Physico-Biochemical and Milk Production Characteristics of Peranakan Etawa (PE) Goats in Banyumas Regency, Indonesia

Yusuf Subagyo*, Hermawan Setyo Widodo, Merryafinola Ifani, Afduha Nurus Syamsi, Mas Yedi Sumaryadi, Sambodo Nugroho, Muhammad Zaky Abdullah Algani

Faculty of Animal Science, Jenderal Sodirman University, Jl. Dr. Soeparno No. 60, Purwokerto Utara, Banyumas, Central Java, Indonesia.

Received | July 01, 2025; Accepted | September 05, 2025; Published | October 20, 2025

*Correspondence | Yusuf Subagyo, Faculty of Animal Science, Jenderal Sodirman University, Jl. Dr. Soeparno No. 60, Purwokerto Utara, Banyumas, Central Java, Indonesia; Email: [email protected]

Citation | Subagyo Y, Widodo HS, Ifani M, Syamsi AN, Sumaryadi MY, Nugroho S, Algani MZA (2025). Physico-biochemical and milk production characteristics of peranakan Etawa (PE) goats in Banyumas Regency, Indonesia. J. Anim. Health Prod. 13(4): 1035-1045.

DOI | https://dx.doi.org/10.17582/journal.jahp/2025/13.4.1035.1045

ISSN (Online) | 2308-2801

Copyright: 2025 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

INTRODUCTION

In numerous tropical areas, dual-purpose goats are essential for sustaining rural livelihoods by supplying both milk and meat, particularly in smallholder farming systems. The Etawa Crossbreed (Peranakan Etawa (PE)) is a goat resulting from the hybridization of Jamunapari (Etawa) goats with Kacang goats as indigenous Indonesian breeds. It has become a significant genetic resource in Indonesia owing to its adaptability to tropical climates and its comparatively high productivity (Devendra and Liang, 2012; Pazla and Arief, 2023). Banyumas Regency in Central Java Province has been identified as a national development center for PE goats, as specified in the Ministry of Agriculture Decree No. 43/Kpts/PD.410/1/2015. This breed functions as a dual-purpose goat, significantly contributing to both milk and meat production in the area.

The productivity of PE goats is influenced by a range of factors, including genetic quality, nutritional status, management practices, and overall animal health (Devendra and Liang, 2012). Crossbreeding yields genetic advantages that enhance performance compared to local breeds, particularly in milk production and growth rate (Mestawet et al., 2014). Nonetheless, among these determinants, nutrition is a pivotal factor, ranking just below genetics in its influence on milk production (Park, 2008). Nutritional management frequently poses considerable challenges in smallholder contexts. Insufficient and unbalanced feeding practices can result in deficiencies of vital dietary elements, including energy, protein, minerals, and vitamins. These nutritional imbalances compromise the health, reproductive performance, and productivity of the animals, while also increasing their vulnerability to disease (Lu and Miller, 2019; Tsiplakou et al., 2016).

One of the most critical physiological stages affecting productivity is the transition period, which spans the weeks before and after parturition. At this time, goats are particularly vulnerable to metabolic disorders and infectious diseases due to immunosuppression (Inglingstad et al., 2014). A primary concern at this stage is the emergence of Negative Energy Balance (NEB), a metabolic state wherein energy and protein requirements surpass dietary consumption. NEB is nearly inevitable in ruminants during early lactation, when nutrient requirements increase sharply while feed intake remains inadequate (Harvey et al., 2021). Prolonged NEB not only impairs animal health but also affects the expression of genes involved in lipid metabolism, adipose tissue regulation, and reproductive functions. Therefore, appropriate nutritional strategies are essential to mitigate the adverse effects of NEB and to support metabolic adaptation during this vulnerable period.

