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Seed reserve mobilization is a crucial physiological process during seed germination. Enhancing the reserve mobilization in sweet corn is vital for seed germination and seedling growth. In this study, a genome-wide association analysis (GWAS) was conducted to predict candidate genes for regulating the weight of mobilized reserved seeds (WMRS) and kernel weight (KW) in sweet corn. GWAS was performed using the BLINK model with the maize 56K SNP chip. The results indicated that there was a positive correlation between the WMRS and KW, with coefficients of variation of 68.18% and 44.63%. Association analysis identified thirteen SNPs associated with two traits, and linkage disequilibrium analysis revealed that eight of these SNPs were in strong linkage. A total of 298 candidate genes were identified within the confidence interval, of which 79 were annotated. About 20 candidate genes were identified through the comparison of homologous genes in Arabidopsis. These genes were enriched in regulating ribosome biogenesis, signal transduction, hormone synthesis, and RNA degradation processes. This study provides important insights into the genetic mechanisms governing germination traits in sweet corn, aiding further research into the localization and cloning of genes involved in the mobilization of reserve materials. [ABSTRACT FROM AUTHOR]

Introduction: Maize (Zea mays L.) is one of the most important crops worldwide, the kernel size-related traits are the major components of maize grain yield. Methods: To dissect the genetic architecture of four kernel-related traits of 100-kernel weight, kernel length, kernel width, and kernel diameter, a genome-wide association study (GWAS) was conducted in the waxy and sweet maize panel comprising of 447 maize inbred lines re-sequenced at the 5× coverage depth. GWAS analysis was carried out with the mixed linear model using 1,684,029 high-quality SNP markers. Results: In total, 49 SNPs significantly associated with the four kernel-related traits were identified, including 46 SNPs on chromosome 3, two SNPs on chromosome 4, and one SNP on chromosome 7. Haplotype regression analysis identified 338 haplotypes that significantly affected these four kernelrelated traits. Genomic selection (GS) results revealed that a set of 10,000 SNPs and a training population size of 30% are sufficient for the application of GS in waxy and sweet maize breeding for kernel weight and kernel size. Forty candidate genes associated with the four kernel-related traits were identified, including both Zm00001d000707 and Zm00001d044139 expressed in the kernel development tissues and stages with unknown functions. Discussion: These significant SNPs and important haplotypes provide valuable information for developing functional markers for the implementation of markerassisted selection in breeding. The molecular mechanism of Zm00001d000707 and Zm00001d044139 regulating these kernel-related traits needs to be investigated further. [ABSTRACT FROM AUTHOR]

Determining sample size to estimate the mean value of a target (infinite) population is of prime importance to have correct results and conclusions. In general, sample sizes differ as the studied objects differ. Another factor affect sample size is number of replicates or groups. Small objects, such as seeds, grains, and bacterial cell sizes require in general larger sample sizes as compared to large objects, such as human patients, large animals, and large trees. In this short article, a mixture of 12 crosses, maize kernels (k) were mixed to have 12,000 kernels. Sample sizes of 50,100, 150, 200, 250, and 300k were taken to find out which sample size fits the mean of target population (12,000 k). Sample size of at least 100 k and up gave the best good fit as compared by Chi-square, P=0.10 and 0.05 at one of degree of freedom. This result was on a heterogenous population of different maize crosses kernel sizes. On the other hand, a population of kernels of a maize cross (32x19), as homogenous population was studied by taking sample sizes of 25, 50, 100, 200, 300, 500, and 1000 k. Depending on Chi-square test, sample sizes of 50 and 100 k gave good estimate to the mean value of kernel weight. A previous research by Elsahookie et al (6) studied the best combination of sample sizes and number of replicates to estimate mean value of plant seed yield of maize and sunflower. They found that the best recommended combination was using sample size of 10 plants with 4 replicates. Hertzog (10) reported that sample size should be over 40, while Julious (12) recommended sample size of 12 per group. More studies on other objects of sample sizes are really required. [ABSTRACT FROM AUTHOR]

The sunflower (Helianthus annuus L.) is one of the most essential oil crops in the world. Several component traits, including flowering time, plant height, stem diameter, seed weight, and kernel weight, determine sunflower seed and oil yield. Although the genetic mechanisms governing the variation of these yield-related traits have been studied using various approaches, genome-wide association studies (GWAS) have not been widely applied to sunflowers. In this study, a set of 342 sunflower accessions was evaluated in 2019 and 2020 using an incomplete randomized block design, and GWAS was conducted utilizing two complementary approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU) model by fitting 226,779 high-quality SNPs. As a result, GWAS identified a number of trait-associated SNPs. Those SNPs were located close to several genes that may serve as a basis for further molecular characterization and provide promising targets for sunflower yield improvement. [ABSTRACT FROM AUTHOR]

