Effects of Long-Term Grazing on Feed Intake and Digestibility of Cattle in Meadow Steppe
by
, , , , ,
Lulu Hou
1,
Xiaoping Xin
1,
Beibei Shen
1,
Qi Qin
1,
Ahmed Ibrahim Ahmed Altome
2,
Yousif Mohamed Zainelabdeen Hamed
2,
Ruirui Yan
1
,
Serekpaev Nurlan
3,
Nogayev Adilbek
4,
Akhylbekova Balzhan
4,
Maira Kussainova
5,
Amartuvshin Amarjargal
6,
Wei Fang
7,
Alim Pulatov
8,
Wenneng Zhou
9,* and
Haixia Sun
10,* 1
National Hulunbuir Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Agricultural Research Corporation, Wad Madani 126, Sudan
3
S. Seifullin Kazakh Agro Technical University, Astana 010000, Kazakhstan
4
LLP “Scientific and Production Center of Grain Farming Named after A.I. Barayev”, Akmola 021600, Kazakhstan
5
Sustainable Agriculture Center, Kazakh National Agrarian Research University, Almaty 050010, Kazakhstan
6
University of the Humanities, Ulaanbaatar P.O. Box 210646/53, Mongolia
7
Department of Biology, Pace University, New York, NY 10038, USA
8
EcoGIS Center, «Tashkent Institute of Irrigation and Agricultural Mechanization Engineers» National Research University (TIIAME NRU), Tashkent 100000, Uzbekistan
9
School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
10
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 150081, China
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(7), 1760; https://doi.org/10.3390/agronomy13071760
Submission received: 2 June 2023
/
Revised: 17 June 2023
/
Accepted: 26 June 2023
/
Published: 29 June 2023
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
Abstract
:(1) Estimation of grazing livestock intake is the basis for studying animal–plant relationships and the nutritional status of grazing livestock and has important implications for grassland composition and productivity. (2) We used the saturated alkanes method to determine the feed intake and vegetation nutrient digestibility of livestock at different grazing intensities and in different months. (3) We found that C31 had the highest concentration in both pasture and fecal output, and the average recovery of C31 was 77.99%. The different grazing intensities significantly affected livestock intake. As the grazing intensity increased, there was a decreasing trend of livestock intake and the highest livestock feed intake was 6.11 kg DM/day in light grazing. With the increase in grazing season months, the highest livestock intake was 6.67 kg DM/day in the cold period in September. The month also had a significant effect on the digestibility of livestock for all nutrient variables when compared to the grazing intensity. Livestock weight and medium palatability species are more important for livestock intake. (4) Our study provides a more accurate measurement of grazing livestock intake, which can be used as a reference for the scientific management of grazing livestock and the rational use of grazing pastures.
1. Introduction
Grassland above-ground net primary production (ANPP) is considered to be a key aspect of ecosystem functioning due to its decisive influence on ecosystem structure and biodiversity [1]. Most productivity studies have used maximum biomass as a proxy, ignoring livestock foraging in grazing ecosystems [2], but an accurate estimation of livestock foraging is beneficial for the assessment of livestock nutrition and digestibility which offers potential ecological and economic benefits. However, estimating grazing livestock intake has always been challenging due to the limitations of available measurement methods and environmental conditions in ecology [3,4]. Currently, the estimation methods of livestock feed intake mainly include the herbage disappearance method, animal performance method, and internal or external markers. However, no methods have been approved as the golden standard for the estimation of herbage intake [5,6]. The alkane technique has been developed principally for use in grazing ruminants. Long-chain n-alkanes (C21~C36), which occur naturally in the waxes of the plant cuticle and are relatively indigestible in the gastrointestinal tract and can be recuperated in the feces, are one of the widely and successfully used markers to estimate dry matter intake (DMI), diet composition, and digestibility of animals [7,8,9]. Long-chain n-alkanes have an obvious trend for recovery to increase with chain length, though it should be noted that even with C35 and C36 alkanes, observed recoveries are still incomplete, especially in cattle, where alkane recoveries are more variable and more work needs to be done to obtain further estimates of alkane recovery [10]. So, observational studies mostly use plant wax markers to estimate the diet composition and dry matter intake of animals with a correction for fecal marker recovery [4,11,12,13,14]. This is a great advantage of this technique to support studies of plant–animal interactions in rangeland environments.
Livestock foraging is a complex, dynamic process in which biotic and abiotic factors interact. In the case of grazing livestock, palatability selection and grassland vegetation community structure are important factors influencing foraging [15]. Grassland above-ground biomass determines the supply of grass, while the grass layer structure (including plant height, carrying capacity, and spatial distribution within the canopy) determines the grazing tolerance of the grass [16]. Long et al. [17] showed that differences in the composition of grassland vegetation and differences in the foraging behavior of livestock resulted in differences in intake. Other factors that influence intake include the physiological characteristics of the livestock (e.g., gender, weight, etc.). The growth of pastures varies between seasons with different ambient temperatures and rainfall. Some studies have shown that suitable temperatures can increase livestock intake, while high temperatures can reduce livestock intake [18]. Grazing intensity is one of the policies of grazing management; accurate estimation of livestock DMI is also a key indicator of judgment whether the stocking rate is reasonable. However, little information exists about cattle intake under different grazing intensities and seasons, especially using the saturated alkanes method to determine cattle intake. We hypothesize that the alkanes would not be fully recovered under grazing cattle and that livestock intake would decrease with increasing grazing intensity. This study will assess the recovery of alkanes in cattle at different grazing intensities, more accurately determine grazing livestock intake and provide a database for the estimation of grassland ANPP on the one hand, and to more rationally manage grazing areas according to livestock forage preferences on the other hand, thus achieving both improvements in livestock production and grassland optimization.
