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Article

Intensive Silvopastoral Systems Mitigate Enteric Methane Emissions from Cattle

by
Gustavo Flores-Coello
1,
Juan H. Hernández-Medrano
2,
Juan Ku-Vera
3,
Daniel Diaz
4,
Francisco J. Solorio-Sánchez
5,
Lucero Sarabia-Salgado
6 and
Francisco Galindo
1,*
1
Department of Ethology, Wildlife and Laboratory Animals, Faculty of Veterinary Medicine and Animal Science, National Autonomous University of Mexico (UNAM), Ciudad Universitaria, Mexico City Z.C. 04510, Mexico
2
Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB T2N4N1, Canada
3
Climate Change and Livestock Laboratory, Faculty of Veterinary Medicine and Animal Science, Autonomous University of Yucatan, Mérida Z.C. 97100, Mexico
4
Center for Complexity Sciences (C3), National Autonomous University of Mexico (UNAM), Ciudad Universitaria, Madrid Z.C. 04510, Mexico
5
Department of Ruminant Nutrition and the Tropic Environment, Faculty of Veterinary Medicine and Animal Science, Autonomous University of Yucatan, Mérida Z.C. 97100, Mexico
6
Faculty of Veterinary Medicine n 1, Autonomous University of Guerrero (UAGro), Altamirano City Z.C. 40665, Mexico
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(5), 863; https://doi.org/10.3390/atmos14050863
Submission received: 25 February 2023 / Revised: 27 April 2023 / Accepted: 28 April 2023 / Published: 12 May 2023
(This article belongs to the Special Issue Observations and Management of Livestock Production Emissions)
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Versions Notes

Abstract

:
Assessments of the efficiency of grazing systems, in terms of productivity and ecological sustainability, are necessary in view of the increased demand for animal protein. In this study, the methane (CH4) emissions (sniffer methodology), dry matter (DM) yield, paddock chemical composition (AOAC and Van Soest methods), nutrient intake (dry matter, DMI; crude protein, CPI; metabolizable energy, MEI), daily milk yield (DMY), body condition score (BCS), and body weight (BW) of cattle, in intensive silvopastoral systems (ISPSs) and monoculture systems (MSs), in the tropics of Mexico were evaluated. In the ISPS, CH4 emissions (18% less) and DMY were lower than in the MS. Cows from MSs tend to disperse across higher values of CH4 emissions per kg of DMI, as well as higher milk production, while cows from the ISPS were dispersed over a higher intake (DMI, CPI, and MEI) and lower CH4 emissions. There were no differences between systems in paddock DM yield, chemical composition, cows’ BCS, and BW, regardless of whether it was the dry (April to May) and rainy (September to October) season. Based on the results obtained in this study, ISPSs contribute to the mitigation of methane emissions in cattle; forage and animal production variables in both systems were similar, with a lower use of imported inputs in the ISPS.

1. Introduction

In 2020, 12.2 million ha of forest were lost due to fires and deforestation, as well as through transformation to pastures, soybean fields, and palm oil plantations, mainly in the tropics [1]. Deforestation is continuing, albeit at a lower rate (ha per year), due to the expansion of agriculture and livestock frontiers [2]. Between 1950 and 1980, more land was converted to crops than in the previous 150 years [2]. Globally, 34% of the arable area is used for livestock feed production [2], which has grown rapidly in response to increasing food demands. The environmental footprint of animal feed production is now considered a major disruptor of global biogeochemical cycles. The origin of this disruption lies in greenhouse gas (GHG) emissions, biodiversity loss, increased water use, and soil erosion, just to name a few drivers [3,4].
Livestock GHG emissions represent 14.5% of total anthropogenic CO2e emissions [5]. It is estimated that 2039.21 Gg of CH4 is released into the atmosphere from cattle [6]. Enteric methane is a GHG derived from the reduction in CO2 by the excess H2 produced during rumen fermentation, and it represents a loss of 6–7% gross energy intake in ruminants [7]. The national herd in Mexico is estimated to be over 35 million head, which were estimated to produce 94% of the latter emissions, based on Tier 2 IPCC [8].
For Latin America and the Caribbean, a 16% increase in demand for livestock products is expected by 2031 [9]. However, associated with the expected growth of livestock activity, an increase in pollutants such as methane and the depletion of natural resources are expected to occur [9]. Indeed, direct emissions of GHG due to livestock activity are forecasted to increase by 0.5% [9]. Therefore, facing the considerable challenge of increasing livestock productivity is fundamental in the development and adoption of sustainable production systems [10] that strategically improve production, reduce environmental impacts, and increase farmers’ income.
Dual-purpose cattle production represents one of the main economic activities in tropical regions because it enables the production of milk and meat at a low cost [11]. It is estimated that, in the tropical regions of the Americas, 78% of cattle are managed under extensive conditions [11]. Historically, grazing systems in tropical and subtropical regions have been based on introduced grasses (mainly as a monoculture), agricultural products (mainly grains), and industrial byproducts to feed livestock. These systems rely on the use of large amounts of herbicides, pesticides, and fertilizers to sustain production [4]. Extensive systems are associated with high environmental impact due to high methane emission estimations, which are partially caused by poor-quality pastures and diets [12]. However, alternative animal production systems are starting to emerge in the tropical and sub-tropical regions, such as the intensive silvopastoral systems (ISPS).
ISPSs are an alternative to conventional (extensive) grazing systems that deliberately combine different vegetation levels or strata. They combine fodder shrubs, at high cultivation density (4000–40,000 plants ha−1), with native or introduced grasses and trees for animal nutrition, using different species at each level/strata, as well as different management strategies [13,14,15,16]. These three-level vegetation strata can maintain animals under intensive rotational grazing management, while allowing adequate resting periods and optimizing the use of available forage, aimed at increasing productivity and lowering the environmental impact [16,17]. ISPSs provide environmental services such as erosion control, watershed protection, carbon sequestration (27–163% more than monoculture systems, MS), reductions in methane emissions, atmospheric nitrogen fixation, reductions in biodiversity loss, and modifications of the microclimate improving animal welfare [13,15,18,19,20,21]. Overall, ISPSs have been reported to be more productive at animal (e.g., higher milk and beef yield) and pasture levels (32% higher dry matter production [22]) per unit of area compared with conventional or monoculture livestock systems [15,22,23,24,25].
ISPSs improve animal welfare through the provision of environmental protection, with a positive impact on health [26,27]. Moreover, these systems are highly adaptable and flexible, enabling the combination of livestock production with fruit, timber, and other products, which offers a diversification strategy for farmers to reduce financial risks, while improving the environmental benefits and making these grazing systems more resilient to climate change [28].
There has been a substantial increase in research interest on silvopastoral systems in the last 20 years, which has contributed to enhancing scientific knowledge of the benefits of these systems not only in terms of productivity but also some transversal practices, such as ecological restoration, better agricultural practices, and socioeconomic benefits [29]. Unfortunately, little is known regarding the comparison between monoculture systems (MSs) and ISPSs in terms of methane emissions under grazing conditions [30]. Thus far, the data reported have been generated under controlled in vivo or in vitro conditions, resulting in equal or lower methane emissions for ISPSs [16,31,32,33,34]. In the search for sustainable dual-purpose cattle production systems, this study aimed to compare (a) dry matter yield, dry matter intake, and milk production; (b) methane (CH4) emissions during milking, in both ISPSs and MSs, during the rainy and dry seasons. This information will be useful to identify sustainable practices in extensive cattle systems that could be adopted by small-scale producers in the tropical regions.

