Crop Science

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.

The importance of underutilized crops in the diversification of diets for both humans and animals, among other uses, has been highlighted in literature in recent times. Underutilized crops are especially important because of their potential to provide nutrient‐packed, climate‐resilient, and sustainable farming practices. One such crop is Bambara groundnut [Vigna subterranea (L.) Verdc], whose genetic potential has not been afforded sufficient research attention. For most of the rural areas in Sub‐Saharan Africa, it is a great source of food and income and is most valued for its nutrient richness and ability to thrive in marginal land. However, farmers grapple with the lack of high agronomic quality seeds where production of the crop is concerned. The aim of this study was to establish an easy basis for selecting seeds that are of favorable agronomic potential by assessing whether a singular characteristic (seed coat color) was sufficient to group landraces. Restriction‐site‐associated DNA (RAD) sequencing was used for the first time to assess the genetic variations and/or similarities in 48 Bambara groundnut landraces. The findings revealed that there are two populations that are genetically variable among the chosen 48 landraces; however, these variations were not as a result of a singular morphological attribute. Therefore, farmers cannot use coat color alone to select for landraces that are of better agronomic quality.

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 (ETa) 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 ETa estimates and to evaluate the spatial variations of TQ and ETa 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 ETa estimates. The TQ and ETa 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 ETa 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 ETa 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 (ETOS) 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 ETOS 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 ETOS for irrigation of turfgrasses and estimating irrigation requirements.

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high‐yield, disease‐resistant, and climate‐resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state‐of‐the‐art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1‐score, and Matthews correlation coefficient (MCC) for model evaluation. The top‐performing models, namely a feedforward neural network classifier (Neural Net), a histogram‐based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data‐generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data‐generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC‐ROC) and MCC. The simulation results highlight that non‐linear models like SVM and HGBC consistently show higher AUC‐ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision‐making in breeding programs.

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.

Wheat crops (Triticum aestivum) that are conventionally planted may exhibit susceptibility to yellow rust (YR). However, the disease can be mitigated if the crops are planted earlier than the recommended planting time. A wheat screening experiment was carried out at the Borlaug Institute of South Asia located in Ludhiana, Punjab, India. The purpose of the study was to gain a deeper understanding of the adaptation patterns of early planted wheat. Early planting was found to be more advantageous for production potential, as well as phenology, stature, and physiological traits. In a separate experiment, each year, the same number of genotypes were screened for YR by artificially inoculating them with pathogen spores. The well‐adapted genotypes for early establishment tend to possess a greater vulnerability to YR infection. Furthermore, the infection type score for the genotype selected for early planting showed a significantly greater proportion of S (susceptible) type reactions than for the genotypes adapted to early planting. Intriguingly, more R (resistant) and moderately resistant types of reactions were observed in early‐adapted genotypes than in timely‐adapted ones. Therefore, further concentrated research on YR screening is required to assess the possibility of breeding early sown wheat in the northwest part of the Indo‐Gangetic region.

This manuscript delves into the intersection of genomics and phenotypic prediction, focusing on the statistical innovation required to navigate the complexities introduced by noisy covariates and confounders. The primary emphasis is on the development of advanced robust statistical models tailored for genomic prediction from single nucleotide polymorphism data in plant and animal breeding and multi‐field trials. The manuscript highlights the significance of incorporating all estimated effects of marker loci into the statistical framework and aiming to reduce the high dimensionality of data while preserving critical information. This paper introduces a new robust statistical framework for genomic prediction, employing one‐stage and two‐stage linear mixed model analyses along with utilizing the popular robust minimum density power divergence estimator (MDPDE) to estimate genetic effects on phenotypic traits. The study illustrates the superior performance of the proposed MDPDE‐based genomic prediction and associated heritability estimation procedures over existing competitors through extensive empirical experiments on artificial datasets and application to a real‐life maize breeding dataset. The results showcase the robustness and accuracy of the proposed MDPDE‐based approaches, especially in the presence of data contamination, emphasizing their potential applications in improving breeding programs and advancing genomic prediction of phenotyping traits.

