## 3 Conclusion and Future work

Rayuela is a gamification platform that can be set up with several citizen science projects and is transparent to the collaboration tools they already have. It allows participating in more than one citizen science project, choosing among a project-specific list of generated and recommended game elements for the player, recording the completion of sampling tasks, and being aware of the progress in the game.

Citizen science projects may apply adaptive gamification to have greater participation from the general public and reach a higher project efficiency.

This tool allows incorporating a gamification engine in a transparent way to a set of CLCS-supported citizen science projects. It also allows materializing a platform of gamified projects that has several advantages. On the one hand, people's linkage for a project can be transferred to other projects, since people discover new projects through the platform. On the other hand, by joining a new project, the user does not start from scratch. This project capitalizes on the experience and knowledge the person acquired by participating in other projects.

<figure><img src="image_55.png" /><figcaption>Fig. 3. Rayuela screens: gamified projects list (A), recommended challenges for user (B), check-in registration (C), and game layer feedback (D)</figcaption></figure>

The challenge-based gamification approach considered the user's profile, the project's coverage goals, area priority configuration, and global system status to adapt the game challenges.

As further work, the inclusion of other game elements and mechanics will be analyzed.

## Acknowledgements

This article publication is based upon the work of the Project RUN Citizen science and co-creation for regeneration of urban rivers and mitigation of risks, which has received funding from the CYTED Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo, under grant agreement No 420RT0008, http://cyted.org/es/run.
References

1. Busch, M., Mattheiss, E., Hochleitner, W., Hochleitner, C., Lankes, M., Fröhlich, P., Orji, R., Tscheligi, M.: Using player type models for personalized game design - an empirical investigation. Interaction Design and Architecture(s) Journal **28**, 145–163 (01 2016)

2. Bockle, M., Novak, J., Bick, M.: TOWARDS ADAPTIVE GAMIFICATION: A SYNTHESIS OF CURRENT DEVELOPMENTS. Research Papers (Jun 2017), https://aisel.aisnet.org/ecis2017\_rp/11

3. Cochero, J.: Appear: A citizen science mobile app to map the habitat quality of continental waterbodies. Ecologia Austral **28**, 467–479 (08 2018)

4. Cochero, J., Pattori, L., Balsalobre, A., Ceccarelli, S., Marti, G.: A convolutional neural network to recognize chagas disease vectors using mobile phone images. Ecological Informatics **68**, 101587 (2022)

5. Dalponte Ayastuy, M., Torres, D.: Relevance of non-activity representation in traveling user behavior profiling for adaptive gamification. In: Proceedings of the XXI International Conference on Human Computer Interaction. Interaccion '21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3471391.3471431, https://doi.org/10.1145/3471391.3471431

6. Deterding, S., Dixon, D., Khaled, R., Nacke, L.: From Game Design Elements to Gamefulness: Defining "Gamification". In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments. pp. 9–15. MindTrek '11, ACM, New York, NY, USA (2011). https://doi.org/10.1145/2181037.2181040, http://doi.acm.org/10.1145/2181037.2181040, event-place: Tampere, Finland

7. Gobel, S., Wendel, V.: Personalization and Adaptation, pp. 161–210. SpringerInternational Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-40612-1 7, https://doi.org/10.1007/978-3-319-40612-1_7

8. Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work? a literature review of empirical studies on gamification (01 2014). https://doi.org/10.1109/HICSS.2014.377

9. Heeter, C., Magerko, B., Medler, B., Lee, Y.H.: Impacts of forced serious game play on vulnerable subgroups. Int. J. Gaming Comput. Mediat. Simul. **3**(3), 34–53 (Jul 2011). https://doi.org/10.4018/jgcms.2011070103, https://doi.org/10.4018/jgcms.2011070103

10. Huotari, K., Hamari, J.: Defining gamification: a service marketing perspective. In: Proceeding of the 16th international academic MindTrek conference. pp. 17–22(2012)

11. Iversen, S.: In the double grip of the game: Challenge and Fallout 3. Game Studies **12** (2012), http://www gamestudies org/1202/articles/in\_the\_double\_grip\_of\_the\_game

12. Nugent, J.: inaturalist, citizen science for 21st-century naturalists. Science Scope **41**(7), 12–13 (2018)
References

1. Busch, M., Mattheiss, E., Hochleitner, W., Hochleitner, C., Lankes, M., Fröhlich, P., Orji, R., Tscheligi, M.: Using player type models for personalized game design - an empirical investigation. Interaction Design and Architecture(s) Journal **28**, 145–163 (01 2016)

2. Bockle, M., Novak, J., Bick, M.: TOWARDS ADAPTIVE GAMIFICATION: A SYNTHESIS OF CURRENT DEVELOPMENTS. Research Papers (Jun 2017), https://aisel.aisnet.org/ecis2017\_rp/11

3. Cochero, J.: Appear: A citizen science mobile app to map the habitat quality of continental waterbodies. Ecologia Austral **28**, 467–479 (08 2018)

4. Cochero, J., Pattori, L., Balsalobre, A., Ceccarelli, S., Marti, G.: A convolutional neural network to recognize chagas disease vectors using mobile phone images. Ecological Informatics **68**, 101587 (2022)

5. Dalponte Ayastuy, M., Torres, D.: Relevance of non-activity representation in traveling user behavior profiling for adaptive gamification. In: Proceedings of the XXI International Conference on Human Computer Interaction. Interaccion '21, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3471391.3471431, https://doi.org/10.1145/3471391.3471431

6. Deterding, S., Dixon, D., Khaled, R., Nacke, L.: From Game Design Elements to Gamefulness: Defining "Gamification". In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments. pp. 9–15. MindTrek '11, ACM, New York, NY, USA (2011). https://doi.org/10.1145/2181037.2181040, http://doi.acm.org/10.1145/2181037.2181040, event-place: Tampere, Finland

7. Gobel, S., Wendel, V.: Personalization and Adaptation, pp. 161–210. SpringerInternational Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-40612-1_7, https://doi.org/10.1007/978-3-319-40612-1_7

8. Hamari, J., Koivisto, J., Sarsa, H.: Does gamification work? a literature review of empirical studies on gamification (01 2014). https://doi.org/10.1109/HICSS.2014.377

9. Heeter, C., Magerko, B., Medler, B., Lee, Y.H.: Impacts of forced serious game play on vulnerable subgroups. Int. J. Gaming Comput. Mediat. Simul. **3**(3), 34–53 (Jul 2011). https://doi.org/10.4018/jgcms.2011070103, https://doi.org/10.4018/jgcms.2011070103

10. Huotari, K., Hamari, J.: Defining gamification: a service marketing perspective. In: Proceeding of the 16th international academic MindTrek conference. pp. 17–22(2012)

11. Iversen, S.: In the double grip of the game: Challenge and Fallout 3. Game Studies **12** (2012), http://www gamestudies org/1202/articles/in\_the\_double\_grip\_of\_the\_game

12. Nugent, J.: inaturalist, citizen science for 21st-century naturalists. Science Scope **41**(7), 12–13 (2018)
# Named Entity Extraction in Requirement
Specification: A Comparison

Luciana Tanevitch<fnref n="1" />, Leandro Antonelli<fnref n="1" /> and Diego Torres<fnref n="1" />,<fnref n="2" />

**Abstract** Software requirements specifications are often written in natural language, but extracting the main concepts from these documents can be challenging for computer agents. Named entity extraction is a task that involves recognizing entities in a text and linking them to a knowledge graph for disambiguation. In the domain of requirements engineering, applying this task can facilitate the representation and efficient management of complex information. This work compares different named entity extraction tools in the task of extracting entities from a requirements specification, considering technical aspects but also performance in terms of precision, recall, f1-score and accuracy.

**Keywords:** *Named Entity Extraction* · *Knowledge Graph* · *Requirements Engineering*

## 1 Introduction

Software Requirements Specifications (SRS) define in an unstructured way the requirements that a system should satisfy. Identifying and extracting the main concepts involved in a requirements specification could be useful for several auto-matic tasks, such as classification and categorization tasks [15]. However, computer agents are not able to process and immediately understand the content and information included in the natural language documents such as SRSs. Nevertheless, research lines introduced techniques to enable machines to convert text into information that can be processed automatically and to deal with the ambiguity of natural language [4,7].

Natural Language Processing (NLP) is a branch of Artificial Intelligence that enables computers to understand texts written in natural language[13,16]. NLP can be used to extract entities from a text using a specific technique called Named Entity Recognition (NER), which allows recognition and classification of named entities in a text into predefined classes [13]. As natural language allows for multiple meanings of the same concept, once entities are detected, it is necessary to disambiguate them to determine their true meaning according to the context in which they occur.

Knowledge Graphs (KG) allow structuring complex information in a proper format for computers [11]. Moreover, knowledge graphs are suitable for auto-matic data processing. Each node in the graph represent a unique concept, so linking a named entity in a text to its corresponding node enables meaning dis-ambiguation. Al-Moslmi et. al propose a pipeline for transforming texts into KGs [3]. This process is known as Named Entity Extraction (NEE) and involves threemain tasks: Named Entity Recognition (NER), Named Entity Disambiguation (NED) and Named Entity Linking (NEL).

<footnote n="1">¹ LIFIA, CICPBA-Facultad de Informática, UNLP, Argentina
/name.surname/@lifia.info.unlp.edu.ar</footnote>

<footnote n="2">² Departamento de Ciencia y Tecnología, UNQ, Argentina</footnote>
# Named Entity Extraction in Requirement
Specification: A Comparison

Luciana Tanevitch<fnref n="1" />, Leandro Antonelli<fnref n="1" /> and Diego Torres<fnref n="1" />,<fnref n="2" />

**Abstract** Software requirements specifications are often written in natural language, but extracting the main concepts from these documents can be challenging for computer agents. Named entity extraction is a task that involves recognizing entities in a text and linking them to a knowledge graph for disambiguation. In the domain of requirements engineering, applying this task can facilitate the representation and efficient management of complex information. This work compares different named entity extraction tools in the task of extracting entities from a requirements specification, considering technical aspects but also performance in terms of precision, recall, f1-score and accuracy.

**Keywords:** *Named Entity Extraction* · *Knowledge Graph* · *Requirements Engineering*

## 1 Introduction

Software Requirements Specifications (SRS) define in an unstructured way the requirements that a system should satisfy. Identifying and extracting the main concepts involved in a requirements specification could be useful for several auto-matic tasks, such as classification and categorization tasks [15]. However, computer agents are not able to process and immediately understand the content and information included in the natural language documents such as SRSs. Nevertheless, research lines introduced techniques to enable machines to convert text into information that can be processed automatically and to deal with the ambiguity of natural language [4,7].

Natural Language Processing (NLP) is a branch of Artificial Intelligence that enables computers to understand texts written in natural language[13,16]. NLP can be used to extract entities from a text using a specific technique called Named Entity Recognition (NER), which allows recognition and classification of named entities in a text into predefined classes [13]. As natural language allows for multiple meanings of the same concept, once entities are detected, it is necessary to disambiguate them to determine their true meaning according to the context in which they occur.

Knowledge Graphs (KG) allow structuring complex information in a proper format for computers [11]. Moreover, knowledge graphs are suitable for auto-matic data processing. Each node in the graph represent a unique concept, so linking a named entity in a text to its corresponding node enables meaning dis-ambiguation. Al-Moslmi et. al propose a pipeline for transforming texts into KGs [3]. This process is known as Named Entity Extraction (NEE) and involves threemain tasks: Named Entity Recognition (NER), Named Entity Disambiguation (NED) and Named Entity Linking (NEL).

<footnote n="1">¹ LIFIA, CICPBA-Facultad de Informática, UNLP, Argentina
/name.surname/@lifia.info.unlp.edu.ar</footnote>

<footnote n="2">² Departamento de Ciencia y Tecnología, UNQ, Argentina</footnote>
The aim of this paper is to compare various NEE tools applied to require-ments domain, considering core features (supported languages, KG used for link-ing and disambiguation technique), and their performance in requirements spe-cification analysis (measured in terms of precision, recall, f1-score and accuracy). This paper is organized as follows. In Section 2, previous work in the liter-ature in this area is introduced. Then, Section 3 defines the evaluation method—including the metrics, the tools to be compared and the data used for the eval-uation. The results of the evaluation are described in Section 4. Finally, Section 5 summarizes the paper's conclusions and suggests some possible future work.

## 2 Related Work

Tedeschi et al. [21] evaluate NER-based strategies that help narrow down the performance gap between systems trained on limited data and those trained on massive corpora. This work is relevant to our study as it explores approaches for improving NER system performance. Checco et al. [6] develop a tool that detects named entities in fashion blogs and links them to a fashion ontology. While their focus is on a different domain, their work demonstrates the potential of entity extraction and linking techniques to discover domain-specific information. Hosseni and Bagheri [12] consider different entity linking techniques for detecting implicit entities, particularly in tweets. Given the potential ambiguity in require-ment specifications, their approach is valuable for detecting and disambiguating implicit references. Vychegzhanin and Kotelnikov [23] conduct a comparison of domain-independent NER tools, focusing on their characteristics and perform ance in recognizing specific entity types on pre-trained news datasets. Rizzo and Troncy [19] propose a framework for evaluating NEE tools based on named entity detection, entity type detection, and entity disambiguation criteria. Abdallaha et al. [2] build a framework for evaluating entity extraction tools applied to texts from various domains. Their criteria consider generic, technical, and advanced features, as well as efficiency measures.

## 3 Evaluation Method

### 3.1 Metrics

**Core features** Following Abdallaha et al. [2], we considered the following core features to evaluate the tools: supported language, disambiguation technique, and knowledge graph used.

The “language” feature specifies supported languages, crucial for multical- tural requirements, as precise terminology may not translate well into inter- mediate languages. The “disambiguation technique” determines entity disam- biguation based on context, impacting accuracy and performance. The tool’s “knowledge graph”, such as DBpedia and Wikidata, is essential for entity link-ing.

**Performance** According to 4 parameters are used for evaluating the performance: precision, recall, f1-score and accuracy.

Precision assesses the model's ability to accurately identify entities by con- sidering both true positives and false positives. Recall measures the model's cap-ability to identify all correct entities, with false negatives representing missed entities. The F1-score balances precision and recall. Accuracy verifies if the linkedresource is contextually correct for the named entity.
The aim of this paper is to compare various NEE tools applied to require-ments domain, considering core features (supported languages, KG used for link-ing and disambiguation technique), and their performance in requirements spe-cification analysis (measured in terms of precision, recall, f1-score and accuracy). This paper is organized as follows. In Section 2, previous work in the liter-ature in this area is introduced. Then, Section 3 defines the evaluation method—including the metrics, the tools to be compared and the data used for the eval-uation. The results of the evaluation are described in Section 4. Finally, Section 5 summarizes the paper's conclusions and suggests some possible future work.

## 2 Related Work

Tedeschi et al. [21] evaluate NER-based strategies that help narrow down the performance gap between systems trained on limited data and those trained on massive corpora. This work is relevant to our study as it explores approaches for improving NER system performance. Checco et al. [6] develop a tool that detects named entities in fashion blogs and links them to a fashion ontology. While their focus is on a different domain, their work demonstrates the potential of entity extraction and linking techniques to discover domain-specific information. Hosseni and Bagheri [12] consider different entity linking techniques for detecting implicit entities, particularly in tweets. Given the potential ambiguity in require-ment specifications, their approach is valuable for detecting and disambiguating implicit references. Vychegzhanin and Kotelnikov [23] conduct a comparison of domain-independent NER tools, focusing on their characteristics and perform ance in recognizing specific entity types on pre-trained news datasets. Rizzo and Troncy [19] propose a framework for evaluating NEE tools based on named entity detection, entity type detection, and entity disambiguation criteria. Abdallaha et al. [2] build a framework for evaluating entity extraction tools applied to texts from various domains. Their criteria consider generic, technical, and advanced features, as well as efficiency measures.

## 3 Evaluation Method

### 3.1 Metrics

**Core features** Following Abdallaha et al. [2], we considered the following core features to evaluate the tools: supported language, disambiguation technique, and knowledge graph used.

The “language” feature specifies supported languages, crucial for multicul- tural requirements, as precise terminology may not translate well into inter- mediate languages. The “disambiguation technique” determines entity disam- biguation based on context, impacting accuracy and performance. The tool’s “knowledge graph”, such as DBpedia and Wikidata, is essential for entity link-ing.

**Performance** According to 4 parameters are used for evaluating the performance: precision, recall, f1-score and accuracy.

Precision assesses the model's ability to accurately identify entities by con- sidering both true positives and false positives. Recall measures the model's cap-ability to identify all correct entities, with false negatives representing missed entities. The F1-score balances precision and recall. Accuracy verifies if the linkedresource is contextually correct for the named entity.
## 3.2 Data

To choose the tools, a systematic review was conducted to gather various NamedEntity Extraction (NEE) tools from studies in a similar domain as this work. Additionally, we performed searches in public code repositories using the keywords *named entity extraction*, *named entity linking*, and *named entity disambiguation* to identify additional tools. Once collected, we began narrowing down the number of tools due to various reasons, such as tools no longer being hosted, unavailable source code, or limited entity recognition in the specific domain of application. Consequently, the tools we will analyze are those that are freely available, support the English language, and have shown promising results in preliminary testing using different small-scale requirement datasets (i.e., high entity detection rates). The selected tools for this work are: Wikifier [5], DBpedia Spotlight [17], Babelfy [18], TagMe [10] and Spacy.

The following text was used as input for the tools, which it was written by a functional analyst, and describes a functional requirement that involves various technical concepts related to the named entity extraction pipeline: *Asa news agency, we want to automatically extract and disambiguate mentions of places, people, and organizations from newspapers*. When a new article is submitted, the system should identify all named entities in the text and link them to their corresponding entities in a knowledge graph such as DBpedia or Wikidata. The system should use contextual information such as the surrounding words and the sentence structure to disambiguate entities that have multiple possible meanings. The system should also be able to handle entity coreference, where different mentions in the text refer to the same entity. The disambiguated entities should be stored in a structured format, such as RDF or JSON, for further processing and analysis. The system should be scalable and able to handle a large volume of articles in real-time. The accuracy of the system should be evaluated against a manually labeled dataset to ensure high precision and recall. An expert analysis can be performed on this data to determine which entities should be automatically detected. It is called *ground truth* dataset because it contains the entities expected to be found by the evaluated tools, and is defined as follows: *news agency, places, persons, organization, article, system, named entity, newspapers, knowledge graph, DBpedia, Wikidata, contextual information, sentence structure, meanings, coreference, RDF, JSON, dataset, accuracy, analysis, precision, recall*. A table with expected entities for each named entity is shown in this document.

## 4 Results

Table 2 compares the core features of the tools mentioned in Section 3. Since all the tools support several languages, Babelfy is the one with the most coverage. NER performance may differ in each language, as each uses a different model. They use machine learning approaches for disambiguation phase, each approach has its own advantages and disadvantages. The evaluated tools can link mentions to Wikidata, DBpedia or BabelNet. As these graphs do not describe specific concepts from the domain of software requirements, some named entities are likely to remain unlink. The importance of BabelNet is that it includes lexical resources, which provide a foundation of structured knowledge, using lexical and semantic knowledge could improve disambiguation tasks.

Table 3 compares the performance of the tools. DBpedia Spotlight achieves perfect precision and accuracy scores, but it has a low recall, indicating that it recognized fewer entities compared to the ground truth. On the other hand, Babelfy recognized a high number of entities but suffered from low precision due to overfitting, detecting irrelevant concepts unrelated to the requirements domain. TagMe and Spacy kept a good balance in all their measures: they recognized almost all the expected words with a precision higher than 65%. Wikifier performs well in entity detection but has a lower recall compared to the aforementioned tools. TagMe obtained the higher results for the used data, but Spacy was so close: it has similar values for accuracy and precision, keeping the same recall. Sowe can say that even if TagMe had a good performance in requirement domain, better results could be obtained using a specific-domain knowledge graph for training Spacy.
## 3.2 Data

To choose the tools, a systematic review was conducted to gather various NamedEntity Extraction (NEE) tools from studies in a similar domain as this work. Additionally, we performed searches in public code repositories using the keywords *named entity extraction*, *named entity linking*, and *named entity disambiguation* to identify additional tools. Once collected, we began narrowing down the number of tools due to various reasons, such as tools no longer being hosted, unavailable source code, or limited entity recognition in the specific domain of application. Consequently, the tools we will analyze are those that are freely available, support the English language, and have shown promising results in preliminary testing using different small-scale requirement datasets (i.e., high entity detection rates). The selected tools for this work are: Wikifier [5], DBpedia Spotlight [17], Babelfy [18], TagMe [10] and Spacy.

