How can nutrition models increase the production efficiency of sheep and goat operations?

Insights into the influence of diet and genetics on feed efficiency and meat production in sheep

Feed costs and carcass yields affect the profitability and sustainability of sheep production. Therefore, it is crucial to select animals with a higher feed efficiency and high-quality meat production. This study focuses on the impact of dietary and genetic factors on production traits such as feed efficiency, carcass quality, and meat quality. Diets promote optimal sheep growth and development and provide sufficient protein can lead to higher-quality meat. However, establishing an optimized production system requires careful consideration and balance of dietary parameters. This includes ensuring adequate protein intake and feeding diets with higher intestinal absorption rates to enhance nutrient absorption in the gut. The study identifies specific genes, such as Callipyge, Calpastatin, and Myostatin, and the presence of causal mutations in these genes, as factors influencing animal growth rates, feed efficiency, and meat fatty acid profiles. Additionally, variants of other reported genes, including PIGY, UCP1, MEF2B, TNNC2, FABP4, SCD, FASN, ADCY8, ME1, CA1, GLIS1, IL1RAPL1, SOX5, SOX6, and IGF1, show potential as markers for sheep selection. A meta-analysis of reported heritability estimates reveals that residual feed intake (0.27 ± 0.07), hot carcass weight (0.26 ± 0.05), dressing percentage (0.23 ± 0.05), and intramuscular fat content (0.45 ± 0.04) are moderately to highly heritable traits. This suggests that these traits are less influenced by environmental factors and could be improved through genetic selection. Additionally, positive genetic correlations exist between body weight and hot carcass weight (0.91 ± 0.06), dressing percentage (0.35 ± 0.15), and shear force (0.27 ± 0.24), indicating that selecting for higher body weight could lead to favorable changes in carcass quality, and meat quality.

Factors affecting superovulation induction in goats (Capra hericus): An analysis of various approaches

Goats are generally called a "poor man's cow" because they not only provide meat and milk but also other assistance to their owners, including skins for leather production and their waste, which can be used as compost for fertilizer. Multiple ovulation and embryo transfer (MOET) is an important process in embryo biotechnology, as it increases the contribution of superior female goats to breeding operations. The field of assisted reproductive biotechnologies has seen notable progress. However, unlike in cattle, the standard use of superovulation and other reproductive biotechnologies has not been widely implemented for goats. Multiple intrinsic and extrinsic factors can alter the superovulatory response, significantly restricting the practicability of MOET technology. The use of techniques to induce superovulation is a crucial step in embryo transfer (ET), as it accelerates the propagation of animals with superior genetics for desirable traits. Furthermore, the conventional superovulation techniques based on numerous injections are not appropriate for animals and are labor-intensive as well as expensive. Different approaches and alternatives have been applied to obtain the maximum ovarian response, including immunization against inhibin and the day-0 protocol for the synchronization of the first follicular wave. While there are several studies available in the literature on superovulation in cattle, research on simplified superovulation in goats is limited; only a few studies have been conducted on this topic. This review describes the various treatments with gonadotropin that are used for inducing superovulation in various dairy goat breeds worldwide. The outcomes of these treatments, in terms of ovulation rate and recovery of transferrable embryos, are also discussed. Furthermore, this review also covers the recovery of oocytes through repeated superovulation from the same female goat that is used for somatic cell nuclear transfer (SCNT).

