DOI: 10.5061/dryad.47d7wm3q5
title: West African cocoa farmer tree valuation data

description: The study was conducted at 15 sites across Côte d’Ivoire, covering a
 South-North climate and vegetation gradient from evergreen to
 semi-deciduous forests. These sites were selected to represent varying
 cocoa field characteristics, including structural complexity (ranging from
 monocultures to agroforests), cocoa field age (young to mature), and cocoa
 tree productivity (low to high). Each site had 10 cocoa plots, ranging
 from 0.3 to 5 hectares. The study also spanned different "cocoa
 loops," covering areas from the eastern to western parts of the
 country. Cocoa farmers were involved in a ranking exercise using
 the stone method to assign importance to tree species across 10 categories
 of use.
description: The adopted approach involved the use of the stone ranking method
 developed by Sheil et al. (2004) and applied by Jagoret et al. (2014) in
 cocoa fields in Cameroon. A comprehensive table with tree species listed
 in rows and 10 use categories in columns was presented to each cocoa
 farmer. Each farmer had a bag with an ample number of small stones, more
 than enough for the exercise. The farmer was then asked to place between 0
 and 10 stones in each cell of the table, based on the importance they
 assigned to each combination of tree species and use. Due to the 10
 categories of use, the farmer could distribute up to 100 stones per tree
 species.  Three response variables were calculated to
 answer research questions: 
 <strong>M<sub>p</sub></strong>: Global
 motivations, representing total use values of trees per
 hectare. 
 <strong>M<sub>p<sup>U</sup></sub></strong>: Motivations by category of use, calculated per use category and per hectare. <strong>S<sub>p</sub></strong>: Specialization of tree uses, the level of specialization of uses, which we have defined as the opposite of the observed diversity of uses. To obtain it, we calculated, for each field, the first-order diversity <strong>M<sub>p<sup>U</sup></sub></strong> transformed into a Hill number. Since the maximum possible value is 10 (all uses have exactly the same level of motivation, i.e. maximum diversity), we transformed the obtained value by subtracting it from 10. Thus, we obtain a measure of the specialization of uses. Calculations of <strong>S<sub>p </sub></strong>were performed using R’s ‘entropart’ package.
description: # West African cocoa farmer tree valuation data
 [https://doi.org/10.5061/dryad.47d7wm3q5](https://doi.org/10.5061/dryad.47d7wm3q5) ## Description of the data and file structure * Three Response Variables were calculated: Global motivations, Motivations by category of use, and Specialization of tree uses * Thirteen Explanatory Variables were gathered through interviews or national land use maps and included characteristics related to the farmers (e.g., land ownership, education), cocoa fields (e.g., field size, tree density), spatial positions (e.g., distance to the forest edge), and the history of the cocoa fields. ### Files and variables #### File: Data\_survey.csv **Description:** Data collected in the field ##### Variables * Site: Name of the sampling site (Côte d'Ivoire) * id_plot: Plot ID * Gen_Motiv: General Motivations: Sum of all Use Values of all trees (per hectare) * Agronomy: Use Value of all trees (per hectare) in the Agronomy category * Build: Use Value of all trees (per hectare) in the Building category * Craft: Use Value of all trees (per hectare) in the Craft category * Cultural: Use Value of all trees (per hectare) in the Cultural category * Esthetic: Use Value of all trees (per hectare) in the Esthetic category * Financial: Use Value of all trees (per hectare) in the Financial category * Food: Use Value of all trees (per hectare) in the Food category * Fuel: Use Value of all trees (per hectare) in the Fuel category * Medicinal: Use Value of all trees (per hectare) in the Medicinal category * Social: Use Value of all trees (per hectare) in the Social category * Specialization: Specialization level * Origin: Producer's origin (native of the locality or not) * Knowledge: Seedling recognition score * Status: Owner or non-owner status (owner (inheritance, gift, purchase); not owner (tenant, worker)) * Education: Education level (No education, Primary, Secondary, Higher education) * Area: Plot area (ha) * Remnant: Proportion of remnant trees per hectare * Cocoa_Trees: Cocoa tree density per hectare * Forest_Cover: Percentage of forest cover within 10 km of the plots * Forest_Edge: Distance (m) between the plots and the nearest forest * Plot_Home: Distance (m) between the plots and the producers' residences * Previous: Previous land use (forest, no forest) * Plot_Age: Age of the plots in 2023 (years) * Cocoa_Loop: Cocoa production Loop (1 = Oldest loop ; 2 = 2nd loop; 3 =3rd loop ; 4= Newest loop) #### File: Metadata.xlsx **Description:** Metadata of the Data Survey ## Code/software To view the data_survey, open it in R. To view the Metadata, open it in Excel.