Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Remove the so-called lumps like this: [1–4]. Because it looks unprofessional. In many cases it occurs but in the current work it would be advisable to avoid it. It is more valid if one idea is attributed to one author. Rework it.

The abstract, results and especially conclusions should also include the global impacts of your work. Therefore, think about and interpret the transferability of your impacts in a global context if possible, but according to what I have researched, it has the potential.

What about biodiversity (social, ecological, economic)? These are aspects that you should also mention in parallel with your output.

The discussion should also focus on the currently very acute area of ​​sustainability. Explore and be inspired by the following: https://doi.org/10.1016/j.jafr.2021.100190; DOI: https://doi.org/10.1007/s11356-023-26462-y; DOI: 10.1088/1757-899X/603/2/022022; https://doi.org/10.1016/j.crsust.2021.100028 and etc.

Figures number: 3A-B, 4B, 7 are of low readability compared to the others. I suggest improving their quality or increasing the font size to make it readable. This will enhance the quality of what you want to present.

Did you examine the analyzed data for normality? Or what other software did you use for data analysis or editing? All of this must be included in the methodological part of the work.

In the introductory part, you have set certain hypotheses, but they are not clearly confirmed or refuted in the conclusion, so you should comment on this matter in the final part or conclusion.

In principle, I am missing the aforementioned impact, both local and global, i.e., wherever the transferability of knowledge resulting from your work, both in the practical and scientific fields, is possible. Add it, it will enhance the validity and benefit of your work.

Author Response

Dear reviewer:

On behalf of all the authors of the article entitled: Open Data Reuse in Agricultural, Livestock, and Environmental Systems: A Global Scoping Review with a Case Analysis of Ecuador; We appreciate your kind comments and suggestions, as they have allowed us to improve the scientific quality of the manuscript. Below we present in detail and by section the changes made to the text.

 

The authors

 

 

Reviewer 1:  

1. a) Remove the so-called lumps like this: [1–4]. Because it looks unprofessional. In many cases it occurs but in the current work it would be advisable to avoid it. It is more valid if one idea is attributed to one author. Rework it.

Response:

Answer: Thanks for your suggestion! We have thoroughly reviewed the text and have proceeded to rewrite it more clearly.

Old paragraph:

New paragraph in the introduction:

Open science has redefined how research is conceived and shared across agriculture, livestock, and environmental sciences. The adoption of interoperable infrastructures for data exchange fosters transparency, reproducibility, and sustained innovation in complex production systems. Recent studies highlight tangible progress in trustworthy frameworks for agricultural data sharing, including livestock event information schemas, farm-to-fork traceability models, and open data services that enhance hydrological and remote-sensing platforms [1–4].

Open science has redefined how research is conceived and shared across agriculture, livestock, and environmental sciences. The adoption of interoperable infrastructures for data exchange strengthens transparency, reproducibility, and sustained innovation in complex production systems. Recent studies demonstrate substantial advances in trustworthy frameworks for agricultural data sharing, including livestock event information schemas developed to standardize data exchange in animal production systems [1], farm-to-fork traceability models that incorporate pre-harvest and post-harvest transparency across agri-food supply chains [2], and trust-based agricultural data spaces that support structured data governance in agri-food value chains [3]. Open global data services also expand the capacity of hydrological and remote sensing platforms by supplying foundational datasets for distributed watershed modeling [4] At the same time, critical analyses of data governance and corporate strategies of data association indicate that, without inclusive interoperability standards and equitable governance structures, open data ecosystems could reinforce existing power asymmetries [5].

In this scoping review, “open data reuse” is defined as the secondary utilization of openly accessible datasets for analytical, monitoring, and decision-support applications beyond their original purpose. This definition encompasses the reuse of geospatial, agricultural, environmental, and biodiversity-related open data. The conceptual framing adopted here is consistent with foundational scholarship on open data use and reuse across public-sector, scientific, and environmental domains [6,7] and aligns with established principles of data reusability articulated in the FAIR framework [8]. It further reflects documented practices of data sharing, interoperability, and data-driven analysis in ecological, environmental, and agri-food systems [9,10].

 

 

Within section 3. Results and Discussion, section 3.2.2. Thematic areas, the following text has been replaced:

Cluster 1, located in the upper right region of the plot and labeled Intelligent Digital Cluster 1 – Intelligent Digital Agriculture & Sustainability (red) included terms such as machine learning, artificial intelligence, precision agriculture, agricultural robotics, and sustainable development. This cluster reflected research focused on collecting and using real time farm data from IoT based sensors to monitor plant and livestock conditions [6,860]. These datasets were processed with machine and deep learning algorithms for modeling and prediction [7,10,61,62], supporting applications such as disease detection, yield estimation, and biomass prediction. The sequence linking data acquisition, analysis, and decision-making illustrated a maturing path toward data reuse aimed at improving productivity, reducing environmental impacts, and advancing sustainability goals.