Seed reserve mobilization is a crucial physiological process during seed germination. Enhancing the reserve mobilization in sweet corn is vital for seed germination and seedling growth. In this study, a genome-wide association analysis (GWAS) was conducted to predict candidate genes for regulating the weight of mobilized reserved seeds (WMRS) and kernel weight (KW) in sweet corn. GWAS was performed using the BLINK model with the maize 56K SNP chip. The results indicated that there was a positive correlation between the WMRS and KW, with coefficients of variation of 68.18% and 44.63%. Association analysis identified thirteen SNPs associated with two traits, and linkage disequilibrium analysis revealed that eight of these SNPs were in strong linkage. A total of 298 candidate genes were identified within the confidence interval, of which 79 were annotated. About 20 candidate genes were identified through the comparison of homologous genes in Arabidopsis. These genes were enriched in regulating ribosome biogenesis, signal transduction, hormone synthesis, and RNA degradation processes. This study provides important insights into the genetic mechanisms governing germination traits in sweet corn, aiding further research into the localization and cloning of genes involved in the mobilization of reserve materials.

Foliar fertilization is an important agricultural practice. The response of crop plants to foliar application of mineral nutrients such as nitrogen (N), phosphorus, and potassium has been well recognized. However, little is known about crop plants' response to the foliar spray of organic sources of nutrient, in particular, under field conditions. Here, we sprayed glutamine (Gln) at V6, V8, V12, R1 (silk), and R3 (dough) stages of maize hybrid ZD958 to investigate the physiological impacts on grain yield and nutritional quality under sufficient and low-N conditions for two consecutive years. Exogenous application of Gln (1.25 mM) significantly enhanced grain yield approximately by 20.0% under sufficient N and 38.0% under low N, as a combinatorial effect of increases in kernel number and kernel weight. The higher N concentrations in the ear and delaying leaf senescence favor the kernel setting and grain filling, preconditioning higher grain yield at harvest. Further, Gln spray resulted in higher nutritional quality kernels as indicated by higher concentrations of crude proteins, minerals, oil, unsaturated fatty acids (i.e., oleic, and linoleic acids), total carotenoids (i.e., β-carotene, lutein, zeaxanthin, antheraxanthin, and violaxanthin), and α-tocopherol, partially attributing to larger nutrient-rich embryos. Taken together, our findings revealed how Gln promotes maize production and an additional potential to improve grain yield and nutritional quality traits of maize and other closely related cereal crops. [ABSTRACT FROM AUTHOR]

Brassinosteroids (BRs) are a class of plant steroid hormones that are essential for plant growth and development. BRs control important agronomic traits and responses to abiotic stresses. Through the signaling pathway, BRs control the expression of thousands of genes, resulting in a variety of biological responses. The key effectors of the BR pathway are two transcription factors (TFs): BRASSINAZOLE RESISTANT 1 (BZR1) and BRI1-EMSSUPPRESSOR 1 (BES1). Both TFs are phosphorylated and inactivated by the Glycogen synthase kinase 3 BRASSINOSTEROID INSENSITIVE2 (BIN2), which acts as a negative regulator of the BR pathway. In our study, we describe the functional characteristics of HvGSK1.1, which is one of the GSK3/SHAGGY-like orthologs in barley. We generated mutant lines of HvGSK1.1 using CRISPR/Cas9 genome editing technology. Next Generation Sequencing (NGS) of the edited region of the HvGSK1.1 showed a wide variety of mutations. Most of the changes (frameshift, premature stop codon, and translation termination) resulted in the knock-out of the target gene. The molecular and phenotypic characteristics of the mutant lines showed that the knock-out mutation of HvGSK1.1 improved plant growth performance under salt stress conditions and increased the thousand kernel weight of the plants grown under normal conditions. The inactivation of HvGSK1.1 enhanced BR-dependent signaling, as indicated by the results of the leaf inclination assay in the edited lines. The plant traits under investigation are consistent with those known to be regulated by BRs. These results, together with studies of other GSK3 gene members in other plant species, suggest that targeted editing of these genes may be useful in creating plants with improved agricultural traits. [ABSTRACT FROM AUTHOR]

Abstract Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.