The evaluation of goats’ physiological condition during critical phases can be efficiently performed via blood biochemical and protein fraction analyses. Blood serves as the principal transport medium for nutrients, gases, hormones, and metabolic waste, with its composition indicating the animal’s health, nutritional condition, and metabolic functions (Herve et al., 2019; Manat et al., 2016). Commonly utilized blood biochemical indicators encompass glucose, urea, albumin, total protein, cholesterol, and creatinine. Specifically, blood protein fractions like albumin serve as informative indicators of nutritional and immunological status. Molecular analysis of milk through sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) elucidates the composition and variability of milk proteins, shaped by genetic and nutritional factors (Kumar et al., 2013; Widodo et al., 2021). SDS-PAGE facilitates the identification of distinct milk protein bands, rendering it an essential instrument for assessing milk quality and recognizing protein variants pertinent to animal breeding and dairy processing.

Despite Banyumas Regency plays a pivotal role in the advancement of PE goats, there is presently an absence of thorough data regarding the blood biochemical and milk protein profiles of this population. The lack of baseline data impedes the capacity to assess and evaluate the health, nutritional condition, and productive potential of these animals. This study intends to evaluate blood biochemical parameters and protein fractions, in addition to characterizing milk protein profiles through SDS-PAGE in PE goats in Banyumas Regency. The study aims to establish a scientific basis for enhancing goat health management, nutritional strategies, and milk production efficiency in tropical smallholder systems by examining the correlations among these physiological indicators.

MATERIALS AND METHODS

Study design and material used

This study employed an analytical observational approach using a cross-sectional design. It concentrated on observing naturally occurring events within the study population and simultaneously examining the association between causative and outcome-related parameters. The study was carried out in Gumelar District, Banyumas Regency, Central Java, recognized as a designated development center for PE goats. Ninety-eight lactating goats were obtained using purposive sampling. The inclusion criteria comprised goats in their first to third lactation, aged one to four years, with either singleton or multiple births, and reared under a semi-intensive management style by smallholder farmers. Animals were selected according to these criteria to ensure that the resulting data reliably reflect the overall actual conditions. Farmers’ cooperative data indicate that Gumelar has around 3,000 PE goats, with approximately 255 in lactation. A total of 98 lactating goats were sampled, and their distribution across lactation phases followed the proportional ratio observed in the local population. Specifically, the animals consisted of 40 in early, 44 in mid, and 14 in late lactation stages, and were further categorized into 32 in the first, 42 in the second, and 44 in the third lactation periods.

Biochemical examinations of blood samples employed several reagents, including EDTA as an anticoagulant and commercial quantitative reagent kits for the assessment of glucose, cholesterol, triglycerides, total protein, albumin, urea, and creatinine (DiaSys, Germany). SDS-PAGE was performed for protein fraction analysis, utilizing the following reagents and components: A 30% acrylamide:bis-acrylamide solution (29:1) (Himedia, India), Sodium Dodecyl Sulfate (SDS) (Merck, Singapore), Tris-HCl buffer (Himedia, India), ammonium persulfate (Himedia, India), TEMED (N,N,N′,N′-tetramethylethylenediamine) (Himedia, India), and a sample buffer containing β-mercaptoethanol (Merck, Singapore). Gels were stained with Coomassie Brilliant Blue R-250 (Himedia, India) and subsequently destained using a suitable solution to visualize protein bands.

Collection and preparation of samples

Samples of blood were obtained from the jugular vein of each goat using sterile syringes and immediately placed into 3 mL EDTA tubes. Milk samples were collected in sterile 50 mL centrifuge tubes during the morning milking, as the goats were milked only once per day. Representative feed samples were collected from each farm for proximate analysis. All samples were briefly preserved in gel ice trays to ensure their integrity throughout transportation. Laboratory analyses were performed at the Animal Biotechnology and Dairy Animal Production Laboratory, Faculty of Animal Science, Jenderal Soedirman University, Indonesia.

Analysis of feed composition

The feedstuffs analyzed in the study were corn stover, cassava leaves (Manihot esculenta), Odot grass (Pennisetum purpureum cv. Mott), Kaliandra leaves (Calliandra calothyrsus), and Onggok (a by-product of tapioca). The composition was determined using proximate analysis as standardized approach comprised the quantification of dry matter (DM), crude protein (CP), crude fiber (CF), ether extract (EE), ash, and nitrogen-free extract (NFE). The analyses were performed according to the protocols established by the AOAC (1990), using a Soxhlet extraction unit (Velp Scientifica, Milan, Italy).