Effects of the Level and Composition of Concentrate Supplements before Breeding and in Early Gestation on Production of Different Hair Sheep Breeds

Female hair sheep, 27 Dorper (DOR), 41 Katahdin (KAT), and 39 St. Croix (STC), were used to determine influences of the nutritional plane before breeding and in early gestation on feed intake, body weight, body condition score, body mass indexes, blood constituent concentrations, and reproductive performance. There were 35 multiparous and 72 primiparous sheep, with initial ages of 5.6 ± 0.25 years and 1.5 ± 0.01 years, respectively (average overall initial age of 2.8 ± 0.20 years). Wheat straw (4% crude protein; dry matter [DM] basis) was consumed ad libitum and supplemented with approximately 0.15% initial body weight (BW) of soybean meal (LS) or a 1:3 mixture of soybean meal and rolled corn at 1% BW (HS; DM). The supplementation period was 162 days, with the breeding of animals in two sets sequentially, with the pre-breeding period 84 and 97 days, and that after breeding began at 78 and 65 days, respectively. Wheat straw DM intake (1.75, 1.30, 1.57, 1.15, 1.80, and 1.38% BW; SEM = 0.112) was lower (p < 0.05), but average daily gain (-46, 42, -44, 70, -47, and 51 g for DOR-LS, DOR-HS, KAT-LS, KAT-HS, STC-LS, and STC-HS, respectively; SEM = 7.3) was greater (p < 0.05) for HS than LS treatment during the supplementation period. Additionally, changes in body condition score during the supplementation period (-0.61, 0.36, -0.53, 0.27, -0.39, and -0.18; SEM = 0.058), and changes in body mass index based on height at the withers and body length from the point of the shoulder to the pin bone (BW/[height × length], g/cm²) from 7 days before supplementation (day -7) to day 162 were -1.99, 0.07, -2.19, -0.55, -2.39, and 0.17 for DOR-LS, DOR-HS, KAT-LS, KAT-HS, STC-LS, and STC-HS, respectively; (SEM = 0.297) were affected by supplement treatment. All blood constituent concentrations and characteristics addressed varied with the day of sampling (-7, 14, 49, 73, and 162) as well as the interaction between the supplement treatment and the day (p < 0.05), with few effects of interactions involving breed. Birth rate (66.7, 93.5, 84.6, 95.5, 82.8, and 100.0; SEM = 9.83) and individual lamb birth weight (4.50, 4.61, 4.28, 3.98, 3.73, and 3.88 kg; SEM = 0.201) were not affected by supplement treatment (p = 0.063 and 0.787, respectively), although litter size (0.92, 1.21, 1.17, 1.86, 1.12, and 1.82; SEM = 0.221) and total litter birth weight (5.84, 5.74, 5.92, 7.52, 5.04, and 6.78 kg for DOR-LS, DOR-HS, KAT-LS, KAT-HS, STC-LS, and STC-HS, respectively; SEM = 0.529) were greater (p < 0.05) for HS than for LS. In conclusion, although there was some compensation in wheat straw intake for the different levels of supplementation, soybean meal given alone rather than with cereal grain adversely affected BW, BCS, BMI, and reproductive performance, the latter primarily through litter size but also via a trend for an effect on the birth rate. Hence, the supplementation of low-protein and high-fiber forage such as wheat straw should include a consideration of the inclusion of a feedstuff(s) high in energy in addition to nitrogen.

Dry Matter Intake Prediction from Milk Spectra in Sarda Dairy Sheep

Individual dry matter intake (DMI) is a relevant factor for evaluating feed efficiency in livestock. However, the measurement of this trait on a large scale is difficult and expensive. DMI, as well as other phenotypes, can be predicted from milk spectra. The aim of this work was to predict DMI from the milk spectra of 24 lactating Sarda dairy sheep ewes. Three models (Principal Component Regression, Partial Least Squares Regression, and Stepwise Regression) were iteratively applied to three validation schemes: records, ewes, and days. DMI was moderately correlated with the wavenumbers of the milk spectra: the largest correlations (around ±0.30) were observed at ~1100-1330 cm^-1 and ~2800-3000 cm^-1. The average correlations between real and predicted DMI were 0.33 (validation on records), 0.32 (validation on ewes), and 0.23 (validation on days). The results of this preliminary study, even if based on a small number of animals, demonstrate that DMI can be routinely estimated from the milk spectra.

