# Sheep Flock [Data Management](/blog/guides/data-management-basics-principles-processes-and-best-practices): Records, Analysis, and Decision Support ## Key Takeaways - Effective sheep flock management necessitates systematic data collection, analysis, and application to production decisions, moving beyond anecdotal observation to identify subclinical disease, poor fertility, or inefficient feed conversion. - Key Performance Indicators (KPIs) such as lambing percentage, mortality rates (neonatal, pre-weaning, adult), and feed conversion efficiency are critical for trend analysis and targeted interventions, with neonatal lamb mortality directly influenced by housing and management practices. - Data quality is paramount, requiring clear definitions, standardized entry (e.g., recording lamb birth weight within 12 hours using a calibrated scale), and regular verification to mitigate issues like missing identification or inconsistent measurement methods. - Record-keeping systems range from paper notebooks for small flocks to dedicated software and cloud-based platforms with EID integration for larger operations, each with distinct advantages and limitations regarding analysis, scalability, and cost. - A structured decision framework involving benchmarking against targets, investigating root causes through record analysis (e.g., drilling down into individual ewe records for low lambing percentages), implementing prioritized interventions, and evaluating outcomes is essential for diagnosing and correcting production gaps. - Health records are vital for biosecurity, disease surveillance (e.g., tracking disease incidence by season and age group to inform control strategies for conditions like caseous lymphadenitis or sheep scab), and ensuring food safety through accurate recording of withdrawal periods for veterinary treatments. --- Effective sheep flock management depends on systematic data collection, analysis, and application to production decisions. This article covers record-keeping systems, key performance indicators, data analysis methods, and decision-support tools for sheep farmers aiming to improve flock productivity and profitability. The content draws on peer-reviewed research and official agricultural guidance, with emphasis on practical implementation, measurement, and professional escalation criteria. ## At a Glance: Core Record-Keeping Systems for Sheep Flocks | Record System | Primary Data Collected | Typical Use Case | Key Limitation | |---------------|------------------------|------------------|----------------| | Paper-based notebooks or cards | Lambing dates, ewe ID, treatments, sales | Small flocks under 50 ewes with low turnover | Difficult to analyze trends, risk of loss or damage | | Spreadsheet (e.g., Excel, Google Sheets) | Individual ewe records, lamb weights, health events, financial transactions | Medium flocks of 50 to 300 ewes with basic analysis needs | Requires manual data entry, prone to entry errors, limited scalability | | Dedicated flock management software or mobile app | Full individual animal records, automated calculations, reporting, integration with EID readers | Large flocks over 300 ewes or precision management | Higher cost, requires training, data portability concerns | | Cloud-based platform with EID integration | Real-time data capture, remote access, multi-user collaboration, analytics dashboards | Commercial operations with multiple staff or sites | Internet dependency, subscription fees, data security considerations | ## Principles of Flock Data Management ### Why Records Matter for Sheep Production Systematic record keeping enables farmers to identify underperforming animals, track disease patterns, evaluate breeding decisions, and measure financial returns. Research on sheep production records shows that farms with structured health management programs tend to have better flock performance outcomes. The Ontario Sheep Health Program, a structured health management program for intensively reared flocks, demonstrates how organized record systems support preventive health decisions (Small Ruminant Research, 2006, https://doi.org/10.1016/j.smallrumres.2005.07.033). Without reliable records, farmers rely on memory and anecdotal observation, which can mask chronic problems such as subclinical disease, poor fertility, or inefficient feed conversion. The USDA Agricultural Research Service provides resources on livestock production systems that emphasize the role of data in improving efficiency (www.ars.usda.gov). The USDA Natural Resources Conservation Service offers guidance on grazing and pasture management records that integrate with flock data systems (www.nrcs.usda.gov). The Food and Agriculture Organization of the United Nations supports animal production record standards through its Animal Production and Health division (www.fao.org/animal-production/en). ### Key Performance Indicators for Sheep Flocks Selecting the right metrics is essential