AAVS_14_1_173-183

Introduction

Swamp buffalo or mud buffalo, also known as “hadangan” or “kalang” buffalo (Hamdan and Sabran, 2005) is one of the endemic livestock in South Kalimantan that has great potential to be developed (Widaningsih et al., 2023a). The population of this type of buffalo is still classified as safe (not endangered) and its existence is widespread in almost all districts in South Kalimantan (Widaningsih et al., 2025), as well as several areas in Central and East Kalimantan (Prihandini et al., 2023). The spread of swamp buffalo in South Kalimantan is very potential, considering that the swamp land area in South Kalimantan reaches 70,842.43 Ha. On the other hand, swamp buffalo have unique adaptations to swamp, river, and lake environments, making them a valuable regional biodiversity resource (Hegde, 2019), as well as easy to maintain (Talib et al., 2014), as swamp buffalo can convert low-quality fibrous feed into meat and milk and can utilize coarse feed, and are more efficient than cattle (Rostini et al., 2018). In addition, swamp buffalo also has high adaptability and disease resistance (Borghese et al., 2022).

Swamp buffalo contributes significantly to the livelihoods of smallholder farmers through the provision of meat and dairy, organic fertilizers, and labor (Prihandini et al., 2023). In addition, swamp buffalo also contributes to food diversification, with buffalo meat serving as an alternative or substitute for beef, while the milk provides higher essential nutrients than cow’s milk, including lower cholesterol content and rich in iron, phosphorus, and vitamin A (Matondang and Talib, 2015). The meat and milk are a source of food and participate in realizing national meat self-sufficiency. Its contribution plays an important role in realizing a stable meat supply for the nation (Widaningsih et al., 2022). However, the consumption of raw buffalo milk is not as popular as cow’s milk, thus limiting its potential use in the local market (Prihandini et al., 2023). In addition, the use of swamp buffalo also includes cultural roles, such as in the Kuda Gipang dance in South Kalimantan, which is an expression of gratitude to God Almighty (Putri and Syahrial, 2024).

The development of swamp buffalo in South Kalimantan, especially in North Hulu Sungai Regency, is very potential, considering that the swamp land area of North Hulu Sungai Regency is 46.01% of the existing swamp land area in South Kalimantan, which is 32,594.98 Ha. However, the productivity level of swamp buffalo in North Hulu Sungai Regency is still very low, which has decreased significantly in the last 10 years, both in terms of population and meat production. BPS South Kalimantan 2024 data shows that the number of swamp buffaloes in South Kalimantan has fallen to only 8,803, a decrease of 15,906 head or approximately 64.37%. A decrease also occurred for swamp buffalo meat production, from 680,350 kg in 2015, to 320,094 kg in 2024 or a decrease of around 52.95%. This is influenced by many factors that are very complex so that it is very difficult to overcome all existing problems (Atmoko et al., 2023). Some researchers report that the factor causing the decline in the population and production of swamp buffalo livestock is the change in land use (Widi et al., 2021). Climate change also negatively impacts forage provision (Yani et al., 2022). In terms of reproduction, the decline in the productivity of swamp buffalo is also caused by low productivity of female livestock (Reswati et al., 2021) as well as low access to information sources and lack of expertise in buffalo processing. In addition, the problems in the buffalo cattle business are quite varied for each region, so experts have developed various theoretical and practical steps in evaluating the factors that influence the development of the swamp buffalo farming business, but until now it has not been successful because the main determining factors are not known for sure. In addition, research on swamp buffalo livestock is still very minimal, when compared to other livestock research such as cattle and poultry.