Embryo production and transplantation in non-breeding season of meat sheep breeds by stimulating superovulation with different follicle-stimulating hormone preparations

The objective of this study was to investigate the response of Charolais and Ile-de-France meat sheep breeds to stimulate superovulation with various follicle-stimulating hormone (FSH) preparations. A total of 14 adult ewes of meat sheep breeds were used in our study as donors, including Charolais breed (n = 8) and Ile-de-France breed (n = 6). Donors ewes were randomly divided into two groups in equal numbers (first group, n = 7; second group, n = 7), every group included Charolais breed (n = 4) and Ile-de-France breed (n = 3). Ewes in the first group were treated with Folltropin-V (total dose of 200 mg per ewe, seven injections), and ewes in the second group were treated with FSH-P (total of 280 IU per ewe, six injections). Thirty-seven ewes of Edilbay breed used as recipients were divided into two groups (first group, n = 20; second group, n = 17). Our results showed that the number of corpora lutea in donor groups treated with Folltropin-V was significantly higher than in donor groups treated with FSH-P (p < .01). A greater number of embryos recovery and embryos suitable for transplantation were found in the first group compared with the second group of donors. After 30 days from transplantation, transabdominal ultrasonography showed that the presence of pregnancy in recipients groups was found in 16 recipient ewes (43.2%), in the first group of recipients were registered nine pregnant ewes of 20 recipient ewes (45.0%), and in the second group of recipients were registered seven pregnant ewes of 17 recipient ewes (41.2%). In conclusion, using Folltropin-V in Charolais and Ile-de-France meat sheep breeds is a more effective scheme for stimulating superovulation than using FSH-P.

Invited review: integration of technologies and systems for precision animal agriculture-a case study on precision dairy farming

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence on the health, security, and welfare of animals.

Sustainability in the Sardinian sheep sector: A systems perspective, from good practices to policy

Three million sheep raised on 10 000 active farms operating in traditional and innovative farming systems in Sardinia, Italy, account for 13% of sheep milk production in the European Union (EU). Almost all of the milk delivered is processed to sheep cheese and is destined for world trade. The Sardinian dairy sheep sector also emits approximately 1600 kt CO₂ eq/year, approximately 60% of regional livestock greenhouse gas (GHG), prompting the need for regional mitigation plans. The SheepToShip LIFE project (EU-LIFE Climate Change Action 2014-2020) is a regional case study to test emission mitigation strategies. Based on the SheepToShip LIFE findings, this paper presents a systems perspective against the backdrop of the Sustainable Development Goals (SDGs) framework, with the aim of underlining system interlinkages between environmental, societal, and economic objectives. The project included (i) a life cycle thinking approach featuring environmental and socioeconomic traits of 18 sheep farms, (ii) on-farm implementation and demonstration of eco-innovative mitigation techniques indicating the most viable actions to reduce impact, (iii) focus groups discussing beliefs and reactions of the main stakeholders, and (iv) group model building producing a causal loop diagram from a systems thinking approach and exploring insights for regional policy-making that aligns with the SDGs. Causal links connect public interventions and stakeholder interaction (SDG 17) to boost farm eco-innovations (SDGs 9 and 8) and education and farmer training (SDG 4), and they foster efficient production (SDG 12) and high-quality food provisioning (SDG 2). These benefits contribute to climate change mitigation (SDG 13), water quality (SDG 6), and farm ecosystem services (SDG 15). Integr Environ Assess Manag 2022;18:1187-1198. © 2022 SETAC.