Cluster 2 – Geospatial Monitoring & Land Management (blue) comprised keywords such as GIS, remote sensing, land use, and environmental monitoring. Research in this group emphasized spatial analyses of agricultural and pastoral systems using GIS and remote sensing. Studies documented forest and pasture conversion to cropland, with major biodiversity impacts, while deep learning methods improved land cover detection [63–65]. UAV and satellite imagery supported crop and ecosystem monitoring, increasingly enhanced by edge computing and IoT based sensor networks [66–68]. This cluster highlighted the reuse of geospatial and environmental data as key inputs for planning, livestock management, and sustainable land use strategies [69–71].

New paragraph

3.2.2. Thematic areas

A total of 881 documents from the core dataset of 1,261 peer reviewed articles were analyzed based on author keywords. Following the third phase of the BiblioConsensus Framework, thematic clusters were generated through Multiple Correspondence Analysis (MCA) applied to normalized keywords. Figure 5 shows the conceptual structure of keyword co-occurrence across agriculture, livestock, and environmental domains in the context of open data reuse.

Cluster 1, located in the upper right region of the plot and labeled Intelligent Digital Cluster 1 – Intelligent Digital Agriculture & Sustainability (red) included terms such as machine learning, artificial intelligence, precision agriculture, agricultural robotics, and sustainable development. This cluster reflected research focused on collecting and using real time farm data from IoT based sensors to monitor plant and livestock conditions [66]. These datasets were processed with machine and deep learning algorithms for modeling and prediction [67,68], supporting applications such as disease detection, yield estimation, and biomass prediction. The sequence linking data acquisition, analysis, and decision-making illustrated a maturing path toward data reuse aimed at improving productivity, reducing environmental impacts, and advancing sustainability goals.

Cluster 2 – Geospatial Monitoring & Land Management (blue) comprised keywords such as GIS, remote sensing, land use, and environmental monitoring. Research in this group emphasized spatial analyses of agricultural and pastoral systems using GIS and remote sensing. Studies documented forest and pasture conversion to cropland, with major biodiversity impacts [72], while deep learning methods improved land cover detection [73,74]. UAV and satellite imagery supported crop and ecosystem monitoring [75], increasingly enhanced by edge computing and IoT based sensor networks [76,77]. This cluster highlighted the reuse of geospatial and environmental data as key inputs for planning, livestock management [28], and sustainable land use strategies [78,79].

 

1. b) The abstract, results and especially conclusions should also include the global impacts of your work. Therefore, think about and interpret the transferability of your impacts in a global context if possible, but according to what I have researched, it has the potential.

According to your suggestions, we have highlighted the global relevance and transferability of the findings by explicitly incorporating the potential international impacts in the abstract, results, and conclusions

Old Abstract:

Open data reuse has become a strategic component in the digital transformation of agricultural, livestock, and environmental systems, although substantial disparities persist in regions with limited technological infrastructure. This global scoping review maps existing evidence on open data reuse and highlights its thematic, geographic, and socioeconomic dimensions. The search was conducted in Scopus and restricted to peer-reviewed articles published in English between 1993 and 2025. A total of 2863 records were identified, and 1261 articles met the eligibility criteria for full analysis. Evidence charting combined bibliometric mapping, Multiple Correspondence Analysis, Principal Component Analysis, and a modified Delphi method to characterize thematic domains and institutional alignment. Results reveal three dominant research clusters Intelligent Digital Agriculture & Sustainability, Geospatial Monitoring & Land Management, and Biodiversity & Livestock Dynamics along with a persistent imbalance favoring high-income regions. A case analysis of Ecuador, based on eight articles, indicates an emerging but fragmented research landscape shaped by institutional capacity and structural constraints. Overall, findings show that open data reuse operates as a structural facilitator that reduces informational asymmetries and supports sustainable, data-driven innovation. This review provides an evidence-based foundation for policy design, capacity building, and international collaboration.

 

New Abstract:

Open data reuse has become a strategic driver of the digital transformation of agricultural, livestock, and environmental systems. In this industry yet significant disparities persist in regions with limited technological and institutional capacity. This global scoping reviews systematically maps providing the scientific evidence on open data reuse and examines its thematic, geographic, and socioeconomic dimensions in relation to sustainability, food security, and biodiversity conservation. The search, conducted in Scopus for peer-reviewed articles from 1993 to 2025, identified 2,863 records, of which 1,261 met the eligibility criteria. Evidence charting combined Bibliometric mapping, Multiple Correspondence Analysis, Principal Component Analysis, and a modified Delphi method to characterize thematic domains and research alignment. Results reveal three dominant global clusters: Intelligent Digital Agriculture & Sustainability, Geospatial Monitoring & Land Management, and Biodiversity & Livestock Dynamics alongside persistent geographic inequalities that favor high-income regions. A case analysis of Ecuador illustrates how open data reuse is emerging in a peripheral context shaped by structural constraints. Overall, findings show that open data reuse reduces informational asymmetries, enables cross scale environmental and production monitoring, and supports data driven innovation for climate resilience. The proposed BiblioConsensus Framework offers a transferable basis for policy design, capacity building, and international collaboration aimed at strengthening inclusive global open data ecosystems.