for meaningful analysis. Common KPIs include: - Lambing percentage (lambs born per ewe mated) - Weaning weight and growth rate - Ewe longevity and replacement rate - Mortality rates (neonatal, pre-weaning, adult) - Disease incidence (e.g., caseous lymphadenitis, sheep scab, coenurosis) - Feed conversion efficiency - Cost per lamb produced - Gross margin per ewe Each KPI should be calculated consistently across years to allow trend analysis. For example, neonatal lamb mortality is influenced by housing conditions and management practices, as documented in Norwegian sheep flocks (Preventive [Veterinary Medicine](/blog/careers/veterinary-medicine-careers-from-clinical-practice-to-public-health), 2012, https://doi.org/10.1016/j.prevetmed.2012.06.007). Tracking mortality rates by cause and season helps target interventions. Research using lamb sales data to investigate associations between implementation of disease preventive practices and sheep flock performance demonstrates how production records can reveal the impact of health management decisions (Animal, 2019, https://pubmed.ncbi.nlm.nih.gov/31094306). This study underscores the value of linking health records with production outcomes. ### Data Quality and Consistency Accurate records depend on clear definitions, standardized data entry, and regular verification. Common data quality issues include: - Missing or incomplete animal identification - Inconsistent recording of dates (e.g., birth date versus tagging date) - Variable measurement methods (e.g., weight taken at different times of day) - Duplicate or conflicting entries for the same animal Implementing a standard operating procedure for data collection reduces these errors. For example, record lamb birth weight within 12 hours of birth using a calibrated scale, and enter data into the system on the same day. The Merck Veterinary Manual provides guidance on management and nutrition record keeping that supports consistent data collection (www.merckvetmanual.com/management-and-nutrition). ## Practical Workflow for Implementing a Flock Data System ### Step 1: Define Objectives and Select Metrics Start by identifying the primary decisions you need to support. Common objectives include: - Improving lamb survival and growth - Reducing veterinary costs - Selecting replacement ewes and rams - Evaluating feed and pasture management - Tracking profitability by enterprise For each objective, define one to three measurable KPIs. For example, if the goal is to reduce neonatal mortality, track the number of lambs born alive, deaths within 24 hours, and deaths before weaning. ### Step 2: Choose a Record-Keeping System Evaluate available options based on flock size, technical comfort, budget, and long-term goals. Consider: - Paper systems: Suitable for small flocks but limit analysis. Use pre-printed forms for lambing, health treatments, and sales. - Spreadsheets: Offer flexibility and basic analysis. Create separate sheets for ewe records, lamb records, health events, and financial transactions. Use data validation to reduce entry errors. - Dedicated software: Provides built-in calculations, reporting, and integration with electronic identification (EID) readers. Examples include programs designed for sheep enterprise budget Excel templates or specialized flock management platforms. - Mobile apps: Allow field data entry and real-time synchronization. Research on technology adoption in sheep farming indicates that farmers attitudes and behaviors influence the successful integration of precision tools (Preventive Veterinary Medicine, 2019, https://pubmed.ncbi.nlm.nih.gov/31421497). A mobile precision flock management tool for intensively managed meat sheep has been developed and tested ([Precision Livestock Farming](/knowledge/animal-farming/farm-management/precision-livestock-farming-technologies-benefits-and-implementation-challenges) 2019, https://api.elsevier.com/content/abstract/scopus_id/85073746272). ### Step 3: Establish Data Collection Protocols Define who collects what data, when, and how. For example: - Lambing season: Record ewe ID, lamb ID (if using individual tags), birth date, birth weight, sex, and any assistance or complications. - Weaning: Record weaning weight, date, and any health treatments. - Health events: Record date, animal ID, clinical signs, diagnosis, treatment, and outcome. - Sales and purchases: Record date, animal ID, weight, price, and buyer or seller. Use standardized codes for common events (e.g., AB for antibiotic treatment, VX for vaccination) to speed data entry and reduce ambiguity. ### Step 4: Train Staff and Ensure Consistency All personnel involved in data collection should receive training on the system and protocols. Regular audits (e.g., monthly review of 10 percent of records) help identify and correct errors. Document any changes to protocols or definitions. ### Step 5: Analyze Data and Make Decisions Schedule regular analysis intervals (e.g., after