Validating a Decision-Support Framework for Optimal Calf Weaning in South African Beef Systems Using the Delphi Technique

Abstract

Calf weaning plays a fundamental role in the sustainability of cow-calf production systems. In South Africa, conventional weaning at six to nine months is widely practiced, but increasing climatic variability has highlighted early weaning as an adaptive strategy. To support producers in determining the optimal weaning age, we developed a Calf Weaning Decision-Support Framework through an extensive literature review. To ensure its practicality, we validated the framework using the Delphi technique, incorporating real-world insights from livestock experts. A two-round Delphi study was conducted with ten experts in livestock production and research, evaluating key factors influencing weaning age decisions. The study also used the Relative Importance Index (RII) to rank these factors based on expert consensus. The main findings showed strong agreement on productive factors, including weaning weight, conception rate, and dam body condition score alongside financial aspects that influence profitability, such as calf health and feeding expenses, as well as income generated at weaning. Experts identified three additional factors—cattle breed, enterprise cash flow needs, and veld type, emphasizing the need for flexible weaning strategies tailored to specific conditions. This study concludes that the expert-validated framework is a practical and adaptable tool, empowering South African beef producers to make informed, context-specific weaning decisions.

1. Introduction

Weaning age is a critical management decision in cow-calf production systems, shaping not only the immediate productivity of calves but also the long-term sustainability of cattle operations [1]. Decisions around the timing of weaning significantly influence feed efficiency, dam recovery, reproduction rates, and overall resource allocation, which are factors that have become increasingly essential under the dual pressures of climate variability and shifting market demands [2]. As such, adjusting weaning strategies can contribute directly to Sustainable Development Goal (SDG) 2 (Zero Hunger) by enhancing livestock productivity, improving reproductive efficiency, and ultimately increasing food availability through more stable and predictable beef supply chains. Simultaneously, early and contextually appropriate weaning practices support SDG 12 (Responsible Consumption and Production) by promoting the more efficient use of grazing, land, and water resources, especially during times of drought or forage scarcity [2], which are conditions characteristic to South Africa. As weaning practices influence cow-calf productivity and the overall resilience of beef production, understanding and refining this management decision is essential for farmers navigating the complexities of modern cattle farming [3,4,5].

Globally, weaning ages vary significantly across cattle operations [1,6]. In South Africa, conventional weaning (CW), typically between six and nine months, remains the standard practice in beef cow-calf systems largely due to its alignment with seasonal forage availability and dam lactation cycles [7]. However, the increasing prevalence of climatic challenges, such as drought and inconsistent rainfall, has underscored the potential of early weaning (EW) as a climate-smart adaptive strategy that can reduce the nutritional burden on dams, enhance body condition recovery, and improve conception rates under harsh conditions [8,9]. These adaptive benefits have meaningful implications not only for farm-level resilience but also for national food security, environmental stewardship, and sustainable livestock production, which are key targets within both SDGs 2 and 12.

Despite the potential benefits of EW, its adoption in South African beef production still needs to be improved. Additionally, producers base weaning decisions on factors beyond climatic conditions, such as calf solid feed intake, housing capacity, and overall calf health [5,10]. The existing literature highlights a significant variation in adopting weaning practices globally, with some producers demonstrating reluctance to implement EW due to concerns over calf welfare, reduced weaning weights, and increased operational costs [11,12]. However, several studies underscore the economic and environmental benefits of adjusting weaning practices to align with specific farm conditions, particularly under high stocking rates [9,13].

Given the complexities surrounding weaning age decisions, there are few structured, evidence-based tools or decision-support frameworks explicitly designed to guide producers in selecting optimal weaning ages. Globally, most existing weaning frameworks are limited to productivity-based or partial economic assessments, often focusing narrowly on nutritional requirements, post-weaning growth, or economic returns without integrating the multifactorial nature of real-world decision making [4]. Moreover, these tools are typically developed in regions outside sub-Saharan Africa contexts with little adaptation to diverse production environments such as those in sub-Saharan Africa. This gap in practical, regionally adapted tools highlights the need for frameworks that consider a broader range of biological, environmental, and economic variables, especially under resource-constrained and variable climatic conditions accustomed to South Africa.

In response, Jammer et al. [14] developed an Integrated Calf Weaning Framework following the Best Fit Framework Synthesis by Carroll et al. [15], synthesizing key factors influencing optimal weaning age in cow-calf production systems. That study systematically synthesized existing frameworks and decision-support models using the BeHEMoTh strategy, which guided the development of an a priori framework based solely on pre-existing conceptual frameworks of calf weaning. This was followed by the SPIDER technique to identify and integrate findings from primary research studies (quantitative and qualitative) that validated and extended the framework by incorporating factors that were missing or underrepresented in existing models. This process resulted in an integrated science-informed, literature-based framework addressing key financial, reproductive, and management factors influencing calf weaning decisions.

However, despite its evidence-based foundation, the Integrated Calf Weaning Framework remained a theoretical tool without practical validation. As Inglis [16] have emphasized, the practical utility and effectiveness of any decision-support framework depend on its validation in real-world settings, ensuring it reflects the diverse production environments, priorities, and constraints that actual producers face. Without practical validation, frameworks risk being theoretically sound but functionally limited when applied by farmers or advisors involved in everyday farming operations.

This study addresses that limitation gap by practically validating the Integrated Calf Weaning Framework through expert consensus using the Delphi method, which is a widely recognized approach for testing and refining frameworks for complex decision making. Unlike the earlier framework development phase by Jammer et al. [14], this study engages a panel of experienced livestock experts, producers, and advisors from in Argentina and South Africa’s varied agro-ecological zones, incorporating their real-world insights to judge the framework’s relevance, completeness, and usability for beef cattle producers. In doing so, this study contributes a significant and novel methodological advance by moving beyond a literature-based framework to practical, expert-informed refinement. It ensures that the framework is not only scientifically effective but also applicable, adaptable, and valuable as a decision-support tool for cow-calf producers operating under diverse South African production conditions.