10. c) What about biodiversity (social, ecological, economic)? These are aspects that you should also mention in parallel with your output. The discussion should also focus on the currently very acute area of ​​sustainability. Explore and be inspired by the following: https://doi.org/10.1016/j.jafr.2021.100190; DOI: https://doi.org/10.1007/s11356-023-26462-y; DOI: 10.1088/1757-899X/603/2/022022; https://doi.org/10.1016/j.crsust.2021.100028 and etc.

Taking your suggestions into account, we have reviewed the bibliography and added the following paragraph in yellow:

3.2.2. Thematic areas

A total of 881 documents from the core dataset of 1,261 peer reviewed articles were analyzed based on author keywords. Following the third phase of the BiblioConsensus Framework, thematic clusters were generated through Multiple Correspondence Analysis (MCA) applied to normalized keywords. Figure 5 shows the conceptual structure of keyword co-occurrence across agriculture, livestock, and environmental domains in the context of open data reuse.

Cluster 1 – Intelligent Digital Agriculture & Sustainability (red) included terms such as machine learning, artificial intelligence, precision agriculture, agricultural robotics, and sustainable development. This cluster reflected research focused on collecting and using real time farm data from IoT based sensors to monitor plant and livestock conditions [65]. These datasets were processed with machine and deep learning algorithms for modeling and prediction [66,67], supporting applications such as disease detection, yield estimation, and biomass prediction. The sequence linking data acquisition, analysis, and decision-making illustrated a maturing path toward data reuse aimed at improving productivity, reducing environmental impacts, and advancing sustainability goals.

Research within Cluster 1 indicates a growing consolidation of intelligent digital agriculture, where IoT devices, machine learning models, and automated sensing systems improve productivity and resource efficiency through real-time monitoring. These tools enhance predictive capacity for disease detection, stress responses, and yield forecasting, while simultaneously strengthening sustainability indicators by reducing input waste and optimizing water and nutrient use [69]. Climate-smart livestock studies demonstrate that precision monitoring, wearables, and adaptive management systems increase resilience to climatic variability and mitigate environmental pressures across production systems [70]. Integrated crop–livestock approaches further show that data-enabled decisions support economic and social dimensions of biodiversity by diversifying production, stabilizing incomes, and improving food security in low- and middle-income regions [71]. Collectively, these findings indicate that digital agriculture offers global transferability and the potential to advance sustainability and socioecological resilience, although uneven digital infrastructure and limited technical capacity remain major barriers.

Cluster 2 – Geospatial Monitoring & Land Management (blue) comprised keywords such as GIS, remote sensing, land use, and environmental monitoring. Research in this group emphasized spatial analyses of agricultural and pastoral systems using GIS and remote sensing. Studies documented forest and pasture conversion to cropland, with major biodiversity impacts [71], while deep learning methods improved land cover detection [72,73]. UAV and satellite imagery supported crop and ecosystem monitoring [74], increasingly enhanced by edge computing and IoT based sensor networks [75,76]. This cluster highlighted the reuse of geospatial and environmental data as key inputs for planning, livestock management [28], and sustainable land use strategies [77,78].

Local-scale sustainability studies show that integrating ecological footprint metrics with geospatial data strengthens territorial planning and supports evidence-based policies for sustainable resource use [80]. Additionally, research on ecological urban agriculture demonstrates that strategically planned urban and peri-urban food systems can mitigate the ecological pressures of urban expansion and reinforce socioecological sustainability in metropolitan landscapes [81]. Collectively, this cluster demonstrates that geospatial data reuse provides globally transferable tools for biodiversity conservation and sustainable land use strategies, although disparities in data access and technical capacity continue to limit adoption in resource constrained regions.

Cluster 3 – Biodiversity & Livestock Dynamics (green) research in Cluster 3 highlights the interplay among livestock systems, land-use change, and ecological resilience, emphasizing how ecological and spatial data clarify biodiversity patterns. IoT and AI tools support species identification and help manage human wildlife interactions [82], although recent analyses show that these relationships are shaped by cultural norms, social tensions, and competing stakeholder interests rather than by ecological factors alone [83]. Evidence from East Africa indicates that fencing and land conversion fragment habitats and reduce biodiversity, with direct implications for ecosystem stability and rural livelihoods [84]. Complementary research on food waste shows that inefficiencies in agri-food chains intensify pressure on land and biomass resources, while circular strategies that convert waste into feed or bioenergy reinforce environmental sustainability and food security [85]. GIS-based approaches integrate genetic, demographic, and environmental data to guide conservation planning and identify landscapes where livestock production and wildlife can coexist under managed levels of risk [86]. Collectively, this cluster demonstrates that data reuse strengthens the capacity to address the ecological, social, and economic dimensions of biodiversity and supports globally transferable strategies for coexistence-oriented and sustainable livestock management.

 

1. d) Figures number: 3A-B, 4B, 7 are of low readability compared to the others. I suggest improving their quality or increasing the font size to make it readable. This will enhance the quality of what you want to present.

