Genetic analysis of the yield and physical quality of wheat revealed complex genetic control, including strong effects of photoperiod-sensitivity loci. Environmental conditions such as moisture deficit and high temperatures during the growing period affect the grain yield and grain characteristics of bread wheat (Triticum aestivum L.). The aim of this study was to map quantitative trait loci (QTL) for grain yield and grain quality traits using a Drysdale/Gladius bread wheat mapping population grown under a range of environmental conditions in Australia and Mexico. In general, yield and grain quality were reduced in environments exposed to drought and/or heat stress. Despite large effects of known photoperiod-sensitivity loci (Ppd-B1 and Ppd-D1) on crop development, grain yield and grain quality traits, it was possible to detect QTL elsewhere in the genome. Some of these QTL were detected consistently across environments. A locus on chromosome 6A (TaGW2) that is known to be associated with grain development was associated with grain width, thickness and roundness. The grain hardness (Ha) locus on chromosome 5D was associated with particle size index and flour extraction and a region on chromosome 3B was associated with grain width, thickness, thousand grain weight and yield. The genetic control of grain length appeared to be largely independent of the genetic control of the other grain dimensions. As expected, effects on grain yield were detected at loci that also affected yield components. Some QTL displayed QTL-by-environment interactions, with some having effects only in environments subject to water limitation and/or heat stress.

Armillaria species are important root rot pathogens with a wide host range and a worldwide distribution. The taxonomy of these fungi has been problematic for many years but the understanding of the relationships between them has been substantially improved through the application of DNA sequence comparisons. In this study, relationships between different Armillaria species were determined using elongation factor 1-alpha DNA sequence data for the first time. A total of 42 isolates, representing the majority of Armillaria species, with diverse geographic distributions and hosts, were included in this study. PCR amplification yielded products of 600 bp for all the isolates. Phylogenetic trees resulting from parsimony analysis showed that this gene region is useful for studying relationships between species. Generally, results were similar to those emerging from previous comparisons using ITS and IGS-1 sequence data. Phylogenetic trees generated from the dataset grouped the African taxa in a strongly supported clade, basal to the rest of the Armillaria species included in the study. The Armillaria species originating from the Northern Hemisphere formed a monophyletic group. Within this group, isolates of A. mellea constituted four subclades, representing their geographical origin. The phylogenetic relationships among species from the Southern Hemisphere were not entirely resolved. However, A. pallidula, A. fumosa and A. hinnulea grouped in a strongly supported clade and isolates of A. limonea formed a sister clade with those of A. luteobubalina. This is the first time a single-copy protein coding gene has been used to study phylogenetic relationships in Armillaria, and overall the data support previously held views regarding the relationships between species.

HighlightWe describe new quantitative trait loci for growth and transpiration in wheat under two water regimes using an imaging platform, and co-location with loci for yield components in the field.

ORCID IDs: 0000-0002-7600-9592 (B.P.); 0000-0003-3851-8617 (J.B.); 0000-0001-9494-400X (P.L.); 0000-0002-7077-4103 (D.F.).Yield is subject to strong genotype-by-environment (G 3 E) interactions in the field, especially under abiotic constraints such as soil water deficit (drought [D]) and high temperature (heat [H]). Since environmental conditions show strong fluctuations during the whole crop cycle, geneticists usually do not consider environmental measures as quantitative variables but rather as factors in multienvironment analyses. Based on 11 experiments in a field platform with contrasting temperature and soil water deficit, we determined the periods of sensitivity to drought and heat constraints in wheat (Triticum aestivum) and determined the average sensitivities for major yield components. G 3 E interactions were separated into their underlying components, constitutive genotypic effect (G), G 3 D, G 3 H, and G 3 H 3 D, and were analyzed for two genotypes, highlighting contrasting responses to heat and drought constraints. We then tested the constitutive and responsive behaviors of two strong quantitative trait loci (QTLs) associated previously with yield components. This analysis confirmed the constitutive effect of the chromosome 1B QTL and explained the G 3 E interaction of the chromosome 3B QTL by a benefit of one allele when temperature rises. In addition to the method itself, which can be applied to other data sets and populations, this study will support the cloning of a major yield QTL on chromosome 3B that is highly dependent on environmental conditions and for which the climatic interaction is now quantified.

No abstract

Chickpea is the main legume rotation crop within farming systems in northern New South Wales (NSW), Australia, and is grown mainly under rainfed conditions. Recent expansion of chickpea growing areas in southern and central western NSW expose them to abiotic stresses; however, knowledge about how these stresses affect overall crop development is limited. This study aimed to examine the influence of sowing time on the timing and duration of key chickpea phenological growth phases in southern and central western environments of NSW. Experiments were conducted over two years in southern NSW (Leeton, Wagga Wagga and Yanco (one year)) and central western NSW (Trangie) to identify phenology responses. Climatic, phenology and experimental site data was recorded, and the duration of growth phases and growing degree days calculated. Early sowing (mid-April) generally delayed flowering, extending the crop’s vegetative period, and the progressive delay in sowing resulted in shorter vegetative and podding growth phases. All genotypes showed photoperiod sensitivity, and the mean daily temperature at sowing influenced time to emergence and to some extent crop establishment. This study concludes that environmental factors such as temperature, moisture availability and day length are the main drivers of phenological development in chickpea.

Nitrogen use efficiency (NUE) in crops is generally low, with more than 60% of applied nitrogen (N) being lost to the environment, which increases production costs and affects ecosystems and human habitats. To overcome these issues, the breeding of crop varieties with improved NUE is needed, requiring efficient phenotyping methods along with molecular and genetic approaches. To develop an effective phenotypic screening method, experiments on wheat varieties under various N levels were conducted in the automated phenotyping platform at Plant Phenomics Victoria, Horsham. The results from the initial experiment showed that two relative N levels—5 mM and 20 mM, designated as low and optimum N, respectively—were ideal to screen a diverse range of wheat germplasm for NUE on the automated imaging phenotyping platform. In the second experiment, estimated plant parameters such as shoot biomass and top-view area, derived from digital images, showed high correlations with phenotypic traits such as shoot biomass and leaf area seven weeks after sowing, indicating that they could be used as surrogate measures of the latter. Plant growth analysis confirmed that the estimated plant parameters from the vegetative linear growth phase determined by the “broken-stick” model could effectively differentiate the performance of wheat varieties for NUE. Based on this study, vegetative phenotypic screens should focus on selecting wheat varieties under low N conditions, which were highly correlated with biomass and grain yield at harvest. Analysis indicated a relationship between controlled and field conditions for the same varieties, suggesting that greenhouse screens could be used to prioritise a higher value germplasm for subsequent field studies. Overall, our results showed that this phenotypic screening method is highly applicable and can be applied for the identification of N-efficient wheat germplasm at the vegetative growth phase.

Precision agriculture represents the new age of conventional agriculture. This is made possible by the advancement of various modern technologies such as the internet of things. The unparalleled potential for data collection and analytics has resulted in an increase in multi-disciplinary research within machine learning and agriculture. However, the application of machine learning techniques to agriculture seems to be out of step with core machine learning research. This gap is further exacerbated by the inherent challenges associated with agricultural data. In this work, we conduct a systematic review of a large body of academic literature published between 2000 and 2022, on the application of machine learning techniques to agriculture. We identify and discuss some of the key data issues such as class imbalance, data sparsity and high dimensionality. Further, we study the impact of these data issues on various machine learning approaches within the context of agriculture. Finally, we identify some of the common pitfalls in the machine learning and agriculture research including the misapplication of machine learning evaluation techniques. To this end, this survey presents a holistic view on the state of affairs in the cross-domain of machine learning and agriculture and proposes some suitable mitigation strategies to address these challenges.