2. Materials and Methods

This research was approved by the Institutional Subcommittee for the Care and Use of Experimental Animals (SICUAE-UNAM; Protocol No. DC-2017-1/1) of the Faculty of Veterinary Medicine and Animal Science of the National Autonomous University of Mexico (UNAM).

2.1. Animal Management

The trial was carried out at the localities of Xmatkuil (20°51′57.432″ N, 89°37′20.651″ O) and Dzununcán (20°49′51.808″ N, 89°38′13.069″ O), municipality of Mérida in the Yucatán State, México. The region has a warm sub-humid climate (Aw0 (i′) gw″) [35]. The mean annual temperature for the region is 25.0 to 27.6 °C [36], with a mean annual precipitation of 1021 mm, with low deciduous rainforest as the predominant vegetation [37]. This observational study was carried out in commercial farms, in the aforementioned locations, with management practices established by the farmers.
There were 60 mature, dual-purpose, lactating Gyr (Bos indicus) × Holstein (Bos taurus) cows, kept under a commercial monoculture system (MS, n = 40) or intensive silvopastoral system (ISPS, n = 20), used in this study. Cows were evaluated during the dry (April to May) and rainy seasons (September to October) in 2016, followed by further evaluation for the MS in 2017. Cows were selected according to parity (Me: 3.5, range: 2–5 calvings) and body weight (BW; 522.8 ± 54.2 kg, mean ± SEM). Cows were weighed on a digital scale (1000 kg ± 0.5 kg), and the body condition score (BCS; 5-point scale [38]) was estimated at the start and end of each sampling period. Milk yield data were collected for 10 consecutive days during each season.
Cows were milked between 07:00 and 09:00 h and kept in a holding pen with their calves until 12:00 h, when the cows returned to the paddocks. The total grazing area in both systems (MS = 3.5 ha and ISPS = 2.5 ha) was divided into 10 equally sized paddocks with electric fencing, resulting in individual paddocks of 0.35 and 0.25 ha in the MS and ISPS, respectively. Cows in both systems grazed for 17–19 h, in groups, following an intensive rotational grazing system (paddock occupation time = 1 day). Paddocks were allowed to rest (i.e., no animals grazed) for 35 and 42 days during the dry season and 25 and 28 days during the rainy season for the MS and ISPS, respectively. Animal grazing management was similar in both systems.
Cows received concentrate supplementation during milking (see the methane emission estimations), in both systems, as part of the regular management of the farms for the milk production. Concentrate was prepared on the farm using similar ingredients in both systems: corn, dry turkey manure, soybean meal, and wheat bran). Following milking while at the holding pen, only MS cows were supplemented with concentrate and grass hay (Cynodon nlemfuensis) during the dry season and orange by-product silage with dry turkey manure during the rainy season, before being released into the corresponding paddock (12:00 h).
As mentioned above, paddocks were of comparable size but with different vegetation compositions. MS vegetation consisted, predominantly, of African star grass (Cynodon nlemfuensis) and isolated Azadirachta indica trees used as hedges. The ISPS vegetation were comprised of grasses (Megathyrsus maximus and Cynodon nlemfuensis), shrub legumes (Leucaena leucocephala at a 1.8 m height), and trees (Brosimum alicastrum, Ceiba pentandra, Piscidia piscipula, Bursera simaruba, Lysiloma latisiliquum) as hedgerows to provide shade (310 to 592 m2 of shade per ha−1 [27]). Grazing paddocks, in both systems, were regularly irrigated during the dry season using portable sprinklers. However, only MS paddocks received nitrogen fertilizer (urea; dose: 100 kg ha−1) and herbicides, twice yearly, to maintain herbage mass production.

