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. 2026 Sep 16;34:100873. doi: 10.1016/j.vas.2026.100873

Influence of postpartum disorders, parity, and season on estimated methane emissions in dairy cows in northwest of Spain

J Álvarez a,b, A Acción a,b,⁎, R Barrionuevo a, U Yáñez a, JJ Becerra a,b, AI Peña a, J Cainzos c, R Fouz d, LÁ Quintela a,b
PMCID: PMC13627955  PMID: 42824441

Highlights

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    Warm-season cows had lower milk yield but higher methane intensity.

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    Multiparous cows emitted more methane overall, but less methane per unit of milk.

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    Methane intensity was the highest in early lactation.

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    Dairy herd improvement based methane estimates can assess herd emission efficiency.

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    Better transition health may lower methane intensity.

Keywords: Dairy cattle, Lactation stage, Dairy herd improvement, Heat stress, Emission intensity

Abstract

This study evaluated the influence of postpartum disorders, parity, stage of lactation, and season on estimated enteric methane emissions in dairy cows from northwestern Spain using dairy herd improvement (DHI) data. A total of 2,440 lactating Holstein cows from 50 commercial dairy farms were included. Data from four official monthly DHI controls conducted in 2017 included daily milk yield, milk composition, parity, and postpartum disorders recorded within 21 days in milk. Methane emissions (MEE; g CH₄/day) were estimated using a prediction equation based on energy-corrected milk, and methane emission intensity (MEEI) was calculated as g CH₄/kg of daily milk yield. MEEI was highest in early lactation and declined as milk yield increased. Retained placenta, metritis, subclinical mastitis, and abomasal displacement were associated with higher MEEI, whereas ketosis was associated with higher milk yield and total MEE but not with MEEI. Multiparous cows had higher total MEE but lower MEEI than primiparous cows. During warm months, cows produced less milk and had lower total MEE but higher MEEI than during cold months. These findings indicate that DHI-based methane estimates can be used as practical indicators of emission efficiency in dairy herds. Differences in methane intensity were mainly associated with milk production efficiency and were influenced by health status, parity, lactation stage, and season. Improving early-lactation health and reducing seasonal stress may help lower methane emissions per unit of milk, although direct methane measurements are needed to validate these estimates. Future studies should validate these estimates using direct on-farm methane measurements.

Implications

Methane emissions from cattle are an environmental concern because they contribute to climate change and are linked to production efficiency, however direct measurement on farms is difficult. This study evaluated the influence of postpartum disorders, parity, and season on estimated methane emissions in dairy cows. Methane emission intensity varied across lactation. Cows in early lactation or affected by postpartum disorders produced less milk and emitted more methane per kilogram of milk. Multiparous cows emitted more total methane but were more efficient. These findings may benefit farmers, advisors, and breeding organizations by providing practical indicators of environmental efficiency.

Introduction

Livestock systems account for approximately 5% of total anthropogenic methane (CH₄) emissions (Kamalanathan et al., 2023), contributing substantially to global climate change due to the high global warming potential of CH₄ relative to carbon dioxide (Kwaśny and Bombalska, 2023; Rafalska et al., 2023). In ruminants, enteric methane production also represents an energy loss from feed intake, reducing production efficiency (Mohamad Salleh et al., 2023). Therefore, reducing CH₄ emissions while maintaining milk production is a key goal for sustainable dairy systems (Richardson et al., 2021).

Direct measurement of methane emissions in dairy cattle requires specialized equipment and infrastructure, limiting its applicability in commercial conditions (Tedeschi et al., 2022). Indirect approaches based on milk yield and composition, including milk fatty acid profiles and dairy herd improvement (DHI) data, provide a more practical option for estimating methane emissions at herd and population levels (Dijkstra et al., 2011).

