Evidence map›Paper›PMID 36624499›Full record

ArticleJournal of animal science and biotechnology2023

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.

Rui Shi, Wenqi Lou, Bart Ducro, Aart van der Linden, Han A Mulder, Simon J Oosting, Shengli Li, Yachun Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of animal science and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 3 countries.

Rui ShiLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Wenqi LouLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Bart DucroWageningen University & Research Animal Breeding and Genomics, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
Aart van der LindenAnimal Production System Group, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
Han A MulderWageningen University & Research Animal Breeding and Genomics, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
Simon J OostingAnimal Production System Group, Wageningen University & Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
Shengli LiLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
Yachun WangLaboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China. wangyachun@cau.edu.cn.ORCID http://orcid.org/0000-0003-3629-2802
Wageningen University & Research · NLChina Agricultural University · CNMinistry of Agriculture · ID

Funding

Beijing Sanyuan Breeding Technology Ltd. Co. funded project SYZYZ20190005China Agriculture Research System CARS-36China Scholarship Council No. 201913043National Agricultural Genetic Improvement Program 2130135Program for Changjiang Scholar and Innovation Research Team in University IRT_15R62
6 · The paper itself

Abstract

backgroundNitrate 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.

resultsA 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 (R

conclusionThe 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.

Indexed as

Dairy cowEnvironmentMid-infrared spectraNitrogen use efficiencyPrediction

Identifiers

PMID36624499
PMCPMC9830822
OpenAlexW4313886456

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.