We appreciate this observation, and the figures indicated will be provided to the editorial team in high-resolution versions with improved readability and appropriately increased font size

 

1. e) Did you examine the analyzed data for normality? Or what other

The cubic regression applied in this study corresponds to a polynomial specification estimated using ordinary least squares (OLS) within the classical General Linear Model (GLM) framework. As established in methodological literature, polynomial regression is linear in its parameters and therefore does not require the original variables to follow a normal distribution (Kutner et al., 2005; Montgomery et al., 2012). In this context, the cubic model employed is appropriate for the objectives of this research, which aims to describe and interpret empirical patterns rather than to perform statistical inference or forecast future values. For this purpose, the model remains valid even in the presence of non-normal bibliometric time-series data. This clarification is particularly relevant because bibliometric trajectories typically exhibit non-linear, right-skewed growth patterns that rarely conform to normality, an observation widely documented in research (de Solla Price, 1963; Egghe & Rousseau, 2006).

Regarding the additional analytical procedures used in this study—chi-square tests, Multiple Correspondence Analysis (MCA), and Principal Component Analysis (PCA), none of them requires multivariate normality. Moreover, PCA fulfilled the adequacy conditions indicated by Bartlett’s test of sphericity and the Kaiser–Meyer–Olkin (KMO) measure.

Taken together, these considerations support the robustness and validity of the results even under the assumption of non-normality.

 

1. f) In the introductory part, you have set certain hypotheses, but they are not clearly confirmed or refuted in the conclusion, so you should comment on this matter in the final part or conclusion.

We have modified the conclusions and have also added the following paragraph.

 

This scoping review provides an integrated and data-driven assessment of global trends in open data reuse within agriculture, environmental sciences, and livestock research, with a particular emphasis on Ecuador’s peripheral yet emerging role within the international research landscape. Through the implementation of the five phase BiblioConsensus Framework, we combined bibliometric mapping, semantic normalization, expert validation, and multivariate analysis to identify thematic clusters, evaluate institutional alignment, and formulate strategic recommendations grounded in empirical evidence.

The results confirm both initial hypotheses. Global scientific output on open data reuse has grown consistently since 1993 but remains geographically and thematically unbalanced. High-income countries dominate research on digital agriculture and sustainability, while lower- and middle-income regions exhibit concentration in geospatial monitoring and limited representation in bioeconomy and livestock-related themes. Within this context, Ecuador’s scholarly output although nascent demon-strates growing diversification, with selected institutions such as UTM, ESPOCH, UNIANDES, UTPL, and UDLA showing differentiated but complementary thematic alignments across smart farming, remote sensing, and biodiversity monitoring.

The BiblioConsensus Framework thus represents a methodological innovation that can be replicated in other underrepresented contexts to inform national research agendas, promote thematic convergence, and strengthen participation in international scientific networks. The evidence-based strategies proposed ranging from national collaboration clusters and open-data repositories to South–South integration and ca-pacity-building programs provide a coherent roadmap for enhancing Ecuador’s integration into global knowledge ecosystems.

Future research could expand this framework by conducting longitudinal analyses of institutional transformation, implementing cross-country benchmarking, and integrating advanced metrics of data reuse such as FAIR compliance and user-centered impact indicators. These extensions would help clarify how open data reuse contributes to sustainable innovation across diverse socioeconomic and governance contexts.

Strengthening institutional capacities in open data governance, interoperability, and digital infrastructure will be critical for ensuring that Ecuador and other emerging economies participate equitably and effectively in the global transition toward da-ta-driven, sustainable agricultural innovation.

 

1. g) In principle, I am missing the aforementioned impact, both local and global, i.e., wherever the transferability of knowledge resulting from your work, both in the practical and scientific fields, is possible. Add it, it will enhance the validity and benefit of your work.

Thank you for your observation; Table 3 has been modified, and a new paragraph has been incorporated.

Strategic Action	Core Objective	Rationale
1. Consolidate thematic clusters based on existing institutional strengths	Strengthen national specialization and reduce fragmentation	The PCA revealed well-defined but disconnected niches (IoT and sensor systems; remote sensing and land-use analytics; biodiversity monitoring). Improving coordination could enhance national research coherence.
2. Prioritize research areas where Ecuador is underrepresented relative to global trends	Align national production with leading international themes	MCA results showed limited Ecuadorian presence in Intelligent Digital Agriculture & Sustainability, the most influential global cluster. Strengthening this line would reduce thematic gaps.
3. Expand and diversify international collaboration networks	Increase visibility and reduce dependency on a few partner countries	The global co-authorship network positioned Ecuador at the periphery, with strong reliance on Spain and the United States. Broader partnerships would enhance research integration and impact.
4. Establish national guidelines for open-data governance and interoperability	Improve data quality, standardization, and reusability	Delphi results highlighted conceptual inconsistencies and the absence of unified standards for open and interoperable data reuse across institutions.
5. Develop federated national repositories for agricultural, livestock, and environmental data	Reduce informational asymmetries and support local research capacity	Ecuador relies heavily on international datasets (e.g., FAO HIH, Copernicus). National repositories would enhance autonomy and facilitate open-data reuse.
6. Strengthen digital and analytical infrastructure for advanced agri-environmental research	Enable participation in high-impact thematic areas	The income-level analysis showed that lower- and middle-income countries, including Ecuador, concentrate in geospatial monitoring due to limited digital capacity. Infrastructure investments would broaden thematic coverage.
7. Promote research in smart livestock systems and sustainable bioeconomy	Address national gaps in globally growing fields	The global cluster Biodiversity & Livestock Dynamics is prominent, but Ecuador shows low representation. Developing this line would enhance environmental monitoring and production sustainability.
8. Integrate grey literature and non-indexed sources in future assessments	Obtain a more complete and accurate representation of national research	The exclusive use of Scopus excluded local reports, theses, and institutional studies, underrepresenting Ecuador’s research efforts.

