Abstract Existing and emerging disease threats create the need to continuously identify and develop cultivars that can more efficiently defend against devastating pathogens. For two decades Fusarium oxysporum f. sp. vasinfectum (FOV) race 4 (FOV4), the fungus that causes the Fusarium wilt disease, has threatened cotton ( Gossypium spp.) production in the San Joaquin Valley of California and more recently in New Mexico and in the El Paso, TX region. In this study, a comprehensive genetic quantitative trait locus (QTL) mapping and genomic host–plant interaction research approach was conducted to identify simple sequence repeat and single nucleotide polymorphism markers and a set of biomarker‐candidate genes involved in Fusarium wilt resistance. Results from this research revealed resistant transgressive segregants—ancestral reciprocal rearrangements—duplication between gene‐ancestral forms in the A and D subgenomes, especially chromosomes A02 and A03 and translocation between A03 and D02, and the complexity of FOV4 resistance. In addition, QTL analyses revealed that the largest contribution (29%–61% of vascular root staining variation) to the FOV4 resistance resided in A02 ( qtlA02‐Fov4 1 ) and D03 ( qtlD03Fov4 1 ). Genes linked to enhanced phenylpropanoid metabolism and identified expressed genes are research targets to dissect polygenic forms of disease resistance, providing an opportunity for new strategies for marker and/or genomic selection. A final set of putative QTL markers and quantitative trait differentially expressed genes loci (QTDEL) associated with FOV4 resistance in cotton is proposed for identifying and selecting resistant phenotypes under naturally infested FOV4 field and artificial FOV4 inoculations in the greenhouse. This novel approach could also be applied to other pathogen–host systems.
Abstract Sorghum ( Sorghum bicolor (L.) Moench) is a diploid crop that is primarily grown in regions with high temperatures and limited irrigation. Breeding efforts often focus on stay‐green, a trait associated with green leaf area after the crop experiences post‐flowering drought. When present, this trait is assessed using subjective visual categorical ratings. The objective of this study was to assess stay‐green across nine environments and to develop a machine learning model that can estimate stay‐green using vegetative indices collected by an unoccupied aerial system. Hybrids were derived from a partial factorial design that included material from three public and one private breeding program. Results showed that each program had heritable variation for this trait and the benefit of sharing material to develop top‐performing stay‐green hybrids varied among each breeding program. Five machine learning models were built to estimate stay‐green; they produced accuracies up to 92% (gradient boosting model) in testing and training. The validation dataset, which contained data from four unseen environments, produced average accuracies up to 66% (naïve Bayes model). The naïve Bayes model stay‐green estimates also resulted in a heritability 16% higher than that of the field‐collected ratings. Overall, the naïve Bayes and gradient boosting models performed best, demonstrating potential for more efficient and less biased stay‐green rating collection in grain sorghum.
Abstract Maize is the most important genetic model for agriculture, with ∼3500 phenotypic mutants curated over 120 years. Its large heterochromatic chromosomes, extensive mutant resources, high nucleotide (nt) diversity, and strong genic collinearity with other grasses have made maize an excellent model organism for basic and applied research. We investigated the genetic basis of a nuclear recessive, seedling‐lethal albino mutant ( alb‐605F ) isolated from a Maize Genetics Cooperation Stock Center line, which exhibits yellowish‐white seedlings. To identify the causal mutation, we performed bulk‐segregant analysis (BSA) coupled with whole‐genome re‐sequencing using pooled DNA from 89 homozygous alb‐605F mutants and 205 wild‐type relatives derived from B73 backcrossed lines. De novo single nucleotide polymorphisms and insertion/deletions were called using Genome Analysis Toolkit, and the mutant locus was fine‐mapped with QTLseqr. Mapping localized the alb‐605F locus to a ∼10 Mb interval at the distal end of the short arm of chromosome 9 containing 31 genes. Among these, alanyl‐tRNA synthetase ( ZmAlaRS ; Zm00001d044745 / Zm00001eb371640 ) was identified as the principal candidate causal gene. Homozygous mutants carried a two nt deletion that produced a frameshift near the N‐terminus of the encoded protein, confirmed by complementation with an independent mutator‐induced allele. Additionally, re‐sequencing coverage indicated increased copy numbers of both chloroplast and mitochondrial genomes in the mutant. Together, our findings identify ZmAlaRS as the gene underlying the alb‐605F phenotype and demonstrate the effectiveness of BSA‐based whole‐genome re‐sequencing for rapid gene identification in maize. The results highlight the essential role of AlaRS in chloroplast function and organellar genome homeostasis during early seedling development.
Abstract Conservation agricultural practices, such as cover crops (CCs), are associated with increased microbial activity and improved soil ecosystem function. Our objective was to evaluate the effects of CC on soil microbial activity and chemical properties in commercial peanut‐based systems. Winter wheat ( Triticum aestivum ), rye ( Secale cereale L.), and vetch ( Vicia villosa ) were established as treatments: wheat (W), rye (R), and vetch/rye mixture (V/R), using a nested hierarchical design. Soil analysis included extracellular enzyme activities (β‐D‐glucuronidase, β‐xylosidase, cellobiohydrolase, β‐glucosidase, leucine aminopeptidase [LEU], β‐1,4‐N‐acetylglucosaminidase [NAG], and acid phosphatase [PHOS]), general microbial parameters, and soil chemical properties. A soil quality index (SQI) was calculated using a minimal dataset. Enzyme activities were significantly influenced by CC management, especially by the inclusion of V/R, particularly in NAG, LEU, and PHOS. General microbial indicators showed no significant differences between treatments. However, glomalin‐related soil protein was higher under rye than under wheat or vetch/rye. Most soil chemical properties were improved by the inclusion of vetch/rye. The SQI results indicate that CC mixtures enhanced overall soil quality compared with single‐species CC. SQI values ranged from 1.622 (W) to 2.455 (V/R), with the vetch/rye mixture showing significantly higher overall soil quality. These findings demonstrate that rye–vetch mixtures promote nutrient cycling and improve soil chemical properties, supporting their use as a sustainable soil management practice in peanut‐based cropping systems.
Abstract Waxy maize ( Zea mays L. var. ceratina Kulesh) serves as both a staple food and industrial raw material, and its starch physicochemical properties are affected by fertilization regimes. This study investigated the starch synthesis enzyme activities and physicochemical properties of two waxy maize cultivars (Jingkenuo 768, JKN768, and Suyunuo 2, SYN2) under three fertilization treatments: no nitrogen fertilizer (T1), chemical nitrogen fertilizer (T2), and combined chemical and organic nitrogen fertilizers (T3). Compared with T2, T3 enhanced starch synthesis enzyme activities, which coincided with the increasing of total starch and amylopectin contents. Concomitantly, T3 improved grain flexibility. chewiness, and soluble sugar content, and the grain hardness decreased by 4.49% in JKN768 and 6.77% in SYN2, respectively. Moreover, T3 increased the average starch granule size by 3.47% in JKN768 but exerted an opposite effect in SYN2. In both two cultivars, T3 decreased the proportions of short amylopectin chains, along with peak viscosity, trough viscosity, final viscosity, and relative crystallinity, while increasing the proportions of long amylopectin chains. This study elucidates the regulatory mechanisms of combined chemical‑organic fertilizer application on starch quality and provides theoretical support for optimizing fertilizer management in waxy maize production.
