What is he best known for?
The fields of study he is best known for:
Majid R. Foolad spends much of his time researching Agronomy, Biotechnology, Genetically modified crops, Botany and Genetics.
Majid R. Foolad combines subjects such as Molecular breeding and Abiotic stress with his study of Biotechnology.
He applies his multidisciplinary studies on Genetically modified crops and Osmoprotectant in his research.
His research in the fields of Horizontal resistance, Gibberellin and Alternaria solani overlaps with other disciplines such as Abiotic component.
Majid R. Foolad usually deals with Genetics and limits it to topics linked to Lycopersicon and Germplasm, Genomics, Plant physiology and Solanaceae.
His work in Heat shock protein addresses issues such as Germination, which are connected to fields such as Chaperone.
His most cited work include:
- Roles of glycine betaine and proline in improving plant abiotic stress resistance (2657 citations)
- Heat tolerance in plants: An overview (1914 citations)
- Pre‐Sowing Seed Treatment—A Shotgun Approach to Improve Germination, Plant Growth, and Crop Yield Under Saline and Non‐Saline Conditions (497 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Genetics, Lycopersicon, Botany, Quantitative trait locus and Horticulture.
His biological study spans a wide range of topics, including Germination and Backcrossing.
His Botany research includes elements of Plant disease resistance, Genetic variation, Abiotic stress and Solanaceae.
His studies deal with areas such as Radicle, Lycopene, Allele and Molecular marker as well as Quantitative trait locus.
As a part of the same scientific family, he mostly works in the field of Horticulture, focusing on Heritability and, on occasion, Genetic analysis and Genetic gain.
His Cultivar research incorporates themes from Vegetative reproduction and Biotechnology.
He most often published in these fields:
- Genetics (37.00%)
- Lycopersicon (34.00%)
- Botany (30.00%)
What were the highlights of his more recent work (between 2012-2020)?
- Blight (24.00%)
- Solanum (17.00%)
- Horticulture (28.00%)
In recent papers he was focusing on the following fields of study:
Majid R. Foolad mainly focuses on Blight, Solanum, Horticulture, Plant disease resistance and Genetics.
His Blight research is multidisciplinary, incorporating elements of Solanum pimpinellifolium, Oomycete and Heritability.
His study looks at the relationship between Solanum and fields such as Cultivar, as well as how they intersect with chemical problems.
In Fungicide, Majid R. Foolad works on issues like Genomics, which are connected to Biotechnology.
In most of his Plant disease resistance studies, his work intersects topics such as Botany.
His work in Genetics is not limited to one particular discipline; it also encompasses Lycopene.
Between 2012 and 2020, his most popular works were:
- Crop breeding for salt tolerance in the era of molecular markers and marker‐assisted selection (123 citations)
- The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor. (121 citations)
- Validation and fine mapping of lyc12.1, a QTL for increased tomato fruit lycopene content (34 citations)
In his most recent research, the most cited papers focused on:
Majid R. Foolad mostly deals with Quantitative trait locus, Solanum, Genetic diversity, Genetics and Biotechnology.
The concepts of his Quantitative trait locus study are interwoven with issues in Genotyping, Restriction fragment length polymorphism, Single-nucleotide polymorphism, Computational biology and Candidate gene.
His Solanum study incorporates themes from Cultivar, Blight, Phytophthora infestans and Oomycete.
His Cultivar study is concerned with the field of Botany as a whole.
His research in Genetic diversity intersects with topics in Fungicide, Domestication, Gene, Allele and Pan-genome.
His Biotechnology research is multidisciplinary, relying on both Molecular breeding, Plant breeding, Abiotic stress and Gene–environment interaction.
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.