The following text was used as input for the tools, which it was written by a functional analyst, and describes a functional requirement that involves various technical concepts related to the named entity extraction pipeline: *Asa news agency, we want to automatically extract and disambiguate mentions of places, people, and organizations from newspapers*. When a new article is submitted, the system should identify all named entities in the text and link them to their corresponding entities in a knowledge graph such as DBpedia or Wikidata. The system should use contextual information such as the surrounding words and the sentence structure to disambiguate entities that have multiple possible meanings. The system should also be able to handle entity coreference, where different mentions in the text refer to the same entity. The disambiguated entities should be stored in a structured format, such as RDF or JSON, for further processing and analysis. The system should be scalable and able to handle a large volume of articles in real-time. The accuracy of the system should be evaluated against a manually labeled dataset to ensure high precision and recall. An expert analysis can be performed on this data to determine which entities should be automatically detected. It is called *ground truth* dataset because it contains the entities expected to be found by the evaluated tools, and is defined as follows: *news agency, places, persons, organization, article, system, named entity, newspapers, knowledge graph, DBpedia, Wikidata, contextual information, sentence structure, meanings, coreference, RDF, JSON, dataset, accuracy, analysis, precision, recall*. A table with expected entities for each named entity is shown in this document.

## 4 Results

Table 2 compares the core features of the tools mentioned in Section 3. Since all the tools support several languages, Babelfy is the one with the most coverage. NER performance may differ in each language, as each uses a different model. They use machine learning approaches for disambiguation phase, each approach has its own advantages and disadvantages. The evaluated tools can link mentions to Wikidata, DBpedia or BabelNet. As these graphs do not describe specific concepts from the domain of software requirements, some named entities are likely to remain unlink. The importance of BabelNet is that it includes lexical resources, which provide a foundation of structured knowledge, using lexical and semantic knowledge could improve disambiguation tasks.

Table 3 compares the performance of the tools. DBpedia Spotlight achieves perfect precision and accuracy scores, but it has a low recall, indicating that it recognized fewer entities compared to the ground truth. On the other hand, Babelfy recognized a high number of entities but suffered from low precision due to overfitting, detecting irrelevant concepts unrelated to the requirements domain. TagMe and Spacy kept a good balance in all their measures: they recognized almost all the expected words with a precision higher than 65%. Wikifier performs well in entity detection but has a lower recall compared to the aforementioned tools. TagMe obtained the higher results for the used data, but Spacy was so close: it has similar values for accuracy and precision, keeping the same recall. Sowe can say that even if TagMe had a good performance in requirement domain, better results could be obtained using a specific-domain knowledge graph for training Spacy.
# 5 Conclusions

The domain of requirements is highly ambiguous and complex. In order to enable automatic processing of requirement specifications, techniques such as knowledge graphs can be employed. Linking a concept to a knowledge graph allows for dis-ambiguation of its meaning, and entity extraction tools can be used for this purpose. Evaluation results exhibit considerable variability in accuracy, precision, and recall. While precision and recall are useful metrics, accuracy is critical in determining whether the tool is performing correctly.

It is possible that the combination of different tools can improve results and provide greater accuracy in identifying requirement entities. Moreover, using a KG specific for requirements domain could help to solve the problem about those unrecognized entities due there is not an entry in a general-purpose KG.

This evaluation may be applied to different domains to determine whether the tools have the same ability to recognize entities as they do in the requirements domain. The next step could be to detect relationships between the identified entities in order to build a knowledge graph. A more specific study could be performed using massive datasets of the requirement domain, what it would require techniques for annotating massive data. In addition, automatic techniques should be considered to properly choose the expected entities for each of the knowledge graphs to be evaluated.
# 5 Conclusions

The domain of requirements is highly ambiguous and complex. In order to enable automatic processing of requirement specifications, techniques such as knowledge graphs can be employed. Linking a concept to a knowledge graph allows for dis-ambiguation of its meaning, and entity extraction tools can be used for this purpose. Evaluation results exhibit considerable variability in accuracy, precision, and recall. While precision and recall are useful metrics, accuracy is critical in determining whether the tool is performing correctly.

It is possible that the combination of different tools can improve results and provide greater accuracy in identifying requirement entities. Moreover, using a KG specific for requirements domain could help to solve the problem about those unrecognized entities due there is not an entry in a general-purpose KG.

This evaluation may be applied to different domains to determine whether the tools have the same ability to recognize entities as they do in the requirements domain. The next step could be to detect relationships between the identified entities in order to build a knowledge graph. A more specific study could be performed using massive datasets of the requirement domain, what it would require techniques for annotating massive data. In addition, automatic techniques should be considered to properly choose the expected entities for each of the knowledge graphs to be evaluated.
<table><caption>Table 2. Core features comparison results</caption><thead><tr><td>Tool</td><td>Language</td><td>Disambiguation technique</td><td>Knowledge graph</td></tr></thead><tbody><tr><td>DBpedia Spot-light</td><td>Among the supported languages are German, English, Spanish, French, Italian, and Portuguese. The full list can be found at [9]</td><td>They modeled DBpedia resource occurrences in a Vector Space Model (VSM) using a variant of the TF-IDF algorithm to weigh words based on their ability to distinguish between candidates for a given surface form. Therefore, they use cosine similarity to compare the similarity between context vectors and the context surrounding the surface form. In an improved version [8], they use a generative model to calculate the probability that a candidate is correct for a mention, which improves disambiguation accuracy, as well as time performance and required space</td><td>DBpedia</td></tr><tr><td>Wikifier</td><td>It supports around 100 languages among which are German, English, Spanish, French, Italian, Portuguese, and Chinese.</td><td>They build a bipartite graph where each node on the left represents mentions and each node on the right represents Wikipedia entries for those mentions. The graph is augmented by edges between concepts based on their semantic relatedness. They use the graph to calculate the PageRank score for each vertex, and after some iterations, they obtain the relevant concepts for each mention. A threshold value can be specified by the user to discard all candidates that were scored below that value.</td><td>The entities are linked to wikipedia pages, although the service includes information from the Wikidata ID and DBpedia URI in the response.</td></tr><tr><td>Babelfy</td><td>Provides support for 271 languages [1], including English</td><td>They build a directed graph which relates mentions in the text with their Babel candidate. They construct a graph that relates mentions in the text to their possible candidates in Babel, also linking those candidates that are semantically similar. In this way, all possible interpretations are obtained for each mention, and the heuristic of the densest subgraph is applied to obtain the best candidate for a mention (based on lexical and semantic coherence), and thus arrive at the most coherent semantic interpretation.</td><td>BabelNet</td></tr><tr><td>TagMe</td><td>English, Italian and German</td><td>They apply a voting scheme where the goal is to reach a “collective agreement” on the real meaning of some mentions. To select the best candidate, they tested two algorithms: Disambiguation by Classifier (DC), which computes the probability of correct disambiguation for every candidate of a mention and then selects the best one; and Disambiguation by Threshold (DT), which selects the top n-best candidates. Then other works improved that initial version by topical classification and clustering [22] [20].</td><td>Entities are linked to a Wikipedia page, but Wikidata URIs could be inferred from them</td></tr><tr><td>Spacy</td><td>English, although a model can be trained to recognize other languages</td><td>Starting from a defined knowledge graph, candidates for entities can be generated. Then, a machine learning model selects the most suit-able candidate. The power of Spacy lies in the fact that each component is customizable, allowing the user to improve accuracy by combining techniques for candidate generation and scoring.</td><td>It allows configuring the desired knowledge graph, although in this work, an implementation with Wikidata was used.</td></tr></tbody></table>
<table><caption>Table 2. Core features comparison results</caption><thead><tr><td>Tool</td><td>Language</td><td>Disambiguation technique</td><td>Knowledge graph</td></tr></thead><tbody><tr><td>DBpedia Spot-light</td><td>Among the supported languages are German, English, Spanish, French, Italian, and Portuguese. The full list can be found at [9]</td><td>They modeled DBpedia resource occurrences in a Vector Space Model (VSM) using a variant of the TF-IDF algorithm to weigh words based on their ability to distinguish between candidates for a given surface form. Therefore, they use cosine similarity to compare the similarity between context vectors and the context surrounding the surface form. In an improved version [8], they use a generative model to calculate the probability that a candidate is correct for a mention, which improves disambiguation accuracy, as well as time performance and required space</td><td>DBpedia</td></tr><tr><td>Wikifier</td><td>It supports around 100 languages among which are German, English, Spanish, French, Italian, Portuguese, and Chinese.</td><td>They build a bipartite graph where each node on the left represents mentions and each node on the right represents Wikipedia entries for those mentions. The graph is augmented by edges between concepts based on their semantic relatedness. They use the graph to calculate the PageRank score for each vertex, and after some iterations, they obtain the relevant concepts for each mention. A threshold value can be specified by the user to discard all candidates that were scored below that value.</td><td>The entities are linked to wikipedia pages, although the service includes information from the Wikidata ID and DBpedia URI in the response.</td></tr><tr><td>Babelfy</td><td>Provides support for 271 languages [1], including English</td><td>They build a directed graph which relates mentions in the text with their Babel candidate. They construct a graph that relates mentions in the text to their possible candidates in Babel, also linking those candidates that are semantically similar. In this way, all possible interpretations are obtained for each mention, and the heuristic of the densest subgraph is applied to obtain the best candidate for a mention (based on lexical and semantic coherence), and thus arrive at the most coherent semantic interpretation.</td><td>BabelNet</td></tr><tr><td>TagMe</td><td>English, Italian and German</td><td>They apply a voting scheme where the goal is to reach a “collective agreement” on the real meaning of some mentions. To select the best candidate, they tested two algorithms: Disambiguation by Classifier (DC), which computes the probability of correct disambiguation for every candidate of a mention and then selects the best one; and Disambiguation by Threshold (DT), which selects the top n-best candidates. Then other works improved that initial version by topical classification and clustering [22] [20].</td><td>Entities are linked to a Wikipedia page, but Wikidata URIs could be inferred from them</td></tr><tr><td>Spacy</td><td>English, although a model can be trained to recognize other languages</td><td>Starting from a defined knowledge graph, candidates for entities can be generated. Then, a machine learning model selects the most suit-able candidate. The power of Spacy lies in the fact that each component is customizable, allowing the user to improve accuracy by combining techniques for candidate generation and scoring.</td><td>It allows configuring the desired knowledge graph, although in this work, an implementation with Wikidata was used.</td></tr></tbody></table>
<table><caption>**Table 3.** Performance evaluation results</caption><thead><tr><th>Tool</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td>DBpedia Spotlight</td><td>1</td><td>1</td><td>0.27</td><td>0.42</td></tr><tr><td>Wikifier</td><td>0.85</td><td>0.66</td><td>0.63</td><td>0.65</td></tr><tr><td>Babelfy</td><td>0.9</td><td>0.39</td><td>0.95</td><td>0.55</td></tr><tr><td>TagMe</td><td>0.75</td><td>0.71</td><td>0.9</td><td>0.80</td></tr><tr><td>Spacy</td><td>0.68</td><td>0.66</td><td>0.9</td><td>0.76</td></tr></tbody></table>

## References

1. (Apr 2015), http://babelfy.org/javadoc/it/uniroma1/lcl/jlt/util/Language.html

2. Abdallah, Z.S., Carman, M., Haffari, G.: Multi-domain evaluation framework for named entity recognition tools. Computer Speech & Language 43, 34–55 (May 2017)

3. Al-Moslmi, T., Gallofré Ocaña, M., Opdahl, A., Veres, C.: Named Entity Extraction for Knowledge Graphs: A Literature Overview. IEEE Access 8, 32862–32881 (Feb 2020)

4. Antonelli, L., Delle Ville, J., Dioguardi, F., Fernández, A., Tanevitch, L., Torres, D.: An iterative and collaborative approach to specify scenarios using natural language. In: Workshop on Requirements Engineering (WER22), (Modalidad virtual, 23 al 26 de agosto de 2022) (2022)

5. Brank, J., Leban, G., Grobelnik, M.: Annotating documents with relevant wikiedia concepts. Proceedings of SiKDD 472 (2017)

6. Checco, A., Demartini, G., Löser, A., Arous, I., Khayati, M., Dantone, M., Koop- manschap, R., Stalinov, S., Kersten, M., Zhang, Y.: Fashionbrain project: A vision for understanding europe's fashion data universe. arXiv preprint arXiv:1710.09788(2017)

7. Corral, A., Sánchez Crespo, L.E., Antonelli, L.: Building an integrated requirements engineering process based on intelligent systems and semantic reasoning on the basis of a systematic analysis of existing proposals. JUCS - Journal of Universal Computer Science 28, 1136–1168 (11 2022)

8. Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems. pp. 121–124. ACM, Graz Austria (Sep 2013) Named Entity Extraction in Requirement Specification: A Comparison 7

9. DBpedia Spotlight - Shedding light on the web of documents: FAQ / www.dbpedia-spotlight.org/faq (accessed March 8, 2023)

10. Ferragina, P., Scaiola, U.: TAGME: on-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM international conference on Information and knowledge management. pp. 1625–1628. ACM, Toronto ON Canada (Oct 2010)

11. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Gayo, J.E.L., Kirrane, S., Neumaier, S., Polleres, A., Navigli, R., Ngomo, A.C.N., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., Zim-mermann, A.: Knowledge Graphs. ACM Computing Surveys 54(4), 1–37 (May 2022), arXiv:2003.02320 [cs]

12. Hosseini, H., Bagheri, E.: From Explicit to Implicit Entity Linking: A Learn to Rank Framework. In: Goutte, C., Zhu, X. (eds.) Advances in Artificial Intelligence, vol. 12109, pp. 283–289. Springer International Publishing, Cham (2020), series Title: Lecture Notes in Computer Science

13. Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications 82(3), 3713–3744 (Jan 2023)

14. Malik, G., Cevik, M., Bera, S., Yildirim, S., Parikh, D., Basar, A.: Software requirement specific entity extraction using transformer models. Proceedings of the Canadian Conference on Artificial Intelligence (May 2022)

15. Malik, G., Cevik, M., Khedr, Y., Parikh, D., Başar, A.: Named Entity Recognition on Software Requirements Specification Documents. Proceedings of the Canadian Conference on Artificial Intelligence (Jun 2021)

16. Martinez, A.R.: Natural language processing. Wiley Interdisciplinary Reviews: Computational Statistics 2(3), 352–357 (2010)
<table><caption>**Table 3.** Performance evaluation results</caption><thead><tr><th>Tool</th><th>Accuracy</th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td>DBpedia Spotlight</td><td>1</td><td>1</td><td>0.27</td><td>0.42</td></tr><tr><td>Wikifier</td><td>0.85</td><td>0.66</td><td>0.63</td><td>0.65</td></tr><tr><td>Babelfy</td><td>0.9</td><td>0.39</td><td>0.95</td><td>0.55</td></tr><tr><td>TagMe</td><td>0.75</td><td>0.71</td><td>0.9</td><td>0.80</td></tr><tr><td>Spacy</td><td>0.68</td><td>0.66</td><td>0.9</td><td>0.76</td></tr></tbody></table>

## References

1. (Apr 2015), http://babelfy.org/javadoc/it/uniroma1/lcl/jlt/util/Language.html

2. Abdallah, Z.S., Carman, M., Haffari, G.: Multi-domain evaluation framework for named entity recognition tools. Computer Speech & Language 43, 34–55 (May 2017)

3. Al-Moslmi, T., Gallofré Ocaña, M., Opdahl, A., Veres, C.: Named Entity Extraction for Knowledge Graphs: A Literature Overview. IEEE Access 8, 32862–32881 (Feb 2020)

4. Antonelli, L., Delle Ville, J., Dioguardi, F., Fernández, A., Tanevitch, L., Torres, D.: An iterative and collaborative approach to specify scenarios using natural language. In: Workshop on Requirements Engineering (WER22), (Modalidad virtual, 23 al 26 de agosto de 2022) (2022)

5. Brank, J., Leban, G., Grobelnik, M.: Annotating documents with relevant wikiedia concepts. Proceedings of SiKDD 472 (2017)

6. Checco, A., Demartini, G., Löser, A., Arous, I., Khayati, M., Dantone, M., Koop- manschap, R., Stalinov, S., Kersten, M., Zhang, Y.: Fashionbrain project: A vision for understanding europe's fashion data universe. arXiv preprint arXiv:1710.09788(2017)

7. Corral, A., Sánchez Crespo, L.E., Antonelli, L.: Building an integrated requirements engineering process based on intelligent systems and semantic reasoning on the basis of a systematic analysis of existing proposals. JUCS - Journal of Universal Computer Science 28, 1136–1168 (11 2022)

8. Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems. pp. 121–124. ACM, Graz Austria (Sep 2013) Named Entity Extraction in Requirement Specification: A Comparison 7

9. DBpedia Spotlight - Shedding light on the web of documents: FAQ. www.dbpedia-spotlight.org/faq (accessed March 8, 2023)

10. Ferragina, P., Scaiola, U.: TAGME: on-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM international conference on Information and knowledge management. pp. 1625–1628. ACM, Toronto ON Canada (Oct 2010)

11. Hogan, A., Blomqvist, E., Cochez, M., d'Amato, C., de Melo, G., Gutierrez, C., Gayo, J.E.L., Kirrane, S., Neumaier, S., Polleres, A., Navigli, R., Ngomo, A.C.N., Rashid, S.M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., Zim-mermann, A.: Knowledge Graphs. ACM Computing Surveys 54(4), 1–37 (May 2022), arXiv:2003.02320 [cs]

12. Hosseini, H., Bagheri, E.: From Explicit to Implicit Entity Linking: A Learn to Rank Framework. In: Goutte, C., Zhu, X. (eds.) Advances in Artificial Intelligence, vol. 12109, pp. 283–289. Springer International Publishing, Cham (2020), series Title: Lecture Notes in Computer Science

13. Khurana, D., Koli, A., Khatter, K., Singh, S.: Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications 82(3), 3713–3744 (Jan 2023)

14. Malik, G., Cevik, M., Bera, S., Yildirim, S., Parikh, D., Basar, A.: Software requirement specific entity extraction using transformer models. Proceedings of the Canadian Conference on Artificial Intelligence (May 2022)

15. Malik, G., Cevik, M., Khedr, Y., Parikh, D., Başar, A.: Named Entity Recognition on Software Requirements Specification Documents. Proceedings of the Canadian Conference on Artificial Intelligence (Jun 2021)

16. Martinez, A.R.: Natural language processing. Wiley Interdisciplinary Reviews: Computational Statistics 2(3), 352–357 (2010)
17. Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems. pp. 1–8. ACM, Graz Austria (Sep 2011)

18. Navigli, R., Ponzetto, S.P.: Babelnet: Building a very large multilingual semantic network. In: Proceedings of the 48th annual meeting of the association for compu-tational linguistics. pp. 216–225 (2010)

19. Rizzo, G., Troncy, R.: NERD: Evaluating Named Entity Recognition Tools in the Web of Data (2011)

20. Scaiella, U., Ferragina, P., Marino, A., Ciaramita, M.: Topical clustering of search results. In: Proceedings of the fifth ACM international conference on Web search and data mining. pp. 223–232. ACM, Seattle Washington USA (Feb 2012)

21. Tedeschi, S., Conia, S., Cecconi, F., Navigli, R.: Named Entity Recognition for Entity Linking: What Works and What's Next. In: Findings of the Association for Computational Linguistics: EMNLP 2021. pp. 2584–2596. Association for Computational Linguistics, Punta Cana, Dominican Republic (Nov 2021)

22. Vitale, D., Ferragina, P., Scaiella, U.: Classification of Short Texts by Deploy-ing Topical Annotations. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cam-bazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) Advances in Informa-tion Retrieval. pp. 376–387. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg (2012)

23. Vychegzhanin, S., Kotelnikov, E.: Comparison of Named Entity Recognition Tools Applied to News Articles. In: 2019 Ivannikov Ispras Open Conference (ISPRAS).pp. 72–77 (Dec 2019)
17. Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems. pp. 1–8. ACM, Graz Austria (Sep 2011)

18. Navigli, R., Ponzetto, S.P.: Babelnet: Building a very large multilingual semantic network. In: Proceedings of the 48th annual meeting of the association for compu-tational linguistics. pp. 216–225 (2010)

19. Rizzo, G., Troncy, R.: NERD: Evaluating Named Entity Recognition Tools in the Web of Data (2011)

20. Scaiella, U., Ferragina, P., Marino, A., Ciaramita, M.: Topical clustering of search results. In: Proceedings of the fifth ACM international conference on Web search and data mining. pp. 223–232. ACM, Seattle Washington USA (Feb 2012)

21. Tedeschi, S., Conia, S., Cecconi, F., Navigli, R.: Named Entity Recognition for Entity Linking: What Works and What's Next. In: Findings of the Association for Computational Linguistics: EMNLP 2021. pp. 2584–2596. Association for Computational Linguistics, Punta Cana, Dominican Republic (Nov 2021)

22. Vitale, D., Ferragina, P., Scaiella, U.: Classification of Short Texts by Deploy-ing Topical Annotations. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cam-bazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) Advances in Informa-tion Retrieval. pp. 376–387. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg (2012)

23. Vychegzhanin, S., Kotelnikov, E.: Comparison of Named Entity Recognition Tools Applied to News Articles. In: 2019 Ivannikov Ispras Open Conference (ISPRAS).pp. 72–77 (Dec 2019)
# Formulation of a model to determine current and potential areas (Persea americana Mill) of Hass variety avocado crops, in the department of Risaralda, based on edaphoclimatic variables and fruit quality.