Methane emissions in dairy cows are influenced by multiple factors, including feed intake, milk production, stage of lactation, parity, health status, and environmental conditions (Allen and Piantoni, 2013; Becker et al., 2021; Islam et al., 2023). The early postpartum period is characterized by negative energy balance and increased incidence of metabolic and infectious disorders, which may affect production efficiency and, consequently, methane emission intensity (Ramin and Huhtanen, 2013; Liu et al., 2022). In addition, parity and seasonal conditions, particularly heat stress, can also influence milk yield and feed intake, thereby modifying methane emission patterns (Llonch et al., 2016).

The objective of this study was to estimate enteric methane emissions using prediction equations based on DHI data and to evaluate the influence of postpartum disorders, parity, stage of lactation, and season on total estimated methane emissions and methane emission intensity in dairy cows from northwestern Spain.

Materials and methods

Methane estimation equations

An extensive literature search was conducted between February and March 2025 using PubMed and Google Scholar to identify published methane prediction equations. Search terms included “methane”, “prediction”, “equation”, and “milk fatty acids”. No restrictions were applied regarding year of publication. Bibliographies of selected studies were also screened.

A total of 51 methane prediction equations from 14 peer-reviewed studies were identified (Table S1). Equations were excluded if they could not be applied using routinely collected DHI variables, which allows the assessment of large cow populations under commercial conditions without experimental intervention. Equations incorporating direct measurements of dry matter intake (DMI) or methane emissions were not included, as the collection of such data in large-scale commercial dairy herds presents substantial logistical challenges and considerable costs, limiting the feasibility of mechanistic analyses at this scale. At the same time, equations were excluded if they showed low predictive performance (R² < 0.75), were developed from datasets including fewer than 1,000 cows, or relied on variables not available in DHI records (Fig. 1). Enteric methane emissions were estimated using one equation proposed by Niu et al. (2018), derived from a large intercontinental database comprising 2,566 dairy cows and validated through cross-validation procedures across regions. The selected equation (CH₄ = 247 + 4.30 × ECM) corresponds to the European regional model, which demonstrated good predictive performance within European production systems. Although the selected equation relies on ECM as a single composite predictor, ECM integrates milk yield and composition and is therefore less sensitive than models based on a single raw milk trait alone.(Niu et al., 2018). This equation was selected as a representative proxy; however, results should be interpreted in relative terms and may vary depending on the prediction model used.

Fig. 1.

Fig. 1

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Selection of methane prediction equations applicable to DHI data.

Flow diagram illustrating the identification, screening, and selection of published methane (CH₄) prediction equations. A total of 51 equations were initially identified through a systematic literature search. Equations were excluded if they were not applicable using routinely collected dairy herd improvement (DHI) variables, showed low predictive performance (R² < 0.80), were based on datasets including fewer than 1,000 cows, or relied on variables not available in DHI records. One equation based on energy-corrected milk (ECM) was retained as an operational proxy for estimated enteric methane emissions.

DHI: Dairy herd improvement.

Animals and milk sampling

The study was carried out in Lugo province (northwestern Spain) between January and October 2017. Seasonal classification was restricted to the months included in the study period. Data were obtained from four official monthly DHI controls conducted approximately one month apart. The dataset comprised 2,440 lactating Holstein cows from 50 intensive commercial dairy farms, with herd sizes ranging from 11 to 178 cows (Table 1).

Table 1.

Descriptive characteristics of the study population. Descriptive statistics of the study population comprising 2,440 lactating Holstein cows from 50 commercial dairy farms in northwestern Spain. Variables include daily milk yield (dMY), milk composition, days in milk (DIM) and estimated methane emission variables (total estimated methane emissions (MEE; g CH₄/day), and methane emission intensity (MEEI; g CH₄/kg dMY)). Values are presented as mean ± standard deviation unless otherwise stated. Different superscript letters within the same row indicate significant differences among DIM groups after Bonferroni-adjusted pairwise comparisons (p ≤ 0.05; Kruskal–Wallis test). dMY: daily milk yield; DIM: days in milk; MF: milk fat; MP: milk protein; MEE: total estimated methane emissions; MEEI: methane emission intensity.

