Forage maize type influences on methane emissions, nutrient degradation, and fermentation profiles in ruminants

Abstract

The selection of forage maize (Zea mays L.) cultivars significantly impacts ruminant nutrition and environmental sustainability. This study evaluated four maize types (TWC647, SC704, D5, and a SC704 + D5 mixture) for their effects on methane emissions, nutrient degradability, and rumen fermentation using in vitro techniques. SC704 exhibited the highest gas production (236.8 ml/g DM), metabolizable energy (5.17 MJ/kg DM), and net energy for lactation (2.64 MJ/kg DM). TWC647 showed the highest degradability of dry matter, crude protein, and NDF. The SC704 + D5 mixture enhanced volatile fatty acid production but also resulted in the highest methane emissions (71.5 l/kg FOM). These findings emphasize the importance of cultivar selection in optimizing fermentation efficiency and reducing environmental impact.

Introduction

The growing global population and the ongoing depletion of natural food resources pose significant challenges to ensuring an adequate food supply in the future. According to the Food and Agriculture Organization (FAO)¹, feeding an estimated 9.1 billion people by 2050 will require a 70% increase in total food production. Livestock systems, as a critical component of global food security, face the dual challenge of meeting rising demand while minimizing environmental impact.

Forage maize (Zea mays L.) is one of the most important high-yielding forages used in ruminant nutrition due to its considerable biomass production and rich content of fermentable carbohydrates^2,3. The nutritional value of forage maize varies widely among cultivars, especially in terms of fiber composition, starch concentration, and digestibility. These differences can significantly influence ruminal fermentation patterns, nutrient degradability, and methane production. Therefore, evaluating the fermentative and nutritional characteristics of forage maize cultivars is essential for optimizing ruminant feed efficiency and minimizing environmental impact.

In recent decades, advancements in breeding programs have led to the development of diverse maize genotypes with varying agronomic and nutritional traits, as highlighted by previous studies^4–6. These genotypes vary in maturation rates, energy composition (cell wall vs. starch content), and fermentation profiles. Recent research further highlights the importance of selecting forage maize varieties that balance productivity and environmental sustainability^7,8. Additionally, breeding initiatives increasingly focus on improving forage digestibility and reducing enteric methane emissions, aligning with global climate change mitigation efforts⁹.

Methane emissions from ruminants, primarily resulting from rumen fermentation, represent a significant loss of dietary energy while contributing to greenhouse gas emissions. Variations in forage maize characteristics can markedly affect fermentation kinetics, volatile fatty acid production, and nutrient degradation, as reported by Offner et al.¹⁰ and Montes et al.¹¹. Investigating these interactions is essential to designing feeding strategies that enhance production efficiency and environmental sustainability.

Despite substantial progress in understanding the nutritional variability among maize genotypes, previous studies often focused on either digestibility or methane emissions in isolation, and rarely evaluated these parameters together in a comparative framework using multiple cultivars under identical in vitro conditions. Moreover, there is limited research on the synergistic or antagonistic effects of cultivar mixtures (e.g., SC704 + D5) on rumen fermentation profiles and environmental outcomes. This study uniquely combines a comprehensive analysis of nutrient degradability, gas production kinetics, volatile fatty acid profiles, and methane emissions across diverse maize genotypes (D5, single cross SC704, TWC 647, and SC704 + D5) using standardized in vitro techniques. By integrating both productivity and environmental indicators, our work addresses the knowledge gap regarding the trade-offs and complementarities among forage maize types, providing actionable insights for selecting cultivars that optimize both animal performance and environmental sustainability.

Materials and methods

Forage maize varieties

In 2023, a randomized complete block design with three replications was implemented at the Agricultural Research Station of Moghan Agro-Industry and Livestock Co¹² to evaluate four forage maize cultivars: TWC647 (♀KSC647 × MO17♂), SC704 (♀B73 × MO17♂), D5 (♀M41 × L51♂), and a SC704 + D5 mixture. These cultivars were selected based on their agronomic diversity and relevance to regional production systems. SC704 (late-maturing) and TWC647 (medium-maturing) are commonly grown hybrids in Iran, while D5 is a tropical, late-maturing genotype with distinct fiber traits. The SC704 + D5 mixture was included to explore potential interactions between contrasting genotypes in terms of fermentation efficiency and environmental output.

