## 3 Material and Methods
### HMP-Coffee Model
#### 3.2.1 Contextual Knowledge Phase  
Key aspects related to profitability in small-scale coffee crops are gathered from different knowledge sources (e.g., scientific papers, technical reports, and human beings). From those aspects, a conceptual hierarchical model is proposed. In order to build up the conceptual hierarchical model, we used a multi-level granularity analysis useful for knowledge discovery from a global to a specific perspective [28]. The multi-level granularity analysis involves two steps: the coarse granularity and the fine granularity. In the **Coarse Granularity step**, we performed a brainstorming about concepts related to \"profitability in small coffee productions.\" This step included three activities: _Literature review_ to identify available documentation regarding profitability in small coffee production, _Textual analysis_ to extract the frequency of different concepts from the documents, and _Iterative conceptualization_ to identify the relevant concepts on the analyzed information [29]. Documentary selection: to cover the relevant aspects of the estimation of profitability in small coffee productions, three query strings were defined: \"Small-scale Coffee Production\" (SCP), \"Economic Coffee Profitability\" (ECP), and \"Small-scale Coffee Profitability\" (SCPR). Query strings were used as search keys on scientific databases (i.e., SCOPUS, Science Direct, and Google Scholar), and official pages concerning the coffee activity, such as ICO [17], Coffee Institute of Costa Rica (ICAFE) [30], and Caravela Coffee [31]. Figure 3 shows the retrieved documents (45) resulting from the documentary selection activity, including 19 technical reports, 19 scientific papers, and 7 grey literature documents. SCP focused on small coffee production documents, with 20 results: 8 technical reports, 10 scientific papers, and 2 grey literature documents. ECP focused on the documents related to economic profitability variables for coffee production, with 15 results: 7 technical reports, 5 scientific papers, and 3 grey literature documents. Finally, SCPR focused on documents related to profitability and small-scale coffee crops, with 10 results: 4 technical reports, 4 scientific papers, and 2 grey literature documents. _Textual analysis:_ The \"Frequency Words\" statistical method is used for textual analysis activity [32]. The method identifies the frequency of concepts in written information [29]. Figure 4 describes the process performed in the textual analysis using the R-statistics tool version 1.2 [33]. Frequency Words technique involves the identification of the iterative  
Figure 3: The x-axis represents the query strings: Small-scale Coffee Production (SCP), Economic Coffee Profitability (ECP), and Small-scale Coffee Profitability (SCPR). The y-axis represents the number of documents retrieved the colors blue, black, and green represent technical reports, scientific papers and grey literature. The documents retrieved are shown by the query strings, quantity, and type of document. concepts about a group of documents (i.e., the 45 documents collected), the elimination of redundant words or \"Stop Words\" (e.g., articles, pronouns, and prepositions), and the selection of the more frequent concepts. As a result, the activity provides around 40 most frequent words in 45 analyzed documents. _Iterative conceptualization_: the most frequent concepts were mapped by query string in a Venn diagram as shown in Figure 5. Subsequently, the words located in the overlapping regions were selected (words in bold depicted in Figure 5). Accordingly, 24 words were identified during the brainstorming, including concepts such as **workforce** belonging to SCP and EPC query strings; **labor** belongs to EPC and SCP query strings; **chemical** belongs to SCP and SCP query string, and **harvest** belongs to the three query strings. Furthermore, Table 2 presents the selected concepts. For each concept is defined: id, name, description, and frequency. Figure 4: The process of text analysis. 1.