Variable Group 1 (5-32 DIM) Group 2 (33-60 DIM) Group 3 (61-84 DIM) Group 4 (85-114 DIM) Group 5 (115-210 DIM)
dMY (kg of milk) 36.91 ± 10.62 42.37 ± 10.85 42.37 ± 10.36 40.63 ± 9.91 38.68 ± 9.19
MF (% of total milk) 4.43 ± 1.35 3.90 ± 1.15 3.88 ± 1.17 3.94 ± 1.16 3.94 ± 1.11
MP (% of total milk) 3.21 ± 0.40 2.94 ± 0.25 3.04 ± 0.26 3.16 ± 0.28 3.27 ± 0.29
MEEI (g CH₄/kg dMY) 11.91 ± 3.42a 10.41 ± 2.06d 10.36 ± 1.88d 10.69 ± 2.11c 11.12 ± 2.08b
MEE (g CH₄/day) 411.21 ± 51.95e 421.78 ± 50.71b 422.04 ± 47.61a 417.47 ± 45.10c 412.19 ± 43.01d
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Days in milk ranged from 5 to 215 days (mean: 74.44 days). All farms were enrolled in the official DHI program. No experimental manipulation of animals or feeding practices was performed. Postpartum disorders, including mastitis, metritis, retained placenta, ketosis, subclinical mastitis, and abomasal displacement, occurring within the first 21 DIM were recorded. Clinical diagnoses of mastitis, metritis, and abomasal displacement were established according to standard veterinary criteria by experienced veterinarians during routine herd health monitoring. Ketosis was defined as β-hydroxybutyrate ≥ 0.14 mmol/L of milk, subclinical mastitis as somatic cell count (SCC) ≥ 200,000 cells/mL of milk, and retained placenta as failure to expel fetal membranes within 24 hours after calving.

Milk samples were collected during routine DHI testing, preserved using bronopol, and analyzed by mid-infrared spectroscopy at the Laboratorio Interprofesional Galego de Análise do Leite (LIGAL, A Coruña, Spain) to determine milk fat, milk protein, and milk fatty acid profiles. Individual cow data, including parity, days in milk, recorded postpartum diseases, and daily milk yield, were obtained from the DHI database (Table 1).

Weather and heat stress indicators

Meteorological data were obtained from Meteogalicia (2023). The overall mean maximum temperature–humidity index (MTHI) in the study area during 2017 was 66.95 ± 8.90. The mean daily MTHI was used to classify months as warm months (mean daily MTHI 72.99 ± 6.97; May–October) or cold months (mean daily MTHI 57.69 ± 8.78; January–April). Monthly averages were calculated from daily maximum temperature and relative humidity records.

The MTHI was calculated according to Antanaitis et al. (2023). Heat stress risk was categorized as MTHI ≤ 72 (comfort or low risk) and MTHI > 72 (increased heat stress risk).

Data classification and variables

Cows were grouped according to days in milk into five lactation stages: 5–32, 33–60, 61–84, 85–114, and 115–210 DIM. Cows were classified according to postpartum disorder status as healthy or affected. Parity was classified as primiparous or multiparous.

Estimated methane emissions (MEE; g CH₄/day) were calculated using the selected prediction equation. Methane emission intensity (MEEI) was calculated as MEE divided by daily milk yield (g CH₄/kg dMY). Energy-corrected milk (ECM, kg/day) was calculated according to Hoffmann et al. (2016) using the following equation: ECM = dMY × (1.05 + 0.38 × MF + 0.21 × MP) / 3.28.

