Reproductive Performance of Bali Cattle in West Papua: Evaluating the Effectiveness of Artificial Insemination under the Upsus Siwab Program

Reproductive Performance of Bali Cattle in West Papua: Evaluating the Effectiveness of Artificial Insemination under the Upsus Siwab Program

*Andoyo Supriyantono¹, Fredy Yusak Felle², Johan Koibur¹, Azchar Prianka Piawan Putra¹

¹Department of Animal Science, University of Papua, Manokwari, Indonesia

²Alumnus, Faculty of Animal Science, University of Papua, Manokwari, Indonesia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000554

Received: 15 September 2025; Accepted: 24 September 2025; Published: 17 October 2025

ABSTRACT

The demand for beef in Indonesia continues to rise, while domestic production remains insufficient, leading to reliance on imports. To address this gap, the government introduced the Upsus Siwab program utilizing artificial insemination (AI). This study aimed to evaluate the reproductive performance of Bali cows under the program in Manokwari Regency, West Papua, using indicators of service per conception (S/C), conception rate (CR), and calving rate (CvR). The study involved 560 acceptor cows owned by 274 farmers across six districts, with 112 sampled. Primary data were collected through interviews and field observation, while secondary data came from official livestock services. Results showed an average S/C of 2.3, CR of 85.91%, and CvR of 78.57%. The S/C was more favorable than in other Papua regions, while CR exceeded the national threshold (≥60%) and approached levels observed in advanced areas. However, CvR remained slightly below the international standard (85–95%), mainly due to embryonic loss, dystocia, climate variability, and low pasture quality. Farmer literacy in estrus detection, inseminator training quality, and veterinary support during late pregnancy emerged as critical determinants of AI success.  Recommendations include improving targeted farmer and inseminator training, enhancing pasture and nutritional management, integrating climate-adaptive strategies, and strengthening reproductive health interventions to reduce embryonic loss and dystocia.

Keywords: artificial insemination, Bali cattle, Upsus Siwab, Manokwari, reproductive performance

INTRODUCTION

The demand for beef in Indonesia continues to increase in line with population growth, urbanization, and changes in consumption patterns. This trend aligns with global projections that the demand for ruminant meat in developing countries will continue to rise due to increasing income and urbanization (FAO, 2020). However, domestic production capacity remains limited, so Indonesia still relies on beef imports to cover the consumption deficit (Priyanti et al., 2021). This dependency has become a strategic issue because it creates vulnerability to fluctuations in global prices and international supply.

To address this gap, the government launched the Upsus Siwab (Special Effort for Mandatory Pregnancy in Breeding Cows) program in 2016. The program focuses on the intensification of reproductive technologies, particularly artificial insemination (AI). AI is widely recognized as an effective method to improve reproductive productivity, enhance genetic quality, and reduce the costs of maintaining bulls (Handiwirawan and Subandriyo, 2020). Nevertheless, its effectiveness strongly depends on semen quality, the availability of liquid nitrogen (N₂), reproductive management, and the technical capacity of field officers (FAO, 2020).

Although AI has long been introduced in Manokwari, infrastructural and managerial challenges persist. Beyond semen quality and inseminator availability, reproductive success is also shaped by climate variability, seasonal pasture quality, and prevalence of reproductive diseases such as brucellosis and leptospirosis. These external factors interact with on-farm practices, yet have received limited research attention in eastern Indonesia. Moreover, although farmer literacy and inseminator skills are repeatedly mentioned as constraints, few studies examine the effectiveness of training content, frequency, and delivery methods in ensuring sustained AI performance. With the launch of Upsus Siwab in 2017, the central government provided support in the form of semen straws, liquid nitrogen, technical training, and expert assistance. This support created an opportunity to evaluate the reproductive performance of Bali cows as AI acceptors using physiological indicators such as service per conception (S/C), conception rate (CR), and calving rate (CvR) (Diskin and Kenny, 2016).

International studies reveal that challenges in AI implementation across developing countries share similar patterns. In India, limited access to quality semen straws and trained technicians is a major barrier to AI success in rural areas (Kumar et al., 2021). In Vietnam, the success of AI programs is highly influenced by integration with intensive extension services and smallholder farmer support (Nguyen et al., 2022). In East Africa, research on local cattle highlighted that access to liquid nitrogen and inseminator training were key determinants of AI success across regions (Musinguzi et al., 2023). Compared with these cases, the context of West Papua is unique: although AI has long been introduced, infrastructural and logistical constraints remain fundamental challenges.