Each block included four plots, each assigned to one maize treatment. Plots consisted of four rows (6.5 × 0.75 m) with 0.14 m spacing between hills, and a planting density of approximately 92,000 plants/ha. Standard agronomic practices were applied, including nitrogen fertilization at 60 kg/ha at planting and again four weeks later.

At the soft dough stage of kernel development, each plot was sampled three times. In each sampling, five maize plants were randomly harvested, yielding 15 plants per plot and a total of 45 plants per cultivar across replicates. The harvested plants were chopped, air-dried, and initially ground to pass through a 2-mm sieve for use in the in vitro fermentation and degradability assays. Subsequently, a portion of each composite sample was further ground to pass through a 1-mm sieve to obtain sufficient material for chemical composition analysis.

Experimental design

The study was conducted in two independent but complementary in vitro experiments using the same forage maize samples. Experiment 1 focused on evaluating gas production kinetics and rumen fermentation characteristics, while Experiment 2 assessed the in vitro degradability of dry matter, crude protein, and neutral detergent fiber.

Experiment 1 – Gas production and rumen fermentation characteristics

In the first experiment, the effects of forage maize type on fermentation characteristics were evaluated using in vitro gas measuring technique according to Parnian-khajehdizaj et al.¹³. Forage maize samples were ground in a Wiley Mill to pass a 2 mm screen (Arthur H. Thomas, Philadelphia, PA, USA) and weighed (500 mg) into 50 ml glass vials. Six vials containing no feed sample were included as blank vials. Rumen fluid was obtained 2 h after morning feeding from two non-lactating Holstein cows (BW 650 ± 23 kg) fitted with ruminal cannulas. Animals fed a diet comprising 60% forages and 40% concentrates¹⁴, with feedings occurring twice daily from 08:00 to 15:00. The obtained rumen fluid samples were then transferred into pre-warmed thermos flask, which were mixed using a manual agitator, filtered through four layers of cheesecloth and purged with CO₂. McDougall buffer solution¹⁵ was prepared and placed in a water bath at 39 °C. The rumen fluid was then mixed with prepared buffer solution (at a 1:2 ratio v/v; rumen fluid to buffer). Each vial received 20 ml of the prepared rumen fluid-buffer mixture and purged with CO₂. The vials were immediately sealed with rubber caps and aluminum rings after loading and were secured to a rotary shaker platform (lab-line instruments Inc Melors dark, USA) and housed in an incubator (39 °C). Gas production was measured in each vial after 2, 4, 6, 8, 12, 16, 24, 36, 48, 72, and 96 h of incubation using a water displacement system¹⁶.

Experiment 2 – In vitro degradability of DM, CP, and NDF

In order to investigate effects of forage maize type on degradability for DM, CP and NDF, an in vitro technique was performed similarly to in vitro gas measuring technique in the second experiment^17,18. Ground forage maize samples were weighed (500 mg) into 2.5 × 3 cm nylon bags (45 μm pore size), heat sealed and placed into 120 vials (4 treatments in 6 replicates) for five incubation periods (6, 12, 24, 48, and 72 h). Serum vials were loaded with prepared buffered inoculum, and then were affixed to a rotary shaker platform at 39 °C according to in vitro gas production technique. Produced gas was emptied during incubation via needles installed on the rubber caps of vials. At the end of each incubation time, respective vials for each incubation time were removed and the pH of content was measured immediately. The bags in the vials were pulled out, rinsed with phosphate buffer¹³ to ensure removal of any microbial contamination by rumen microorganisms. The washed bags were dried at 60 °C for 48 h and then weighed. The laboratory degradability of dry matter was obtained by subtracting the amount of dry matter remaining after incubation from the amount of dry matter in the sample. The residues were analysed for CP and NDF content and in vitro degradability (IVD) of CP and NDF were calculated as:

To analyze VFA, a 10 ml portion of vial content was frozen in 15 mL polyethylene tubes with 2 ml of 25% metaphosphoric acid (wt/vol). Additionally, 2 ml of a 5% solution (V/V) of sulfuric acid was immediately mixed with another 10 ml of vial content. The resulting solution was subsequently stored at a temperature of − 20 °C until analysis for ammonia nitrogen (NH₃ − N) concentration.