Statistical analysis

Statistical analyses were performed using SPSS version 28.0 (IBM Corp., Armonk, NY, USA). Normality was assessed using the Shapiro–Wilk test, and as several variables showed non-normal distribution, non-parametric tests were applied. Differences in methane emission intensity across categories of days in milk (DIM), postpartum disease status, and parity were evaluated independently using the Kruskal–Wallis test, followed by Bonferroni-adjusted pairwise comparisons when appropriate. Seasonal differences were evaluated using the Wilcoxon rank-sum test. Each variable was analysed separately; no multivariable adjustment model was constructed. DIM, disease status, parity, and season were treated as categorical variables. Statistical significance was set at p ≤ 0.05. All results are interpreted as associations derived from observational data and predictive models.

Results

Methane emissions across lactation

Methane emission intensity (MEEI) differed significantly among days-in-milk groups (p ≤ 0.05), except between groups 33–60 and 61–84 DIM. The highest MEEI values were observed during early lactation (11.91 g CH₄/kg dMY), followed by lower values during mid-lactation and a slight increase in later stages (Table 1).

Total estimated methane emissions also differed among DIM groups (p ≤ 0.05), increasing from early lactation to a peak during mid-lactation and declining thereafter (Table 1). Based on these estimates, a representative farm with 60 lactating cows would emit approximately 9,103 kg of CH₄ per year.

Influence of postpartum disorders

Cows experiencing postpartum disorders within the first 21 DIM did not differ from healthy cows in daily milk yield or total estimated methane emissions (Table 2). However, affected cows exhibited higher methane emission intensity and higher milk fat production (p < 0.001).

Table 2.

Effect of postpartum disorders on milk production and estimated methane emissions. Daily milk yield (dMY, kg of milk/day), total estimated methane emissions (MEE; g CH₄/day), and methane emission intensity (MEEI; g CH₄/kg dMY) in healthy cows (No) and cows affected by postpartum disorders (Yes) within the first 21 days in milk. Values are presented as mean ± standard deviation. Different letters within each disorder indicate statistically significant differences between categories (p ≤ 0.05; Kruskal–Wallis test). dMY: daily milk yield; MF: milk fat; MP: milk protein; MEE: total estimated methane emissions; MEEI: methane emission intensity.

Status Cows (n) dMY (kg of milk/day) MF (%) MP (%) MEEI (g CH₄/kg dMY) MEE (g CH₄/day)
Disease No 1,694 40.37 ± 8.45 3.95 ± 0.82 b 3.13 ± 0.22 10.76 ± 1.60 b 416.14 ± 38.32
Yes 746 39.54 ± 9.15 4.23 ± 0.87 a 3.13 ± 0.24 11.29 ± 2.33 a 417.31 ± 43
Ketosis No 2,141 39.81 ± 8.61b 3.99 ± 0.83 b 3.14 ± 0.23 10.92 ± 1.84 415.00 ± 38.89 b
Yes 299 41.60 ± 9.11 a 4.36 ± 0.89 a 3.04 ± 0.21 10.93 ± 2.02 427.25 ± 43.93 a
Mastitis No 2,362 40.16 ± 8.64 4.04 ± 0.84 3.13 ± 0.33 10.90 ± 1.80 416.69 ± 39.69
Yes 78 38.60 ± 9.58 4.87 ± 0.89 3.32 ± 0.40 11.58 ± 3.17 410.77 ± 40.88
Placenta retention No 2,250 40.27 ± 8.59 a 4.02 ± 0.85 b 3.13 ± 0.23 b 10.85 ± 1.73 b 416.95 ± 39.45
Yes 190 38.17 ± 9.49 b 4.21 ± 0.79 a 3.17 ± 0.24 a 11.72 ± 2.92 a 411.10 ± 42.68
Metritis No 2,239 40.41 ± 8.58 a 4.03 ± 0.85 b 3.13 ± 0.23 10.82 ± 1.72 b 417.61 ± 39.44 a
Yes 201 36.79 ± 9.04 b 4.15 ± 0.76 a 3.15 ± 0.23 11.92 ± 2.88 a 404.14 ± 40.92 b
Subclinical mastitis No 2,134 40.35 ± 8.63 a 4.00 ± 0.83 b 3.12 ± 0.22 b 10.81 ± 1.68 b 416.86 ± 39.41
Yes 306 38.47 ± 8.84 b 4.28 ± 0.93 a 3.19 ± 0.25 a 11.62 ± 2.75 a 413.95 ± 41.90
Abomasal displacement No 2,396 40.16 ± 8.67 4.03 ± 0.85 3.13 ± 0.23 10.90 ± 1.85 b 416.67 ± 39.75
Yes 44 37.44 ± 8.64 4.25 ± 0.82 3.11 ± 0.29 11.88 ± 2.42 a 406.96 ± 38.24
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Retained placenta, metritis, and subclinical mastitis were associated with lower daily milk yield and higher MEEI (Table 2). Cows with ketosis showed higher daily milk yield and higher total estimated methane emissions compared with healthy cows. MEEI did not differ between cows with ketosis or clinical mastitis and healthy cows, whereas cows with retained placenta, metritis, abomasal displacement, or subclinical mastitis exhibited higher MEEI (Table 2).

