This study evaluated the effects of implants on weight gain in suckling calves. Forty-eight Angus cross spring born calf pairs were randomly assigned to implant or control groups. Calves received implants at the same time as vaccinations between 45-150 days old. Final weights were taken at 114 days, showing no significant difference in average daily gain between implanted and non-implanted steers or heifers. Poor forage quality during the study may have limited the impact of implants. Coordinating implants with existing practices reduced stress and costs. More research is needed on how forage quality affects implant response in suckling calves.
This document summarizes a study that investigated the effects of total protein intake (TPro) and changes in TPro on bone mineral density (BMD) and bone mineral content (BMC) in overweight and obese adults following a 36-week exercise and diet intervention. The study found that neither TPro nor changes in TPro were associated with changes in total or regional BMD or BMC. Higher protein intake through supplementation was found to not negatively impact bone health during the intervention.
1) The study examined whether previous ballistic or dynamic conditioning contractions could enhance subsequent throwing performance in competitive rugby players.
2) The results showed that a ballistic conditioning contraction significantly improved maximum displacement and velocity in the subsequent throw, while both ballistic and dynamic contractions significantly improved peak velocity and velocity at peak power.
3) The findings indicate that ballistic activity can improve aspects of throwing performance related to velocity and distance, while dynamic and ballistic contractions can both enhance velocity.
Implant study poster layout edits in color (003)Dana Zook
1) The document discusses using growth promoting implants in suckling calves to increase weight gain. It implemented a study using 48 Angus cross calves randomly assigned to implanted or control groups.
2) The results of the study showed no significant difference in average daily gain between implanted and non-implanted steers or heifers. Previous research has found inconsistent responses to implants in suckling calves.
3) The lack of effect may have been due to poor forage quality decreasing milk production and weight gains. Coordinating implants with current practices kept added stress low for producers. More research is needed on forage quality's impact on implant responses in suckling calves.
Dr. Nick Gabler - The impact of PRRSV on feed efficiency, digestibility and t...John Blue
The impact of PRRSV on feed efficiency, digestibility and tissue accretion in grow-finisher pigs - Dr. Nick Gabler, Department of Animal Science, Iowa State University, from the 2013 Allen D. Leman Swine Conference, September 14-17, 2013, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2013-leman-swine-conference-material
This study compared chick length and hatch day body weight as predictors of future broiler performance. Over 400 chicks were measured for length and weighed at hatch, then grown out and weighed again at 1, 2, 3, and 6 weeks. Both chick length and hatch day weight were found to be fair predictors of performance for the first 2 weeks, but differences became less significant over time. Chick length appeared to be a slightly better early predictor. The study did not account for gender differences, which could have impacted results. More research is needed comparing length and weight while accounting for sex.
1) Gait analysis provides quantitative measures of walking ability, but the measures need to accurately detect meaningful changes for clinical use.
2) The Gait Profile Score (GPS) summarizes overall gait deviation and has a Minimal Clinically Important Difference (MCID) of 1.6 degrees, meaning changes less than this are not noticeable.
3) An analysis of children who underwent surgery found that 66% improved over the MCID, 32% saw no meaningful change, and 2% deteriorated, showing the ability of GPS to evaluate the effects of interventions.
1) Gait analysis uses temporal-spatial parameters like walking speed and gait indices to evaluate outcomes. Walking speed is best measured independently of gait labs using walk tests.
2) Gait indices like the Gait Profile Score (GPS) and Gait Deviation Index (GDI) provide a single number to reflect gait quality, with GPS above 6 degrees considered abnormal.
3) Studies have found the minimal clinically important difference for the GPS is 1.6 degrees, and that 66% of children improved over this threshold after surgery like single-event multilevel surgery (SEMLS), while 2% deteriorated.
This study evaluated the effects of implants on weight gain in suckling calves. Forty-eight Angus cross spring born calf pairs were randomly assigned to implant or control groups. Calves received implants at the same time as vaccinations between 45-150 days old. Final weights were taken at 114 days, showing no significant difference in average daily gain between implanted and non-implanted steers or heifers. Poor forage quality during the study may have limited the impact of implants. Coordinating implants with existing practices reduced stress and costs. More research is needed on how forage quality affects implant response in suckling calves.
This document summarizes a study that investigated the effects of total protein intake (TPro) and changes in TPro on bone mineral density (BMD) and bone mineral content (BMC) in overweight and obese adults following a 36-week exercise and diet intervention. The study found that neither TPro nor changes in TPro were associated with changes in total or regional BMD or BMC. Higher protein intake through supplementation was found to not negatively impact bone health during the intervention.
1) The study examined whether previous ballistic or dynamic conditioning contractions could enhance subsequent throwing performance in competitive rugby players.
2) The results showed that a ballistic conditioning contraction significantly improved maximum displacement and velocity in the subsequent throw, while both ballistic and dynamic contractions significantly improved peak velocity and velocity at peak power.
3) The findings indicate that ballistic activity can improve aspects of throwing performance related to velocity and distance, while dynamic and ballistic contractions can both enhance velocity.
Implant study poster layout edits in color (003)Dana Zook
1) The document discusses using growth promoting implants in suckling calves to increase weight gain. It implemented a study using 48 Angus cross calves randomly assigned to implanted or control groups.
2) The results of the study showed no significant difference in average daily gain between implanted and non-implanted steers or heifers. Previous research has found inconsistent responses to implants in suckling calves.