The kernel serves as the storage organ and harvestable component of maize, and it plays a crucial role in determining crop yield and quality. Understanding the molecular and genetic mechanisms of kernel development is of considerable importance for maize production. In this study, we obtained a mutant, which we designated defective kernel 407 (dek407), through ethyl methanesulfonate mutagenesis. The dek407 mutant exhibited reduced kernel size and kernel weight, as well as delayed grain filling compared with those of the wild type. Positional cloning and an allelism test revealed that Dek407 encodes a nitrate transporter 1/peptide transporter family (NPF) protein and is the allele of miniature 2 (mn2) that was responsible for a poorly filled defective kernel phenotype. A transcriptome analysis of the developing kernels showed that the mutation of Dek407 altered the expression of phytohormone-related genes, especially those genes associated with indole-3-acetic acid synthesis and signaling. Phytohormone measurements and analysis indicated that the endogenous indole-3-acetic acid content was significantly reduced by 66% in the dek407 kernels, which may be the primary cause of the defective phenotype. We further demonstrated that natural variation in Dek407 is associated with kernel weight and kernel size. Therefore, Dek407 is a potential target gene for improvement of maize yield. [ABSTRACT FROM AUTHOR]

Durum wheat breeding relies on grain yield improvement to meet its upcoming demand while coping with climate change. Kernel size and shape are the determinants of thousand kernel weight (TKW), which is a key component of grain yield, and the understanding of the genetic control behind these traits supports the progress in yield potential. The present study aimed to dissect the genetic network responsible for kernel size components (length, width, perimeter, and area) and kernel shape traits (width-to-length ratio and formcoefficient) as well as their relationships with kernel weight, plant height, and heading date in durum wheat. Quantitative Trait Locus (QTL) mapping was performed on a segregating population of 110 recombinant inbred lines, derived from a cross between the domesticated emmer wheat accession MG5323 and the durum wheat cv. Latino, evaluated in four different environments. A total of 24 QTLs stable across environments were found and further grouped in nine clusters on chromosomes 2A, 2B, 3A, 3B, 4B, 6B, and 7A. Among them, a QTL cluster on chromosome 4B was associated with kernel size traits and TKW, where the parental MG5323 contributed the favorable alleles, highlighting its potential to improve durum wheat germplasm. The physical positions of the clusters, defined by the projection on the T. durum reference genome, overlapped with already known genes (i.e., BIG GRAIN PROTEIN 1 on chromosome 4B). These results might provide genome-based guidance for the efficient exploitation of emmer wheat diversity in wheat breeding, possibly through yield-related molecular markers. [ABSTRACT FROM AUTHOR]

Durum wheat breeding relies on grain yield improvement to meet its upcoming demand while coping with climate change. Kernel size and shape are the determinants of thousand kernel weight (TKW), which is a key component of grain yield, and the understanding of the genetic control behind these traits supports the progress in yield potential. The present study aimed to dissect the genetic network responsible for kernel size components (length, width, perimeter, and area) and kernel shape traits (width-to-length ratio and formcoefficient) as well as their relationships with kernel weight, plant height, and heading date in durum wheat. Quantitative Trait Locus (QTL) mapping was performed on a segregating population of 110 recombinant inbred lines, derived from a cross between the domesticated emmer wheat accession MG5323 and the durum wheat cv. Latino, evaluated in four different environments. A total of 24 QTLs stable across environments were found and further grouped in nine clusters on chromosomes 2A, 2B, 3A, 3B, 4B, 6B, and 7A. Among them, a QTL cluster on chromosome 4B was associated with kernel size traits and TKW, where the parental MG5323 contributed the favorable alleles, highlighting its potential to improve durum wheat germplasm. The physical positions of the clusters, defined by the projection on the T. durum reference genome, overlapped with already known genes (i.e., BIG GRAIN PROTEIN 1 on chromosome 4B). These results might provide genome-based guidance for the efficient exploitation of emmer wheat diversity in wheat breeding, possibly through yield-related molecular markers.

Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut. [ABSTRACT FROM AUTHOR]