Blood biochemical analysis

Glucose

Blood glucose concentration was determined using the Glucose Oxidase–Phenol Aminophenazone (GOD-PAP) enzymatic colorimetric method. A volume of 10 μL of serum or standard solution was mixed with 1000 μL of glucose reagent. The solution was vortexed for 10 seconds and incubated at 37 °C for 10 minutes. Absorbance was measured at a wavelength of 546 nm using a spectrophotometer (Thermo Scientific, USA).

Cholesterol

Total cholesterol concentration was measured using the Cholesterol Oxidase–Phenol Aminophenazone (CHOD-PAP) method. A volume of 10 μL of blood serum or cholesterol standard was combined with 1000 μL of reagent. After vortexing for 10 seconds, the mixture was incubated at room temperature for 10 minutes, and absorbance was read at 500 nm.

Triglycerides

Serum triglyceride concentration was analyzed using the Glycerol-3-Phosphate Oxidase–Phenol Aminophenazone (GPO-PAP) method. A volume of 10 μL of serum or standard was added to 1000 μL of triglyceride reagent, vortexed, and incubated at 37°C for 10 minutes. The absorbance was recorded at 546 nm.

Total protein

Total protein concentration was determined using the Biuret method. A mono reagent was prepared by mixing Biuret reagents in a 4:1 ratio, then 20 μL of serum was added to 1000 μL of the mono reagent, vortexed, and incubated at room temperature for 5 minutes. Absorbance was measured at 540 nm against a blank and standard solution.

Albumin

Albumin concentration was measured using the Bromocresol Green (BCG) dye-binding method. Blood serum or albumin standard 10 μL of was added to 1000 μL of BCG reagent, vortexed, and incubated at room temperature for 5 minutes. Absorbance was then measured at 630 nm. The intensity of the green-colored complex formed is proportional to the albumin concentration in the sample.

Blood urea nitrogen (BUN)

Urea concentration was determined using the Urease–Glutamate Dehydrogenase (GLDH) enzymatic method. Blood serum or standard of 10 μL of was mixed with 1000 μL of urea reagent, vortexed, and incubated at 37 °C for 10 minutes. The absorbance was then recorded at 578 nm.

Creatinine

Creatinine concentration was analyzed using the Jaffe kinetic method, in which 200 μL of serum was mixed with 500 μL of picric acid reagent (26 mmol/L) and 500 μL of alkaline buffer solution (1.6 mol/L sodium hydroxide). After vortexing, the solution was allowed to stand for 30 seconds, and the initial absorbance was measured at 492 nm. A second reading was taken after 2 minutes to correct for non-specific chromogen interference.

Milk production and composition analysis

Goat milk production in this study was measured based on both volume and weight. A graduated cylinder was used to measure the volume (mL) and a digital scale to measure the weight (grams) of the milk after it was milked. To standardize milk production based on lactation month and number, proportional distributions adapted from Tiesnamurti et al. (2023) were used, with milk yield assumed to be 20% in the first month, followed by 30%, 25%, 15%, and 10% in the subsequent months. Additionally, correction factors from Ciappesoni et al. (2004) were applied to account for lactation number, using values of 1.4 for first, 1.2 for second, and 1.0 for third and later lactations.

A Lactoscan Milk Analyzer (Milkoscan, Bulgaria) utilizing ultrasonic technology was used to test milk composition. This device rapidly quantifies the percentages of key milk components, such as fat, protein, lactose, minerals, Solids Non Fat (SNF), Total Solids (TS), and density (g/ml).

