The effect of stage of regrowth on the physical composition and nutritive value of the various vertical strata of kikuyu (Cenchrus clandestinus) pastures

A plot study was conducted at the Gatton Research Dairy, Queensland, Australia, to quantify the effects of 5 regrowth periods (9, 11, 14, 16 and 18 days) and 4 vertical strata on the composition and nutritive value of kikuyu (Cenchrus clandestinus) pastures using a block factorial design with 4 replicates. Pasture samples were analyzed for crude protein (CP), ethanol-soluble carbohydrates (ESC), acid detergent fiber (ADF), neutral detergent fiber (aNDFom), in vitro indigestible neutral detergent fibre (iNDF240) and minerals. Metabolizable energy (ME) was then calculated from the concentrations of other nutrients. Regardless of the stage of regrowth, stems were located mainly in the bottom 1 or 2 strata, while leaves were present mainly in the top 2 or 3 strata. CP, ESC and ME declined, but aNDFom, ADF and iNDF240 increased with stage of regrowth and from top to bottom of the swards (P<0.05). While herbage quality variables were affected by both factors, vertical stratum had a much larger impact on quality than stage of regrowth. These results indicate that grazing management of kikuyu pastures should be based not only on stage of regrowth but also on level of defoliation, as both have strong impacts on the nutritive value of the consumed forage

Use of \u3cem\u3eN\u3c/em\u3e-Alkane Technique to Estimate Sheep Dry Matter Intake

Given the complexity of evaluating intake on grazing, some compounds have been studied to promote qualitative and quantitative estimates of animal physiology. In this sense, the technique using n-alkanes as a marker has been used in several animal species, especially in grazing ruminants (Dove and Mayes 1996). By definition, validation under grazing or browsing conditions is not possible, because actual intakes are unknown (Dove and Mayes 2005). Thus, the aim of the study was to evaluate the methodology of n-alkanes to estimate herbage intake by sheep in metabolic cages

Methane Emissions by Lactating Ewes Grazing Italian Ryegrass

Agriculture contributes 13.5% of global emissions of greenhouse gases (GHG) (IPCC 2007), and about 50% of CH4 and 60% N2O from anthropogenic sources, while livestock contributes an additional 18% of global GHG emissions (FAO, 2006). Among the various sources with a potential negative impact on the environment, methane emissions for which livestock are mainly responsible have been highlighted for the agricultural sector. Studies on means to mitigate these emissions, and understand how integrated crop and livestock production systems may contribute to the reduction of greenhouse gases, are essential for the creation of public policies for environmental preservation. The objective in this study was to evaluate how strategies for grazing management can influence animal production and emission of methane in areas of crop-livestock integration

Italian Ryegrass Establishment by Self-Seeding in Integrated Crop-Livestock Systems: Effects of Grazing Management

Recent reports have indicated that integrated crop-livestock systems (ICLS) can enhance sustained crop and livestock production by efficiently using agricultural system resources (Liu et al. 2012). In the subtropical South American regions, soybean (Glycine max L. Merril) and maize (Zea Mays L.) crops are widely grown after Italian ryegrass (Lolium multiflorum Lam) pastures. In this system, the pasture may be established by self-seeding. Self-seedling reduces pasture production costs and extends the grazing period. The stoking method, and especially the grazing intensity, can greatly affect the quantity of seeds added to the soil by affecting the demography of the reproductive tillers. In subtropical areas where Italian ryegrass is used for winter pastures in ICLSs, the effects of crop rotation, stocking methods or grazing intensities on the subsequent ability of Italian ryegrass to self-seed are unknown. The objectives of this study are to evaluate the effects of management practices (crop rotation, stocking method and herbage allowance) on the establishment of Italian ryegrass pastures by self-seedling in an ICLS

Low-Intensity, High-Frequency Grazing Positively Affects Defoliating Behavior, Nutrient Intake and Blood Indicators of Nutrition and Stress in Sheep