Seasonal and heat stress effects

Cows produced more milk during cold months (40.81 ± 10.91 vs 39.65 ± 9.94 kg of dMY, p < 0.001), whereas milk protein percentage was higher during warm months (3.09 ± 0.32 vs 3.16 ± 0.32, p < 0.001). Milk fat production was not affected by season (4.05 ± 1.20 vs 4.04 ± 1.22, p > 0.05).

Methane emission intensity was higher during warm months (Table 3), whereas total estimated methane emissions were lower compared with cold months (p < 0.001).

Table 3.

Effect of parity and season on milk production and estimated methane emissions. Daily milk yield (dMY), total estimated methane emissions (MEE; g CH₄/day), and methane emission intensity (MEEI; g CH₄/kg dMY) in parity and season groups. Values are presented as mean ± standard deviation. Different letters within each factor and variable indicate statistically significant differences between categories (p ≤ 0.05). dMY: daily milk yield; MF: milk fat; MP: milk protein; MEE: total estimated methane emissions; MEEI: methane emission intensity.

Categories Cows (n) dMY (kg of milk) MF (%) MP (%) MEEI (g CH₄/kg dMY) MEE (g CH₄/day)
Parity Primiparous 816 34.21 ± 8.34 b 3.96 ± 1.09 b 3.14 ± 0.30 a 12.15 ± 2.44 a 384.51 ± 32.59 b
Multiparous 1,624 42.98 ± 10.12 a 4.08 ± 1.26 a 3.12 ± 0.33 b 10.31 ± 2.22 b 432.01 ± 46.57 a
Season Cold months 1,431 40.81 ± 10.91 a 4.05 ± 1.20 3.09 ± 0.32 b 10.82 ± 2.52 419.22 ± 50.05 a
Warm months 1,585 39.65 ± 9.94 b 4.04 ± 1.22 3.16 ± 0.32 a 10.97 ± 2.38 414.92 ± 46.03 b
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Parity effects

Primiparous cows produced less daily milk yield and milk fat but more milk protein than multiparous cows. MEEI was higher in primiparous cows, whereas total estimated methane emissions were higher in multiparous cows (Table 3, p < 0.001).

Discussion

The present study evaluated the influence of postpartum disorders, parity, stage of lactation, and season on estimated enteric methane emissions in dairy cows using prediction equations derived from routinely collected dairy herd improvement (DHI) data. Overall, methane emission intensity was more clearly affected than total estimated methane emissions (Table 1). This finding supports the use of emission intensity indicators when assessing the environmental efficiency of dairy production systems.

Validity of methane estimates and comparison with previous studies

The methane estimation approach used in this study was deliberately constrained to variables available through DHI records, allowing prediction equations to be applied at scale under commercial conditions. Although the selected equation relies on ECM as a single composite predictor, ECM integrates milk yield and composition variables, providing a composite indicator of milk energy output.