Towards Improving the Outcomes of Multiple Ovulation and Embryo Transfer in Sheep, with Particular Focus on Donor Superovulation

Considerable improvements in sheep multiple ovulation and embryo transfer (MOET)protocols have been made; however, unlike for cattle, MOET is poorly developed in sheep, and thus has not been broadly applicable as a routine procedure. The tightly folded nature of the ewe cervix, the inconsistent ovarian response to various superovulatory treatments, and the requirement of labor to handle animals, particularly during large-scale production, has limited the implementation of successful MOET in sheep. Moreover, several extrinsic factors (e.g., sources, the purity of gonadotrophins and their administration) and intrinsic factors (e.g., breed, age, nutrition, reproductive status) severely limit the practicability of MOET in sheep and other domestic animals. In this review, we summarize the effects of different superovulatory protocols, and their respective ovarian responses, in terms of ovulation rate, and embryo recovery and transfer. Furthermore, various strategies, such as inhibin immunization, conventional superovulation protocols, and melatonin implants for improving the ovarian response, are discussed in detail. Other reproductive techniques and their relative advantages and disadvantages, such as artificial insemination (AI), and donor embryo recovery and transfer to the recipient through different procedures, which must be taken into consideration for achieving satisfactory results during any MOET program in sheep, are also summarized in this article.

Efficiency Analysis as a Tool for Revealing Best Practices and Innovations: The Case of the Sheep Meat Sector in Europe

Simple Summary

The European sheep meat sector faces technical, market and financial challenges that threaten its economic performance and overall sustainability. At the same time, the sector is characterized by poor and slow adoption of innovations that could help towards facing these challenges. In this study, the technical efficiency of extensive, semi-intensive and intensive sheep meat farms in France, Spain and the UK was explored to reveal the profile of the most efficient ones and identify the best practices and innovations that these farms apply. The most efficient sheep meat farms reared large flocks, used available infrastructure at full capacity and managed human labor in a rational way. These best farms emphasized feeding and grazing innovations, marketing strategies, breeding programs and use of digital technologies. The uptake of such practices and innovations by farms of similar production systems could help to increase the productivity and economic performance of the sheep meat sector.

Abstract

The slow adoption of innovations is a key challenge that the European sheep sector faces for its sustainability. The future of the sector lies on the adoption of best practices, modern technologies and innovations that can improve its resilience and mitigate its dependence on public support. In this study, the concept of technical efficiency was used to reveal the most efficient sheep meat farms and to identify the best practices and farm innovations that could potentially be adopted by other farms of similar production systems. Data Envelopment Analysis was applied to farm accounting data from 458 sheep meat farms of intensive, semi-intensive and extensive systems from France, Spain and the UK, and the structural and economic characteristics of the most efficient farms were analyzed. These best farmers were indicated through a survey, which was conducted within the Innovation for Sustainable Sheep and Goat Production in the Europe (iSAGE) Horizon 2020 project, the management and production practices and innovations that improve their economic performance and make them better than their peers.

Nutrient requirement equations for Indian goat by multiple regression analysis and least cost ration formulation using a linear and non-linear stochastic model

An elaborate multiple regression analysis was done to arrive a nutrient requirement equation for goat including dry matter intake, DMI (kg/day), total digestible nutrient, TDN (g/day) and crude protein, CP (g/day) based on animal body weight (BW)(kg) and average daily gain (ADG)(g/day). The derived equations were highly significant (p < 0.001) and had high R² (0.99) values. The estimated values of TDN, CP and DMI are compared with NRC (1981), Kearl (Nutrient Requirements of Ruminants in Developing Countries, All Graduate Theses and Dissertations, 1982), as well as ICAR (Livestock Management, 2013). The estimated total TDN and CP requirements at different body weights and ADG are close to the values of recommended feeding standards of Mandal et al. (Small Ruminant Res., 58, 2005, 201). The estimated DMI values are close to the values of ICAR (Livestock Management, 2013) but lower (26.5%-43.8%) as compared to NRC (1981). Regressed values are used to develop a linear programming (LP) model and a stochastic model (SM) for least-cost ration formulation for the Indian goat breed, whose average BW is about 45 kg and ADG is 130 (g/day), and which is solved using LP simplex and Generalised Reduced Gradient (GRG) nonlinear of Microsoft Excel. The models satisfy the nutrient requirement calculated by regression equations with minimum specified level of variation (usually 5%-10%) in CP and TDN. Both methods adequately meet the nutritional requirements. Therefore, an electronic sheet is developed in Excel to calculate DMI, TDN and CP for different body weights, ADG and formulate the ration by LP and stochastic model.