Meadow steppe is the most productive grassland type of Eurasian steppe, with rich plant species and relatively high vegetation productivity, and is the main production base for forage resources in China [19], making it particularly important to accurately estimate livestock intake. The present study was conducted in a meadow steppe with a 10-year grazing history, where community composition has shifted at different grazing intensities, for example, from tall grasses to short forbs at heavy grazing intensities, with the objectives of (1) measuring the alkanes recovery in grazing cattle, (2) quantifying the intake and digestibility of cattle at different grazing intensities, and (3) understanding the cattle foraging palatability, by analyzing the relationship between forage intake and community composition.
2. Materials and Methods
2.1. Location of the Study Site
The study area is located at Hulunbuir Grassland Ecosystem Observation and Research Station (49°32′~49°34′ N, 119°94′~119°96′ E), with an altitude of 670~677 m. It belongs to the temperate semi-arid continental climate, with an average annual temperature of −3~1 °C, a frost-free period of about 110 days, and an average annual precipitation of 350~400 mm. The precipitation is mostly concentrated from July to September. The soil type is chernozem, and the vegetation type is Leymus chinensis—weeds meadow grassland. The main species are Leymus chinensis, Stipa baicalensis, Carex pediformis, Galium verum, and Bupleurum scorzonerifolium [20].
2.2. Experiment Design
2.2.1. Experiment Platform
The grazing experiment was established in 2008 on relatively flat terrain and consistent soil as well as vegetation conditions steppe. Based on the daily feed intake of livestock and the utilization rate of local forage, 1 Au is equal to 500 kg adult cattle, with 0.46 cattle units per hectare (0.46 cow. Au/ha) as the theoretical stocking rate. There were six grazing intensities in total, with stocking rates ranging from 0, 0.23, 0.34, 0.46, 0.69, to 0.92 cow.Au/ha, replicated three times. Each experimental plot was 5 ha. Three grazing intensities were selected as the experiment units, namely light (G0.23: 0.23 cow. Au/ha), moderate (G0.46: 0.46 cow. Au/ha), and heavy (G0.92: 0.92 cow. Au/ha). The plots were simulated with two, four, and eight 250–300 kg adult cattle per plot. Wire fences were established in the experimental plot without herders; there was no supplementary feeding and sufficient water was available throughout the free grazing season (June–September) each year [20].
2.2.2. Animals–Plant Sampling
- Animals
Livestock from each experimental plot was weighed from June to the end of September 2018. From July to September (i.e., days 14–18 of the month), three cattle were selected from each plot to follow during the day, with the exception of light grazing (only two cattle), and as soon as the livestock excreted fecal output, it was shoveled into buckets to collect the volume of excrement. Each plot had a separate enclosure (with an area large enough for one livestock to move around for one night), and each livestock was whisked into the enclosure at night and released early the next morning to continue the tracking and collect the manure from the enclosure.
- Plants
Plant community surveys were based on five randomly selected 1 m × 1 m quadrats per plot at the beginning of each month from July through September 2018. The height and abundance of each plant species were recorded. The aboveground component of each species was cut, collected, and dried to constant weight at 65 °C for 48 h. The sum of the dry weights of individual species in the quadrats was termed aboveground biomass (AGB). Species richness, which in the present study is defined as a total number of species occurring per unit area (e.g., 1-m2 plot), is a simple and easily interpretable indicator of biological diversity [21]. Species abundance is the study of how common a particular species occurs in a given community. The plants were sampled in the morning, mid-day, and evening simulating the livestock foraging pattern (tongue roll) while tracking the livestock. The samples were mixed, stored at −20 °C, and then lyophilized. Based on the palatability of the vegetation combined with expert experience, the vegetation species were classified as high, medium, low, or poisonous palatable vegetation [22].
2.2.3. Recovery Experiment
In early August, one cattle from each of the light, moderate, and heavy grazing plots was placed in a paddock (the pasture was removed from the paddock) and fed above the stem to simulate the feeding pattern of the cattle. Three feedings time per day (8:00, 12:00, 18:00) were carried out with a total feeding intake of approximately 1.0 kg DM/100 kg LW [23]. The fresh weight of the forage was weighed before each feeding, as well as the fresh weight of the forage left over from the previous feeding, and a portion was taken to determine the dry matter content of the forage. The amount of manure excreted by the cattle was collected in a bucket at the same time as feeding. The trial period was four days for acclimatization and four days for testing. One part of each forage and manure sample was lyophilized to determine the alkanes content and the other part was dried to determine the dry matter content. The recovery was calculated according to Formula (1):
where Cn is the concentration of alkanes in the grass and fecal samples.
Recovery = Cn(fecal) ∗ Total Fecal/Cn(grass) ∗ Feeding intake
2.3. Alkane and Nutrient Analysis
2.3.1. Alkane Analysis
Alkane was determined according to the method of Mayes et al. [7], Sun et al. [24], and Dove et al. [25], with some modifications. Briefly, 2 g of plant or 1 g of fecal sample was weighed into a pyrex bottle with three replicates; two internal standards (2 mg C22 and 2 mg C34) and 15 mL ethanolic KOH (1.5 mol/L) were added to each sample. The tubes were capped tightly and heated for 4.5 h at 90 °C. The extraction of alkanes was performed by adding 7 mL heptane plus 5 mL distilled water, with ultrasonic treatment for 5 min, followed by transferring the heptane layer to an evaporating dish. The extraction was repeated twice with 5 mL heptane. The evaporating dish was heated in a water bath at 60 °C and the heptane solution was evaporated to approximately 1 mL. The solution was then transferred into a silica gel column (70–230 mesh), and the dish was rinsed four times with 2-, 2-, 3-, and 4-mL heptane. The lipid in the sample was absorbed into the gel, and alkanes were eluted. The eluate was collected in a tube, blown dry using nitrogen, and reconstituted in 1 mL heptane for the determination of alkane concentrations (Figure 1).