lambing, at weaning, annually). Compare current KPIs to historical averages and targets. Investigate deviations by drilling down into individual animal or group data. For example, if weaning weights are lower than target, examine lamb birth weights, ewe condition scores, and pasture quality records. ### Step 6: Review and Refine the System Annually evaluate whether the record system is meeting objectives. Consider adding or removing metrics, changing software, or adjusting protocols. Seek feedback from staff and advisors. ## Records and Measurements ### Essential Records for Each Animal Maintain a permanent record for every ewe, ram, and replacement lamb. Include: - Unique identification (ear tag, EID, or tattoo) - Breed or cross - Date of birth - Sire and dam - Purchase date and source (if not homebred) - Breeding and lambing history (mating dates, lambing dates, number of lambs born, weaning weights) - Health events (vaccinations, treatments, diagnoses, test results) - Culling or death date and reason For lambs intended for sale or breeding, record birth weight, weaning weight, and any health interventions. ### Financial Records Track all income and expenses related to the flock. Categories include: - Feed and pasture costs - Veterinary and medicine costs - Breeding stock purchases - Marketing and transport - Labor - Infrastructure and equipment - Sales of lambs, wool, cull ewes, and manure A sheep enterprise budget Excel template can help organize these data and calculate gross margins. Compare actual costs to budgeted amounts to identify overspending or opportunities for savings. ### Health and Treatment Records Maintain a log of all health events, including: - Date and animal ID - Clinical signs and diagnosis - Treatment administered (product, dose, route, withdrawal period) - Outcome (recovered, culled, died) - Any laboratory test results These records support disease surveillance, treatment efficacy evaluation, and compliance with food safety regulations. Research on flock management risk factors associated with Q fever infection in sheep in Saudi Arabia highlights the importance of systematic health records for identifying disease patterns (Animals, 2021, https://pubmed.ncbi.nlm.nih.gov/34208803). A survey of British sheep farmers on sheep scab management found that record keeping practices influence disease control decisions (Preventive Veterinary Medicine, 2023, https://pubmed.ncbi.nlm.nih.gov/36931179). The USDA National Agricultural Library provides resources on animal health and welfare that support record-based disease monitoring (www.nal.usda.gov/animal-health-and-welfare). ### Production and Performance Records Track production metrics by group or individual: - Lambing percentage (lambs born alive per ewe mated) - Weaning percentage (lambs weaned per ewe mated) - Average daily gain from birth to weaning - [Feed conversion ratio](/knowledge/animal-farming/poultry/feed-conversion-ratio-measuring-improving-poultry-efficiency) (if measured) - Wool weight and quality (if applicable) Compare these metrics to breed averages or farm targets. For example, if lambing percentage is below 150 percent for a prolific breed, investigate ewe nutrition, ram fertility, and mating management. ## Common Failure Patterns in Flock Data Management ### Incomplete or Inconsistent Data Entry The most common failure is failing to record events promptly or consistently. This leads to gaps in analysis and reliance on memory. Mitigation: Use pre-printed forms or mobile apps with mandatory fields. Assign one person responsibility for data entry and verification. ### Overcomplication Trying to record too many variables can overwhelm staff and reduce compliance. Mitigation: Start with a core set of essential metrics. Add additional data points only when they directly support a specific decision. ### Lack of Regular Analysis Collecting data without analyzing it provides no benefit. Mitigation: Schedule fixed analysis periods (e.g., first week of each month) and produce a simple report comparing current KPIs to targets. Share results with staff and advisors. ### Poor Data Quality Errors in identification, dates, or measurements undermine analysis. Mitigation: Use EID readers to reduce transcription errors. Calibrate scales regularly. Cross-check records against physical animals during handling. ### Failure to Act on Insights Even good data are useless if not used to change management. Mitigation: For each analysis, identify one to three actionable changes. Implement them and track the impact in subsequent periods. ## Quality and Welfare Controls ### Using Records to Monitor Animal Welfare Systematic records help identify welfare problems early. For example, tracking lamb mortality by cause and location can reveal issues with housing, nutrition, or disease. Research on housing conditions and management practices associated with neonatal lamb mortality in Norwegian sheep flocks demonstrates how data can guide