Crop Science最新文献

There is growing demand across the turfgrass industry for turfgrasses that require minimal watering. St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze], a warm-season turfgrass favored in the southeastern United States for its shade tolerance and vigorous stoloniferous growth, falls short in drought resistance. Integrating genomic and conventional breeding methodologies could accelerate the introduction of cultivars that thrive with less water. In this study, a population derived from the cross of breeding lines XSA10098 and XSA10127 was evaluated for drought resistance in field trials, where percent green cover and normalized difference vegetation index were collected by unmanned aerial vehicle-based phenotyping. A multiple quantitative trait loci (QTL) mapping approach identified 22 QTL, with overlapping regions on linkage groups 1, 2, 4, and 9 between this and previous studies. In addition, a detailed transcriptomic analysis on the roots of two St. Augustinegrass genotypes with contrasting drought responses revealed 1642 and 2669 differentially expressed genes (DEGs) in the drought-tolerant and drought-sensitive genotypes, respectively. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes classification showed different pathways adopted by the two genotypes in response to drought stress. Moreover, integration of QTL mapping and transcriptomic analyses identified five DEGs co-localized in overlapping QTL regions, which exhibit great value to potentially serve as targets to facilitate marker-assisted selection. The findings in this study contribute to a deeper understanding of the genetic basis of drought tolerance in St. Augustinegrass, facilitating the development of more robust breeding strategies for enhancing drought resilience in this important turfgrass species.

Variety testing programs (VTPs) use multi-environment trials (MET) to evaluate and report the performance of commercially available and pre-commercial soybean (Glycine max L. Merr.) varieties targeting a specific set of environments. Adequate modeling of the environmental variability and genotype–environment interactions (G × E) within the VTP would help farmers and seed companies decide which variety to choose or recommend. We propose an approach to characterize environments using the soybean data from the University of Missouri VTP. We modeled an environmental trend (EnvT) based on the phenotypic mean performance and the observed phenotype in each environment. The environments were classified into four different EnvT environment types, and soil and climate data were used as predictors of the EnvT through eXtreme Gradient Boosting (XGBoost) model. Temperature on late vegetative and flowering, soil-saturated hydraulic conductivity, and silt content were key drivers of EnvT. The approach identified overrepresented environments (62%) and increased the ratio between variety and G × E variance. A simulation case study verified that the random removal of overrepresented sites from the dataset quickly degraded G × E analysis, implying that increasing the number of underrepresented sites is recommended. Our results demonstrate that environmental characterization is essential for optimizing resource allocation within VTP, thereby supporting the end goal of aiding farmers to utilize the best varieties for their production environment.

The objective of this study was the characterization of commercial cultivars, differentiating lines/cultivars of Phytophthora sojae carrying Rps (resistance Phytophthora Sojae) genes, inoculated with different pathotypes. Thirty-one differentiating soybeans (Glycine max (L.) Merrill) lines/cultivars carrying Rps genes and six commercial cultivars were evaluated for virulence pattern to PS2.4, PS14.4, PS36.1, PS34.1, and CMES1608 pathotypes. Inoculations were performed using the toothpick technique, with reaction evaluation about 15 days after infection, where the number of healthy, infected, and dead seedlings was quantified. There was a difference in resistance for the pathotypes, and the most virulent were PS34.1 and PS36.1. The Rps1k, Rps11, and Rp12 genes deserve to be highlighted by resistance to the PS34.1 pathotype and the Rps1k, Rps11, Rp12, and Rps8 genes to the PS36.1 pathotype. The line L77-1863 (Rps1b) showed resistance to the PS2.4 and PS14.4 pathotypes. The characterization of the genotypes allowed the updating of information about them and the identification of new possibilities of resistance sources.

Bacterial leaf spot (BLS), caused by Pseudomonas syringae pathovar aptata (Psa), is a seedborne, foliar disease affecting members of the Amaranthaceae and Cucurbitaceae families, including table beet and Swiss chard crops. There is no known resistance to BLS in beet or chard. A diversity panel, modified from the Wisconsin Beta Diversity Panel (WBDP) and comprised of 219 accessions from the Beta vulgaris crop complex, was assembled and genotyped for single nucleotide polymorphism data. These accessions were screened by foliar inoculation of Psa and visually evaluated for percentage of diseased leaf tissue. Overall, sugar beet and Beta vulgaris subsp. maritima accessions had the lowest BLS response, whereas table beet accessions had the largest range of responses. Phenotypic means were adjusted using best linear unbiased estimates, and two different software programs, GWASpoly and GAPIT3, were utilized to conduct a genome-wide association study (GWAS). Leaf color was found to be significantly associated with and correlated with BLS response scores, and was used as a covariate in GWAS analysis. An association with BLS response was detected on chromosome 1 in the full WBDP, explaining upward of 21% of the variation in the phenotype. The marker associated with this quantitative trait locus (QTL), Chr1_61344476, showed an additive relationship between dosage and BLS response. Eleven candidate genes, described and annotated in sugar beet, were associated with this QTL. Some of these include F Box domains, RNA-binding proteins, and calcium-dependent kinases, all of which have roles in plant defense responses. Marker Chr1_61344476 may be useful in breeding for BLS resistance in members of the Beta vulgaris crop complex.