The estimated methane emissions obtained were broadly consistent with values reported in the literature across different production systems and geographic regions. Although methane was predicted from ECM, emission intensity was expressed per kilogram of raw milk (dMY) to provide a practical indicator of production efficiency at farm level. This approach allows assessment of methane relative to actual saleable milk output rather than corrected milk energy. Specifically, mean daily methane emissions were comparable to those reported by Kandel et al. (2017), Donadia et al. (2023), van Gastelen et al. (2023), Engelke et al. (2019), and Manzanilla-Pech et al. (2021). However, the estimates observed in the present study were higher than those reported for cows measured during milking events (Pszczola et al., 2017), cows receiving lipid supplementation (Engelke et al., 2019), and average emissions reported for Denmark and Canada (Manzanilla-Pech et al., 2021). Conversely, methane emissions were lower than those reported for multiparous cows in other intensive systems (Kandel et al., 2017; Martínez-Marín et al., 2024) and for Australian dairy herds (Manzanilla-Pech et al., 2021).

These differences likely reflect variation in feeding strategies, productivity levels, and herd structure. Dairy farms in Lugo typically operate under intensive management throughout the year with relatively stable diets based on approximately 60% forage and 40% concentrate, predominantly corn and grass silage. Given that Lugo province is an important dairy-producing region within the Spanish dairy sector, the present estimates may be broadly indicative of emission patterns in intensive dairy systems in northwestern Spain with comparable feeding and management conditions.

Methane emissions across lactation

Methane emission intensity was highest during early lactation and declined as milk yield increased, while total estimated methane emissions varied only modestly across days in milk (Table 1). In the first stage of lactation (5–32 DIM), cows produced less milk (36.91 kg/day), resulting in the significantly highest methane emission intensity despite relatively moderate total methane emissions. During mid-lactation (33–84 DIM), milk yield reached its highest values (approximately 42 kg/day), which coincided with slightly higher total methane emissions but significantly lower methane emission intensity. As lactation progressed and milk production gradually declined (85–210 DIM), total methane emissions decreased slightly, whereas methane emission intensity increased again. These patterns indicate that changes in methane emission intensity across lactation were primarily driven by variations in milk yield rather than substantial changes in total methane emissions.

Total methane emissions in the present study followed similar patterns to those described in previous studies (Vanrobays et al., 2016; Kandel et al., 2017; Manzanilla-Pech et al., 2021). Studies using mid-infrared predicted methane emissions, sulfur hexafluoride (SF6), and GreenFeed devices (C-Lock Technology Inc., Rapid City, South Dakota, USA) across lactation have reported low emissions during early lactation followed by an increase between approximately 30 and 90 DIM (Vanrobays et al., 2016; Kandel et al., 2017; Manzanilla-Pech et al., 2021). This pattern is consistent with studies reporting reduced methane emissions during the first weeks after calving due to lower dry matter intake (DMI), followed by increased emissions as intake rises during mid-lactation (Hardan et al., 2023; McParland et al., 2024). Because DMI is considered the primary determinant of methane production (Ramin and Huhtanen, 2013; Liu et al., 2022), the gradual increase in total methane emissions across lactation observed in the present study is expected. Nevertheless, the increase in milk yield during mid-lactation resulted in lower methane emission intensity. Because methane emissions were estimated using an ECM-based prediction equation, the estimates primarily reflect milk energy output and production efficiency rather than short-term variation in feed intake or rumen fermentation. In contrast, studies using direct methane measurements may capture metabolic and intake-related changes occurring immediately after calving. Pszczola et al. (2017) reported stable methane emissions from the 20 DIM onwards across lactation, which the authors attributed to limited sample size.

Overall, these results emphasize that improving milk yield efficiency, particularly in lower-producing cows, may help reduce methane emissions per kilogram of milk produced. Given that a relatively small proportion of cows contributes disproportionately to global milk production (Donadia et al., 2023), improvements in efficiency could reduce the number of cows required to maintain production levels, thereby lowering sector-wide methane emissions.