2.2. Forage and Feed Analysis

  • Forage sampling
Paddock forage biomass availability and dry matter yield were estimated daily after cows moved out of the paddock. For the MS, 7 random plots of 0.25 m2 (0.5 m × 0.5 m) were collected in a zigzag pattern (technique adapted from [39]), and forage mass was estimated by direct (destructive) sampling. Due to the complexity of the vegetation levels/strata associated with ISPS, sampling was carried out as previously reported and validated by Sarabia [40]. Briefly, 5 plots of 2 m2 were selected, with grass cut at 5 cm from the ground and L. leucocephala samples obtained from leaves and stems of <5 mm diameter (edible stem). Fresh samples collected from MS and ISPS paddocks were weighed, placed in paper bags, and dried in a forced-air oven at 55 °C, for 72 h, to estimate the total dry matter (DM) composition. Subsamples from each paddock were separated and stored for further chemical analysis.
  • Concentrate and supplements
To determine the chemical composition of the concentrate and dry turkey manure, samples were taken at five random points (10% of the total). Hay composition was determined from 3 sample points, in each bale, from 10% of the total number of bales used. For orange by-product silage, samples were taken at ten different points in the bunker. Samples collected from each ingredient and concentrate were homogenized; subsamples were collected and dehydrated for chemical composition analysis.
  • Chemical Composition Analysis
Crude protein (CP) and ash were calculated using the methods proposed by AOAC, 2006 [41]; neutral detergent fiber (NDF) and acid detergent fiber (ADF) content was determined according to the methodology proposed by Van Soest et al. (1991) [42] at the Animal Nutrition Laboratory (UNAM). Additionally, metabolizable energy (ME) of the ingredients was determined using the following previously published equations:
  • Concentrate ME (Mcal/kg DM) = 2.76–0.00165 (g ADF/kg DM) [43];
  • Grass and hay ME (MJ/kg DM) = 15.9–0.019 ADF [44];
  • L. leucocephala ME (MJ/kg DM) = 12–0.019 ADF [44].

2.3. DM, CP, NDF, and ADF Intake

DM and nutrient intake from pasture were determined from residual biomass and residual DM, as described previously. DM intake (DMI) was estimated by subtracting the initial available DM and residual DM, divided by the total number of cows grazing in each paddock in both systems. Similarly, the DMI for concentrate in both systems—stargrass hay and orange bagasse silage mixed with dry turkey manure in the MS—was calculated as the difference between the amount offered and rejected, divided by the number of cows in the holding pen. Once the DMI was estimated, the nutrient intake, corresponding to CP, NDF, and ADF, was estimated based on the chemical composition analysis of the pasture and concentrates using the following formulas:
  • CPI = DMI × CP%
  • ME intake (MJ d−1/animal) = DMI × ME
where CPI means crude protein intake expressed as g d−1/animal, DMI is the dry matter intake expressed as g d−1/animal, CP% is the crude protein content of the feed; ME intake means metabolizable energy intake expressed as megaJoules d−1/animal. ME is the metabolizable energy content of the feed expressed as megaJoules/kg of DM.

2.4. Grazing Pressure Index

The grazing pressure index was calculated based on the number of animal units grazing per hectare. An animal unit is the standardized measurement to estimate the DM intake on grazing animals, considering a 450 kg animal (as standard) with a DM intake of 3% BW (i.e., 13.5 kg DM daily) of available fodder biomass [45].

2.5. Methane Emission Estimates

Methane (CH4) emission estimates were carried out daily during milking (07:00 and 09:00) for 10 consecutive days. During this time, cows received concentrate in a custom-made headbox fitted with a methane IR analyzer (range 0–10,000 ppm; Guardian Plus; Edinburgh Instruments Ltd., Livingston, UK), based on the methodology developed by Garnsworthy et al. (2012a) [46]. The headbox measured 58 × 47 × 102 cm, with a trough where concentrate was supplied (Figure 1). Air around the mouth and nose of the cow was sampled using a tube ring (10 cm diameter with 8 holes of 0.4 × 0.3 cm) attached to the methane analyzers by a 6 m plastic tubing. Sampling periods ranged from 6 to 20 min depending on milking duration. A period of two weeks was allowed for acclimatization to the headboxes before measurements were collected. Recordings were carried out in early to mid-lactation cows without interference in the regular daily management routine in each system. Daily methane emissions (g∙d−1) and methane emissions per kg DM intake (g∙kg) were calculated based on the equations reported by Garnsworthy et al. (2012a) [46].

2.6. Statistical Analysis

To compare herbage mass (DM yield), the chemical composition (CP, NDF, and ADF), and system productivity (intake of DM, CP, and ME), two-way ANOVA was used. The model included both system (MS vs. ISPS) and season (dry vs. rainy) as the main factors, as well as their interaction. Tukey’s post hoc test was used for pairwise comparisons. Similarly, two-way ANOVA was used to compare (between systems and seasons) the grazing pressure index, productive parameters (body weight, body condition score, and milk production), and CH4 emissions (total, g∙d−1, g∙d−1, and g∙kg DM intake). CH4 emissions were adjusted per liter of milk. A one-sample t-test was used to assess whether the daily intake of DM, CP, and ME in each system (MS and ISPS) differed from the NRC [47] reference values for cattle producing 6 or 8 L/day during the first third of lactation [47]. Statistical analyses were performed and graphs were drawn using Prism 9 (GraphPad Software, San Diego, CA, USA). A value of p < 0.05 was considered significant.
Principal component analysis (PCA) was used to assess whether a higher productivity was associated with increased CH4 emissions in MSs and ISPSs. For both systems, the following components were considered: (1) productivity (milk production); (2) diet intake (DMI, crude protein, and metabolizable energy); (3) estimated CH4 emissions (CH4∙kg of DMI and CH4∙kg of milk produced). For the description of the model, we present: (a) the outcome loadings for the two first principal components; (b) a distribution pattern of the individual scores of cows from each system, according to PCA axes 1 and 2 [48]. PCA was performed in SAS University Edition (SAS Institute, Cary, NC, USA). A value of p < 0.05 was considered significant.