Golf courses are increasingly affected by water scarcity and climate change. An understanding of spatial variability of actual evapotranspiration (ET_a) and turfgrass quality (TQ) site-specific management zones (SSMZ) is important for the implementation of precision turfgrass management. Therefore, the main objectives of this study were to quantify the relationship between remotely sensed TQ and ET_a estimates and to evaluate the spatial variations of TQ and ET_a at a golf course in Utah. Ground-based normalized difference vegetation index was collected using a TCM-500 sensor, and aerial multispectral and thermal imagery data were acquired from unpiloted aircraft systems (UAS) in 2021, 2022, and 2023. A remote sensing TQ-random forest (RF) model was developed using six datasets of UAS spectral indices and the RF algorithm. The spatial data were analyzed to determine the correlation between TQ and ET_a estimates. The TQ and ET_a SSMZ were created and integrated with irrigation heads on the golf course using the Thiessen polygons tool. Results demonstrated that TQ-RF model was accurate within a root mean square error of 0.05. The correlation between TQ-RF and ET_a was stronger for fairways (R² = 0.74), tees (R² = 0.66), and roughs (R² = 0.75) as compared to greens (R² = 0.25) and the driving range (R² = 0.36) on July 20, 2022. Actual evapotranspiration SSMZ, in combination with TQ-RF SSMZ, is useful for irrigation scheduling, addressing the question of how much and where to irrigate. This study demonstrates the ability of TQ-RF and ET_a SSMZ to identify spatial variation for the purpose of landscape irrigation management in semi-arid areas.

Previous literature documented an imbalance for sorghum [Sorghum bicolor (L.) Moench] between source (leaves) and sink (grains), favoring the source. Therefore, reducing leaf number, anticipating maturity, and placing the dry-down with more favorable environment might be advantageous for producers to fit another crop in the rotation. The aims of this study were to (1) evaluate via in-silico the effects of leaf removal during the grain filling and (2) explore those impacts using a field dataset for sorghum yield. For the first objective, the APSIM (Agricultural Production Systems Simulator) sorghum model was tested with four hybrids across 12 locations in the United States (2015–2023) resulting in an RRMSE (relative root mean squared error) of 25% for yield. As a second step, an APSIM defoliation module was developed using field data of one site-year, demonstrating an RRMSE of 17% for yield. As a last step, the model was used to simulate the effect of sequential defoliations on yield, across 38 years of weather data (1984–2022), without showing any yield penalties when removing up to four leaves after flowering. Leaf area removal after flowering indicated a positive imbalance in source:sink ratio (i.e., source excess). For the second objective, a field dataset from 21 sorghum hybrids with different attainable leaf numbers and cycle duration did not result in significant yield differences. Early maturity hybrids with fewer leaves give farmers the opportunity to intensify crop sequences. Less focus in sorghum improvement for early relative to late maturing hybrids has been reported; therefore, there is still ample room for future yield gains.

The wheat (Triticum spp.) stem rust pathogen, Puccinia graminis f. sp. tritici Eriks. and E. Henn. (Pgt), has continued to be a devastating biotic stress in wheat production. Over previous decades, scientists have identified several resistance genes effective against Pgt. However, the ever-evolving Pgt and low availability of durable resistance necessitates continuous identification and wise deployment of resistance genes. To elucidate the identity of our previously reported stem rust resistance in hard red winter wheat cultivar Gage, we used recombinant inbred lines (RILs) developed from the cross of Bill Brown × Gage and evaluated them for 3 years for response to six different stem rust pathogen races individually at the seedling stage in the greenhouse and a mixture of these races in the field. Using molecular markers, we determined the genomic regions that affect stem rust resistance in Gage, which identified two quantitative trait loci (QTLs) at the seedling stage and one major QTL at the adult stage, giving insight into why Gage has superior stem rust resistance. The seedling stem rust resistance was from SrTmp and likely from an Sr7 allele. QTLs conferring adult plant resistance in Gage were mainly from Sr2, but molecular analysis suggested additional minor-effect QTLs were involved.