Influence of postpartum disorders

Postpartum disorders, excluding ketosis, were associated with higher methane emission intensity, mainly due to reduced milk yield rather than increased total methane emissions (Table 2). This finding aligns with previous studies showing that cows affected by disorders such as metritis, retained placenta, or subclinical mastitis experience reductions in DMI and milk yield during early lactation (Schirmann et al., 2016; Salamone et al., 2024).

In the present study, cows with postpartum disorders did not consistently exhibit lower total methane emissions, despite the expected reduction in DMI. This apparent discrepancy may be explained by the monthly frequency of DHI sampling, which may not capture short-term reductions in intake or methane production occurring immediately after disease onset. Consequently, milk yield may recover more rapidly than intake-related effects are detected in estimated methane outputs.

Subclinical mastitis was associated with lower milk yield and higher methane emission intensity, consistent with previous reports linking udder inflammation to altered milk composition and reduced production efficiency (Malek dos Reis et al., 2013; Panchal et al., 2024). In contrast, cows with clinical mastitis did not differ from healthy cows in estimated methane emissions or emission intensity. However, the need to discard milk from clinically mastitic cows represents an additional environmental burden not captured by methane intensity indicators alone, effectively increasing methane emissions per unit of saleable milk at the farm level.

Cows with metritis and retained placenta showed lower milk yield and higher emission intensity, despite showing no consistent reduction in total estimated methane emissions (Table 2). These results are in line with previous studies reporting reduced appetite and production following these disorders (Laven and Peters, 1996; Schirmann et al., 2016; Castro-Montoya et al., 2022), and again highlight the importance of production efficiency in determining emission intensity. Abomasal displacement showed a similar pattern, with higher emission intensity likely reflecting reduced milk yield during the disorder and partial recovery by the time of sampling (Giesteira et al., 2023).

Ketosis showed a different pattern from the other postpartum disorders. Cows affected by ketosis showed higher milk yield and higher total estimated methane emissions, but no difference in emission intensity compared with healthy cows (Table 2). This agrees with previous studies indicating that high-producing cows are more susceptible to ketosis and may maintain elevated milk yield despite metabolic imbalance (Duffield et al., 2009; Ha et al., 2023). The higher methane emissions observed in ketotic cows may also be related to alterations in rumen fermentation patterns, particularly an increased acetate-to-propionate ratio, which favors methanogenesis (Kong et al., 2025).

Parity effects

Parity significantly influenced methane emission patterns. Multiparous cows emitted more total methane but less methane per unit of milk produced compared with primiparous cows (Table 3). These results are consistent with studies reporting higher absolute methane emissions in older cows due to greater feed intake and rumen capacity (Clauss et al., 2020; Fresco et al., 2023), alongside improved production efficiency.

Differences in milk fatty acid profiles associated with parity may also contribute to the observed patterns, as fatty acid variables included in prediction equations tend to be higher in multiparous cows (Rodríguez-Bermúdez et al., 2023). While some studies have reported higher methane emissions in younger cows (Hardan et al., 2023), the present results support the interpretation that increased productivity in multiparous cows offsets higher absolute emissions, resulting in lower emission intensity.

Seasonal and heat stress effects

Seasonal conditions influenced both milk production and methane emission estimates (Table 3). Cows during warm months produced less milk and had lower total methane emissions but higher methane emission intensity than cows during cold months (Table 3). These findings are consistent with previous research showing that heat stress reduces feed intake and milk production, leading to lower absolute methane emissions but reduced production efficiency (Souza et al., 2023; Giannone et al., 2023). At the same time, they suggest that seasonal stress may increase methane emissions per kilogram of milk even when total daily methane emissions decrease.