Laboratory analyses and calculations

Dry matter (method ID 934.01), ash (method ID 942.05), ether extract (method ID 920.30), and crude protein (method ID 984.13) of forage maize samples were determined according to AOAC procedures¹⁹. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were determined with a Fibertec™ (Foss, Denmark) fiber analyzer using reagents described by Van Soest et al.²⁰ without heat-stable amylase but with sodium sulfite and ash correction.

Frozen samples of vial contents were thawed and then centrifuged at 10,000 g for 10 min at 4 °C, and ammonia was determined by a phenol-hypochlorite assay using a spectrophotometer at 630 nm according to Broderick and Kang²¹. Volatile fatty acids (VFA) were measured by gas chromatography with a 15 m (0.53 mm i.d.) fused silica column. The metabolizable energy (ME) and net energy for lactation (NEL) content of forage maize samples were calculated using the equations of Menke and Steingass²² as:

ME (MJ/kg DM) = 1.06 + 0.1570GP + 0.0084 CP + 0.022 CF − 0.0081 CA.

NEL (MJ/kg DM) = − 0.36 + 0.1149GP + 0.0054 CP + 0.0139 CF − 0.0054 CA.

Where GP is 24 h net gas production (ml/200 mg DM); CP, CF and CA are crude protein, crude fat and crude ash (% DM), respectively. Partitioning factor (PF) for 12 and 24 h of incubation time calculated as the ratio of milligrams (mg) of substrate degraded to ml of corresponding gas produced²³. Rumen fermentable organic matter (FOM) and methane production were estimated stoichiometrically from VFA proportions using the equations proposed by Demeyer²⁴:

FOM (g/mol VFA) = 162 (0.5A + 0.5P + B) (V).

where 162 = mol.wt% of theoretical carbohydrate polymer unit, A, P and B = molar proportion.

of acetate, propionate and butyrate, respectively.

Acetate (mol/kg FOM) = (l000/V)A (W).

Propionate (mol/kg FOM) = (l000/V)P (X).

Butyrate (mol/kg FOM) = (l000/V)B (Y).

Methane (mol/kg FOM) = (1.8W – 1.1X + 1.61Y)/4 (Z).

Methane (1/kg FOM) = Z × 22.4.

Microbial protein was calculated as 20 g bacterial protein per 100 g of organic matter fermented in the vial content²⁵.

Curve peeling and statistical analysis

To describe the dynamics of gas production over time the following Gompertz function²⁶ was chosen:

GP = g_m exp{– exp [1 + (r_me/g_m)(Lag – t)]}

where GP is cumulative gas production (ml), g_m the theoretical maximum of gas production (ml), r_m the maximum rate of gas production (ml/h), which occurs at the point of inflection of the curve, LAG the lag time (h), which is defined as the time-axis intercept of a tangent line at the point of inflection, t is time (h) and e is the Euler constant. The parameters g_m, r_m, and Lag were estimated using Marquardt method with NLIN procedure of SAS 9.4. The analysis of variance for all data was performed using the SAS’s GLM procedures (version 9.4) using the model:

Y_ij = µ + α_i + ε_ij,

where Y_ij is the value of each individual observation for the dependent variables, µ the overall mean, α_i the effect of treatment, and ε_ij the random residual error. Differences among treatments within incubation were determined using LSMEANS with PDIFF statement.

Results

Chemical composition

The chemical composition of the four maize types is summarized in Table 1. The crude protein content ranged from 8.23% (TWC647) to 11.91% (SC704), while ether extract content was highest in SC704 at 2.26%, while the other maize types had lower values: 1.10% for TWC647 and D5, and 1.00% for SC704 + D5. Neutral detergent fiber (NDF), an important measure of the plant’s cell wall content, ranged from 49.03% in SC704 to 64.18% in SC704 + D5. Non-fiber carbohydrates (NFC), calculated as the difference between dry matter and the sum of crude protein, ether extract, NDF, and ash, were highest in SC704 at 29.50% and lowest in SC704 + D5 at 16.03%.