Milk protein profile

The milk protein profile was analyzed using Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) following the protocol described by Widodo et al. (2021), using equipment from Clever Scientific (UK). Milk samples were first centrifuged at 5,000 rpm for 15 minutes to remove the fat layer. The resulting milk serum was collected and diluted tenfold with double-distilled water. The diluted serum was then mixed with 4X sample buffer and the mixture was heated at 95°C for 5 minutes to denature the proteins followed by immediate cooling. A 10 µL of each sample was loaded into the wells of a polyacrylamide gel consisting of a 4% stacking gel and a 15% separating gel. Electrophoresis was conducted at a constant voltage of 80 V for 2.5 hours at 15°C. The gel was stained with Coomassie Brilliant Blue R-250 to visualize the protein bands. This method enables the identification and comparison of milk protein fractions based on their molecular weights and relative density of each band using ImageJ. Images were first converted to 8-bit grayscale, and background subtraction was performed using the ‘Rolling Ball’ algorithm with a radius of 50 pixels. Rectangular selections were drawn around each sample lane and added to the ROI Manager. Lane profiles were generated through the ‘Analyze-Gels-Plot Lanes’ function, producing intensity histograms for each lane. Protein bands were defined by manually setting peak boundaries, and the area under each peak was measured using the wand tool. Band intensities were normalized against the total intensity per lane to allow for quantitative comparison of milk protein fractions.

Data analysis

All collected data were first subjected to descriptive statistical analysis to summarize the distribution and central tendency of the measured parameters. These included physiological condition, nutrient intake and blood biochemical parameters, milk production and composition, and milk protein profile. Pearson correlation analysis was then performed to examine the relationships among those parameters. Principal Component Analysis (PCA) was applied to further identify the key factors that most significantly influence milk production and composition in PE goats. This multivariate technique allowed for the reduction of dimensionality and the identification of the most influential variables contributing to milk productivity and quality. All statistical analyses were conducted using GraphPad Prism 9.

RESULTS AND DISCUSSION

Physiological characteristics of lactating pe goats

Table 1 presents the physiological parameters of PE goats. The goats’ mean lactation period revealed they were in mid-lactation, a stage during which milk supply progressively diminishes following its peak at 4-6 weeks (Park and Haenlein, 2013). Moreover, the average lactation period and parity indicate that the animals were predominantly primiparous or in their second lactation. Parity and lactation period are critical determinants of milk production, as multiparous goats with more developed mammary glands typically provide greater quantities (Cecchinato et al., 2015).

 

Table 1: Physiological characteristics of lactating Peranakan Etawa (PE) goats in banyumas regency.

Parameter	Mean	Standard deviation	Min	Max
Lactation week	9.88	4.48	1.00	20.00
Lactation period	1.92	0.76	1.00	3.00
Parity	1.40	0.49	1.00	2.00

 

The physiological state of the animal characterized by lactation stage and maturity, was recognized as a significant factor influencing both milk composition and protein profile. The lactation stage as indicated by lactation week, exhibited a classic inverse relationship with milk volume, supported by a strong negative correlation with milk yield and positive correlations with fat and protein concentrations (Figure 1). In addition, the intra-lactation change characterized by lactation period and parity as animal maturity was recognized through PCA as a significant secondary source of variation (Figure 2). Consequently, the analysis suggests that the short-term lactation stage is responsible for the trade-off between milk quantity and solid concentration, whereas the long-term physiological maturation of the goat is associated with more subtle but significant changes in the specific types of protein synthesized (El-Tarabany et al., 2018).

 

 

Table 2: Feed nutrient content (%) and daily intake (g/day) of lactating Peranakan Etawa (PE) goats.

Feed stuffs	Ratio in diet	DM	CP	CF	EE	Ash	NFE	TDN
Corn stover	25.54	21.10	10.70	29.60	2.10	5.28	52.32	59.00
Cassava leaves (Manihot esculenta)	14.55	20.35	18.95	30.95	1.46	0.71	47.93	37.42
Odot grass (Pennisetum purpureum cv. Mott)	23.82	16.59	12.72	32.35	2.28	10.35	42.30	62.56
Kaliandra leaves (Calliandra calothyrsus)	15.66	23.00	23.52	27.70	2.90	8.48	37.40	66.00
Onggok (tapioca by-product)	20.63	85.69	1.55	10.44	0.36	1.03	86.62	77.24
Average nutrient intake (g/day)		758.72± 82.72	94.85± 10.34	198.8± 21.67	13.78± 1.5	41.33± 4.51	409.97± 44.7	467.13± 50.93

 

DM: Dry Mater; CP: Crude Protein; CF: Crude Fiber; EE: Ether Extract; NFE: Nitrogen-Free Extract; TDN: Total Digestible Nutrients.