2. Materials and Methods

2.1. The Proposed Calf Weaning Framework (Figure 1)

The calf weaning framework was initially developed in response to a key research gap of limited existing frameworks that can be used to comprehensively guide beef cattle producers on all the factors to consider when deciding on the optimal weaning age of calves. A defined research question was then structured: “What productive and financial factors should be incorporated into a framework to support South African beef cattle producers in making informed decisions about optimal calf weaning age?”

Figure 1 schematically illustrates the defined research question.

Figure 1. “Thinking through weaning?” A cattle producer’s decision journey. Source: Authors’ compilation.

Figure 1. “Thinking through weaning?” A cattle producer’s decision journey. Source: Authors’ compilation.

Furthermore, Figure 2 presents the Integrated Calf Weaning Decision-Support Framework [14], placing weaning age at the center of the decision-making process for beef cattle producers. The framework illustrates the dynamic interaction between productive and financial factors, emphasizing how biological performance and economic considerations jointly influence weaning age decisions. On the productive side, elements such as dam re-conception and calf growth interact with nutritional factors, reflecting how herd biology responds to changes or adjustments in weaning age.

On the financial side, revenues from calf sales are weighed against costs related to feed, labor, and replacement heifers. The framework visually underscores trade-offs; for instance, EW may enhance reproductive efficiency and dam recovery but increase calf rearing costs, whereas CW may optimize calf weights but extend resource demands on the dam. This integrative structure helps producers navigate complex decisions by balancing short-term costs with long-term productivity goals.

By analyzing these interconnected factors, the framework guides farmers in making an informed weaning decision that aligns with their production goals, environmental conditions, and financial constraints. To enhance its practical relevance, it is now essential to validate the framework [15] in a real-world South African beef cattle producer setting. The following section provides an in-depth discussion of the validation process.

2.2. Validating the Framework Using the Delphi Technique

Several methods exist for validating frameworks, as Inglis [16] outlined, including literature reviews, expert panel consultation, empirical research, survey research, pilot projects, interviews and case studies. Among these, the Delphi technique [17] is widely used to establish expert consensus on complex issues within specific domains [18]. This method allows researchers to obtain input from geographically dispersed experts without requiring physical meetings, thereby reducing time constraints for participants [19]. In validating a quality framework, expert panelists may be asked to assess specific scenarios, in this case, determining the optimal weaning age for beef calves [20]. The Delphi technique was therefore selected as an appropriate approach for this study phase.

This method ensures anonymity, minimizing biases such as group conformity and encouraging the open expression of expert opinions [17,21]. Furthermore, it supports the structured collection of insights from diverse experts, making it particularly valuable for refining emerging frameworks, guidelines, or policies [22]. Initially developed by Dakey and Helmer [23], the Delphi process involves expert selection, questionnaire design, scoring methods, multiple iteration rounds, and data analysis. Key features include anonymity, iterative feedback, the statistical aggregation of responses, and expert-driven consensus [24,25]. This structured approach enhances the reliability of findings by reducing individual bias while fostering collective expertise, making it an effective method for framework validation.

2.3. Delphi Data Collection and Obtaining Consensus Among Experts

The Delphi study was conducted between December 2024 and February 2025 using a structured questionnaire in each phase of two rounds to collect primary data from a panel of 10 experts in livestock production and research. The panel comprised livestock production and research specialists from South Africa and Argentina. It included six primary livestock producers actively managing cow-calf production systems or serving as cattle association board members as well as four academics or research specialists focusing on livestock production or animal breeding. To ensure the accuracy and reliability of the study, the authors established selection criteria for panelists, ensuring that only individuals with relevant expertise and experience contributed to the Delphi process [23]. This approach helped obtain well-informed opinions and enhance the credibility of the findings.

To qualify as an expert, participants were required to meet at least one of the following criteria:

• Conducted independent research in beef cattle production and breeding.
• Authored and published peer-reviewed research articles on livestock production or cattle breeding.
• Possessed at least 10 years of industry experience in beef cattle management, breeding, or genomic selection.
• Held a leadership role in a livestock producer association or advisory board related to cattle production.
• Contributed to developing or implementing livestock policies, management strategies, or genetic improvement programs.
• Provided expert consultation or advisory services to cattle producers on breeding, nutrition, or herd management.

Contextual relevance and geographical representation

To ensure contextual relevance and regional applicability, the expert panel was intentionally composed to include participants from diverse agro-ecological zones within South Africa with one expert based in Argentina to provide an external comparative perspective. This geographic spread was important for capturing variation in environmental constraints, management practices, and production goals across different cattle-farming regions.

Specifically, the panel included a cow-calf producer from Vrede in the Free State, representing temperate grassland systems with a mix of cultivated pastures and variable seasonal forage. Moreover, one expert was based in Bultfontein, Free State, a region known for its high maize production and mixed farming systems, and also operated farms in the North West province, which is characterized by bushveld grazing systems and lower rainfall variability. Other participants served as a breed technical advisors, contributing insights from a national scope across multiple production zones. A producer from the Kalahari region in the Northern Cape, where extreme low rainfall and extensive grazing systems dominate, brought perspectives on early weaning under arid conditions. In contrast, a participant from the Aliwal North region in the Eastern Cape, characterized by sourveld grazing and moderate rainfall, emphasized the seasonal nature of pasture quality and associated weaning flexibility. Additionally, one expert from KwaZulu-Natal, a high-rainfall and subtropical region, contributed knowledge relevant to intensive grazing systems with higher biomass availability.

The remaining panelists were livestock researchers affiliated with South African universities, offering complementary scientific and technical expertise. Their inclusion ensured that empirical and theoretical insights supported practitioner perspectives.

By drawing from experts across varied production settings, the Delphi panel allowed the study to integrate both scientific knowledge and practical, region-specific realities. This diversity strengthened the framework’s relevance and adaptability to South Africa’s heterogeneous beef production systems

Sample Size Justification

The existing literature suggests that a Delphi panel should consist of approximately 8 to 18 experts to minimize variations, reduce the risk of false consensus, and enhance data reliability [26,27,28]. In line with these recommendations, 10 experts who met the inclusion criteria were identified and invited via email, which outlined the study’s objectives and methodology. Ethical approval was granted by the Institutional Review Board (or Ethics Committee) of the University of the Free State (ethical clearance no: UFS-HSD2024/2549, date of approval: 12 December 2024 for round 1 and 20 February 2024 for round two).

