How the Inclusion of Pigeon Pea in Beef Cattle Diets Affects CH₄ Intensity: An In Vitro Fermentation Assessment

Abstract

Tropical pastures intercropped with legumes have been gaining prominence for increasing the efficiency of livestock production systems when compared to pasture monocultures. Here, our objective was to understand the fermentation processes that tropical grass and legumes underwent when included in ruminant diets, which have previously been found to optimize animal performance while reducing the intensity of enteric CH₄ emissions. For this purpose, three areas containing pigeon pea (Cajanus cajan) and Urochloa spp. were sampled. Samples were dried, grounded, chemically analyzed, and included in five proportions (0%, 25%, 50%, 75%, and 100%) of pigeon pea in the diet. The diets were then analyzed using an in vitro fermentation technique. Statistical analysis was performed using SAS statistical software, considering bottles as replicates, and our results suggest that a 25% inclusion of pigeon pea is optimal for balancing CH₄ mitigation and fermentation efficiency, highlighting the importance of more studies with this legume due to its benefits, especially as a supplement during drought periods that impact the production and quality of tropical pastures. It is important to consider that pigeon pea’s secondary compounds may have positively modulated the fermentation process and reduced CH₄ emissions. However, excessive legume inclusion can negatively affect digestibility and animal health, impairing animal performance and the sustainability of pasture-based production systems.

1. Introduction

It is estimated that there are 50 million ha of pastures in advanced stages of degradation in Brazil [1]. One strategy for the recovery of these degraded pastures is the consortium between grasses and legumes, which has been reported in several studies as an excellent alternative, improving the nutritional quality of the diet and consequently increasing the productivity of ruminants with the potential to mitigate enteric methane (CH₄) emissions [2,3,4].

The search for alternative feeds in animal nutrition is driven by the need to reduce the environmental impact of animal production and for lower feed costs. Including new ingredients in animal diets can offer several benefits, such as improved feed efficiency, enhanced nutrient absorption, and reduced greenhouse gas emissions (GHG) [5]. However, the adoption of these feeds requires a thorough assessment of their nutritional value, safety, and impact on animal performance. It is essential to find a balance between the benefits and challenges associated with the use of alternative feeds, ensuring the sustainability and efficiency of animal production [5].

In addition, the amount of pasture available as feed for animal grazing can vary according to the seasons of the year, especially in periods of greater water scarcity, as occurs in the dry season [6]. Recently, Pezzopane et al. [7] studied the shrub legume pigeon pea [Cajanus cajan (L. Millsp.) cv. BRS Mandarim] intercropped with Urochloa spp., demonstrating its high productivity and resistance to drought, reaching an increment of 4-ton DM ha⁻¹ of forage accumulation during this period. In another study, Furtado et al. [3], adopting the principle of isotopic difference between C₃ and C₄ plants, found that the animals were consuming up to 40% of this legume during the dry season, indicating that intercropping pigeon pea with tropical grasses is an interesting strategy to meet the nutritional requirements of the animals compared to other pasture-based systems.

However, it is important to notice that the consumption proportion between legumes (C₃) and grasses (C₄) can vary greatly in mixed pastures, so several studies with the inclusion of legumes have chosen to perform in vitro experiments [8,9,10] since these techniques represent reduced costs and require fewer animals and samples, with the results being quickly obtained and consistent with in vivo or in situ techniques [11]. Another advantage of in vitro experiments is the possibility of safely testing high inclusions of legumes, which, if consumed in high concentrations, can cause bloating in ruminants [9].

Here, the aim of this study was to better understand the fermentation processes and establish the best proportions of pigeon pea and tropical grasses to optimize animal production while also reducing the enteric emissions of CH₄.

2. Materials and Methods

The experiment was approved by and followed the guidelines of the Committee for the Use and Care of Institutional Animals (CEUA) of Embrapa (03/2020 No. 20.19.00.047.00.00). This work complements the publications of Furtado et al. [3] and Pezzopane et al. [7] that are part of a thematic project “Strategic practices for mitigating GHG emissions in grassland systems of the Brazilian Southeast” supported by the São Paulo Research Foundation (FAPESP), grant number #2017/20084-5.

