Data-Driven Strategic Sustainability Initiatives of Beef and Dairy Genetics Consortia: A Comprehensive Landscape Analysis of the US, Brazilian and European Cattle Industries

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Reviewer Comments

Author Remarks

1

There is no clear description of the research design and analytical methods.

As a narrative landscape review, the study does not follow an experimental research design; instead, it employs a structured qualitative synthesis of published literature, industry reports, and data platforms to compare regional genetic programs, germplasm strategies, and modeling frameworks across the USA, Europe, and Brazil.

2

There is a lack of quantitative analysis and comparative research.

3

Some data references cite relatively old literature.

Out of approximately 160 cited articles, only about seven were published before 2000, and these were included as essential foundational references necessary to establish historical context and baseline concepts

4

There is a lack of the latest industry data and trend analysis.

The review includes all relevant and recently reported genetic programs and data platforms across major cattle industries, with individual program names and direct website links explicitly provided in Figure 3, while the primary objective is to synthesize structural trends in genetic programs, germplasm strategies, and decision-support frameworks rather than to provide real-time market or short-term industry trend analyses.

5

The conclusion section is relatively general and lacks specific policy recommendations.

We have revised the conclusion to include specific policy recommendations, such as incentivizing genomic selection and precision livestock technologies, mandating carbon and water footprint reporting in certification programs

6

The analysis of differences between different regions is not in-depth enough.

Our study was structured around three main objectives: (1) highlighting the multi-trait nature of sustainability dimensions across the U.S., Europe, and Brazil, (2) examining the role of genetic consortia in propagating multi-trait optimized germplasm aligned with dynamic sustainability metrics, and (3) emphasizing the integration of decision-support models and data-driven platforms for informed management. Based on these objectives, we have included the maximum available details from current literature, and a deeper quantitative analysis is planned for future research

7

The color of figure 3 has some problem.

We have shared a higher-resolution, clearer image

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Reviewer Comments

Author Remarks

1

"Targeting production, longevity, health, and reproduction which include..." can be revised to "targeting production, longevity, health, and reproduction, with outcomes including..."  

We have carefully revised the sentence as recommended

2

When "germplasm programs" is mentioned for the first time, a brief annotation can be added (e.g., "germplasm programs, i.e., breeding programs focusing on genetic resources").  

Added sentence “breeding and conservation programs focusing on genetic resources”

3

For the cases of the USA, Europe, and Brazil, 1–2 sets of core data or representative programs can be supplemented (e.g., "U.S. programs, such as the National Beef Cattle Evaluation Program, combine genomic selection...").  

The specific programs and initiatives are explicitly illustrated in Figure 3, which lists representative genetic programs for the USA, Europe, and Brazil along with direct website links to each program.

4

For the expression "positioning genetics and technology as key drivers", specific orientations can be added (e.g., "key drivers of low-carbon, high-efficiency cattle farming") to make the conclusion more concrete.

Included more clarifying sentence “Key drivers of genetically resilient and sustainable breeding systems”

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Reviewer Comments

Author Remarks

1

Abstract lacks specific examples of genetic indices, models, or quantifiable outcomes (emission reductions or efficiency gains), which could strengthen its impact and demonstrate tangible progress beyond general claims.

Specific examples of widely used genetic indices and quantitative sustainability outcomes were omitted from the abstract to comply with strict word-limit constraints and are discussed in detail in the main text. Moreover, we did not conduct any comparative analysis or assign weightage to genetic selection indices. Instead, we report the genetic selection indices provided by different companies across various geographical regions.

2

Lines 62-96: The text suffers from awkward phrasing and grammatical issues, such as the run-on final sentence ("Even though Brazil... remains lower"), incomplete thoughts, and inconsistent tense usage (e.g., "peaked at 250.65 million in 1985 but declined to 87.2 million in 2024" mixes past and present). It also lacks smooth transitions between regions and omits explicit discussion of cited challenges like sustainability or biodiversity, reducing analytical depth despite data richness.

We have revised Lines 62–96 to improve grammatical clarity, correct tense consistency, eliminate run-on and incomplete sentences, and enhance the smoothness of transitions between regions.