3) The lack of effect may have been due to poor forage quality decreasing milk production and weight gains. Coordinating implants with current practices kept added stress low for producers. More research is needed on forage quality's impact on implant responses in suckling calves.
Dr. Nick Gabler - The impact of PRRSV on feed efficiency, digestibility and t...John Blue
The impact of PRRSV on feed efficiency, digestibility and tissue accretion in grow-finisher pigs - Dr. Nick Gabler, Department of Animal Science, Iowa State University, from the 2013 Allen D. Leman Swine Conference, September 14-17, 2013, St. Paul, Minnesota, USA.
More presentations at http://www.swinecast.com/2013-leman-swine-conference-material
This study compared chick length and hatch day body weight as predictors of future broiler performance. Over 400 chicks were measured for length and weighed at hatch, then grown out and weighed again at 1, 2, 3, and 6 weeks. Both chick length and hatch day weight were found to be fair predictors of performance for the first 2 weeks, but differences became less significant over time. Chick length appeared to be a slightly better early predictor. The study did not account for gender differences, which could have impacted results. More research is needed comparing length and weight while accounting for sex.
1) Gait analysis provides quantitative measures of walking ability, but the measures need to accurately detect meaningful changes for clinical use.
2) The Gait Profile Score (GPS) summarizes overall gait deviation and has a Minimal Clinically Important Difference (MCID) of 1.6 degrees, meaning changes less than this are not noticeable.
3) An analysis of children who underwent surgery found that 66% improved over the MCID, 32% saw no meaningful change, and 2% deteriorated, showing the ability of GPS to evaluate the effects of interventions.
1) Gait analysis uses temporal-spatial parameters like walking speed and gait indices to evaluate outcomes. Walking speed is best measured independently of gait labs using walk tests.
2) Gait indices like the Gait Profile Score (GPS) and Gait Deviation Index (GDI) provide a single number to reflect gait quality, with GPS above 6 degrees considered abnormal.
3) Studies have found the minimal clinically important difference for the GPS is 1.6 degrees, and that 66% of children improved over this threshold after surgery like single-event multilevel surgery (SEMLS), while 2% deteriorated.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
2802 REMOTE SENSING INDICATORS FOR CROP GROWTH MONITORING AT DIFFERENT SCALES...grssieee
This document summarizes research on using remote sensing indicators to monitor crop growth at different scales. At the canopy scale, soil-adjusted vegetation indices (SAVI) with different L values were found to be suitable for monitoring winter wheat growth across different phenological stages. At the regional scale, SAVI had limitations for dense crop coverage areas. For dense coverage, the relationship between vegetation indices and crop growth improved with larger pixel sizes, but this trend was not observed for low crop coverage areas. The research aimed to identify optimal indicators for crop monitoring at different scales.
Improving the Estimation of Crop of Rice Using Higher Resolution Simulated La...iosrjce
This document presents a method for improving the estimation of rice crop areas using higher resolution simulated Landsat images. The method involves enhancing the resolution of Landsat images using discrete wavelet transform and stationary wavelet transform. Histogram specification is then used to match the histograms of the enhanced resolution images to the originals. Normalized difference vegetation index and scatterplot classification methods are applied to the original and enhanced resolution images to estimate rice areas. The estimated areas are validated by comparing them to actual cultivated areas reported by the Iraqi Ministry of Agriculture. The results show that the enhanced and histogram specified images improve the accuracy of rice area estimation compared to using the original Landsat images.
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
This document discusses decision support systems (DSS) in agronomy and crop modeling. It provides examples of how DSS are applied in agronomy through modeling software like Stella and modeling applications like DSSAT and CropSyst. A sample wheat model is described that uses 7 state variables like phenology, water, and biomass to mathematically represent biological and environmental processes. Weather generators, phenology models, and biomass and water balance models are outlined.
Prediction of Surface Subsidence and Its MonitoringVR M
This dissertation examines surface subsidence prediction and monitoring related to underground coal mining in India. The author develops an empirical relationship to predict subsidence profiles based on collected subsidence data. Various conventional and advanced surveying techniques for monitoring subsidence are also studied. It is recommended to use tacheometry surveys and GPS to efficiently monitor vertical and horizontal ground movements in Indian coal mines.
This document discusses plant growth hormones, including their definitions, types, functions, and uses in agriculture. It covers the major classes of plant hormones: auxins, gibberellins, cytokines, ethylene, inhibitors, and growth retardants. For each class, it describes their roles in plant growth and development processes. It also provides examples of practical applications in crop production, such as stimulating root growth, increasing fruit size, controlling flowering, and accelerating ripening. Overall, the document outlines how plant growth hormones regulate important physiological functions and how their exogenous application can help improve crop yields and quality.
These slides are about how crop and weather are interlinked an d how their association can be an impressive tools in the hands of the creative minds of the scientific world.
Operational Agriculture Monitoring System Using Remote SensingMary Adel
This document discusses using remote sensing for operational agricultural monitoring. It describes how remote sensing can be applied to monitor vegetation, soil, forests, and land cover. Applications of remote sensing discussed include crop type identification, crop condition assessment, crop area estimation, crop growth monitoring, crop yield prediction, and crop damage assessment. The document outlines methodologies for crop area estimation, crop growth monitoring, and crop yield estimation that utilize remote sensing data like NDVI. It also discusses using field networks for validation and improving monitoring accuracy.