For this reason, it is important to conduct comprehensive research, including research for the development of swamp buffalo livestock business in South Kalimantan, with an Exploratory Factor Analysis (EFA) approach. EFA is a process for identifying the meanings, constructs, or dimensions that are evaluated by observed covariance (Panter et al., 1997). This method is well-suited for exploring complex linkages in new or under-researched domains, such as the specific ecological and socio-economic dynamics of swamp buffalo farming (Rohaeni et al., 2024). EFA is a useful method for understanding data structures and identifying latent factors underlying variables, so that it can reduce the data dimension by combining related variables and can understand the data structure and relationships between variables (Floriandi et al., 2020). In addition, the EFA serves as a basic step to uncover the basic constructs that might explain the observed correlations between various variables, allowing researchers to condense large data sets into factors that are easier to manage and interpret. This is especially valuable when researchers have limited prior hypotheses about the mechanisms underlying a phenomenon, allowing the identification of latent factors that reconstruct the complexity of the observed data in essential form (Sürücü et al., 2022). This analysis allows researchers to identify highly correlated variables and group them into more meaningful factors, often used to reduce and summarize the information contained in the observed variables (Ledesma et al., 2021). This method is particularly valuable in the early stages of research to distinguish the underlying linkages between a large set of variables (Shrestha, 2021).

With the increasing demand for meat and milk, which is in line with the increase in the population, including in South Kalimantan, various efforts continue to be made, including efforts to develop the swamp buffalo livestock business as an effort to meet the needs of meat sourced from animals. This study aims to find out the base area for the development of the swamp buffalo livestock business in South Kalimantan, as well as to find out the main factors for its development.

Materials and Methods

Research approach

The research approach used is combination research, which is a combination of qualitative and quantitative approaches. The research approach is seen as quite appropriate (good) to answer the research objectives, which require both qualitative and quantitative approaches at the same time. The combination research approach allows researchers to take advantage of the advantages of both methods at once, so that they can better answer complex research questions (Oktariyani et al., 2025), and gain a more comprehensive understanding of a phenomenon, so that the data obtained becomes more valid, reliable, and objective (Nasruddin et al., 2019). The qualitative approach in the study is intended to determine the base area for the development of swamp buffalo livestock in South Kalimantan. Meanwhile, the quantitative approach is intended to find out the main factors that affect the development of swamp buffalo livestock in South Kalimantan.

The research design used is a Concurrent Embedded Design, which is the quantitative method that is the main method to determine the factors in the development of swamp buffalo livestock in South Kalimantan, and uses a qualitative method to determine the base area of swamp buffalo livestock development in South Kalimantan. In this design, quantitative and qualitative data are collected simultaneously, but one of the methods has a more dominant weight in achieving the research objectives (Rofiqoh et al., 2020). Furthermore, Nasruddin et al., (2019) stated that the concurrent embedded design method is a type of combination (mixed) research, which is a type of research that integrates qualitative and quantitative approaches simultaneously, where one method (primary) is assisted or supported by another method (secondary) within a larger design framework. In this study, the quantitative method served as the primary method with data sources from 160 respondents. Meanwhile, the qualitative research method as a secondary method is intended to obtain an overview of the importance of swamp buffalo development obtained from 7 expert respondents. It is further noted that this approach allows researchers to integrate the findings of both methods for a more comprehensive understanding, where qualitative data can provide context and in-depth explanation of the quantitative results.

Data types and sources

The types of data used in this combination study are primary data types, and secondary data. The primary data in this study are the results of filling out a questionnaire with a number of questions using a Likert scale (1-5), where 1= strongly disagree, 2= disagree, 3= hesitate, 4= agree, 5= strongly agree. The questionnaire consists of a number of variables and factors that are questions in the research, namely; the role of the government, maintenance system, knowledge of information system technology, zoning regulations, infrastructure facilities, livestock processing management, waste management, profits, extension institutions, seasons, distance of business locations, traditions, signal availability from providers, feed/forage technology, the existence of animal markets, community consumption patterns, internal conflicts, and environmental sanitation.

Meanwhile, the secondary data in this study is the data used to analyze the base area with the main data being the number of swamp buffalo populations in sub-district and district areas. The data was obtained from the district/city BPS in South Kalimantan.