Consensus Measurement Approach

The degree of consensus reached after each Delphi round determines whether additional rounds are required in the research process. Various methods can be used to evaluate the degree of agreement among respondents with differing opinions. These methods include the coefficient of variation, interquartile range, and standard deviation (SD). According to Loughlin and Moore [29], consensus can be established with 51% agreement among respondents. Eagle and Iverson [30] reported a consensus threshold of 60% agreement for a specific score on a five-point Likert scale. The coefficient of variation is widely used in Delphi studies to assess the dispersion of opinions and monitor consensus progression across successive rounds. A decrease in the coefficient of variation from one round to the next signifies increasing agreement among respondents [31]. A coefficient of variation below 0.5 is generally considered indicative of reasonable internal agreement [32].

Table 1. Coefficient of variation cut-off points and decision rules.

Coefficient of Variation	Decision Rule
0 ≤ V ≤ 0.5	A good degree of consensus, no additional round
0.5 < V < 0.8	Less than satisfactory consensus, possible need for another round
0.8 ≤ V	There is a poor degree of consensus regarding, need for an additional round

Source: [33].

presents the consensus levels determined using the coefficient of variation.

Additionally, standard deviation (SD) is a measurement used to assess the variation in a population. Standard deviation indicates stability in consensus and convergence in the round agreement [19]. The lower the standard deviation, the higher the level of agreement between the respondents [19]. This study implemented the level of consensus indicated by Grobbelaar [34] as a guideline on which to base consensus decisions in terms of the SD, as shown in

Table 2. Decision criteria used to determine the consensus levels based on the standard deviation.

Standard Deviation	Level of Consensus Achieved
0 ≤ X ≤ 1	High level
1.1 ≤ X ≤ 1.49	Reasonable/fair level
1.5 ≤ X ≤ 2	Low level
2.1 ≤ X	Non-consensus

Source: [34].

.

These metrics were calculated using Microsoft Excel (2013) to track consensus between rounds. The use of SD provided an effective view of agreement levels and helped guide the decision to conclude the Delphi process after two rounds [34].

2.4. The Delphi Questionnaire

In the first round, each one of the experts was asked to complete a questionnaire comprising components within the Calf Weaning Framework. The experts were asked to provide qualitative feedback on the framework components in this round. The questionnaire consisted of statements to which the panel of experts answered according to a Likert scale from 1 to 5, indicating their level of agreement with factors in the framework that influence a beef cattle producer’s decision regarding the weaning age of calves (early or conventional). A score of 5 indicated that the respondents strongly agree with the factor influencing calf weaning age decisions. In contrast, a score of 1 indicated that the livestock experts strongly disagree that this factor would influence a producer’s decision regarding calf weaning age.

Round 1 of the Delphi Study:

The first round of questions in the Delphi study were presented as follows:

Question 1:

“Would the following factors be considered by cattle producers when deciding on the weaning age of calves?”

Productive factors:

• Conception rate of dams after weaning at a particular age (early or conventional);
• The mortality rate of calves after weaning at a specific age (early or conventional);
• Calving age of the dam;
• Birth weight of the calf;
• Average Daily Gain (ADG) of the calf after weaning at a particular age (early or conventional);
• Weaning weight of the calf after weaning at a particular age (early or conventional);
• The objective of the farmer is to finish calves in the feedlot (feedlot finishing);
• Feed intake and Feed Conversion Ratio (FCR) of calves after weaning at a particular age (early or conventional);
• Body Condition Score (BCS) dams after calving and weaning at a particular age (early or conventional);
• Stocking rate provisioned by a particular weaning age;
• Weaning season (summer vs. autumn weaning);
• Rainfall (wet vs. dry year of production).

Financial factors:

• Revenue generated from calf sales after weaning at a particular age (early or conventional);
• Revenue generated from culled cow sales after weaning at a particular age (early or conventional);
• Calf labor cost associated with a particular weaning age (early or conventional);
• Calf feed cost associated with a particular weaning age (early or conventional);
• Calf health cost associated with a particular weaning age (early or conventional);
• Calf housing cost associated with a particular weaning age (early or conventional);
• Supplement cost of herd pre- and post-weaning;
• Health cost of herd pre- and post-weaning;
• Labor cost of herd pre- and post-weaning;
• Cost of developing replacement heifers after weaning them at a particular age (early or conventional).

Respondents were also asked to suggest any additional factors not currently considered to influence calf weaning age decisions by beef cattle producer’s factors. Question two was asked as an open-ended question.

Question 2:

Are there any additional factors influencing calf weaning age decisions that were not previously considered in the proposed framework? Responses to this question were analyzed using a basic thematic categorization approach [15], which is suitable for structured, short-text responses where participants directly named factors and provided brief contextual justifications. Explanatory notes accompanying each factor were used to understand how the producer framed its relevance within the South African production context. The findings from this question were then used to extend the original framework by incorporating these additional context-specific opinions.

Round 2 of the Delphi Study:

Additionally, the second round of the Delphi study aimed to assess the degree to which respondents agreed with the initial findings while incorporating additional information and factors identified in the first round. The revised questionnaire was distributed to the same panel of experts, allowing respondents to confirm or adjust their previous answers and modify their agreement level as needed. Panel members were also encouraged to score the additional factors identified in the initial round. Furthermore, the second round sought to evaluate the level of consensus among respondents’ revised answers, ensuring a more refined and representative outcome.

2.5. Establishing a Relative Importance Index of Framework Factors

After confirming the factors within the framework, this study applied the Relative Importance Index (RII) method [35] to assess each factor’s level of importance according to the livestock experts. The RII, commonly used in decision-making studies, ranks factors based on expert evaluations [35]. This research study employed the RII to determine the relative importance of various factors influencing producers’ decisions on the optimal weaning age of calves. The ranking reflects livestock experts’ perceptions and levels of agreement gathered through surveys or research studies. Consequently, the RII would provide valuable insights for informed decision making in cow-calf management. The following equation (Equation (1)) was used:

$$RII={\displaystyle \frac{\sum w}{A\times N}}$$

(1)

where

W = response given by each expert (Likert scale value);

A = highest possible Likert score (5, using a 1–5 Lickert scale);

N = total number of responses (experts).