The three main processes in plant physiology are photosynthesis, respiration, and transpiration. Photosynthesis allows plants to produce their own food (carbohydrates) from carbon dioxide, water, and sunlight using chlorophyll in their leaves. Respiration breaks down carbohydrates to release energy, which is used for growth and tissue building. Transpiration is the process by which plants lose water, primarily through their leaves, which serves purposes like mineral transport and cooling the plant.
1. GIS is useful for irrigation and agriculture by identifying suitable cultivation areas, classifying soil types for different crops, and assessing water availability and demand over space and time.
2. Remote sensing and GIS allow for irrigation water management through determining crop types, yields, problems like waterlogging, and evaluating irrigation system performance.
3. Various maps generated by remote sensing, like soil type, crop coverage, rivers/canals, land use, and contours, can be analyzed in GIS to plan, maintain, and improve irrigation systems.
Application of gis and remote sensing in agricultureRehana Qureshi
This document summarizes the applications of remote sensing and GIS in agriculture as presented by Rehana Khaliq. It discusses how GIS systems capture and analyze geospatial data to integrate information and perform analysis. Remote sensing is defined as obtaining information about objects without physical contact using sensors. The document outlines how remote sensing and GIS have been applied to agriculture for tasks like crop mapping and monitoring, yield estimation, and precision agriculture. It also discusses their applications in forestry, land use mapping, and urban planning. While remote sensing provides valuable data, it notes that measurement errors and data interpretation can sometimes be challenging. In conclusion, the document argues that remote sensing and GIS are promising tools to enhance sustainable agriculture and development through
Application of Remote Sensing in AgricultureUTTAM KUMAR
Remote sensing has been found to be a valuable tool in evaluation, monitoring and management of land, water and crop resources. The launching of the Indian remote sensing satellite (IRS) has enhanced the capabilities for better utilization of this technology and significant progress has been made in soil and land cover mapping, land degradation studies, monitoring of waste land, assessment of crop conditions crop acreage and production estimates
Validation of Producer-Recorded Health Event Data and Use in Genetic Improvem...John B. Cole, Ph.D.
This document summarizes a presentation on genetic improvement of dairy cattle health using producer-recorded health event data. It discusses validation of such field data and its potential use in genetic evaluations. Standard health event codes have been developed and a new data exchange format will facilitate collection of health data for research and selection. Challenges include dataset size and estimating economic impacts of diseases.
Genetic evaluation and best prediction of lactation persistencyJohn B. Cole, Ph.D.
At the same level of production cows with high persistency milk more at the end than the beginning of lactation. Best prediction of persistency is calculated as a function of trait-specific standard lactation curves and the linear regression of a cow’s test day deviations on days in milk.
Application of Physiologically-based Kinetic Models in Exposure ModelingIES / IAQM
This document summarizes a land condition symposium presentation on applying physiologically-based kinetic models in land contamination exposure modelling. The presentation introduced key terms like bioaccessibility and bioavailability, and described a study that used physiologically-based pharmacokinetic (PBPK) models to improve understanding of actual human exposure to contaminants like arsenic, cadmium, chromium, nickel, and lead in soil. Soil, produce, blood, and urine samples were collected and analyzed from allotment users. PBPK models were produced and evaluated against literature data. The models can estimate uptake fractions and relative
The study had two specific goals: 1) Determine the impact of a simulated reduced dose rendering on the detection of skeletal fractures in children, and 2) Evaluate the effect of enhanced skeletal processing on the same detection task. The methodology and results of this study were on display at RSNA 2015. Read the blog at http://www.carestream.com/blog/2016/04/12/carestream-pediatric-fracture-detection-study-and-potential-dose-reduction/#more-7700 or visit www.carestream.com/medical
Dairy Reproduction: Identifying Problems and Solutions for Your HerdDAIReXNET
Ray Nebel of Select Sires, Inc. presented this information for DAIReXNET on March 17, 2014. A recording of the full presentation can be found at http://www.extension.org/pages/15830/archived-dairy-cattle-webinars#.Uyigy86nbZU,
Subclinical Hypocalcemia: How to eliminate the unseen beast.Lasse Jakobsen
Cornell Feed Dealers Seminars from 2017. The data presented in this presentation is based primarily on the newly finished Cornell research trial. The study was focused on the low calcium approach to preventing subclinical hypocalcemia in today's modern dairy herds; by the application of the calcium binder, X-Zelit.
This document discusses broiler chicken lighting programs and their effects on performance. It covers:
Light Intensity (lux)
24
23
18
0
1
6
20-60
20-60
---> 5-10**
15
18
23
23
9
6
1
1
5-10
5-10
5-10
5-10 ---> increase****
* Age at which to make light change.
** Light intensity can be decreased to 5-10 lux after light restriction begins.
*** If thinning a flock, follow the program for the whole flock. After the flock is thinned, revert back to
Stability based validation of dietary patterns obtained by cluster (1)SarathvarmaTirumalar
This study aimed to objectively select the most appropriate clustering method and number of clusters for describing dietary patterns using a stability-based methodology. Cluster analysis was performed on dietary data from the NESCAV study using K-means and principal component analysis (PCA). The most stable solution was obtained using K-means clustering with three clusters. This identified three main dietary patterns in the population: a "Convenient" and "Non-Prudent" pattern associated with higher cardiovascular risk, and a "Prudent" pattern associated with decreased risk.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
2802 REMOTE SENSING INDICATORS FOR CROP GROWTH MONITORING AT DIFFERENT SCALES...grssieee
This document summarizes research on using remote sensing indicators to monitor crop growth at different scales. At the canopy scale, soil-adjusted vegetation indices (SAVI) with different L values were found to be suitable for monitoring winter wheat growth across different phenological stages. At the regional scale, SAVI had limitations for dense crop coverage areas. For dense coverage, the relationship between vegetation indices and crop growth improved with larger pixel sizes, but this trend was not observed for low crop coverage areas. The research aimed to identify optimal indicators for crop monitoring at different scales.