The identification of alkanes was determined by using a Gas Chromatograph–Mass Spectrometer (GC-MS, Agilent 7000C, Santa Clara, CA, USA), which offers higher sensitivity and more definitive compound identification. The initial column temperature was set at 200 °C, held for 1 min, increased to 250 °C at a rate of 20 °C/min, then ramped to 300 °C at 10 °C/min. Then, 1 μL of the reconstitute was injected at split mode. Individual alkanes were identified from their retention times and quantitated according to their peak areas in reference to the internal standards C22 (n-docosane) and C34 (tetratriacontane). Figure 2 shows that there were almost no impurities in the extraction process of this method.
2.3.2. Nutrient Analysis
Plant and fecal samples were analyzed for C, N, neutral detergent fiber (NDF), acid detergent fiber (ADF), gross energy (GE), dry matter (DM), and organic matter (OM) contents (Table A1). Dry matter was determined by drying the samples to a constant weight at 65 °C for 48 h. The C and N concentrations were determined with an elemental analyzer (Vario EL III; Elementar Analysensysteme GmbH, Langenselbold, Germany). Crude protein (CP) was calculated as N × 6.25 [26]. ADF and NDF were determined with an ANKOM 200 automatic fiber analyzer. Ash was determined by muffle furnace combustion at 550 °C for 6 h. GE was determined with the MTZW-A4 high-precision dual-purpose automatic oxygen bomb calorimeter (Shanghai Mitong, Shanghai, China). Metabolizable energy (ME) content was then calculated as 0.82 ∗ Digestible energy content which was the difference between gross energy intake (Dry matter intake × GE) and the fecal energy (fecal output × energy content in feces) [24].
2.4. Estimation of Feed Intake and Digestibility
Livestock feed intake and digestibility was calculated according to Formulas (2) and (3):
where Fecal is the total dry fecal output.
Dry matter intake (kg/d·cattle) = Cn(fecal) ∗ Fecal/Cn(grass) ∗ recovery
Digestibility (%) = 1 − Fecal/Intake
2.5. Statistical Analysis
All data were plotted with Origin v. 2021 (OriginLab, Northampton, MA, USA). The data were subjected to two-way ANOVA to analyze the effect of the month and grazing intensity on cattle intake and digestibility using SPSS Statistics v. 23 (IBM Corp., Armonk, NY, USA). Regression analysis was used to examine the plant–livestock factors which influence cattle intake. Duncan’s multiple range test was used to determine significance at the 95% confidence interval.
3. Results
3.1. Chemical Composition and n-Alkanes Pattern of Plant and Fecal Matter
The chemical composition of plant and fecal output differed significantly at different grazing intensities and months (p < 0.05) (Table A1 and Table A2). The plants DM, OM, C, N, CP, and GE were significantly different (p < 0.05) across months with the lowest DM content of 39.36% in August. As grazing intensity increased, plant and fecal nutrients gradually decreased, but the highest values of CP were observed under heavy grazing.
The concentration of alkanes was higher in the fecal output of cattle compared to the grasses at different grazing intensity. The highest concentrations of C31 were found in grass and fecal output at different grazing intensities, with mean values of 237.13 mg/kg and 128.37 mg/kg, respectively (Table 1). In addition, odd alkane concentrations were greater than even alkane concentrations, with feces and grass samples accounting for 89.34% and 91.56% of total alkanes, respectively. There was the same trend in different grazing intensities.
3.2. C31 Recovery Rate
Based on feeding experiments, it was clear that the amount of fecal output excreted by livestock was directly proportional to the amount of feed. The average digestibility of livestock was 66.90%, with the highest digestibility of 70.33% for heavy grazing. The concentration of alkanes was greater in the fecal output than in the forage, with the highest concentration in C31. The recovery of alkanes increased progressively with increasing alkane length, except for C33 and C35 where recovery exceeded 100%; we therefore only show the recoveries for C31 selected for this study. C31 recovery was 77.63%, 75.25%, and 81.09% for light, moderate, and heavy grazing, respectively, but there was no significant difference (Table 2).
3.3. Intake and Digestibility of Cattle under Different Grazing Intensities and Months
The general trend in grazing cattle intake showed the highest intake in September and from light grazing (Figure 3). Significant differences were observed among the grazing intensities and months for all the observed variables except for CP under grazing intensities (p < 0.001) (Figure 3). Average values of daily dry matter intake (DMI) were lower under heavy grazing (G0.92) compared to the other treatments, with an average of 4.37 kg DM/ day. In addition, animals in G0.92 showed a relatively low intake of ~1.24% LW, whereas animals in G0.23 showed an intake close to 1.53% LW. Considering the DMI, cattle consumed 27.77% less in G0.92 than in G0.23. Due to the lower DMI at G0.92, the NDF and ADF intake were also lower in this treatment. Grazing intensity and month had a significant effect on the dry matter intake of cattle, as well as on NDF and ADF intake (p < 0.001, Figure 3), but there was no significant effect on the interaction of month and grazing intensity.
The general trend in grazing cattle digestibility showed that dry matter and nutrient digestibility (except CP) of livestock were lowest during heavy grazing and in September (Table 3). The digestibility of CP increased significantly in August under heavy grazing (64.11%). The month had a significant effect on the digestibility of livestock for all variables compared to the grazing intensity (p < 0.001, Table 3). The month explained greater variance in cattle dry matter and nutrient digestibility than grazing intensity. However, the interaction between grazing intensity and month on nutrient digestibility in cattle was not significant.
