FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366998746> ?p ?o ?g. }

• W4366998746 endingPage "4244" @default.
• W4366998746 startingPage "4232" @default.
• W4366998746 abstract "Body condition score (BCS) is a subjective estimate of body reserves in cows. Body condition score and its change in early lactation have been associated with cow fertility and health. The aim of the present study was to estimate change in BCS (ΔBCS) using mid-infrared spectra of the milk, with a particular focus on estimating ΔBCS in cows losing BCS at the fastest rate (i.e., the cows most of interest to the producer). A total of 73,193 BCS records (scale 1 to 5) from 6,572 cows were recorded. Daily BCS was interpolated from cubic splines fitted through the BCS records, and subsequently used to calculate daily ΔBCS. Body condition score change records were merged with milk mid-infrared spectra recorded on the same week. Both morning (a.m.) and evening (p.m.) spectra were available. Two different statistical methods were used to estimate ΔBCS: partial least squares regression and a neural network (NN). Several combinations of variables were included as model features, such as days in milk (DIM) only, a.m. spectra only and DIM, p.m. spectra only and DIM, and a.m. and p.m. spectra as well as DIM. The data used to estimate ΔBCS were either based on the first 120 DIM or all 305 DIM. Daily ΔBCS had a standard deviation of 1.65 × 10-3 BCS units in the 305 DIM data set and of 1.98 × 10-3 BCS units in the 120 DIM data set. Each data set was divided into 4 sub-data sets, 3 of which were used for training the prediction model and the fourth to test it. This process was repeated until all the sub-data sets were considered as the test data set once. Using all 305 DIM, the lowest root mean square error of validation (RMSEV; 0.96 × 10-3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.82) was achieved with NN using a.m. and p.m. spectra and DIM. Using the 120 DIM data, the lowest RMSEV (0.98 × 10-3 BCS units) and the strongest correlation between actual and estimated ΔBCS (0.87) was achieved with NN using DIM and either a.m. spectra only or a.m. and p.m. spectra together. The RMSEV for records in the lowest 2.5% ΔBCS percentile per DIM in early lactation was reduced up to a maximum of 13% when spectra and DIM were both considered in the model compared with a model that considered just DIM. The performance of the NN using DIM and a.m. spectra only with the 120 DIM data was robust across different strata of farm, parity, year of sampling, and breed. Results from the present study demonstrate the ability of mid-infrared spectra of milk coupled with machine learning techniques to estimate ΔBCS; specifically, the inclusion of spectral data reduced the RMSEV over and above using DIM alone, particularly for cows losing BCS at the fastest rate. This approach can be used to routinely generate estimates of ΔBCS that can subsequently be used for farm decisions." @default.
• W4366998746 created "2023-04-27" @default.
• W4366998746 creator A5033957848 @default.
• W4366998746 creator A5042156705 @default.
• W4366998746 creator A5049795579 @default.
• W4366998746 creator A5053986875 @default.
• W4366998746 date "2023-06-01" @default.
• W4366998746 modified "2023-10-09" @default.
• W4366998746 title "Estimation of body condition score change in dairy cows in a seasonal calving pasture-based system using routinely available milk mid-infrared spectra and machine learning techniques" @default.
• W4366998746 cites W1538242449 @default.
• W4366998746 cites W1964464800 @default.
• W4366998746 cites W1977404830 @default.
• W4366998746 cites W1989838590 @default.
• W4366998746 cites W2003946092 @default.
• W4366998746 cites W2022805378 @default.
• W4366998746 cites W2024055184 @default.
• W4366998746 cites W2036421323 @default.
• W4366998746 cites W2037648658 @default.
• W4366998746 cites W2038242845 @default.
• W4366998746 cites W2042127308 @default.
• W4366998746 cites W2053559523 @default.
• W4366998746 cites W2073503722 @default.
• W4366998746 cites W2088519406 @default.
• W4366998746 cites W2108439743 @default.
• W4366998746 cites W2109342785 @default.
• W4366998746 cites W2110328184 @default.
• W4366998746 cites W2118051155 @default.