3. Results

3.1. Paddock Yield and Chemical Composition of Forages and External Feeding Inputs

There was a higher production of forage biomass during the rainy season (p < 0.0001), with DM yield increasing 3.2 and 2.1-fold compared with the dry season in the MS and ISPS, respectively (Table 1). Forage CP content varied according to the season in the MS, with a higher concentration during the dry season compared with the rainy season (p < 0.0001; Table 1). Conversely, in the ISPS, CP content in forage was comparable in both seasons. NDF and ADF values (p > 0.05) did not differ between seasons in both systems (Table 1).
Table 2 shows the nutritional composition mean (±SD) of the external inputs (ingredients) used to supplement diet of cows in dairy production in both seasons.

3.2. DM and Nutrient Intake

Cattle in the ISPS exhibited a higher DMI in both seasons compared with the MS (p < 0.001, Table 3). Forage represented about two-thirds of the DMI in the ISPS (75–80%) and nearly half in the MS (55–60%), with the remaining portion obtained from external sources (i.e., concentrates and supplements; 20–25% in the ISPS and 40–45% in the MS). Concentrate intake was higher during the dry season in the MS, but it was comparable during the rainy season between both systems (Table 3). The average DMI of L. leucocephala in the ISPS was higher during the dry season (0.82 kg DM) compared with the rainy season (0.23 kg).
With respect to CP intake, cows in the ISPS demonstrated a higher intake (p < 0.001) during both seasons, compared with the MS, due to the higher DMI (Table 3). Moreover, in the ISPS, the CPI from L. leucocephala was 56 and 179 g CP for the dry and rainy seasons, respectively. In both systems, cattle had a higher MEI (p < 0.001) during the dry season compared with the rainy season (Table 3). In fact, cattle grazing on the ISPS during the dry season had the highest MEI (p < 0.001).
Additionally, we evaluated whether cattle grazing under the MS and ISPS fulfilled their nutritional requirements based on NRC recommendations [47] for cattle in early lactation. The DMI was above the requirements in both systems (Figure 2A). Regarding CPI, MS cows were within the recommended intake, but in ISPS cows, the intake was significantly higher (p < 0.0001) [47], with CP requirements exceeding 32% and 43% during rainy and dry seasons, respectively (Figure 2B). In contrast, cattle in both systems had lower MEI than NRC [47] recommended intake, with cows in the ISPS having a higher MEI (p < 0.001; Figure 2C).

3.3. Grazing Pressure Index and Stocking Rate

There was a higher grazing pressure index during the rainy season in both systems (p < 0.001; Table 4). Grazing pressure during the rainy season increased 2.5 and 1.8-fold in the MS and ISPS (p < 0.001), respectively, compared with the dry season (Table 4). Both systems demonstrated a similar grazing pressure during the dry season (p > 0.05).

3.4. Milk Production, Body Weight, and BCS

There was no significant effect (p > 0.05) of season on the BW and BCS of cattle grazing under the MS and ISPS (Table 5). Daily milk yield (DMY) was higher in MS compared with the ISPS, which was more evident during the dry season. Milk production in the ISPS was comparable during dry and rainy seasons, whereas in the MS, production decreased from the dry season to the rainy season (p < 0.05; Figure 3).

3.5. Methane Production

Cows in the ISPS emitted 18% less enteric methane (p < 0.05) than cows in the MS (Figure 4A). The MS demonstrated differences between seasons (p < 0.05), and there was a wide variation (264 to 663 g CH4 d−1 in MS and 261 to 491 g CH4 d−1 in ISPS) in methane emissions between cows within systems (Figure 4A). Cows in ISPS had lower methane emissions per kg of DM intake (Figure 4B).

3.6. Multivariate Integration

According to the multivariate analysis, principal components 1 and 2 separated the main characteristics of both systems in a differential pattern that explained 82.12% of the total variance. (Figure 5A). Principal component 1 separated the outcomes of diet intake (dry matter, crude protein, and metabolizable energy) and the emissions of CH4 per kg of DM in opposite directions, suggesting that cows with a higher feed intake tended to produce lower amounts of CH4. In contrast, CH4 emissions per kg of milk and total yield were separated in opposite directions, indicating that cows with higher milk production had lower CH4 emissions.
As shown in Figure 5B, the individual scores for MS and ISPS cows were separated into two distinctive groups, according to the main characteristics of each system. Cows from the MS tended to disperse across higher values of CH4 emissions per kg of DMI, as well as higher milk production; cows from the ISPS were dispersed over higher intakes (DMI, CPI, and MEI) and higher CH4 emissions per kg of milk.