Table 1.

Chemical composition (% of DM) of forage maize types.

	SC704	TWC647	D5	SC704 + D5
Dry matter, % of as-is	26.50	27.00	26.00	27.50
Crude protein	11.91	8.23	9.99	9.89
Ether extract	2.26	1.10	1.10	1.00
Ash	7.30	10.60	9.00	8.90
Neutral detergent fibre	49.03	59.00	57.09	64.18
Acid detergent fibre	22.97	29.50	28.16	31.42
Non fiber carbohydrate	29.50	21.07	22.82	16.03
Hemicellulose	26.06	29.50	28.93	32.76

Non fiber carbohydrate = DM – (CP + EE + NDF + Ash).

Hemicellulose = Neutral detergent fiber – Acid detergent fiber.

In vitro cumulative gas production and fermentation kinetics

The cumulative in vitro gas production and fermentation characteristics of different forage maize types (SC704, TWC647, D5, SC704 + D5) were evaluated over 96 h of fermentation. Significant differences (P < 0.05) were observed in cumulative gas production at several time points (Table 2). At 2 h of fermentation, gas production did not differ (P > 0.05) among treatments, while significant differences were observed at 4 h, with SC704 (35.2 ml/g DM) and TWC647 (36.9 ml/g DM) showing higher gas production than D5 (30.2 ml/g DM) and SC704 + D5 (33.9 ml/g DM). At 8 h, SC704 had significantly higher cumulative gas production than all other treatments. At 12 h, SC704 continued to exhibit significantly greater gas production (78.4 ml/g DM) compared to other forage maize varieties. At subsequent time points (16, 24, 36, 48, 72, and 96 h), gas production across the treatments gradually increased, but no significant differences (P > 0.05) were observed after 24 h, indicating stabilization of fermentation (Fig. 1).

Table 2.

Cumulative in vitro gas production and fermentation characteristics of forage maize types.

	Forage maize types				SEM	P-Value
	SC704	TWC647	D5	SC704 + D5
Cumulative gas production, mL/g of DM
6 h	46.9	43.5	41.9	40.9	1.94	0.18
12 h	78.4a	62.5b	62.5b	61.2b	2.90	0.001
24 h	128.0a	108.4b	104.8b	114.3ab	5.32	0.03
48 h	207.5	180.4	199.3	206.5	8.10	0.09
72 h	230.7	206.0	230.4	239.1	10.38	0.16
Fermentation characteristics according to Gompertz function (Schofield et al., 1994)
gm, ml/g of DM	236.8	219.9	247.8	254.1	10.70	0.15
rm, ml/h	4.99a	3.92c	4.26bc	4.60ab	0.175	0.002
Lag time,/h	–2.7ab	–3.9b	–1.8a	–1.9a	0.54	0.01
ME, MJ/kg DM	5.17a	4.47b	4.39b	4.68ab	0.167	0.01
NEl, MJ/kg DM	2.64a	2.13b	2.07b	2.29ab	0.122	0.02

SEM, standard error of means.

g_m, the theoretical maximum of gas production.

r_m, the maximum rate of gas production.

ME, estimated metabolisable energy.

NEl, estimated net energy for lactation.

Fig. 1.

Cumulative gas production of individual forage maize at different times of incubation (TWC647 “▲”, from the medium-maturing group; SC704 “■”, from the late-maturing group; D5 “●”, from the very late-maturing group; and SC704 + D5 “◆” a mixture of varieties SC704 and D5).

