Classification and prediction of drought and salinity stress tolerance in barley using GenPhenML.

Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience.

Detection of closely linked QTLs and candidate genes controlling germination indices in response to drought and salinity stresses in barley.

The aim of current study was to identify closely linked QTLs and candidate genes related to germination indices under control, salinity and drought conditions in barley. A total of nine (a major), 28 (eight major) and 34 (five major) closely linked QTLs were mapped on the seven chromosomes in response to control, drought and salinity conditions using genome-wide composite interval mapping, respectively. The major QTLs can be used in marker-assisted selection (MAS) projects to increase tolerance to drought and salinity stresses during the germination.

Mega Meta-QTLs: A Strategy for the Production of Golden Barley ( L.) Tolerant to Abiotic Stresses.

Abiotic stresses cause a significant decrease in productivity and growth in agricultural products, especially barley. Breeding has been considered to create resistance against abiotic stresses. Pyramiding genes for tolerance to abiotic stresses through selection based on molecular markers connected to Mega MQTLs of abiotic tolerance can be one of the ways to reach Golden Barley.