Gloria Edith Guerrero, Julio César Chavarro, Cesar Manuel Castillo, César Augusto Jaramillo, Juan Pablo Arrubla and Andrés Alfonso Patiño

*E-mail(s) Authors: gguerrero@utp.edu.co; jchavar@utp.edu.co; cesar.castillo@utp.edu.co; swokosky@utp.edu.co; juanpablo77@utp.edu.co; andrespatinomartinez@gmail.com;

## Abstract

Agriculture is one of the fundamental pillars of any worldwide population, and the proper management of information allows timely decisions to move any company forward. National and departmental government agencies support agricultural sectors that emerge as an excellent opportunity to increase production levels and product commercialization [1], such as the Hass avocado (Persea americana Mill) crop. One of the challenges facing this type of crop is to find potential planting and productivity zones to contribute to technological developments in the agricultural sector, benefiting the Hass avocado growers of the department of Risaralda. Therefore, this study proposes the formulation of a model to determine current and potential cultivation areas of Hass avocado (Persea americana Mill) in the department, based on edaphoclimatic variables and fruit quality, taking advantage of current trends in precision agriculture, including techniques derived from Machine Learning, such as the use of Supervised Learning algorithms, among which is Random Forest.

**Keywords:** *Persea americana Mill*, *Machine Learning*, *Random Forest*, *potential crop areas*

## 1 Introduction

Food security has become a pressing challenge due to rapid population growth, climate change and water scarcity, especially in developing countries [2]. Nowadays, modifications and adaptations are made in agricultural practices to improve soil fertility and the different climatic changes that are currently occurring. Therefore, it is necessary to evaluate the environmental impacts of agriculture in terms of water, nutrients (soil) and atmospheric components [3].

Avocado (Persea americana Mill) Hass variety is the most common commercial avocado crop in the world due to the number of essential nutrients and important phytochemicals [4]. This fruit is grown in Colombia, where the Hass avocado production system has recently increased due to the excellent economic opportunities and the high unsatisfied domestic demand [5]. The Hass avocado-producing departments are Tolima, Antioquia, Caldas, Santander, Bolivar, Quindío, Cesar, Valle del Cauca, Risaralda, and Cundinamarca; in Risaralda, avocado is grown in 13 of the 14 municipalities [6]. To address the problems of avocado cultivation, information and communication technology (ICT) approaches are adopted, such as precision agriculture or real-time crop data collection [7]. In this sense, ICT topics for the agricultural sector include procedures for the digital management of geographic information on crops, decision-making systems for mechanizing processes based on georeferencing, and epidemiological early warning information systems, among others [7] [8].

<footnote n="1">¹School of Chemistry, Universidad Tecnológica de Pereira, Colombia.</footnote>

<footnote n="2">²Faculty of Engineering, Universidad Tecnológica de Pereira, Colombia.</footnote>
# Formulation of a model to determine current and potential areas (Persea americana Mill) of Hass variety avocado crops, in the department of Risaralda, based on edaphoclimatic variables and fruit quality.

Gloria Edith Guerrero, Julio César Chavarro, Cesar Manuel Castillo, César Augusto Jaramillo, Juan Pablo Arrubla and Andrés Alfonso Patiño

*E-mail(s) Authors: gguerrero@utp.edu.co; jchavar@utp.edu.co; cesar.castillo@utp.edu.co; swokosky@utp.edu.co; juanpablo77@utp.edu.co; andrespatinomartinez@gmail.com;

## Abstract

Agriculture is one of the fundamental pillars of any worldwide population, and the proper management of information allows timely decisions to move any company forward. National and departmental government agencies support agricultural sectors that emerge as an excellent opportunity to increase production levels and product commercialization [1], such as the Hass avocado (Persea americana Mill) crop. One of the challenges facing this type of crop is to find potential planting and productivity zones to contribute to technological developments in the agricultural sector, benefiting the Hass avocado growers of the department of Risaralda. Therefore, this study proposes the formulation of a model to determine current and potential cultivation areas of Hass avocado (Persea americana Mill) in the department, based on edaphoclimatic variables and fruit quality, taking advantage of current trends in precision agriculture, including techniques derived from Machine Learning, such as the use of Supervised Learning algorithms, among which is Random Forest.

**Keywords:** *Persea americana Mill*, *Machine Learning*, *Random Forest*, *potential crop areas*

## 1 Introduction

Food security has become a pressing challenge due to rapid population growth, climate change and water scarcity, especially in developing countries [2]. Nowadays, modifications and adaptations are made in agricultural practices to improve soil fertility and the different climatic changes that are currently occurring. Therefore, it is necessary to evaluate the environmental impacts of agriculture in terms of water, nutrients (soil) and atmospheric components [3].

Avocado (Persea americana Mill) Hass variety is the most common commercial avocado crop in the world due to the number of essential nutrients and important phytochemicals [4]. This fruit is grown in Colombia, where the Hass avocado production system has recently increased due to the excellent economic opportunities and the high unsatisfied domestic demand [5]. The Hass avocado-producing departments are Tolima, Antioquia, Caldas, Santander, Bolivar, Quindío, Cesar, Valle del Cauca, Risaralda, and Cundinamarca; in Risaralda, avocado is grown in 13 of the 14 municipalities [6]. To address the problems of avocado cultivation, information and communication technology (ICT) approaches are adopted, such as precision agriculture or real-time crop data collection [7]. In this sense, ICT topics for the agricultural sector include procedures for the digital management of geographic information on crops, decision-making systems for mechanizing processes based on georeferencing, and epidemiological early warning information systems, among others [7] [8].

<footnote n="1">¹School of Chemistry, Universidad Tecnológica de Pereira, Colombia.</footnote>

<footnote n="2">²Faculty of Engineering, Universidad Tecnológica de Pereira, Colombia.</footnote>
However, difficulties are observed with crop management given the variability in soil and edaphoclimatic conditions of the department. This situation causes effects such as heterogeneity of fruit quality, besides the lack of scientific work to understand the behavior of the crop in the region. This study proposes to formulate a model to determine current and potential zones of Hass avocado (Persea americana Mill) cultivation in the department of Risaralda, based on edaphoclimatic variables and fruit quality, using the current trends of precision agriculture and machine learning, to contribute to technological developments in the agricultural sector, benefiting the Hass avocado growers of the department.

## 2 Bibliographic Review

Machine learning (ML) is an important decision support tool in fields such as crop yield prediction, including supporting decisions on what agricultural products to grow and what to do during the crop growing season; therefore, several machine learning algorithms have been applied to support research aimed at crop yield prediction or suggestion, where the most commonly used characteristics are temperature, precipitation and soil type [9]; In that sense, the different uses of the data obtained from the soil, such as the selection of the dataset for training, as well as the choice of soil environmental covariates, could boost the accuracy of automatic learning techniques [10]. One of the tools used within automatic learning are the crop-oriented recommendation systems where, based on provided variables, a model is created to predict or suggest which crop can be cultivated; to establish this, the models use historical data, such as climatic data (temperature, humidity, pH, rainfall) and fertilizer values (nitrogen, potassium, phosphorus) [11]. Machine learning employs methods such as supporting processes in charge of data analysis [12]. In [13] it is mentioned that different Artificial Intelligence techniques have been proposed and among these techniques that are part of Precision Agriculture, more specifically in the fields of crop recommendation systems, it is observed that algorithms such as k nearest neighbors (KNN), similarity-based models, set-based models, neural networks, among others, are included. These algorithms consider several external characteristics, such as meteorological data, and others, such as the soil profile, to provide the best recommendations, by examining the data, the most important attributes are obtained using techniques such as principal component analysis (PCA) and linear discrimination analysis (LDA). These extracted features are used to train models such as Naïve-Bayes (NBC), Random Forest, KNN; using training data and the performance is evaluated on test data using techniques such as cross-validation, RMSE or accuracy. Reliable predictions of crop yields are difficult for the development of agriculture; crop production varies according to different climatic conditions, having conditions such as dry periods and increasing temperatures; these forces strengthen the need for analysis of crop production in different climatic conditions, in this sense, [14] analyzes the automatic learning method, the Random Forest supervised algorithm can analyze the growth of crops concerning the current climatic conditions and biophysical change. Similarly, in [10], the ability of the Random Forest algorithm to predict soil classes from different training data sets and extrapolate such information to a similar area was evaluated.

Another aspect to consider is the usage of automatic learning models/algorithms and their possible applications to geospatial data, where special attention is given to the models used that are based on artificial neural networks (multilayer perceptron, general regression neural networks, self-organizing maps), statistical learning theory (support vector machines) [15], geo-statistical techniques such as Ordinary Kriging [16], or algorithms such as Random Forest Spatial Interpolation (RFSI) [17].

Obtaining the visualization of different crops and their associated characteristics, high resolution yield maps are used, which are an essential tool in modern agriculture, and these are obtained by spatial interpolation, however, spatial interpolation is generally performed using methods that can be computationally demanding [18]; therefore, some works have been carried out to explicitly take into account the spatial component in machine learning, where observations on the prediction location are included, performed using Random Forest Spatial Interpolation, comparing it with deterministic interpolation methods, such as ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) where it is observed that in the case studies of precipitation and temperature, RFSI mostly outperformed regression kriging, inverse distance weighting, Random Forest and RFsp, moreover, RFSI was substantially faster than RFsp, mainly when the training data set was large and high resolution prediction maps were made [17].
However, difficulties are observed with crop management given the variability in soil and edaphoclimatic conditions of the department. This situation causes effects such as heterogeneity of fruit quality, besides the lack of scientific work to understand the behavior of the crop in the region. This study proposes to formulate a model to determine current and potential zones of Hass avocado (Persea americana Mill) cultivation in the department of Risaralda, based on edaphoclimatic variables and fruit quality, using the current trends of precision agriculture and machine learning, to contribute technological developments in the agricultural sector, benefiting the Hass avocado growers of the department.

## 2 Bibliographic Review

Machine learning (ML) is an important decision support tool in fields such as crop yield prediction, including supporting decisions on what agricultural products to grow and what to do during the crop growing season; therefore, several machine learning algorithms have been applied to support research aimed at crop yield prediction or suggestion, where the most commonly used characteristics are temperature, precipitation and soil type [9]; In that sense, the different uses of the data obtained from the soil, such as the selection of the dataset for training, as well as the choice of soil environmental covariates, could boost the accuracy of automatic learning techniques [10]. One of the tools used within automatic learning are the crop-oriented recommendation systems where, based on provided variables, a model is created to predict or suggest which crop can be cultivated; to establish this, the models use historical data, such as climatic data (temperature, humidity, pH, rainfall) and fertilizer values (nitrogen, potassium, phosphorus) [11]. Machine learning employs methods such as supporting processes in charge of data analysis [12]. In [13] it is mentioned that different Artificial Intelligence techniques have been proposed and among these techniques that are part of Precision Agriculture, more specifically in the fields of crop recommendation systems, it is observed that algorithms such as k nearest neighbors (KNN), similarity-based models, set-based models, neural networks, among others, are included. These algorithms consider several external characteristics, such as meteorological data, and others, such as the soil profile, to provide the best recommendations, by examining the data, the most important attributes are obtained using techniques such as principal component analysis (PCA) and linear discrimination analysis (LDA). These extracted features are used to train models such as Naïve-Bayes (NBC), Random Forest, KNN; using training data and the performance is evaluated on test data using techniques such as cross-validation, RMSE or accuracy. Reliable predictions of crop yields are difficult for the development of agriculture; crop production varies according to different climatic conditions, having conditions such as dry periods and increasing temperatures; these forces strengthen the need for analysis of crop production in different climatic conditions, in this sense, [14] analyzes the automatic learning method, the Random Forest supervised algorithm can analyze the growth of crops concerning the current climatic conditions and biophysical change. Similarly, in [10], the ability of the Random Forest algorithm to predict soil classes from different training data sets and extrapolate such information to a similar area was evaluated.

Another aspect to consider is the usage of automatic learning models/algorithms and their possible applications to geospatial data, where special attention is given to the models used that are based on artificial neural networks (multilayer perceptron, general regression neural networks, self-organizing maps), statistical learning theory (support vector machines) [15], geo-statistical techniques such as Ordinary Kriging [16], or algorithms such as Random Forest Spatial Interpolation (RFSI) [17].

Obtaining the visualization of different crops and their associated characteristics, high resolution yield maps are used, which are an essential tool in modern agriculture, and these are obtained by spatial interpolation, however, spatial interpolation is generally performed using methods that can be computationally demanding [18]; therefore, some works have been carried out to explicitly take into account the spatial component in machine learning, where observations on the prediction location are included, performed using Random Forest Spatial Interpolation, comparing it with deterministic interpolation methods, such as ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) where it is observed that in the case studies of precipitation and temperature, RFSI mostly outperformed regression kriging, inverse distance weighting, Random Forest and RFsp, moreover, RFSI was substantially faster than RFsp, mainly when the training data set was large and high resolution prediction maps were made [17].
# 3 Methodology

This study aims to formulate a recommendation model based on the Random Forest algorithm complemented with Random Forest Spatial Interpolation (a methodology for spatial interpolation used in Machine Learning) involving three data sources: climatic and edaphic variables and fruit quality. Considering the formulated model, developing an information system that allows establishing current and potential fruit production zones is proposed in the Department of Risaralda, based on edaphoclimatic and fruit quality variables, thanks to the ongoing execution of the project "Development of an information system to determine current and potential zones for avocado (Persea americana Mill) Hass variety crops in the Department of Risaralda, based on edaphoclimatic and fruit quality variables (contract 424-201 MinCiencias)"<fnref n="1" />.

## 3.1 Description of the Data Set

Sampling will be conducted for a study of soils, fruit quality and climatic data collection in Hass avocado-producing farms in the municipalities of Pereira, Dosquebradas, Santa Rosa de Cabal, Marsella, Apía, Belén de Umbría, Guática and Quinchía. For the data collection of the first data set (dataset), seven (7) LynkBOX CLIMA PLUS climate stations have been installed in the different farms located in the municipalities selected for the study, allowing the recording of data on climate variables Temperature (°C), Relative humidity (%), Precipitation (mm), Solar radiation (W/m2), Wind direction and Wind speed, and the building variables humidity, soil temperature and electrical conductivity, which will be recorded for one year long. The second set of data will be based on a soil fertility analysis, where the parameters, pH, organic matter, elemental analysis (K, Ca, Mg and Na), and phosphorus (P) will be evaluated according to Colombian technical standards NTC 5264, 5403, 5349 and 5350, respectively. Additionally, in the third set of data, variables such as nitrogen content will be calculated based on organic matter content, aluminum using the KCI IM-EAA volumetric technique and texture to the touch Bouyoucos with sodium pyrophosphate - USDA triangular diagram classification - Cl (clayey), L (loamy), S (sandy).

## 3.2 Data Preparation

It will be considered the generated datasets presented in a different format, as observed in the datasets described above; for this reason, it is imperative to clean and normalize the data for subsequent use with the algorithms. Techniques from the Random Forest algorithm [19] will be used for missing data. A data scaling or normalization process is also conducted to convert the dates and times of the different data sources.

## 3.3 Random Forest Spatial Interpolation

When the data sets are configured, the prediction process will be carried out, which is framed in the use of the Random Forest Spatial Interpolation algorithm, using the local spatial information, it is to say, spatial variables to select the spatial dependencies and the complex spatial patterns that are presented [20]; a first training of the data will be done using the algorithm Random Forest, since being an algorithm based on decision trees, a prediction is made through a series of partitioning rules; the spatial correlation between the data obtained is not included in Random Forest standard, it will be taken into account that the nearby data contain information about a prediction location, for this purpose, additional spatial variables will be incorporated in the Random Forest model.

## 3.4 Accuracy Evaluation

The following accuracy metrics will be used to verify the predictions: coefficient of determination, Accuracy, mean absolute error (MAE) and root mean square error (RMSE) [17].

<footnote n="1">¹Project supported by MinCiencias, Gobernación de Risaralda, and Alcaldía de Pereira, work executed by the research groups GIA and Oleoquímica of Universidad Tecnológica de Pereira</footnote>
# 3 Methodology

This study aims to formulate a recommendation model based on the Random Forest algorithm complemented with Random Forest Spatial Interpolation (a methodology for spatial interpolation used in Machine Learning) involving three data sources: climatic and edaphic variables and fruit quality. Considering the formulated model, developing an information system that allows establishing current and potential fruit production zones is proposed in the Department of Risaralda, based on edaphoclimatic and fruit quality variables, thanks to the ongoing execution of the project "Development of an information system to determine current and potential zones for avocado (Persea americana Mill) Hass variety crops in the Department of Risaralda, based on edaphoclimatic and fruit quality variables (contract 424-201 MinCiencias)"<fnref n="1" />.

## 3.1 Description of the Data Set

Sampling will be conducted for a study of soils, fruit quality and climatic data collection in Hass avocado-producing farms in the municipalities of Pereira, Dosquebradas, Santa Rosa de Cabal, Marsella, Apía, Belén de Umbría, Guática and Quinchía. For the data collection of the first data set (dataset), seven (7) LynkBOX CLIMA PLUS climate stations have been installed in the different farms located in the municipalities selected for the study, allowing the recording of data on climate variables Temperature (°C), Relative humidity (%), Precipitation (mm), Solar radiation (W/m2), Wind direction and Wind speed, and the building variables humidity, soil temperature and electrical conductivity, which will be recorded for one year long. The second set of data will be based on a soil fertility analysis, where the parameters, pH, organic matter, elemental analysis (K, Ca, Mg and Na), and phosphorus (P) will be evaluated according to Colombian technical standards NTC 5264, 5403, 5349 and 5350, respectively. Additionally, in the third set of data, variables such as nitrogen content will be calculated based on organic matter content, aluminum using the KCI IM-EAA volumetric technique and texture to the touch Bouyoucos with sodium pyrophosphate - USDA triangular diagram classification - Cl (clayey), L (loamy), S (sandy).

## 3.2 Data Preparation

It will be considered the generated datasets presented in a different format, as observed in the datasets described above; for this reason, it is imperative to clean and normalize the data for subsequent use with the algorithms. Techniques from the Random Forest algorithm [19] will be used for missing data. A data scaling or normalization process is also conducted to convert the dates and times of the different data sources.