Therefore, evaluating the Upsus Siwab program in Manokwari Regency is important as it can provide new insights into how central government interventions help overcome infrastructural limitations while promoting herd population growth in areas with restricted access. The novelty of this study lies in its geographical focus on West Papua, a region rarely studied in the literature; its evaluative approach that assesses program effectiveness not only by population outcomes but also by physiological reproductive indicators; and its contribution to global literature by enriching discussions on AI implementation strategies in developing countries facing infrastructural limitations, with comparisons to cases in India, Vietnam, and Africa.

The objective of this research is to analyze the reproductive performance of Bali cows participating in the Upsus Siwab program in Manokwari Regency and to identify supporting and inhibiting factors of its success. Practically, the findings are expected to provide recommendations for local governments in designing sustainable strategies. Academically, this study contributes to international literature on the effectiveness of AI in developing countries, particularly in geographically isolated areas with limited infrastructure.

Research Method

This study was conducted in six districts of Manokwari Regency, West Papua: Manokwari Barat, Manokwari Selatan, Manokwari Utara, Prafi, Masni, and Sidey. The study sites were selected because they are centers for beef cattle development in West Papua and part of the Upsus Siwab program implementation. The research subjects were 274 farmers whose cattle were AI acceptors, and 7 inseminators. The objects of the study included 560 Bali cows inseminated under the program between February 2017 and December 2018. From this population, 20% (112 acceptors) were selected as representative samples using purposive random sampling. Each acceptor was assigned an identification number, and samples were drawn by lottery. Based on ownership, 45 farmers and their corresponding inseminators were included.

The data consisted of primary and secondary sources. Primary data were obtained through interviews with structured questionnaires and direct field observation, while secondary data were collected from official documents of the Provincial Livestock and Animal Health Service. The main variables observed were farmer performance, inseminator performance, and reproductive physiological indicators of the acceptor cows.

The reproductive indicators used were:

1. Service per Conception (S/C)

S/C = Number of pregnant cows / Total inseminations

Indicates the average number of inseminations required to achieve one conception.

2. Conception Rate (CR)

CR (%) = (Number of pregnant cows / Total inseminations) × 100

Reflects the percentage of pregnancies achieved after AI.

3. Calving Rate (CvR)

(%) = (Number of live calves born / Total inseminated cows) × 100

Measures the percentage of cows that successfully produced live calves after conception.

Data analysis was carried out both quantitatively and qualitatively. Quantitative data from S/C, CR, and CvR calculations were analyzed by computing averages and percentages, then presented in tables and graphs. Qualitative data from interviews were analyzed thematically to identify supporting and inhibiting factors of AI success. This mixed-methods approach aligns with current livestock research trends that integrate quantitative and qualitative analyses to provide a comprehensive assessment of program effectiveness (Nguyen et al., 2022; Musinguzi et al., 2023).

RESULTS AND DISCUSSION

General Overview of AI Implementation

The implementation of the Artificial Insemination (AI) program under Upsus Siwab in Manokwari Regency began in February 2017, involving seven inseminators across six districts (Figure 1).

Figure 1. Map of AI Implementation Districts in Manokwari Regency

The semen straws were supplied by the Lembang Artificial Insemination Center (BIB Lembang), including bulls of Limousin, Simmental, Bali, and Ongole Crossbred (PO). By December 2018, there were 560 acceptor cows from 274 farmers, with the majority (81.96%) located in Masni District. For evaluation, 112 acceptors (20%) were observed using Service per Conception (S/C), Conception Rate (CR), and Calving Rate (CvR) indicators (Table 1).

Table 1. Response of female cattle to artificial insemination in the Upsus Siwab program

Source: Primary data processed, 2025

Service per Conception (S/C)

The average service per conception (S/C) in Manokwari Regency was recorded at 2.3±0.91 (Table 1). For an introduction region in eastern Indonesia, this achievement is considered good, as it falls below the benchmark of 3–5 inseminations per conception (Ditjen PKH, 2017). Compared to previous studies in Prafi (4.5) and Sorong (3.0), the value of 2.3 indicates improved reproductive efficiency. Nevertheless, performance still needs to be optimized towards self-reliant regions (target <2), as reported in successful AI hubs such as Gorontalo, to further reduce the cost per conception.