2.1. Planning, Location, and Pastures

The samples were taken from three mixed experimental areas (1.5 ha each) of Urochloa decumbens cv. Basilisk and Urochloa brizantha cv. Marandu intercropped with Cajanus cajan (L. Millsp.) cv. BRS Mandarim during the dry (June) and rainy (January) seasons of 2021 and 2022 at Embrapa Pecuária Sudeste, São Carlos, SP, Brazil (21°58′41″ S 47°51′12″ W and Köppen classification: tropical at altitude with dry winter). Climate data were obtained through an automatic meteorological station close to the experimental area. Factors such as forage production and water balance can be found in the work of Pezzopane et al. [7]. As described by Furtado et al. [3], pastures were established with Urochloa spp. in 1996, removing the previous native pasture. In 2011, pigeon pea planting began, using a no-till seeder adjusted to 180,000 viable seeds per ha. It is known that over time the plant population tends to decline due to trampling and grazing by animals, adverse weather conditions, and plant senescence, requiring replanting every three years [3]. The grazing units received Nellore steers under a continuous stocking regime, with adjustments to the stocking rate using the “put and take” technique described by Mott and Lucas [12].

The grazing simulation methodology developed by Sollenberger et al. [13] was chosen for sampling the forages. This methodology consists of observing the grazing habits of the animals in each experimental area for 24 min, on three consecutive days. The sample represented what the animals consumed, including leaves, thin twigs, pods, and flowers. Pigeon pea and Urochiloa spp. samples were separately stored in paper bags and placed in a forced ventilation oven at 65 °C for 72 h (the pigeon pea samples were dried at 40 °C until the weight became constant). After drying and grinding, a pool of samples was created by combining the paddock in equal proportions in the different seasons and years. As a result, one sample of pigeon pea and one of Urochloa spp. were obtained. To establish the best proportion of pigeon pea in the diet of grazing ruminants, five levels of legume inclusion were tested in the in vitro assay: 0%, 25%, 50%, 75%, and 100% of pigeon pea. Increasing levels may represent a mixed pasture with different legume availability for grazing, making it possible to understand fermentation processes while reducing CH₄ emission. In addition, the inclusion levels assist in future research with this ingredient.

The chemical analysis of the different diets (

Table 1. Chemical composition of different diets according to the inclusion of pigeon pea.

Inclusion of Pigeon Pea				Variables
	CP (%)	NDF (%)	ADF (%)	Lig (%)	EE (%)	Ash (%)	NFC (%)	GE (cal/g)	CT *
0%	8.8	69.1	38.5	3.5	2.0	8.7	11.5	3687.2	0.6
25%	11.6	63.1	35.7	6.9	2.8	7.8	14.6	3875.9	12.7
50%	14.4	57.2	32.9	10.3	3.6	7.0	17.8	4064.5	24.7
75%	17.2	51.2	30.1	13.7	4.4	6.2	21.0	4253.1	36.7
100%	20.0	45.3	27.3	17.1	5.3	5.4	24.2	4441.7	48.8

* Eq. g leucocyanidin/kg of dry matter (g/kg of DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), lignin (Lig), ether extract (EE), mineral matter (Ash), non-fibrous carbohydrates (NFC), gross energy (GE) expressed as calories per gram (cal/g), and condensed tannins (CT).

) was based on the methodologies described in AOAC [14], Method 934.01, Method 923.03, Method 920.87, and Method 920.85 for dry matter (DM), mineral matter (Ash), crude protein (CP), and ether extract (EE), respectively. The fiber concentrations (NDF and ADF) followed the methodologies described by Goering and Van Soest [15]. Gross energy (GE) was measured using a bomb calorimeter (IKA WERKE^®, model C 5000, Staufen—Germany). The concentrations of condensed tannins (CT) were determined according to Makkar [16]. Non-fibrous carbohydrates (NFC) were obtained using the following equation [17]:

$$\mathrm{NFC} =100- \mathrm{CP} - \mathrm{EE} - \mathrm{MM} - \mathrm{NDF}$$

2.2. In Vitro Fermentation

The in vitro analyses were performed at the Ruminant Nutrition Laboratory of the University of São Paulo, Pirassununga, SP, Brazil. The in vitro fermentation technique was adapted to quantify gas production, dry matter digestibility (IVDMD), and the production of short-chain fatty acids (SCFA) according to Figure 1.