Improving the Estimation of Crop of Rice Using Higher Resolution Simulated La...iosrjce
This document presents a method for improving the estimation of rice crop areas using higher resolution simulated Landsat images. The method involves enhancing the resolution of Landsat images using discrete wavelet transform and stationary wavelet transform. Histogram specification is then used to match the histograms of the enhanced resolution images to the originals. Normalized difference vegetation index and scatterplot classification methods are applied to the original and enhanced resolution images to estimate rice areas. The estimated areas are validated by comparing them to actual cultivated areas reported by the Iraqi Ministry of Agriculture. The results show that the enhanced and histogram specified images improve the accuracy of rice area estimation compared to using the original Landsat images.
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
This document discusses decision support systems (DSS) in agronomy and crop modeling. It provides examples of how DSS are applied in agronomy through modeling software like Stella and modeling applications like DSSAT and CropSyst. A sample wheat model is described that uses 7 state variables like phenology, water, and biomass to mathematically represent biological and environmental processes. Weather generators, phenology models, and biomass and water balance models are outlined.
Prediction of Surface Subsidence and Its MonitoringVR M
This dissertation examines surface subsidence prediction and monitoring related to underground coal mining in India. The author develops an empirical relationship to predict subsidence profiles based on collected subsidence data. Various conventional and advanced surveying techniques for monitoring subsidence are also studied. It is recommended to use tacheometry surveys and GPS to efficiently monitor vertical and horizontal ground movements in Indian coal mines.
This document discusses plant growth hormones, including their definitions, types, functions, and uses in agriculture. It covers the major classes of plant hormones: auxins, gibberellins, cytokines, ethylene, inhibitors, and growth retardants. For each class, it describes their roles in plant growth and development processes. It also provides examples of practical applications in crop production, such as stimulating root growth, increasing fruit size, controlling flowering, and accelerating ripening. Overall, the document outlines how plant growth hormones regulate important physiological functions and how their exogenous application can help improve crop yields and quality.
These slides are about how crop and weather are interlinked an d how their association can be an impressive tools in the hands of the creative minds of the scientific world.
Operational Agriculture Monitoring System Using Remote SensingMary Adel
This document discusses using remote sensing for operational agricultural monitoring. It describes how remote sensing can be applied to monitor vegetation, soil, forests, and land cover. Applications of remote sensing discussed include crop type identification, crop condition assessment, crop area estimation, crop growth monitoring, crop yield prediction, and crop damage assessment. The document outlines methodologies for crop area estimation, crop growth monitoring, and crop yield estimation that utilize remote sensing data like NDVI. It also discusses using field networks for validation and improving monitoring accuracy.
The three main processes in plant physiology are photosynthesis, respiration, and transpiration. Photosynthesis allows plants to produce their own food (carbohydrates) from carbon dioxide, water, and sunlight using chlorophyll in their leaves. Respiration breaks down carbohydrates to release energy, which is used for growth and tissue building. Transpiration is the process by which plants lose water, primarily through their leaves, which serves purposes like mineral transport and cooling the plant.
1. GIS is useful for irrigation and agriculture by identifying suitable cultivation areas, classifying soil types for different crops, and assessing water availability and demand over space and time.
2. Remote sensing and GIS allow for irrigation water management through determining crop types, yields, problems like waterlogging, and evaluating irrigation system performance.
3. Various maps generated by remote sensing, like soil type, crop coverage, rivers/canals, land use, and contours, can be analyzed in GIS to plan, maintain, and improve irrigation systems.
Application of gis and remote sensing in agricultureRehana Qureshi
This document summarizes the applications of remote sensing and GIS in agriculture as presented by Rehana Khaliq. It discusses how GIS systems capture and analyze geospatial data to integrate information and perform analysis. Remote sensing is defined as obtaining information about objects without physical contact using sensors. The document outlines how remote sensing and GIS have been applied to agriculture for tasks like crop mapping and monitoring, yield estimation, and precision agriculture. It also discusses their applications in forestry, land use mapping, and urban planning. While remote sensing provides valuable data, it notes that measurement errors and data interpretation can sometimes be challenging. In conclusion, the document argues that remote sensing and GIS are promising tools to enhance sustainable agriculture and development through
Application of Remote Sensing in AgricultureUTTAM KUMAR
Remote sensing has been found to be a valuable tool in evaluation, monitoring and management of land, water and crop resources. The launching of the Indian remote sensing satellite (IRS) has enhanced the capabilities for better utilization of this technology and significant progress has been made in soil and land cover mapping, land degradation studies, monitoring of waste land, assessment of crop conditions crop acreage and production estimates
Validation of Producer-Recorded Health Event Data and Use in Genetic Improvem...John B. Cole, Ph.D.
This document summarizes a presentation on genetic improvement of dairy cattle health using producer-recorded health event data. It discusses validation of such field data and its potential use in genetic evaluations. Standard health event codes have been developed and a new data exchange format will facilitate collection of health data for research and selection. Challenges include dataset size and estimating economic impacts of diseases.