## 3.3 Random Forest Spatial Interpolation

When the data sets are configured, the prediction process will be carried out, which is framed in the use of the Random Forest Spatial Interpolation algorithm, using the local spatial information, it is to say, spatial variables to select the spatial dependencies and the complex spatial patterns that are presented [20]; a first training of the data will be done using the algorithm Random Forest, since being an algorithm based on decision trees, a prediction is made through a series of partitioning rules; the spatial correlation between the data obtained is not included in Random Forest standard, it will be taken into account that the nearby data contain information about a prediction location, for this purpose, additional spatial variables will be incorporated in the Random Forest model.

## 3.4 Accuracy Evaluation

The following accuracy metrics will be used to verify the predictions: coefficient of determination, Accuracy, mean absolute error (MAE) and root mean square error (RMSE) [17].

<footnote n="1">¹Project supported by MinCiencias, Gobernación de Risaralda, and Alcaldía de Pereira, work executed by the research groups GIA and Oleoquímica of Universidad Tecnológica de Pereira</footnote>
# 4 Final Considerations

The information system proposed in the execution of the project (contract 424-201 MinCiencias) will allow establishing by means of a suggestion if the current production areas of avocado cultivation are the most appropriate and to determine the potential areas of cultivation.

# References

1. Perfetti, J.J., Bravo-Ureta, B.E., García, A., Pantoja, J., Delgado, M., Blanco, J., Jara, R., Moraga, C., Paredes, G., Naranjo, J., et al.: Adecuación de tierras y el desarrollo de la agricultura colombiana: políticas e instituciones. *Fedesarrollo* **447**, 456 (2019)

2. El-Bendary, N., Elhariri, E., Hazman, M., Saleh, S.M., Hassanien, A.E.: Cultivation-time recommender system based on climatic conditions for newlyreclaimed lands in Egypt. *Procedia Computer Science* **96**, 110–119 (2016)

3. Singh, R., Kumari, T., Verma, P., Singh, B.P., Raghubanshi, A.S.: Compatible package-based agriculture systems: an urgent need for agro-ecological balance and climate change adaptation. *Soil Ecology Letters* **4**(3), 187–212 (2022)

4. Dreher, M.L., Davenport, A.J.: Hass avocado composition and potential health effects. *Critical reviews in food science and nutrition* **53**(7), 738–750 (2013)

5. Ramírez-Gil, J.G., Ramelli, E.G., Osorio, J.G.M.: Economic impact of the avo-cado (cv. hass) wilt disease complex in antioquia, colombia, crops under different technological management levels. *Crop protection* **101**, 103–115 (2017)

6. MADR, M.d.A.y.D.R.: CADENA DE AGUACATE, Indicadores e instrumentos. *Lect Econ. 2019;52(52):165–94* (2019)

7. Martínez, D.H.F., Galvis, C.P.U.: Tic para la investigación, desarrollo e inno-vación del sector agropecuario (2018)

8. Vite Cevallos, H., Carvajal Romero, H., Barrezueta Unda, S.: Aplicación de algoritmos de aprendizaje automático para clasificar la fertilidad de un suelo bananero. *Conrado* **16**(72), 15–19 (2020)

9. Katarya, R., Raturi, A., Mehndiratta, A., Thapper, A.: Impact of machine learn-ing techniques in precision agriculture. In: 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 1–6 (2020). IEEE

10. Geetha, V., Punitha, A., Abarna, M., Akshaya, M., Illakiya, S., Janani, A.: An effective crop prediction using random forest algorithm. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5 (2020). IEEE

11. Pozdnoukhov, A., Kanevski, M.: Machine learning algorithms for analysis and modeling of geospatial data. In: Annual Conference of International Association for Mathematical Geology (IAMG 07), Beijing, China, 25-31 August (2007)

12. Carranza, J.P., Salomón, M.J., Piumetto, M.A., Monzani, F., MONTENE-GRO CALVIMONTE, M., Córdoba, M.A.: Random forest como técnica de valuación masiva del valor del suelo urbano: una aplicación para la ciudad de río cuarto, córdoba, argentina. In: *Congresso Brasileiro de Cadastro Técnico Multifinalitário-COBRAC* (2018)

13. Sekulić, A., Kilibarda, M., Heuvelink, G.B., Nikolić, M., Bajat, B.: Random forest spatial interpolation. *Remote Sensing* **12**(10), 1687 (2020)

14. Mariano, C., Monica, B.: A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping. *Computers and Electronics in Agriculture* **184**, 106094 (2021)

15. Kuhn, M., Johnson, K., Kuhn, M., Johnson, K.: Classification trees and rule-based models. *Applied predictive modeling*, 369–413 (2013)

16. Talebi, H., Peeters, L.J., Otto, A., Tolosana-Delgado, R.: A truly spatial ran-dom forests algorithm for geoscience data analysis and modelling. *Mathematical Geosciences* **54**, 1–22 (2022)
# 4 Final Considerations

The information system proposed in the execution of the project (contract 424-201 MinCiencias) will allow establishing by means of a suggestion if the current production areas of avocado cultivation are the most appropriate and to determine the potential areas of cultivation.

# References

1. Perfetti, J.J., Bravo-Ureta, B.E., García, A., Pantoja, J., Delgado, M., Blanco, J., Jara, R., Moraga, C., Paredes, G., Naranjo, J., et al.: Adecuación de tierras y el desarrollo de la agricultura colombiana: políticas e instituciones. *Fedesarrollo* **447**, 456 (2019)

2. El-Bendary, N., Elhariri, E., Hazman, M., Saleh, S.M., Hassanien, A.E.: Cultivation-time recommender system based on climatic conditions for newlyreclaimed lands in Egypt. *Procedia Computer Science* **96**, 110–119 (2016)

3. Singh, R., Kumari, T., Verma, P., Singh, B.P., Raghubanshi, A.S.: Compatible package-based agriculture systems: an urgent need for agro-ecological balance and climate change adaptation. *Soil Ecology Letters* **4**(3), 187–212 (2022)

4. Dreher, M.L., Davenport, A.J.: Hass avocado composition and potential health effects. *Critical reviews in food science and nutrition* **53**(7), 738–750 (2013)

5. Ramírez-Gil, J.G., Ramelli, E.G., Osorio, J.G.M.: Economic impact of the avo-cado (cv. hass) wilt disease complex in antioquia, colombia, crops under different technological management levels. *Crop protection* **101**, 103–115 (2017)

6. MADR, M.d.A.y.D.R.: CADENA DE AGUACATE, Indicadores e instrumentos. *Lect Econ. 2019;52(52):165–94* (2019)

7. Martínez, D.H.F., Galvis, C.P.U.: Tic para la investigación, desarrollo e inno-vación del sector agropecuario (2018)

8. Vite Cevallos, H., Carvajal Romero, H., Barrezueta Unda, S.: Aplicación de algoritmos de aprendizaje automático para clasificar la fertilidad de un suelo bananero. *Conrado* **16**(72), 15–19 (2020)

9. Katarya, R., Raturi, A., Mehndiratta, A., Thapper, A.: Impact of machine learn-ing techniques in precision agriculture. In: 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 1–6 (2020). IEEE

10. Geetha, V., Punitha, A., Abarna, M., Akshaya, M., Illakiya, S., Janani, A.: An effective crop prediction using random forest algorithm. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5 (2020). IEEE

11. Pozdnoukhov, A., Kanevski, M.: Machine learning algorithms for analysis and modeling of geospatial data. In: Annual Conference of International Association for Mathematical Geology (IAMG 07), Beijing, China, 25-31 August (2007)

12. Carranza, J.P., Salomón, M.J., Piumetto, M.A., Monzani, F., MONTENE-GRO CALVIMONTE, M., Córdoba, M.A.: Random forest como técnica de valuación masiva del valor del suelo urbano: una aplicación para la ciudad de río cuarto, córdoba, argentina. In: *Congresso Brasileiro de Cadastro Técnico Multifinalitário-COBRAC* (2018)

13. Sekulić, A., Kilibarda, M., Heuvelink, G.B., Nikolić, M., Bajat, B.: Random forest spatial interpolation. *Remote Sensing* **12**(10), 1687 (2020)

14. Mariano, C., Monica, B.: A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping. *Computers and Electronics in Agriculture* **184**, 106094 (2021)

15. Kuhn, M., Johnson, K., Kuhn, M., Johnson, K.: Classification trees and rule-based models. *Applied predictive modeling*, 369–413 (2013)

16. Talebi, H., Peeters, L.J., Otto, A., Tolosana-Delgado, R.: A truly spatial ran-dom forests algorithm for geoscience data analysis and modelling. *Mathematical Geosciences* **54**, 1–22 (2022)
# Multidimensional Energy Analysis in Agricultural Coffee Production Systems to Assess Sustainability.

Cristian Méndez-Rodríguez<fnref n="1" />,<fnref n="2" />, Cristian Camilo Ordoñez Quintero<fnref n="2" />, Claudia SofiaIdrobo<fnref n="3" />, Luis Freddy Muñoz Sanabria<fnref n="3" />.

## Abstract.

A coffee farm is sustainable when it uses the system inputs (energy/materials) and transforms them into high quality beans, avoiding the loss of energy in waste and pollutants. The aim of the research is to evaluate sustainability in coffee agricultural production systems, through multidimensional energy analysis (with the energy tool). This research was conducted in the farm "La Angostura" in Popayán, Colombia, during the years 2018-2020. The results show that the farm achieved its best energy performance and was most sustainable in 2020, producing 8750 kg/ha of cherry coffee, with the lowest Transformity (1.35E+06 seJ/J) and the highest Emergency Sustainability Index (0.97), for the three years analyzed. In addition, natural inputs contribute approximately 57% of total emergency, and those coming from the economy 43%. This study provides a precise analysis of the energy flows that interact in the system, the significant uses of energy and the sources of energy at each stage of the production process. This research provides a basis for the management and planning of the territory in relation to its agricultural strengths.

**Keywords:** Sustainability, Agricultural coffee production system, Emergency Analysis.

## 1 Introduction.

In 2016, Colombia was the third largest coffee producer in the world with approximately 14 million bags (equivalent to 9.23% of world production), followed by Brazil and Vietnam [1]. These data consolidate coffee as the main agricultural product of the country and demonstrate its great contribution to the Colombian rural economy. The Cauca department is a region that stands out for its high-quality coffee and denomination of origin; where the aim is to generate a product recognized as excellent at an international level [2].

Coffee is a common crop in the traditional farms of the department of Cauca. According to the Comité de Cafeteros del Cauca, the department occupies the fourth place nationally in coffee production, with 93.000 families, 31 municipalities of the department of Cauca have 64% of the land for agricultural production to coffee cultivation [1]. A significant part of the department's coffee production is carried out on the "Popayán plateau", which includes the municipalities of Popayán, Piendamó, Morales, Cajibío, El Tambo and Timbío, with traditional and technified production systems. This research will be carried out at the "La Angostura" farm in the village Clarete Bajo of the Popayán city, where there is a traditional coffee plantation (small producers), with approximately 800 coffee trees of the Castillo variety.

<footnote n="1">¹Environmental Studies Group (GEA), Environmental Sciences, Department of Biology, University of Cauca, Popayán 190002, Colombia. cristianmendez@unicauca.edu.co.</footnote>

<footnote n="2">²Intelligent Management System (IMS), Faculty of Engineering, University Foundation of Popayán, Popayán 190002, Colombia.</footnote>

<footnote n="3">³LOGICIEL, Faculty of Engineering, University Foundation of Popayán, Popayán 190002, Colombia.</footnote>
# Multidimensional Energy Analysis in Agricultural Coffee Production Systems to Assess Sustainability.

Cristian Méndez-Rodríguez<fnref n="1" />,<fnref n="2" />, Cristian Camilo Ordoñez Quintero<fnref n="2" />, Claudia SofiaIdrobo<fnref n="3" />, Luis Freddy Muñoz Sanabria<fnref n="3" />.

## Abstract.

A coffee farm is sustainable when it uses the system inputs (energy/materials) and transforms them into high quality beans, avoiding the loss of energy in waste and pollutants. The aim of the research is to evaluate sustainability in coffee agricultural production systems, through multidimensional energy analysis (with the energy tool). This research was conducted in the farm "La Angostura" in Popayán, Colombia, during the years 2018-2020. The results show that the farm achieved its best energy performance and was most sustainable in 2020, producing 8750 kg/ha of cherry coffee, with the lowest Transformity (1.35E+06 seJ/J) and the highest Emergency Sustainability Index (0.97), for the three years analyzed. In addition, natural inputs contribute approximately 57% of total emergency, and those coming from the economy 43%. This study provides a precise analysis of the energy flows that interact in the system, the significant uses of energy and the sources of energy at each stage of the production process. This research provides a basis for the management and planning of the territory in relation to its agricultural strengths.

**Keywords:** Sustainability, Agricultural coffee production system, Emergency Analysis.

## 1 Introduction.

In 2016, Colombia was the third largest coffee producer in the world with approximately 14 million bags (equivalent to 9.23% of world production), followed by Brazil and Vietnam [1]. These data consolidate coffee as the main agricultural product of the country and demonstrate its great contribution to the Colombian rural economy. The Cauca department is a region that stands out for its high-quality coffee and denomination of origin; where the aim is to generate a product recognized as excellent at an international level [2].

Coffee is a common crop in the traditional farms of the department of Cauca. According to the Comité de Cafeteros del Cauca, the department occupies the fourth place nationally in coffee production, with 93.000 families, 31 municipalities of the department of Cauca have 64% of the land for agricultural production to coffee cultivation [1]. A significant part of the department's coffee production is carried out on the "Popayán plateau", which includes the municipalities of Popayán, Piendamó, Morales, Cajibío, El Tambo and Timbío, with traditional and technified production systems. This research will be carried out at the "La Angostura" farm in the village Clarete Bajo of the Popayán city, where there is a traditional coffee plantation (small producers), with approximately 800 coffee trees of the Castillo variety.

<footnote n="1">¹Environmental Studies Group (GEA), Environmental Sciences, Department of Biology, University of Cauca, Popayán 190002, Colombia. cristianmendez@unicauca.edu.co.</footnote>

<footnote n="2">²Intelligent Management System (IMS), Faculty of Engineering, University Foundation of Popayán, Popayán 190002, Colombia.</footnote>

<footnote n="3">³LOGICIEL, Faculty of Engineering, University Foundation of Popayán, Popayán 190002, Colombia.</footnote>
In recent years, the agricultural sector of Cauca has focused on the development of strategies for the generation of value chain, with coffee being a key link, focusing on the production of specialty coffee [3], where the improvement of efficiency and sustainability constitutes an innovative and necessary bet for the growth of the productive chain. In addition to the importance of coffee in the region, and the scientific advances in this area, it is necessary to study the use of conceptual and methodological tools that allow the modeling of a productive chain in relation to its multidimensional energy flows (coming from the natural, social and economic systems) in order to have an integral evaluation of the sustainability of coffee production systems [4]–[6].

A coffee farm is efficient when it uses the energy/materials input to the system and transforms them into high quality beans [7], [8], avoiding the loss of energy in waste and pollutants. The objective of this article is to model the stages of the production process and identify the multidimensional energy flows involved, such as: natural re- sources (sun, wind, rain, soil, etc.) and resources from the socioeconomic component (fertilizers, equipment and machinery, infrastructure, labor, technical services, etc.), in order to evaluate the sustainability of the farm. This paper uses the methodological tool proposed in [9], where the processes of the production chain are modeled and an Emergency evaluation is carried out to estimate the sustainability of the farm. The study's main contribution is to provide baseline information on the sustainability of the coffee farm and the most significant uses of resources (in energy terms).

The article is organized as follows: section 2 presents the methodology (the general context of the research, the first modeling phase, and the Emergency tool to evaluate sustainability in agricultural systems); section 3 shows the main results of the research; and finally, the conclusions are presented.

# 2 Methodology.

## 2.1 Agricultural coffee production system description

### – “La Angostura” farm.

The research was carried out taking as a reference the data provided by the Traditional Farm "La Angostura", located in the village Clarete Bajo, north of the city of Popayán, Cauca, Colombia, and has an area of 5.8 Hectares. This farm has a traditional polyculture of Castillo variety coffee, which is characterized for being one of the Colombian coffee varieties resistant to rust, and stands out for its aroma and citric acidity. The crops planted in semi-shade, with bushes such as orange, mandarin, lemon, and “guamos” trees. Currently there are 800 coffee plants, it is a young coffee plantation (6 years old), and has a planting density equivalent to 5000 trees/ha. The geolocation of the farm (average elevation 1800 m.a.s.l), the tropical climate of the region and the biodiversity favor coffee production. Three stages were identified on the farm: planting, harvesting, and processing, where the final product obtained is dry parchment coffee. The analysis of energy flows for the "La Angostura" farm was carried out for the years 2018, 2019 and 2020, due to the unavailability of information from the owners.

## 2.2 Phase 1: Modeling – ANSI/ISA 88 Standard.

This tool allows modeling industrial processes, by describing equipment and procedures [9]. The ISA-88 committee has published 5 parts of the standard. The Part 1: Models and Terminology, which seeks to model the production process in relation to the chronological procedures and associated resources, is very useful (Figure 1) [10]. Phase 1 is applied at each stage of an agricultural production system. This process modeling using ISA 88 helps to characterize reliably the energy flows (considering convention proposed by Emergency Analysis) present in the agricultural production system.
In recent years, the agricultural sector of Cauca has focused on the development of strategies for the generation of value chain, with coffee being a key link, focusing on the production of specialty coffee [3], where the improvement of efficiency and sustainability constitutes an innovative and necessary bet for the growth of the productive chain. In addition to the importance of coffee in the region, and the scientific advances in this area, it is necessary to study the use of conceptual and methodological tools that allow the modeling of a productive chain in relation to its multidimensional energy flows (coming from the natural, social and economic systems) in order to have an integral evaluation of the sustainability of coffee production systems [4]–[6].

A coffee farm is efficient when it uses the energy/materials input to the system and transforms them into high quality beans [7], [8], avoiding the loss of energy in waste and pollutants. The objective of this article is to model the stages of the production process and identify the multidimensional energy flows involved, such as: natural re-sources (sun, wind, rain, soil, etc.) and resources from the socioeconomic component (fertilizers, equipment and machinery, infrastructure, labor, technical services, etc.), in order to evaluate the sustainability of the farm. This paper uses the methodological tool proposed in [9], where the processes of the production chain are modeled and an Emergency evaluation is carried out to estimate the sustainability of the farm. The study's main contribution is to provide baseline information on the sustainability of the coffee farm and the most significant uses of resources (in energy terms).

The article is organized as follows: section 2 presents the methodology (the general context of the research, the first modeling phase, and the Emergency tool to evaluate sustainability in agricultural systems); section 3 shows the main results of the research; and finally, the conclusions are presented.

# 2 Methodology.

## 2.1 Agricultural coffee production system description
– “La Angostura” farm.

The research was carried out taking as a reference the data provided by the Traditional Farm "La Angostura", located in the village Clarete Bajo, north of the city of Popayán, Cauca, Colombia, and has an area of 5.8 Hectares. This farm has a traditional polyculture of Castillo variety coffee, which is characterized for being one of the Colombian coffee varieties resistant to rust, and stands out for its aroma and citric acidity. The crops planted in semi-shade, with bushes such as orange, mandarin, lemon, and “guamos” trees. Currently there are 800 coffee plants, it is a young coffee plantation (6 years old), and has a planting density equivalent to 5000 trees/ha. The geolocation of the farm (average elevation 1800 m.a.s.l), the tropical climate of the region and the biodiversity favor coffee production. Three stages were identified on the farm: planting, harvesting, and processing, where the final product obtained is dry parchment coffee. The analysis of energy flows for the "La Angostura" farm was carried out for the years 2018, 2019 and 2020, due to the unavailability of information from the owners.