Protecting floral buds (squares) from insect damage in cotton during early growth is a priority for crop managers despite unclear implications for yield potential and increased system risks from early‐season insecticide use. This study was conducted to determine the compensatory responses of high‐yielding Bacillus thuringiensis (Bt) cotton, Gossypium hirsutum L. cultivars, following manual square damage across 30 experiments, spanning different seasons and environments under commercial production conditions. Square removal from the first five sympodia (fruiting branches) before flowering reduced yield by 9% in one experiment, increased yield by 9%–12% in three experiments and had no effect in the remaining experiments. The most damaging treatment, with squares removed twice across 10 sympodia, reduced yield in just nine experiments by 10%–23%. Lint strength and length remained high, exceeding Australian market preferences. Micronaire decreased with later or more severe square loss particularly in shorter season environments, but economic impact varied. Compensatory growth following pre‐flowering square loss increased fruiting site production without raising total biomass or boll proportion commensurately and caused only minor boll opening delay (<4 days). Yield compensation occurred through increased boll retention at the first position on upper canopy sympodia and more distal positions on remaining sympodia and was un‐reliant on growth of additional mainstem sympodia. Square loss impacts were greater after commencement of flowering or when pre‐flowering losses continued during the early‐flowering period. Crop managers can have confidence to reduce pre‐flowering pesticide use without jeopardizing high yields, which may produce additional systems benefits.

Nitrogen is an essential macronutrient for crop development and growth. However, nitrogen‐based fertilizer application not only increases the cost of crop production systems, but also causes serious environmental pollution and low nitrogen use efficiency (NUE) in cereal crops. To enhance the NUE of wheat (Triticum aestivum L.), the transgenic approach was used to create a new variety. In this study, the rice (Oryza sativa L.) alanine‐aminotransferase1 (OsAlaAT1), an important nitrogen assimilation enzyme affecting the NUE of plants, was transformed into wheat under the control of TaAnt1 promoter. Special expression of OsAlaAT1 enhanced the height and 1000‐kernel weight of wheat. It also affected the expression level of starch synthesis‐related genes in seeds at 10 and 15 days after pollination. Independent lines expressing OsAlaAT1 exhibited higher grain production than wild‐type plants under high‐nitrogen and, especially, low‐nitrogen supplementation. Our results suggest that OsAlaAT1 improves the NUE of wheat.

Seedling vigor is a critical trait for successful cover crop varieties. Selection for seed size can impact fall seedling vigor in the cover crop hairy vetch (Vicia villosa Roth). Fall vigor and seed size measurements from 1239 plants and fall vigor measurements from 13,923 progeny across 25 different growing environments were used to calculate narrow sense heritabilities and identify relationships between variance components of fall vigor and seed size. Standardized parent–progeny heritabilities were estimated across multiple years and environments and used to determine the impact of seed size on offspring fall vigor. A genome‐wide association study for seed size (n = 853, m = 1,010,403, two environments) was conducted to explore genetic determinants associated with this trait. Seed size was influenced by parent genetics and parent growing environments and had a significant impact on the fall vigor of the offspring. The narrow sense heritability of seed size was greater than visual fall vigor scores (0.580 and 0.111, respectively) and was less influenced by genotype‐by‐environment variance. Finally, parental seed size had a strong and consistent genetic correlation to offspring fall vigor relative to direct parental measurements of fall vigor (standardized parent–progeny heritability 0.17 for parent seed size‐progeny fall vigor compared to nearly zero for parent fall vigor_offspring fall vigor). The genome‐wide association study did not find significant loci controlling seed size. The strong correlation between seed size and fall vigor highlights an important consideration for growers since larger seed size may increase fall vigor, thus impacting profitability for producers.

Barley (Hordeum vulgare L.) is a major cereal crop grown worldwide for human consumption, malting, and animal feed. Drought is one of the major abiotic stresses that reduce grain yield and quality in barley. This study was conducted to evaluate a set of 250 barley lines grown under irrigated, water‐stressed, and rainfed conditions and to identify genomic regions associated with 10 traits related to grain yield and quality across eight independent field environments. Variability was observed among barley lines for tolerance to water‐stressed conditions in all tested environments. Genotype and environment both contributed to the phenotypic variation of the barley lines. Population structure analysis identified two subpopulations using 20,700 single nucleotide polymorphism (SNP) markers. Genome‐wide association mapping detected 74 significant SNPs (p ≤ 6.5 × 10⁻⁶), representing 14 quantitative trait loci (QTLs), on all barley chromosomes, except 3H. The QTL, QBG.ARS.7H, associated with beta‐glucan (BG), was consistently detected across environments and explained 13.93% of phenotypic variation. Carriers of the minor allele for the BG‐associated SNP, JHI‐Hv50k‐2016‐488035, exhibited up to 14.65% higher BG content, on average, compared with carriers of the common allele. This study advances our understanding of the genetics of barley response to water‐stress conditions and suggests molecular markers for QTL, which may be used in barley improvement.