3.4. Plant Species Composition Influencing Cattle Intake
Livestock intake was not significantly correlated with AGB, while livestock body weight was significantly correlated with livestock intake (R2 = 0.46, p < 0.01) (Figure 4a,b). The number of species also showed a significant correlation with livestock intake (R2 = 0.34, p < 0.01), with a significant reduction in mainly medium palatable plant species richness (R2 = 0.44, p < 0.01), while high and low palatable species richness were not associated with livestock intake (Figure 4c). There was no overall significant trend between livestock intake and palatable forage abundance, but there was a relationship between palatable species abundance and livestock intake at different grazing intensities. With increasing feed intake of cattle, the high and low palatability species numbers remained constant, but the abundance decreased, e.g., Leymus chinensis (high), Iris tectorum (low), and the rate of decrease in heavy grazing increased (slope increased); the number of medium palatability species decreased, species abundance decreased in light and medium grazing, and increased in heavy grazing, e.g., Artemisia frigida and Potentilla chinensis (medium) (Figure 4d).
4. Discussion
4.1. Chemical Composition and Alkanes Recovery Rate
Grazing intensity is the management tool that truly drives ecosystem functioning. Pasture samples are taken from the remainder of the animal’s diet as well as from the new growth under grazing. Plant nutrient composition undergoes dramatic changes across seasons and plant physiological stages. Forage growth and nutrient content were at their peak in August at the Eurasian meadow steppe. In the case of heavy grazing, with few standing dead plants, mainly fresh new-growth grasses, the chemical composition of the fresh grass was characterized by low DM content and high CP (Table A1) [27].
Many studies have shown that the chain alkane patterns of both herbage and fecal output are greater even than odd alkanes. In addition, they show that the concentration of alkanes in fecal output is much greater than in plants, and demonstrate an increase in fecal recovery with increasing carbon chain length [12,23], which was consistent with the results of this study. However, the recoveries increase with the length of the chain and may be greater than 100% due to errors, such as C33 and C35. Our study found the highest concentrations of C31 and the highest recoveries in grazing cattle. However, different species have different n-alkane patterns—such as Artemisia frigida and Iris ventricose, which have the highest concentrations of C29 (unpublished data)—so the results of this study may only apply to temperate meadow grasslands.
4.2. Animal Intake and Digestibility
Most studies estimate livestock intake based on the analysis of representative samples of daily fecal output because it is laborious and prohibitive for large-scale experiments. Total collection of feces also obviates the need for markers to measure fecal output. Thus, in the current study, we collected total fecal output, fed the livestock without additional markers, and measured the alkanes in the feces to determine the amount of forage taken by the livestock. We found that as grazing intensity increases, the amount of dry matter taken by livestock gradually decreases. This may be explained that as grazing intensity increases, interspecific competition for livestock increases, the grazing resources will decrease, and livestock feed intake will decrease as observed in light grazing. Similarly, as grazing intensity increases, livestock forage time increases, single-mouth intake decreases, and highly palatable forage decrease, so daily intake decreases. Therefore, lower forage intake and animal nutritional status under high stocking rates are explained by lower forage availability [28]. We also found that livestock feed intake was highest in September, probably due to the fact that local temperatures in the study area were already significantly lower in September than in July and August and that livestock need to feed more to meet their energy requirements in cold conditions.
With respect to cattle digestibility, the month had a significant effect on the digestibility of livestock for all variable measuring compared to the grazing intensity. Digestibility reflects the nutritional quality of the forage, and the nutritional quality of forage varies considerably from month to month depending on the stage of growth, resulting in a significant effect of the month on nutrient digestibility for livestock. However, it was shown that grazing management also significantly affects livestock digestibility [29], i.e., livestock dry matter digestibility is significantly lower at low grazing rates than at high grazing rates. Our feeding trials showed that dry matter digestibility was higher in heavily grazed livestock than in light and medium grazing, however, there was no significant difference among them (Table 2). The free grazing trials showed no significant effect of grazing intensity on livestock dry matter digestibility (Table 3), probably because on the one hand, the feeding trials obtained vegetation consisting entirely of fresh grass above plant stems, even in heavy grazing. On the other hand, for livestock under free grazing in heavy intensity, the low digestibility of the forage may be due to the vegetation obtained in the heavy grazing trials being entirely fresh grass. However, the livestock were more likely to forage on ash due to the short vegetation under heavy grazing, resulting in low digestibility. This phenomenon was observed in our experiments from the measurement of acid-insoluble ash in forage and fecal output. In addition, on the other hand, the apparent nutrient digestibility of animals is, in general, correlated negatively with their dry matter intake. With sufficient precipitation in 2018, the effect of grazing intensity on livestock nutrient digestibility was weaker when herbage was enough available at each grazing gradient.