The present study is aimed at the postulation of Ut genes in loose smut-resistant bread wheat (Triticum aestivum L.) genotypes and establishing a correlation with their pedigree. Loose smut caused by Ustilago segetum tritici (Ust) is an internal seed-borne disease of wheat that can be managed through chemical seed treatment. However, due to the absence of evident symptoms, seed treatment is not a regular practice in the farming community. Thus, the use of resistant cultivars is an efficient and sustainable approach for the management of loose smut of wheat. The majority of current wheat cultivars are susceptible to loose smut. Therefore, there is a pressing need for the development of resistant cultivars, which requires the identification of resistant donors with known resistant genes. In this study, field screening for 3 years resulted in the identification of 124 bread wheat genotypes conferring stable resistance against Ust race T11. Molecular marker-based identification of Ut genes (Ut4–Ut11) revealed the presence of these genes either singly or in combination in 118 genotypes. Among them, six genotypes showed different combinations of five Ut genes, namely, WH 1218 and HI 1633 (Ut4, Ut6, Ut8, Ut9, Ut11), HD 3377 (Ut4, Ut6, Ut8, Ut9, Ut10), WH 1218 and HI 1633 (Ut4, Ut6, Ut9, Ut10, Ut11), and HD 3226 (Ut4, Ut5, Ut6, Ut9, Ut11). The genotypes with multiple genes for loose smut resistance can be used as donors for transferring the resistance into the high-yielding cultivars. Furthermore, the pedigree of each genotype was analyzed to find the gene source of the postulated Ut genes. None of the genotypes showed consistent association with the gene source of the postulated Ut gene present in the pedigree. Thus, no association between molecular marker-based postulation and pedigree of genotypes was inferred. However, the root pedigree of common parents revealed five putative sources of loose smut resistance, that is, Chris, Thatcher, Federation, New-Thatch, and Ostka-Galicyjska, in most of the genotypes under evaluation in the present study.

Deficit irrigation is a water conserving practice that involves watering below an estimated evapotranspiration (ET) replacement level. Research is limited to comparing cool-season (CS) and warm-season (WS) turfgrass varieties grown in arid regions under varying deficit irrigation replacement levels. This study investigated the effects of five levels of reference evapotranspiration for short grass (ET_OS) replacement (55%, 70%, 85%, 100%, and 115%) on the performance and fall recovery of several turfgrasses in the southwestern United States. Three years of field research evaluated green cover and visual quality of three CS Kentucky bluegrass (Poa pratensis L.) (four cultivars), tall fescue [Schedonorus arundinaceus (Schreb.)] (three cultivars), and perennial ryegrass (Lolium perenne L.) (three cultivars), and two WS turfgrasses bermudagrass (Cynodon dactylon L.) (three cultivars) and buffalograss Buchloe dactyloides (two cultivars). CS grasses required higher ET_OS replacement than WS grasses to maintain acceptable quality (1–9, ≥6 = minimum acceptable) and coverage. Among CS grasses, Barserati Kentucky bluegrass maintained the best quality and green cover under deficit irrigation and demonstrated the most consistent ability to recover. Notably, bermudagrass performed well under deficit irrigation, maintaining acceptable visual quality and better green cover than CS species like Kentucky bluegrass and tall fescue at lower irrigation levels. Overall, there were significant differences among cultivars, demonstrating the importance of the selection process in drought tolerance. These findings support the promotion of drought-resistant WS grasses to conserve water in arid regions without compromising turfgrass functionality. Future research should focus on variable and seasonal ET_OS for irrigation of turfgrasses and estimating irrigation requirements.

Plants are often exposed to fluctuating light from a few seconds to a few minutes due to cloud movements, mutual shading of leaves, and change in the angle of the sun. Slow stomatal response to fluctuating light leads to carbon loss, but the influence of planting density on light fluctuation frequency and on stomatal response and carbon gain has yet to be fully explored. To fill this knowledge gap, we investigated leaf morphology, stomatal anatomy and response rate, nitrogen content, biomass, and yield under low density, moderate density, and high density (HD) of cotton cultivar (Gossypium hirsutum L.). The results showed that higher planting density significantly increased light fluctuation frequency at the lower canopy. Stomatal size significantly decreased with the increase in planting density, while total stomatal density was consistent. Stomatal density had greater plasticity of determining maximum stomatal conductance than stomatal size. Faster stomatal response rate to fluctuating light under HD was attributed to smaller and denser stomata in the abaxial leaf side. Therefore, cotton under HD treatment had faster photosynthetic induction rate under light induction, resulting in greater carbon gain. We conclude that faster stomatal response rate achieved by the optimization of stomatal anatomy, especially the abaxial side, plays a crucial role in obtaining more carbon gain, biomass, and yield under HD cotton field. This finding indicates that selecting varieties with rapid stomatal response traits and planting at appropriate densities may optimize fluctuating light use to achieve higher yields.