Wild rice species are invaluable resources for genetic improvement of cultivated rice. “Breeding super rice using double advantages of wide cross and polyploidization” is a novel pathway in rice breeding. To exploit the favorable genes of wild rice fully, a technical system composed of hormone treatment, repeated pollination, hybrid embryo rescue, and chromosome doubling was established for efficient creation of distant hybrids and allopolyploids. Using the technical system, the diploid and tetraploid interspecific hybrids between Oryza sativa L. and Oryza barthii A. Chev. were successfully established. Morphological traits such as lemma tip color, stigma color and exsertion, awn color and length of interspecific hybrids exhibited the characters of their wild rice parents. Some traits such as plant type and grain length were intermediate their parents. Overall, the fertility of hybrids was very low. The results of simple sequence repeat molecular marker detection indicated that the interspecific hybrids possessed the bands of both parents. After chromosome doubling, the grain size, and the seed‐set rates of the tetraploid interspecific hybrids increased significantly, but the tiller number per plant, panicle length, and the total grain number per panicle decreased significantly. The results showed the efficiency and importance of the technical system in establishment of distant hybrids and allopolyploids. The study provides novel germplasm resources for rice breeding and theoretical research.

Sweetpotato (Ipomoea batatas [L.] Lam) is highly adaptable to different soils and climates, but it is more sensitive to cold due to its tropical origin. Low‐temperature stress is a key factor affecting storage and has a significant impact on sweetpotato quality. During sweetpotato storage, prolonged exposure to low temperatures causes chilling damage to the root system, altering its physiological functions. This is manifested by wilting of the tuberous roots, nutrient loss, and reduced antioxidant enzyme activity. At the same time, it leads to a significant downregulation of genes associated with cold signaling pathways. Understanding the physiological and molecular mechanisms of sweetpotato's response to low‐temperature stress, which is crucial for improving its quality during storage. In addition, methods such as high‐voltage alternating electric field, controlled atmosphere, hot water treatment, and hot air treatment can better preserve the nutrients of sweetpotato and maintain their high commercial quality during low‐temperature storage. This article reviews and summarizes key studies on the nutrient and physiological changes, as well as the molecular regulatory mechanisms of sweetpotato during low‐temperature storage, and identifies unresolved questions in this field. It provides insights for further research on low‐temperature stress in sweetpotato.

BC204 is a citrus‐based plant extract applied as a plant biostimulant on a variety of plant species in South Africa, China, and Australia. Although there are reports that it elicits physiological responses such as an increase in crop yield, abiotic and biotic stress tolerance, and fruit quality, no molecular data are available to explain the specific mechanisms underlying these effects. In this study, an RNA sequencing approach was adopted to elucidate the effects of BC204 at the molecular level in Arabidopsis thaliana and Solanum lycopersicum. BC204, applied via either a 0.01% (v/v) soil drench to A. thaliana or a 0.05% (v/v) foliar spray to S. lycopersicum, stimulated above‐ground biomass production whilst eliciting a large change in gene expression levels across several primary and secondary biochemical pathways in shoot tissues. Of the entire transcriptomic profile examined, 8.212% of genes were significantly differentially expressed between the treated and control groups in A. thaliana and 18.059% of genes for S. lycopersicum. Most notably, genes involved in photosynthesis, several aspects of cell wall biogenesis, remodeling and restructuring, carbohydrate metabolism, signaling, stress, and secondary metabolism were upregulated, which could explain the observed increase in plant growth. Little correlation in types of gene and pathway induction was observed between the two model organisms. Genes related to transcription and RNA regulation were both strongly up‐ and downregulated, which suggests that BC204 plays a role in inducing and suppressing several pathways. This novel study provides valuable information to be used as a starting point for targeted future research and identifying new targets for enhanced plant growth and vigor.

It has been argued that metabolic content can be used as a selection marker to accelerate crop improvement because metabolic profiles in crops are often under genetic control. Evaluating the role of genetics in metabolic variation is a long‐standing challenge. Rice, one of the world's most important staple crops, is known to be sensitive to recent increases in nighttime temperatures. Quantification of metabolic levels can help measure rice responses to high night temperature (HNT) stress. However, the extent of metabolic variation that can be explained by regression on whole‐genome molecular markers remains to be evaluated. In the current study, we generated metabolic profiles for mature grains from a subset of rice diversity panel accessions grown under optimal and HNT conditions. Metabolite accumulation was low to moderately heritable, and genomic prediction accuracies of metabolite accumulation were within the expected upper limit set by their genomic heritability estimates. Genomic heritability estimates were slightly higher in the control group than in the HNT group. Genomic correlation estimates for the same metabolite accumulation between the control and HNT conditions indicated the presence of genotype‐by‐environment interactions. Reproducing kernel Hilbert spaces regression and image‐based deep learning improved prediction accuracy, suggesting that some metabolite levels are under non‐additive genetic control. Joint analysis of multiple metabolite accumulation simultaneously was effective in improving prediction accuracy by exploiting correlations among metabolites. The current study serves as an important first step in evaluating the cumulative effect of markers in influencing metabolic variation under control and HNT conditions.