Differences between the present study and studies conducted in pasture-based systems, where higher methane emissions have been reported during summer (Nunes et al., 2023), likely reflect differences in feeding strategies. Most farms included in this study operate under intensive management with relatively stable diets throughout the year, reducing the influence of seasonal forage quality on methane production. The use of the maximum temperature–humidity index (MTHI) further supports the relevance of heat stress as a driver of reduced efficiency during warm periods.

Strengths and limitations

A major strength of this study is the use of routinely collected DHI data, enabling large-scale evaluation of methane emission patterns under commercial conditions. All methane values reported in this study were derived from ECM-based prediction equations and should therefore be interpreted as estimated indicators rather than direct measurements. This approach provides practical and scalable indicators of environmental efficiency at herd level. We recognize the importance of local validation with direct measurements and advocate that future research integrates DHI-based estimates with on-farm methane data to refine predictive models and improve accuracy.

Despite these limitations, the results consistently show that methane emission intensity is strongly influenced by milk production efficiency, health status, parity, and seasonal factors. Strategies that enhance early-lactation health, optimize production efficiency, and mitigate heat stress may effectively reduce methane emissions per unit of milk. Combining DHI-based estimates with direct measurements in future studies will be critical to validate these findings and guide mitigation strategies that improve the sustainability of dairy production systems.

Conclusions

Estimated methane emissions derived from DHI data provide a practical approach to assess methane emission patterns and efficiency in dairy herds. Postpartum health status, parity, stage of lactation, and season were associated with meaningful differences in methane emission intensity, largely driven by milk production efficiency. Improving cow health during early lactation and mitigating seasonal stressors may contribute to reducing methane emissions per unit of milk produced.

Ethical approval statement

The Ethics Committee of the University of Santiago de Compostela (USC) reviewed the nature of the study and confirmed that the project does not fall under the legislation for the protection of animals used for scientific purposes, according to Spanish Royal Decree RD53/2013 and Directive 2010/63/EU. The Committee considered that the study has no impact on animal welfare and falls within the exceptions referred to in Article 2(5.f) of this legislation. Consequently, the project was exempted from ethics review and did not require approval by the USC Ethics Committee.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT 5.2 to improve the English language and the structure of the article. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Funding statement

This work was funded by the European Union under the Horizon Europe programme (Pillar II – Global Challenges and European Industrial Competitiveness), Cluster 6: Food, Bioeconomy, Natural Resources, Agriculture and Environment, project ClimateSmartResearch (CSR), call HORIZON-CL6-2024-CLIMATE-02.

CRediT authorship contribution statement

J. Álvarez: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. A. Acción: Writing – review & editing, Supervision. R. Barrionuevo: Writing – review & editing. U. Yáñez: Methodology, Formal analysis, Conceptualization. J.J. Becerra: Visualization, Methodology. A.I. Peña: Validation, Supervision. J. Cainzos: Data curation. R. Fouz: Resources. L.Á. Quintela: Methodology, Conceptualization.

Declaration of competing interest

The authors confirm that this work represents an original contribution and has been approved by all co-authors and declare no conflicts of interest.

Acknowledgements

Álvarez J. holds a predoctoral contract funded by the Fundación Caixa Rural Tomás Notario Vacas (2024 call). Acción A. holds a predoctoral contract funded by the Xunta de Galicia (Ref. ED481A2025-031). Yáñez U. holds a postdoctoral contract funded by the Xunta de Galicia (Ref. ED481_033/2024).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.vas.2026.100873.

Supplementary materials

mmc1.docx (45.8KB, docx)

Data availability

  • The data used in this study were obtained from the official DHI program and are subject to strict data protection agreements. For reasons of confidentiality and data protection regulations, they cannot be made publicly available or shared with third parties.

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Associated Data

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Supplementary Materials

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Data Availability Statement

  • The data used in this study were obtained from the official DHI program and are subject to strict data protection agreements. For reasons of confidentiality and data protection regulations, they cannot be made publicly available or shared with third parties.

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