## 2.2 Phase 1: Modeling – ANSI/ISA 88 Standard.

This tool allows modeling industrial processes, by describing equipment and procedures [9]. The ISA-88 committee has published 5 parts of the standard. The Part 1: Models and Terminology, which seeks to model the production process in relation to the chronological procedures and associated resources, is very useful (Figure 1) [10]. Phase 1 is applied at each stage of an agricultural production system. This process modeling using ISA 88 helps to characterize reliably the energy flows (considering convention proposed by Emergency Analysis) present in the agricultural production system.
<figure><img src="image_42.png" /><figcaption>**Fig. 1.** Process modeling - ANSI/ISA 88 Standard.</figcaption></figure>

## 2.3 Phase 2: Emergy

The emergy is defined as the available energy previously required, which has been used directly or indirectly in the transformations necessary to manufacture a product or service (Y = R + N + F, Figure 2). The unit of measurement of emergy is solar emjoules (seJ). Energy flows are represented in three categories of resources: (i) R as renewable,

(ii) N as non-renewable (R and N flows are provided by the environment) and (iii) F the inputs of the economy (provided by the market and flows coming from the economy) [11]. Emergy is a quantitative tool, based on the law of conservation of energy, thermodynamics, systems theory and systems ecology [12], achieving an integral analysis of the production system [13].

<figure><img src="image_43.png" /><figcaption>**Fig. 2.** Agricultural production system model, based on the Emergy analysis [14].</figcaption></figure>

# 3 Results and Discussion.

## 3.1. Stages modeling - production process.

The unit procedure, operations, by-products flows, and phases of these stage are presented in Figure 3, and Table 1.
<figure><img src="image_56.png" /><figcaption>**Fig. 1.** Process modeling - ANSI/ISA 88 Standard.</figcaption></figure>

## 2.3 Phase 2: Emergy

The emergy is defined as the available energy previously required, which has been used directly or indirectly in the transformations necessary to manufacture a product or service (Y = R + N + F, Figure 2). The unit of measurement of emergy is solar emjoules (seJ). Energy flows are represented in three categories of resources: (i) R as renewable,

(ii) N as non-renewable (R and N flows are provided by the environment) and (iii) F the inputs of the economy (provided by the market and flows coming from the economy) [11]. Emergy is a quantitative tool, based on the law of conservation of energy, thermodynamics, systems theory and systems ecology [12], achieving an integral analysis of the production system [13].

<figure><img src="image_57.png" /><figcaption>**Fig. 2.** Agricultural production system model, based on the Emergy analysis [14].</figcaption></figure>

# 3 Results and Discussion.

## 3.1. Stages modeling - production process.

The unit procedure, operations, by-products flows, and phases of these stage are presented in Figure 3, and Table 1.
<figure><img src="image_44.png" /><figcaption>Fig. 3. Operations and flows of plantation and harvesting stages.</figcaption></figure>

<table><caption>**Table 1.** Description of plantation and harvesting stages.</caption><thead><tr><th>Unit<br>Procedure</th><th>Pro-<br>Operation</th><th>Phase</th></tr></thead><tbody><tr><td rowspan="6">Seed germination</td><td>Construction</td><td>Building the germinator</td></tr><tr><td>Planting 1</td><td>Sowing seeds in germinator</td></tr><tr><td>Shade</td><td>Protecting seeds from sunlight</td></tr><tr><td>Irrigation 1</td><td>Keeping the germinator moist</td></tr><tr><td>Preparation</td><td>Preparing substrate organic fertilize</td></tr><tr><td>Fill</td><td>Filling coffee bags</td></tr><tr><td rowspan="6">Coffee nursery<br>Plantation and<br>harvesting cof-fee</td><td>Selection 1</td><td>Selecting seedbeds</td></tr><tr><td>Transplant</td><td>Transplanting seedbeds to bags</td></tr><tr><td>Irrigation 2</td><td>Keeping bag soil moist</td></tr><tr><td>Plot-spacing</td><td>Establish planting density</td></tr><tr><td>Tillage</td><td>Digging holes for each seedling</td></tr><tr><td>Selection 2</td><td>Select seedlings</td></tr><tr><td rowspan="2">Plantation</td><td>Planting 2</td><td>Sowing the seedlings in the pits</td></tr><tr><td>Fertilization and<br>Pest/weeds Management</td><td>Fertilize growth<br>Trimming<br>Fertilize production</td><td>Fertilizing coffee in growth<br>Pruning coffee plants<br>Fertilizing coffee in production</td></tr><tr><td>Harvesting</td><td>Harvesting</td><td>Harvesting coffee beans</td></tr></tbody></table>

## 3.2. Emergency Analysis.

In order to identify the energy flows in a coffee agricultural system, it is necessary to characterize the stages that are carried out in this productive process. In this way, it is possible to detail the energy flows that are contributing to the system in each of these stages. This detailed inventory is shown in Appendix A, where all the energy flows involved in each stage of the production process are presented (for the year 2018). This table is designed according to Emergency's methodology.

Figure 4 shows the energy contributions of nature to the farm (green bars) and the con-tribution of the economy (orange bars) for the year 2018. In general, it can be seen that most of the energy comes from non-renewable resources, mainly from energy provided by the soil (nature) and energy from labor (economy). The data in Table 2 show that the stage that demands the most energy resources is planting, with approximately 84% of total energy use, harvesting contributes 15% and the milling stage only contributes about 1%.
<figure><img src="image_58.png" /><figcaption>Fig. 3. Operations and flows of plantation and harvesting stages.</figcaption></figure>

<table><caption>**Table 1.** Description of plantation and harvesting stages.</caption><thead><tr><th>Unit<br>Procedure</th><th>Pro-<br>Operation</th><th>Phase</th></tr></thead><tbody><tr><td rowspan="6">Seed germination</td><td>Construction</td><td>Building the germinator</td></tr><tr><td>Planting 1</td><td>Sowing seeds in germinator</td></tr><tr><td>Shade</td><td>Protecting seeds from sunlight</td></tr><tr><td>Irrigation 1</td><td>Keeping the germinator moist</td></tr><tr><td>Preparation</td><td>Preparing substrate organic fertilize</td></tr><tr><td>Fill</td><td>Filling coffee bags</td></tr><tr><td rowspan="6">Coffee nursery<br>Plantation and<br>harvesting cof-fee</td><td>Selection 1</td><td>Selecting seedbeds</td></tr><tr><td>Transplant</td><td>Transplanting seedbeds to bags</td></tr><tr><td>Irrigation 2</td><td>Keeping bag soil moist</td></tr><tr><td>Plot-spacing</td><td>Establish planting density</td></tr><tr><td>Tillage</td><td>Digging holes for each seedling</td></tr><tr><td>Selection 2</td><td>Select seedlings</td></tr><tr><td rowspan="2">Plantation</td><td>Planting 2</td><td>Sowing the seedlings in the pits</td></tr><tr><td>Fertilization and<br>Pest/weeds Management</td><td>Fertilize growth<br>Trimming<br>Fertilize production</td><td>Fertilizing coffee in growth<br>Pruning coffee plants<br>Fertilizing coffee in production</td></tr><tr><td>Harvesting</td><td>Harvesting</td><td>Harvesting coffee beans</td></tr></tbody></table>

## 3.2. Emergency Analysis.

In order to identify the energy flows in a coffee agricultural system, it is necessary to characterize the stages that are carried out in this productive process. In this way, it is possible to detail the energy flows that are contributing to the system in each of these stages. This detailed inventory is shown in Appendix A, where all the energy flows involved in each stage of the production process are presented (for the year 2018). This table is designed according to Emergency's methodology.

Figure 4 shows the energy contributions of nature to the farm (green bars) and the con-tribution of the economy (orange bars) for the year 2018. In general, it can be seen that most of the energy comes from non-renewable resources, mainly from energy provided by the soil (nature) and energy from labor (economy). The data in Table 2 show that the stage that demands the most energy resources is planting, with approximately 84% of total energy use, harvesting contributes 15% and the milling stage only contributes about 1%.
<figure><img src="image_45.png" /><figcaption>**Fig. 4.** Emergency flows contributed by Nature (green) and the Economy (orange) (Renewable and non-Renewables), 2018.</figcaption></figure>

<table><caption>**Table 2.** Emergency contributions by stages of the production process, 2018-2020.</caption><thead><tr><td>Year</td><td>Plantation</td><td>Harvesting</td><td>Drying</td><td>TOTAL Emergency</td></tr></thead><tbody><tr><td>2018</td><td>1,86E+16</td><td>3,38E+15</td><td>1,75E+14</td><td>2,21E+16</td></tr><tr><td>2019</td><td>1,80E+16</td><td>3,02E+15</td><td>1,53E+14</td><td>2,11E+16</td></tr><tr><td>2020</td><td>1,74E+16</td><td>3,24E+15</td><td>1,46E+14</td><td>2,08E+16</td></tr></tbody></table>

The emergency for 2020 decreased by 0.13E+16 (SeJ) with respect to 2018, and 0.3 E+16 (SeJ) with reference to 2019, indicating that less energy was required for the operation of the productive system. In addition, it is important to know the significant energy uses in each stage of the productive process (Figure 5). The plantation is the one that consumes the most resources with 83.9%. The resources that contribute most energy are the soil, renewable natural resources (sun, wind, rain, etc.), and human labor. In the harvest stage, 15.2% of energy is consumed, and the resource that contributes most is human labor. And only 0.78% of energy is used at the milling stage, with human labor contributing the most.

<figure><img src="image_46.png" /><figcaption>**Fig. 4.** Significant emergency use in each stage of the production process, 2018. Finally, different indicators were calculated to evaluate the sustainability, efficiency and productivity of the production farm (Table 3).</figcaption></figure>
<figure><img src="image_59.png" /><figcaption>**Fig. 4.** Emergency flows contributed by Nature (green) and the Economy (orange) (Renewable and non-Renewables), 2018.</figcaption></figure>

<table><caption>**Table 2.** Emergency contributions by stages of the production process, 2018-2020.</caption><thead><tr><td>Year</td><td>Plantation</td><td>Harvesting</td><td>Drying</td><td>TOTAL Emergency</td></tr></thead><tbody><tr><td>2018</td><td>1,86E+16</td><td>3,38E+15</td><td>1,75E+14</td><td>2,21E+16</td></tr><tr><td>2019</td><td>1,80E+16</td><td>3,02E+15</td><td>1,53E+14</td><td>2,11E+16</td></tr><tr><td>2020</td><td>1,74E+16</td><td>3,24E+15</td><td>1,46E+14</td><td>2,08E+16</td></tr></tbody></table>

The emergency for 2020 decreased by 0.13E+16 (SeJ) with respect to 2018, and 0.3 E+16 (SeJ) with reference to 2019, indicating that less energy was required for the operation of the productive system. In addition, it is important to know the significant energy uses in each stage of the productive process (Figure 5). The plantation is the one that consumes the most resources with 83.9%. The resources that contribute most energy are the soil, renewable natural resources (sun, wind, rain, etc.), and human labor. In the harvest stage, 15.2% of energy is consumed, and the resource that contributes most is human labor. And only 0.78% of energy is used at the milling stage, with human labor contributing the most.

<figure><img src="image_60.png" /><figcaption>**Fig. 4.** Significant emergency use in each stage of the production process, 2018. Finally, different indicators were calculated to evaluate the sustainability, efficiency and productivity of the production farm (Table 3).</figcaption></figure>
<table><caption>Table 3. Emergency Indices.</caption><thead><tr><th>Indices</th><th>Formula</th><th>2018</th><th>2019</th><th>2020</th></tr></thead><tbody><tr><td>Total emergency [Sej]</td><td>Y<sub>coffee</sub> = R + N + F</td><td>2.21E+1</td><td>2.11E+1</td><td>2.08E+1</td></tr><tr><td></td><td></td><td>6</td><td>16</td><td>16</td></tr><tr><td>Transformity [Sej/J]</td><td><div style="text-align: center; line-height: 1.2em;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Y<sub>coffee</sub></div><div style="padding-top: 1px;">T = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">E</div><div style="padding-top: 1px;">Total emergency</div></div></div> = <div style="text-align: center; line-height: 1.2em;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Product energy</div><div style="padding-top: 1px;">Total energy</div></div></td><td>1.79E+06</td><td>1.50E+06</td><td><strong>1.35E+06</strong></td></tr><tr><td>Emergency Yield Ratio</td><td><div style="text-align: center; line-height: 1.2em;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">Y<sub>coffee</sub></div><div style="padding-top: 1px;">EYR = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">F</div><div style="padding-top: 1px;">N + F<sub>N</sub></div></div></div></td><td>2.31</td><td>2.46</td><td>2.52</td></tr><tr><td>Environmental Loading Ratio</td><td><div style="text-align: center; line-height: 1.2em;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">ELR = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">R + F</div><div style="padding-top: 1px;">F<sub>N</sub> = M<sub>N</sub> + S<sub>N</sub> y F<sub>R</sub> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">M<sub>R</sub> + S<sub>R</sub></div><div style="padding-top: 1px;">M<sub>R</sub> + S<sub>R</sub></div></div></div></div></td><td>2.77</td><td>2.66</td><td>2.61</td></tr><tr><td>Energy Investment Ratio</td><td><div style="text-align: center; line-height: 1.2em;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">EIR = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">F</div><div style="padding-top: 1px;">N + R</div></div></div></td><td>0.76</td><td>0.68</td><td>0.66</td></tr><tr><td>Energy Sustainability Index</td><td><div style="text-align: center; line-height: 1.2em;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">EYR</div><div style="padding-top: 1px;">ESI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding-bottom: 1px;">ELR</div><div style="padding-top: 1px;">ELR</div></div></div></td><td>0.83</td><td>0.93</td><td><strong>0.97</strong></td></tr><tr><td>Productivity - cherrycoffee</td><td>kg/ha</td><td>7500</td><td>8200</td><td><strong>8750</strong></td></tr></tbody></table>

Comparing the three years analyzed, the “La Angostura” farm had its best environmental and energy performance in 2020, with a production of 8750 kg/ha of cherry coffee, where the Transformity was the lowest, 1.35E+06 seJ/J, and the Emergency Sustainability Index (ESI) increased (0,97). This indicates that the decisions taken in recent years have led to a notable improvement in the efficiency and sustainability of the system. The Emergency value decreased each year, demonstrating that the system was more energy efficient in 2020 compared to 2018.

## 4 Conclusions

The sustainable development of the department of Cauca requires the planning of efficient agricultural production systems, where the management of resources and respect for nature are considered. For this purpose, the evaluation of the sustainability of a coffee farm was proposed, considering the energy flows that intervene in the processes of the productive chain. For this purpose, a systemic approach was considered, which would allow for an integral characterization of the components that interact in the system, and the multiple energy flows that intervene, such as: sun, wind, rain, soil, fertilizers, equipment, machinery, labor, etc. This modeling of the production chain was carried out by means of operations and flow modeling, process flow diagrams (PFD), using the ISA 88 standard, thus allowing a broad understanding of the coffee system.

After having the inventory of all the energy flows involved in the system, the Emergency method was used, which helps to establish a model that indicates the energy footprint of a product, in this case coffee. Likewise, the tool provides a series of indicators that help to improve the decision-making processes of the coffee producing communities.

The work was carried out in the traditional farm "La Angostura", located in the village of Clarete Bajo, in the city of Popayán, data from the years 2018, 2019, and 2020 were considered. For the three years analyzed, the planting stage is the one that consumes the most resources with approximately 84%. The resources that contribute the most energy are: soil, renewable natural resources (sun, wind, rain, etc.), and human labor; the harvest stage contributes approximately 15% of the energy, here the resource that contributes the most is human labor; and only 1% of the energy is used in the milling stage, with human labor contributing the most.
<table><caption>Table 3. Emergency Indices.</caption><thead><tr><th>Indices</th><th>Formula</th><th>2018</th><th>2019</th><th>2020</th></tr></thead><tbody><tr><td>Total emergency [Sej]</td><td>Y<sub>coffee</sub> = R + N + F</td><td>2.21E+1</td><td>2.11E+1</td><td>2.08E+1</td></tr><tr><td></td><td></td><td>6</td><td>16</td><td>16</td></tr><tr><td></td><td><div style="text-align: center; line-height: 1.2;">Y<sub>coffee</sub><br>T = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">E</div><div style="padding: 0 5px;">Total emergency</div></div></td><td>1.79E+0</td><td>1.50E+0</td><td><strong>1.35E+0</strong></td></tr><tr><td>Transformity [Sej/J]</td><td>= <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">Product energy</div><div style="padding: 0 5px;">Total energy</div></div></td><td>6</td><td>06</td><td><strong>06</strong></td></tr><tr><td>Emergency Yield Ratio</td><td><div style="text-align: center; line-height: 1.2;">Y<sub>coffee</sub><br>EYR = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">F</div><div style="padding: 0 5px;">EYR</div></div></td><td>2.31</td><td>2.46</td><td>2.52</td></tr><tr><td>Environmental<br>Loading Ratio</td><td><div style="text-align: center; line-height: 1.2;">N + F<sub>N</sub><br>ELR = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">R + F</div><div style="padding: 0 5px;">F<sub>N</sub></div></div></td><td>2.77</td><td>2.66</td><td>2.61</td></tr><tr><td></td><td><div style="text-align: center; line-height: 1.2;">F<sub>N</sub> = M<sub>N</sub> + S<sub>N</sub> y F<sub>R</sub> = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">M<sub>R</sub> + S<sub>R</sub></div><div style="padding: 0 5px;">F<sub>R</sub></div></div></td><td></td><td></td><td></td></tr><tr><td>Energy Investment Ratio</td><td>EIR = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">F</div><div style="padding: 0 5px;">EIR</div></div></td><td>0.76</td><td>0.68</td><td>0.66</td></tr><tr><td>Energy Sustainability Index</td><td><div style="text-align: center; line-height: 1.2;">EYR<br>ESI = <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">N + R</div><div style="padding: 0 5px;">EYR</div></div></div></td><td>0.83</td><td>0.93</td><td><strong>0.97</strong></td></tr><tr><td>Productivity - cherrycoffee</td><td>kg/ha</td><td>7500</td><td>8200</td><td><strong>8750</strong></td></tr></tbody></table>

Comparing the three years analyzed, the “La Angostura” farm had its best environmental and energy performance in 2020, with a production of 8750 kg/ha of cherry coffee, where the Transformity was the lowest, 1.35E+06 seJ/J, and the Emergency Sustainability Index (ESI) increased (0,97). This indicates that the decisions taken in recent years have led to a notable improvement in the efficiency and sustainability of the system. The Emergency value decreased each year, demonstrating that the system was more efficient in 2020 compared to 2018.

## 4 Conclusions

The sustainable development of the department of Cauca requires the planning of efficient agricultural production systems, where the management of resources and respect for nature are considered. For this purpose, the evaluation of the sustainability of a coffee farm was proposed, considering the energy flows that intervene in the processes of the productive chain. For this purpose, a systemic approach was considered, which would allow for an integral characterization of the components that interact in the system, and the multiple energy flows that intervene, such as: sun, wind, rain, soil, fertilizers, equipment, machinery, labor, etc. This modeling of the production chain was carried out by means of operations and flow modeling, process flow diagrams (PFD), using the ISA 88 standard, thus allowing a broad understanding of the coffee system.

After having the inventory of all the energy flows involved in the system, the Emergency method was used, which helps to establish a model that indicates the energy footprint of a product, in this case coffee. Likewise, the tool provides a series of indicators that help to improve the decision-making processes of the coffee producing communities.

The work was carried out in the traditional farm "La Angostura", located in the village of Clarete Bajo, in the city of Popayán, data from the years 2018, 2019, and 2020 were considered. For the three years analyzed, the planting stage is the one that consumes the most resources with approximately 84%. The resources that contribute the most energy are: soil, renewable natural resources (sun, wind, rain, etc.), and human labor; the harvest stage contributes approximately 15% of the energy, here the resource that contributes the most is human labor; and only 1% of the energy is used in the milling stage, with human labor contributing the most.
Approximately 59% of the energy used by the farm comes from nature, of which 23% is from renewable sources and 36% from non-renewable sources. The economy contributes 41% of the energy, of which only 4% is from renewable sources and 37% from non-renewable sources. Energy for 2020 decreased by 0.13E+16 (SeJ) compared to 2018, and 0.3 E+16 (SeJ) compared to 2019, indicating that less energy was required for coffee production on the farm.