welfare improvements (Preventive Veterinary Medicine, 2012, https://doi.org/10.1016/j.prevetmed.2012.06.007). Records of lameness, body condition scores, and treatment outcomes support welfare monitoring. ### Biosecurity and Disease Surveillance Health records are essential for biosecurity planning. Track disease incidence by group and season. Investigate outbreaks by reviewing animal movements, introductions, and contact with other flocks. Research on rearing management and its impact on caseous lymphadenitis in sheep shows how management practices influence disease risk (Animals, 2024, https://pubmed.ncbi.nlm.nih.gov/38791721). For reportable diseases (e.g., scrapie, Q fever), maintain records that can be shared with veterinary authorities. A case-control study on scrapie in Norwegian sheep flocks highlights the role of records in disease investigations (Preventive Veterinary Medicine, 2001, https://doi.org/10.1016/S0167-5877(01)00225-2). Research on treatment and management of coenurosis by Taenia multiceps using field data from outbreaks in endemic regions demonstrates how outbreak records inform control strategies (Parasites and Vectors, 2024, https://pubmed.ncbi.nlm.nih.gov/39123250). ### Food Safety and Withdrawal Periods Accurate treatment records are critical for food safety. Record the product, dose, route, and withdrawal period for every treatment. Ensure that treated animals are clearly identified and not sent to slaughter before the withdrawal period expires. This is especially important for sheep destined for meat or milk production. ### Worker Safety Records can also support worker safety. Track incidents of injury during handling, shearing, or treatment. Identify patterns (e.g., specific handling facilities or times of year) and implement corrective measures. ## Limitations and Professional Escalation Criteria ### Limitations of Flock Data Systems - Data quality depends on human input, errors are inevitable. - Small flocks may not generate enough data for meaningful [statistical analysis](/blog/guides/statistical-analysis). - Software and hardware require ongoing investment and maintenance. - Integration between different systems (e.g., farm management software and veterinary practice records) can be challenging. - Privacy and data security concerns when using cloud-based platforms. ### When to Seek Professional Help Escalate to a veterinarian, agricultural advisor, or data specialist when: - Disease incidence exceeds historical norms or industry benchmarks, and on-farm investigation has not identified the cause. - Reproductive performance (e.g., lambing percentage, conception rate) is consistently below targets despite management changes. - Financial records show persistent negative margins or unexplained cost increases. - Data analysis reveals patterns that suggest a systemic problem (e.g., high mortality in a specific age group or season). - You need assistance setting up or migrating to a new record-keeping system. - You require advanced [statistical analysis](/blog/guides/statistical-analysis) or modeling (e.g., genetic evaluation, economic optimization). For example, if lamb mortality exceeds 15 percent in multiple lambing seasons, consult a veterinarian to investigate potential infectious causes, nutritional deficiencies, or management factors. Research on management strategies, reproductive performance, and causes of infertility in sheep flocks in the central region of Saudi Arabia illustrates how professional investigation can identify underlying issues (Tropical Animal Health and Production, 2020, https://doi.org/10.1007/s11250-019-02182-9). ## Decision Framework: Using Flock Records to Diagnose and Correct Production Gaps A systematic decision framework transforms raw records into actionable management changes. Without a structured approach, farmers may collect data but fail to identify the root causes of underperformance. This section presents a practical framework for using flock records to diagnose production gaps, prioritize interventions, and monitor outcomes. The framework follows a four-stage process: benchmark, investigate, intervene, and evaluate. ### Stage 1: Benchmark Current Performance Against Targets Begin by calculating your flock's current KPIs and comparing them to established targets. Targets can come from breed averages, historical farm performance, or regional benchmarks. The Food and Agriculture Organization of the United Nations provides guidance on production benchmarks through its Animal Production and Health division (www.fao.org/animal-production/en). For each KPI, define a target range and a threshold that triggers investigation. Create a simple table for each production period: | KPI | Current Value | Target Range | Investigation Threshold | Status | |-----|---------------|--------------|------------------------|--------| | Lambing percentage | 145% | 150-180% | Below 140% | Investigate | | Pre-weaning mortality | 12% | Below 8% | Above 