Data collection methods

The data collection method used consisted of a survey method for filling out questionnaires which was carried out to 160 respondents who are swamp buffalo farmers spread across 13 districts/cities in South Kalimantan Province (North Hulu Sungai Regency 20 respondents, Barito Kuala 12 respondents, South Hulu Sungai 12 respondents, Hulu Sungai Tengah 12 respondents, Kotabaru 12 respondents, Banjarbaru 12 respondents, Tanah Bumbu 12 respondents, Tanah Laut 12 respondents, Tapin 12 respondents, Banjarmasin 12 respondents, Balangan 12 respondents, Banjarbaru 10 respondents, and Balangan Regency 10 respondents), and 7 respondents as experts in the field of swamp buffalo farming. The determination of respondents was carried out by the purposive sampling method, namely only swamp buffalo farmers and livestock experts were the target respondents. The expert respondents in this study are 7 people, namely; (1) Head of the North Hulu Sungai Regency Agriculture Office, (2) Head of the North Hulu Sungai Regency Environmental Service, (3) Head of the South Kalimantan Provincial Forestry Service, (4) Head of the North Hulu Sungai Regency Regional Research and Development Agency, (5) Head of the Panggang Lake BPP and Field Extension Coordinator of Paminggir District, (6) Head of Livestock and Animal Health of North Hulu Sungai Regency, and (7) Chairman of Gapoktan Paminggir District. The selected expert respondents have representativeness, namely representing various perspectives or interests that can improve the quality and balance of analysis results. The criteria of the experts are; (a) Have a commitment to the problem being studied, (b) Have a reputation, position/position in their competence with the field being studied, (c) Have abilities and competencies in the field being studied, and (d) Be neutral and open in receiving input/opinions from other respondents.

Meanwhile, the desk study method is used to obtain secondary data, namely the number of population (swamp buffalo) at the sub-district and district/city levels in South Kalimantan, through data tracing at BPS.

Data analysis methods

The data analysis method used is Location Quotient (LQ) analysis to determine the base area for the development of swamp buffalo livestock in South Kalimantan with the following formula:

Where; xi: Total livestock population i in the sub-district area, xt: The number of population of the livestock subsector in the sub-district area, Ni: Number of livestock population i in the district area, Nt: Number of population of livestock subsector t in the district area.

The results of the Location Quotient (LQ) analysis will be obtained from the base area for the development of swamp buffalo livestock in South Kalimantan. LQ with a value of more than >1.0 is categorized as a base/flagship sector and has export potential, LQ less than <1.0 is categorized as a non-base sector, or still depends on supply from outside the region.

Meanwhile, to find out the factors that influence the development of the swamp buffalo farming business in South Kalimantan, it was analyzed using the Exploratory Factor Analysis (EFA) method with the help of SPSS Statistics. Comprehensive statistical analysis, often involving software such as SPSS, is also essential for testing the validity of research instruments, such as questionnaires, in measuring the intended construct (Soma et al., 2025). The standard normal distribution is verified with a Kurtosis value (<7) and a skewness value (<2) are values that are within the acceptable range (Harerimana and Mtshali, 2020). This ensures that the data collected from the questionnaire accurately represents the variables being studied, which further supports the reliability of the study findings (Tambunan, 2024). The overall reliability is 0.950 with a minimum limit of 0.70 (Xi et al., 2020). Furthermore, the Kaiser-Meyer-Olkin (KMO) Measure of sampling adequacy (>0.5) and Bartlett’s test of Sphericity (<0.05) (Taherdoost et al., 2014) were analyzed. This test is critical to determining the suitability of the data for factor analysis, ensuring that the intercorrelation between variables is sufficient to identify the underlying dimensions (Hidayat et al., 2021). Specifically, the Kaiser-Meyer-Olkin statistics assess the proportion of variance in variables that may be caused by the underlying factors, with loading values above >0.5 generally indicating suitability for factor analysis or being categorized as significant (Sawiji et al., 2023). The mathematical model for EFA is as follows:

Where; Xi: Observed variable i-i (questionnaire item), aij: Factor loading correlation coefficient between variable and factor XiFj, Fj: The j latent (unobserved) factor, εi: The unique error term for variable Xi, m: The number of factors formed.