 

The empirical findings of this review showed that open data reuse had expanded across agricultural, livestock, and environmental research, although its development remained uneven. Three principal thematic domains were identified: Intelligent Digital Agriculture and Sustainability, Geospatial Monitoring and Land Management, and Biodiversity and Livestock Dynamics. Participation patterns across these domains were clearly stratified. High-income countries led research in digital agriculture and sustainability, while lower- and middle-income regions were more concentrated in geospatial monitoring, largely due to infrastructural constraints. Ecuador reflected these global disparities, displaying a modest yet diversified body of research aligned with isolated institutional strengths identified through PCA, while occupying a peripheral position within international collaboration networks. Overall, the results indicated that although open data reuse had acted as a strategic driver of scientific innovation, its diffusion and impacts were limited by structural inequalities in digital capacity, governance, and access to interoperable data infrastructures.

 

Furthermore, we have modified the conclusions and limitations of the study.

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript conducts a range review and quantitative analysis of Scopus data from 1993 to 2025 using the PRISMA process, R-Bibliometrix/VOSviewer, and the self-built “BiblioConsensus Framework”. And conduct case analyses at the national level using eight papers from Ecuador. The topic selection of the article is well-matched, and the data volume (1,261 articles) and analytical methods (MCA, PCA, Delphi) are also quite rich. However, the following problems exist:

1. When previewing the article, the image resolution is not high and the content is not displayed clearly.
2. There are slight differences in the format of the references. For instance, the DOI numbers of 13, 15, 16, 17, and 20 are different from those of others
3. The key words of the article include “BiblioConsensus Framework”, but they do not appear in the abstract, and the “Scoping Review”that appears in the abstract is not included in the key words either.
4. “open data reuse” should be clearly stated in the text. Which literatures are truly involved?
5. The title of this manuscript is “A Global Scoping Review”, but the data source is limited to Scopus. Is it necessary to emphasize the potential impact of a single database within the limitations?
6. Why is the BiblioConsensus framework needed and what innovative problems does it solve?
7. The analysis of Ecuador is slightly disconnected from that of the global part. Should it be fully explained how the case of Ecuador confirms, supplements or challenges the universal conclusion of global trends?
8. Can the eight cases in Ecuador fully cover the national level? Does it have certain limitations?
9. Could the strategies in Table 3 be more targeted? Extract some conclusions from the empirical findings of this article.
10. What is the basis for time division? Is it subjective? Should some policy nodes be added to enhance logic?
11. The article studies open Data, but the Data Availability Statement indicates that “data needs to be applied for from the author”. Will it cause misunderstandings?
12. Can the conclusion propose what aspects of research can be carried out in the future?

Author Response

Dear reviewer:

On behalf of all the authors of the article entitled: Open Data Reuse in Agricultural, Livestock, and Environmental Systems: A Global Scoping Review with a Case Analysis of Ecuador; We appreciate your kind comments and suggestions, as they have allowed us to improve the scientific quality of the manuscript. Below we present in detail and by section the changes made to the text.

 

The authors

 

 

Reviewer 2:

On behalf of all the authors of the article entitled “Open Data Reuse in Agricultural, Livestock, and Environmental Systems: A Global Scoping Review with a Case Analysis of Ecuador”, we sincerely appreciate your constructive comments and thoughtful suggestions. Your observations have substantially contributed to strengthening the scientific rigor and overall quality of the manuscript. Below, we provide a detailed, point-by-point description of the revisions incorporated in response to each of your remarks.

This manuscript conducts a range review and quantitative analysis of Scopus data from 1993 to 2025 using the PRISMA process, R-Bibliometrix/VOSviewer, and the self-built “BiblioConsensus Framework”. And conduct case analyses at the national level using eight papers from Ecuador. The topic selection of the article is well-matched, and the data volume (1,261 articles) and analytical methods (MCA, PCA, Delphi) are also quite rich. However, the following problems exist:

1. a) When previewing the article, the image resolution is not high and the content is not displayed clearly.

Thank you for your observation. The figures appeared with reduced clarity due to automatic compression during the submission process. High resolution versions of all images will be provided to the editorial team to ensure optimal quality in the final publication.

 

1. b) There are slight differences in the format of the references. For instance, the DOI numbers of 13, 15, 16, 17, and 20 are different from those of others

We have reviewed the entire reference list and adjusted the formatting to fully comply with the journal’s guidelines.

 

1. c) The key words of the article include “BiblioConsensus Framework”, but they do not appear in the abstract, and the “Scoping Review”that appears in the abstract is not included in the key words either.

Many thanks! You are absolutely right, and we appreciate this observation. We have carefully revised the abstract and updated the keywords to ensure consistency, including the terms “BiblioConsensus Framework” and “Scoping Review.”