The test was carried out in triplicate, weighing one gram of dry sample ground at 1 mm according to the proportion of grasses and legumes in each diet, and then placed in 250 mL graduated transparent bottles with screw caps that served as fermenters. The cap of each bottle has a system for collecting gasses, measuring the volume of gasses produced by a pressure transducer (Datalloger GN200, Genesis^®, Barueri, Brazil) as shown in Figure 1.

The inoculum was collected from two animals cannulated in the rumen and maintained on an exclusive pasture diet with mineral supplements ad libitum. The inoculum was manually removed from the rumen of these animals, separated into solid and liquid phases, and stored in a thermal container for transport. In the laboratory, the liquid was prepared as described by Bueno et al. [18]. Each bottle contained 1 g of sample, 15 mL of the processed inoculum, and 35 mL of artificial saliva (McDougall’s Solution), according to McDougall [19].

Incubation was carried out in a forced ventilation oven at 39 °C. Evaluations were performed at 6, 12, 24, 36, 48, 72, 96, and 144 h in triplicate, with three “blanks” (without sample, only with inoculum). At each sampling time, 9 mL of gas was collected for CH₄ analysis using gas chromatography (Trace 1300, Thermo Fisher Scientific^®, Rodano, Milan, Italy) [20]. In addition, 1 mL of the liquid content of the fermentation bottle was sampled at each time point for the quantification of SCFAs by gas chromatography (Focus GC, Thermo Scientific^®, Rodano, Milan, Italy).

After quantifying the fermentation products of each sample over time, each product (CH₄, acetate, propionate, and butyrate) was multiplied by its heat of combustion to express the CH₄ production in relation to the fermentation energy produced. Thus, the relative energy loss (REL) was the ratio between the energy of the CH₄ produced and the sum of the energy of all quantified fermentation products (CH₄ and SCFA), expressed as a percentage [21].

The data were submitted to Gompertz’s non-linear sigmoidal model (Figure 2) according to the model below [22]:

$$y=A{e}^{-B{e}^{-Ct}}$$

where: e, exponential; A, maximum production; B, integration constant (without biological meaning or indicates the lag time); C or K, specific production rate as a function of time; t, time. Ti, time in inflection point [ln(B)/K]; Yi, concentration at inflection point (A/2.718281828).

2.3. In Vitro Digestibility

At the end of the experimental period (144 h), the contents of the fermentation bottles were transferred to 50 mL tubes and centrifuged at 1.6× g for 15 min. The supernatant was discarded, and the material was dried in a forced ventilation oven at 65 °C for 72 h. After drying, the material was weighed on an analytical scale, and the in vitro degradability of DM was calculated using the following equation:

$$IV\mathrm{DMD}=100\times \left({\displaystyle \frac{Pi-\left(Pf-Pr\right)}{Pi}}\right)$$

(1)

IVDMD, in vitro degradability of DM; Pi, incubated sample weight; Pf, dry sample weight; Pr, weight of residue from the inoculum (blanks).

2.4. Statistical Analysis

For statistical analysis, bottles were considered the experimental units. Data were analyzed using SAS 9.4 statistical software (SAS Institute Inc., Cary, NC, USA). Prior to analysis, outliers were identified, and the normality of residuals was tested (Shapiro–Wilk). Means separation and p values were determined using the LSMEANS statement with the PDIFF option. The level effect was evaluated using orthogonal polynomials (p < 0.05), separating the effects in linear, quadratic, cubic, and cubic deviation [23]. After removing outliers, PROC CORR was used to perform Pearson correlation between the desired parameters.

3. Results

Except for propionate Ti and total SCFA K, all parameters presented a significant linear effect (p < 0.05) according to the level of pigeon pea inclusion in the diet (

Table 2. Products of the in vitro fermentation of different diets according to the inclusion of pigeon pea.

