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<jats:p>Monitoring genetic gains within breeding programs is a critical component for continuous improvement. While several national breeding programs in Africa have assessed genetic gain using era studies, this study is the first to use two decades of historical data to estimate genetic trends within a national breeding program. The objective of this study was to assess genetic trends within the final two stages of Zimbabwe’s Department of Research &amp;amp; Specialist Services maize breeding pipeline between 2002 and 2021. Data from 107 intermediate and 162 advanced variety trials, comprising of 716 and 398 entries, respectively, was analyzed. Trials were conducted under optimal, managed drought stress, low nitrogen stress, low pH, random stress, and disease pressure (maize streak virus (MSV), grey leaf spot (GLS), and turcicum leaf blight under artificial inoculation. There were positive and significant genetic gains for grain yield across management conditions (28–35 kg ha<jats:sup>-1</jats:sup> yr<jats:sup>-1</jats:sup>), under high-yield potential environments (17–61 kg ha<jats:sup>-1</jats:sup> yr<jats:sup>-1</jats:sup>), and under low-yield potential environments (0–16 kg ha<jats:sup>-1</jats:sup> yr<jats:sup>-1</jats:sup>). No significant changes were observed in plant and ear height over the study period. Stalk and root lodging, as well as susceptibility to MSV and GLS, significantly decreased over the study period. New breeding technologies need to be incorporated into the program to further increase the rate of genetic gain in the maize breeding programs and to effectively meet future needs.</jats:p>

<jats:sec><jats:title>Background</jats:title><jats:p>Cannabidiol (CBD), as an important therapeutic property of the cannabis plants, is mainly produced in the flower organs. Auxin response factors (ARFs) are play a crucial role in flower development and secondary metabolite production. However, the specific roles of ARF gene family in cannabis remain unknown.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In this study, various bioinformatics analysis of <jats:italic>CsARF</jats:italic> genes were conducted using online website and bioinformatics, quantitative real time PCR technology was used to investigate the expression patterns of the <jats:italic>CsARF</jats:italic> gene family in different tissues of different cannabis varieties, and subcellular localization analysis was performed in tobacco leaf.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this study, 22 <jats:italic>CsARF</jats:italic> genes were identified and found to be unevenly distributed across 9 chromosomes of the cannabis genome. Phylogenetic analysis revealed that the ARF proteins were divided into 4 subgroups. Duplication analysis identified one pair of segmental/whole-genome duplicated <jats:italic>CsARF</jats:italic>, and three pairs of tandemly duplicated <jats:italic>CsARF</jats:italic>. Collinearity analysis revealed that two <jats:italic>CsARF</jats:italic> genes, <jats:italic>CsARF4</jats:italic> and <jats:italic>CsARF19</jats:italic>, were orthologous in both rice and soybean. Furthermore, subcellular localization analysis showed that <jats:italic>CsARF2</jats:italic> was localized in the nucleus. Tissue-specific expression analysis revealed that six genes were highly expressed in cannabis male flowers, and among these genes, 3 genes were further found to be highly expressed at different developmental stages of male flowers. Meanwhile, correlation analysis between the expression level of <jats:italic>CsARF</jats:italic> genes and CBD content in two cultivars ‘H8’ and ‘Y7’ showed that the expression level of <jats:italic>CsARF13</jats:italic> was negatively correlated with CBD content, while the expression levels of six genes were positively correlated with CBD content. In addition, most of <jats:italic>CsARF</jats:italic> genes were responsive to IAA treatment.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our study laid a foundation for the further studies of CsARFs function in cannabis, and provides candidate genes for breeding varieties with high CBD yield in cannabis production.</jats:p></jats:sec>

<jats:p>Maize, a salt-sensitive crop, frequently suffers severe yield losses due to soil salinization. Enhancing salt tolerance in maize is crucial for maintaining yield stability. To address this, we developed an introgression line (IL76) through introgressive hybridization between maize wild relatives <jats:italic>Zea perennis</jats:italic>, <jats:italic>Tripsacum dactyloides</jats:italic>, and inbred Zheng58, utilizing the tri-species hybrid MTP as a genetic bridge. Previously, genetic variation analysis identified a polymorphic marker on <jats:italic>Zm00001eb244520</jats:italic> (designated as ZmSC), which encodes a vesicle-sorting protein described as a salt-tolerant protein in the NCBI database. To characterize the identified polymorphic marker, we employed gene cloning and homologous cloning techniques. Gene cloning analysis revealed a non-synonymous mutation at the 1847th base of <jats:italic>ZmSC<jats:sup>IL76</jats:sup></jats:italic>, where a guanine-to-cytosine substitution resulted in the mutation of serine to threonine at the 119th amino acid sequence (using <jats:italic>ZmSC<jats:sup>Z58</jats:sup></jats:italic> as the reference sequence). Moreover, homologous cloning demonstrated that the variation site derived from <jats:italic>Z. perennis</jats:italic>. Functional analyses showed that transgenic <jats:italic>Arabidopsis</jats:italic> lines overexpressing <jats:italic>ZmSC<jats:sup>Z58</jats:sup></jats:italic> exhibited significant reductions in leaf number, root length, and pod number, alongside suppression of the expression of genes in the SOS and CDPK pathways associated with Ca<jats:sup>2+</jats:sup> signaling. Similarly, fission yeast strains expressing <jats:italic>ZmSC<jats:sup>Z58</jats:sup></jats:italic> displayed inhibited growth. In contrast, the <jats:italic>ZmSC<jats:sup>IL76</jats:sup></jats:italic> allele from <jats:italic>Z. perennis</jats:italic> alleviated these negative effects in both <jats:italic>Arabidopsis</jats:italic> and yeast, with the lines overexpressing <jats:italic>ZmSC<jats:sup>IL76</jats:sup></jats:italic> exhibiting significantly higher abscisic acid (ABA) content compared to those overexpressing <jats:italic>ZmSC<jats:sup>Z58</jats:sup></jats:italic>. Our findings suggest that ZmSC negatively regulates salt tolerance in maize by suppressing downstream gene expression associated with Ca2+ signaling in the CDPK and SOS pathways. The <jats:italic>ZmSC<jats:sup>IL76</jats:sup></jats:italic> allele from <jats:italic>Z. perennis</jats:italic>, however, can mitigate this negative regulatory effect. These results provide valuable insights and genetic resources for future maize salt tolerance breeding programs.</jats:p>

<jats:p>Seed quality traits of oilseed rape, <jats:italic>Brassica napus</jats:italic> (<jats:italic>B. napus</jats:italic>), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in <jats:italic>B. napus</jats:italic> seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, <jats:italic>cinnamoyl-CoA-reductase</jats:italic> (<jats:italic>CCR1</jats:italic>), and <jats:italic>TRANSPARENT TESTA</jats:italic> genes <jats:italic>TT4</jats:italic>, <jats:italic>TT6</jats:italic>, <jats:italic>TT8</jats:italic>, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.</jats:p>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>It shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.</jats:p></jats:sec>