4. Discussion

Overall, the results obtained in this study are consistent with previous publications, both qualitatively and quantitatively. We found that ISPSs show better indicators of sustainability than monoculture systems, which coincides with previous work in the region [49,50,51]. Furthermore, our data show a decrease in methane emissions, as measured by sniffer methodology [46] These results are also consistent with previous studies that have quantified methane emissions [25,31,52].

4.1. Dry Matter Yield and Chemical Composition of Pasture

Results of the DM yield and chemical composition are in accordance with previous research carried out in the same localities [27]. However, Peniche-González et al. (2014) [53] reported that, in an MS based on Stargrass (Cynodon nlemfuensis), pasture production was similar in both seasons of the year. It is important to highlight that both systems were managed under intensive rotational grazing, both were at similar stocking rates per ha in the dry season, and the highest pressure index in MS in the rainy season. Therefore, the fact that both systems showed similar DM yields supports the notion that ISPSs are a rational alternative to MSs that rely on fertilizers and herbicides, which, when used in excess and mismanaged, could be associated with soil acidification [54], GHG emissions [55], decreased soil biodiversity [56], and several other detrimental effects on the environment [57,58,59,60,61]. According to Lira-Junior et al. (2020) [62], after 2–4 years, ISPSs retain greater amounts of N and C, which are associated with legume N fixation [63], as well as tree, shrub, and grass root C exudates [64]. In a parallel study, in the same paddocks in which our study was developed, a higher water retention capacity was reported for the ISPS [65]. Of the last factors mentioned, N, C, and water retention in ISPS can explain the similar forage production in both systems without the application of agrochemicals in the ISPS.
This study did not specifically evaluate the effect of trees on the productivity of the systems; we did not observe an effect of shade on DM yield. Although such a result is in accordance with Orefice (2019) [66], other reports found that shade in the ISPS decreased forage production by 30% [67,68]. According to Rodrigues et al. (2019) [24], biomass production of Mombasa grass (Megathyrsus maximus) is affected by the shadow projected by the trees. In a similar study, Alvarado-Figueroa (2017) [65] reported a higher water retention capacity for the ISPS, which is associated with lower water losses due to evapotranspiration, favoring the availability of groundwater and the improvement of soil fertility.
Results on chemical composition differ from those found by other authors, who suggested a higher nutritional quality in the ISPS due to the effects of shade and nitrogen fixed by tree legumes [14,23,68,69]. Shelton and Dalzell (2007) [70] reported that leucaena forage contains 20% crude protein, is highly digestible, and provides a consistently high-quality diet throughout the year. However, in this study, L. leucocephala contributed 3.9% of the total DM in the dry season and 1.7% in the rainy season, contrasting data obtained by Sarabia-Salgado et al. (2020) [23] and Bottini-Luzardo et al. (2016) [71], where L. leucocephala represented 29% and 27–39% of available DM, respectively (probably due to differences in shrub density per ha−1 among trials).

4.2. Dry Matter, Crude Protein, and Metabolizable Energy Intake

The dry matter intake of L. leucocephala in the ISPS was lower than the 5.1 kg DM intake reported by Bottini-Luzardo et al. (2016) for the same type of production system. Dry matter intake estimated for both systems is within the range estimated by different authors under grazing conditions in similar systems [72,73]. Bottini-Luzardo et al. (2016) [71] reported a similar CP intake in both systems. The same authors reported 1479 g in the ISPS, from which almost 50% corresponded to L. leucocephala, which is different from the results presented here. According to López-Vigoa et al. (2017) [74], care must be taken to use supplements that improve the energy balance of grazing animals because, if animals consume crude protein above their requirements, for a particular level of production, they will produce milk; however, when the energy supplied is insufficient, this energy will most likely originate from the body reserves (adipose tissue), as can be reflected in the BCS. Sierra-Montoya et al. (2017) [75] pointed out that it is important to pay careful attention when feeding cows because DM, CP, and ME in excess can be associated with reproductive disorders.
In accordance with Jennings et al. (2018) [76], the excess in crude protein and metabolizable protein intakes from a source high in crude protein, which is not degradable in the rumen, increases maintenance energy requirements. In addition, excess protein can modify microbial protein production due to the fact that NH3–N concentration in the rumen will increase [77,78], and once it is absorbed into the bloodstream and metabolized in the liver through the synthesis of urea, it will represent an additional energy expenditure for the cow [79]; this can have an impact on milk production [80] and its performance [81] on the body condition score or on reproductive physiology [82]. Dry matter intake increases with high levels of crude protein in the diet [83], which could possibly explain the higher consumption of DM in ISPS.
On-farm feeds in ISPS represented 75–80%, while in MS, these represented 54–60% of the total DMI. The lower the dependence on off-farm feeds, the more sustainable the system, due to the level of pollution that decreases because of the relationship represented by the life cycle of external inputs [84].

4.3. Grazing Pressure Index

The grazing pressure index is influenced by management in both systems, although in the MS, it is higher for the rainy season. Based on the data obtained in this study, both systems have the possibility of increasing this because grazing efficiency was 32.35% and 35.95% in the ISPS and 28.94% and 20.96% in the MS, for the dry and rainy seasons, respectively. Solorio et al. (2016) [85] and Murgueitio et al. (2015) [17] reported similar grazing pressure index values to those in this study for ISPS. Improvements of the stocking rate management and grazing efficiency will lead to an increase in soil carbon stock, as well as concentrations and improvements in ecosystemic services [86,87].

