FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313886456> ?p ?o ?g. }

• W4313886456 abstract "Nitrate leaching to groundwater and surface water and ammonia volatilization from dairy farms have negative impacts on the environment. Meanwhile, the increasing demand for dairy products will result in more pollution if N losses are not controlled. Therefore, a more efficient, and environmentally friendly production system is needed, in which nitrogen use efficiency (NUE) of dairy cows plays a key role. To genetically improve NUE, extensively recorded and cost-effective proxies are essential, which can be obtained by including mid-infrared (MIR) spectra of milk in prediction models for NUE. This study aimed to develop and validate the best prediction model of NUE, nitrogen loss (NL) and dry matter intake (DMI) for individual dairy cows in China.A total of 86 lactating Chinese Holstein cows were used in this study. After data editing, 704 records were obtained for calibration and validation. Six prediction models with three different machine learning algorithms and three kinds of pre-processed MIR spectra were developed for each trait. Results showed that the coefficient of determination (R2) of the best model in within-herd validation was 0.66 for NUE, 0.58 for NL and 0.63 for DMI. For external validation, reasonable prediction results were only observed for NUE, with R2 ranging from 0.58 to 0.63, while the R2 of the other two traits was below 0.50. The infrared waves from 973.54 to 988.46 cm-1 and daily milk yield were the most important variables for prediction.The results showed that individual NUE can be predicted with a moderate accuracy in both within-herd and external validations. The model of NUE could be used for the datasets that are similar to the calibration dataset. The prediction models for NL and 3-day moving average of DMI (DMI_a) generated lower accuracies in within-herd validation. Results also indicated that information of MIR spectra variables increased the predictive ability of models. Additionally, pre-processed MIR spectra do not result in higher accuracy than original MIR spectra in the external validation. These models will be applied to large-scale data to further investigate the genetic architecture of N efficiency and further reduce the adverse impacts on the environment after more data is collected." @default.
• W4313886456 created "2023-01-10" @default.
• W4313886456 creator A5008956416 @default.
• W4313886456 creator A5013402799 @default.
• W4313886456 creator A5013799425 @default.
• W4313886456 creator A5039020647 @default.
• W4313886456 creator A5048062762 @default.
• W4313886456 creator A5049927222 @default.
• W4313886456 creator A5053299755 @default.
• W4313886456 creator A5069101595 @default.
• W4313886456 date "2023-01-10" @default.
• W4313886456 modified "2023-10-14" @default.
• W4313886456 title "Predicting nitrogen use efficiency, nitrogen loss and dry matter intake of individual dairy cows in late lactation by including mid-infrared spectra of milk samples" @default.
• W4313886456 cites W1885547432 @default.
• W4313886456 cites W1979603098 @default.
• W4313886456 cites W2002448960 @default.
• W4313886456 cites W2032844392 @default.
• W4313886456 cites W2038242845 @default.
• W4313886456 cites W2046319435 @default.
• W4313886456 cites W2051275357 @default.
• W4313886456 cites W2053559523 @default.
• W4313886456 cites W2091614621 @default.
• W4313886456 cites W2119387367 @default.
• W4313886456 cites W2131609588 @default.
• W4313886456 cites W2134497527 @default.
• W4313886456 cites W2141068923 @default.
• W4313886456 cites W2145035307 @default.
• W4313886456 cites W2167692544 @default.
• W4313886456 cites W2170917242 @default.
• W4313886456 cites W2292946783 @default.
• W4313886456 cites W2303320765 @default.
• W4313886456 cites W2552961485 @default.
• W4313886456 cites W2963148657 @default.
• W4313886456 cites W2967531254 @default.
• W4313886456 cites W3010501026 @default.
• W4313886456 cites W3019328443 @default.
• W4313886456 cites W3026477450 @default.
• W4313886456 cites W3047071846 @default.
• W4313886456 cites W3048372262 @default.
• W4313886456 cites W3126734739 @default.
• W4313886456 cites W4210761283 @default.
• W4313886456 doi "https://doi.org/10.1186/s40104-022-00802-3" @default.
• W4313886456 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36624499" @default.
• W4313886456 hasPublicationYear "2023" @default.
• W4313886456 type Work @default.
• W4313886456 citedByCount "1" @default.
• W4313886456 countsByYear W43138864562023 @default.
• W4313886456 crossrefType "journal-article" @default.
• W4313886456 hasAuthorship W4313886456A5008956416 @default.
• W4313886456 hasAuthorship W4313886456A5013402799 @default.
• W4313886456 hasAuthorship W4313886456A5013799425 @default.
• W4313886456 hasAuthorship W4313886456A5039020647 @default.
• W4313886456 hasAuthorship W4313886456A5048062762 @default.
• W4313886456 hasAuthorship W4313886456A5049927222 @default.
• W4313886456 hasAuthorship W4313886456A5053299755 @default.
• W4313886456 hasAuthorship W4313886456A5069101595 @default.
• W4313886456 hasBestOaLocation W43138864561 @default.
• W4313886456 hasConcept C140793950 @default.
• W4313886456 hasConcept C178790620 @default.
• W4313886456 hasConcept C185592680 @default.
• W4313886456 hasConcept C18903297 @default.
• W4313886456 hasConcept C2776659692 @default.
• W4313886456 hasConcept C2776977481 @default.
• W4313886456 hasConcept C2779234561 @default.
• W4313886456 hasConcept C2780138947 @default.
• W4313886456 hasConcept C33923547 @default.
• W4313886456 hasConcept C39432304 @default.
• W4313886456 hasConcept C48743137 @default.
• W4313886456 hasConcept C537208039 @default.
• W4313886456 hasConcept C54355233 @default.
• W4313886456 hasConcept C86803240 @default.
• W4313886456 hasConceptScore W4313886456C140793950 @default.
• W4313886456 hasConceptScore W4313886456C178790620 @default.
• W4313886456 hasConceptScore W4313886456C185592680 @default.
• W4313886456 hasConceptScore W4313886456C18903297 @default.
• W4313886456 hasConceptScore W4313886456C2776659692 @default.
• W4313886456 hasConceptScore W4313886456C2776977481 @default.
• W4313886456 hasConceptScore W4313886456C2779234561 @default.
• W4313886456 hasConceptScore W4313886456C2780138947 @default.
• W4313886456 hasConceptScore W4313886456C33923547 @default.
• W4313886456 hasConceptScore W4313886456C39432304 @default.
• W4313886456 hasConceptScore W4313886456C48743137 @default.
• W4313886456 hasConceptScore W4313886456C537208039 @default.
• W4313886456 hasConceptScore W4313886456C54355233 @default.
• W4313886456 hasConceptScore W4313886456C86803240 @default.
• W4313886456 hasFunder F4320322725 @default.
• W4313886456 hasIssue "1" @default.
• W4313886456 hasLocation W43138864561 @default.
• W4313886456 hasLocation W43138864562 @default.
• W4313886456 hasLocation W43138864563 @default.
• W4313886456 hasLocation W43138864564 @default.
• W4313886456 hasOpenAccess W4313886456 @default.
• W4313886456 hasPrimaryLocation W43138864561 @default.
• W4313886456 hasRelatedWork W1990408512 @default.
• W4313886456 hasRelatedWork W2030184170 @default.
• W4313886456 hasRelatedWork W2107839362 @default.
• W4313886456 hasRelatedWork W2111716958 @default.
• W4313886456 hasRelatedWork W2138600269 @default.
• W4313886456 hasRelatedWork W2207845687 @default.
• W4313886456 hasRelatedWork W2262443970 @default.

• next