The rotation method used is Promax. The Promax method was chosen on the theoretical assumption that the measured factors are interconnected. In addition, the Promax method results in a simpler factor structure, especially if the factors are highly correlated (Ahmed and Maruod, 2025).

Results and Discussion

Base region analysis

The determination of the base region for the development of the swamp buffalo business in South Kalimantan is carried out using the Location Quotient (LQ) analysis approach with a regional basis is all districts/cities within the territory of South Kalimantan Province. Location Quotient (LQ) is one of the regional economic analysis tools that is commonly used to measure the level of specialization or comparative advantage of a sector in a certain area compared to a wider reference area, such as a district within a province. Location Quotient (LQ) aims to find out whether a sector is a base sector that produces more than local needs, or a non-base sector that only serves the needs of the region itself. Based on the results of the Location Quotient (LQ) calculation, the base area for the development of swamp buffalo livestock in South Kalimantan Province is detailed in Table 1.

Table 1: Base region based on LQ value.

No	Region (Regency/City)	Value of location quotient
1	Hulu Sungai Utara (HSU)	11.30
2	Barito Kuala	1.70
3	Hulu Sungai Selatan (HSS)	1.58
4	Hulu Sungai Tengah (HST)	1.25
5	Kotabaru	1.10
6	Banjar	0.85
7	Tanah Bumbu	0.42
8	Tanah Laut	0.28
9	Tapin	0.17
10	Banjarmasin	0.09
11	Balangan	0.03
12	Banjarbaru	0.01
13	Tabalong	0.00

Source: Secondary data analysis (2025).

The results of the LQ analysis were obtained that there were 5 districts that had an LQ value of more than >1, namely; North Hulu Sungai Regency (HSU), Barito Kuala, South Hulu Sungai (HSS), Hulu Sungai Tengah (HST) and Kotabaru Regency. A district with an LQ value >1 indicates that the area has a relatively large population of swamp buffalo livestock compared to other areas. Thus, it can be declared as a base area for swamp buffalo farming and can be used as a leading livestock in South Kalimantan. The overview of the level of livestock population in a certain area is a measure to determine strategies for increasing livestock populations (Begna and Masho, 2024). This strategy is crucial to achieve meat self-sufficiency, considering that the low productivity of local livestock, both in terms of body weight and meat quality, is the main obstacle (Susanti et al., 2025). Increasing livestock populations and intensifying animal feed production need to be guaranteed to support the sustainability of the livestock industry (Daeng et al., 2023). The following is an overview of the development of the number of swamp buffalo livestock in South Kalimantan in the last 10 years (2015-2024) (Figure 1).

The type of buffalo, which is generally found in South Kalimantan, is a swamp buffalo that is located in the same expanse of swamps and part of the watershed (Watershed) of the Barito River and its tributaries with almost the same characteristics of buffalo livestock because they come from a relatively similar genetic diversity. These buffalo populations are well-adapted to the region’s unique hydrological conditions, characterized by extensive swamps and high rainfall, making them an integral component of the local agro-ecosystem (Nugroho et al., 2021).

