Geographical indications and FDI in Europe

Crescenzi,

De Filippis,

Giua

et al. 2023

Papers in Regional Science

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The Geographical Indications (GIs) scheme of the European Union guarantees visibility and protection to high‐quality agri‐food products associated with a demarcated region of origin. This paper estimates the impact of the scheme in attracting agri‐food Foreign Direct Investment (FDI) in European NUTS3 regions, using a novel dataset and a Generalized Propensity Score Matching approach. Areas endorsed with GIs attract more FDI in agri‐food related activities than their non‐GI counterparts. Positive effects, estimated for FDI inflows, related job creation, and inter‐sectoral spillovers on local employment, involves territories with lower institutional quality.

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Section: Literature Reviewmentioning

confidence: 99%

From local to global, and return: Geographical indications and FDI in Europe

Crescenzi,

De Filippis,

Giua

et al. 2023

Papers in Regional Science

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“…2012/1151). GI is a quality label used to differentiate agri‐food products that have a specific geographical origin and possess a reputation that essentially or exclusively results from the natural elements and human‐specific knowledge of their region of origin (Resce & Vaquero‐Piñeiro, 2022). GIs are formally identified as protected geographical indications (PGI) and protected designation of origin (PDO), respectively.…”

Section: Introductionmentioning

confidence: 99%

Assessing the role of geographical indications in affecting the quality of imports

Vaquero Piñeiro,

Curzi

2024

J Agricultural Economics

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Geographical indications (GIs) aim to protect the names of specific high‐quality products (food and wine) to preserve and promote the uniqueness linked to their geographical origin and traditional know‐how. EU and extra‐EU countries register domestic products with GIs. This study disentangles the effects of such registrations on the quality of imports using country‐level product import data for the fruit, vegetable, coffee, tea, and spice sectors. Our results show that the registration of domestic products as GIs enhances the quality of imported goods only if the importing country has a lower level of quality of domestic production. The introduction of GIs into high‐quality domestic markets can discourage import quality upgrades. This is because domestic producers may prefer to compete for quality rather than price, and imported goods represent a less expensive alternative to high‐quality national goods for consumers. Conversely, in countries where domestic product quality is lower, the introduction of GIs may enhance import quality upgrading because the diffusion of GIs induces domestic consumers to become more demanding in terms of quality for foreign products.

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“…A machine learning method has also been developed to predict which municipalities will obtain geographical Indications in the future, showcasing which main features (e.g., territorial conditions, socio-economic factors, etc.) are more relevant in predicting their success [ 31 ]. These works, although not presenting exhaustively all possible applications of machine learning in the wine sector, demonstrate the merits of the data-driven approaches, which can improve the performance on some tasks or provide essential tools for various stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sugar Content in Wine Grapes via In Situ VNIR–SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques

Kalopesa

Karyotis

Tziolas

et al. 2023

Sensors

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Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR–SWIR spectrum (350–2500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (°Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the °Brix content from the VNIR–SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2>0.8, RPIQ≥4), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.

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Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications

Cited by 14 publications

References 56 publications

From local to global, and return: Geographical indications and FDI in Europe

From local to global, and return: Geographical indications and FDI in Europe

Assessing the role of geographical indications in affecting the quality of imports

Estimation of Sugar Content in Wine Grapes via In Situ VNIR–SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques

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