| Literature DB >> 35405874 |
Severiano R Silva1, Laura Sacarrão-Birrento2, Mariana Almeida1, David M Ribeiro2, Cristina Guedes1, José Ramiro González Montaña3, Alfredo F Pereira4, Konstantinos Zaralis5, Ana Geraldo4, Ouranios Tzamaloukas6, Marta González Cabrera7, Noemí Castro7, Anastasio Argüello7, Lorenzo E Hernández-Castellano7, Ángel J Alonso-Diez3, María J Martín8, Luis G Cal-Pereyra9, George Stilwell10, André M de Almeida2.
Abstract
Sheep and goat extensive production systems are very important in the context of global food security and the use of rangelands that have no alternative agricultural use. In such systems, there are enormous challenges to address. These include, for instance, classical production issues, such as nutrition or reproduction, as well as carbon-efficient systems within the climate-change context. An adequate response to these issues is determinant to economic and environmental sustainability. The answers to such problems need to combine efficiently not only the classical production aspects, but also the increasingly important health, welfare, and environmental aspects in an integrated fashion. The purpose of the study was to review the application of technological developments, in addition to remote-sensing in tandem with other state-of-the-art techniques that could be used within the framework of extensive production systems of sheep and goats and their impact on nutrition, production, and ultimately, the welfare of these species. In addition to precision livestock farming (PLF), these include other relevant technologies, namely omics and other areas of relevance in small-ruminant extensive production: heat stress, colostrum intake, passive immunity, newborn survival, biomarkers of metabolic disease diagnosis, and parasite resistance breeding. This work shows the substantial, dynamic nature of the scientific community to contribute to solutions that make extensive production systems of sheep and goats more sustainable, efficient, and aligned with current concerns with the environment and welfare.Entities:
Keywords: PLF; extensive; goat; omics; sheep; technology; welfare
Year: 2022 PMID: 35405874 PMCID: PMC8996830 DOI: 10.3390/ani12070885
Source DB: PubMed Journal: Animals (Basel) ISSN: 2076-2615 Impact factor: 2.752
Summary of research work to assess behavior, lambing time, lameness and live weight in sheep by kinematic and kinetic approaches.
| WMT | n/Breed | Aim | Technique | Results | Ref. |
|---|---|---|---|---|---|
| B | Cheviot ewes | Determine if different behavior types associated with grazing | Sensor accelerometer-integrated collars | Accuracy 90% | [ |
| B | 29 Scottish Blackface ewes | General activity and circadian rhythm of activity with sheep body weight change | Sensor accelerometer-integrated collars | [ | |
| B | 3 Merino | Behavioral and movement patterns of individuals | Tri-axial sensors, temperature sensor, and GPS | Accuracy > 75% | [ |
| B | 6 sheep | Continuous surveillance of eating behavior for monitoring ruminant health, productivity, and welfare | Tri-axial gyroscope and tri-axial accelerometer | Accuracy > 86% | [ |
| B | 50 Merino | Low-cost solution to monitoring of the location of all the animals in a herd and the continuous updating of location data | GPS collars (25 ewes) and BLE (25 ewes) | [ | |
| B | 10 Norwegian White | System that automatically generated individual animal behavior and localization | Real-time sensor tags and tri-axial accelerometers (ST LIS2DE) | SE = 98.16% (standing); SE = 100% (lying) | [ |
| B | Serra da Estrela breed | Autonomous system to control sheep posture and monitor their location in real-time. | Collar set of sensors (inertial and ultrasound) and a microcontroller and actuators (i.e., stimulation devices, namely sound and electrostatic) | [ | |
| La | 40 ewes | Predictive model to identify the day of lambing in extensive sheep | GNSS tracking collars | Accuracy 83.0%, SE = 63.6%, SP = 84.1% | [ |