Likert scale values: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree and 5 = Strongly Agree.

According to Peter et al. [35], higher values indicate stronger agreement. Higher RII values (closer to 1.0) indicate a strong expert agreement that a specific factor is important in weaning decisions. Meanwhile, lower RII values (closer to 0.0) indicate less agreement among experts on the importance of the factor.

Figure 3 illustrates the validation process of a conceptual framework using the Delphi technique. Initially, a Delphi study (Round 1) is conducted to gather expert opinions, leading to a revised framework. If consensus is not reached or new factors emerge from Round 1, a second Delphi study (Round 2) is conducted. Upon achieving expert consensus, the framework is accepted; otherwise, further revisions and Delphi repetition will be necessary. This iterative process ensures the framework is refined based on expert input.

3. Results and Discussion

This section will present the responses obtained from the Delphi study. Firstly,

Table 3. Summary of results for the initial round of the Delphi study, illustrating the average, SD, mode, mean, and consensus level for factors mentioned by the panel of livestock experts.

Factor	Average	SD	Mode	Median	Level of Consensus
Productive factors
Dam conception rate	4.7	0.67	5	5	High
Calf mortality rate	3.7	1.52	5	4	Low
Calving age of the dam	4.1	0.73	4	4	High
Birth weight of the calf	2.5	1.08	3	3	Reasonable/Fair
Average Daily Gain (ADG) of the calf	3.9	1.19	5	4	Reasonable/Fair
Weaning weight of the calf	4.7	0.48	5	5	High
Feedlot finishing	3.2	1.35	3	3	Reasonable/Fair
Feed intake and Feed Conversion Ratio (FCR)	3.5	0.85	3	3.5	High
Body Condition Score (BCS) dams	4.1	0.99	5	4.5	High
Stocking rate	3.1	0.94	4	3	High
Weaning season	3.7	0.82	3	3.5	High
Wet vs. dry year of production	4	0.94	3	4	High
Financial factors
Revenue generated from calf sales	4.4	1.10	5	5	Reasonable/Fair
Revenue generated from culled cows	3.8	0.97	4	4	High
Calf labor cost	3.3	1.11	3	3	Reasonable/Fair
Calf feed cost	4	1.32	5	4	Reasonable/Fair
Calf health cost	4.1	0.86	5	5	High
Calf housing cost	3.1	1.43	1	3	Reasonable/Fair
Supplement cost of herd	3.6	1.32	3	4	Reasonable/Fair
Health cost of herd	3.7	1.65	5	4	Low
Labor cost of herd	2.9	1.27	3	3	Reasonable/Fair
Cost of developing replacement heifers	4	1.5	5	5	Low

Source: Authors’ calculations.

summarizes the results for the initial round of the Delphi study, illustrating the average, SD, mode, mean, and consensus level for factors mentioned by the panel of livestock experts.

Figure 4 graphically represents the responses of livestock experts to the productive factors included in the proposed Calf Weaning Framework.

A Likert scale from 1 to 5 was used to capture the experts’ level of agreement with a score of 5 indicating strong agreement and 1 indicating strong disagreement regarding the relevance of each factor.

Consensus Analysis

The consensus level among experts was evaluated using standard deviation (SD) to guide the inclusion or exclusion of factors within the framework [31]. Table 3 shows a substantial prevalence of consensus among the expert responses with 91% of all the factors demonstrating a reasonably fair to high level of agreement. Regarding the productive factors of the framework, Table 3 indicates that the majority (67%) of the factors attained a high level of consensus, whereas 25% of the factors attained a reasonable/fair level of consensus. Notably, only one factor, the mortality rate of calves after weaning, exhibited a low level of consensus (8%), suggesting it may be less critical in influencing weaning age decisions. However, none of the factors received a complete lack of consensus, affirming the validity of all the proposed productive factors for inclusion in the Calf Weaning Framework.

Key Findings

The Delphi study identified key productive factors (Figure 3) influencing South African beef cattle producers’ weaning decisions based on expert consensus (average agreement score ≥ 4). The most significant factors included the following:

• Weaning weight of the calf (4.7)—a critical determinant of calf readiness for weaning and subsequent growth performance.
• Conception rate of dams after weaning at a specific calf age (4.5)—reflecting the impact of weaning timing on reproductive efficiency.
• Body Condition Score (BCS) of the dam (4.2)—indicating the dam’s nutritional status and ability to conceive post-weaning.
• Calving age of the dam (4.0)—highlighting the influence of maternal age on weaning management and herd productivity.

These findings provide valuable insights for optimizing weaning strategies in South African beef production systems, which aligns with studies by Meyer et al. [9] and Taylor et al. [12], emphasizing the critical importance of these factors in optimizing weaning practices to enhance herd productivity. Taylor et al. [12] explicitly mentioned that in the case of first-time calving heifers, beef cattle producers need to critically manage the calving age, which would ensure re-conception after weaning as well as managing adequate BCS favorable for conception by younger dams. Thus, experts viewed these factors as pivotal for ensuring reproductive efficiency, calf performance, and overall herd profitability.

Conversely, factors such as calf birth weight and stocking rate in relation to weaning age (early or conventional) achieved lower levels of agreement among the expert panel. This suggests that while these factors remain relevant within the broader production system, they may not be primary determinants in weaning decisions. This observation contrasts with the conclusions of Julien and Tess [11], who argued that when evaluating alternative weaning dates, especially the stocking rate associated with a specific weaning age serves as a key indicator of herd management strategies for cow-calf producers.

However, in practice, multiple factors influence weaning age, and these determinants may vary depending on the environmental and contextual conditions in which a producer operates, as corroborated by Vaz et al. [36] and Mapiye et al. [37]. This divergence between expert consensus and some literature-based priorities can be explained by contextual factors specific to South African beef production systems. For instance, breed-specific growth curves may reduce the perceived importance of birth weight. Indigenous and locally adapted breeds such as the Bonsmara, Nguni, and Afrikaner often demonstrate moderate birth weights but high postnatal growth efficiency, potentially reducing the need to emphasize birth weight as a selection criterion for weaning decisions [38]. Furthermore, economic pressures, including fluctuating feed prices, variable rainfall, and the cost of maintaining reproductive efficiency frequently cause producers to place greater emphasis on fertility-related traits such as dam conception rate and BCS over traits like stocking rate or calf birth weight. As noted by Mapiye et al. [37], South African cattle systems are often shaped by risk mitigation and cost-efficiency.

