Genotypic variability in cotton's transpiration response under progressive soil drying.

Introduction: Crop yields in food and fibre production systems throughout the world are significantly limited by soil water deficits. Identifying water conservation mechanisms within existing genotypes is pivotal in developing varieties with improved performance in water-limited conditions. The objective of this study was to screen Australian germplasm for variability in the transpiration response to progressive soil drying using a glasshouse dry-down experiment.

HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait.

Background: Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS.

The importance of species-specific and temperature-sensitive parameterisation of A/C models: A case study using cotton (Gossypium hirsutum L.) and the automated 'OptiFitACi' R-package.

Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO assimilation to variable intercellular CO concentrations (A/C response curves) are used to model the maximum (potential) rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; J). The standard Farquhar-von Caemmerer-Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO (g) derived from tobacco that may be inapplicable across species.

Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD).

Understanding and Exploiting Transpiration Response to Vapor Pressure Deficit for Water Limited Environments.

More frequent droughts and an increased pressure on water resources, combined with social licence to operate, will inevitably decrease water resources available for fully irrigated cotton production. Therefore, the long-term future of the cotton industry will require more drought tolerant varieties that can perform well when grown in rainfed cropping regions often exposed to intermittent drought. A trait that limits transpiration (TR) under an increased vapour pressure deficit (VPD) may increase crop yield in drier atmospheric conditions and potentially conserve soil water to support crop growth later in the growing season.

Core Collections: Is There Any Value for Cotton Breeding?

Global plant breeding activities are reliant on the available genetic variation held in extant varieties and germplasm collections. Throughout the mid- to late 1900s, germplasm collecting efforts were prioritized for breeding programs to archive precious material before it disappeared and led to the development of the numerous large germplasm resources now available in different countries. In recent decades, however, the maintenance and particularly the expansion of these germplasm resources have come under threat, and there has been a significant decline in investment in further collecting expeditions, an increase in global biosecurity restrictions, and restrictions placed on the open exchange of some commercial germplasm between breeders.

Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods.

Genomic selection or genomic prediction (GP) has increasingly become an important molecular breeding technology for crop improvement. GP aims to utilise genome-wide marker data to predict genomic breeding value for traits of economic importance. Though GP studies have been widely conducted in various crop species such as wheat and maize, its application in cotton, an essential renewable textile fibre crop, is still significantly underdeveloped.

HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance.

Background: Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of global natural fibre production, and in this crop leaf hairiness also affects fibre yield and value. Currently, this key phenotype is measured visually which is slow, laborious and operator-biased.

A standardised approach for determining heat tolerance in cotton using triphenyl tetrazolium chloride.

Improving the heat tolerance of cotton is a major concern for breeding programs. To address this need, a fast and effect way of quantifying thermotolerant phenotypes is required. Triphenyl tetrazolium chloride (TTC) based enzyme viability testing following high-temperature stress can be used as a vegetative heat tolerance phenotype.

Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments.

Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there has not been a study to evaluate GS prediction models that may be used for predicting cotton breeding lines across multiple environments. In this study, we evaluated the performance of Bayes Ridge Regression, BayesA, BayesB, BayesC and Reproducing Kernel Hilbert Spaces regression models.

Vapour pressure deficit aids the interpretation of cotton canopy temperature response to water deficit.

Crop canopy temperature (Tc) is coupled with transpiration, which is a function of soil and atmospheric conditions and plant water status. Thus, Tc has been identified as a real-time, plant-based tool for crop water stress detection. Such plant-based methods theoretically integrate the water status of both the plant and its environment.