| La | 39 Merino | Monitor changes in sheep behavior around the time of lambing | Accelerometer ear-tags (Axivity AX3) | [ | |
| L | 10 Merino Poll Dorset ewes | Ability of a tri-axial accelerometer to discriminate between sound and lame gait | Accelerometers (GCDC X16) on 3 points: neck collar, ear, and leg | Accuracy 82% (ear), 35% (collar), and 87% (leg) | [ |
| L | 20 various breeds | Relationship between sheep hoof-health status and the load a sheep distributes to each hoof | Hoof weigh crate raceway two strain-gauge cantilever load cells | SE = 100%, SP = 95% | [ |
| LW | 4 flocks | LW as an indicator of nutritional status | WoW | Repeatability 0.20–0.76 | [ |
| LW | 900 ewes | Ewe performance of two different methods of feed allocation | Automatic weigh and drafting crates coupled with EID technology | Accuracy 52% | [ |
| LW | Romane ewes | LW data were recorded as each ewe entered voluntarily and walked throughout the WoW | WoW | Accuracy 0.89 and 0.98 | [ |
WMT: Welfare/management target; B: behavior; La: lambing; L: lameness: LW: live weight; GPS: global positioning system; SE: sensitivity = true positive/(true positive + false negative) × 100; SP: specificity = true negative/(true negative + false positive) × 100; BLE: low-cost Bluetooth low energy tags; WoW: walk-over weighing, GNSS: global navigation satellite system; EID: electronic identification; Ref: reference.
Figure 1Omics information was obtained within the context of small-ruminant production systems, comparing dairy goat breeds (Majorera and Palmera) and meat-producing sheep (Australian Merino, Damara, and Dorper) under seasonal weight loss (SWL). Image created using biorender.com.
Variables traditionally measured in the diagnosis of pregnancy toxemia in sheep and goats.
| Parameter | Reference Interval and Problematic Values | References |
|---|---|---|
| Glycemia | 50–70 mg/dL (2.8–3.9 mmol/L); | [ |
| Blood ketone bodies (especially ß-hydroxybutyrate (BOHB) | <1.1 mmol/L; | [ |
| >2 mmol/L (36.03 mg/dL); | [ | |
| >5.0 mmol/L (90.09 mg/dL); | [ | |
| 19.0 mmol/l. | [ | |
| BOHB in aqueous humor or cerebrospinal fluid | >2.5 mmol/L (45.0 mg/dL) y > 0.5 mmol/L (9.0 mg/dL), respectively. | [ |
| Total proteinemia, albuminemia and globulins in blood | Significant fall, due to liver injury and anorexia; possible false increase due to dehydration. | [ |
| Urea, blood urea nitrogen (BUN) and serum creatinine | Increased urea, blood urea nitrogen (BUN), and serum creatinine, due to renal dysfunction. | [ |
| Serum cortisol and thyroid hormones (T3 and T4) | Increased cortisol, justified by hyperactive adrenal glands. | [ |
| Decreased T3 and T4, due to hypersecretion of cortisol. | [ | |
| Blood enzymes: aspartate aminotransferase (AST); alanine aminotransferase (ALT); gamma glutamyl transferase (GGT); lactate dehydrogenase (LDH) and creatine kinase (CK) | Increased AST, ALT, GGT, LDH, and CK, possibly due to liver dysfunction. | AST: [ |
Other, less conventional parameters measured in ewes and goats affected by pregnancy toxemia.
| Parameter | Modification of the Values and Justification | References |
|---|---|---|
| Fructosamine and glycosylated hemoglobin | Both indicate not the current glycemia, but over a long period prior to measurement; low values suggest persistent hypoglycemia. | [ |
| Minerals: potassium (K), sodium (Na), calcium (Ca) and phosphorus (P) in blood | Large decrease in K, Na and Ca. | [ |
| No changes in phosphorus. | [ | |
| Due by starvation, dehydration, metabolic acidosis, electrolyte imbalance and renal dysfunction, as well as increased lipolysis that can induce hypocalcemia. | [ | |
| High calcium demand of late gestation leads to a significant decrease in maternal calcemia. | [ | |
| Metabolic acidosis and aciduria | Metabolic acidosis (lactate and pyruvate measured in blood). | [ |
| Aciduria, measured in urine using semi-quantitative test strips. | [ |