What is he best known for?
The fields of study he is best known for:
- Animal science
- Biochemistry
- Food science
The scientist’s investigation covers issues in Animal science, Silage, Dry matter, Crossbreed and Completely randomized design.
His Animal science research is multidisciplinary, incorporating perspectives in Urea, Rumen, Body water and Urinary excretion.
His research investigates the connection with Silage and areas like Latin square which intersect with concerns in Zebu, Abomasum and Omasum.
His Dry matter study frequently links to related topics such as Forage.
His Crossbreed research is multidisciplinary, incorporating elements of Breed, Nellore cattle and Carcass composition.
Marcos Inácio Marcondes has researched Completely randomized design in several fields, including Spot urine, Excretion and Feedlot.
His most cited work include:
- Consumo, digestibilidade e excreção de uréia e derivados de purinas em vacas de diferentes níveis de produção de leite (43 citations)
- Consumo, digestibilidade e excreção de uréia e derivados de purinas em novilhas de diferentes pesos (39 citations)
- Avaliação de indicadores em estudos com ruminantes: digestibilidade (32 citations)
What are the main themes of his work throughout his whole career to date?
Marcos Inácio Marcondes mainly investigates Animal science, Dry matter, Crossbreed, Silage and Beef cattle.
His work deals with themes such as Body weight, Forage and Nutrient, which intersect with Animal science.
His research integrates issues of Rumen, Latin square and Digestion in his study of Dry matter.
His studies deal with areas such as Breed, Feed conversion ratio and Nellore cattle as well as Crossbreed.
His work is dedicated to discovering how Silage, Urea are connected with Excretion, Urine, Allantoin and Uric acid and other disciplines.
Marcos Inácio Marcondes has included themes like Grazing and Zebu in his Beef cattle study.
He most often published in these fields:
- Animal science (86.67%)
- Dry matter (41.03%)
- Crossbreed (23.59%)
What were the highlights of his more recent work (between 2017-2021)?
- Animal science (86.67%)
- Dry matter (41.03%)
- Crossbreed (23.59%)
In recent papers he was focusing on the following fields of study:
Marcos Inácio Marcondes focuses on Animal science, Dry matter, Crossbreed, Beef cattle and Rumen.
His Animal science study integrates concerns from other disciplines, such as Body weight, Digestion and Latin square.
His Dry matter research includes themes of Starch, Silage, Feed conversion ratio, Forage and Grazing.
His work carried out in the field of Crossbreed brings together such families of science as Breed, Dairy cattle and Zebu.
In his research, Crossbred cattle is intimately related to Carcass weight, which falls under the overarching field of Beef cattle.
The study incorporates disciplines such as Zoology, Microbial ecology, Bacteroides and Fatty acid in addition to Rumen.
Between 2017 and 2021, his most popular works were:
- Bacterial Community Dynamics across the Gastrointestinal Tracts of Dairy Calves during Preweaning Development (25 citations)
- Review: Overview of factors affecting productive lifespan of dairy cows. (15 citations)
- Dietary protein reduction on microbial protein, amino acid digestibility, and body retention in beef cattle: 2. Amino acid intestinal absorption and their efficiency for whole-body deposition. (10 citations)
In his most recent research, the most cited papers focused on:
- Animal science
- Biochemistry
- Food science
His primary areas of investigation include Animal science, Dry matter, Rumen, Beef cattle and Animal feed.
His Animal science study combines topics from a wide range of disciplines, such as Body weight, Propionate and Methionine.
His research in Dry matter intersects with topics in Feed conversion ratio, Digestion and Weaning.
His Rumen research incorporates elements of Caecum, Genus Acetobacter and Fatty acid.
His Animal feed research integrates issues from Randomized block design, Grazing, Panicum and Pasture.
His biological study spans a wide range of topics, including Feedlot, Backgrounding and Crossbreed.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
