Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record

The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accur...
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Autor*in:	Jimmy Semakula [verfasserIn] Rene Anne Corner-Thomas [verfasserIn] Stephen Todd Morris [verfasserIn] Hugh Thomas Blair [verfasserIn] Paul Richard Kenyon [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	accuracy multivariate variable prediction error record

Übergeordnetes Werk:	In: Animals - MDPI AG, 2011, 10(2020), 7, p 1182
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:7, p 1182

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ani10071182

Katalog-ID:	DOAJ016349253

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allfields	10.3390/ani10071182 doi (DE-627)DOAJ016349253 (DE-599)DOAJ8c2a59ee04604e4986f249b6ac853a3a DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 Jimmy Semakula verfasserin aut Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for. accuracy multivariate variable prediction error record Veterinary medicine Zoology Rene Anne Corner-Thomas verfasserin aut Stephen Todd Morris verfasserin aut Hugh Thomas Blair verfasserin aut Paul Richard Kenyon verfasserin aut In Animals MDPI AG, 2011 10(2020), 7, p 1182 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:10 year:2020 number:7, p 1182 https://doi.org/10.3390/ani10071182 kostenfrei https://doaj.org/article/8c2a59ee04604e4986f249b6ac853a3a kostenfrei https://www.mdpi.com/2076-2615/10/7/1182 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1182
spelling	10.3390/ani10071182 doi (DE-627)DOAJ016349253 (DE-599)DOAJ8c2a59ee04604e4986f249b6ac853a3a DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 Jimmy Semakula verfasserin aut Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for. accuracy multivariate variable prediction error record Veterinary medicine Zoology Rene Anne Corner-Thomas verfasserin aut Stephen Todd Morris verfasserin aut Hugh Thomas Blair verfasserin aut Paul Richard Kenyon verfasserin aut In Animals MDPI AG, 2011 10(2020), 7, p 1182 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:10 year:2020 number:7, p 1182 https://doi.org/10.3390/ani10071182 kostenfrei https://doaj.org/article/8c2a59ee04604e4986f249b6ac853a3a kostenfrei https://www.mdpi.com/2076-2615/10/7/1182 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1182
allfields_unstemmed	10.3390/ani10071182 doi (DE-627)DOAJ016349253 (DE-599)DOAJ8c2a59ee04604e4986f249b6ac853a3a DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 Jimmy Semakula verfasserin aut Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for. accuracy multivariate variable prediction error record Veterinary medicine Zoology Rene Anne Corner-Thomas verfasserin aut Stephen Todd Morris verfasserin aut Hugh Thomas Blair verfasserin aut Paul Richard Kenyon verfasserin aut In Animals MDPI AG, 2011 10(2020), 7, p 1182 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:10 year:2020 number:7, p 1182 https://doi.org/10.3390/ani10071182 kostenfrei https://doaj.org/article/8c2a59ee04604e4986f249b6ac853a3a kostenfrei https://www.mdpi.com/2076-2615/10/7/1182 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1182
allfieldsGer	10.3390/ani10071182 doi (DE-627)DOAJ016349253 (DE-599)DOAJ8c2a59ee04604e4986f249b6ac853a3a DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 Jimmy Semakula verfasserin aut Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for. accuracy multivariate variable prediction error record Veterinary medicine Zoology Rene Anne Corner-Thomas verfasserin aut Stephen Todd Morris verfasserin aut Hugh Thomas Blair verfasserin aut Paul Richard Kenyon verfasserin aut In Animals MDPI AG, 2011 10(2020), 7, p 1182 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:10 year:2020 number:7, p 1182 https://doi.org/10.3390/ani10071182 kostenfrei https://doaj.org/article/8c2a59ee04604e4986f249b6ac853a3a kostenfrei https://www.mdpi.com/2076-2615/10/7/1182 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1182
allfieldsSound	10.3390/ani10071182 doi (DE-627)DOAJ016349253 (DE-599)DOAJ8c2a59ee04604e4986f249b6ac853a3a DE-627 ger DE-627 rakwb eng SF600-1100 QL1-991 Jimmy Semakula verfasserin aut Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for. accuracy multivariate variable prediction error record Veterinary medicine Zoology Rene Anne Corner-Thomas verfasserin aut Stephen Todd Morris verfasserin aut Hugh Thomas Blair verfasserin aut Paul Richard Kenyon verfasserin aut In Animals MDPI AG, 2011 10(2020), 7, p 1182 (DE-627)657589306 (DE-600)2606558-7 20762615 nnns volume:10 year:2020 number:7, p 1182 https://doi.org/10.3390/ani10071182 kostenfrei https://doaj.org/article/8c2a59ee04604e4986f249b6ac853a3a kostenfrei https://www.mdpi.com/2076-2615/10/7/1182 kostenfrei https://doaj.org/toc/2076-2615 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 7, p 1182
language	English
source	In Animals 10(2020), 7, p 1182 volume:10 year:2020 number:7, p 1182
sourceStr	In Animals 10(2020), 7, p 1182 volume:10 year:2020 number:7, p 1182
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	accuracy multivariate variable prediction error record Veterinary medicine Zoology
isfreeaccess_bool	true
container_title	Animals
authorswithroles_txt_mv	Jimmy Semakula @@aut@@ Rene Anne Corner-Thomas @@aut@@ Stephen Todd Morris @@aut@@ Hugh Thomas Blair @@aut@@ Paul Richard Kenyon @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	657589306
id	DOAJ016349253
language_de	englisch
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callnumber-first	S - Agriculture
author	Jimmy Semakula
spellingShingle	Jimmy Semakula misc SF600-1100 misc QL1-991 misc accuracy misc multivariate misc variable misc prediction error misc record misc Veterinary medicine misc Zoology Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record
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topic_title	SF600-1100 QL1-991 Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record accuracy multivariate variable prediction error record
topic	misc SF600-1100 misc QL1-991 misc accuracy misc multivariate misc variable misc prediction error misc record misc Veterinary medicine misc Zoology
topic_unstemmed	misc SF600-1100 misc QL1-991 misc accuracy misc multivariate misc variable misc prediction error misc record misc Veterinary medicine misc Zoology
topic_browse	misc SF600-1100 misc QL1-991 misc accuracy misc multivariate misc variable misc prediction error misc record misc Veterinary medicine misc Zoology
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title	Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record
ctrlnum	(DE-627)DOAJ016349253 (DE-599)DOAJ8c2a59ee04604e4986f249b6ac853a3a
title_full	Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record
author_sort	Jimmy Semakula
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author_browse	Jimmy Semakula Rene Anne Corner-Thomas Stephen Todd Morris Hugh Thomas Blair Paul Richard Kenyon
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author-letter	Jimmy Semakula
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title_sort	predicting ewe body condition score using lifetime liveweight and liveweight change, and previous body condition score record
callnumber	SF600-1100
title_auth	Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record
abstract	The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for.
abstractGer	The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for.
abstract_unstemmed	The body condition score (BCS) in sheep (<i<Ovis aries</i<) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (<i<n</i< = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (<i<p</i< < 0.01) the R<sup<2</sup< value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for.
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title_short	Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record
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score	7.3982153

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Predicting Ewe Body Condition Score Using Lifetime Liveweight and Liveweight Change, and Previous Body Condition Score Record

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