 

Nutrient intake and blood biochemical profiles of lactating PE goats

The proximate analysis of feedstuffs indicates possible deficiencies in nutritional sufficiency (Table 2). The average total dry matter intake was 758.72±82.72 g/day, with an estimated TDN of 467.13 ± 50.93 g/day; nonetheless, the quality and content of the feed are concerning. Onggok (tapioca by-product), comprising 20.63% of the diet, exhibited a high dry matter content (85.69%) but contained low levels of crude protein (1.55%) and crude fiber (10.44%), resulting in a nutritionally unbalanced profile. Although it contains a high TDN value (77.24%), the lack in protein and fiber indicates it may predominantly function as an energy source, potentially leading to metabolic imbalance if inadequately supplied (Syamsi et al., 2021). Farmers rely on onggok because of its abundant availability and low cost in rural regions; yet, in the absence of appropriate balancing with protein-rich and fiber-balanced forages, it may impede rather than support optimal milk production and component synthesis (Rimbawanto et al., 2015).

 

Table 3: Blood biochemical parameters of lactating Peranakan Etawa (PE) goats.

Parameter	Lactation stage
	Early	Mid	Late
Glucose (mg/dL)	65.81±6.48	47.32±5.05	37±1.45
Cholesterol (mg/dL)	60.11±12.19	108.62±13.19	128.53±7.62
Triglycerides (mg/dL)	11.71±2.57	33.45±8.79	51.82±3.10
Total protein (g/dL)	7.87±0.27	6.75±0.32	6.04±0.06
Albumin (g/dL)	3.43±0.50	2.04±0.43	1.53±0.01
Blood urea nitrogen (mg/dL)	45.75±3.8	59.02±4.43	68.96±1.94
Creatinine (mg/dL)	1.52±0.27	1.42±0.26	1.42±0.20

 

Table 3 shows blood biochemical profile of lactating PE goats. The high BUN levels observed in the PE goats in this study may be attributed to the diet’s inclusion of high percentage non-protein nitrogen (NPN) feed constituents, such as Calliandra leaves. Calliandra is noted for its enhanced crude protein content, comprising significant quantities of non-protein nitrogen (NPN) and tannins (Subagyo et al., 2024). The advantageous crude protein content required sufficient fermentable carbohydrates in the rumen to facilitate microbial protein synthesis due to the presence of non-protein nitrogen (NPN). The rumen microorganisms are unable to fully utilize the nitrogen in the absence of sufficient readily available carbohydrates, such as those from high-energy feeds like maize or molasses, resulting in an excess of ammonia (Subagyo et al., 2024). This ends up in a buildup in the blood circulation, which is then turned to urea by the liver, manifesting as increased BUN levels.

This situation when the dietary energy-protein ratio is uneven, presumably occurring in the current study due to the incorporation of onggok (a tapioca by-product) as a feed component. While onggok contains some carbohydrates, it is characterized by minimal protein, resulting in an inconsistent nutritional profile. The dependence on onggok is mostly attributed to its accessibility and affordability in rural regions; yet, it may lack sufficient fermentable energy for effective nitrogen usage (Widodo et al., 2019). The elevated BUN levels may signify both increased dietary protein intake and inadequate carbohydrate availability for optimal nitrogen metabolism in lactating PE goats.