The decline in the swamp buffalo population in South Kalimantan, especially in the last 4 years (2021-2024), is caused by various factors, both natural factors, as well as technological, institutional, and economic factors. This decline mirrors a broader national trend, where the Indonesian buffalo population experienced a substantial 39.35% reduction between 2002 and 2017, positioning it among the steepest declines globally after Sri Lanka and Thailand (Prihandini et al., 2023). This alarming decrease is attributed to factors such as low reproductive rates and suboptimal management systems. According to Haryanto (2015) there are 4 (four) main problems in swamp buffalo farming, namely 1) Natural or environmental factors, such as high mortality rates, and the development area is getting narrower due to land conversion, ecosystem disturbance and environmental damage. The decline in the population of swamp buffalo cattle is due to internal factors or the nature of buffalo livestock (silent heat and long gestational period). Additionally, the limited availability of quality feed, especially during the dry season, poses a significant challenge to improving livestock productivity in Indonesia, including buffaloes (Susanti et al., 2025).

Social factors, namely unclear territorial boundaries, ownership status and the occurrence of potential conflicts between communities, 3) Technological factors, namely traditional maintenance patterns, lack of technological inputs, limited field officers, lack of veterinary medical personnel, and lack of animal health facilities, and 4) Institutional and regulatory factors, including a lack of clear regulations, on grazing area boundaries, and low institutional levels of swamp buffalo farmers. According to Suhaimi et al. (2015) that the decline in the swamp buffalo population is due to limited capital factors and lack of expertise in processing livestock products. The decline in the population of swamp buffalo cattle is due to the relatively low reproductive ability of swamp buffaloes. The decline in the swamp buffalo population is also due to the existence of natural factors/traits of buffalo livestock, namely silent heat and long gestational periods. For this reason, it is very important to know and identify leverage factors in the development of swamp buffalo farming business in South Kalimantan.

Key factors analysis

The determination of the main factors in the development of the buffalo livestock business in South Kalimantan is carried out using the Exploratory Factor Analysis (EFA) analysis approach. The EFA technique is used to find the latent (hidden) structure of a complex set of variables, so that key factors can be determined that are easier to understand. The EFA technique was carried out using the main component analysis and the Kaiser-Meyer-Olkin and Bartlett test applied before evaluating the results of the EFA analysis with the KMO Index ranges from 0 to 1, with a value above 0.60 considered suitable for factor analysis (Ebneter and Latner, 2013). The results of the KMO and Bartlett’s test of this research are presented in Table 2.

Table 2: Kaiser-Meyer-Olkin and Bartlett’s test.

Kaiser-meyer-olkin measure of sampling adequacy		0.817
Bartlett's test of sphericity	Approx. Chi-Square	739.594
	Df	185
	Sig.	0.001

Source: Primary data analysis (2025).

Table 3: Total of variance explained.

Factor	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
Factor	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	4.207	23.370	23.370	4.207	23.370	23.370	2.465	13.697	13.697
2	2.593	14.407	37.777	2.593	14.407	37.777	2.463	13.682	27.379
3	1.902	10.564	48.342	1.902	10.564	48.342	2.441	13.560	40.939
4	1.531	8.507	56.848	1.531	8.507	56.848	2.266	12.589	53.527
5	1.358	7.543	64.392	1.358	7.543	64.392	1.956	10.864	64.392
6	.911	6.728	71.120
7	.908	6.155	77.274
8	.860	5.334	82.609
9	.728	4.045	86.654
10	.601	3.341	89.995
11	.450	2.501	92.496
12	.367	2.041	94.537
13	.329	1.830	96.367
14	.259	1.439	97.806
15	.153	.853	98.659
16	.120	.664	99.323
17	.063	.353	99.675
18	.058	.325	100.000
Extraction Method: Principal Component Analysis.

Source: Primary data analysis (2025).

The KMO results obtained a value of 0.817, which means that the samples taken were considered adequate (sufficient) and valid to perform factor analysis and Bartlett’s test of Sphericity was significant. This shows that the relationships between variables are strong and the data are suitable for EFA. A probability value (sig.) of 0.001 or less than <0.05 can be interpreted that the sample size and normality have met to perform the analysis of the main components. This is in accordance with Ghozali (2011), that if the probability (sig.) is <0.05, then the research variable can be analyzed further, and vice versa. Further analysis was carried out was factor rotation by looking at the loading value of each manifest variable. The value of loading factors is detailed as in the table of total variation factors in Table 3.