This work is an important basis that would help coffee growers in the region to make decisions regarding the management of their farms. Similarly, as a perspective it would be interesting to integrate this type of sustainability evaluation work with environmental management issues and the obtaining of green seals. This last option would be interesting to explore, as it would allow the coffee producing communities to have a tool to "demonstrate" that they are carrying out sustainable processes and continuous improvement within their farms, allowing them to have easier access to this type of seals, also achieving a better positioning in the markets.

## References

1. Federación Nacional de Cafeteros de Colombia, "ESTADISTICAS HISTORICAS," *ESTADISTICAS HISTORICAS - Área cultivada - anual desde 2002*, 2017.

2. Federación Nacional de Cafeteros de Colombia, "Organos gremiales de la Fe-deración de Cafeteros," 2017. https://www.federaciondecafeteros.org/clientes/es/que_hacemos/representacion_gremial/organos_gremiales_de_la_federacion_de_cafeteros/ (accessed Nov. 17, 2017).

3. Gobernación del Cauca, "Plan Departamental de Desarrollo 2020-2023." 2020.

4. J. F. Martin, S. A. W. Diemont, E. Powell, M. Stanton, and S. Levy-tacher, "Emergency evaluation of the performance and sustainability of three agricultural systems with different scales and management," vol. 115, pp. 128-140, 2006, doi: 10.1016/j.agee.2005.12.016.

5. C. M. Rodríguez, C. F. R. Rodas, J. C. C. Muñoz, and A. F. Casas, "A multi-criteria approach for comparison of environmental assessment methods in the analysis of the energy efficiency in agricultural production systems," *J Clean Prod*, vol. 228, pp. 1464-1471, 2019, doi: https://doi.org/10.1016/j.jcle-pro.2019.04.388.

6. M. Giampietro, G. Pastore, and S. Ulgiati, "Agricultura italiana e conceitos desustentabilidade," 2016.

7. M. Giampietro, "Economic growth, human disturbance to ecological systems, and sustainability," *ECOSYSTEMS OF THE WORLD*, pp. 723-746, 1999.

8. T. Clayton and N. J. Radcliffe, *Sustainability: a systems approach*. Routledge, 2015.

9. M. Vegetti and G. P. Henning, "ISA-88 Formalization. A Step Towards its Integration with the ISA-95 Standard.," in *FOMI@ FOIS*, 2014.

10. E. Munoz, E. Capon-Garcia, and L. Puigjaner, "ANSI/ISA 88-95 Standards Based-Approach for Improved Integration of Recipes and Operational Tasks Supported by Knowledge Management," in *Computer Aided Chemical Engineering*, Elsevier, 2017, pp. 2335-2340.

11. M. T. Brown and S. Ulgiati, "Energy quality, emergency, and transformity: H. T. Odum's contributions to quantifying and understanding systems," *Ecological Modelling*, vol. 178, pp. 201-213, 2004, doi: 10.1016/j.ecolmodel.2004.03.002.

12. H. T. Odum, *Environmental accounting: emergency and environmental decisionmaking*. New York: John Wiley and Sons, 1996.

13. G. C. Rótolo and C. Francis, "Los servicios ecosistémicos en el 'corazón' agrí-cola de Argentina," *Ediciones INTA*, vol. 44, 2008.

i. B. F. Giannetti, Y. Ogura, S. H. Bonilla, and C. M. V. B. Almeida, "Accounting emergency flows to determine the best production model of a coffee plantation,"

ii. *Energy Policy*, vol. 39, no. 11, pp. 7399-7407, 2011, doi: 10.1016/j.en-pol.2011.09.005.

14. H. T. Odum, *Environmental accounting*. Wiley, 1996.

15. H. T. Odum and E. P. Odum, "The energetic basis for valuation of ecosystem services," *Ecosystems*, vol. 3, no. 1, pp. 21-23, 2000.
Approximately 59% of the energy used by the farm comes from nature, of which 23% is from renewable sources and 36% from non-renewable sources. The economy contributes 41% of the energy, of which only 4% is from renewable sources and 37% from non-renewable sources. Energy for 2020 decreased by 0.13E+16 (SeJ) compared to 2018, and 0.3 E+16 (SeJ) compared to 2019, indicating that less energy was required for coffee production on the farm.

This work is an important basis that would help coffee growers in the region to make decisions regarding the management of their farms. Similarly, as a perspective it would be interesting to integrate this type of sustainability evaluation work with environmental management issues and the obtaining of green seals. This last option would be interesting to explore, as it would allow the coffee producing communities to have a tool to "demonstrate" that they are carrying out sustainable processes and continuous improvement within their farms, allowing them to have easier access to this type of seals, also achieving a better positioning in the markets.

## References

1. Federación Nacional de Cafeteros de Colombia, "ESTADISTICAS HISTORICAS," *ESTADISTICAS HISTORICAS - Área cultivada - anual desde 2002*, 2017.

2. Federación Nacional de Cafeteros de Colombia, "Organos gremiales de la Fe-deración de Cafeteros," 2017. https://www.federaciondecafeteros.org/clientes/es/que_hacemos/representacion_gremial/organos_gremiales_de_la_federacion_de_cafeteros/ (accessed Nov. 17, 2017).

3. Gobernación del Cauca, "Plan Departamental de Desarrollo 2020-2023." 2020.

4. J. F. Martin, S. A. W. Diemont, E. Powell, M. Stanton, and S. Levy-tacher, "Emergency evaluation of the performance and sustainability of three agricultural systems with different scales and management," vol. 115, pp. 128-140, 2006, doi: 10.1016/j.agee.2005.12.016.

5. C. M. Rodríguez, C. F. R. Rodas, J. C. C. Muñoz, and A. F. Casas, "A multi-criteria approach for comparison of environmental assessment methods in the analysis of the energy efficiency in agricultural production systems," *J Clean Prod*, vol. 228, pp. 1464-1471, 2019, doi: https://doi.org/10.1016/j.jcle-pro.2019.04.388.

6. M. Giampietro, G. Pastore, and S. Ulgiati, "Agricultura italiana e conceitos desustentabilidade," 2016.

7. M. Giampietro, "Economic growth, human disturbance to ecological systems, and sustainability," *ECOSYSTEMS OF THE WORLD*, pp. 723-746, 1999.

8. T. Clayton and N. J. Radcliffe, *Sustainability: a systems approach*. Routledge, 2015.

9. M. Vegetti and G. P. Henning, "ISA-88 Formalization. A Step Towards its Integration with the ISA-95 Standard.," in *FOMI@ FOIS*, 2014.

10. E. Munoz, E. Capon-Garcia, and L. Puigjaner, "ANSI/ISA 88-95 Standards Based-Approach for Improved Integration of Recipes and Operational Tasks Supported by Knowledge Management," in *Computer Aided Chemical Engineering*, Elsevier, 2017, pp. 2335-2340.

11. M. T. Brown and S. Ulgiati, "Energy quality, emergency, and transformity : H. T.

i. Odum's contributions to quantifying and understanding systems," *Ecological Modelling*, vol. 178, pp. 201-213, 2004, doi: 10.1016/j.ecolmodel.2004.03.002.

12. H. T. Odum, *Environmental accounting: emergency and environmental decisionmaking*. New York: John Wiley and Sons, 1996.

13. G. C. Rótolo and C. Francis, "Los servicios ecosistémicos en el 'corazón' agrí-cola de Argentina," *Ediciones INTA*, vol. 44, 2008.

i. B. F. Giannetti, Y. Ogura, S. H. Bonilla, and C. M. V. B. Almeida, "Accounting emergency flows to determine the best production model of a coffee plantation,"

ii. *Energy Policy*, vol. 39, no. 11, pp. 7399-7407, 2011, doi: 10.1016/j.en-pol.2011.09.005.

14. H. T. Odum, *Environmental accounting*. Wiley, 1996.

15. H. T. Odum and E. P. Odum, "The energetic basis for valuation of ecosystem services," *Ecosystems*, vol. 3, no. 1, pp. 21-23, 2000.
16. M. Cuadra and T. Rydberg, "Emergency evaluation on the production, processing and export of coffee in Nicaragua," *Ecol Modell*, vol. 6, no. 96, pp. 421-433, 2006, doi: 10.1016/j.ecolmodel.2006.02.010.

17. C. M. V. B. A. B.F. Giannetti, Y. Ogura, S.H. Bonilla, "Accounting emergency flows to determine the best production model of a coffee plantation," *Energy Policy*, vol. 39, pp. 7399-7407, 2011, doi: 10.1016/j.enpol.2011.09.005.

18. M. Jafari, M. Reza, M. Ramroudi, M. Galavi, and G. Hadarbadi, "Sustainability assessment of date and pistachio agricultural systems using energy, emergency and economic approaches," *J Clean Prod*, vol. 193, pp. 642-651, 2018, doi:10.1016/j.jclepro.2018.05.089.

19. J. Björklund, U. Geber, and T. Rydberg, "Emergency analysis of municipal wastewater treatment and generation of electricity by digestion of sewage sludge," *Resour Conserv Recycl*, vol. 31, no. 4, pp. 293-316, 2001.

20. M. Panzieri, N. Marchettini, and T. G. Hallam, "Importance of the Bradhyrizo-bium japonicum symbiosis for the sustainability of a soybean cultivation," *EcolModell*, vol. 135, no. 2-3, pp. 301-310, 2000.

21. A. A. Buenfil, *Emergency evaluation of water*. University of Florida, 2001.
16. M. Cuadra and T. Rydberg, "Emergency evaluation on the production, processing and export of coffee in Nicaragua," *Ecol Modell*, vol. 6, no. 96, pp. 421-433, 2006, doi: 10.1016/j.ecolmodel.2006.02.010.

17. C. M. V. B. A. B.F. Giannetti, Y. Ogura, S.H. Bonilla, "Accounting emergency flows to determine the best production model of a coffee plantation," *Energy Policy*, vol. 39, pp. 7399-7407, 2011, doi: 10.1016/j.enpol.2011.09.005.

18. M. Jafari, M. Reza, M. Ramroudi, M. Galavi, and G. Hadarbadi, "Sustainability assessment of date and pistachio agricultural systems using energy, emergency and economic approaches," *J Clean Prod*, vol. 193, pp. 642-651, 2018, doi:10.1016/j.jclepro.2018.05.089.

19. J. Björklund, U. Geber, and T. Rydberg, "Emergency analysis of municipal wastewater treatment and generation of electricity by digestion of sewage sludge," *Resour Conserv Recycl*, vol. 31, no. 4, pp. 293-316, 2001.

20. M. Panzieri, N. Marchettini, and T. G. Hallam, "Importance of the Bradhyrizo-bium japonicum symbiosis for the sustainability of a soybean cultivation," *EcolModell*, vol. 135, no. 2-3, pp. 301-310, 2000.

21. A. A. Buenfil, *Emergency evaluation of water*. University of Florida, 2001.
Appendix A. Emergy “La Angostura” farm, Popayán, Cauca, 2018.

<table><thead><tr><th>Item</th><th>Description</th><th>Class</th><th>Annual flow (unit/year ha)</th><th>Emergy per Unit (seJ/unit)</th><th>Emergy (seJ/year ha)</th><th>Refs.</th></tr></thead><tbody><tr><td></td><td>Plantation*</td><td></td><td>2 first years/30 years</td><td>Year of 2018</td><td>Total</td><td></td></tr><tr><td>1</td><td>Solar insolation (J)</td><td>R</td><td>2,72E+12</td><td>4,07E+13</td><td>4,34E+13</td><td>1,00E+00</td><td>4,34E+13</td><td>[15]</td></tr><tr><td>2</td><td>Wind, kinetic energy (J)</td><td>R</td><td>3,54E+06</td><td>5,31E+07</td><td>5,67E+07</td><td>2,52E+03</td><td>1,43E+11</td><td>[15]</td></tr><tr><td>3</td><td>Rain, chemical energy (J)</td><td>R</td><td>4,01E+09</td><td>6,01E+10</td><td>6,41E+10</td><td>3,06E+04</td><td>1,96E+15</td><td>[15]</td></tr><tr><td>4</td><td>Rain, geopotential energy (J)</td><td>R</td><td>8,65E+07</td><td>1,30E+09</td><td>1,38E+09</td><td>1,76E+04</td><td>2,44E+13</td><td>[15]</td></tr><tr><td>5</td><td>Evapotranspiration (J)</td><td>R</td><td>4,52E+09</td><td>6,78E+10</td><td>7,23E+10</td><td>3,98E+04</td><td>2,88E+15</td><td>[16]</td></tr><tr><td>6</td><td>Soil erosion (J)</td><td>N</td><td>6,46E+09</td><td>9,68E+10</td><td>1,03E+11</td><td>7,40E+04</td><td>7,64E+15</td><td>[15]</td></tr><tr><td>7</td><td>Nitrogen (g)</td><td>F</td><td>3,33E+03</td><td>5,00E+04</td><td>5,33E+04</td><td>6,62E+09</td><td>3,53E+14</td><td>[17]</td></tr><tr><td>8</td><td>Phosphate (g)</td><td>F</td><td>6,67E+02</td><td>1,00E+04</td><td>1,07E+04</td><td>9,35E+09</td><td>9,97E+13</td><td>[17]</td></tr><tr><td>9</td><td>Potassium (g)</td><td>F</td><td>4,67E+03</td><td>7,00E+04</td><td>7,47E+04</td><td>9,32E+08</td><td>6,96E+13</td><td>[17]</td></tr><tr><td>10</td><td>Urea (g)</td><td>F</td><td>1,00E+04</td><td>1,50E+05</td><td>1,60E+05</td><td>6,62E+09</td><td>1,06E+15</td><td>[17]</td></tr><tr><td>11</td><td>Cal (g)</td><td>F</td><td>6,67E+02</td><td>1,00E+04</td><td>1,07E+04</td><td>1,68E+09</td><td>1,79E+13</td><td>[18]</td></tr><tr><td>12</td><td>Organic fertilizer (J)</td><td>80% R</td><td>2,00E+03</td><td>6,00E+04</td><td>6,20E+04</td><td>3,87E+09</td><td>2,40E+14</td><td>[19]</td></tr></tbody></table>
Appendix A. Emergy “La Angostura” farm, Popayán, Cauca, 2018.

<table><thead><tr><th>Item</th><th>Description</th><th>Class</th><th>Annual flow (unit/year ha)</th><th>Emergy per Unit (seJ/unit)</th><th>Emergy (seJ/year ha)</th><th>Refs.</th></tr></thead><tbody><tr><td colspan="7">Plantation*</td></tr><tr><td></td><td></td><td>2 first years/30 years</td><td>Year of 2018</td><td>Total</td><td></td><td></td></tr><tr><td>1</td><td>Solar insolation (J)</td><td>R</td><td>2,72E+12</td><td>4,07E+13</td><td>4,34E+13</td><td>1,00E+00</td><td>4,34E+13</td><td>[15]</td></tr><tr><td>2</td><td>Wind, kinetic energy (J)</td><td>R</td><td>3,54E+06</td><td>5,31E+07</td><td>5,67E+07</td><td>2,52E+03</td><td>1,43E+11</td><td>[15]</td></tr><tr><td>3</td><td>Rain, chemical energy (J)</td><td>R</td><td>4,01E+09</td><td>6,01E+10</td><td>6,41E+10</td><td>3,06E+04</td><td>1,96E+15</td><td>[15]</td></tr><tr><td>4</td><td>Rain, geopotential energy (J)</td><td>R</td><td>8,65E+07</td><td>1,30E+09</td><td>1,38E+09</td><td>1,76E+04</td><td>2,44E+13</td><td>[15]</td></tr><tr><td>5</td><td>Evapotranspiration (J)</td><td>R</td><td>4,52E+09</td><td>6,78E+10</td><td>7,23E+10</td><td>3,98E+04</td><td>2,88E+15</td><td>[16]</td></tr><tr><td>6</td><td>Soil erosion (J)</td><td>N</td><td>6,46E+09</td><td>9,68E+10</td><td>1,03E+11</td><td>7,40E+04</td><td>7,64E+15</td><td>[15]</td></tr><tr><td>7</td><td>Nitrogen (g)</td><td>F</td><td>3,33E+03</td><td>5,00E+04</td><td>5,33E+04</td><td>6,62E+09</td><td>3,53E+14</td><td>[17]</td></tr><tr><td>8</td><td>Phosphate (g)</td><td>F</td><td>6,67E+02</td><td>1,00E+04</td><td>1,07E+04</td><td>9,35E+09</td><td>9,97E+13</td><td>[17]</td></tr><tr><td>9</td><td>Potassium (g)</td><td>F</td><td>4,67E+03</td><td>7,00E+04</td><td>7,47E+04</td><td>9,32E+08</td><td>6,96E+13</td><td>[17]</td></tr><tr><td>10</td><td>Urea (g)</td><td>F</td><td>1,00E+04</td><td>1,50E+05</td><td>1,60E+05</td><td>6,62E+09</td><td>1,06E+15</td><td>[17]</td></tr><tr><td>11</td><td>Cal (g)</td><td>F</td><td>6,67E+02</td><td>1,00E+04</td><td>1,07E+04</td><td>1,68E+09</td><td>1,79E+13</td><td>[18]</td></tr><tr><td>12</td><td>Organic fertilizer (J)</td><td>80% R</td><td>2,00E+03</td><td>6,00E+04</td><td>6,20E+04</td><td>3,87E+09</td><td>2,40E+14</td><td>[19]</td></tr></tbody></table>
12

<table><tr><td>13</td><td>Seeds (J)</td><td>F</td><td>3,77E+05</td><td>0,00E+00</td><td>3,77E+05</td><td>5,85E+04</td><td>2,20E+10</td><td>[17]</td></tr><tr><td>14</td><td>Machinery and equipment (g)</td><td>F</td><td>7,33E+01</td><td>1,10E+03</td><td>1,17E+03</td><td>6,70E+09</td><td>7,86E+12</td><td>[20]</td></tr><tr><td>15</td><td>Human Labor (USD)</td><td>10% FR</td><td>1,97E+01</td><td>1,66E+02</td><td>1,85E+02</td><td>2,25E+13</td><td>4,17E+15</td><td>[19]</td></tr><tr><td>16</td><td>Pesticides and fungicides (g)</td><td>F</td><td>6,67E+00</td><td>1,00E+02</td><td>1,07E+02</td><td>1,48E+10</td><td>1,58E+12</td><td>[21]</td></tr><tr><td colspan="4">Total for plantation</td><td colspan="4">1,86E+16</td><td></td></tr></table>

<table><thead><tr><th colspan="2">Harvesting</th><th colspan="4">Annual flow (unit/year ha) - 2018</th></tr></thead><tbody><tr><td>17</td><td>Machinery and equipment (J)</td><td>F</td><td></td><td>1,00E+02</td><td>6,70E+09</td><td>6,70E+11</td><td>[20]</td></tr><tr><td>18</td><td>Fuel and lubricants (J)</td><td>F</td><td></td><td>7,90E+08</td><td>1,11E+05</td><td>8,78E+13</td><td>[18]</td></tr><tr><td>19</td><td>Human Labor (USD)</td><td>10% FR</td><td></td><td>1,46E+02</td><td>2,25E+13</td><td>3,29E+15</td><td>[19]</td></tr><tr><td colspan="4">Total for Harvesting</td><td colspan="4">3,38E+15</td><td></td></tr></tbody></table>

<table><thead><tr><th colspan="6">Drying</th></tr></thead><tbody><tr><td>20</td><td>Solar insolation (J)</td><td>R</td><td></td><td>2,23E+12</td><td>1,00E+00</td><td>2,23E+12</td><td>[15]</td></tr><tr><td>21</td><td>Wind, kinetic energy (J)</td><td>R</td><td></td><td>5,31E+07</td><td>2,52E+03</td><td>1,34E+11</td><td>[15]</td></tr></tbody></table>
12