The intensity and frequency of grazing affect the defoliating strategy of ruminants, their daily nutrient intake, thus nutrition and physiological status. Italian ryegrass (Lolium multiflorum Lam.) pastures were grazed by sheep either under a low-intensity/high-frequency grazing strategy (Rotatinuous stocking; RN) with nominal pre- and post-grazing sward heights of 18 and 11 cm, respectively, or under a high-intensity/low-frequency strategy (traditional rotational stocking; RT) with nominal pre- and post-grazing sward heights of 25 and 5 cm, respectively. Treatments were arranged under a complete randomized design and evaluated over two periods, in different years. In 2017, the aim was to depict the type of bites that sheep perform during the grazing-down and associate them to the grazing management strategy according to their relative contribution to the diet ingested. In 2018 we estimated the total nutrient intake and evaluated blood indicators of the nutritional status and immune response to stress of sheep. The bite types accounting the most for the diet ingested by RN sheep were those performed on the “top stratum” of plants with around 20, 15, and 25 cm, whereas the type of bites accounting the most for the diet of RT sheep were those performed on “grazed plants” with around 10, 5, and ≤ 3 cm. In 2018, the RN sheep increased by 18% the total organic matter (OM) intake and by 20–25% the intake of soluble nutrients (i.e., crude protein, total soluble sugars, crude fat), digestible OM and of metabolizable energy, and had 17.5, 18, and 6.1% greater blood concentration of glucose, urea nitrogen (BUN) and albumin, respectively, but 17% lower blood neutrophil-to-lymphocyte (N:L) ratio. Sheep grazing vegetative Italian ryegrass pastures under the low-intensity/high-frequency grazing strategy (RN) ingested a diet of better quality from bites allocated on the top stratum of plants, had greater intake of soluble nutrients and blood parameters positively associated with nutritional status and immune response to stress

Does grazing management provide opportunities to mitigate methane emissions by ruminants in pastoral ecosystems?

peer reviewedAgriculture, and livestock production in particular, is criticized for being a contributor to global environmental change, including emissions of greenhouse gases (GHG). Methane (CH4) from grazing ruminants accounts for most of livestock's carbon footprint because a large share of them are reared under suboptimal grazing conditions, usually resulting in both low herbage intake and animal performance. Consequently, the CH4 quota attributed to animal maintenance is spread across few or no animal outputs, increasing the CH4 intensity [g CH4/kg live weight (LW) gain or g CH4/kg milk yield]. In this review, the generalized idea relating tropical pastures with low quality and intrinsically higher CH4 intensity is challenged by showing evidence that emissions from animals grazing tropical pastures can equal those of temperate grasses. We demonstrate the medium-to-high mitigation potential of some grazing management strategies to mitigate CH4 emissions from grazing ruminants and stress the predominant role that sward canopy structure (e.g., height) has over animal behavioral responses (e.g., intake rate), daily forage intake and resulting CH4 emissions. From this ecological perspective, we identify a grazing management concept aiming to offer the best sward structure that allows animals to optimize their daily herbage intake, creating opportunities to reduce CH4 intensity. We show the trade-off between animal performance and CH4 intensity, stressing that mitigation is substantial when grazing management is conducted under light-to-moderate intensities and optimize herbage intake and animal performance. We conclude that optimizing LW gain of grazing sheep and cattle to a threshold of 0.14 and 0.7 kg/day, respectively, would dramatically reduce CH4 intensity to approximately 0.2 kg CH4/kg LW gain, as observed in some intensive feeding systems. This could represent a mitigation potential of around 55% for livestock commodities in pasture-based systems. Our results offer new insights to the debate concerning mitigation of environmental impacts of pastoral ecosystems

INOVAÇŌES NO MANEJO DE PASTAGEM COM PEQUENOS RUMINANTES: O PASTOREIO “ROTATÍNUO”

peer reviewe

Prediction of enteric methane emissions by sheep using an intercontinental database

Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions

Prediction of enteric methane emissions by sheep using an intercontinental database

Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions

Prediction of enteric methane emissions by sheep using an intercontinental database

Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions