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Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line × tester mating design producing 36 F1 cross combinations. These F1s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments. [ABSTRACT FROM AUTHOR]

A core collection of 100 breeding lines and three approved cultivars of barley (Hordeum vulgare L.) were established based on 32 CAPS markers for seed color polymorphism in Pakistani barley. A "powercore" of barley lines was developed after multiple trials of three years conducted in Pakistan and China. The integrated population structure of the studying breeding lines was divided into two subpopulations (PI, PII) and two main linkages (L-I and L-II). In total, 95 alleles were detected using 32 CAPS markers from the DNA of 100 selected lines and three cultivars. Four alleles were detected using CAPS markers CAPS028, CAPS032, CAPS050, CAPS072, and CAPS166. However, an average of 2.96 alleles per CAPS marker were found in CAPS038, CAPS081, CAPS110, and CAPS158. The 23 CAPS which amplify three alleles per marker were moderately less polymorphic compared with markers with four alleles. The PIC values of these markers ranged from 0.05 (CAPS032) to 0.37, with a mean of 0.34 for up to eighteen markers. The number of observed alleles detected by the 32 CAPS markers in all selected lines was Na = 2. The highest number of effective alleles detected by CAPS133 and CAPS163 markers were Ne = 1.89 and Ne = 1.84, respectively. The lowest number of effective alleles in the selected lines was Ne = 1.06, detected by CAPS032 marker. Shannon's index information based on marker allele detection ranged from 0.13 to 0.66. The genetic diversity was highest (0.97) with marker CAPS032 and followed by CAPS0633 with 0.83 index genetic diversity. In addition, the core collection of Pakistani barley lines appeared to harbor minor seed color alleles. These findings insight into the genetic diversity in 100 breeding lines of barley using CAPS markers. [ABSTRACT FROM AUTHOR]

Background: Wild cotton Gossypium darwinii, an allotetraploid harbours important traits useful for tolerating abiotic stress, i.e., drought, salt and good genetic stability, hence these characteristics can be transferred to cultivated cotton for genetic improvement. Materials and methods: In this study, we analyzed the RNA-seq transcriptomes from leaves of G. darwinii seedlings with and without drought stress. A total of 86.7 million valid reads with an average length of 95.79 bp were generated from the two samples and 58,960 transcripts with a length of more than 500 bp were assembled. We searched the known proteins on the strength of sequence similarity; these transcripts were annotated with COG, KEGG and GO functional categories. According to gene expression abundance RPKM value, we carried out RT-qPCR analysis to determine the expression pattern of the obtained transcription factors. Results: A total of 58,960 genes was differentially expressed (DEG), with 32,693 and 25,919 genes found to be upregulated and downregulated, respectively. Through gene ontology and KEGG pathways, the upregulated genes were found to associate with all the GO terms, molecular functions (MF), biological process (BP) and cellular components (CC), which are highly linked to enhancing drought stress tolerance. Conclusion: The study provides an in-depth knowledge of regulation of pathways and genes involved in photosynthesis during drought stress in G. darwinii. These pathways and genes were found to be significantly downregulated and this information could be further utilized by cotton breeders in developing a more drought tolerant cotton germplasm. [ABSTRACT FROM AUTHOR]

Micronutrient application rate and tillage practices are considered important factors in triggering the productivity of oilseed crops, especially canola (Brassica napus L.). The soil in the area is deficient in available zinc (Zn) and sulfur (S), which necessitates the supplementary application of these nutrients for sustainable crop development. The following study was conducted to evaluate the interactive effect of tillage and micronutrient on the growth, quality, and yield of canola and weed density. The present study was executed consisting of two tillage systems along with four treatments of micronutrients application including control, soil application of recommended doses of zinc (10 kg ha−1), soil application of a recommended dose of sulfur (45 kg ha−1), and combined application of a recommended dose of Zn + S (10 kg + 45 kg ha−1) during the crop growing seasons of 2019–2020 and 2020–2021. The results showed that the combined use of Zn and S under different tillage systems significantly affected crop growth, yield, and quality parameters. An increase of 0.99% in plant height, 6.33% in number of branches, 33% in silique length, 6.25% number of siliques per plant, 10% in number of seeds per silique, 4.64% in thousand seed weight, 0.74% in grain yield, and 0.42% in biological yield was recoded under conventional tillage practices as compared to zero tillage. In crux, the combined application of Zn and S at 10 and 45 kg ha−1 respectively, to canola crops under conventional tillage practices could be a sustainable option to improve canola crop growth, yield, and productivity. [ABSTRACT FROM AUTHOR]