Review: Managing sheep and goats for sustainable high yield production

This review discusses the most relevant aspects of nutritional, reproductive and health management, the three pillars of flock efficiency, production and sustainability regarding the intensification of production in sheep and goats. In small ruminants, reproductive management is dependent on seasonality, which in turn depends on breed and latitude. Nutrition represents the major cost for flocks and greatly affects their health, the quality of their products and their environmental impact. High-yielding sheep and goats have very high requirements and dietary intake, requiring nutrient-dense diets and sophisticated nutritional management that should always consider the strong interrelationships among nutrition, immunity, health, reproduction, housing and farm management. The reproductive pattern is to a great extent assisted by out-of-season breeding, facilitating genetic improvement schemes, and more recently by advanced reproductive technologies. Heath management aims to control or eradicate economic and zoonotic diseases, ensuring animal health and welfare, food safety and low ecosystem and environmental impacts in relation to chemical residues and pathogen circulation. In highly producing systems, nutrition, genetic and hazard factors assume a complex interrelationship. Genomic and management improvement research and technological innovation are the keys to sustain sheep and goat production in the future.

Nanotechnology and Reproductive Management of Farm Animals: Challenges and Advances

Reproductive efficiency of farm animals has central consequences on productivity and profitability of livestock farming systems. Optimal reproductive management is based on applying different strategies, including biological, hormonal, nutritional strategies, as well as reproductive disease control. These strategies should not only guarantee sufficient reproductive outcomes but should also comply with practical and ethical aspects. For example, the efficiency of the biological- and hormonal-based reproductive strategies is mainly related to several biological factors and physiological status of animals, and of nutritional strategies, additional factors, such as digestion and absorption, can contribute. In addition, the management of reproductive-related diseases is challenged by the concerns regarding the intensive use of antibiotics and the development of antimicrobial resistant strains. The emergence of nanotechnology applications in livestock farming systems may present innovative and new solutions for overcoming reproductive management challenges. Many drugs (hormones and antibiotics), biological molecules, and nutrients can acquire novel physicochemical properties using nanotechnology; the main ones are improved bioavailability, higher cellular uptake, controlled sustained release, and lower toxicity compared with ordinary forms. In this review, we illustrate advances in the most common reproductive management strategies by applying nanotechnology, considering the current challenges of each strategy.

Detection of Antimicrobial Resistance of Bacteria Staphylococcus chromogenes Isolated from Sheep's Milk and Cheese

Antimicrobial and multidrug resistance is detected in nonaureus staphylococci, including Staphylococcus chromogenes, which commonly causes intramammary infections. Recent clinical studies point to the presence of methicillin-resistant S. chromogenes. Therefore, this study aims to determine the prevalence of this species in samples of sheep‘s milk and cheeses made from them. Isolates were identified by polymerase chain reaction and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF). A total of 208 staphylococcal isolates were identified. Of these, 18% were identified as S. chromogenes. The antimicrobial resistance of the identified isolates was determined using the agar dilution method against penicillin, ceftaroline, teicoplanin, gentamicin, erythromycin, tetracycline, and ofloxacin. The highest resistance was found to penicillin (95%), tetracycline (86%), and oxacillin (81%). The highest sensitivity was confirmed for gentamicin (55%). The study also confirmed the presence of methicillin resistant staphylococcal isolates (30%) based on the phenotypic manifestation of antimicrobial resistance and detection of the presence of the mecA gene. The study shows that the tested isolates (62%) were multidrug resistant. Resistance to two antibiotics was most often found (39%).