10% | Investigate | | Average weaning weight | 32 kg | 35-40 kg | Below 33 kg | Investigate | | Ewe replacement rate | 22% | 15-20% | Above 25% | Monitor | Flag any KPI that falls outside the investigation threshold. Research using lamb sales data to investigate associations between implementation of disease preventive practices and sheep flock performance demonstrates how benchmarking against industry data can reveal opportunities for improvement (Animal, 2019, https://pubmed.ncbi.nlm.nih.gov/31094306). ### Stage 2: Investigate Root Causes Using Record Analysis For each flagged KPI, drill down into the records to identify patterns. Use a systematic questioning approach: **For low lambing percentage:** - Examine individual ewe records. Are specific ewes or groups underperforming? - Review ram fertility records and mating management. - Check ewe body condition scores at mating and lambing. - Look for disease patterns that may affect fertility. Research on management strategies, reproductive performance and causes of infertility in sheep flocks in the central region of Saudi Arabia highlights how records can identify specific infertility causes (Tropical Animal Health and Production, 2020, https://doi.org/10.1007/s11250-019-02182-9). **For high pre-weaning mortality:** - Sort mortality records by cause, age at death, and ewe ID. - Identify clusters of deaths by location (e.g., specific pens or paddocks). - Review ewe nutrition records, especially late pregnancy feeding. - Check lambing assistance records for patterns of difficult births. - Research on housing conditions and management practices associated with neonatal lamb mortality in Norwegian sheep flocks demonstrates how environmental factors contribute to mortality patterns (Preventive Veterinary Medicine, 2012, https://doi.org/10.1016/j.prevetmed.2012.06.007). **For low weaning weights:** - Compare lamb birth weights and growth rates across groups. - Review ewe milk production indicators (udder health, body condition). - Examine pasture quality and feed supplementation records. - Check for disease outbreaks that may have affected growth. **For high disease incidence:** - Map disease events by season, age group, and source of animals. - Review biosecurity records, including quarantine and vaccination protocols. - Research on rearing management and its impact on caseous lymphadenitis in sheep shows how management practices influence disease risk (Animals, 2024, https://pubmed.ncbi.nlm.nih.gov/38791721). - A survey of British sheep farmers on sheep scab management found that record keeping practices influence disease control decisions (Preventive Veterinary Medicine, 2023, https://pubmed.ncbi.nlm.nih.gov/36931179). ### Stage 3: Prioritize and Implement Interventions Based on the investigation, identify the most likely root causes and rank interventions by expected impact, cost, and feasibility. Use a simple priority matrix: | Intervention | Expected Impact | Cost | Feasibility | Priority | |--------------|-----------------|------|-------------|----------| | Improve ewe nutrition pre-lambing | High | Moderate | High | 1 | | Implement lambing pen hygiene protocol | Medium | Low | High | 2 | | Replace underperforming rams | High | High | Moderate | 3 | | Vaccinate against clostridial diseases | Medium | Low | High | 4 | Implement the highest priority interventions first. Document the changes made, the date of implementation, and the expected timeline for seeing results. The Ontario Sheep Health Program, a structured health management program for intensively reared flocks, provides a model for systematic intervention planning (Small Ruminant Research, 2006, https://doi.org/10.1016/j.smallrumres.2005.07.033). ### Stage 4: Evaluate Outcomes and Adjust After implementing interventions, continue recording data and recalculate KPIs at the next production cycle. Compare post-intervention values to both the previous period and the target range. Ask: - Did the KPI move toward the target? - Was the change clinically or economically meaningful? - Are there unintended consequences (e.g., increased costs, other health issues)? If the KPI improved but remains below target, consider additional interventions or a longer timeline. If there was no improvement, revisit the root cause analysis. Research on treatment and management of coenurosis by Taenia multiceps using field data from outbreaks in endemic regions demonstrates how iterative evaluation of interventions improves outcomes (Parasites and Vectors, 2024, https://pubmed.ncbi.nlm.nih.gov/39123250). ### Records and Measurements for the Framework To support this decision framework, maintain the following specific records: - **Production cycle summary sheets**: One-page reports for each lambing, weaning, and breeding period showing key KPIs and comparison to targets. - **Root cause investigation logs**: A simple form documenting the flagged KPI, data sources reviewed, patterns identified, and suspected