The auxin efflux carrier PIN1a regulates vascular patterning in cereal roots.

Barley (Hordeum vulgare) is an important global cereal crop and a model in genetic studies. Despite advances in characterising barley genomic resources, few mutant studies have identified genes controlling root architecture and anatomy, which plays a critical role in capturing soil resources. Our phenotypic screening of a TILLING mutant collection identified line TM5992 exhibiting a short-root phenotype compared with wild-type (WT) Morex background. Outcrossing TM5992 with barley variety Proctor and subsequent SNP array-based bulk segregant analysis, fine mapped the mutation to a cM scale. Exome sequencing pinpointed a mutation in the candidate gene HvPIN1a, further confirming this by analysing independent mutant alleles. Detailed analysis of root growth and anatomy in Hvpin1a mutant alleles exhibited a slower growth rate, shorter apical meristem and striking vascular patterning defects compared to WT. Expression and mutant analyses of PIN1 members in the closely related cereal brachypodium (Brachypodium distachyon) revealed that BdPIN1a and BdPIN1b were redundantly expressed in root vascular tissues but only Bdpin1a mutant allele displayed root vascular defects similar to Hvpin1a. We conclude that barley PIN1 genes have sub-functionalised in cereals, compared to Arabidopsis (Arabidopsis thaliana), where PIN1a sequences control root vascular patterning.

A mixture of grass-legume cover crop species may ameliorate water stress in a changing climate.

Climate change models predict increasing precipitation variability in the mid-latitude regions of Earth, generating a need to reduce the negative impacts of these changes on crop production. Despite considerable research on how cover crops support agriculture in a changing climate, understanding is limited of how climate change influences the growth of cover crops. We investigated the early development of two common cover crop species-crimson clover (Trifolium incarnatum) and rye (Secale cereale)-and hypothesized that growing them in the mixture would ameliorate stress from drought or waterlogging. This hypothesis was tested in a 25-day greenhouse experiment, where the two factors (species number and water stress) were fully crossed in randomized blocks, and plant responses were quantified through survival, growth rate, biomass production and root morphology. Water stress negatively influenced the early growth of these two species in contrasting ways: crimson clover was susceptible to drought while rye performed poorly under waterlogging. Per-plant biomass in rye was always greater in mixture than in monoculture, while per-plant biomass of crimson clover was greater in mixture under drought. Both species grew longer roots in mixture than in monoculture under drought, and total biomass of mixtures did not differ significantly from the more-productive monoculture (rye) in any water condition. In the face of increasingly variable precipitation, growing crimson clover and rye together has potential to ameliorate water stress, a possibility that should be further investigated in field experiments.

The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.

Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.