The Gompertz model analysis revealed that SC704 had the highest maximum theoretical gas production (236.8 ml/g DM), followed by D5 (247.8 ml/g DM) and SC704 + D5 (254.1 mL/g DM), while TWC647 showed the lowest value (219.9 ml/g DM). The rate of gas production (r_m) was highest in SC704 (4.99 ml/h), significantly greater than TWC647 (3.92 ml/h) and intermediate for D5 (4.26 ml/h) and SC704 + D5 (4.60 ml/h). The lag time was significantly shorter for SC704 and D5 compared to TWC647, with SC704 showing a lag time of − 2.7 h, followed by − 1.8 and − 1.9 h for D5 and SC704 + D5, respectively, and the longest lag time observed for TWC647 at −3.9 h (P = 0.01). SC704 had the highest energy values estimated ME and NEL, with values of 5.17 MJ/kg DM and 2.64 MJ/kg DM, respectively. In contrast, TWC647 had the lowest ME (4.47 MJ/kg DM) and NEL (2.13 MJ/kg DM), while D5 and SC704 + D5 exhibited intermediate values.

In vitro nutrient degradability

Forage maize types differentially affected ruminal fermentation, methane production, and nutrient degradability are presented in Table 3; Figs. 2, 3, 4 and 5. At 12 h of incubation, ruminal pH was significantly higher in TWC647, D5, and SC704 + D5 treatments compared to SC704 (P < 0.0001; Table 3). A similar trend was observed after 24 h (Fig. 5), with TWC647 and SC704 + D5 maintaining the highest pH levels throughout the incubation period. Ammonia concentrations were significantly lower in TWC647 and SC704 + D5 compared to SC704 and D5 after 24 h (P < 0.0001). Total VFA concentration differed significantly among treatments (P < 0.0001). At 12 and 24 h, SC704 + D5 had the highest total VFA levels, followed by TWC647, SC704, and D5. Acetate and propionate molar proportions were highest in SC704 + D5 (Table 3). Methane production was significantly greater in SC704 + D5 compared to other treatments, reaching 71.5 l/kg FOM at 12 h and 68.7 l/kg FOM at 24 h (P < 0.0001). Figure 2 shows in vitro dry matter (DM) degradability curves for the maize types. TWC647 had the greatest DM degradability after 24 h (56%; P < 0.01), significantly higher than SC704, D5, and SC704 + D5. Crude protein degradability trends (Fig. 3) mirrored those for DM, with TWC647 showing the highest CP degradation (69.34%; P < 0.01). Neutral detergent fiber (NDF) degradability (Fig. 4) was also greatest in TWC647 (47.87%; P = 0.03). These results demonstrate superior nutrient degradability of TWC647 and the enhanced fermentation efficiency of SC704 + D5. However, the latter also resulted in the highest methane production, potentially indicating greater energy loss compared to other treatments.

Table 3.

Effects of forage maize type on in vitro ruminal fermentation pattern.