4.4. Body Weight, Body Condition, and Milk Production

As mentioned before, no differences were found on BW and BCS between systems. This is most probably associated with the fact that DM yields and chemical composition of the diet is similar in both the ISPA and MS. Similar results were found by Huertas et al. (2021) [88].
Lower milk production in the ISPS can be associated with high crude protein intake because additional energy (i.e. ATP) is required to remove excess nitrogen (NH3-N), through metabolic pathways, for the synthesis of urea in the liver and excretion via urine [76]. Feeding with excess N increases heat production and reduces the energy retained in the body and the gross energy of milk; this is associated with the catabolism of excess amino acids accompanied by a high energy expenditure [89]. Diets with adequate crude protein levels support maximum production in dairy cows, with a minimal excretion of N to the environment, compared to diets with crude protein above the requirements [80]. The higher levels of blood urea nitrogen (BUN) found in the ISPS suggest a lower efficiency of N utilization at the whole body level [71].
Cardozo-Herrán et al. (2019) [90], evaluating goats under similar conditions to those in this study, found similar individual milk yields in both systems; however, they obtained a higher milk yield per unit area in the MS. Bacab and Solorio (2011) [72] reported higher milk production in a traditional system, offering a greater quantity of concentrate. In a monoculture system, an average milk yield of 6.26 kg/head/day, at a stocking rate of 1 AU·ha−1, has been reported [91], which is rather different from our data.

4.5. Methane Emissions

This study found a decrease in CH4 emissions from the ISPS compared with the MS, as previously reported by another group of researchers [31]. Although we observed similar methane production per kg DMI, as previously reported for grazing cattle (15–30 g CH4/kg DMI) [77,92,93,94,95], cows in the ISPS demonstrated an increase in DMI, which reduced methane emissions. According to Knapp et al. (2014) [96], methane production diminishes by 2% to 6% with each kg of DMI.
Despite the lower emissions observed in the ISPS, cows showed large individual variation (51–64%) in methane production, as reported elsewhere [77,78] in ruminants. There was considerable individual variation in methane emissions within the systems, registering data outside the range (278–456 g−1 CH4/animal/d−1) reported by Garnsworthy et al. (2012b) [97] for specialized dairy cattle. Jonker et al. (2017) [98] pointed out that DMI, alone, explains 90.8% of the variation in CH4 production (g/day). The decrease in CH4 emissions, with the increase in the DMI, is influenced by the proportions of starch and NDF in the diet, as well as that of cellulose and hemicellulose in forages [96]; however, there is no evident influence of these parameters on CH4 emissions, as the concentrations of NDF and ADF were similar for both systems.
Previous studies have reported lower methane emissions when tropical legume shrubs or trees are incorporated in cattle diets due to the presence of secondary metabolites, such as condensed tannins and saponins [31,32,99,100,101,102,103]. Despite the relatively low intake of L. leucocephala in this study, the decrease in methane emissions may be a result of the inclusion of this legume in the diet [7,32,100]. This reduction in emissions could be due to the presence of condensed tannins, as previously reported in studies carried out in the region where the study took place [32,100].

4.6. Integrated Biological Implications of the Results

Our findings have important environmental impacts. In the ISPS, GHG emissions are reduced directly and indirectly, both due to the lower emissions from livestock and the lower consumption of external inputs for animal feed [104,105].
Incorporating trees and shrubs into these systems contributes to the provision of environmental services, such as carbon sequestration in plants and soil [106] and increasing the soil–water retention capacity. Rotational grazing with a moderate stocking rate increases soil organic matter [107] and allows for vegetation resting periods, which increases the lifetime pasture productivity [108]. This information adds up to other findings of this working group that show that silvopastoral systems have sustainability elements that can be useful for climate change adaptation and mitigation, as well as for increasing biodiversity and the welfare of cattle [49,50,65].

5. Conclusions

The study was carried out in commercial cattle systems; thus, it has the limitation that the conditions of the study could not be controlled thoroughly. The systems were evaluated just as the management within the system was carried out. However, this is also an opportunity, because data were obtained through a scientific methodology, for a more realistic portrayal of grazing animal production systems in tropical regions. Economic data of the production in both systems were not taken into account.
It is important to quantify methane emissions from livestock, as well as other GHGs emitted in animal production systems; however, the whole scenario must be studied. Therefore, we considered that C income, C sequestration, and C storage in the soil and vegetation of agroecosystems must be quantified to determine the general C balances of grazing systems. It is important to evaluate costs and income to determine the profitability of livestock systems because the economic aspect is one of the pillars of sustainability.
In general, the productive results obtained in this study were similar in both systems, and the methane emissions were lower in the ISPS; therefore, these systems represent sustainable models of animal grazing compared with the MS. Integrating trees and shrubs encourages greater biodiversity of agroecosystems and the consequent provision of concatenated ecosystem services. This is coupled with the fact that fewer external inputs for animal feed and agrochemicals are used, which reduces the dependence of producers on these products, as well as their environmental impact. These systems should be incorporated as climate change adaptation and mitigation strategies that support achieving Sustainable Development Goals.

Author Contributions

Conceptualization, G.F.-C., F.G., J.H.H.-M. and J.K.-V.; methodology, G.F.-C., F.G., J.H.H.-M., F.J.S.-S. and L.S.-S.; software, J.H.H.-M. and D.D.; validation, G.F.-C., J.H.H.-M. and L.S.-S.; formal analysis, D.D.; investigation, G.F.-C. and L.S.-S.; resources, G.F.-C., F.G., J.H.H.-M., J.K.-V. and F.J.S.-S.; data curation, G.F.-C., D.D. and L.S.-S.; writing—original draft preparation, G.F.-C.; writing—review and editing, G.F.-C., F.G., J.H.H.-M., J.K.-V. and F.J.S.-S.; visualization, G.F.-C., J.H.H.-M. and D.D.; supervision, F.G., J.H.H.-M., J.K.-V. and F.J.S.-S.; project administration, F.G.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Autonomous University of Mexico (UNAM), through Support Program for Research and Innovation Projects (PAPIIT-UNAM), projects DC-2017-1/1 and IG-201621 and supported by the doctoral scholarship of National Grants Program for Graduate Studies of the National Council of Science and Technology (CONACYT).