Old Abstract:

Open data reuse has become a strategic component in the digital transformation of agricultural, livestock, and environmental systems, although substantial disparities persist in regions with limited technological infrastructure. This global scoping review maps existing evidence on open data reuse and highlights its thematic, geographic, and socioeconomic dimensions. The search was conducted in Scopus and restricted to peer-reviewed articles published in English between 1993 and 2025. A total of 2863 records were identified, and 1261 articles met the eligibility criteria for full analysis. Evidence charting combined bibliometric mapping, Multiple Correspondence Analysis, Principal Component Analysis, and a modified Delphi method to characterize thematic domains and institutional alignment. Results reveal three dominant research clusters Intelligent Digital Agriculture & Sustainability, Geospatial Monitoring & Land Management, and Biodiversity & Livestock Dynamics along with a persistent imbalance favoring high-income regions. A case analysis of Ecuador, based on eight articles, indicates an emerging but fragmented research landscape shaped by institutional capacity and structural constraints. Overall, findings show that open data reuse operates as a structural facilitator that reduces informational asymmetries and supports sustainable, data-driven innovation. This review provides an evidence-based foundation for policy design, capacity building, and international collaboration.

 

New Abstract:

Open data reuse has become a strategic driver of the digital transformation of agricultural, livestock, and environmental systems, yet significant disparities persist in regions with limited technological and institutional capacity. This global scoping review systematically maps the scientific evidence on open data reuse and examines its thematic, geographic, and socioeconomic dimensions in relation to sustainability, food security, and biodiversity conservation. The search, conducted in Scopus for peer-reviewed articles from 1993 to 2025, identified 2,863 records, of which 1,261 met the eligibility criteria. Evidence charting combined bibliometric mapping, Multiple Correspondence Analysis, Principal Component Analysis, and a modified Delphi method to characterize thematic domains and research alignment. Results reveal three dominant global clusters Intelligent Digital Agriculture & Sustainability, Geospatial Monitoring & Land Management, and Biodiversity & Livestock Dynamics alongside persistent geographic inequalities that favor high-income regions. A case analysis of Ecuador illustrates how open data reuse is emerging in a peripheral context shaped by structural constraints. Overall, findings show that open data reuse reduces informational asymmetries, enables cross-scale environmental and production monitoring, and supports data-driven innovation for climate resilience. The proposed BiblioConsensus Framework offers a transferable basis for policy design, capacity building, and international collaboration aimed at strengthening inclusive global open-data ecosystems.

 

1. d) “open data reuse” should be clearly stated in the text. Which literatures are truly involved?

We appreciate this comment. The authors have incorporated an explicit paragraph addressing “open data reuse,” supported by the relevant literature indicated in the revised manuscript:

New paragraph in the introduction:

Open science has redefined how research is conceived and shared across agriculture, livestock, and environmental sciences. The adoption of interoperable infrastructures for data exchange fosters transparency, reproducibility, and sustained innovation in complex production systems. Recent studies highlight tangible progress in trustworthy frameworks for agricultural data sharing, including livestock event information schemas, farm-to-fork traceability models, and open data services that enhance hydrological and remote-sensing platforms [1–4].

Open science has redefined how research is conceived and shared across agriculture, livestock, and environmental sciences. The adoption of interoperable infrastructures for data exchange strengthens transparency, reproducibility, and sustained innovation in complex production systems. Recent studies demonstrate substantial advances in trustworthy frameworks for agricultural data sharing, including livestock event information schemas developed to standardize data exchange in animal production systems [1], farm-to-fork traceability models that incorporate pre-harvest and post-harvest transparency across agri-food supply chains [2], and trust-based agricultural data spaces that support structured data governance in agri-food value chains [3]. Open global data services also expand the capacity of hydrological and remote sensing platforms by supplying foundational datasets for distributed watershed modeling [4] At the same time, critical analyses of data governance and corporate strategies of data association indicate that, without inclusive interoperability standards and equitable governance structures, open data ecosystems could reinforce existing power asymmetries [5].

In this scoping review, “open data reuse” is defined as the secondary utilization of openly accessible datasets for analytical, monitoring, and decision-support applications beyond their original purpose. This definition encompasses the reuse of geospatial, agricultural, environmental, and biodiversity-related open data. The conceptual framing adopted here is consistent with foundational scholarship on open data use and reuse across public-sector, scientific, and environmental domains [6,7] and aligns with established principles of data reusability articulated in the FAIR framework [8]. It further reflects documented practices of data sharing, interoperability, and data-driven analysis in ecological, environmental, and agri-food systems [9,10].

 

 

1. e) The title of this manuscript is “A Global Scoping Review”, but the data source is limited to Scopus. Is it necessary to emphasize the potential impact of a single database within the limitations?

We appreciate this observation. Perhaps the manuscript title seems pretentious, and you are right to point out that the data source is limited to Scopus. However, the title follows the guidelines of the "PRISMA-ScR Preferred Reporting Elements Checklist for Systematic Reviews and Meta-Analyses," specifically item 1, which states: "Identify the report as a scoping review." This criterion is defined in Tricco et al. (2018), PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation, Annals of Internal Medicine, 169:467–473, doi:10.7326/M18-0850.