	Forage maize types				SEM	P-Value
	SC704	TWC647	D5	SC704 + D5
12 h
pH	6.20b	6.42a	6.39a	6.36a	0.028	< 0.0001
Ammonia (mg/dl)	6.15	6.34	7.10	6.61	0.429	0.45
VFA concentration (mM)
Total VFA	49.27c	57.44b	45.60c	71.56a	1.293	< 0.0001
Acetate	32.00c	37.47b	28.12d	49.55a	1.014	< 0.0001
Propionate	12.90c	15.21ab	13.41bc	17.08a	0.644	< 0.0001
Butyrate	3.18ab	3.30a	2.91b	3.42a	0.105	0.02
Iso-Butyrate	0.26	0.27	0.24	0.29	0.022	0.43
Valerate	0.40b	0.64a	0.42b	0.67a	0.013	< 0.0001
Iso-Valerate	0.52	0.56	0.51	0.54	0.025	0.45
Branched VFA*	0.78	0.83	0.74	0.83	0.038	0.32
Acetate/propionate	2.54ab	2.48bc	2.13c	2.91a	0.131	0.004
Methane (l/kg FOM)	65.4b	65.3b	59.3c	71.5a	1.89	< 0.01
FOM (g/mol VFA)	84.3a	83.6bc	84.1ab	83.2c	0.212	< 0.01
Bacterial protein yield (g/mol VFA)	16.86a	16.72bc	16.82ab	16.63c	0.042	0.004
In vitro DM degradability, %	38.00	36.37	39.60	34.33	2.299	0.43
In vitro CP degradability, %	64.45a	62.65ab	50.90b	50.73b	4.299	0.05
In vitro NDF degradability, %	25.65	26.80	20.02	20.33	2.908	0.25
PF, mg DM/mL	4.92b	5.81ab	6.43a	5.62ab	0.414	0.01
24 h
pH	6.01b	6.29a	6.11b	6.27a	0.038	< 0.0001
Ammonia (mg/dl)	8.02a	3.98c	6.19b	4.71c	0.374	< 0.0001
VFA concentration (mM)
Total VFA	90.38a	67.10c	50.37d	81.43b	1.188	< 0.0001
Acetate	45.44b	41.98c	28.20d	53.34a	1.011	< 0.0001
Propionate	32.56a	17.44c	17.44c	19.41b	0.533	< 0.0001
Butyrate	8.86a	5.41c	3.09d	6.35b	0.181	< 0.0001
Iso-Butyrate	0.38a	0.35a	0.25b	0.37a	0.010	< 0.0001
Valerate	2.37a	1.21c	0.89d	1.29b	0.018	< 0.0001
Iso-Valerate	0.78a	0.70ab	0.50c	0.68b	0.029	< 0.0001
Branched VFA	1.16a	1.05b	0.72c	1.05b	0.029	< 0.0001
Acetate/propionate	1.40d	2.41b	1.63c	2.75a	0.067	< 0.0001
Methane (l/kg FOM)	43.5d	64.1b	48.7c	68.7a	1.49	< 0.0001
FOM (g/mol VFA)	85.8a	84.8b	83.3c	85.0b	0.210	< 0.0001
Bacterial protein yield (g/mol VFA)	17.16a	16.96b	16.67c	17.00b	0.042	< 0.0001
In vitro DM degradability, %	44.00b	56.0a	43.8b	40.0b	2.446	< 0.01
In vitro CP degradability, %	69.34a	63.64a	55.61b	56.02b	1.974	< 0.01
In vitro NDF degradability, %	42.89ab	47.87a	39.75ab	35.34b	2.841	0.03
PF, mg DM/mL	3.50b	5.15a	4.22b	3.53b	0.253	< 0.001

SEM, standard error of means.

Branched VFA = Iso-butyrate + Iso-valerate.

PF = partitioning factor.

Fig. 2.

In vitro dry matter degradability curves of forage maize types overall incubation times (TWC647 “▲”, from the medium-maturing group; SC704 “■”, from the late-maturing group; D5 “●”, from the very late-maturing group; and SC704 + D5 “◆” a mixture of varieties SC704 and D5).

Fig. 3.

In vitro crude protein degradability curves of forage maize types overall incubation times (TWC647 “▲”, from the medium-maturing group; SC704 “■”, from the late-maturing group; D5 “●”, from the very late-maturing group; and SC704 + D5 “◆” a mixture of varieties SC704 and D5).

Fig. 4.

In vitro neutral detergent fibre degradability curves of forage maize types overall incubation times (TWC647 “▲”, from the medium-maturing group; SC704 “■”, from the late-maturing group; D5 “●”, from the very late-maturing group; and SC704 + D5 “◆” a mixture of varieties SC704 and D5).

Fig. 5.

Changes in in vitro batch culture pH overall incubation times (TWC647 “▲”, from the medium-maturing group; SC704 “■”, from the late-maturing group; D5 “●”, from the very late-maturing group; and SC704 + D5 “◆” a mixture of varieties SC704 and D5).

Discussion

This study investigated how four forage maize types (SC704, TWC647, D5, and SC704 + D5) differ in their effects on ruminal fermentation dynamics, nutrient degradability, and methane emissions. Through controlled in vitro evaluation of these cultivars, we provide a more integrative understanding of how forage genotype selection can influence not only animal productivity but also environmental outcomes such as greenhouse gas emissions. Our results demonstrate the complex interactions between maize chemistry, fermentation behavior, and microbial activity.