Institutional Review Board Statement

This research was approved by SICUAE-UNAM (Institutional Subcommittee for the Care and Use of Experimental Animals, National Autonomous University of Mexico; protocol No. DC-2017-1/1).

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

GFC with CVU number 229868 thanks the National Grants Program for Graduate Studies of the National Council of Science and Technology (CONACYT) for the grant awarded for the completion of his research project. This work was supported by PAPIIT–UNAM IV200715 and IG201621. The authors wish to thank Hugo Anibal Solorio Esquivel and Erick Chi Cocom for their skilled technical assistance.

Conflicts of Interest

The authors declare no conflict of interest.

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Atmosphere 14 00863 g001 550
Figure 1. Diagram of a feeder (head-box with trough) employed to measure methane emission during milking.
Figure 1. Diagram of a feeder (head-box with trough) employed to measure methane emission during milking.
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Atmosphere 14 00863 g002 550
Figure 2. Comparison of mean (±SD) of the daily intake of (A) DM, (B) CP, and (C) ME during early lactation in MS and ISPS cows in this study. Lines represent the NRC [47] reference values for cattle producing 6 or 8 L milk/day during early lactation.
Figure 2. Comparison of mean (±SD) of the daily intake of (A) DM, (B) CP, and (C) ME during early lactation in MS and ISPS cows in this study. Lines represent the NRC [47] reference values for cattle producing 6 or 8 L milk/day during early lactation.
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Figure 3. Mean (±SD) daily milk yield in the MS and ISPSs (A), as well as during the dry and rainy seasons (B).
Figure 3. Mean (±SD) daily milk yield in the MS and ISPSs (A), as well as during the dry and rainy seasons (B).
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Atmosphere 14 00863 g004 550
Figure 4. Estimated methane emissions, expressed as total emissions (g d−1; (A)), and emission rate by DM intake (g kg DM−1; (B)) from cows in the MS and ISPS during two different seasons.
Figure 4. Estimated methane emissions, expressed as total emissions (g d−1; (A)), and emission rate by DM intake (g kg DM−1; (B)) from cows in the MS and ISPS during two different seasons.
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Figure 5. Pattern of association of the main features of productivity, diet intake, and CH4 production in cows from MS and ISPS. (A) Outcome loadings for the two first principal components; (B) individual scores of the cows from each system. Notably, the features of both systems effectively separated the observations for each system. To define both groups, 95% confidence prediction ellipses were drawn.
Figure 5. Pattern of association of the main features of productivity, diet intake, and CH4 production in cows from MS and ISPS. (A) Outcome loadings for the two first principal components; (B) individual scores of the cows from each system. Notably, the features of both systems effectively separated the observations for each system. To define both groups, 95% confidence prediction ellipses were drawn.
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Table
Table 1. Comparison of mean (±SD) of dry matter yield and chemical composition of paddocks between the monoculture system (MS) and intensive silvopastoral system (ISPS).
Table 1. Comparison of mean (±SD) of dry matter yield and chemical composition of paddocks between the monoculture system (MS) and intensive silvopastoral system (ISPS).
ParameterMSISPSSeason Effect
DryRainyDryRainyp Value *
DM yield (kg ha−1)1144.8 ± 234 b3707.2 ± 619 a1631.3 ± 345 b3443.1 ± 281 a<0.0001
CP (g kg−1 DM)108.3 ± 10 a80.9 ± 7 c102.1 ± 14 a,b85.7 ± 8 b,c<0.0001
NDF (g kg−1 DM)720.7 ± 74 a,b765.9 ± 53 a640.8 ± 39 b739.9 ± 59 a,b0.012
ADF (g kg−1 DM)374.7 ± 35 a380.7 ± 174 a365.5 ± 29 a474.4 ± 98 a0.19
DM, dry matter; CP, crude protein; NDF, neutral detergent fiber; ADF, acid detergent fiber. * Significant effect for the test of season factor in two-way ANOVA models. a,b,c indicate significant differences at level p < 0.05 for comparisons between columns.
Table
Table 2. Nutritional composition mean (±SD) of concentrate and supplements (external inputs) fed to cows during the dry and rainy seasons in the monoculture system (MS) and intensive silvopastoral system (ISPS).
Table 2. Nutritional composition mean (±SD) of concentrate and supplements (external inputs) fed to cows during the dry and rainy seasons in the monoculture system (MS) and intensive silvopastoral system (ISPS).