4.3. Factors Influencing Feed Intake
The most important factors determining intake are the quantity and quality of the forage provided. In pastures, these in turn are influenced by factors such as plant species composition, plant community structure, season (forage maturity), and the grazing history of the site in question. The composition of the plant, the energy level and palatability of the feed, and the physicochemical properties of the feed also affect the animal’s intake [30]. The metabolic energy requirements of heavier livestock are also higher, so their feed intake is also relatively high, which is consistent with the results of this study. We usually assume that livestock intake is closely related to grassland AGB, but this study found that livestock intake was poorly correlated with AGB. In our previous study, we found a negative correlation between livestock foraging time and AGB, and foraging intake [31], and therefore suggest that foraging intake may be indirectly related to AGB through livestock behavior (foraging time). Animal feed intake is also influenced by several factors, including age, experience, stress, disease, and external conditions for the same animal. Livestock weight is linearly correlated with livestock feed intake (R2 = 0.46, p < 0.01), which is due to the fact that heavier livestock also requires higher metabolic energy and so feed intake is relatively high. Due to the selective foraging of livestock, the palatability of different plants affects livestock foraging [32]. Consequently, we suggest that medium palatability plants were significantly associated with livestock intake, and heavy grazing increased the abundance of medium palatability plants (Figure 4c,d). Related studies have also shown that Artemisia frigida and Potentilla chinensis, as degradation indicator species, increased significantly under heavy grazing [33]. The abundance of highly palatable species such as Leymus chinensis decreased at an accelerated rate with increasing grazing intensity, and we have also found that the importance value of Leymus chinensis decreased significantly with increasing grazing intensity [34]. The dominant plant species are usually palatable, and grazing reduces their dominance in meadow steppe, which is consistent with our study [35].
Understanding the impacts of grazing on livestock intake may help improve our prediction for future livestock production and grassland dynamics. However, our study has some limitations. First and foremost, the studies included in our dataset were distributed in temperate regions. Our findings have, therefore, little capacity to predict livestock intake in typical steppe or alpine grasslands. Meanwhile, we measure individual livestock feed intake in the first half of each month, and it is undeniable that feed intake gradually increases as livestock gain weight (Figure 4b), so an underestimation of livestock feed intake throughout the month will occur. The second noteworthy point is that our selected studies were largely shorter than 3 years. This short duration may also influence our findings [32]. The lack of large and complete datasets from long-term studies likely limits our ability to better understand the long-term effects of grazing and seasons on livestock intake. Third, grazing intensity significantly affects livestock intake, but the effect of livestock grazing on ecosystem functioning (e.g., ANPP) was also regulated by environmental fluctuations, such as precipitation and nutrient availability [36]. Furthermore, we found that species richness of different palatability affects livestock intake, that intake is a major component of ANPP under grazing, and that the relationship between ANPP and species richness remains unclear [37].
5. Conclusions
Different grazing intensities and seasons can significantly alter livestock feed intake in temperate meadow grasslands. The livestock intake from light grazing was higher than from heavy grazing, and the daily intake of livestock was significantly higher in September than in July and August. The months had a greater effect on the dry matter and nutrient digestibility of livestock than grazing intensity. In addition, seasons had a significant effect on the digestibility of livestock for all nutrient variables compared to the grazing intensity. Livestock weight and medium-palatability species are more important for livestock intake. Our study provides a reference basis for the scientific management of grazing livestock and the rational use of grazing pastures. Given the difficulty of collecting fecal output from livestock, it is not always possible to collect fecal output to estimate livestock intake in the future and a model can be introduced to estimate this. This experiment also provides a database of future models of predicted intake.
Author Contributions
Conceptualization, X.X. and H.S.; methodology, H.S.; software, L.H., B.S., Q.Q.; investigation, L.H., A.I.A.A. and Y.M.Z.H.; data curation, L.H.; writing—original draft preparation, L.H.; writing—review and editing, X.X., R.Y., S.N., N.A., A.B., M.K., A.A., W.F., A.P. and W.Z.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.
Funding
This study was supported by the National Natural Science Foundation of China (32130070, 31971769), the National Key Research and Development Program of China (2021YFD1300500, 2021YFF0703904), Central Public-Interest Scientific Institution Basal Research Fund (1610132021016), Special Funding for the Modern Agricultural Technology System from the Chinese Ministry of Agriculture (CARS-34).