The PCA in Figure 2 emphasizes that BUN significantly contributes to the primary “Feed vs. Production” axis (PC1). This places BUN in close proximity to milk production and composition variables that also exhibit strong negative loadings. This contrasts markedly with the positive loadings of feed and dietary nutrient variables (e.g., DMI, CP) on PC1. In contrast, the negligible coordinate of BUN on PC2 suggests that it does not have a significant impact on the secondary axis, which is associated with milk protein fraction or animal maturity. The correlation matrix further corroborates these PCA findings by illustrating a robust inverse relationship between BUN and CP or TDN intake. This shows that BUN levels reduced as feed intake increased. This observation is in contradistinction to certain studies that have reported a positive correlation between BUN and CP consumption (Figure 1). This correlation frequently arises when animals are fed diets with enhanced DMI and overall nutrient intake, resulting in an excess of protein (Sitaresmi et al., 2020; Widodo et al., 2019). Surplus nitrogen is not entirely utilized for production in these instances, resulting in excretion and an increase in BUN levels. Conversely, the animals were exhibiting a nutrient imbalance, specifically an energy deficit in relation to protein supply, as indicated by the robust negative correlation between BUN and both CP and TDN in the current study (Tsiplakou et al., 2016). This resulted in a higher BUN despite an increase in protein intake, as confirmed by the significant negative correlation, as it prevented the efficient capture of nitrogen by rumen microbes (Kholif et al., 2014). Additionally, the total grams of protein in the milk and milk production exhibit a highly significant negative correlation with BUN. The animals with the lowest BUN levels were the most productive because they converted food nitrogen into milk protein instead of excreting it as urea.

The nutrient imbalance causing negative energy balance is further substantiated by the findings of blood lipids. The concentrations of cholesterol and triglycerides seem to indicate the degradation of adipose tissue reserves (Kessler et al., 2014). This interpretation is corroborated by the PCA, which clusters these lipids in opposition to feed intake on PC1, and the correlation matrix, which demonstrate a strong negative correlation with DMI. This indicates that the inadequate nutritional intake from feed failed to satisfy lactational requirements, forcing the animals to mobilize their body fat (Sitaresmi et al., 2020). The animals are compelled to rely more heavily on these endogenous reserves to facilitate milk production, a circumstance that is further exacerbated by the progression of the lactation stage, which is negatively correlated with DMI (Lôbo et al., 2017). The levels of the remaining metabolites, such as albumin and glucose, which are closely related to the amount of energy and protein precursors available in the food for milk formation, further reflect this strained metabolic state.

Milk production and composition of lactating PE goats

The standardized mean milk produced by PE goats in Banyumas was 629.83±229.20 g/day and 127.85±46.53 kg for 203 days production (Table 4). The comparatively low average output relative to the possible yields from lactating dairy goats may indicate inadequate nutritional conditions, particularly when considering the moderate parity and lactation period (Table 1).

The milk quality results show the variation of PE goats compared to other studies. The density of the milk corresponds with the common number for goat milk. The TS content and fat content are within the typical range

 

Table 4: Milk production and composition of lactating Peranakan Etawa (PE) goats.

Parameter	Mean	Standard deviation	Minimum	Maximum
Standardized milk production (g/day)	629.83	229.20	321.47	1186.82
203 days production (kg)	127.85	46.53	65.26	240.92
Milk production and composition by lactation stage
	Early	Mid	Late
Milk production (g/day)	826.25±97.53	494.55±89.79	223.93±55.2
4% Fat corrected milk (g/day)	739.67±61.93	494.71±69.2	255.98±53.63
Milk production (mL/day)	803.62±96.42	478.43±87.53	215.65±53.38
Density (g/mL)	1.028±0.002	1.034±0.002	1.039±0.001
Total solids (%)	11.25±0.19	12.41±0.43	13.99±0.47
Solids non-fat (%)	7.92±0.08	8.35±0.13	8.96±0.21
Fat (%)	3.33±0.21	4.06±0.32	5.03±0.34
Protein (%)	3.3±0.39	4.09±0.17	4.61±0.18
Lactose (%)	3.31±0.43	2.86±0.11	2.83±0.22
Minerals (%)	0.81±0.03	0.88±0.02	0.95±0.02
Total solids (g)	92.76±9.51	60.98±9.17	31.09±6.71
Solids non-fat (g)	65.48±8.02	41.19±6.96	20±4.62
Fat (g)	27.28±1.57	19.79±2.23	11.09±2.11
Protein (g)	26.89±1.05	20.09±2.9	10.25±2.25
Lactose (g)	27.73±6.78	14.21±2.98	6.36±1.66
Minerals (g)	6.65±0.52	4.33±0.70	2.11±0.48