Furthermore, and in accordance with your suggestion, the study limitations have been incorporated, emphasizing the potential impact of a single database within these constraints.

 

1. f) Why is the BiblioConsensus framework needed and what innovative problems does it solve?
   
   

We appreciate this observation and have incorporated the following paragraph into the manuscript:

The BiblioConsensus Framework is highly useful because bibliometric analyses alone do not guarantee that the identified thematic groups are contextually relevant or aligned with research needs on the ground. Previous studies have shown that open data reuse patterns and research development are constrained by structural limitations, institutional fragmentation, and unequal data governance, particularly in capacity-limited regions [5,41]. In this context, the Framework provided an integrative and replicable approach by combining a data-driven scoping review with a modified Delphi process. This procedure allowed experts to validate, refine, or reassign keywords based on local priorities, governance conditions, and technological gaps; dimensions that could not be inferred solely from bibliometric data. By triangulating quantitative evidence with expert judgment, the framework addressed the limitations of relying exclusively on either approach and provided a more robust and contextualized basis for defining research agendas, particularly in regions where data infrastructures and capabilities remained uneven.

 

1. g) The analysis of Ecuador is slightly disconnected from that of the global part. Should it be fully explained how the case of Ecuador confirms, supplements or challenges the universal conclusion of global trends?

The following paragraph has been added:

3.3. Ecuador’s research output and network position

The inclusion of Ecuador as a focal case was analytically complementary to the global findings. As a peripheral and capacity-constrained research system, Ecuador provided a critical test of whether global patterns of open data reuse held in contexts shaped by infrastructural and governance limitations [5,98]. The case analysis complemented global trends by showing how institutional fragmentation and uneven data governance influenced the uptake and impact of open data reuse in lower-capacity regions, where access to international open geospatial and environmental datasets could help reduce informational asymmetries and support research development [41,100].

 

 

Furthermore, the concluding remarks have been strengthened in accordance with your feedback.

 

1. h) Can the eight cases in Ecuador fully cover the national level? Does it have certain limitations?

We acknowledge that the Ecuadorian subset comprises only eight peer-reviewed articles. However, this number represents the entirety of the Scopus-indexed scientific output produced nationally on open data reuse within the study period, and it includes publications from all major Ecuadorian research institutions active in the field. Therefore, although small in number, the dataset is comprehensive at the national level under the scope of this review.

Nonetheless, we agree that this approach has limitations. The analysis did not include grey literature, local technical reports, policy documents, or non-indexed publications, which may capture additional national research activities. This limitation has now been acknowledged in the manuscript.

The following text has been incorporated.

Limitations and Future Research

This review does not include a direct assessment of the quality and interoperability of open data repositories in Ecuador, even though the accuracy, completeness, and standardization of datasets are essential for effective data reuse and evidence-based policymaking. The Ecuadorian subset consisted of only eight peer-reviewed articles, which represents the full Scopus indexed national output on the topic but introduces limitations in terms of scope and generalizability. Reliance on Scopus also excludes local publications, institutional reports, policy documents, and other forms of grey literature that may reflect additional national research efforts.

Future research should therefore incorporate systematic evaluations of Ecuador’s agri-environmental open data repositories considering accessibility, update frequency, metadata quality, and FAIR compliance while also integrating grey literature and triangulating multiple data sources. These steps would allow for a more comprehensive characterization of Ecuador’s research landscape and support the development of stronger open data governance frameworks.

 

1. i) Could the strategies in Table 3 be more targeted? Extract some conclusions from the empirical findings of this article.

Thank you for your observation; Table 3 has been modified, and a new paragraph has been incorporated.

Strategic Action	Core Objective	Rationale
1. Consolidate thematic clusters based on existing institutional strengths	Strengthen national specialization and reduce fragmentation	The PCA revealed well-defined but disconnected niches (IoT and sensor systems; remote sensing and land-use analytics; biodiversity monitoring). Improving coordination could enhance national research coherence.
2. Prioritize research areas where Ecuador is underrepresented relative to global trends	Align national production with leading international themes	MCA results showed limited Ecuadorian presence in Intelligent Digital Agriculture & Sustainability, the most influential global cluster. Strengthening this line would reduce thematic gaps.
3. Expand and diversify international collaboration networks	Increase visibility and reduce dependency on a few partner countries	The global co-authorship network positioned Ecuador at the periphery, with strong reliance on Spain and the United States. Broader partnerships would enhance research integration and impact.
4. Establish national guidelines for open-data governance and interoperability	Improve data quality, standardization, and reusability	Delphi results highlighted conceptual inconsistencies and the absence of unified standards for open and interoperable data reuse across institutions.
5. Develop federated national repositories for agricultural, livestock, and environmental data	Reduce informational asymmetries and support local research capacity	Ecuador relies heavily on international datasets (e.g., FAO HIH, Copernicus). National repositories would enhance autonomy and facilitate open-data reuse.
6. Strengthen digital and analytical infrastructure for advanced agri-environmental research	Enable participation in high-impact thematic areas	The income-level analysis showed that lower- and middle-income countries, including Ecuador, concentrate in geospatial monitoring due to limited digital capacity. Infrastructure investments would broaden thematic coverage.
7. Promote research in smart livestock systems and sustainable bioeconomy	Address national gaps in globally growing fields	The global cluster Biodiversity & Livestock Dynamics is prominent, but Ecuador shows low representation. Developing this line would enhance environmental monitoring and production sustainability.
8. Integrate grey literature and non-indexed sources in future assessments	Obtain a more complete and accurate representation of national research	The exclusive use of Scopus excluded local reports, theses, and institutional studies, underrepresenting Ecuador’s research efforts.