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
Chemical composition (mean ± SD, g/kg DM) and Gross energy (MJ/kg DM) of plant under different months and grazing intensities.
Table A1.
Chemical composition (mean ± SD, g/kg DM) and Gross energy (MJ/kg DM) of plant under different months and grazing intensities.
| Month | GI | DM | NDF | ADF | AIA | OM | C | CP | Gross Energy |
|---|---|---|---|---|---|---|---|---|---|
| 7 | G0.23 | 429.5 ± 20.83 | 592.05 ± 31.61 | 287.38 ± 26.98 | 18.22 ± 1.22 | 938.36 ± 2.73 | 454.57 ± 2.47 | 109.14 ± 20.26 | 18.21 ± 0.11 |
| 7 | G0.46 | 429.94 ± 35.01 | 575.77 ± 21.58 | 273.5 ± 22.12 | 20.77 ± 4.97 | 936.7 ± 3.13 | 457.43 ± 0.71 | 112.11 ± 13.09 | 18.09 ± 0.06 |
| 7 | G0.92 | 388.92 ± 28.64 | 571.93 ± 10.24 | 238.85 ± 5.86 | 22.65 ± 1.59 | 930.53 ± 0.39 | 453.77 ± 0.89 | 149.03 ± 14.41 | 18.01 ± 0.1 |
| 8 | G0.23 | 430.36 ± 12.23 | 607.53 ± 5.22 | 306.42 ± 11.22 | 24.92 ± 7.17 | 932.11 ± 1.98 | 456.42 ± 2.15 | 117.53 ± 11.03 | 18 ± 0.08 |
| 8 | G0.46 | 411.56 ± 49.47 | 565.76 ± 41.03 | 296.95 ± 20.15 | 26.75 ± 2.92 | 919.54 ± 3.89 | 451.1 ± 2.28 | 116.62 ± 9.81 | 17.92 ± 0.13 |
| 8 | G0.92 | 338.82 ± 31.18 | 540.17 ± 33.39 | 247.84 ± 18.21 | 29.45 ± 8.08 | 905.7 ± 13.96 | 450.2 ± 4.32 | 162.71 ± 14.08 | 17.52 ± 0.17 |
| 9 | G0.23 | 494.93 ± 15.48 | 581 ± 12.49 | 296.85 ± 7.76 | 27.19 ± 3.85 | 932.89 ± 3.34 | 456.45 ± 1.62 | 101.05 ± 10.74 | 18.29 ± 0.18 |
| 9 | G0.46 | 481.61 ± 49.01 | 550.17 ± 57.07 | 277.21 ± 36.13 | 39.69 ± 3.94 | 921.8 ± 1.79 | 451.43 ± 1.73 | 105.18 ± 9.14 | 17.53 ± 0.07 |
| 9 | G0.92 | 424.52 ± 55.78 | 507.83 ± 68.8 | 256.23 ± 33.26 | 31.83 ± 10.02 | 911.68 ± 5.05 | 449.7 ± 1.4 | 115.5 ± 5.16 | 17.33 ± 0.25 |
| Main effect | |||||||||
| Month | 7 | 416.122b | 579.915a | 266.577a | 20.544c | 935.197a | 455.258a | 123.425b | 18.099a |
| 8 | 393.582b | 571.152a | 283.736a | 27.042b | 919.114b | 452.572b | 132.287a | 17.817b | |
| 9 | 467.016a | 546.331a | 276.763a | 32.901a | 922.122b | 452.526b | 107.242a | 17.72b | |
| GI | G0.23 | 451.596A | 593.527A | 296.883A | 23.443A | 934.453A | 455.811A | 109.24B | 18.168A |
| G0.46 | 441.037A | 563.897AB | 282.55A | 29.07A | 926.013B | 453.317B | 111.302B | 17.848B | |
| G0.92 | 384.087B | 539.975B | 247.643B | 27.975A | 915.967C | 451.227B | 142.412A | 17.621C | |
| p-value | Month | 0.001 | 0.17 | 0.293 | 0.001 | <0.001 | 0.025 | 0.002 | <0.001 |
| GI | 0.002 | 0.024 | 0.001 | 0.107 | <0.001 | 0.001 | <0.001 | <0.001 | |
| Month ∗ GI | 0.818 | 0.739 | 0.875 | 0.382 | 0.093 | 0.026 | 0.145 | 0.001 |
Notes: Lower-case letters indicate differences between months and upper-case letters indicate differences between grazing intensities. The same as below.
Appendix B
Table A2.
Chemical composition (mean ± SD, g/kg DM) and Gross energy (MJ/kg DM) of fecal under different months and grazing intensities.
Table A2.
Chemical composition (mean ± SD, g/kg DM) and Gross energy (MJ/kg DM) of fecal under different months and grazing intensities.
| Month | GI | DM | NDF | ADF | AIA | OM | C | CP | Gross Energy |
|---|---|---|---|---|---|---|---|---|---|
| 7 | G0.23 | 163.91 ± 17.5 | 615.78 ± 10.87 | 366.56 ± 8.11 | 82.55 ± 8.44 | 856.23 ± 10.35 | 439.9 ± 7.1 | 131.19 ± 5.5 | 18.09 ± 0.18 |
| 7 | G0.46 | 177.73 ± 8.87 | 608.92 ± 26.16 | 361.05 ± 11.64 | 94.78 ± 6.75 | 844.25 ± 8.12 | 436.05 ± 6.03 | 130.45 ± 8.94 | 17.87 ± 0.37 |
| 7 | G0.92 | 168.95 ± 12.89 | 572.88 ± 19.15 | 350.37 ± 16.5 | 137.39 ± 17.85 | 783.33 ± 24.66 | 403.39 ± 14.27 | 143.05 ± 6.72 | 17.04 ± 0.33 |
| 8 | G0.23 | 165.11 ± 16.62 | 616.15 ± 14.06 | 389.84 ± 13.99 | 107.74 ± 10.28 | 827.17 ± 11.63 | 424.76 ± 19.57 | 123.05 ± 5.79 | 17.84 ± 0.58 |
| 8 | G0.46 | 179.94 ± 10.41 | 606.76 ± 32.26 | 374.72 ± 20.55 | 118.77 ± 17.26 | 813.48 ± 18.16 | 425.56 ± 5.07 | 124.97 ± 9.17 | 17.51 ± 0.37 |
| 8 | G0.92 | 169.3 ± 10.51 | 571.93 ± 41.66 | 372.13 ± 28.56 | 167.86 ± 20.71 | 755.25 ± 25.98 | 422.85 ± 7.35 | 133.05 ± 8.01 | 16.24 ± 0.57 |
| 9 | G0.23 | 182.14 ± 14.74 | 587.13 ± 36.56 | 367.15 ± 14.06 | 83.17 ± 5.69 | 852.46 ± 13.9 | 438.6 ± 5.83 | 109.55 ± 8.95 | 17.91 ± 0.2 |
| 9 | G0.46 | 195.02 ± 7.54 | 578.12 ± 38.36 | 370.12 ± 16.93 | 90.6 ± 7.33 | 837.1 ± 10.97 | 435.68 ± 3.9 | 113.66 ± 4.93 | 18.03 ± 0.23 |
| 9 | G0.92 | 191.27 ± 10.27 | 560.02 ± 31.38 | 360.06 ± 21.5 | 144.42 ± 15.31 | 775.4 ± 18.87 | 401.43 ± 10.25 | 126.3 ± 4.39 | 16.55 ± 0.65 |
| Main effect | |||||||||
| Month | 7 | 170.198b | 599.192a | 359.328b | 104.906b | 827.937a | 426.444a | 134.897a | 17.665a |
| 8 | 171.45b | 598.277a | 378.893a | 131.458a | 798.635b | 411.84b | 127.022b | 17.197b | |
| 9 | 189.476a | 575.09b | 365.777b | 106.063b | 821.654a | 425.234a | 116.503c | 17.497a | |
| GI | G0.23 | 170.387B | 606.351A | 374.517A | 91.152C | 845.287A | 434.686A | 121.262B | 17.946A |
| G0.46 | 184.232A | 597.932A | 368.63AB | 101.382B | 831.612B | 431.525A | 123.025B | 17.802A | |
| G0.92 | 176.505B | 568.275B | 360.852B | 149.893A | 771.327C | 397.307B | 134.134A | 16.611B | |
| p-value | Month | <0.001 | 0.013 | 0.002 | <0.001 | <0.001 | <0.001 | <0.001 | 0.002 |
| GI | 0.001 | <0.001 | 0.052 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | |
| Month ∗ GI | 0.931 | 0.88 | 0.75 | 0.819 | 0.994 | 0.993 | 0.651 | 0.138 |
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Figure 1.