The effect of dilution rate and successive semen collections on sperm quality and sexual motivation of sexually mature South African Merino rams

This study investigated whether dilution rate and successive semen collections influenced sperm viability, morphology, motility and male sexual motivation in sexually mature South African Merino rams (SAMR). Semen was collected from 11 rams up to either sperm or behavioural exhaustion. Semen was then immediately serial diluted to make 0, 2, 4, 8 and 16× dilutions with Hams F10 diluent. Following dilution, sperm motility was evaluated using computer-assisted sperm analysis (SCA®), while sperm morphology and viability were assessed using nigrosin-eosin staining and SYBR14/PI, respectively. Male sexual motivation was recorded by reaction time to first mount, while male dexterity was calculated as the ratio of mounts to ejaculations. An increase of dilution rate did not affect sperm motility (P > 0.05) but resulted in a significant decrease in the percentage of live normal sperm (P < 0.05). Furthermore, while sperm concentration and number decreased with semen collection number (P < 0.05), no effect was detected on sperm viability, morphology and motility (P > 0.05), except for average curvilinear velocity which showed a biphasic trend (P < 0.05). Mating success and reaction time were negatively affected by successive semen collections (P < 0.05), while male dexterity was unaffected (P > 0.05). Nevertheless, relatively high numbers of motile sperm (>2 billion) were collected up to the 4th successive semen collection, with a short initial reaction period (<25 s) and good success rate (>65%). SAMR thus withstood frequent semen collections without affecting their sperm reserves or sexual motivation. Further studies are required to investigate optimal conditions for semen collection and artificial insemination in this breed.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Influence of nutrient restriction on finishing Morada Nova lambs

Our objective was to evaluate the effect of 15% crude protein (CP) and total digestible nutrient (TDN) restriction vis-á-vis that recommended by NRC (2007) on feed intake, digestibility, and average daily gain (ADG) of confined Morada Nova lambs. Twenty lambs with 18.8 ± 2.8 kg liveweight were assigned to a randomized 2 × 2 factorial experiment with either early- or late-maturity feed formulation according to "Nutrient Requirements of Small Ruminants" with or without 15% CP and TDN restriction. The early-maturity diet, independent of restriction, resulted in greater dry matter and organic matter intake and increased rumination efficiency and feed conversion. Early-maturity diets also had the greatest nutrient digestibility as well as ADG and total gains. With late-maturity diets, independent of restriction, there were greater CP and TDN digestibilities as well as neutral detergent fiber (NDF) feed efficiency. However, when 15% of nutrients were restricted for late-maturity lamb diets, there was greater NDF intake and greater ether extract digestibility. Unrestricted early-maturity lamb diets was the better diet for confined Morada Nova lambs. Attention should be given to minimum effective NDF consumed and physically effective for this diet formulation. The use of late-maturity diets as recommended by NRC (2007) allows for 15% restriction of CP and TDN in Morada Nova lambs without negative effects on performance. This restriction should be tested in other tropical breeds to determine wider application.