<table><tr><td>13</td><td>Seeds (J)</td><td>F</td><td>3,77E+05</td><td>0,00E+00</td><td>3,77E+05</td><td>5,85E+04</td><td>2,20E+10</td><td>[17]</td></tr><tr><td>14</td><td>Machinery and equipment (g)</td><td>F</td><td>7,33E+01</td><td>1,10E+03</td><td>1,17E+03</td><td>6,70E+09</td><td>7,86E+12</td><td>[20]</td></tr><tr><td>15</td><td>Human Labor (USD)</td><td>10% FR</td><td>1,97E+01</td><td>1,66E+02</td><td>1,85E+02</td><td>2,25E+13</td><td>4,17E+15</td><td>[19]</td></tr><tr><td>16</td><td>Pesticides and fungicides (g)</td><td>F</td><td>6,67E+00</td><td>1,00E+02</td><td>1,07E+02</td><td>1,48E+10</td><td>1,58E+12</td><td>[21]</td></tr><tr><td colspan="4">Total for plantation</td><td colspan="4">1,86E+16</td><td></td></tr></table>

<table><thead><tr><th colspan="2">Harvesting</th><th colspan="4">Annual flow (unit/year ha) - 2018</th></tr></thead><tbody><tr><td>17</td><td>Machinery and equipment (J)</td><td>F</td><td></td><td>1,00E+02</td><td>6,70E+09</td><td>6,70E+11</td><td>[20]</td></tr><tr><td>18</td><td>Fuel and lubricants (J)</td><td>F</td><td></td><td>7,90E+08</td><td>1,11E+05</td><td>8,78E+13</td><td>[18]</td></tr><tr><td>19</td><td>Human Labor (USD)</td><td>10% FR</td><td></td><td>1,46E+02</td><td>2,25E+13</td><td>3,29E+15</td><td>[19]</td></tr><tr><td colspan="4">Total for Harvesting</td><td colspan="4">3,38E+15</td><td></td></tr></tbody></table>

<table><thead><tr><th colspan="6">Drying</th></tr></thead><tbody><tr><td>20</td><td>Solar insolation (J)</td><td>R</td><td></td><td>2,23E+12</td><td>1,00E+00</td><td>2,23E+12</td><td>[15]</td></tr><tr><td>21</td><td>Wind, kinetic energy (J)</td><td>R</td><td></td><td>5,31E+07</td><td>2,52E+03</td><td>1,34E+11</td><td>[15]</td></tr></tbody></table>
<table><tr><td>22</td><td>Evaporation (g)</td><td>R</td><td>3,72E+06</td><td>1,45E+05</td><td>5,39E+11</td><td>[22]</td></tr><tr><td>23</td><td>Water (J)</td><td>R</td><td>1,78E+07</td><td>8,60E+04</td><td>1,53E+12</td><td>[15]</td></tr><tr><td>24</td><td>Drying yard or “paseras” (g)</td><td>F</td><td>7,06E-01</td><td>2,65E+13</td><td>1,87E+13</td><td>[17]</td></tr><tr><td>25</td><td>Machinery and equipment (USD)</td><td>F</td><td>2,23E+12</td><td>1,00E+00</td><td>2,23E+12</td><td>[17]</td></tr><tr><td>26</td><td>Human Labor (USD)</td><td>10% FR</td><td>5,66E+00</td><td>2,25E+13</td><td>1,27E+14</td><td>[19]</td></tr><tr><td>27</td><td>Buildings (USD)</td><td>F</td><td>5,08E-01</td><td>2,65E+13</td><td>1,35E+13</td><td>[18]</td></tr><tr><td>28</td><td>Electricity (USD)</td><td>F</td><td>3,44E-01</td><td>2,65E+13</td><td>9,12E+12</td><td>[17]</td></tr><tr><td>29</td><td>Sacos Jute (USD)</td><td>F</td><td>1,00E+02</td><td>2,31E+10</td><td>2,31E+12</td><td>[17]</td></tr><tr><td colspan="4">Total for Drying</td><td colspan="3">1,75E+14</td></tr><tr><td colspan="4">(Y) Total Emergy</td><td colspan="3">2,21E+16 Calculated in this study</td></tr></table>

<footnote>*The planting stage was carried out in 2013, where the energy flows are applied (items 1-16), however, this is an investment that is being made in the long term, in this study, and according to the indications of experts in coffee production, it is estimated that the crop can have a productive life of 30 years (Arcila et al., 2007b). For this reason, it is necessary to divide the energy applied in the first 2 years (where the crop had no production), between the 30 years of useful life. Similarly, each year (year analyzed in the 2018 table), it is necessary to perform the maintenance of the crop where energy flows must be added (items 1-16, except 13 seeds). This energy applied during the first 2 years, which is a long-term investment, is only done for the plantation stage.</footnote>
<table><tbody><tr><td>22</td><td>Evaporation (g)</td><td>R</td><td>3,72E+06</td><td>1,45E+05</td><td>5,39E+11</td><td>[22]</td></tr><tr><td>23</td><td>Water (J)</td><td>R</td><td>1,78E+07</td><td>8,60E+04</td><td>1,53E+12</td><td>[15]</td></tr><tr><td>24</td><td>Drying yard or “paseras” (g)</td><td>F</td><td>7,06E-01</td><td>2,65E+13</td><td>1,87E+13</td><td>[17]</td></tr><tr><td>25</td><td>Machinery and equipment (USD)</td><td>F</td><td>2,23E+12</td><td>1,00E+00</td><td>2,23E+12</td><td>[17]</td></tr><tr><td>26</td><td>Human Labor (USD)</td><td>10% FR</td><td>5,66E+00</td><td>2,25E+13</td><td>1,27E+14</td><td>[19]</td></tr><tr><td>27</td><td>Buildings (USD)</td><td>F</td><td>5,08E-01</td><td>2,65E+13</td><td>1,35E+13</td><td>[18]</td></tr><tr><td>28</td><td>Electricity (USD)</td><td>F</td><td>3,44E-01</td><td>2,65E+13</td><td>9,12E+12</td><td>[17]</td></tr><tr><td>29</td><td>Sacos Jute (USD)</td><td>F</td><td>1,00E+02</td><td>2,31E+10</td><td>2,31E+12</td><td>[17]</td></tr><tr><td colspan="4">Total for Drying</td><td colspan="3">1,75E+14</td></tr><tr><td colspan="4">(Y) Total Emergy</td><td colspan="3">2,21E+16 Calculated in this study</td></tr></tbody></table>

<footnote>*The planting stage was carried out in 2013, where the energy flows are applied (items 1-16), however, this is an investment that is being made in the long term, in this study, and according to the indications of experts in coffee production, it is estimated that the crop can have a productive life of 30 years (Arcila et al., 2007b). For this reason, it is necessary to divide the energy applied in the first 2 years (where the crop had no production), between the 30 years of useful life. Similarly, each year (year analyzed in the 2018 table), it is necessary to perform the maintenance of the crop where energy flows must be added (items 1-16, except 13 seeds). This energy applied during the first 2 years, which is a long-term investment, is only done for the plantation stage.</footnote>
# Un sistema de recomendación para la reutilización de fármacos utilizando embeddings sobre Grafos de Conocimiento

Diego López Yse¹ and Diego Torres¹,²,³

¹ Universidad Austral, Buenos Aires, Argentina

² LIFIA, CIBPBA-Facultad de Informática, UNLP, La Plata, Argentina

³ Depto. CyT., Universidad Nacional de Quilmes, Bernal, Argentina

**Abstract.** El proceso de desarrollo tradicional de nuevos fármacos es un camino largo, ineficiente y costoso. Afortunadamente, la reutilización de fármacos representa una muy buena alternativa para superar las limitaciones de los métodos de desarrollo tradicionales. En este sentido, los métodos computaciones de Grafos de Conocimiento y su representación vectorial (embeddings) se han utilizado en el campo biomédico para la reutilización de farmacos con gran efectividad, mejorando los resultados dos producidos por otras técnicas tradicionales de Machine Learning. El presente trabajo propone un esquema para desarrollar un sistema de recomendación para la reutilización de fármacos utilizando embeddings sobre Grafos de Conocimiento, con la flexibilidad suficiente para cen- trarse en múltiples enfermedades e incorporar otros tipos de modelos predictivos para identificar a los mejores.

**Keywords:** Machine Learning · Grafos de Conocimiento · Biotecnología

## 1 Introducción

El proceso de desarrollo tradicional de nuevos fármacos es un camino largo y costoso, que se estima toma en promedio 14 años y cuesta aproximadamente USD 1.8 mil millones para desarrollar un medicamento [7]. Afortunadamente existen otros métodos más aptos para enfrentar la velocidad que nos plantea la biología sobre las enfermedades. La reutilización de fármacos es el proceso de identificación de indicaciones novedosas para fármacos existentes, y representa una muy buena alternativa para superar las limitaciones de los métodos de desarrollo tradicionales, reduciendo drásticamente los plazos, las tasas de fracaso y los costos de desarrollo.

Los enfoques principales para la reutilización de fármacos se clasifican en métodos experimentales y computacionales [4], y el éxito de la reutilización está sujeto a cómo se combinan estos dos enfoques en colaboración.

En particular, los Grafo de Conocimiento se han utilizado en el campo biomédico para la reutilización de fármacos y otras actividades como la pri- orización de genes relevantes en enfermedades [5]. La combinación de Grafos de Conocimiento y su representación vectorial (embeddings) propone una visión novedosa para enfrentar el desafío de reutilización de fármaco al establecer nodos y relaciones entre los datos en contraposición a las clásicas bases de información estructuradas, y análisis en espacios vectorizados en lugar de clásicos modelados multidimensionales.
# Un sistema de recomendación para la reutilización de fármacos utilizando embeddings sobre Grafos de Conocimiento

Diego López Yse¹ and Diego Torres¹,²,³

¹ Universidad Austral, Buenos Aires, Argentina

² LIFIA, CIBPBA-Facultad de Informática, UNLP, La Plata, Argentina

³ Depto. CyT., Universidad Nacional de Quilmes, Bernal, Argentina

**Abstract.** El proceso de desarrollo tradicional de nuevos fármacos es un camino largo, ineficiente y costoso. Afortunadamente, la reutilización de fármacos representa una muy buena alternativa para superar las limitaciones de los métodos de desarrollo tradicionales. En este sentido, los métodos computaciones de Grafos de Conocimiento y su representación vectorial (embeddings) se han utilizado en el campo biomédico para la reutilización de farmacos con gran efectividad, mejorando los resultados dos producidos por otras técnicas tradicionales de Machine Learning. El presente trabajo propone un esquema para desarrollar un sistema de recomendación para la reutilización de fármacos utilizando embeddings sobre Grafos de Conocimiento, con la flexibilidad suficiente para cen- trarse en múltiples enfermedades e incorporar otros tipos de modelos predictivos para identificar a los mejores.

**Keywords:** Machine Learning · Grafos de Conocimiento · Biotecnología

## 1 Introducción

El proceso de desarrollo tradicional de nuevos fármacos es un camino largo y costoso, que se estima toma en promedio 14 años y cuesta aproximadamente USD 1.8 mil millones para desarrollar un medicamento [7]. Afortunadamente existen otros métodos más aptos para enfrentar la velocidad que nos plantea la biología sobre las enfermedades. La reutilización de fármacos es el proceso de identificación de indicaciones novedosas para fármacos existentes, y representa una muy buena alternativa para superar las limitaciones de los métodos de desarrollo tradicionales, reduciendo drásticamente los plazos, las tasas de fracaso y los costos de desarrollo.

Los enfoques principales para la reutilización de fármacos se clasifican en métodos experimentales y computacionales [4], y el éxito de la reutilización está sujeto a cómo se combinan estos dos enfoques en colaboración.

En particular, los Grafo de Conocimiento se han utilizado en el campo biomédico para la reutilización de fármacos y otras actividades como la pri- orización de genes relevantes en enfermedades [5]. La combinación de Grafos de Conocimiento y su representación vectorial (embeddings) propone una visión novedosa para enfrentar el desafío de reutilización de fármaco al establecer nodos y relaciones entre los datos en contraposición a las clásicas bases de información estructuradas, y análisis en espacios vectorizados en lugar de clásicos modelados multidimensionales.
## 2 Trabajos Relacionados

Los enfoques computacionales para la reutilización de fármacos han ganando popularidad debido a la disponibilidad de grandes datos biológicos. Un gran número de trabajos basados en métodos de Machine Learning aprovechan de- scriptores construidos manualmente para realizar predicciones (ej., propiedades moleculares), con el fin de identificar posibles fármacos candidatos para ensayos clínicos posteriores [6].

Pero existen métodos más efectivos para representar información biomédica, y trabajos actuales se han centrado en utilizar técnicas de embeddings sobre Grafos de Conocimiento [2], mapeando la información a un espacio vectorial continuo, y preservando la estructura de proximidad del gráfico de conocimiento para ejecutar algoritmos de Machine Learning.

## 3 Problemática

El proceso de desarrollo de nuevos fármacos es una tarea compleja que requiere el conocimiento de numerosos dominios biológicos y químicos. Posiblemente el mayor obstáculo en este proceso se relaciona con la toxicidad, dando como re- sultado numerosos compuestos que fallan en las últimas etapas de los ensayos clínicos o que se retiran del mercado [1]. De esta forma, la reutilización de fármacos es una posible alternativa a este problema, acelerando el descubrim- iento de nuevas aplicaciones cuando los perfiles de seguridad de los medicamentos que se reutilizan se han evaluado en el contexto del desarrollo de medicamentos para otra enfermedad [3].

La reutilización de fármacos no solo conduce a menores gastos y acelera el desarrollo, sino que también agrega conocimiento sobre los efectos secundarios, la farmacocinética y las interacciones farmacológicas de los fármacos en cuestión [4].

## 4 Contexto

Para enfrentar el desafío de reutilización de fármacos es posible utilizar distintas técnicas y representaciones de datos biológicos. En este sentido, las técnicas de Machine Learning que utilizan descriptores moleculares son populares y suelen alcanzar niveles aceptables de performance. Pero es posible expandir el espacio exploración de soluciones utilizando técnicas de embeddings sobre Grafos de Conocimiento que vinculen el amplio universo de datos biológicos disponibles (ej., genes o efectos secundarios relacionados a fármacos).

Por esta razón, el presente trabajo propone un esquema para desarrollar un sistema de recomendación para la reutilización de fármacos, integrando datos biológicos y distintos modelos predictivos basados en embeddings sobre Grafos de Conocimiento para lograr resultados con mayores probabilidades de éxito.
## 2 Trabajos Relacionados

Los enfoques computacionales para la reutilización de fármacos han ganando popularidad debido a la disponibilidad de grandes datos biológicos. Un gran número de trabajos basados en métodos de Machine Learning aprovechan de- scriptores construidos manualmente para realizar predicciones (ej., propiedades moleculares), con el fin de identificar posibles fármacos candidatos para ensayos clínicos posteriores [6].

Pero existen métodos más efectivos para representar información biomédica, y trabajos actuales se han centrado en utilizar técnicas de embeddings sobre Grafos de Conocimiento [2], mapeando la información a un espacio vectorial continuo, y preservando la estructura de proximidad del gráfico de conocimiento para ejecutar algoritmos de Machine Learning.

## 3 Problemática

El proceso de desarrollo de nuevos fármacos es una tarea compleja que requiere el conocimiento de numerosos dominios biológicos y químicos. Posiblemente el mayor obstáculo en este proceso se relaciona con la toxicidad, dando como re- sultado numerosos compuestos que fallan en las últimas etapas de los ensayos clínicos o que se retiran del mercado [1]. De esta forma, la reutilización de fármacos es una posible alternativa a este problema, acelerando el descubrim- iento de nuevas aplicaciones cuando los perfiles de seguridad de los medicamentos que se reutilizan se han evaluado en el contexto del desarrollo de medicamentos para otra enfermedad [3].

La reutilización de fármacos no solo conduce a menores gastos y acelera el desarrollo, sino que también agrega conocimiento sobre los efectos secundarios, la farmacocinética y las interacciones farmacológicas de los fármacos en cuestión [4].

## 4 Contexto

Para enfrentar el desafío de reutilización de fármacos es posible utilizar distintas técnicas y representaciones de datos biológicos. En este sentido, las técnicas de Machine Learning que utilizan descriptores moleculares son populares y suelen alcanzar niveles aceptables de performance. Pero es posible expandir el espacio exploración de soluciones utilizando técnicas de embeddings sobre Grafos de Conocimiento que vinculen el amplio universo de datos biológicos disponibles (ej., genes o efectos secundarios relacionados a fármacos).

Por esta razón, el presente trabajo propone un esquema para desarrollar un sistema de recomendación para la reutilización de fármacos, integrando datos biológicos y distintos modelos predictivos basados en embeddings sobre Grafos de Conocimiento para lograr resultados con mayores probabilidades de éxito.
References

1. Barratt, M.J., Frail, D. (eds.): Drug repositioning: bringing new life to shelved assets and existing drugs. John Wiley & Sons, Hoboken, N. J (2012)

2. Florin Ratajczak, et al.: Task-driven knowledge graph filtering im-proves prioritizing drugs for repurposing. BMC Bioinformatics **23**(84) (2022).
https://doi.org/10.1186/s12859-022-04608-y,
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04608-y

3. Li, X., Yu, J., Zhang, Z., Ren, J., Peluffo, A.E., Zhang, W., Zhao, Y., Wu, J., Yan, K., Cohen, D., Wang, W.: Network bioinformatics analysis provides insight into drug repurposing for COVID-19. Medicine in Drug Discovery **10**, 100090 (Jun 2021). elsevier.com/retrieve/pii/S2590098621000117

4. Malik, J.A., Ahmed, S., Jan, B., Bender, O., Al Hagbani, T., Alqarni, A., An-war, S.: Drugs repurposed: An advanced step towards the treatment of breast cancer and associated challenges. Biomedicine & Pharmacotherapy **145**, 112375 https://linkinghub.(Jan 2022). https://doi.org/10.1016/j.biopha.2021.112375, https://linkinghub.elsevier.com/retrieve/pii/S0753332221011598

5. Nicholson, D.N., Greene, C.S.: Constructing knowledge graphs and their biomedical applications. Computational and Structural Biotechnology Journal **18**, 1414–1428 (2020). https://doi.org/10.1016/j.csbj.2020.05.017, https://linkinghub.elsevier.com/retrieve/pii/S2001037020302804

6. Pan, X., Lin, X., Cao, D., Zeng, X., Yu, P.S., He, L., Nussinov, R., Cheng, F.: Deep learning for drug repurposing: methods, databases, and applications (2022).

7. Sang, S., Yang, Z., Liu, X., Wang, L., Lin, H., Wang, J., Dumontier, M.: GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures. IEEE Access **7**, 8404–8415 (2019).
https://doi.org/10.1109/ACCESS.2018.2886311, https://ieeexplore.ieee.org/document/8574025/
References

1. Barratt, M.J., Frail, D. (eds.): Drug repositioning: bringing new life to shelved assets and existing drugs. John Wiley & Sons, Hoboken, N. J (2012)

2. Florin Ratajczak, et al.: Task-driven knowledge graph filtering im-proves prioritizing drugs for repurposing. BMC Bioinformatics **23**(84) (2022).
https://doi.org/https://doi.org/10.1186/s12859-022-04608-y,
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04608-y

3. Li, X., Yu, J., Zhang, Z., Ren, J., Peluffo, A.E., Zhang, W., Zhao, Y., Wu, J., Yan, K., Cohen, D., Wang, W.: Network bioinformatics analysis provides insight into drug repurposing for COVID-19. Medicine in Drug Discovery **10**, 100090 (Jun 2021). elsevier.com/retrieve/pii/S2590098621000117

4. Malik, J.A., Ahmed, S., Jan, B., Bender, O., Al Hagbani, T., Alqarni, A., An-war, S.: Drugs repurposed: An advanced step towards the treatment of breast cancer and associated challenges. Biomedicine & Pharmacotherapy **145**, 112375 https://linkinghub.(Jan 2022). https://doi.org/10.1016/j.biopha.2021.112375, https://linkinghub.elsevier.com/retrieve/pii/S0753332221011598

5. Nicholson, D.N., Greene, C.S.: Constructing knowledge graphs and their biomedical applications. Computational and Structural Biotechnology Journal **18**, 1414–1428 (2020). https://doi.org/10.1016/j.csbj.2020.05.017, https://linkinghub.elsevier.com/retrieve/pii/S2001037020302804

6. Pan, X., Lin, X., Cao, D., Zeng, X., Yu, P.S., He, L., Nussinov, R., Cheng, F.: Deep learning for drug repurposing: methods, databases, and applications (2022).