 

reported for goat milk, although they are on the lower side compared to some studies on Indonesian Peranakan Etawah (PE) goats which have reported fat and TS above 4.4% and 13.5%, respectively (Sumarmono, 2022). The finding is reasonable, as the animals were in mid-lactation, a stage during which fat and total solids content may normally fall before increasing back in late lactation (Bittante et al., 2015; Lôbo et al., 2017). On the other hand, the protein content is noticeably higher than the values for PE goats (3.60%) and other tropical breeds in other studies (Rusdiana et al., 2015; Widodo et al., 2021). The lactose content is below the typical average of 4.0-5.0% reported in most goat milk studies; yet, a decline in lactose as lactation progresses is a well-established phenomenon (Park, 2017). The minerals and SNF are firmly within the standard and expected ranges for good goat milk. The daily production in grams of total solids, protein, fat, and lactose indicates a minimal food supply for optimum milk synthesis, particularly at peak lactation (Table 1). The mid lactation stage and parity suggest an ability for increased productivity (Inglingstad et al., 2014), nevertheless, actual performance is limited, perhaps due to insufficient nutritional quality and nutrient imbalance which indicated on further results.

In further correlation analysis, the substantial positive correlations between Milk production (g/day) and the grams of TS, SNF, fat, and protein demonstrate the positive association with milk component production (Figure 1). This conclusion is logical, as a larger volume of milk inherently contains a greater absolute mass of solids. The first principal component (PC1) of the PCA on Figure 2 further substantiates this by grouping all of these yield-based variables with milk production, indicating that they function as a single, cohesive productive unit.

In contrast, the analysis emphasizes a substantial inverse relationship between component concentration and milk volume. The results indicate a robust negative correlation between the concentration of fat (%) and protein (%) and the production of milk (g/day). The dilution effect is the scientific term for this phenomenon. The rate of milk synthesis and secretion in high-yielding animals may increase, but the synthesis of components such as fat and protein may not increase at the same rate (Laroche et al., 2022).

Protein fractions in milk of lactating PE goats

Table 5 and Figure 3 showing the relative density of protein bands of PE goats milk. The β-casein fraction was the most abundant protein in this study, with a mean relative density of 35.08%. This finding is consistent with recent research indicating that β-casein is the primary protein in goat milk, comprising 35-45% of total protein (Kumar et al., 2013; Widodo et al., 2021). The observed variance within β-casein is mostly due to the genetic makeup of individual animals. Indonesian dairy goats have been found to possess genetic variations, including Single Nucleotide Polymorphisms (SNPs) within the β-casein gene (CSN2) (Asim et al., 2022). The variance in β-casein expression that is observed is likely linked to these genetic variations. In addition, the variance in β-casein may be influenced by non-genetic variables such as lactation period and nutritional availability.

 

Table 5: Relative density of milk proteins in Peranakan Etawa (PE) goats from Banyumas.

Parameter	Lactation stage
	Early	Mid	Late
αS1-casein (%)	23.4±1.13	26.19±0.75	29.1±1.07
αS2-casein (%)	15.97±2.51	14.26±2.66	12.07±3.56
β-casein (%)	36.15±3.11	34.77±3.21	33.01±3.43
κ-casein (%)	4.88±0.91	5.33±0.82	5.92±1.08
α-lactalbumin (%)	13.44±2.11	13.32±2.2	13.26±2.25
β-lactoglobulin (%)	6.15±1.21	6.13±1.23	6.64±0.93

 