Biologically and managerially, a low S/C indicates that most acceptors: (i) meet physiological requirements (normal estrous cycle, adequate postpartum interval, optimal body condition score/BCS), (ii) are at productive age, and (iii) are managed with adequate husbandry practices (basic feed plus supplementation, reproductive disease prevention, and record-keeping). Cross-country evidence confirms that three main determinants of AI success—accurate estrus detection, inseminator skills, and semen/cold-chain quality (liquid nitrogen)—consistently reduce S/C and increase pregnancy rates (Handiwirawan and Subandriyo, 2020; FAO, 2020; Nguyen et al., 2022; Musinguzi et al., 2023).

From the program implementation perspective, two elements were critical. First, the timing of insemination relative to peak estrus—affected by farmers’ literacy in estrus signs and accessibility of inseminators—was directly correlated with reduced S/C (Nguyen et al., 2022). Second, inseminator service quality (accurate semen deposition, asepsis, straw handling), supported by regular training and supervision, contributed significantly to reproductive efficiency (Handiwirawan and Subandriyo, 2020; Musinguzi et al., 2023). The reported improvements in field facilities and logistical support align with FAO (2020) recommendations for remote regions: ensuring liquid nitrogen continuity, guaranteeing semen quality from AI centers, and strengthening service networks through scheduled/holding-ground-based systems.

Despite these achievements, the gap to self-reliant performance (<2) indicates room for improvement. Based on recent evidence, three strategies should be prioritized:

Strengthening estrus detection (short AM-PM rule training, use of heat mount detectors/simple tail paint) to reduce S/C to 1.6–2.0, as seen in smallholder best practices (Nguyen et al., 2022).

Ensuring cold-chain quality (monitoring container temperature, straw stock rotation, routine liquid nitrogen audits), proven to reduce post-AI failures in African and Asian contexts (FAO, 2020; Musinguzi et al., 2023).

Sustained inseminator capacity building (refresher training, field audits) and performance feedback based on S/C and CR indicators at the district level (Handiwirawan and Subandriyo, 2020; Priyanti et al., 2021).

Thus, the S/C of 2.3±0.91 in Manokwari should be seen as a transition milestone from introduction to development. By maintaining logistical support, improving farmers’ estrus literacy, and formalizing AI quality assurance, there is a strong opportunity to reach <2 inseminations per conception, thereby reducing cost per pregnancy and accelerating the sustainable increase of pregnant cows (FAO, 2020; Priyanti et al., 2021).

Conception Rate (CR)

The average conception rate (CR) in this study was 85.91%±12.22%, with inter-period variation ranging from 78.26% to 100% (Table 1). This figure is well above the minimum threshold set by the Directorate General of Livestock and Animal Health (≥60%), indicating good pregnancy efficiency among AI acceptor cows in Manokwari Regency.

Compared with other regions (Table 2), the CR in Manokwari was higher than Magelang (45.75%) and slightly better than Jayapura (76.7%). Such inter-regional differences highlight variations in AI success influenced by reproductive management, semen quality, and inseminator skills. Recent studies emphasize that CR >70% is a strong indicator of AI program success in smallholder farming systems (Handiwirawan and Subandriyo, 2020; Priyanti et al., 2021).

Table 2. Comparison of Conception Rates (CR) between regions

High CR in Manokwari can be attributed to several technical factors. First, accurate estrus detection by farmers. Most could identify estrus signs (restlessness, reduced appetite, mucus discharge, vulva swelling), enabling timely AI during peak estrus. A study in Vietnam confirmed that estrus detection by farmers significantly improved CR, even more than feed management (Nguyen et al., 2022).

Second, the use of superior bull semen from BIB Lembang, quality-assured through motility, morphology, and viability tests. FAO (2020) emphasized that semen quality and distribution via cold-chain systems are key to reducing fertilization failure. In Manokwari, government support in ensuring stable liquid nitrogen availability reduced straw damage risks, enhancing AI effectiveness.

Third, inseminator skills were crucial. All inseminators in this study had formal AI training and valid licenses (SIMI), ensuring technical competence. Musinguzi et al. (2023) reported that pregnancy success in East African smallholder farms increased significantly when inseminators had advanced training, especially in proper semen deposition techniques. This aligns with the Manokwari findings.