7. Sang, S., Yang, Z., Liu, X., Wang, L., Lin, H., Wang, J., Dumontier, M.: GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures. IEEE Access **7**, 8404–8415 (2019).
https://doi.org/10.1109/ACCESS.2018.2886311, https://ieeexplore.ieee.org/document/8574025/
# Collaborative Framework for the Implementation of Digital Transformation Processes in Higher Education
## Institutions: Case Study Unicomfacauca

Alex Armando Torres Bermúdez¹

¹ Corporación Universitaria Comfacauca-Unicomfacauca, Popayán, Colombiaatorres@unicomfacauca.edu.co

**Abstract.** The purpose of this Doctoral Research Project is to present a collaborative work frame-work to support the implementation of digital transformation processes in higher education insti-tutions in order to improve the efficiency of mission processes; the case study is the Corporación Universitaria Comfacauca for the implementation of digital transformation processes and the val-idation of the proposed framework in the mission research process. Through a systematic reviewof the literature, it became evident that there are few frameworks validated in higher education scenarios to implement Digital Transformation processes, mediated by a collaborative work en-vironment to contribute to the continuous improvement of the mission processes. The proposal focuses on a projective analytical research methodology, the method of quantitative action re-search type planned, makes it possible to analyze the data of a population in 7 institutions of higher education in the city of Popayan, department of Cauca (Colombia).

**Key words:** Digital Transformation, Digitalization, Framework.

## 1 Introduction

Digital transformation is emerging as a topic of interest not only in scientific commu-nities, but also as an increasingly recurrent practice in organizational dynamics, whichare subject to the pressures imposed by digital technologies, new business models that are manifested throughout the value chain, and the personalized demands of users in anenvironment of hyper-connectivity [1]. More than a few authors have tried to explore the concept of digital transformation through three main areas of change: consumer behavior, business processes and business models [2], [3], [4]. From the literature re-view, the concept of digital transformation is defined as an evolutionary process of change in the organizational culture that is governed in an environment of hypercon- nectivity with collaborative principles for the digitization of processes in all activities of the value chain, which is enabled by the adoption of digital technologies and impactson management processes, business models, value creation, efficiency and operationalperformance; feasible to implement from frameworks and can be evaluated through maturity models and key performance indicators. In addition to this, research agrees that the education sector is one of the last industries that has initiated changes in a digitalculture because it is adhering to old methods and practices, the education sector is no exception [5].

## 2 State of the art

Regarding models and frameworks for digital transformation, the systematic literaturereview [6] showed that there are hardly any contributions of systematic methods as a research methodology on Digital Transformation, while it is more frequent to find con-ceptual models, frameworks and strategies; and much more frequent to find case studiesfocused on very specific areas. In addition, as a result of academic interest, numerous digital transformation frameworks have been published to address different sectors andtheir needs [7]. Despite the identified frameworks, however, there are few validated frameworks in higher education scenarios to implement Digital Transformation pro-cesses, mediated by a collaborative work environment to contribute to the improvementof missional processes, as mentioned by [8], there is a shortage of practical, imple- mentable and simple digital transformation models that combine technologies, systems and educational phenomena. This theoretical gap is therefore evidence that it remains unclear what type of sustainable digital transformation model(s) could be adopted [8]. According to the literature, it can be established that there are digital transformation frameworks or models for different sectors or areas of industry from a conceptual pointof view, with very little evidence of practical, implementable frameworks with achieved results, which indicates a knowledge gap as a state of solution in the educationsector.
# Collaborative Framework for the Implementation of Digital Transformation Processes in Higher Education
## Institutions: Case Study Unicomfacauca

Alex Armando Torres Bermúdez¹

¹ Corporación Universitaria Comfacauca-Unicomfacauca, Popayán, Colombiaatorres@unicomfacauca.edu.co

**Abstract.** The purpose of this Doctoral Research Project is to present a collaborative work frame-work to support the implementation of digital transformation processes in higher education insti-tutions in order to improve the efficiency of mission processes; the case study is the Corporación Universitaria Comfacauca for the implementation of digital transformation processes and the val-idation of the proposed framework in the mission research process. Through a systematic reviewof the literature, it became evident that there are few frameworks validated in higher education scenarios to implement Digital Transformation processes, mediated by a collaborative work en-vironment to contribute to the continuous improvement of the mission processes. The proposal focuses on a projective analytical research methodology, the method of quantitative action re-search type planned, makes it possible to analyze the data of a population in 7 institutions of higher education in the city of Popayan, department of Cauca (Colombia).

**Key words:** Digital Transformation, Digitalization, Framework.

## 1 Introduction

Digital transformation is emerging as a topic of interest not only in scientific commu-nities, but also as an increasingly recurrent practice in organizational dynamics, whichare subject to the pressures imposed by digital technologies, new business models that are manifested throughout the value chain, and the personalized demands of users in anenvironment of hyper-connectivity [1]. More than a few authors have tried to explore the concept of digital transformation through three main areas of change: consumer behavior, business processes and business models [2], [3], [4]. From the literature re-view, the concept of digital transformation is defined as an evolutionary process of change in the organizational culture that is governed in an environment of hypercon- nectivity with collaborative principles for the digitization of processes in all activities of the value chain, which is enabled by the adoption of digital technologies and impactson management processes, business models, value creation, efficiency and operationalperformance; feasible to implement from frameworks and can be evaluated through maturity models and key performance indicators. In addition to this, research agrees that the education sector is one of the last industries that has initiated changes in a digitalculture because it is adhering to old methods and practices, the education sector is no exception [5].

## 2 State of the art

Regarding models and frameworks for digital transformation, the systematic literaturereview [6] showed that there are hardly any contributions of systematic methods as a research methodology on Digital Transformation, while it is more frequent to find con-ceptual models, frameworks and strategies; and much more frequent to find case studiesfocused on very specific areas. In addition, as a result of academic interest, numerous digital transformation frameworks have been published to address different sectors andtheir needs [7]. Despite the identified frameworks, however, there are few validated frameworks in higher education scenarios to implement Digital Transformation pro-cesses, mediated by a collaborative work environment to contribute to the improvementof missional processes, as mentioned by [8], there is a shortage of practical, imple- mentable and simple digital transformation models that combine technologies, systems and educational phenomena. This theoretical gap is therefore evidence that it remains unclear what type of sustainable digital transformation model(s) could be adopted [8]. According to the literature, it can be established that there are digital transformation frameworks or models for different sectors or areas of industry from a conceptual pointof view, with very little evidence of practical, implementable frameworks with achieved results, which indicates a knowledge gap as a state of solution in the educationsector.
## 3 Problem statement and contributions

At present, there is little evidence of validated frameworks in higher education scenarios to implement digital transformation processes in higher education institutions that contribute to the efficiency of mission processes mediated by a collaborative environment [4, 8]. Similarly, [9] recognize that digital transformation is part of this change faced by all types of organizations. In this context, customer demands have changed [10], demanding customized requirements [10]. For this, organizations will have to respond quickly, with the support of collaborative work [11]. This indicates that another factor of Digital Transformation is Collaborative Work. For [1], end-to-end collaboration throughout the value chain is one of the key principles of digital transformation. Considering the aforementioned knowledge problems, the research question oriented to the development of this project arises: How to implement a collaborative work framework of digital transformation to improve the efficiency of the missional processes in a higher education institution?

## 4 Methodology and research approach

In order to carry out the systematic literature review, the procedure defined by Petersen et al. [12, 13] was followed; however, it is complemented by the research procedures proposed by Hoyos Botero [14]. For the development of the proposal, it focuses on a projective analytical research methodology [15], the quantitative action research method proposed [16], makes it possible to analyze the data of a population in 7 instances of higher education in the city of Popayán, department of Cauca (Colombia). Asample of one (1) HEI (UNICOMFACAUCA) is extracted where a pre- and post-test will be conducted to know the level of adoption of the digital transformation processes and then the implementation of the process mediated by the technological tool "MCTD"- Collaborative Framework for the Implementation of Digital Transformation Processes, the above conformed from the quantitative research approaches.

## 5 Evaluation plan

The proposed framework will be assessed by validating its efficiency in relation to the contribution that will be provided in the research mission process at the Corporación Universitaria Comfacauca - UNICOMFACACUA, specifically the research process, allowing it to be a reference framework for higher education institutions in everything related to the current state, maturity level and implementation of digital transformation processes. For this, the evaluation plan will carry out: a) evaluation of collaborative work framework components, b) design of metrics and heuristic evaluation instruments for measuring the usability and interaction of the tool, c) validation of framework usability through framework design experts and software developers, d) verification of the impact of the proposed framework design and refinement of elaborated models, e) elaboration of general document and technical support documents for the project.

## 6 Preliminary or intermediate results

There is a characterization of the different models and/or frameworks of digital trans- formation obtained from the respective literature review, 41 digital transformation frameworks for different industry sectors [7], which supports the knowledge gaps de- scribed in section 2.

## 7 Conclusions and Lessons Learned

There is a lot of literature related to the topic of digital transformation for organizations, facing this topic, there are conceptual, maturity and some exclusive models or frame- works with the name of digital transformation models or frameworks, however, there is no evidence of results of implementation of digital transformation processes in the higher education sector in a specific area. A framework is understood as a standardized set of concepts, practices, guidelines and criteria to approach a particular type of prob-lem, which serves as a reference in a specific domain and can be used to address or solve new problems of a similar nature, which may serve as a basis for the implemen- tation of transformation processes in the education sector.

## 8 Doctor. Stage: Early.
## 3 Problem statement and contributions

At present, there is little evidence of validated frameworks in higher education scenarios to implement digital transformation processes in higher education institutions that contribute to the efficiency of mission processes mediated by a collaborative environment [4, 8]. Similarly, [9] recognize that digital transformation is part of this change faced by all types of organizations. In this context, customer demands have changed [10], demanding customized requirements [10]. For this, organizations will have to respond quickly, with the support of collaborative work [11]. This indicates that another factor of Digital Transformation is Collaborative Work. For [1], end-to-end collaboration throughout the value chain is one of the key principles of digital transformation. Considering the aforementioned knowledge problems, the research question oriented to the development of this project arises: How to implement a collaborative work framework of digital transformation to improve the efficiency of the missional processes in a higher education institution?

## 4 Methodology and research approach

In order to carry out the systematic literature review, the procedure defined by Petersen et al. [12, 13] was followed; however, it is complemented by the research procedures proposed by Hoyos Botero [14]. For the development of the proposal, it focuses on a projective analytical research methodology [15], the quantitative action research method proposed [16], makes it possible to analyze the data of a population in 7 instances of higher education in the city of Popayán, department of Cauca (Colombia). Asample of one (1) HEI (UNICMFACAUCA) is extracted where a pre- and post-test will be conducted to know the level of adoption of the digital transformation processes and then the implementation of the process mediated by the technological tool "MCTD"- Collaborative Framework for the Implementation of Digital Transformation Processes, the above conformed from the quantitative research approaches.

## 5 Evaluation plan

The proposed framework will be assessed by validating its efficiency in relation to the contribution that will be provided in the research mission process at the Corporación Universitaria Comfacauca - UNICMFACACUA, specifically the research process, allowing it to be a reference framework for higher education institutions in everything related to the current state, maturity level and implementation of digital transformation processes. For this, the evaluation plan will carry out: a) evaluation of collaborative work framework components, b) design of metrics and heuristic evaluation instruments for measuring the usability and interaction of the tool, c) validation of framework usability through framework design experts and software developers, d) verification of the impact of the proposed framework design and refinement of elaborated models, e) elaboration of general document and technical support documents for the project.

## 6 Preliminary or intermediate results

There is a characterization of the different models and/or frameworks of digital trans- formation obtained from the respective literature review, 41 digital transformation frameworks for different industry sectors [7], which supports the knowledge gaps de- scribed in section 2.

## 7 Conclusions and Lessons Learned

There is a lot of literature related to the topic of digital transformation for organizations, facing this topic, there are conceptual, maturity and some exclusive models or frame- works with the name of digital transformation models or frameworks, however, there is no evidence of results of implementation of digital transformation processes in the higher education sector in a specific area. A framework is understood as a standardized set of concepts, practices, guidelines and criteria to approach a particular type of prob-lem, which serves as a reference in a specific domain and can be used to address or solve new problems of a similar nature, which may serve as a basis for the implemen- tation of transformation processes in the education sector.

## 8 Doctor. Stage: Early.
References

1. T. Delgado Fernández, “Taxonomía de transformación digital,” 2020. [Online]. Available: https://orcid.org/0000-0002-4323-9674

2. K. Schwertner, “Digital transformation of business,” *Trakia J. Sci.*, vol. 15, no. Suppl.1, pp. 388–393, 2017, doi: 10.15547/tjs.2017.s.01.065.

3. A. Bockshecker, S. Hackstein, and U. Baumöl, “Systematization of the term digital transformation and its phenomena from a socio-technical perspective – A literature review,” 2018.

4. M. H. Ismail, M. Khater, and M. Zaki, “Digital Business Transformation and Strategy: What Do We Know So Far? University of Cambridge,” *J. Univ. Cambridge*, no. November 2017, 2018, [Online]. Available: www.cambridgeservicealliance.org

5. K. Kerroum, A. Khiat, A. Bahnasse, E. S. Aoula, and Y. Khiat, “The proposal of an agile model for the digital transformation of the University Hassan II of Casablanca 4.0,” *Procedia Comput. Sci.*, vol. 175, pp. 403–410, 2020, doi: 10.1016/j.procs.2020.07.057.

6. C. Gebayew, I. R. Hardini, G. H. A. Panjaitan, N. B. Kurniawan, and Suhardi, “A Systematic Literature Review on Digital Transformation,” 2018 *Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2018* - *Proc.*, pp. 260–265, 2018, doi: 10.1109/ICITSI.2018.8695912.

7. Z. Van Veldhoven and J. Vanthienen, “Digital transformation as an interaction-driven perspective between business, society, and technology,” *Electron. Mark.*, 2021, doi: 10.1007/s12525-021-00464-5.

8. M. A. Mohamed Hashim, I. Tlemsani, and R. Duncan Matthews, “A sustainable University: Digital Transformation and Beyond,” *Educ. Inf. Technol.*, 2022, doi: 10.1007/s10639-022-10968-y.

9. “Taxonomía de Transformación Digital | Revista Cubana de Transformación Digital.” https://rctd.uic.cu/rctd/article/view/62 (accessed Jun. 20, 2021).

10. R. Morakanyane, A. Grace, and P. O’Reilly, “Conceptualizing digital transformation in business organizations: A systematic review of literature,” *30th Bled eConference Digit. Transform. - From Connect. Things to Transform. our Lives, BLED 2017*, pp. 427–444, 2017, doi: 10.18690/978-961-286-043-1.30.

11. C. P. Lopez, J. Aguilar, and M. Santorum, “Autonomous VOs management based on industry 4.0: a systematic literature review,” *J. Intell. Manuf.*, 2021, doi: 10.1007/s10845-021-01850-8.

12. K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” *12th Int. Conf. Eval. Assess. Softw. Eng. EASE 2008*, pp. 1–10, 2008, doi: 10.14236/ewic/ease2008.8.

13. K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” *Inf. Softw. Technol.*, vol. 64, pp. 1–18, 2015, doi: 10.1016/j.infsof.2015.03.007.

14. C. H. Botero, *Un modelo para investigación documental: guia teórico-práctica sobre construcción de estados del arte con importantes reflexiones sobre la investigación*. Señal Editora, 2000. [Online]. Available: https://books.google.com.co/books?id=Wa3PAQAACAAJ

15. M. N. Córdoba and C. Monsalve, “Tipos de investigación, predictiva, interactiva, confirmatoria y evaluativa,”

i. *Fund. Sypal*, pp. 139–140, 2008.

16. R. Hernández Sampieri, C. Fernández Collado, and M. del P. Baptista Lucio, *Metodología de la Investigación*. Mexico: McGRAW-HILL / INTERAMERICANA EDITORES, S.A. DE C.V., 2005.
References

1. T. Delgado Fernández, “Taxonomía de transformación digital,” 2020. [Online]. Available: https://orcid.org/0000-0002-4323-9674

2. K. Schwertner, “Digital transformation of business,” *Trakia J. Sci.*, vol. 15, no. Suppl.1, pp. 388–393, 2017, doi: 10.15547/tjs.2017.s.01.065.

3. A. Bockshecker, S. Hackstein, and U. Baumöl, “Systematization of the term digital transformation and its phenomena from a socio-technical perspective – A literature review,” 2018.

4. M. H. Ismail, M. Khater, and M. Zaki, “Digital Business Transformation and Strategy: What Do We Know So Far? University of Cambridge,” *J. Univ. Cambridge*, no. November 2017, 2018, [Online]. Available: www.cambridgeservicealliance.org

5. K. Kerroum, A. Khiat, A. Bahnasse, E. S. Aoula, and Y. Khiat, “The proposal of an agile model for the digital transformation of the University Hassan II of Casablanca 4.0,” *Procedia Comput. Sci.*, vol. 175, pp. 403–410, 2020, doi: 10.1016/j.procs.2020.07.057.

6. C. Gebayew, I. R. Hardini, G. H. A. Panjaitan, N. B. Kurniawan, and Suhardi, “A Systematic Literature Review on Digital Transformation,” 2018 *Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2018 Proc.*, pp. 260–265, 2018, doi: 10.1109/ICITSI.2018.8695912.

7. Z. Van Veldhoven and J. Vanthienen, “Digital transformation as an interaction-driven perspective between business, society, and technology,” *Electron. Mark.*, 2021, doi: 10.1007/s12525-021-00464-5.

8. M. A. Mohamed Hashim, I. Tlemsani, and R. Duncan Matthews, “A sustainable University: Digital Transformation and Beyond,” *Educ. Inf. Technol.*, 2022, doi: 10.1007/s10639-022-10968-y.

9. “Taxonomía de Transformación Digital | Revista Cubana de Transformación Digital.” https://rctd.uic.cu/rctd/article/view/62 (accessed Jun. 20, 2021).

10. R. Morakanyane, A. Grace, and P. O’Reilly, “Conceptualizing digital transformation in business organizations: A systematic review of literature,” *30th Bled eConference Digit. Transform. - From Connect. Things to Transform. our Lives, BLED 2017*, pp. 427–444, 2017, doi: 10.18690/978-961-286-043-1.30.

11. C. P. Lopez, J. Aguilar, and M. Santorum, “Autonomous VOs management based on industry 4.0: a systematic literature review,” *J. Intell. Manuf.*, 2021, doi: 10.1007/s10845-021-01850-8.

12. K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic mapping studies in software engineering,” *12th Int. Conf. Eval. Assess. Softw. Eng. EASE 2008*, pp. 1–10, 2008, doi: 10.14236/ewic/ease2008.8.

13. K. Petersen, S. Vakkalanka, and L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update,” *Inf. Softw. Technol.*, vol. 64, pp. 1–18, 2015, doi: 10.1016/j.infsof.2015.03.007.

14. C. H. Botero, *Un modelo para investigación documental: guia teórico-práctica sobre construcción de estados del arte con importantes reflexiones sobre la investigación*. Señal Editora, 2000. [Online]. Available: https://books.google.com.co/books?id=Wa3PAQAACAAJ

15. M. N. Córdoba and C. Monsalve, “Tipos de investigación, predictiva, interactiva, confirmatoria y evaluativa,”

i. *Fund. Sypal*, pp. 139–140, 2008.

16. R. Hernández Sampieri, C. Fernández Collado, and M. del P. Baptista Lucio, *Metodología de la Investigación*. Mexico: McGRAW-HILL / INTERAMERICANA EDITORES, S.A. DE C.V, 2005.
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