Alkane extraction process.
Figure 2.
Gas chromatograph spectra of control, grass, and fecal samples.
Figure 3.
Daily dry matter intake and nutrients intake at different grazing intensities and months. The differences were analyzed using a two-way ANOVA with Duncan post hoc test. **: p ≤ 0.01, ***: p ≤ 0.001. (n = 24 each month).
Figure 3.
Daily dry matter intake and nutrients intake at different grazing intensities and months. The differences were analyzed using a two-way ANOVA with Duncan post hoc test. **: p ≤ 0.01, ***: p ≤ 0.001. (n = 24 each month).
Figure 4.
The relationship between livestock intake and vegetation. Cattle intake with AGB (a), liveweight (b), species richness (c), and species abundance (d).
Figure 4.
The relationship between livestock intake and vegetation. Cattle intake with AGB (a), liveweight (b), species richness (c), and species abundance (d).
Table 1.
Alkane concentrations (mg/kg DM) in fecal and grass samples.
| Fecal Alkane Concentrations | Grass Alkane Concentrations | |||||
|---|---|---|---|---|---|---|
| G0.23 | G0.46 | G0.92 | G0.23 | G0.46 | G0.92 | |
| C21 | 7.22 | 7.91 | 7.49 | 1.68 | 1.85 | 2.16 |
| C22 | 200.97 | 200.92 | 200.87 | 100.38 | 100.43 | 100.39 |
| C23 | 15.32 | 15.08 | 13.15 | 4.73 | 3.93 | 3.84 |
| C24 | 6.08 | 7.40 | 6.32 | 1.66 | 1.80 | 1.81 |
| C25 | 30.95 | 34.56 | 30.90 | 9.80 | 11.24 | 10.88 |
| C26 | 8.01 | 8.82 | 6.99 | 3.32 | 3.59 | 2.60 |
| C27 | 75.13 | 77.54 | 74.87 | 30.14 | 32.07 | 28.83 |
| C28 | 15.51 | 17.35 | 15.85 | 5.30 | 6.58 | 5.96 |
| C29 | 131.64 | 134.55 | 140.73 | 62.94 | 74.85 | 75.37 |
| C30 | 26.29 | 30.55 | 32.30 | 8.59 | 13.40 | 13.94 |
| C31 | 224.84 | 242.02 | 244.55 | 118.71 | 134.68 | 131.71 |
| C32 | 18.50 | 18.53 | 20.15 | 4.62 | 7.16 | 6.95 |
| C33 | 160.29 | 148.72 | 145.86 | 65.00 | 66.90 | 58.80 |
| C34 | 200.67 | 200.70 | 200.69 | 100.03 | 100.08 | 100.04 |
| C35 | 12.03 | 14.59 | 9.96 | 5.15 | 4.24 | 2.58 |
| Total | 731.80 | 757.62 | 749.09 | 321.64 | 362.28 | 345.42 |
| Total even chain | 657.42 | 674.97 | 667.49 | 298.15 | 329.75 | 314.17 |
Table 2.
Daily feed and dry fecal output weight, C31 concentration, and C31 recovery at different grazing intensities. (n = 3 each grazing intensity).
Table 2.
Daily feed and dry fecal output weight, C31 concentration, and C31 recovery at different grazing intensities. (n = 3 each grazing intensity).
| GI | Feed Intake (kg/d) | Fecal (kg/d) | Digestibility (%) | C31 (Grass) Concentration (mg/kg DM) | C31 (Fecal) Concentration (mg/kg DM) | C31 Recovery (%) |
|---|---|---|---|---|---|---|
| G0.23 | 4.61 ± 0.15 | 1.55 ± 0.27 | 66.15 ± 5.82 | 106.79 ± 0.72 | 246.35 ± 1.83 | 77.63 ± 12.30 a |
| G0.46 | 4.67 ± 0.24 | 1.68 ± 0.20 | 64.23 ± 2.49 | 145.34 ± 2.89 | 295.69 ± 3.16 | 75.25 ± 7.69 a |
| G0.92 | 3.49 ± 0.29 | 1.06 ± 0.22 | 70.33 ± 4.34 | 120.06 ± 0.78 | 334.36 ± 2.15 | 81.09 ± 5.80 a |
a indicates no significant difference between treatments.
Table 3.
Two-way ANOVA for dry matter (DM) digestibility and nutrient digestibility (%) of cattle at different grazing intensities and months, including NDF, ADF, C, CP, OM, GE, and ME, with ME calculated as ME/DMI. (n = 24 each month).