Beyond technical factors, institutional support also played a role. The local government provided holding grounds and gangways (handling alleys), enabling collective AI management. This increased service efficiency and ensured that cows in estrus were inseminated promptly. Similar practices in other developing countries have also significantly improved CR through facility integration and technical assistance (Nguyen et al., 2022; Priyanti et al., 2021).

Thus, the CR of 85.91%±12.22% in Manokwari reflects strong synchronization among cattle biology, inseminator competence, semen quality, and government program support. While there is room for improvement toward the international ideal standard (90–95%), the achievement demonstrates that even introduction regions like Manokwari can match the performance of more developed areas.

Calving Rate (CvR)

Table 1 presents the number of inseminated cows in periods I, II, and III, resulting in a total of 88 calvings. From this, the calving rate (CvR) of AI acceptor cows in Manokwari Regency was calculated at 78.57%. This figure is considered fairly good compared to Jayapura (73.5%) and Sorong (68.04%), which are also AI introduction regions. However, it is still slightly below the international ideal calving rate standard of 85–95% (FAO, 2020).  The suboptimal CvR indicates the presence of calving constraints, including:
(i) early embryonic loss, often caused by environmental stress, limited quality feed, or unstable physiological conditions of the dam; (ii) dystocia, related to size mismatches between semen donor bulls (often large-framed breeds) and local Bali cows, resulting in calves that are too large and cause difficult labor.

Early embryonic loss was frequently associated with nutritional stress during the dry season when pasture biomass and quality were limited, leading to insufficient energy and micronutrient intake (Diskin and Kenny, 2016; Mellado et al., 2020). Studies confirm that mineral deficiencies, particularly selenium and phosphorus, increase early embryonic mortality (Cerri et al., 2021; FAO, 2020). The extensive grazing system, still dominant in Manokwari.  In this system, cattle are released on open pastures with limited supervision, making pregnancy monitoring difficult. Consequently, miscarriage, embryonic loss, or calving complications often go undetected and untreated. This is consistent with reports from Vietnam (Nguyen et al., 2022) and East Africa (Musinguzi et al., 2023), showing that extensive systems are at high risk of reproductive failure when not accompanied by adequate technical support.

Dystocia was linked to mismatched semen from large-framed bulls (Simmental, Limousin) used on smaller Bali cows, resulting in oversized calves (Priyanti et al., 2021; Nguyen et al., 2022). This is consistent with recent findings in smallholder systems, where sire–dam size mismatches were associated with higher perinatal calf mortality (Mee et al., 2023). Practical interventions include sire selection adjusted to dam size, farmer training in calving management, and ensuring emergency veterinary assistance (Musinguzi et al., 2023; FAO, 2022).

The high CR (85.91%) in this study, not fully followed by optimal CvR, indicates that some pregnancies did not end in live calves. According to Handiwirawan and Subandriyo (2020), this may be caused by a management gap between pregnancy and the peripartum phase. Poor-quality feed during the last trimester, lack of mineral supplementation, and limited reproductive health services are key issues.

To improve CvR toward the ideal standard, several strategies are recommended:

1. Selecting bull semen appropriate for the body size of local cows to reduce dystocia risks (FAO, 2020; Priyanti et al., 2021).
2. Strengthening semi-intensive systems with holding grounds for pregnant cows to facilitate easier monitoring (Nguyen et al., 2022).
3. Providing reproductive assistance by inseminators and veterinarians, including pregnancy checks, feed supplementation, and management of high-risk calvings (Musinguzi et al., 2023).
4. Educating farmers on managing pregnant cows and detecting signs of difficult labor, proven to reduce embryonic loss and improve calving success in smallholder systems (Handiwirawan and Subandriyo, 2020).

Thus, the CvR of 78.57% in Manokwari can be categorized as good, though improvements are still needed. If the causes of calving failures are minimized through technical and managerial interventions, achieving the international standard (85–95%) is possible.

Comparison of CR and CvR

The comparative graph (Figure 2) shows a difference between conception rate (CR) and calving rate (CvR) in AI implementation in Manokwari (2017–2018). CR was recorded at 85.91%, while CvR was slightly lower at 78.57%.

Figure 2. Comparison of Conception Rate (CR) and Calving Rate (CvR)

This indicates that high pregnancy rates did not fully translate into live calf births. Such a phenomenon is common in tropical smallholder systems, where high CR is achieved through superior semen and inseminator skills, but late-pregnancy failures (embryonic loss, abortion, or dystocia) reduce CvR. According to Handiwirawan and Subandriyo (2020), the CR–CvR gap is often linked to management deficiencies during late pregnancy, such as poor feed quality and limited reproductive health services.