Based on the results of the analysis as in Table 3 above, it is obtained that there are 5 factors that can be formed with an eigenvalue of more than >1. Eigenvalue is used to analyze the feasibility of a factor. A feasible condition is a factor when the eigenvalue is more than >1, while if it is less than <1, then the factor will be excluded from the model (not used) (Wood et al., 2015). The number of factors is determined based on the cumulative proportional value, which is 64.39%. This suggests that the factor can be chosen as the main factor because the cumulative proportional value is more than >60%. This factor extraction aligns with established practices for exploratory factor analysis, where eigenvalues exceeding one indicate robust underlying constructs (Sappaile et al., 2023). This method, often referred to as the Kaiser criterion, is widely applied in social science research to identify the optimal number of factors to retain (Costello and Osborne, 2005). Details of the loading factor values as shown in Table 4.

Based on the results of the analysis, it was obtained that the smallest loading factor was 0.510 and the highest was 0.850. The value of the loading factor can be used to explain the components that make up the factor, where the value is above the threshold of >0.4. This is in accordance with the opinion of Osborne et al. (2014), that factor loading is above the minimum threshold of 0.40, which suggests that the structure of the factors resulting from this EFA can be accounted for statistically and theoretically. Thus, all factor items in this study are included in the model, namely as many as 18 factors that affect the development of the swamp buffalo farming business in South Kalimantan. The grouping of variables into factors is presented in Table 5.

Table 4: Loading factor.

Variabel manifest	Factors
Variabel manifest	1	2	3	4	5
F1.1	0.754
F1.2	0.710
F1.3	0.709
F1.4	0.638
F2.1		0.802
F2.2		0.790
F2.3		0.580
F2.4		0.751
F3.1			0.850
F3.2			0.789
F3.3			0.590
F3.4			0.513
F4.1				0.818
F4.2				0.703
F4.3				0.608
F5.1					0.755
F5.2					0.706
F5.3					0.510
Contribution	23.37%	14.41%	10.56%	8.51%	7.54%

Source: Primary data analysis (2025).

Based on the grouping results, Factor 1 (Government institutions, business managerial, and information technology) was able to explain the development model of the swamp buffalo livestock business by 23.37%. Factor 2 (Infrastructure, business management, business revenue, environmental management) was able to explain the development model of the swamp buffalo livestock business by 14.41%. Factor 3 (Institutional, ecological, socio-cultural) was able to explain the development model of the swamp buffalo livestock business by 10.56%. Factor 4 (Information technology, business technology, economic infrastructure), and factor 5 (Socio-cultural, environmental health) have a contribution of less than <10%. Thus, the main factor in the development of the swamp buffalo farming business in South Kalimantan is institutional, managerial, and information system technology factors whose position is in the factor 1 cluster. These three factors are the main factors in the development of the swamp buffalo livestock business in South Kalimantan.

Government institutions, business managerial, and information technology factors are elements in the development of the buffalo livestock business in South Kalimantan. These elements are essential to promote a sustainable swamp buffalo business by increasing productivity, standardizing quality, and increasing farmers’ incomes, ultimately reducing dependence on imported meat and promoting regional economic development (Rusdiana and Talib, 2019; Sumarsono et al., 2016). Effective communication channels between breeders nd extension workers, coupled with advances in cage technology and disease management, further contribute a to the sustainability of these farming businesses

Table 5: Grouping variables into factors.