Variables *	Pigeon Pea					Average	SEM	Statistical Probabilities
	0%	25%	50%	75%	100%			Linear	Quadratic	Cubic	Cubic Deviation
Acetate
A	3.46	3.06	2.82	2.63	2.33	2.86	0.10	<0.0001	0.3037	0.1896	0.8685
K	0.058	0.055	0.058	0.064	0.063	0.060	0.001	0.0226	0.5154	0.1294	0.7382
Ti	18.58	16.68	13.63	13.05	12.50	14.89	0.67	<0.0001	0.0515	0.5658	0.2714
Yi	1.27	1.13	1.04	0.97	0.86	1.05	0.04	<0.0001	0.3037	0.1896	0.8685
Propionate
A	1.05	0.95	0.80	0.67	0.43	0.78	0.06	<0.0001	0.1916	0.7199	0.6409
K	0.065	0.051	0.052	0.040	0.037	0.045	0.003	0.0002	0.5162	0.5510	0.1243
Ti	17.97	17.57	16.42	16.32	15.76	16.81	0.42	0.0914	0.8622	0.9126	0.7496
Yi	0.39	0.35	0.29	0.25	0.16	0.29	0.02	<0.0001	0.1916	0.7199	0.6409
Butyrate
A	0.46	0.41	0.37	0.32	0.25	0.36	0.02	<0.0001	0.2926	0.4416	0.8691
K	0.065	0.060	0.054	0.048	0.048	0.055	0.002	0.0029	0.3981	0.6034	0.8647
Ti	20.34	19.76	18.11	16.81	13.90	17.78	0.70	0.0002	0.2153	0.8495	0.6580
Yi	0.17	0.15	0.14	0.12	0.09	0.13	0.01	<0.0001	0.2926	0.4416	0.8691
Total SCFA
A	4.95	4.42	3.99	3.61	3.07	4.01	0.18	<0.0001	0.8639	0.2813	0.7696
K	0.060	0.054	0.054	0.053	0.058	0.056	0.001	0.4544	0.0574	0.9270	0.5670
Ti	18.24	17.15	14.27	13.79	12.73	15.24	0.61	<0.0001	0.3357	0.5699	0.2133
Yi	1.82	1.63	1.47	1.33	1.13	1.47	0.06	<0.0001	0.8639	0.2813	0.7696

* Data accumulated over time, Gompertz model. SEM, standard error of the mean; A, asymptote (production in mmol/g); K, production rate (%/h); Ti, time in inflection point (h); Yi, concentration at inflection point (mmol/g); SCFA, short-chain fatty acids.

). As the inclusion of pigeon pea increased, there was a reduction in the parameters A, Ti, and Yi of acetate, propionate, butyrate, and total SCFA. On the other hand, acetate K increased with higher levels of pigeon pea on the diet, while the opposite was found for this variable when evaluating propionate and butyrate (Table 2). The K is essential to understand the fermentation rate (%/h) of each SCFA, while Yi and Ti help us understand the accumulated volume of each SCFA at the time of greatest production, respectively.

A significant quadratic effect was found for CH₄ A and Yi parameters when both were expressed by mmol/g or mL/g, while for CH₄ K (mmol/g or mL/g) and total gas A, K, Ti, and Yi there was a significant linear effect according to the level of pigeon pea inclusion (

Table 3. The CH₄ and total gas production parameters assessed using the in vitro fermentation technique in the different diets according to the inclusion of pigeon pea.

Variables *	Pigeon Pea					Average	SEM	Statistical Probabilities
	0%	25%	50%	75%	100%			Linear	Quadratic	Cubic	Cubic Deviation
CH4 (mmol/g)
A	2.66	2.52	2.48	2.20	1.86	2.34	0.09	<0.0001	0.0115	0.3993	0.3470
K	0.049	0.044	0.042	0.041	0.033	0.041	0.002	0.0008	0.6305	0.2012	0.8351
Ti	27.45	27.70	29.30	25.64	29.01	27.82	0.57	0.7841	0.8328	0.1689	0.0916
Yi	0.98	0.89	0.95	0.81	0.69	0.86	0.03	<0.0001	0.0328	0.1834	0.4450
Total gas production (mL/g)
A	256.97	213.07	186.93	169.70	108.57	187.05	13.56	<0.0001	0.4764	0.0609	0.5822
K	0.044	0.046	0.052	0.053	0.054	0.050	0.001	0.0026	0.3332	0.7515	0.3613
Ti	24.53	23.80	20.76	15.78	13.68	19.71	1.19	<0.0001	0.1488	0.0665	0.5205
Yi	94.53	78.38	68.77	62.43	39.94	68.81	4.99	<0.0001	0.4764	0.0609	0.5822

* Data accumulated over time, Gompertz model. SEM, standard error of the mean; A, asymptote volume (concentration); K, production rate (%/h); Ti, time in inflection point (h); Yi, volume at inflection point (concentration); CH₄, methane.