Genetic evaluation and best prediction of lactation persistencyJohn B. Cole, Ph.D.
At the same level of production cows with high persistency milk more at the end than the beginning of lactation. Best prediction of persistency is calculated as a function of trait-specific standard lactation curves and the linear regression of a cow’s test day deviations on days in milk.
Application of Physiologically-based Kinetic Models in Exposure ModelingIES / IAQM
This document summarizes a land condition symposium presentation on applying physiologically-based kinetic models in land contamination exposure modelling. The presentation introduced key terms like bioaccessibility and bioavailability, and described a study that used physiologically-based pharmacokinetic (PBPK) models to improve understanding of actual human exposure to contaminants like arsenic, cadmium, chromium, nickel, and lead in soil. Soil, produce, blood, and urine samples were collected and analyzed from allotment users. PBPK models were produced and evaluated against literature data. The models can estimate uptake fractions and relative
The study had two specific goals: 1) Determine the impact of a simulated reduced dose rendering on the detection of skeletal fractures in children, and 2) Evaluate the effect of enhanced skeletal processing on the same detection task. The methodology and results of this study were on display at RSNA 2015. Read the blog at http://www.carestream.com/blog/2016/04/12/carestream-pediatric-fracture-detection-study-and-potential-dose-reduction/#more-7700 or visit www.carestream.com/medical
Dairy Reproduction: Identifying Problems and Solutions for Your HerdDAIReXNET
Ray Nebel of Select Sires, Inc. presented this information for DAIReXNET on March 17, 2014. A recording of the full presentation can be found at http://www.extension.org/pages/15830/archived-dairy-cattle-webinars#.Uyigy86nbZU,
Subclinical Hypocalcemia: How to eliminate the unseen beast.Lasse Jakobsen
Cornell Feed Dealers Seminars from 2017. The data presented in this presentation is based primarily on the newly finished Cornell research trial. The study was focused on the low calcium approach to preventing subclinical hypocalcemia in today's modern dairy herds; by the application of the calcium binder, X-Zelit.
This document discusses broiler chicken lighting programs and their effects on performance. It covers:
Light Intensity (lux)
24
23
18
0
1
6
20-60
20-60
---> 5-10**
15
18
23
23
9
6
1
1
5-10
5-10
5-10
5-10 ---> increase****
* Age at which to make light change.
** Light intensity can be decreased to 5-10 lux after light restriction begins.
*** If thinning a flock, follow the program for the whole flock. After the flock is thinned, revert back to
Stability based validation of dietary patterns obtained by cluster (1)SarathvarmaTirumalar
This study aimed to objectively select the most appropriate clustering method and number of clusters for describing dietary patterns using a stability-based methodology. Cluster analysis was performed on dietary data from the NESCAV study using K-means and principal component analysis (PCA). The most stable solution was obtained using K-means clustering with three clusters. This identified three main dietary patterns in the population: a "Convenient" and "Non-Prudent" pattern associated with higher cardiovascular risk, and a "Prudent" pattern associated with decreased risk.
Stability based validation of dietary patterns obtained by clusterAjay RJ
This study aimed to objectively select the most appropriate clustering method and number of clusters for describing dietary patterns using a stability-based methodology. Cluster analysis was performed on dietary data from the NESCAV study using K-means and principal component analysis (PCA). The most stable solution was obtained using K-means clustering with three clusters. This identified three main dietary patterns in the population: a "Convenient" and "Non-Prudent" pattern associated with higher cardiovascular risk, and a "Prudent" pattern associated with decreased risk.
Everyday Good Health: The Nutrient Rich Way by Lynley DrummondKiwifruit Symposium
Lynley Drummond, Director of Drummond Food Science Advisory, New Zealand. Presented at the 1st International Symposium on Kiwifruit and Health: http://www.kiwifruitsymposium.org/presentations/everyday-good-health-the-nutrient-rich-way/
This presentation considers the role of fruit, in particular kiwifruit, in the diet and how the nutrient-rich, and phytonutrient-rich properties can contribute to the improvement of health outcomes
ESTIMATING FETAL WEIGHT AT VARYING GESTATIONAL AGE USING MACHINE LEARNINGIRJET Journal
This document summarizes research on using machine learning algorithms to estimate fetal weight at varying gestational ages. The researchers trained models using ultrasound parameters like biparietal diameter, abdominal circumference, femur length as features. They used techniques like SMOTE for imbalanced data and different algorithms like SVM, DBN, and ANN. The goal is to help obstetricians more accurately predict fetal weight compared to traditional ultrasound-based methods, in order to reduce prenatal risks. Literature on similar prior research estimating fetal weight with machine learning from maternal characteristics is also reviewed.
Normalization of Large-Scale Metabolomic Studies 2014Dmitry Grapov
This document discusses approaches for normalizing large-scale metabolomics data to minimize analytical variance and remove non-biological artifacts. It describes common normalization methods like analytical standards, quality control-based normalization using LOESS or batch ratios, and variance stabilizing transformations. The document also presents two case studies on normalizing over 5,500 metabolomics samples from the TEDDY study using different normalization approaches like LOESS, batch ratio, qcISTD, and their combinations to minimize analytical variance from over 100 batches and better reveal true biological trends.