No	Manifest variabels	Loading factor	Factors name	% of Variance
1	The Role of Government Livestock rearing system Information technology knowledge Zoning regulations	0.754 0.710 0.709 0.638	Factor 1 (Government institutions, business managerial, and information technology)	23.37
2	Infrastructure Livestock processing management Waste management Profit of the business	0.802 0.790 0.580 0.751	Factor 2 (Infrastructure, business management, business revenue, environmental management)	14.41
3	Counseling institutions Climatic season Distance of business location Community traditions	0.850 0.789 0.590 0.513	Factor 3 (Institutional, ecological, socio-cultural)	10.56
4	Signal from the provider Feed technology Animal markets	0.818 0.703 0.608	Factor 4 (Information technology, business technology, economic infrastructure)	8.51
5	Public consumption patterns Internal conflicts Environmental Sanitation	0.755 0.706 0.510	Factor 5 (Socio-cultural, environmental health)	7.54

Source: Primary data analysis (2025).

(Rohaeni et al., 2024). In addition, government intervention through well-structured programs and significant allocation of budgetary resources, especially for livestock, has a noticeable impact on the expansion of livestock populations and the socio-economic improvement of farmers (Romadhon et al., 2022). Strengthening the government’s institutional aspects through 1) The establishment of a District-Based “Swamp Buffalo Service Unit” that functions for integrated vaccination, superior insemination/mating, population data collection, and consulting services. This is based on research findings from farmer interviews that the level of coordination between agencies is weak, so that the existence of a single unit will minimize service fragmentation, and 2) the Market Partnership Scheme with Slaughterhouses (RPH) and Processed Meat MSMEs to ensure more stable prices and continuous demand. This is based on research findings where farmers complain of price fluctuations and market uncertainty.

Institutional factors are very important, especially including farmer institutions, cooperatives, livestock groups, marketing associations, financial institutions, and local government regulations. This institutional strengthening is essential to facilitate access to better quality feed, capital, and marketing, in order to overcome the productivity challenges of ruminant livestock in Indonesia (Susanti et al., 2025; Rohaeni et al., 2024). The development of partnerships between farmer groups and CPO and PKO processing companies can be used as an alternative feed source in the form of solid waste, which has great potential as a raw material for complete feed (Qomariah et al., 2023). This approach not only reduces dependence on conventional feeds but also promotes a circular economy through the utilization of industrial waste. With the existence of institutions, it ensures coordination and collaboration between actors (farmers, extension workers, government, markets, financial institutions). Without a strong institution, farmers will run alone and have no bargaining power. Institutions strengthen access to capital, inputs, and markets, where through institutions (cooperatives, BUMDes, gapoktan), farmers can obtain collective financing, subsidized feed, and wider access to marketing. In addition, institutions are also the basis for the implementation of community-based regional policies, where the Government can more easily channel assistance programs or interventions in the development of the swamp buffalo livestock business as part of the food self-sufficiency program, especially the fulfillment of meat consumption needs.

Business managerial factors are also the main factor in the development of the swamp buffalo farming business in South Kalimantan. This includes important aspects such as feeding strategies, animal health management, as well as the efficient application of reproductive technology to increase productivity. In addition, effective human and financial resource management also contributes significantly to the operational success of livestock businesses. The application of modern technologies such as automated monitoring systems and the use of innovative feeds can substantially improve the efficiency and production yield of buffalo farms (Longgy and Widianingrum, 2024). Farmers, both individually and in groups, are in dire need of business managerial skills, including in the swamp buffalo farming business. Managerial ability provides the ability to farmers and business managers in planning, organizing, directing, and controlling livestock resources. This is in line with the view that an organization’s competitive success is highly dependent on innovation that is influenced by employee motivation and work ethic. Managerial aspects can be strengthened through 1) Business bookkeeping/recording training programs, for example; amount of feed, labor costs, body weight gain, and reproductive cycles. This is based on research findings that show that business management is still intuitive, where by improving the recording system, it will increase the efficiency and effectiveness of the business which leads to an increase in profits, 2) Technical training on maintenance using local swamp feed, management of fertility and pregnancy, and how to reduce calving intervals.