These contextual influences underscore the value of Delphi-based expert elicitation, which reveals how local production realities, including environmental constraints and breed performance, recalibrate the significance of weaning decision criteria.

Regional and environmental insights

Responses from cow-calf producers provided additional practical context. Producers represented diverse regions across South Africa and Argentina, differing in rainfall patterns and veld types. For example, a producer in the semi-arid Kalahari region of South Africa emphasized the role of environmental conditions in determining weaning practices. During severe droughts in that region, calves are weaned approximately 1–2 months earlier than their conventional 7-month weaning age to conserve grazing resources and optimize herd efficiency as indicated by the respondent. This observation is consistent with Smith et al. [39], who highlighted the impact of droughts in semi-arid savannas, where increased aridity reduces grazing availability, potentially leading to permanent vegetation degradation. Such environmental pressures necessitate adaptive management strategies, including EW, particularly in regions with low and erratic rainfall.

Implications for the Calf Weaning Framework

The strong consensus among experts and the additional producer insights underscores the effectiveness of the proposed framework. The high agreement on key factors such as conception rate, calving age, BCS, and weaning weight highlights their central role in guiding weaning age decisions. Various studies have indicated that these factors are key in cow-calf producer weaning practices [12,40,41]. Savage [40] particularly highlighted how increased calf weaning weights are beneficial for farm gross revenue under Tennessee climatic conditions, especially when weaners are sold to feedlots and stocker yards. This highlights the significant linkage between productive and financial factors within the framework, indicating that beef cattle producers should always balance these factors for optimal production. Furthermore, including flexible strategies to account for climatic variability, as highlighted by the Kalahari producer, strengthens the framework’s practical relevance. It integrates biological and environmental considerations, ensuring producers can make informed, context-specific decisions to optimize productivity and sustainability in their cow-calf systems.

Results: Financial Factors in Round One of the Delphi Study

Experts also expressed their level of agreement on the financial factors in the proposed calf weaning framework (Figure 5). A Likert scale from 1 to 5 was used to capture experts’ level of agreement with a score of 5 indicating strong agreement and 1 indicating strong disagreement regarding the relevance of each factor.

Consensus Analysis

As highlighted by Grobbelaar [31], consensus was assessed based on standard deviation (SD) to determine which financial factors should be included in the framework. The results indicate that all financial factors achieved a consensus sufficient for inclusion. Specifically, Table 3 shows that 60% of the factors attained a reasonable/fair level of consensus, 10% a high level of consensus, and 30% a low level of consensus. Notably, no factor received a complete lack of consensus, validating the relevance of all financial considerations in the framework.

Key Financial Factors Influencing Weaning Age

Livestock experts identified three financial factors surfaced as the most influential when beef cattle producers determine the optimal weaning age for calves in South Africa:

• Revenue generated from weaned calves.
• Calf feed costs.
• Calf health costs.

These factors achieved a high level of consensus, emphasizing their importance in cow-calf production profitability. The findings align with Camargo et al. [4], who highlight that weaning age decisions significantly impact the financial sustainability of cow-calf systems, particularly in cost-intensive production environments.

Impact of These Financial Factors on Weaning Decisions

Revenue through calf sales

Weaning age directly influences farm revenue by affecting calf marketability and herd efficiency [12]. Early weaning (EW) enables producers to market calves sooner, potentially capitalizing on favorable prices while allowing for the earlier culling of unproductive cows. This reduces maintenance costs and improves overall herd productivity as found by Camargo et al. [4]. Conversely, conventional weaning (CW) delays calf sales and may increase feeding expenses, mainly if market conditions fluctuate. However, delayed weaning dates or extended suckling often increases weaning weights, eventually increasing the revenue generated per live weaner weaned and sold [40]. Producers must consider beef price trends and production costs when determining the most profitable weaning age.

Costs related to feeding calves

Weaning age directly impacts feeding expenses. EW calves require costly supplements for longer, while CW calves rely on dam’s milk, reducing direct feed costs but increasing the cow’s nutritional demands. EW calves incur higher direct feeding expenses, as they must be supplemented with creep feeds or concentrate diets to meet their growth requirements in the absence of maternal milk. These additional expenses may influence cow-calf producers decision regarding the timing of weaning, as emphasized by the experts in the Delphi study. Furthermore, forage availability also plays a role, where drought conditions may necessitate EW to ease grazing pressure despite higher cost of feeding.

Tatham et al. [41] emphasize that while EW practices increase feed inputs for calves, they also provide enterprise-level benefits by allowing earlier rebreeding of dams and reducing grazing pressure on pastures, which is a factor especially relevant in drought-prone or overgrazed systems. Their findings suggest that EW can be strategically deployed to stabilize pasture use and improve reproductive efficiency, even if calf nutritional expenses temporarily rise. This also aligns with Arthington and Minton [8], who recommend EW not as a default strategy but as a tactical response by cattle producers to forage limitations, cow condition, and broader production goals. This approach contrasts with the general assumption that CW is always more economical, highlighting the need to evaluate feeding strategies within the context of system-wide efficiency and resource constraints.

Cost related to calf health

Health costs vary with weaning age. EW calves face higher stress and disease risks, which increase veterinary expenses. CW calves benefit from extended maternal immunity, lowering health costs. However, EW with targeted nutrition may be a better option in drought conditions.