Prof. Jon Tobias's presentation from Osteoporosis 2016: Day-to-day levels of high impact physical activity are positively related to lower limb bone strength in older women: findings from a population based study using accelerometers to classify impact magnitude.
Find out more at: https://nos.org.uk/conference
Dr. Ken Stalder - Pork Industry Productivity AnalysisJohn Blue
Pork Industry Productivity Analysis - Dr. Ken Stalder, Iowa State University, from the 2014 World Pork Expo, June 4 - 6, 2014, Des Moines, IA, USA.
More presentations at http://www.swinecast.com/2014-world-pork-expo
Dr. Ken Stalder - Industry Productivity AnalysisJohn Blue
This document summarizes a study analyzing key productivity indicators in the U.S. pork industry from 2007 to 2012 using data from a large record keeping organization. It finds that finishing mortality has declined while market weight has increased. Nursery performance has changed little and sow productivity has improved with pigs per mated sow and litter size increasing. The document provides averages and standard deviations for various metrics across different production phases and graphs trends over time to benchmark performance.
Similar to Estimation of yields for long lactations using best prediction (20)
The national genetic evaluation program
for dairy cattle in the United States is described. Topics include an historical overview of traits and statistical methodology, the structure of the contemporary dairy genetics industry, and the implementation of genomic selection.
Using genotypes to construct phenotypes for dairy cattle breeding programs an...John B. Cole, Ph.D.
Modern dairying uses sophisticated data collection systems to maximize farm profitability. This has traditionally included information on cows and their environments, and now commonly includes genotype information from high-density single nucleotide polymorphism (SNP) panels. The US national database alone contains genotypes for 924,543 bulls and cows as of March 23, 2015, and many other countries are also genotyping animals. As the data continue to grow, the prospect of using genotypes to construct phenotypes directly, instead of measuring phenotypes on animals, becomes more attractive. There are many applications for this genomic information other than the prediction of breeding values. A notable recent application is the use of haplotypes in combination with next-generation sequencing data to identify causal variants associated with recessives. The methodology for identifying recessive haplotypes by searching for a deficit of homozygotes was first used in combination with sequence data to identify the causal variant (APAF1) associated with the HH1 haplotype. The US currently tracks 24 recessive haplotypes in four cattle breeds, and thanks to the work of several teams around the world the causal variants for 17 of them are known. The haplotypes include lethal recessive conditions, such as brachyspina, as well as hair coat color and polledness. There is growing interest in the latter to improve animal welfare and increase economic efficiency, but the polled haplotype has a very low frequency (0.41%, 0.93%, and 2.22% in Brown Swiss, Holstein, and Jersey, respectively). Increasing haplotype frequency by index selection requires known status for all animals. Gene content (GC) for non-genotyped animals was computed using records from genotyped relatives. Prediction accuracy was checked by comparing polled status from recessive codes and animal names to GC for 1,615 non-genotyped Jerseys with known status. 97% (n = 675) of horned animals were correctly assigned GC near 0, and 3% (n = 19) were assigned GC near 1. Heterozygous polled animals had GC near 0 (52%, n = 474) and near 1 (47%; n = 433), although 3 animals were assigned a GC near 2. All homozygous polled animals (n = 11) were assigned GC near 2. Genotype information can also be combined with other data, such as milk spectral data, to predict phenotypes for traits that are expensive or difficult to measure directly. These data can be used for precision farm management, including early culling decisions, monitoring of animals at risk for health problems, and identification of efficient and inefficient cows. The most substantial challenge faced by many dairy managers will be the effective use of the new phenotypes that now are available.
This presentation describes recent changes to the national genetic evaluation system, as well as new research undertaken by AGIL scientists. Topics covered include the 2014 genetic base change, updates to the lifetime net merit selection index, and introduction of the grazing merit index, and the redefinition of daughter pregnancy rate. New research on the use of gene content to predict polled status, and statistical models for accommodating genotype-by-environment interactions also are described.
If we would see further than others: research & technology today and tomorrowJohn B. Cole, Ph.D.
The document discusses the use of technology on dairy farms. It notes that technology provides benefits like making work faster, cheaper, and easier. It then discusses several technologies used on dairy farms today like automated systems for measuring feed and water intake, monitoring cows, and milking systems. The document also looks at challenges like the need for more frequent milk sampling and how collected on-farm data is not always put in a central database. It concludes that sensor technology is producing large amounts of data that could improve management if combined across farms.
Using genotyping and whole-genome sequencing to identify causal variants asso...John B. Cole, Ph.D.
Talk on identification of causal variants given to graduate students at the Universidade Federal de Viçosa in Viçosa, MG, Brasil, on September 9, 2014. It discusses work in my lab to identify causal variants associated with simple and complex modes of inheritance using SNP genotyping and next generation sequencing.
Talk on the genetic and genomic evaluation system for US dairy cattle made to scientists at Embrapa Gado de Leite in Juiz de Fora, MG, Brasil, on September 10, 2014.
The hunt for a functional mutation affecting conformation and calving traits ...John B. Cole, Ph.D.
Presentation from the 10th WCGALP meeting in Vancouver describing our research to identify the causal variant associated with calving and conformation (body shape and size) traits in Holstein cattle.
An updated version of lifetime net merit incorporating additional fertility t...John B. Cole, Ph.D.
The slides for my upcoming talk on the 2014 revision of the lifetime net merit selection index to be presented at the 2014 ASAS-ADSA-CSAS Joint Annual Meeting in Kansas City, MO.