Information system and technology factors are also the main factors in the development of the swamp buffalo farming business in South Kalimantan. This is in line with Widaningsih (2023b) that the development of swamp buffalo livestock in South Kalimantan is also influenced by information system technology factors that are developing today. The use of integrated digital technology allows for significant improvements in operational efficiency and productivity in the agriculture and livestock sectors (Longgy and Widianingrum, 2024). The use of digital technology and information systems will support livestock management, marketing, and livestock population data integration systems. The use of good information technology will increase data-based monitoring and production control, where the use of IoT (Internet of Things) applications or systems allows real-time monitoring of livestock conditions, health, and feed (Widaningsih et al., 2022b). This allows for faster and more precise decision-making, minimizes the risk of loss, and optimizes production yields (Nugraha et al., 2024). The use of information technology, carried out through 1) information systems that are current and current, such as SMS/WhatsApp groups for Price Information, Diseases, and Service Schedules will greatly help ease of business. There is no need for complex applications, just a WA group then information can be received equally and quickly. Research findings show limited digital literacy among farmers, suggesting simple platforms would be adopted more quickly, 2) The use of IoT/AI such as the Water Level and Weather Alert pilot project which aims to monitor inundation, potential disease outbreaks, and buffalo safety during extreme rainy seasons. This is based on the findings of research in the field that swamp buffalo are very sensitive to changes in swamp conditions, so simple IoT technology can prevent large losses that can occur.

Conclusion

The study found that there are 5 (five) districts that are the base areas for swamp buffalo development in South Kalimantan, namely 1) North Hulu Sungai Regency (HSU), 2) Barito Kuala Regency, 3) South Hulu Sungai Regency (HSS), 4) Hulu Sungai Tengah Regency (HST), and 5) Kotabaru Regency. North Hulu Sungai Regency (HSU) is the main base area in the development of swamp buffalo in South Kalimantan with an LQ = 11.30 value. In addition, the main factors for the development of swamp buffalo in South Kalimantan were also obtained based on the results of the EFA analysis with the main factors, namely; 1) institutional factors, 2) managerial factors, and 3) information system technology factors with institutional factors with a contribution rate to the model reaching 23.37%.

Thus, it is hoped that the results of this study can be the basis for consideration of government policies, especially the Regional Government (South Kalimantan Province) to make North Hulu Sungai Regency, as a base area for the development of swamp buffalo livestock business in South Kalimantan, and pay attention to the main factors, namely institutional, managerial and information system technology factors.

Therefore, population conservation and carrying capacity recovery must form as the foundation for business development. In other words, the business is not a standalone goal, but the result of investment in biophysical improvements: grazing pasture rehabilitation, nutritional improvement, reproductive management, inbreeding control, and protection of supporting ecosystems. Without this integration, the title and content of the study would appear contradictory.

Acknowledgments

We thank the Ministry of Higher Education, Science, and Technology, through the Director of Research and Community Service, has provided research funding facilities for 2025, with the Master Contract Number: 132/C3/DT.05.00/PL/2025. We also thank the Faculty of Agriculture, Islam University of Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, and the Government of Hulu Sungai Utara Regency, South Kalimantan Province, for granting permission and the opportunity to conduct research in its area.

Novelty Statement

The main finding of this study is that the main development center for swamp buffaloes is in Hulu Sungai Utara Regency, with the main factors for its development being institutional, managerial, and information system technology factors

Authors Contribution

NW designed research, analyzed data, and wrote the manuscript. AZ designed research and prepared the manuscript. SE validation and revised the manuscript. MY tabulation and data analysis, as well as an in-depth discussion of the study of research results. All authors reviewed the final version and approved the manuscript before submission.

Generative AI and AI-assisted technology statement

Generative AI is used as a tool to find various related research for in-depth discussion and to help find the latest related literature.

Conflict of interest

The authors have declared no conflict of interest.

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