3

Lines 120-132: Vague, self-serving definitions: The GRSB's framework prioritizes "planet, people, animals, and progress" in a buzzword-heavy manner without quantifiable metrics or independent validation, allowing stakeholders like NCBA to greenwash high-impact practices

The GRSB framework is cited in the manuscript as an established, stakeholder-driven reference that conceptualizes sustainability dimensions in beef systems globally. Quantification or the development of independently validated metrics is not the intention of this study

4

- Lines 145-149: Ignores core environmental trade-offs: Beef's status as the most resource-intensive protein per kcal or gram is acknowledged but downplayed by pivoting to nutraceuticals and lipids [20-22], which do nothing to offset deforestation, methane emissions, or water scarcity—global production's tripling by 1975 underscores unchecked growth, not progress.

The discussion in Lines 145–149 acknowledges beef’s high resource intensity and historical production growth. References to nutraceutical and lipid attributes are included to illustrate concurrent nutritional value considerations, not to offset or negate well-documented environmental trade-offs such as deforestation, methane emissions, or water use. Furthermore, the objective of this study is to guide industry stakeholders toward scaling well-designed, research-validated genetic selection indices and mathematical models to mitigate these environmental trade-offs.

5

 Lines 193-198: Optimistic reductions (30% drop since 1975, 5-15% from additives) contradict the defeatist tone, and vague projections like a "4.6% global CO₂e cut by 2030" lack sourcing or feasibility details. Broader omissions abound: no mention of regenerative grazing's carbon sequestration potential (up to 1-2 tons CO₂/ha/year via soil building), methane's short 12-year atmospheric lifespan (vs. CO₂e's centuries), or beef's role in land stewardship on non-arable pastures unfit for crops. Without addressing how industrial chicken/pork systems often perform worse in space, antibiotics, and ethics, animal welfare rhetoric becomes hollow.

We discuss carbon sequestration in the manuscript by citing References 44, 45, and 110. The emission reduction estimates and mitigation ranges presented in Lines 193–198 are derived from the comprehensive synthesis by Tedeschi and Beauchemin (2023) [38], which integrates life-cycle assessment evidence and system-level analyses to contextualize potential mitigation pathways rather than to assert definitive outcomes. Topics such as regenerative grazing, atmospheric methane dynamics, comparative protein-system trade-offs, and land stewardship roles—while important—fall beyond the focused scope of this review and are therefore acknowledged as areas requiring deeper, dedicated investigation. Furthermore, more comprehensive system-level life-cycle analyses are needed to fully capture the genetic dimensions of cattle selection indices.

6

Lines 244-295: These data exhibit a fragmented structure, mixing vague ideas about dairy sustainability with superficial facts lacking depth. They rely on unprocessed citations and promotional claims about dairy's dual role in stewardship and profit, while neglecting significant contradictions like Brazil's high costs, poor milk quality, and welfare concerns. The statistics regarding Brazilian nutrient contributions seem out of place, further diminished by noted consumption levels falling short of recommendations.

Lines 244–295 provide a broad contextual overview of dairy sustainability across regions to highlight structural heterogeneity and contrasting challenges, including economic constraints, quality limitations, and animal welfare issues in Brazil. Region-specific statistics are included to illustrate disparities among nutritional contribution, consumption patterns, and sustainability performance, rather than to serve as promotional claims. The intention of this section is to position the manuscript as a guiding reference for the dairy industry toward the adoption of mathematical models and genomic selection indices that integrate multiple dimensions of dairy sustainability.

7

Lines 307-358: Phrases like "improved profitability and sustainability" diminish credibility in global comparisons. The document cites indices like EcoFeed and NTM but lacks clarity and critical analysis of their effectiveness, particularly for diverse groups. It overlooks significant downsides, such as high genomic testing costs for smallholders and equity issues in Brazil, while failing to address risks associated with prioritizing indices over biodiversity.

Lines 307–358 present genomic indices such as EcoFeed and NTM as representative tools used by genetic consortia to operationalize multi-trait breeding objectives, without asserting universal effectiveness or equity, and the review’s landscape-oriented scope does not extend to cost–benefit analyses for smallholders, equity implications, or biodiversity trade-offs, which are recognized as important considerations for future, targeted studies.

8

- Lines 414-508: Minor issues persist: repetitive phrasing (e.g., multiple mentions of "linear programming" and "multi-objective optimization" without consolidation), citation inconsistencies (e.g., – but gaps like), and run-on sentences in model descriptions. Streamlining redundancies would enhance clarity and impact for journal submission.

The repeated use of the term “linear programming” reflects its widespread implementation across diverse landscape simulation and optimization mathematical modelling frameworks, while the intentional repetition of “multi-objective optimization” underscores its central relevance in balancing economic, environmental, and social objectives within sustainability-focused studies.