 

The empirical findings of this review showed that open data reuse had expanded across agricultural, livestock, and environmental research, although its development remained uneven. Three principal thematic domains were identified: Intelligent Digital Agriculture and Sustainability, Geospatial Monitoring and Land Management, and Biodiversity and Livestock Dynamics. Participation patterns across these domains were clearly stratified. High-income countries led research in digital agriculture and sustainability, while lower- and middle-income regions were more concentrated in geospatial monitoring, largely due to infrastructural constraints. Ecuador reflected these global disparities, displaying a modest yet diversified body of research aligned with isolated institutional strengths identified through PCA, while occupying a peripheral position within international collaboration networks. Overall, the results indicated that although open data reuse had acted as a strategic driver of scientific innovation, its diffusion and impacts were limited by structural inequalities in digital capacity, governance, and access to interoperable data infrastructures.

 

 

1. j) What is the basis for time division? Is it subjective? Should some policy nodes be added to enhance logic?

We appreciate this observation. The temporal segmentation was not subjective; it was first evaluated using interquartile ranges (IQR), which showed clear, non-overlapping distributions across the three periods. For Articles, the IQR increased from 1.75 in Period I to 5.25 in Period II and then to 110.0 in Period III. For Citations, the IQR increased from 91.75 to 115.5 and then to 1378.0, indicating sharp structural shifts in scientific output and impact.

Following this distributional assessment, we conducted a Kruskal–Wallis test, which confirmed significant differences among periods for both Articles (p = 1.07 × 10⁻⁶) and Citations (p = 1.91 × 10⁻⁵). Dunn’s post-hoc comparisons with Bonferroni correction further validated the segmentation, showing significant differences in all pairwise contrasts (Articles: p < 0.001; Citations: p < 0.01).

In response to your suggestion, we have incorporated in the manuscript the IQR analysis, the Kruskal–Wallis test, and the Dunn post-hoc procedure into the Methodology section, and we have clarified their corresponding statistical outputs in the Results section.

 

1. k) The article studies open Data, but the Data Availability Statement indicates that “data needs to be applied for from the author”. Will it cause misunderstandings?

You are correct. Our study emphasizes the value of using open data, and the bibliometric dataset generated for this analysis has been made openly available for other researchers. We have updated the Data Availability Statement according to the journal’s guidelines, ensuring that all materials are accessible without restrictions to any interested user.

 

1. l) Can the conclusion propose what aspects of research can be carried out in the future?

We have modified the conclusions and have also added the following paragraph.

 

This scoping review provides an integrated and data-driven assessment of global trends in open data reuse within agriculture, environmental sciences, and livestock research, with a particular emphasis on Ecuador’s peripheral yet emerging role within the international research landscape. Through the implementation of the five phase BiblioConsensus Framework, we combined bibliometric mapping, semantic normalization, expert validation, and multivariate analysis to identify thematic clusters, evaluate institutional alignment, and formulate strategic recommendations grounded in empirical evidence.

The results confirm both initial hypotheses. Global scientific output on open data reuse has grown consistently since 1993, but remains geographically and thematically unbalanced. High-income countries dominate research on digital agriculture and sustainability, while lower- and middle-income regions exhibit concentration in geospatial monitoring and limited representation in bioeconomy and livestock-related themes. Within this context, Ecuador’s scholarly output although nascent demonstrates growing diversification, with selected institutions such as UTM, ESPOCH, UNIANDES, UTPL, and UDLA showing differentiated but complementary thematic alignments across smart farming, remote sensing, and biodiversity monitoring.

The BiblioConsensus Framework thus represents a methodological innovation that can be replicated in other underrepresented contexts to inform national research agendas, promote thematic convergence, and strengthen participation in international scientific networks. The evidence-based strategies proposed ranging from national collaboration clusters and open-data repositories to South–South integration and ca-pacity-building programs provide a coherent roadmap for enhancing Ecuador’s integration into global knowledge ecosystems.

Future research could expand this framework by conducting longitudinal analyses of institutional transformation, implementing cross-country benchmarking, and integrating advanced metrics of data reuse such as FAIR compliance and user-centered impact indicators. These extensions would help clarify how open data reuse contributes to sustainable innovation across diverse socioeconomic and governance contexts.

Strengthening institutional capacities in open data governance, interoperability, and digital infrastructure will be critical for ensuring that Ecuador and other emerging economies participate equitably and effectively in the global transition toward da-ta-driven, sustainable agricultural innovation.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

satisfied