Implications for the Calf Weaning Framework

The strong consensus on revenue from culled cows, calf health and feed costs underscores their significance in optimizing financial outcomes for beef cattle producers. While factors such as replacement heifer development and herd health costs exhibited lower levels of agreement, they remain relevant and should be retained in the framework based on the SD criteria. Overall, the Delphi results confirm that financial considerations play a crucial role in weaning decisions with producers evaluating multiple cost and revenue components before determining an optimal weaning strategy, which confirms findings in the literature [10,11,42]. Julien and Tess [11] demonstrated that weaning age influences profitability through its effects on feeding expenses, grazing season length, and reproductive timing. Their study showed that EW, while increasing the cost of feeding calves, can enhance system profitability by reducing cow maintenance costs and allowing earlier rebreeding. Similarly, Hudson et al. [42] found that EW calves, though more expensive to feed initially, performed well in feedlot settings and provided flexibility in marketing, highlighting the role of strategic timing in maximizing returns. These findings support a broader view that profitability is shaped not only by reducing costs but by aligning production practices with market opportunities and resource conditions.

By integrating such financial dynamics, the framework reflects the complex practicality South African producers face, especially under resource constraints. While Ahloy-Dallaire et al. [10] link calf welfare indicators to broader production outcomes, it is studies like those of Julien and Tess [11] and Hudson et al. [42] that directly illustrate how weaning decisions shapes financial outcomes in real-world systems.

Furthermore, livestock experts identified three additional factors in round one, which were not included in the first-round questionnaire and proposed framework.

Additional factors influencing weaning age decisions identified by livestock experts include the following:

1.

Cattle Breed

Different breeds exhibit varying growth rates, milk production, and adaptability to environmental conditions, influencing the ideal weaning age. For instance, Bonsmara cattle, commonly used in South Africa, differ in calf growth patterns and resilience to harsh climates [43]. Some livestock experts in the Delphi study emphasized that breeds with higher milk yields may support conventional or later weaning, while less drought-tolerant breeds may be suited for early weaning strategies.

2.

Enterprise Cash Flow Needs

Weaning age directly impacts cash flow by determining when calves can be marketed or when cull cows can be sold, influencing the financial stability of a cow-calf enterprise [12]. Early weaning (EW) allows producers to generate revenue sooner, which can be particularly beneficial when there is an urgent need for liquidity. In contrast, conventional weaning (CW) may align better with seasonal market cycles, potentially yielding higher prices but delaying income. A key insight from the Delphi study was that many South African beef producers operate under financial constraints, often carrying debt from banks or other credit facilities. One livestock expert specifically noted that when debt repayments or critical operational expenses become pressing, farmers may be compelled to wean calves earlier and sell them to boost cash flow and meet immediate financial obligations. This reflects a broader trend in financially strained operations, where weaning age may be adjusted to optimize short-term liquidity rather than purely based on production efficiency. Thus, financial pressures such as loan repayments, seasonal production costs, or unexpected expenses can play a decisive role in determining weaning strategies. In such cases, EW is a potential financial management tool, ensuring cash is available to sustain farm operations. This was also highlighted by Kneirim [44], who indicated that some producers place greater emphasis on the revenue generated per calf weaned, which enables producers to meet operational expense obligations.

3.

Veld Type

South Africa’s diverse grazing lands, from high-rainfall grasslands to arid savannas, affect forage availability and weaning decisions. In South Africa, veld type is classified into sweetveld, sourveld, and mixed veld, influencing grazing quality, seasonal forage availability, and potentially calf weaning decisions [39]. Livestock experts highlighted that later weaning is feasible in the nutrient-rich veld. In contrast, in semi-arid regions like the Kalahari, early weaning may prevent overgrazing and maintain herd productivity, especially during droughts. Additionally, the producer from the Aliwal North (Eastern Cape) region was operating within a sourveld system with moderate rainfall, where declining pasture quality during the dry season would potentially necessitate more adaptive weaning timelines. From KwaZulu-Natal, a region characterized by higher rainfall, the producer contributed perspectives from intensive grazing systems where later weaning is feasible due to prolonged forage availability and better pasture management opportunities, which emphasized the importance of farmer contributions to the framework. Collectively, these contributions underscore how veld type not only determines nutritional constraints but also interacts with regional management practices, production goals, and economic realities, thereby influencing the feasibility and timing of both early and conventional weaning strategies. This diverse input ultimately enriched the framework’s effectiveness, ensuring its adaptability across South Africa’s varied beef production landscapes. This is emphasized in literature by Vaz et al. [36], who found that environmental factors such as annual rainfall and forage availability significantly differ among various regions, necessitating region-specific farm management practices by producers.

Rationale for the Delphi Second Round

The decision to conduct a two-round Delphi process was based on the consensus levels achieved in Round 1, the nature of the additional data generated, and methodological guidance from prior Delphi studies [45,46,47,48]. In the first round, the majority (86%) of the framework components reached a reasonable to high level of consensus, and only three of the twenty-two (14%) demonstrated low consensus. None of the factors were rejected or exhibited no consensus at all, indicating that the panel generally aligned on most components of the weaning framework.

Given these results, a third Delphi round was not deemed necessary. As supported by Hsu and Sandford [18] and Szipilko [46], further rounds in a Delphi study are warranted primarily when low consensus persists or when consensus has not yet been reached. In this study, consensus had already been achieved on the all components by Round 1. Round 2 was purposefully used to refine the framework based on new insights from experts (three new factors were introduced) and to allow panelists to re-score components with the benefit of statistical feedback, which is in accordance with Linstone and Turoff [45].

Moreover, minimizing the number of rounds helped reduce the risk of respondent fatigue, which is a known limitation to Delphi data quality in longer studies [14,47]. Since the newly introduced factors in Round 1 were the only elements requiring additional validation, the two-round design allowed for a focused and efficient process while still ensuring effective, consensus-driven results. This design choice aligns with established best practices recommending that Delphi rounds be limited when early consensus is achieved as emphasized by Powell [48] and Avella [17].

Results of Round 2 of the Delphi Study:

A second questionnaire was developed based on the responses from the first round. In Round 2 of the Delphi study, all panelists retained their original responses for the existing factors identified in Round 1, leading to the same level of consensus as previously achieved [45]. This reaffirmed that these factors should be included in the validated calf weaning framework, as they consistently reflected the most critical considerations for calf weaning decisions among livestock experts. As seen by the consensus levels in

Table 4. Summary of results for the second round of the Delphi study, illustrating the average, SD, mode, mean, and consensus level for factors mentioned by the panel of livestock experts.