root causes. - **Intervention tracking sheets**: Record the intervention, date implemented, cost, expected outcome, and actual outcome. - **Annual review document**: Summarize all production cycles, evaluate overall trends, and set targets for the coming year. ### Common Failure Patterns in Applying the Framework **Skipping the benchmark stage**: Farmers may jump to interventions without establishing current performance levels. This leads to wasted effort on problems that may not exist. Mitigation: Always calculate current KPIs before making changes. **Confirmation bias**: Interpreting data to support preconceived ideas about the cause of a problem. Mitigation: List all possible causes before reviewing data, and seek input from an advisor or veterinarian. **Insufficient data to investigate**: If records are incomplete, the investigation stage fails. Mitigation: Maintain complete records for at least two full production cycles before attempting root cause analysis. **Implementing too many changes at once**: Multiple simultaneous interventions make it impossible to know which one caused any improvement. Mitigation: Implement one or two changes per production cycle and evaluate before adding more. **Failing to document interventions**: Without written records of what was done and when, evaluation is impossible. Mitigation: Use the intervention tracking sheet consistently. ### Quality and Welfare Controls The decision framework directly supports animal welfare by identifying and correcting problems that cause suffering. For example, tracking lameness records through the framework can reveal management practices that increase risk. The USDA National Agricultural Library provides resources on animal health and welfare that support welfare monitoring through records (www.nal.usda.gov/animal-health-and-welfare). The Merck Veterinary Manual offers guidance on management practices that affect welfare outcomes (www.merckvetmanual.com/management-and-nutrition). ### Limitations and Professional Escalation Criteria The framework is limited by data quality and quantity. Small flocks may not have enough records for meaningful pattern analysis. If investigation fails to identify a root cause after two production cycles, or if the KPI remains below target after implementing recommended interventions, escalate to a veterinarian or agricultural advisor. Research on flock management risk factors associated with Q fever infection in sheep in Saudi Arabia demonstrates how professional investigation can identify factors not apparent from farm records alone (Animals, 2021, https://pubmed.ncbi.nlm.nih.gov/34208803). A case-control study on scrapie in Norwegian sheep flocks highlights the role of expert analysis in disease investigations (Preventive Veterinary Medicine, 2001, https://doi.org/10.1016/S0167-5877(01)00225-2). ## Frequently Asked Questions ### What is the minimum data I should record for each ewe? Record unique identification, date of birth, sire and dam, breeding and lambing history (mating dates, lambing dates, number of lambs born, weaning weights), health events, and culling or death date and reason. This core set supports breeding decisions, health management, and culling. ### How often should I analyze my flock records? Analyze records after each major production event (lambing, weaning, breeding) and at least annually for financial performance. Monthly reviews of health and mortality data help detect emerging problems early. ### What is the best record-keeping system for a small flock under 50 ewes? A paper-based system using pre-printed forms or a simple spreadsheet is often sufficient for small flocks. Focus on recording lambing data, health treatments, and sales. As the flock grows, consider transitioning to dedicated software or a mobile app. ### How can I use records to improve lamb survival? Track lamb mortality by cause, age at death, and ewe ID. Analyze patterns to identify risk factors such as poor ewe condition, difficult births, or inadequate colostrum intake. Use this information to adjust management, such as improving ewe nutrition in late pregnancy or providing assistance during lambing. ### What financial records should I keep for my sheep enterprise? Record all income (lamb sales, wool, cull ewes, manure) and expenses (feed, veterinary, breeding stock, marketing, labor, infrastructure). Use a sheep enterprise budget Excel template to calculate gross margins per ewe and identify cost-saving opportunities. ### How do I ensure data quality in my flock records? Use standardized codes and definitions, train all staff, and conduct regular audits. Cross-check records against physical animals during handling. Use EID readers to reduce transcription errors. Calibrate scales and other measurement tools regularly. ### When should I consult a veterinarian about my flock records? Consult a veterinarian if disease incidence exceeds historical norms, reproductive performance is consistently