Genetic Evaluation of Stillbirth in US Holsteins Using a Sire-maternal Grands...John B. Cole, Ph.D.
This document summarizes the genetic evaluation of stillbirth in US Holsteins using a sire-maternal grandsire threshold model. Over 6 million stillbirth records from 1980-2005 were analyzed. Heritability of direct and maternal stillbirth was estimated to be 3.0% and 6.5%, respectively. Genetic and phenotypic trends for stillbirth over time were small. Stillbirth EPDs were included in the US National Milk Progeny Test program starting in August 2006 and the US began participating in Interbull evaluations for calving traits in November 2006.
This document summarizes John Cole's presentation on new genomic tools for dairy cattle. Some key points:
1) Genomic selection works well in dairy cattle due to extensive historical data, genetic evaluation programs, widespread AI use, and high-value animals. Genomics can reduce generation intervals.
2) Different genotyping arrays like BovineSNP50 and BovineHD are used, with over 300,000 animals genotyped as of 2013.
3) Genomic predictions provide information equivalent to dozens of progeny, improving reliability of selection, especially for lowly heritable traits. This allows more rapid genetic improvement.
Opportunities for genetic improvement of health and fitness traitsJohn B. Cole, Ph.D.
This document summarizes a presentation on opportunities for genetic improvement of health and fitness traits in dairy cattle. It discusses challenges such as low heritability of health traits and lack of standardized recording. It also provides examples of ways to increase genetic gain, such as improving reliability through genomics and increasing selection intensity. International efforts to develop guidelines for recording health traits are described. Overall, the presentation argues that improving health and fitness should be a priority for the dairy industry given economic impacts, and that genetic selection can help achieve improvements provided data recording is expanded and standardized.
Genomic selection and systems biology – lessons from dairy cattle breedingJohn B. Cole, Ph.D.
Presentation made to the staff of Keygene, NV, in Wageningen, The Netherlands.
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Use of NGS to identify the causal variant associated with a complex phenotypeJohn B. Cole, Ph.D.
This document summarizes a presentation on using next generation sequencing to identify the causal variant associated with a complex phenotype like dystocia in cattle. It discusses selecting animals to sequence, the sequencing and analysis process, challenges in annotation and validation, and recent successes in identifying causal mutations for other traits in cattle. The presentation outlines using sequencing to investigate a quantitative trait locus for dystocia on chromosome 18 in cattle that affects traits like birth weight and gestation length. It describes analyzing sequence data to identify variants associated with predicted birth weight and discusses ongoing challenges in sequencing, analysis, and validating causal variants.
This document summarizes research on genomic evaluation of dairy cattle health traits. It discusses challenges in evaluating health traits including low heritability and inconsistent definitions. The researchers conducted single and multiple trait genetic and genomic analyses on health event data from over 1 million US dairy cow records. Heritability estimates for various health events ranged from 0.03 to 0.20. Genomic evaluation allowed more accurate prediction of sires' daughters' probabilities of different health events compared to traditional genetic evaluation. The multiple trait genomic analysis found moderate to strong genetic correlations between some health events.
The use and economic value of genomic testing for calves on dairy farmsJohn B. Cole, Ph.D.
This document discusses the use and economic value of genomic testing for calves on dairy farms. It provides information on how genomic selection works and how it can increase genetic gain. Genomic testing can be used for animal identification and parentage verification, early culling decisions, mate selection to produce better calves, and identifying elite cows. The document examines studies that show dairy farms could make improved mating decisions using genomic testing data to pair cows and bulls. Genomic testing provides reliable estimates of animals' genetic merit and can help farmers make better long-term breeding and management decisions.
Genomic evaluation of low-heritability traits: dairy cattle health as a modelJohn B. Cole, Ph.D.
Genetic selection has been very successful when applied to traits of moderate to high heritability, but progress has been slow for traits with low heritabilities. The problem is further compounded when novel traits are considered because data needed to calculate high-reliability PTA generally are not available. A combination of producer-recorded health event data and SNP genotypes may permit the routine calculation of PTA with reasonable reliabilities for health traits.
Poster presented at the 5th International Symposium on Animal Functional Genetics in Guaruja, Brazil, in 2014.
New applications of genomic technology in the US dairy industryJohn B. Cole, Ph.D.
John B. Cole presented on new applications of genomic technology in the US dairy industry. Some key points include: genomic selection has been successful due to extensive historical data and widespread use of AI; over 300,000 animals have been genotyped across several dairy breeds; the percentage of genomically tested young bulls marketed has increased significantly since 2007; non-additive effects, novel recessives, and whole genome sequencing can further improve selection; and new phenotypes like health traits and methane production are being studied but require large datasets for accurate evaluation.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Estimation of yields for long lactations using best prediction
1. J. B. ColeJ. B. Cole1,*1,*
, P. M. VanRaden, P. M. VanRaden11
, and C. M. B., and C. M. B.
DematawewaDematawewa22
1
Animal Improvement Programs Laboratory, Agricultural
Research Service, USDA, Beltsville, MD
2
Department of Dairy Science, Virginia Polytechnic Institute
and State University, Blacksburg
2007
Estimation of yields for long
lactations using best prediction
2. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Best PredictionBest Prediction
VanRaden JDS 80:3015-3022 (1997), 6VanRaden JDS 80:3015-3022 (1997), 6thth
WCGALP XXIII:347-350 (1998)WCGALP XXIII:347-350 (1998)
• Selection IndexSelection Index
− Predict missing yields from measured yields.Predict missing yields from measured yields.