SystemSeasonIngredientCP %NDF %ADF %Ash %
MSDryConcentrate *13.1 ± 1.731.3 ± 6.422.2 ± 11.65.5 ± 1.4
Stargrass hay3.5 ± 1.972.0 ± 5.562.5 ± 0.58.6 ± 0.0
RainyConcentrate *13.8 ± 1.245.6 ± 3.027.0 ± 6.14.1 ± 0.1
Orange by-product silage6.9 ± 0.134.8 ± 2.326.4 ± 6.15.1 ± 0.3
Dry turkey manure22.879.332.415.9
ISPSDryConcentrate *12.645.836.19.1
RainyConcentrate *15.434.015.810.1
* Concentrate was prepared on the farm, consisting of a mixture of corn, dehydrated turkey manure, soybean meal, and wheat bran, and it was balanced according to the farm’s nutritional management.
Table
Table 3. Comparison of mean (±SD) of dry matter intake (DMI), protein intake (CPI), and energy intake (MEI) of cows kept under the monoculture system (MS) and the intensive silvopastoral system (ISPS) in this study.
Table 3. Comparison of mean (±SD) of dry matter intake (DMI), protein intake (CPI), and energy intake (MEI) of cows kept under the monoculture system (MS) and the intensive silvopastoral system (ISPS) in this study.
ParameterMSISPSSeason Effect
DryRainyDryRainyp Value *
DMI
(kg d−1/
animal)		Total11.94 ± 1.3 b12.0 ± 0.5 b15.5 ± 0.2 a15.4 ± 0.5 a0.937
Forage6.5 ± 0.7 b7.1 ± 0.8 b12.4 ± 0.2 a11.6 ± 0.6 a0.681
Concentrate4.7 ± 1.0 a3.7 ± 0.3 b3.1 ± 0.3 b3.8 ± 0.4 b0.604
Other0.7 ± 0.2 b1.1 ± 0.4 a---
CPI
(g d−1/
animal)		Total1357.0 ± 110.0 b1264.0 ± 93.0 b1703.0 ± 185.0 a1571.0 ± 76.0 a<0.001
Forage700.6 ± 109.1 c577.6 ± 94.1 d1316.9 ± 173.8 a983.7 ± 26.6 b<0.001
Concentrate634.3 ± 174.7 a523.0 ± 77.4 b386.9 ± 37.7 c587.6 ± 74.2 a,b0.174
Other21.7 ± 9.1 b164.0 ± 46.2 a---
MEI
(MJ d−1/
animal)		Total114.1 ± 8.4 c103.9 ± 7.6 d138.9 ± 4.7 a126.6 ± 6.6 b<0.001
DM, dry matter; CP, crude protein; MJ, megaJoules. * Effect of the season in a two-way ANOVA comparison; a,b,c,d different letters indicate significant differences at level p < 0.05 between columns.
Table
Table 4. Comparison of mean (±SD) of the grazing pressure index and stocking rate between the monoculture system (MS) and intensive silvopastoral system (ISPS).
Table 4. Comparison of mean (±SD) of the grazing pressure index and stocking rate between the monoculture system (MS) and intensive silvopastoral system (ISPS).
ParameterMSISPSSeason Effect
DryRainyDryRainyp Value *
AU·pad−1·d−14.84 ± 0.57 b14.6 ± 6.43 a9.3 ± 0.35 b16.96 ± 6.54 a<0.001
AU·ha−1·d−124.20 ± 11.76 b48.36 ± 11.82 a18.97 ± 0.72 b34.6 ± 13.35 b<0.001
AU·pad−1·d−1 = animal unit per paddock per day; AU·ha−1·d−1, animal unit per hectare per day. * Effect of the season in a two-way ANOVA comparison; a,b different letters indicate significant differences at level p < 0.05 between columns.
Table
Table 5. Comparison of mean (±SD) of body weight, body condition score, and milk production in the monoculture system (MS) and intensive silvopastoral system (ISPS). (BW, body weight; BCS, body condition score. No differences were found between systems.).
Table 5. Comparison of mean (±SD) of body weight, body condition score, and milk production in the monoculture system (MS) and intensive silvopastoral system (ISPS). (BW, body weight; BCS, body condition score. No differences were found between systems.).
ItemMSISPSSeason Effect
Dry (20)Rainy (20)Dry (10)Rainy (10)p Value
BW506.5 ± 43.2515.7 ± 40.3528.3 ± 61.1541.3 ± 72.00.431
BCS2.7 ± 0.63.2 ± 0.52.8 ± 0.62.7 ± 0.60.265
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Flores-Coello, G.; Hernández-Medrano, J.H.; Ku-Vera, J.; Diaz, D.; Solorio-Sánchez, F.J.; Sarabia-Salgado, L.; Galindo, F. Intensive Silvopastoral Systems Mitigate Enteric Methane Emissions from Cattle. Atmosphere 2023, 14, 863. https://doi.org/10.3390/atmos14050863

AMA Style

Flores-Coello G, Hernández-Medrano JH, Ku-Vera J, Diaz D, Solorio-Sánchez FJ, Sarabia-Salgado L, Galindo F. Intensive Silvopastoral Systems Mitigate Enteric Methane Emissions from Cattle. Atmosphere. 2023; 14(5):863. https://doi.org/10.3390/atmos14050863

Chicago/Turabian Style

Flores-Coello, Gustavo, Juan H. Hernández-Medrano, Juan Ku-Vera, Daniel Diaz, Francisco J. Solorio-Sánchez, Lucero Sarabia-Salgado, and Francisco Galindo. 2023. "Intensive Silvopastoral Systems Mitigate Enteric Methane Emissions from Cattle" Atmosphere 14, no. 5: 863. https://doi.org/10.3390/atmos14050863

APA Style

Flores-Coello, G., Hernández-Medrano, J. H., Ku-Vera, J., Diaz, D., Solorio-Sánchez, F. J., Sarabia-Salgado, L., & Galindo, F. (2023). Intensive Silvopastoral Systems Mitigate Enteric Methane Emissions from Cattle. Atmosphere, 14(5), 863. https://doi.org/10.3390/atmos14050863

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