below targets, or mortality rates are elevated. Share your records to support diagnosis and treatment planning. Research on flock management risk factors associated with Q fever infection in sheep in Saudi Arabia demonstrates how records can guide veterinary investigations (Animals, 2021, https://pubmed.ncbi.nlm.nih.gov/34208803). ### Can I use my flock records to select replacement ewes and rams? Yes. Records of individual ewe performance (lambing percentage, weaning weights, health history) and ram progeny data support selection decisions. Track traits such as growth rate, maternal ability, and disease resistance. Genetic evaluation services can provide estimated breeding values if you have sufficient pedigree and performance data. ## Related Farming Guides - [Systems Biology](/blog/news/systems-biology) - [Rotational Grazing Plans For Sheep Flocks](/knowledge/animal-farming/sheep/rotational-grazing-plans-for-sheep-flocks) - [Dairy Cow Cooling System Management](/knowledge/animal-farming/dairy-cattle/dairy-cow-cooling-system-management) - [Dairy Cow Culling Decisions And Records](/knowledge/animal-farming/dairy-cattle/dairy-cow-culling-decisions-and-records) - [Dairy Farm Financial Records For Production Decisions](/knowledge/animal-farming/dairy-cattle/dairy-farm-financial-records-for-production-decisions) ## Related Clinical & Scientific Guides * [Sheep Grazing Lease: Terms, Rates, and Legal Considerations](/knowledge/animal-farming/sheep/sheep-grazing-lease-terms-rates-and-legal-considerations) * [Sheep Breed Selection for Meat, Wool, Dairy, and Low-Input Systems](/knowledge/animal-farming/sheep/sheep-breed-selection-for-meat-wool-dairy-and-low-input-systems) * [Sheep Barn Flooring for Hoof Health: Best Materials and Practices](/knowledge/animal-farming/sheep/sheep-barn-flooring-hoof-health-materials-practices) ## References and Further Reading - [www.ars.usda.gov](https://www.ars.usda.gov/) - [www.nrcs.usda.gov](https://www.nrcs.usda.gov/) - [www.merckvetmanual.com](https://www.merckvetmanual.com/management-and-nutrition) - [FAO Animal Production and Health](https://www.fao.org/animal-production/en). Food and Agriculture Organization of the United Nations. - [Animal Health and Welfare](https://www.nal.usda.gov/animal-health-and-welfare). USDA National Agricultural Library. - [Flock Management Risk Factors Associated with Q Fever Infection in Sheep in Saudi Arabia.](https://pubmed.ncbi.nlm.nih.gov/34208803). Animals : an open access journal from MDPI, 2021. - [Using lamb sales data to investigate associations between implementation of disease preventive practices and sheep flock performance.](https://pubmed.ncbi.nlm.nih.gov/31094306). Animal : an international journal of animal bioscience, 2019. - [Rearing Management and Its Impact on Caseous Lymphadenitis in Sheep.](https://pubmed.ncbi.nlm.nih.gov/38791721). Animals : an open access journal from MDPI, 2024. - [Treatment and management of coenurosis by Taenia multiceps: field data from outbreaks in endemic regions and literature review.](https://pubmed.ncbi.nlm.nih.gov/39123250). Parasites & vectors, 2024. - [A survey of British sheep farmers: Practices, opinions and knowledge surrounding the management of sheep scab.](https://pubmed.ncbi.nlm.nih.gov/36931179). Preventive veterinary medicine, 2023. - [Technology adoption on farms: Using Normalisation Process Theory to understand sheep farmers' attitudes and behaviours in relation to using precision technology in flock management.](https://pubmed.ncbi.nlm.nih.gov/31421497). Preventive veterinary medicine, 2019. - [The Ontario Sheep Health Program: A structured health management program for intensively reared flocks](https://doi.org/10.1016/j.smallrumres.2005.07.033). Small Ruminant Research, 2006. - [Housing conditions and management practices associated with neonatal lamb mortality in sheep flocks in Norway](https://doi.org/10.1016/j.prevetmed.2012.06.007). Preventive Veterinary Medicine, 2012. - [Management strategies, reproductive performance and causes of infertility in sheep flocks in the central region of Saudi Arabia](https://doi.org/10.1007/s11250-019-02182-9). Tropical Animal Health and Production, 2020. - [Mobile precision flock management tool for intensively managed meat sheep](https://api.elsevier.com/content/abstract/scopus_id/85073746272). Precision [Livestock Farming](/knowledge/animal-farming/farm-management/livestock-farming-an-overview-of-modern-practices-and-challenges) 2019 Papers Presented at the 9th European Conference on Precision [Livestock Farming](/knowledge/animal-farming/farm-management/livestock-farming-an-overview-of-modern-practices-and-challenges) Ecplf 2019, 2019. - [A case-control study on scrapie in Norwegian sheep flocks](https://doi.org/10.1016/S0167-5877%2801%2900225-2). Preventive Veterinary Medicine, 2001. > This article is educational and is not a substitute for veterinary diagnosis, treatment, public-health guidance, or regulatory reporting.