Factor	Average	SD	Mode	Median	Consensus Level
Newly identified factors from round 1
Cattle breed	4	1.3	4	4	Reasonable/Fair
Cashflow needs	3.1	1.4	3	3	Reasonable/Fair
Veld type	4.4	0.7	5	4.5	High

Source: Authors’ calculations.

, the stability in responses highlights a significant [31] level of agreement and reliability in the expert panel’s evaluation, reinforcing the effectiveness of the framework.

Regarding the three newly introduced factors, veld type received a high level of consensus (SD = 0.72), indicating strong agreement on its relevance in calf weaning decisions. This suggests that producers widely recognize the influence of grazing conditions on calf performance and dam recovery post-weaning, particularly in diverse South African environments where forage availability varies significantly.

Cattle breed and cash flow needs received a reasonable or fair level of consensus, suggesting that while these factors are important, their significance may be more context dependent. The variability in consensus for these factors may stem from differences in production systems, financial structures, and breed adaptability to different weaning strategies [9].

Consensus Analysis

From a consensus analysis perspective, the retention of initial responses for existing factors and the moderate-to-high agreement on new factors indicate strong content validity in the finalized framework. The Delphi process successfully refined the framework without requiring unnecessary reassessment of well-established components. For cow-calf producers, these results imply that veld type should be explicitly accounted for in weaning decisions given its direct impact on nutrition and cow-calf dynamics. The inclusion of cattle breed and cash flow needs recognizes the economic and genetic management strategies producers must balance, suggesting that while these factors may not be universally prioritized, they are still relevant considerations in optimizing weaning outcomes under diverse production systems [44]. Overall, the validated calf weaning framework now comprehensively reflects both biophysical and economic realities, ensuring its applicability across varied operational contexts in South African beef production.

The relative importance of these factors for South African beef cattle producers

The Delphi study results highlight the weaning weight of the calf (RII = 0.94) and dam conception rate (RII = 0.95) as the most important productive factors influencing calf weaning decisions, emphasizing their direct impact on herd productivity and reproductive efficiency (

Table 5. Ranking of the most important productive factors influencing beef cattle producer’s decisions on calf weaning age.

Production Factor	RII Value	Factor Importance Rank
Weaning weight of the calf	0.94	1
Dam conception rate	0.95	2
Calving age at the dam	0.83	3
Veld type	0.82	4
Body Condition Score	0.81	5
Wet vs. dry year of production	0.80	6
Calf Average Daily Gain	0.78	7
Weaning season	0.75	8
Calf mortality rate	0.74	9
Cattle breed	0.71	10
Feed intake and Feed Conversion Ratio	0.70	11
Feedlot finishing	0,64	12
Stocking rate	0.62	13
Birth weight of the calf	0.5	14

Source: Authors’ calculations.

).

Table 5 indicates that calf weaning weight is particularly important, as it not only determines post-weaning growth potential but also directly influences financial outcomes through calf liveweight market value, illustrating how some productive factors spill over into financial considerations. These observations corroborate studies by Guimaraes et al. [49] and Savage [40]. Guimarães et al. [49], through simulation modeling, emphasized that calf weight at weaning is a key driver of income in conventional suckler systems, noting that small changes in weaning weight can significantly alter gross margins. Similarly, Savage [40] found that later weaning dates, which typically result in heavier calves, often improve net returns, although this benefit must be weighed against increased cow maintenance and forage demands. By integrating these insights, the framework more accurately reflects the way productive traits like weaning weight influence financial decision making, bridging biological performance with enterprise profitability in line with established economic analyses.

Additionally, dam conception rate is crucial for maintaining annual calving cycles and herd replacement efficiency, as delayed rebreeding can negatively affect long-term productivity. Factors such as calving age at the dam (RII = 0.83), veld type (RII = 0.82), Body Condition Score (RII = 0.81) and weaning seasonal variations (RII = 0.80) reinforce the role of both biological efficiency and environmental conditions in determining the optimal weaning age. A younger dam may still be growing, which can affect milk production and future fertility, while body condition score directly influences reproductive success and the ability of the dam to recover post-weaning [12]. Veld type (RII = 0.82) ranks highly among factors influencing weaning age, highlighting the significance of forage availability and quality in calf growth and dam recovery. This is highlighted by Smith et al. [39], who stated that different veld types ranging from sweetveld with higher nutritional value to sourveld with lower digestibility directly impact the ability of cows to maintain body condition and produce sufficient milk for calves. Producers operating in regions with lower-quality grazing may need to adjust weaning age to prevent excessive nutritional stress on the dam, ensuring both calf performance and herd sustainability.

The relatively lower ranking of cattle breed as a determinant of weaning age among South African beef cattle producers may be attributed to the adaptability of locally prevalent breeds, such as Bonsmara and Nguni [43], which are well suited to extensive grazing systems and variable climatic conditions. Since these breeds have been selected for resilience and efficient postnatal growth, producers may prioritize management factors such as nutritional supplementation and disease control over genetic selection when determining weaning age. Interestingly, birth weight (RII = 0.50) ranked lowest, which was likely because postnatal growth and survival rates have a more significant impact on overall production outcomes. This observation contradicts Johansen and Berger [50] who found that birthweight is a significant contributing factor to calf weaning weight and ultimately influencing producers weaning practices applied. These rankings provide empirical support for the calf weaning decision-support framework, aligning with existing literature on the economic and reproductive trade-offs in cow-calf production systems while also presenting potential areas where producer priorities may diverge from conventional weaning guidelines.

Moreover, the Delphi study results also identify important financial factors influencing beef cattle producers’ decisions on calf weaning age with revenue generated from calf sales (RII = 0.90) ranking as the most important (

Table 6. Ranking of the most important financial factors influencing beef cattle producer’s decisions on calf weaning age.

Production Factor	RII Value	Factor Importance Rank
Revenue generated from calf sales	0.90	1
Cost of developing replacement heifers	0.82	2
Calf health cost	0.80	3
Calf feed cost	0.77	4
Herd health cost	0.76	5
Revenue generated from culled cow sales	0.75	6
Calf feed cost	0.74	7
Calf labor cost	0.68	8
Calf housing cost	0.62	9
Herd labor cost	0.60	10
Cash flow needs	0.58	11

Source: Authors’ compilation.

