French wine dataset to mapping the expected harvest value by county

This database is built from open data as described in the paper entitled ‘French wine: Combination of multiple open data sources to mapping the expected harvest value’ (2024). CODE_CULTU Crop code of the graphic land registry database CodeCdC Crop code in Multi Perils Crop Insurance specification Harvest Value B Harvest value (€/ha organic wine) Harvest Value C Harvest value (€/ha no-organic wine) IDA ID of geographical areas of INAO Insee_Com County code (INSEE) Label_CdC Crop label in Multi Perils Crop Insurance specification Label_Dpt Department Label_Insee_com County Label_RA Agricultural Region (AGRESTE) Label_appellation Appellation (INAO) Label_code3 Crop (FADN) Label_cvi Wine name (vineyard register of customs services) Label_idGeo Geographical ID of Quality Sign (INAO) PxBaremAOP Price listed in Multi Perils Crop Insurance specification (€/hl no-organic) PxBaremAOPBio Price listed in Multi Perils Crop Insurance specification (€/hl organic) RdtMOAOP Harvest wine yield (hl/ha) SurfaceModel Surface of wine as fitted by model code3 Crop code (FADN) code_dept Department code code_regag Code of Agricultural Region (AGRESTE) cvi Wine code (vineyard register of customs services) id_appellation Appellation code (INAO) id_denomination_geo Geographical ID of Quality Sign (INAO) Related Research Paper This dataset supports the research article: French wine: Combination of multiple open data sources to mapping the expected harvest value. The full paper is available via HAL: https://univ-lemans.hal.science/hal-04627672. Methodological Overview This study addresses the challenge of reconstructing macro-scale economic values for French vineyards from incomplete public information. Due to privacy regulations (GDPR), granular production data is often protected, leaving researchers with marginal totals (sums by region and sums by appellation) but lacking the cross-tabulated matrix required for precise risk assessment. We treat this gap as an inverse problem solvable through mathematical programming. The core methodology employs a constrained optimization algorithm (implemented in SAS PROC OPTMODEL) to estimate the latent surface area sac (hectares) for each appellation a in each county c. The model respects the following constraints: Marginal Consistency: Sum of estimated areas must not exceed known totals by appellation (sa) and by county (sc). Geographical Authorization: Areas are forced to zero where an appellation is not authorized in a specific county (data provided by INAO). Objective Function: Maximization of AOP priority to reflect economic weight. Once the surface map is reconstructed, expected harvest values are computed using Olympic average yields (to mitigate extreme weather years) and official insurance scale prices. This results in a high-resolution map of economic exposure, distinct from physical crop load mapping used in Precision Viticulture. Data Sources All data utilized are open-source or publicly accessible government records. Below is the list of primary sources consulted (last viewed June 26, 2024): Official Statistics & Registries Vineyard Registry (CVI): Customs service statistics on area and production by appellation and county.https://www.douane.gouv.fr/la-douane/opendata?f[0]=categorie_opendata_facet:467 National Institute of Origin and Quality (INAO): Catalog of counties authorized for AOP/PGI production and quality specifications.https://www.inao.gouv.frhttps://www.data.gouv.fr/fr/datasets/?q=inao Agreste / Ministry of Agriculture: National agricultural accounts and agricultural regions definitions.https://agreste.agriculture.gouv.fr/agreste-web/methodon/Z.1/!searchurl/listeTypeMethodon/https://agreste.agriculture.gouv.fr/agreste-web/download/publication/publie/Dos2203/2Pages%20de%20Dossier2022-3_CCAN_ChapitreII.pdf Insurance Specifications: Official scale values for crop insurance premium calculation (Ministry of Agriculture).https://info.agriculture.gouv.fr/boagri/document_administratif-4b9ef75e-29a7-449d-9e40-7e5253bfd642/telechargement Supplementary Data Champagne Area Distribution: Detailed breakdown of Champagne vineyard surfaces by county.https://maisons-champagne.com/fr/appellation/aire-geographique/ RICA (FADN): Farm Accountancy Data Network (referenced for context on farm structures).https://www.casd.eu/source/reseau-dinformation-comptable-agricole/?tab=16 Simplified SAS code DATA LINAO; INPUT INSEE_COM CVI AUTHORIZED SINIT; DATALINES; 01001 3B011 0.33 0 01001 3B011M 0.33 0 01001 3B012 0.33 0 01001 3B012M 0.33 0 01001 3B013 0.33 0 .... ;;;; RUN; DATA LA; INPUT CVI S; DATALINES; 1B001D 78.714523339 1B001M 3064.7940186 1B001S 9474.2135637 1B002D 3 1B002S 12.987045088 .... ;;;; RUN; DATA LC; INPUT INSEE_COM SDC; DATALINES; 01001 0.2 01002 5.0859 01003 0.2 01004 1.0499999999 01005 0.2 .... ;;;; RUN; ODS OUTPUT SolutionSummary=SolutionSummary; PROC OPTMODEL PRESOLVER=AUTOMATIC ; SET <STR> Icvi; SET <STR> Icom; SET <STR,STR> Iinao; NUM Authorized {Iinao}; NUM SInit {Iinao}; NUM S {Icvi}; NUM SDC {Icom}; READ DATA LA INTO Icvi=[cvi] S; READ DATA LC INTO Icom=[Insee_com] SDC; READ DATA LINAO INTO Iinao=[cvi Insee_com ] Authorized SInit; SET NODES = union {<cvi,Insee_com> IN Iinao} {cvi,Insee_com}; VAR SurfModel {<cvi,Insee_com> IN Iinao} INIT SInit[cvi,Insee_com] >= 0 <= MAX(0, MIN(S[cvi],SDC[Insee_com])) ; MAX obj= SUM {<cvi,Insee_com> IN Iinao} SurfModel[cvi,Insee_com]*Authorized[cvi,Insee_com]; CON SurCVI {cvi IN Icvi}: SUM {<(cvi),Insee_com> IN Iinao} SurfModel[cvi,Insee_com] <= S[cvi]; CON SurCom {Insee_com IN Icom}: SUM {<cvi,(Insee_com)> IN Iinao} SurfModel[cvi,Insee_com] <= SDC[Insee_com]; SOLVE WITH NLP; CREATE DATA optmodel(RENAME=(SurfModel=SurfModel&i)) FROM [cvi Insee_com] SurfModel; QUIT; File Description optimization_results.csv: The output table of estimated surfaces (ha) per appellation and county, along with calculated harvest values.