Cheatgrass (Bromus tectorum) is a widespread species of concern throughout the western US, as it dominates many low‐elevation rangelands and continues to spread annually. As a winter annual grass, however, cheatgrass can produce high‐quality and cheap protein forage for livestock early in the growing season. Estimating biomass can lead to better management in these western United States and Great Basin plant communities. The present study was designed to evaluate the accuracy of biomass simulations in Great Basin plant communities using the process‐based Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model as it simulates biomass production and competing species interactions. For this study, data were used from the Sagebrush Steppe Treatment Evaluation Project to simulate cheatgrass, perennial grass, forb, and sagebrush biomass across three community types: native, invaded, and a co‐dominated community of sagebrush, perennial grasses, cheatgrass, and forbs at six representative Great Basin sites from 2006 to 2018. Our results indicated a strong relationship between simulated and measured biomass of total cheatgrass and perennial grasses across the three plant communities. Sagebrush and forb biomass were poorly simulated across most plant community types. Model accuracy also varied by site, largely depending on elevation. We saw high variability in simulated biomass across years, likely because of the single point‐in‐time measurements at peak biomass and the low biomass values of cheatgrass and forbs. Collectively, ALMANAC shows potential for assessing biomass production and plant interactions but will require more data and model development to fully comprehend its utility.

Increasing planting density is a common practice to enhance rapeseed (Brassica napus L.) yield via an increase in pod quantity. However, excessive density may lead to a deterioration in pod quality. Therefore, we hypothesized that improving pod quality based on a certain level of pod quantity could further increase seed yield. A randomized block experiment was conducted with five density levels (2.4, 3.6, 4.8, 6.0, and 7.2 × 10⁵ plants ha⁻¹, referred to as D1, D2, D3, D4, and D5) using two hybrid varieties of Qinyou10 and Ningza1838. The plot seed yield reached the maximum value in D2 or D3, and there was no significant difference between these two density levels. An increase in planting density resulted in a decrease in canopy thickness, but an increase in lodging angle and pod density. According to the number of seeds per pod, the pods were categorized into low‐productive pod (≤14), middle‐productive pod (15‒17), and high‐productive pod (≥18). The number of high‐productive pod in D2 and D3 ranged from 48.15 × 10⁶ to 54.22 × 10⁶ ha⁻¹, accounting for 53.76%‒63.28% of the total pod number and 76.89%‒82.83% of the total seed yield. With the planting density increasing from D3 to D5, there was a significant transition from high‐productive pod to middle‐productive and low‐productive pods, causing a decrease in seed yield. Therefore, when the seed yield was targeted as 4500 kg ha⁻¹, the suitable planting density ranged from 3.6 × 10⁵ to 4.8 × 10⁵ plants ha⁻¹, and the optimal number of pods in population ranged from 83.0 × 10⁶ to 94.0 × 10⁶ ha⁻¹, and the quantity proportion of high‐productive pod maintained >50%. This study provides a guide for high‐yield cultivation of rapeseed in China and presents a novel approach to promoting a potential yield of rapeseed.

Dwarf bunt, caused by Tilletia controversa, is a major biotic constraint and grain contaminant in winter wheat (Triticum aestivum L.) production. The conventional approach for evaluating dwarf bunt resistance in wheat cannot be conducted until maturity. Hence, there is a need to develop a method to determine host resistance at an earlier growth stage. A quantitative polymerase chain reaction (qPCR) assay was developed for the quantification of T. controversa biomass in wheat plants that correlates fungal DNA (fDNA) content in the host tissue with host resistance. A previously developed pathogen primer–probe set and host primer pairs as well as a new host probe were used in this study. The respective primer–probe sets were specific to T. controversa and wheat, respectively. The qPCR assay amplified as little as 0.05 pg of fDNA. The assay was validated in field evaluations conducted in a dwarf bunt nursery in Logan, UT, using susceptible and resistant wheat varieties. The assay detected fDNA in both susceptible varieties at all growth stages. In the resistant varieties, fDNA was detected in the first leaves of all varieties, but only a single plant of the resistant variety Juniper exhibited fDNA at the third leaf stage. There was no fDNA detection in plants beyond the third leaf in any of the resistant varieties. These results established the proof of concept that the qPCR technique is rapid, highly sensitive, and easily applicable for the evaluation of dwarf bunt resistance in wheat at an earlier growth stage and may significantly reduce the time required to develop resistant varieties compared to the conventional method.