− Condense test days into lactation yield andCondense test days into lactation yield and
persistency.persistency.
− Only phenotypic covariances are needed.Only phenotypic covariances are needed.
− Mean and variance of herd assumed known.Mean and variance of herd assumed known.
• Reverse predictionReverse prediction
− Daily yield predicted from lactation yield andDaily yield predicted from lactation yield and
persistency.persistency.
• Single or multiple trait predictionSingle or multiple trait prediction
3. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
HistoryHistory
• Calculation of lactation records for
milk (M), fat (F), protein (P), and
somatic cell score (SCS) using best
prediction (BP) began in November
1999.
• Replaced the test interval method
and projection factors at AIPL.
• Used for cows calving in January 1997
and later.
4. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
AdvantagesAdvantages
• Small for most 305-d lactations but
larger for lactations with infrequent
testing or missing component
samples.
• More precise estimation of records
for SCS because test days are
adjusted for stage of lactation.
• Yield records have slightly lower SD
because BP regresses estimates
toward the herd average.
5. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
UsersUsers
• AIPL: Calculation of lactation yields
and data collection ratings (DCR).
− DCR indicates the accuracy of lactation
records obtained from BP.
• Breed Associations: Publish DCR on
pedigrees.
• DRPCs: Interested in replacing test
interval estimates with BP.
− Can also calculate persistency.
− May have management applications.
6. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Restrictions of Original SoftwareRestrictions of Original Software
• Limited to 305-d lactations used since
1935.
• Changes to parameters requires
recompilation.
• Uses simple linear interpolation for
calculation of standard curves.
• It is not possible to obtain BP for
individual days of lactation.
7. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Enhancements in New SoftwareEnhancements in New Software
• Lactations of any length can be modeled.
− Lactation-to-date and projected yields.
• The autoregressive function used to
model correlations among test day yields
was updated.
• Program options set in a parameter file.
• Diagnostic plots available for all traits.
• BP of individual daily yields, test day
yields, and standard curves now output.
8. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Data and EditsData and Edits
• Holstein TD data were extracted from
the national dairy database.
• The edits of Norman et al. (1999)
were applied to the data set used by
Dematawewa et al. (2007).
− 1st through 5th parities were included.
− Lactation lengths were at least 250 d for
the 305 d group and 800 d for the 999 d
group.
− Records were made in a single herd.
− At least five tests were reported.
− Only twice-daily milking was reported.
9. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Summary StatisticsSummary Statistics
First Later
Records 171,970 176,153
Length (d) 362 369
Pct > 305-d 23.9 27.5
Pct > 500-d 3.3 3.4
10. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Correlations among test day yieldsCorrelations among test day yields
Norman et al. JDS 82:2205-2211 (1999)Norman et al. JDS 82:2205-2211 (1999)
• An autoregressive matrix accounts for
biological changes, and an identity
matrix models daily measurement
error.
• Autoregressive parameters (r) were
estimated separately for first-
(r=0.998) and later-parity (r=0.995)
cows.
• These r were slightly larger than
previous estimates due to the
inclusion of the identity matrix.
11. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Modeling Long LactationsModeling Long Lactations
• Dematawewa et al. (2007) recommend
simple models, such as Wood's (1967)
curve, for long lactations.
• Curves were developed for M, F, and P
yield, but not SCS.
− Little previous work on fitting lactation
curves to SCS (Rodriguez-Zas et al., 2000).
• BP also requires curves for the standard
deviation (SD) of yields.
12. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Modeling SCS and SDModeling SCS and SD
• Test day yields were assigned to 30-d
intervals and means and SD were
calculated for each interval.
− First, second, and third-and-later parities.
• Curves were fit to the resulting means
(SCS) and SD (all traits).
• SD of yield modeled with Woods curves.
• SCS means and SD modeled using curve
C4 from Morant and Gnanasankthy
(1989).
13. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Milk Yield (1Mean Milk Yield (1stst
parity) (kg)parity) (kg)
14. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
SD of Milk Yield (first parity) (kg)SD of Milk Yield (first parity) (kg)
15. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Somatic Cell Score (1Mean Somatic Cell Score (1stst
parity)parity)
16. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Somatic Cell Score(3+ parity)Mean Somatic Cell Score(3+ parity)
17. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
SD of Somatic Cell Score (1SD of Somatic Cell Score (1stst
parity)parity)
18. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
SD of Somatic Cell Score (3+ parity)SD of Somatic Cell Score (3+ parity)
19. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Uses of Daily EstimatesUses of Daily Estimates
• Daily yields can be adjusted for
known sources of variation.
− Example: Daily loss from clinical mastitis
(Rajala-Schultz et al., 1999).
• This could lead to animal-specific
rather than group-specific
adjustments.
• Research into optimal management
strategies.
• Management support in on-farm
computer software.
20. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Mean Milk Yield (kg)Mean Milk Yield (kg)
21. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
Accounting for Mastitis LossesAccounting for Mastitis Losses
22. ADSA 2007 – Best prediction and long lactations Cole et al. 2007
ConclusionsConclusions
Correlations among successive test days
may require periodic re-estimation as
lactation curves change.
Many cows can produce profitably for
>305 days in milk, and the revised BP
program provides a flexible tool to
model those records.
Daily BP of yields may be useful for on-
farm management.