). For CH₄ and total gas A, there was a reduction with the inclusion of pigeon pea in the diets. The CH₄ K and total gas Ti decreased, while total gas K increased with higher inclusions of pigeon pea (Table 3).

Table 4. Dry matter digestibility, relative energy loss, and CH₄ per digestibility of different diets according to the inclusion of pigeon pea.

Variables	Pigeon Pea					Average	SEM	Statistical Probabilities
	0%	25%	50%	75%	100%			Linear	Quadratic	Cubic	Cubic Deviation
DMD (%)	61.7	54.8	48.4	45.2	36.6	46.2	0.86	<0.0001	0.9766	0.0539	0.1511
REL (%)	30.3	29.9	32.9	32.7	32.1	31.5	0.43	0.0141	0.2110	0.0877	0.1626
CH4/digestibility (mmol/g DMD)	4.34	4.56	5.04	5.03	5.20	4.8	0.10	0.0001	0.1964	0.8013	0.1860

SEM, standard error of the mean; CH₄, methane; REL, relative energy loss; DMD, dry matter digestibility.

demonstrates that there was a linear effect on DMD, REL, and CH₄ per digestibility with the inclusion of pigeon pea in the different diets, where DMD decreased while REL and CH₄ variables increased with the inclusion of higher levels of pigeon pea.

In Figure 3A–D and Figure 4, it is possible to visualize the Gompertz sigmoid representing the fermentation products in different diets over the 144 h in the in vitro fermentation assay. Overall, with increasing time, there was a reduction on the concentration of fermentation products with higher inclusions of pigeon pea (Figure 3A–D and Figure 4). In addition, there were highly significant correlations between NDF and CH₄ (p = 0.0001, R = 0.8286) and for CP and CH₄ (p = 0.0001, R = −0.8285) in the different diets (Figure 5A,B).

4. Discussion

Legumes, when implemented in pasture systems, are known to bring benefits such as deposition of straw and organic matter-fixing carbon in the soil, reduction in pest infestation, improvement of nutrient cycling with a deep root system capable of symbiosis with N-fixing bacteria, and reduction in animal production costs [3,10]. However, a better understanding of the effects of different levels of legumes in grazing animals’ diets on ruminal fermentation parameters is crucial for improving the nutritional management of the herd, especially during dry seasons when the production and nutritional quality of tropical pastures are impaired, compromising the performance and health of these animals. Additionally, legumes with high concentrations of nutrients (especially CP and NFC) can reduce CH₄ production by modulating ruminal fermentation [10]. On the other hand, grasses that have high levels of structural carbohydrates may favor ruminal fermentation by cellulolytic microorganisms, which are involved in the methanogenesis process [10]. Our results suggest that in diets with lower inclusions of pigeon pea, the tropical grass (Urochloa spp.) was fermented by these cellulolytic microorganisms, favoring the production of CH₄ (Table 3, Figure 4) without impairing DMD (Table 4).

Carbohydrate fermentation in SCFAs produces CO₂ and H₂ as by-products, the leading gasses found in in vitro experiments. These gasses are also primary substrates for methanogenic Archaea to produce CH₄ [8]. Aung et al. [24] evaluated the effects of mixtures of tree legumes (Gliricidia sepium and Sesbania sesban) with a tropical grass (Urochloa híbrida cv. Mulato) and found that these legumes have higher CP and lower NDF contents and also higher CH₄ emissions, with a negative correlation between CH₄ and NDF, which is the opposite of what we found in our study (Figure 5A). This suggests that the more significant amount of NDF (Table 1) may stimulate the action of cellulolytic microorganisms that increase acetate production, as can be observed in Table 2. Diets based on grass only may also lead to the segregation of butyrate (Table 2), consequently releasing carbon atoms in the form of CH₄ [25]. Here, we found that the inclusion of pigeon pea resulted in higher diet levels of CP and lower CH₄ emissions (Figure 5B).

The DMD is directly related to total gas production and may be indirectly related to SCFA production [18]. This suggests that the linear effects found for DMD (Table 4) correlate with those presented in Table 2 and Table 3. Therefore, the decrease in digestibility as pigeon pea inclusion increased resulted in decreases in total gas, CH₄, acetate, propionate, and butyrate production.

The decrease in total SCFAs due to the inclusion of legumes in the diet indicates a general loss of fermentability and a potential reduction in the animal’s energy supply, as ruminants use SCFAs as their primary source of energy [8]. However, in our study, the concentration of gross energy in the diet increased with the inclusion of pigeon pea (Table 1).