Rumen Microbiome Composition Is Altered in Sheep Divergent in Feed Efficiency

Rumen microbiome composition and functionality is linked to animal feed efficiency, particularly for bovine ruminants. To investigate this in sheep, we compared rumen bacterial and archaeal populations (and predicted metabolic processes) of sheep divergent for the feed efficiency trait feed conversion ratio (FCR). In our study 50 Texel cross Scottish Blackface (TXSB) ram lambs were selected from an original cohort of 200 lambs. From these, 26 were further selected for experimentation based on their extreme FCR (High Feed Efficiency, HFE = 13; Low Feed Efficiency, LFE = 13). Animals were fed a 95% concentrate diet ad libitum over 36 days. 16S rRNA amplicon sequencing was used to investigate the rumen bacterial and archaeal communities in the liquid and solid rumen fractions of sheep divergent for FCR. Weighted UniFrac distances separated HFE and LFE archaea communities from the liquid rumen fraction (Permanova, P < 0.05), with greater variation observed for the LFE cohort (Permdisp, P < 0.05). LFE animals exhibited greater Shannon and Simpson diversity indices, which was significant for the liquid rumen fraction (P < 0.05). Methanobrevibacter olleyae (in liquid and solid fractions) and Methanobrevibacter millerae (liquid fraction) were differentially abundant, and increased in the LFE cohort (P.adj < 0.05), while Methanobrevibacter wolinii (liquid fraction) was increased in the HFE cohort (P.adj < 0.05). This suggests that methanogenic archaea may be responsible for a potential loss of energy for the LFE cohort. Bacterial community composition (Permanova, P > 0.1) and diversity (P > 0.1) was not affected by the FCR phenotype. Only the genus Prevotella 1 was differentially abundant between HFE and LFE cohorts. Although no major compositional shifts of bacterial populations were identified amongst the feed efficient cohorts (FDR > 0.05), correlation analysis identified putative drivers of feed efficiency with Ruminococcaceae UCG-014 (liquid, rho = −0.53; solid, rho = −0.56) and Olsenella (solid, rho = −0.40) exhibiting significant negative association with FCR (P < 0.05). Bifidobacterium and Megasphaera showed significant positive correlations with ADG. Major cellulolytic bacteria Fibrobacter (liquid, rho = 0.43) and Ruminococcus 1 (liquid, rho = 0.41; solid, rho = 41) correlated positively with FCR (P < 0.05). Our study provides evidence that feed efficiency in sheep is likely influenced by compositional changes to the archaeal community, and abundance changes of specific bacteria, rather than major overall shifts within the rumen microbiome.

An improved algorithm for solving profit-maximizing cattle diet problems

Feeding cattle with on-pasture supplementation or feedlot diets can increase animal efficiency and system profitability while minimizing environmental impacts. However, cattle system profit margins are relatively small and nutrient supply accounts for most of the costs. This paper introduces a nonlinear profit-maximizing diet formulation problem for beef cattle based on well-established predictive equations. Nonlinearity in predictive equations for nutrient requirements poses methodological challenges in the application of optimization techniques. In contrast to other widely used diet formulation methods, we develop a mathematical model that guarantees an exact solution for maximum profit diet formulations. Our method can efficiently solve an often-impractical nonlinear problem by solving a finite number of linear problems, that is, linear time complexity is achieved through parametric linear programming. Results show the impacts of choosing different objective functions (minimizing cost, maximizing profit and maximizing profit per daily weight gain) and how this may lead to different optimal solutions. In targeting improved ration formulation on feedlot systems, this paper demonstrates how profitability and nutritional constraints can be met as an important part of a sustainable intensification production strategy.

Processing induced changes in physicochemical structure properties and nutrient metabolism and their association in cool-season faba (CSF: Vicia L.), revealed by vibrational FTIR spectroscopy with chemometrics and nutrition modeling techniques

This review aims to update recent progress in processing induced molecular structure changes in the association of physicochemical structure properties with nutritional metabolism in cool-season faba bean (Vicia L.), which was revealed using advanced vibrational molecular spectroscopy in combination with chemometrics and advanced nutrient modeling techniques. The review focused on strategies to improve the utilization of the cool-season faba bean through heat-related technological treatments and the relationship of the processing induced molecular structural changes to nutrient delivery and metabolism in ruminant systems. The updated methods with truly absorption nutrient modeling techniques and advanced vibrational molecular spectroscopy techniques sourced by globar and synchrotron radiation (e.g. NIR, near Infrared, FTIR, Fourier transform infrared, DRIFT, diffuse reflectance infrared Fourier transform, ATR-FTIR, attenuated total reflectance-FTIR, FTIRM, FTIR micro-spectroscopy, SR-FTIRM, synchrotron radiation- FTIRM) to study cool-season faba bean were reviewed. This article provides an insight and a new approach on how to combine advanced nutrient modeling techniques with cutting-edge vibrational molecular spectroscopic techniques to study the processing induced molecular structure change in relation to molecular nutrition of cool-season Vicia faba as well as the interaction between molecular structure and molecular nutrition.