Table 3.
Two-way ANOVA for dry matter (DM) digestibility and nutrient digestibility (%) of cattle at different grazing intensities and months, including NDF, ADF, C, CP, OM, GE, and ME, with ME calculated as ME/DMI. (n = 24 each month).
| Month | GI | DM | OM | NDF | ADF | C | CP | GE | ME |
|---|---|---|---|---|---|---|---|---|---|
| 7 | G0.23 | 65.3 ± 3.66 | 68.34 ± 3.35 | 63.26 ± 3.62 | 56.18 ± 3.43 | 66.35 ± 3.67 | 56.07 ± 7.41 | 65.54 ± 3.63 | 9.79 ± 0.55 |
| 7 | G0.46 | 65.06 ± 1.3 | 68.53 ± 1.11 | 64.11 ± 1.04 | 53.97 ± 1.05 | 66.72 ± 1.17 | 58.79 ± 2.57 | 65.51 ± 1.23 | 9.72 ± 0.19 |
| 7 | G0.92 | 60.99 ± 1.18 | 67.23 ± 0.74 | 60.19 ± 1.23 | 42.73 ± 2.11 | 65.39 ± 0.87 | 62.4 ± 1.44 | 63.11 ± 1.06 | 9.32 ± 0.17 |
| 8 | G0.23 | 57 ± 4.86 | 61.84 ± 4.36 | 57.11 ± 4.93 | 45.68 ± 5.56 | 59.89 ± 4.59 | 54.55 ± 5.76 | 57.38 ± 4.87 | 8.48 ± 0.73 |
| 8 | G0.46 | 57.5 ± 2.89 | 62.42 ± 2.56 | 55.38 ± 2.65 | 46.31 ± 3.6 | 60.18 ± 2.71 | 54.17 ± 3.58 | 58.56 ± 2.73 | 8.6 ± 0.39 |
| 8 | G0.92 | 56.23 ± 1.4 | 63.53 ± 1.15 | 53.07 ± 2.51 | 33.76 ± 3.82 | 62.38 ± 1.28 | 64.11 ± 1.44 | 59.44 ± 1.35 | 8.54 ± 0.21 |
| 9 | G0.23 | 51.91 ± 6.23 | 55.97 ± 5.87 | 50.82 ± 6.53 | 40.73 ± 7.21 | 53.69 ± 6.16 | 48 ± 6.47 | 52.9 ± 6.13 | 7.92 ± 0.9 |
| 9 | G0.46 | 50.37 ± 2.37 | 54.87 ± 2.32 | 46.15 ± 3.28 | 32.69 ± 4.7 | 52.05 ± 2.41 | 46.43 ± 2.22 | 49.03 ± 2.29 | 7.05 ± 0.32 |
| 9 | G0.92 | 49.68 ± 4.69 | 57.36 ± 3.78 | 45.03 ± 3.3 | 30.31 ± 4.23 | 55.23 ± 3.98 | 44.85 ± 5.28 | 52.35 ± 3.86 | 7.42 ± 0.53 |
| Main effect | |||||||||
| Month | 7 | 63.60a | 68.00a | 62.42a | 50.31a | 66.13a | 59.47a | 64.62a | 9.59a |
| 8 | 56.90b | 62.69b | 54.95b | 41.45b | 60.93b | 58.00a | 58.60b | 8.55b | |
| 9 | 50.50c | 56.08c | 46.90c | 33.81c | 53.65c | 46.23b | 51.24c | 7.41c | |
| GI | G0.23 | 58.07A | 62.05A | 57.06A | 47.53A | 59.98A | 52.88A | 58.61A | 8.73A |
| G0.46 | 57.65A | 61.94A | 55.21A | 44.32A | 59.65A | 53.13A | 57.70A | 8.45A | |
| G0.92 | 55.64A | 62.71A | 52.76A | 35.60B | 61.00A | 57.12A | 58.30A | 8.43A | |
| p-value | Month | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
| GI | 0.61 | 0.93 | 0.28 | <0.001 | 0.84 | 0.33 | 0.93 | 0.69 | |
| Month ∗ GI | 0.97 | 0.96 | 0.95 | 0.67 | 0.94 | 0.48 | 0.82 | 0.77 |
Notes: Lower-case letters indicate differences between months and upper-case letters indicate differences between grazing intensities.
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Hou, L.; Xin, X.; Shen, B.; Qin, Q.; Altome, A.I.A.; Hamed, Y.M.Z.; Yan, R.; Nurlan, S.; Adilbek, N.; Balzhan, A.; et al. Effects of Long-Term Grazing on Feed Intake and Digestibility of Cattle in Meadow Steppe. Agronomy 2023, 13, 1760. https://doi.org/10.3390/agronomy13071760
AMA Style
Hou L, Xin X, Shen B, Qin Q, Altome AIA, Hamed YMZ, Yan R, Nurlan S, Adilbek N, Balzhan A, et al. Effects of Long-Term Grazing on Feed Intake and Digestibility of Cattle in Meadow Steppe. Agronomy. 2023; 13(7):1760. https://doi.org/10.3390/agronomy13071760
Chicago/Turabian StyleHou, Lulu, Xiaoping Xin, Beibei Shen, Qi Qin, Ahmed Ibrahim Ahmed Altome, Yousif Mohamed Zainelabdeen Hamed, Ruirui Yan, Serekpaev Nurlan, Nogayev Adilbek, Akhylbekova Balzhan, and et al. 2023. "Effects of Long-Term Grazing on Feed Intake and Digestibility of Cattle in Meadow Steppe" Agronomy 13, no. 7: 1760. https://doi.org/10.3390/agronomy13071760
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