The mean levels of αS1-casein and αS2-casein were 25.47% and 14.64%, respectively. Although the αS2-casein level is within the typical range, the αS1-casein value is at the upper end of the common 10-25% range. It is important to note the significant range of its distribution, which ranges from 20.43% to 31.65%. This substantial variation is a direct result of genetic polymorphism, which regulates the level of protein expression through the action of various alleles. The Indonesian PE goat population has explicitly confirmed the prevalence of polymorphisms that result in the presence of multiple ‘strong’ and ‘weak’ alleles for the αS1-casein gene (CSN1S1) (Dagnachew et al., 2011; Perna et al., 2018; Widodo et al., 2023). The observed variability is primarily caused by this genetic characteristic.

The further results demonstrated that α-lactalbumin constituted the primary component of the whey proteins at 13.36%. This quantity exceeds that of β-lactoglobulin, which was quantified at 6.21%, by more than a factor of two. The increased ratio of α-lactalbumin to β-lactoglobulin constitutes a unique biochemical marker for goat milk in comparison to other ruminant species (Asim et al., 2022). The variance in these whey proteins is affected by hereditary variables and physiological conditions. The variance of β-lactoglobulin is associated with genetic variants in its BLG gene. Moreover, the concentrations of whey proteins are recognized to vary with the lactation stage. Levels are often at their peak during early lactation and gradually diminish as lactation progresses. The goats in this study were sampled at mid-lactation (Table 1), suggesting that this physiological state likely influences the detected concentrations (Dettori et al., 2015).

The caseins were separated along the PC1 based on PCA results (Figure 2). αs1- and κ-casein were linked to production outputs, whereas αS2 and β-casein were more closely linked to dietary inputs, indicating differential regulation. The correlation matrix supports this distinction, showing negative correlations between members of these two groups, such as αs2- and β-casein (Al-Amareen and Jawasreh, 2022). The second principal component (PC2) was identified by a change in protein type that was associated with animal maturity. This shift was characterized by an opposition between whey proteins (α-lactalbumin) and caseins (β-casein), a phenomenon in which the whey protein fraction increases in proportion in later parities.

CONCLUSIONS

In conclusion, the physiological state and nutritional intake of PE goats are essential factors influencing their milk production and composition. The outcomes of this study indicate that appropriate nutrition is essential for obtaining high productivity, whereas an inadequate or imbalanced dietary supply leads to detrimental metabolic effects instead of improving efficiency. Inadequate energy and protein consumption, especially during crucial periods such as early lactation, can result in Negative Energy Balance (NEB), which not only diminishes milk production but also negatively affects its qualitative attributes.

ACKNOWLEDGMENTS

The authors gratefully acknowledge the support of the Livestock and Fisheries Agency of the Banyumas Regency Government and Jenderal Soedirman University. We thank them for providing the official permits and assistance necessary for accessing research sites and collecting the samples that formed the basis of this investigation.

NOVELTY STATEMENT

The findings of this study reflect a prevalent issue within Indonesia’s smallholder farms: Reduced milk production in PE goats is directly linked to diet quality. The reliance on high-inclusion, low-quality feedstuffs such as Onggok, even when protein sources like Calliandra leaves are available, correlates strongly with blood biochemical indicators of poor nutritional status. Given that this feeding strategy is a deeply ingrained and widespread practice, these insights are crucial and warrant a direct response from the farming community to adapt and improve traditional farming for enhanced efficiency under contract No. 6.72/UN23.37/PT.01.03/IV/2023.

AUTHOR’S CONTRIBUTION

YS and HSW: Data analysis and manuscript writing.

MI and MYS: Data analysis.

ANS: Manuscript writing.

SN and MZAA: Sample collection and analysis.

Generative AI and AI-assisted technology statement

We acknowledge the use of Grammarly, Quillbot, and ChatGPT in the preparation of this manuscript for assistance with language editing and refinement. The authors have meticulously verified and revised all content to ensure its originality and accuracy and bear full responsibility for the final publication.

Conflict of interest

The authors have declared no conflict of interests.

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