In Manokwari, the extensive system made early detection of pregnancy problems difficult. Musinguzi et al. (2023) reported a similar situation in East Africa, where CR was high (>70%) but CvR lagged due to limited pregnancy and calving management. Nguyen et al. (2022) also emphasized that intensive monitoring in semi-intensive systems improves CR–CvR synchronization by enabling faster identification of pregnancy loss or dystocia.

In this context, the CR of 85.91%±12.22% already demonstrates technical AI success in Manokwari. However, the CvR of 78.57% signals the need for additional strategies to ensure pregnancies lead to healthy calves. FAO (2020) recommended three key interventions:

(i) using semen from bulls matched to local cow body size to reduce dystocia;

(ii) improving nutrition and supplementation during the last trimester; and

(iii) regular pregnancy monitoring by inseminators/veterinarians.

Thus, while pregnancy efficiency (CR) in Manokwari was good, calving success (CvR) still requires strengthening. An integrated strategy combining AI technology, nutrition, reproductive health monitoring, and farmer education is expected to narrow the CR–CvR gap, bringing reproductive performance in West Papua closer to international standards (85–95%).

Supporting and Inhibiting Factors of AI Success

While inseminator training was highlighted as a supporting factor, qualitative data revealed that refresher training was infrequent (often once every two years) and mostly technical, with limited coverage of reproductive health monitoring and farmer extension methods. Improving training frequency (annual), incorporating modules on estrus literacy, pregnancy monitoring, and calving assistance, and integrating practical field demonstrations would increase effectiveness. Farmer literacy in estrus detection was generally good, yet knowledge gaps persisted in late-pregnancy management and recognition of dystocia risks. Training content should expand beyond heat detection to include nutrition management during gestation, disease prevention, and calving support.  External influences—climate variability, seasonal pasture quality, and disease prevalence—were often overlooked by both farmers and extension agents.  Besides them, some supporting and inhibiting factors of AI success as described below.

Supporting Factors

1. Quality of superior bull semen – Straws from BIB Lembang (Limousin, Simmental, Bali, PO) were quality assured through motility and viability tests (FAO, 2020).
2. Availability of liquid nitrogen (N₂)—Government support in maintaining the semen cold chain reduced the risk of straw damage (Nguyen et al., 2022).
3. Improving reproductive management – A mix of basic feed and simple supplementation maintained optimal body condition scores (Priyanti et al., 2021).

Inhibiting Factors

1. Extensive grazing system – Free grazing limited monitoring of pregnant cows, leading to undetected miscarriages, abortions, or dystocia (Nguyen et al., 2022).
2. Early embryonic loss is triggered by environmental stress, poor-quality feed, and low mineral supplementation, which lowers CvR (FAO, 2020).
3. Dystocia (difficult calving) – Caused by size mismatch between large donor bulls and small local Bali cows, producing oversized calves (Priyanti et al., 2021).
4. Management gap between pregnancy and calving – Suboptimal late-gestation nutrition and limited reproductive health services (Handiwirawan and Subandriyo, 2020).
5. Logistical and infrastructure limitations – Although improved, liquid nitrogen distribution continuity remains a challenge in remote districts (Musinguzi et al., 2023).
6. Limited human resources and technical support – Only seven inseminators for six large districts, sometimes resulting in delayed services.

CONCLUSION

This study concludes that the Upsus Siwab program in Manokwari Regency has improved reproductive performance of Bali cows, as indicated by favorable S/C (2.3±0.91) and CR (85.91%±12.22%). However, CvR (78.57%) remains below the international benchmark due to embryonic loss, dystocia, pasture quality decline, and limited veterinary interventions. To reduce the CR–CvR gap, three integrated strategies are recommended: 1. Targeted training: Increase training frequency for inseminators and farmers, with content expanded to estrus literacy, late-pregnancy management, and calving support. 2. Reproductive health interventions: Provide mineral supplementation, pregnancy monitoring, sire–dam size matching, and veterinary support to address embryonic loss and dystocia. 3. External factor management: Implement climate-adaptive practices (forage conservation, pasture improvement) and strengthen disease surveillance systems. By addressing technical and environmental constraints, reproductive performance in West Papua can move closer to international standards (85–95%), ensuring sustainable regional beef cattle development.

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