Chemical composition and fermentation efficiency

Variation in forage maize composition, particularly in terms of neutral detergent fiber (NDF) and non-fiber carbohydrates (NFC), played a pivotal role in determining fermentation behavior. SC704, characterized by higher NFC content and lower NDF content, promoted more rapid and extensive fermentation. This aligns with established findings that NFC-rich diets accelerate microbial fermentation, increase propionate production, and reduce methane emissions through more efficient hydrogen utilization²⁷. In contrast, the SC704 + D5 mixture, which had a much higher NDF content, showed slower but sustained fermentation, possibly due to the time required to degrade complex structural carbohydrates. While sustained fermentation may benefit rumen fill and microbial stability, it can also result in greater methane emissions. These findings confirm the importance of fiber content and composition in modulating fermentation profiles, and they echo earlier research emphasizing that fiber-rich forages are more likely to support acetate production and methanogenesis²⁸.

Fermentation kinetics and energy yield

The in vitro gas production technique provides a reliable estimate of the in vivo digestibility of ruminant feeds²⁹. Fermentation kinetics revealed distinct performance characteristics among the forage types. SC704 exhibited rapid fermentation kinetics and shorter lag time, suggesting its substrates were more readily accessible to microbial enzymes and supporting high metabolizable energy (ME) and net energy for lactation (NEL) values (Table 2). These characteristics are particularly valuable in high-production dairy systems where rapid energy availability is critical. Although the SC704 + D5 mixture showed slower initial fermentation, exhibited prolonged gas release over time, indicating continued microbial activity, an attribute potentially beneficial for maintaining stable rumen function throughout the day. TWC647 showed the longest lag time and the slowest gas production rate, suggesting more resistant fiber structures or lower microbial affinity. However, such characteristics may help modulate fermentation speed, reducing the risk of acidosis and promoting fiber digestion over extended periods. These dynamics reflect important trade-offs in feed formulation: fast-fermenting forages may enhance performance in the short term but can compromise rumen health, while slower-fermenting feeds may foster more stable microbial environments.

Lag times in our study ranged from − 1.8 to − 3.9 h, with TWC647 exhibiting the longest lag time of − 3.9 h, significantly differing from the other samples. Lag time refers to the initial delay before the fermentation process begins and is an important factor in understanding the adaptation of microbial communities to the substrate. Extended lag times typically indicate delayed microbial adaptation may take longer to adapt to the feed, which could lead to delayed fermentation initiation and potentially lower efficiency. Conversely, the shorter lag times seen in D5 and SC704 + D5 treatments indicated that microbial communities may be more rapidly adapting to these feeds, likely resulting in more efficient fermentation. The negative values for lag time may be due to the way the Gompertz function is fitted, representing the time before the exponential phase of fermentation. Moreover, Krishnamoorthy et al.³⁰ observed negative LAG values for in vitro gas curves of oat, rye, and hay standards incubated for 96 h. They assumed that the negative lag-time values are a consequence of rapid gas production in the early stages of fermentation, which may have been caused by the characteristics of the fermentable substrate.

Energy parameters including metabolizable energy (ME) and net energy for lactation (NEL) followed similar trends, with SC704 showing greater energy yield per unit of dry matter. These findings are supported by Wei et al.³¹, who showed that higher NFC levels lead to improved energy capture by favoring glucogenic fermentation pathways. The lower energy values observed in TWC647 and D5 may reflect a greater proportion of energy lost as methane or tied up in slowly degradable fiber fractions.

Nutrient degradability and microbial efficiency

Dry matter (DM), crude protein (CP), and NDF degradability varied across cultivars, influenced by differences in cell wall structure, lignin content, and carbohydrate availability. TWC647 demonstrated strong DM and CP degradability, suggesting that its fiber was more accessible to microbial enzymes and that it supported effective nitrogen utilization. These characteristics are advantageous for improving feed efficiency and reducing nitrogen excretion in ruminants. In contrast, the SC704 + D5 mixture had comparatively lower degradability metrics (Fig. 3). This may be attributed to fiber–protein complexes or a denser cell wall matrix that restricts microbial access. While this limits nutrient availability, it can prolong rumen retention time and stabilize pH levels (Fig. 5). Therefore, reduced degradability may be advantageous, it may be appropriate for animals with lower nutrient demands or in diets formulated to prevent rapid rumen acidification. Higher NDF degradability in TWC647 also highlights its value in systems prioritizing fiber digestibility and overall energy extraction from roughages³². Improved fiber utilization is critical in dairy systems where high-forage diets are preferred for economic or health reasons. Breeding programs that enhance the digestibility of structural carbohydrates in maize, particularly through reduced lignin content, can help maximize energy availability from the forage base.