• W4366998746 cites W2120443247 @default.
• W4366998746 cites W2131051013 @default.
• W4366998746 cites W2131315622 @default.
• W4366998746 cites W2136106484 @default.
• W4366998746 cites W2137188155 @default.
• W4366998746 cites W2147456113 @default.
• W4366998746 cites W2158007459 @default.
• W4366998746 cites W2159502199 @default.
• W4366998746 cites W2253306814 @default.
• W4366998746 cites W2292946783 @default.
• W4366998746 cites W2339688661 @default.
• W4366998746 cites W2646835289 @default.
• W4366998746 cites W2738536068 @default.
• W4366998746 cites W2772968419 @default.
• W4366998746 cites W2810297782 @default.
• W4366998746 cites W2943326749 @default.
• W4366998746 cites W2947572220 @default.
• W4366998746 cites W2959401958 @default.
• W4366998746 cites W3069362868 @default.
• W4366998746 cites W3124698940 @default.
• W4366998746 cites W3155241274 @default.
• W4366998746 cites W3195581525 @default.
• W4366998746 cites W3202035851 @default.
• W4366998746 doi "https://doi.org/10.3168/jds.2022-22394" @default.
• W4366998746 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37105880" @default.
• W4366998746 hasPublicationYear "2023" @default.
• W4366998746 type Work @default.
• W4366998746 citedByCount "2" @default.
• W4366998746 countsByYear W43669987462023 @default.
• W4366998746 crossrefType "journal-article" @default.
• W4366998746 hasAuthorship W4366998746A5033957848 @default.
• W4366998746 hasAuthorship W4366998746A5042156705 @default.
• W4366998746 hasAuthorship W4366998746A5049795579 @default.
• W4366998746 hasAuthorship W4366998746A5053986875 @default.
• W4366998746 hasBestOaLocation W43669987461 @default.
• W4366998746 hasConcept C105795698 @default.
• W4366998746 hasConcept C121332964 @default.
• W4366998746 hasConcept C126322002 @default.
• W4366998746 hasConcept C1276947 @default.
• W4366998746 hasConcept C140793950 @default.
• W4366998746 hasConcept C19720800 @default.
• W4366998746 hasConcept C22641795 @default.
• W4366998746 hasConcept C2776476923 @default.
• W4366998746 hasConcept C2776659692 @default.
• W4366998746 hasConcept C2779234561 @default.
• W4366998746 hasConcept C33923547 @default.
• W4366998746 hasConcept C54355233 @default.
• W4366998746 hasConcept C58489278 @default.
• W4366998746 hasConcept C71924100 @default.
• W4366998746 hasConcept C86803240 @default.
• W4366998746 hasConceptScore W4366998746C105795698 @default.
• W4366998746 hasConceptScore W4366998746C121332964 @default.
• W4366998746 hasConceptScore W4366998746C126322002 @default.
• W4366998746 hasConceptScore W4366998746C1276947 @default.
• W4366998746 hasConceptScore W4366998746C140793950 @default.
• W4366998746 hasConceptScore W4366998746C19720800 @default.
• W4366998746 hasConceptScore W4366998746C22641795 @default.
• W4366998746 hasConceptScore W4366998746C2776476923 @default.
• W4366998746 hasConceptScore W4366998746C2776659692 @default.
• W4366998746 hasConceptScore W4366998746C2779234561 @default.
• W4366998746 hasConceptScore W4366998746C33923547 @default.
• W4366998746 hasConceptScore W4366998746C54355233 @default.
• W4366998746 hasConceptScore W4366998746C58489278 @default.
• W4366998746 hasConceptScore W4366998746C71924100 @default.
• W4366998746 hasConceptScore W4366998746C86803240 @default.
• W4366998746 hasIssue "6" @default.
• W4366998746 hasLocation W43669987461 @default.
• W4366998746 hasLocation W43669987462 @default.
• W4366998746 hasLocation W43669987463 @default.
• W4366998746 hasOpenAccess W4366998746 @default.
• W4366998746 hasPrimaryLocation W43669987461 @default.

• next