It is important to emphasize that including pigeon pea, as well as other legumes rich in CT, reduces CH₄ emissions by modulating ruminal fermentation [26]. We must be aware of the possible adverse effects that tannins can bring when included in ruminant diets [27]. In this study, the maximum CT dose was 48.8 g/kg of DM, and with doses of 50 g/kg of DM, some authors found a reduction in DMD, digestible protein, decreases in DM intake, and, consequently, a decrease in weight gain in animals [28,29].

The quadratic effects found, especially in CH₄, are linked to a lower inflection point than the other curves. The 100% pigeon pea diet inhibited fermentation, causing an abrupt reduction in fermentation, which resulted in the quadratic effect, which may be justified by deleterious effects of the plant secondary compounds of pigeon pea on the fermenting microorganisms [10].

According to Martínez et al. [30], DMD can be affected by high NDF dietary content, which is a limitation in its use of legumes, this may be a negative effect of the CT in the diet [27]. However, in our study, including legumes even with lower NDF decreased the diet’s digestibility. Legumes are known to have higher levels of Lig; however, according to More and Jung [31], the Lig of legumes is less inhibitory to the digestive process than the Lig of grasses. Furtado et al. [3], in an in vivo assay using internal and external markers, found that Nellore steers grazing pigeon pea plants increased DM digestibility and, consequently, animal productivity when the legume intake represented around 40% of the diet. In addition, unpublished results of our research group suggest that pigeon pea has lower in vitro digestibility but does not affect in vivo digestibility nor animal performance as observed by Furtado et al. [3], being justified by the presence of secondary compounds interfering in the in vitro fermentation process.

Plant secondary compounds such as tannins found in legumes can form complexes with proteins, forming a rumen undegradable protein molecule (RUP) [32]. However, after passing through the abomasum and by the action of an acidic pH, these complexes are fractionated, and the amino acids are then absorbed in the animal’s intestine [32,33]. This fact justifies the greater DMD and animal performance found by Furtado et al. [3] when evaluating the inclusion of pigeon pea in vivo, which also confirms that tannins interfered with the in vitro fermentation process. Highlighting the need for in-depth studies on the digestion of amino acids, carbohydrates, and CT.

The intensity of CH₄ emissions, which here we evaluated by the ratio of CH₄ per gram of digested DM (Table 4), may be directly related to the lower digestibility presented by the inclusion of pigeon pea in the diet. This differs from the findings of Holguín et al. [25], who observed that including Tithonia diversifolia reduced emissions per gram of DMD compared to exclusive grass silage. In contrast, the inclusion of 25% pigeon pea in the diet showed a slight increase in emissions compared to the exclusive grass treatment. The best REL value was found when including 25% pigeon pea. Considering the significant linear effect, we concluded that this inclusion would be responsible for the most significant mitigation and fermentation efficiency of the SCFA. As some authors state that the general ideal proportion of grasses and legumes for ruminant diets is around 70:30 [34], here we suggest that the inclusion of 25% pigeon pea in the diet of grazing cattle may be beneficial for fermentation, mitigating CH₄ and reducing REL.

According to Tupy et al. [35], when performing an economic analysis of the intercropping system, they found that pigeon pea is a low-cost feed and is an excellent alternative for raising cattle during the dry season of the year. Pigeon pea is rich in protein and has a high nitrogen-fixing power in the soil, increasing the production of dry matter in pastures in the medium and long term. It allows for an increase in the stocking rate per hectare since it has a high dry matter production in the dry season, reducing the scarcity of forage, and the animals gain more weight when compared to the protein-based system in the dry season of the year [35]. This makes the intercropping system a sustainable and viable activity for livestock farmers.

5. Conclusions

Pigeon pea, like other legumes, can be a beneficial addition to tropical pastures due to its higher nutrient density. The presence of secondary compounds, mainly tannins, can modulate the fermentation process in the rumen of animals, altering fermentation patterns depending on the inclusion level. Therefore, this study suggests that the intercropping of 25% pigeon pea with Urochloa spp. can be beneficial for fermentation efficiency, reducing energy loss in the form of CH₄, which contributes to GHG mitigation. At the same time, we emphasize the importance of further research to better understand the effects of intercropping on microbial fermentation and whether they occur directly or indirectly.