Volatile fatty acids and rumen environment

All treatments maintained ruminal pH within the optimal range (6.0–6.5, Fig. 4), with some differences reflecting fermentation speed and buffering capacity³³. Faster-fermenting forages like SC704 tended to lower pH slightly due to greater acid production, although this remained within physiologically acceptable bounds. In contrast, fiber-rich treatments like SC704 + D5 maintained higher pH levels, likely due to slower fermentation and increased salivary buffering stimulated by chewing.

Volatile fatty acid profiles substantiated the divergence in fermentation patterns. The SC704 + D5 treatment led to the highest total VFA production, driven largely by acetate and propionate, indicating active fiber fermentation. However, its higher acetate-to-propionate ratio suggests a metabolic shift toward pathways that support methane production, unlike SC704, which likely favored propionate synthesis and more efficient hydrogen capture. These results mirror those of Sun et al.³⁴, who linked VFA profiles to energy yield and methane formation in dairy cattle. The balanced VFA production seen in TWC647 again reinforces its dual value as both a digestible and environmentally favorable feed, capable of supporting fermentation without excessive methane release.

Methane production and environmental impact

We quantified methane production relative to fermented organic matter. In our prior investigation³⁵, we validated the hypothesis that expressing methane emissions on the basis of digestible organic matter may provide a superior framework for evaluating the effectiveness of feed, dietary strategies, and feed additives in terms of mitigation. Methane emissions differed substantially among forage types, largely tracking with fiber content and fermentability. SC704 + D5 produced the most methane, which can be attributed to increased acetate formation and hydrogen release from structural carbohydrate fermentation. Such results are consistent with previous studies indicating that high-fiber diets promote methanogenesis³⁶. In contrast, forages that ferment quickly and promote propionate production, like SC704, tend to result in lower methane output per unit of fermented substrate. D5, despite its slow fermentation, also exhibited relatively low methane emissions, potentially due to limited VFA production. These findings suggest that methane yield is influenced not only by fiber content but also by the efficiency and direction of microbial metabolism.

These findings have important implications for climate-smart livestock systems. Selecting cultivars¹¹ with improved degradability and reduced methane potential, such as TWC647, or employing feed additives (e.g., tannins, essential oils) to inhibit methanogenesis, offers viable strategies for reducing greenhouse gas emissions without compromising productivity³⁶. Moreover, combining cultivars with complementary traits, as in the SC704 + D5 mixture, may offer a balanced approach, though further optimization is needed to minimize associated methane losses.

Conclusion

The selection of forage maize should be aligned with both production goals and sustainability targets. SC704 appears optimal for high-producing livestock requiring energy-dense diets, while TWC647 is a strong candidate for improving fiber digestibility and reducing environmental impact. The SC704 + D5 mixture may be useful for supporting long-duration rumen activity but requires mitigation strategies for its higher methane output. Subsequent studies should assess these cultivars under in vivo conditions to validate their performance in real-world feeding scenarios. Additionally, exploring their interaction with dietary additives, mixed rations, and feeding frequencies could further refine feeding strategies for sustainable ruminant production. Investigating the rumen microbiome’s response to different maize chemistries may also uncover mechanisms behind fermentation efficiency and methane mitigation.

Data Availability

Data is available as supplementary information file on Figshare Data cloud-based communal repository (10.6084/m9.figshare.28359065.v1).

Author contributions

Sajjad Mohramnejad was involved in planting different varieties of forage corn, and Farhad Parnian-khajehdizaj participated in project design, laboratory in vitro work, statistical analysis of data, and writing the article.

Declarations

Competing interests

The authors declare no competing interests.