). This highlights that producers prioritize financial returns when determining weaning strategies, as calf sales represent a primary income stream reflecting the results of Camargo et al. [4] and Taylor et al. [12]. Camargo et al. [4] used an economic sub-model to compare early and conventional weaning practices, and they highlighted that weaning age is a key driver of cow-calf economic performance, particularly income earned through weaner sales, and should thus be managed with precision by producers.

The cost of developing replacement heifers (RII = 0.82) follows closely, emphasizing the long-term investment required to sustain herd productivity. Calf health costs (RII = 0.80) and feed costs (RII = 0.77) further underscore the economic burden associated with calf rearing particularly in systems where supplementation is necessary. Additionally, herd health costs (RII = 0.76) and revenue from culled cow sales (RII = 0.75) indicate that both veterinary expenses and culling strategies factor into financial decision making. Lower-ranked considerations such as calf labor cost (RII = 0.68), housing cost (RII = 0.62), overall labor cost (RII = 0.60) and enterprise cashflow needs suggest that while operational expenses are relevant, they are secondary to direct revenue and major input costs. This ranking reflects the economic trade-offs producers must balance when deciding on optimal weaning age, aligning with the existing literature [8,40,41,43] that emphasizes the need to weigh market returns against input expenditures particularly for South African cow-calf production systems. Mare [43] corroborated that the relationship between weaning weight and post-weaning performance plays a critical role in shaping future profitability. The author [43] mentioned that lighter calves tend to exhibit stronger feedlot performance, faster finishing times, and better carcass traits, which are factors that can command premiums in both domestic and export markets. Finally, the refined and validated calf weaning decision support framework is illustrated in Figure 6.

Figure 7 presents a visual schematic of the validated Calf Weaning Decision-Support Framework, outlining the key factors influencing South African beef cattle producers’ decision-making process regarding weaning age as validated by South African livestock experts.

The framework is divided into two main categories: Productive Factors and Financial Factors, both contributing to the determination of the optimal weaning age.

On the left side, Productive Factors include variables related to biological efficiency, environmental conditions, and herd management, such as feedlot finishing, re-conception, dam calving age, calf daily gain, stocking rate, and veld type. These factors directly impact calf growth, dam recovery, and overall herd performance.

On the right side, Financial Factors highlight the economic considerations producers must weigh, including calf sales, cull cow sales, calf rearing labor costs, feed costs, medication costs, and cash flow needs. These financial aspects influence the profitability and sustainability of weaning strategies.

At the center of the illustration, a farmer is shown contemplating calf weaning age, emphasizing the complexity of the decision-making process. The question marks represent the interaction and uncertainty between these factors, illustrating how South African beef cattle producers must balance both productivity and financial viability to determine the optimal weaning age for their cow-calf operations as validated by this study.

4. Conclusions

This study advances the Integrated Calf Weaning Framework by moving beyond a literature-based structure to a field-validated decision-support tool tailored for South African cow-calf producers. While the original framework was developed through a Best Fit Framework Synthesis to identify productive and financial factors influencing weaning age from existing models and primary studies, it lacked practical testing with those making day-to-day weaning decisions. By incorporating expert consensus via the Delphi method, this study also introduced new critical context-specific variables, such as veld type, cattle breed, and enterprise cash flow needs, which are factors absent from the original framework. The Relative Importance Index (RII) further enhanced the framework by ranking the most influential factors, allowing producers to focus on high priority variables such as calf weaning weight, dam re-conception rate, and key financial drivers relating to calf health and feed expenses. This prioritization strengthens the framework’s utility as a practical, adaptive tool.

For South African beef cattle producers, the expert-validated framework offers a more flexible approach that balances biological performance with economic viability. It supports scenario-based decision making aligned with seasonal forage availability, market fluctuations, and drought risk, enabling producers to optimize weaning strategies under diverse production conditions. For instance, EW may reduce grazing pressure and improve dam recovery during dry periods, while CW can enhance calf selling weights during favorable seasons.

By bridging and expanding scientific evidence with practical realities, the input of livestock production experts helped transform the theoretical framework into a practically usable decision-support tool. It empowers producers to make informed, flexible weaning choices that promote both productivity and profitability in a climate-variable, economically pressured South African beef industry.

Recommendations for future research:

Future research should focus on field trials across varied production systems to test the implementation of the framework under real-world conditions, such as the following:

• On-farm trials comparing the financial and reproductive outcomes of early vs. conventional weaning, using the framework factors to guide decisions across different contexts.
• Case studies applying the framework to emerging or smallholder beef enterprises to assess its usability and impact on herd improvement and resilience.
• Testing the framework’s adaptability under different climatic zones (e.g., arid Northern Cape vs. higher-rainfall KwaZulu-Natal) to measure performance variability and guide region-specific refinements.
• Longitudinal studies measuring long-term effects of framework-guided decisions on dam longevity, calf productivity, and replacement heifer development.

Additionally, participatory research involving farmer-led testing of the framework could increase adoption and provide insights into barriers and enablers of practical implementation. Integration with digital farm management tools and mobile decision-support apps could also be explored to enhance accessibility and real-time decision making.

5. Limitations of the Study

While the Delphi method proved effective for validating the Integrated Calf Weaning Framework, certain limitations must be acknowledged. Firstly, although 13 livestock experts were initially invited to participate, only 11 agreed to be part of the Delphi panel. One expert subsequently withdrew before the first round due to personal reasons, reducing the panel to 10 participants. While this number aligns with commonly accepted Delphi sample sizes, a larger panel may have introduced broader perspectives and avoided potential bias. Secondly, during the second round of data collection, several participants faced time constraints, which was likely due to the start-of-year workload. Three experts did not respond by the initial deadline, and additional follow-up was required to ensure their input was included. This delay, although ultimately resolved, may have influenced the timeliness and completeness of responses. Despite these constraints, the panel’s overall engagement remained strong across rounds, and consensus was achieved on key elements of the framework. Future research may benefit from scheduling Delphi rounds outside of peak professional periods and considering strategies to maintain engagement throughout the process. Another noteworthy limitation is the inclusion of 90% South African experts, which might have resulted in the generalizability of framework inputs.