Dehydration of maize grains in the field can reduce costs associated with drying after harvest. A delayed harvest approach after physiological maturity, in which plants stand in the field to allow the stems to dry, has been widely adopted in maize production. However, it remains unclear how harvesting at different time points during the dehydration stage may affect grain yield and quality. In the present study, experiments were conducted in the Ningxia Irrigation Area of northwest China from 2019 to 2022, we continuously observed and used a linear-plateau model to analyze the changes in grain weight and quality traits (such as bulk density and levels of starch, protein, oil, fiber, and free fatty acids) during the field dehydration period of maize hybrids with differing maturity times. Harvesting at a grain moisture content of >31.0% was shown to affect grain weight, whereas harvesting at a moisture content of <25.9% did not affect grain weight or yield. The stable period for grain weight occurred during the physiological maturity stage for an early-maturing hybrid and 5–12 days before physiological maturity for the mid–late-maturing hybrids. When the field grain moisture content was <37%, harvesting did not affect the grain bulk density. Grain bulk density tended to stabilize one to two weeks earlier than grain weight and two to three weeks before the physiological maturity period. The protein, oil, fiber, and free fatty acid contents in maize kernels at 30 days after silking were not affected by the harvesting period, and the starch contents were unaffected in maize kernels harvested at any time later than 50 days after silking. Overall, maize grain should be harvested during field dehydration and delayed harvesting after physiological maturity with relatively low moisture content to get a better yield with superior quality.; delayed harvesting is therefore an important technical approach to improve the efficient production of high-quality maize. [ABSTRACT FROM AUTHOR]

Summary: Wheat fixes CO2 by photosynthesis into kernels to nourish humankind. Improving the photosynthesis rate is a major driving force in assimilating atmospheric CO2 and guaranteeing food supply for human beings. Strategies for achieving the above goal need to be improved. Here, we report the cloning and mechanism of CO2ASSIMILATION RATE AND KERNEL‐ENHANCED 1 (CAKE1) from durum wheat (Triticum turgidum L. var. durum). The cake1 mutant displayed a lower photosynthesis rate with smaller grains. Genetic studies identified CAKE1 as HSP90.2‐B, encoding cytosolic molecular chaperone folding nascent preproteins. The disturbance of HSP90.2 decreased leaf photosynthesis rate, kernel weight (KW) and yield. Nevertheless, HSP90.2 over‐expression increased KW. HSP90.2 recruited and was essential for the chloroplast localization of nuclear‐encoded photosynthesis units, for example PsbO. Actin microfilaments docked on the chloroplast surface interacted with HSP90.2 as a subcellular track towards chloroplasts. A natural variation in the hexaploid wheat HSP90.2‐B promoter increased its transcription activity, enhanced photosynthesis rate and improved KW and yield. Our study illustrated an HSP90.2–Actin complex sorting client preproteins towards chloroplasts to promote CO2 assimilation and crop production. The beneficial haplotype of Hsp90.2 is rare in modern varieties and could be an excellent molecular switch promoting photosynthesis rate to increase yield in future elite wheat varieties. [ABSTRACT FROM AUTHOR]

Chickpea is the second-most-cultivated legume globally, with India and Australia being the two largest producers. In both of these locations, the crop is sown on residual summer soil moisture and left to grow on progressively depleting water content, finally maturing under terminal drought conditions. The metabolic profile of plants is commonly, correlatively associated with performance or stress responses, e.g., the accumulation of osmoprotective metabolites during cold stress. In animals and humans, metabolites are also prognostically used to predict the likelihood of an event (usually a disease) before it occurs, e.g., blood cholesterol and heart disease. We sought to discover metabolic biomarkers in chickpea that could be used to predict grain yield traits under terminal drought, from the leaf tissue of young, watered, healthy plants. The metabolic profile (GC-MS and enzyme assays) of field-grown chickpea leaves was analysed over two growing seasons, and then predictive modelling was applied to associate the most strongly correlated metabolites with the final seed number plant−1. Pinitol (negatively), sucrose (negatively) and GABA (positively) were significantly correlated with seed number in both years of study. The feature selection algorithm of the model selected a larger range of metabolites including carbohydrates, sugar alcohols and GABA. The correlation between the predicted seed number and actual seed number was R2 adj = 0.62, demonstrating that the metabolic profile could be used to predict a complex trait with a high degree of accuracy. A previously unknown association between D-pinitol and hundred-kernel weight was also discovered and may provide a single metabolic marker with which to predict large seeded chickpea varieties from new crosses. The use of metabolic biomarkers could be used by breeders to identify superior-performing genotypes before maturity is reached. [ABSTRACT FROM AUTHOR]

Plant architecture is a crucial influencing factor of wheat yield and adaptation. In this study, we cloned and characterized TaSPL14, a homologous gene of the rice ideal plant architecture gene OsSPL14 in wheat. TaSPL14 homoeologs (TaSPL14- 7A, TaSPL14-7B and TaSPL14-7D) exhibited similar expression patterns, and they were all preferentially expressed in stems at the elongation stage and in young spikes. Moreover, the expression level of TaSPL14-7A was higher than that of TaSPL14-7B and TaSPL14-7D. Overexpression of TaSPL14-7A in wheat resulted in significant changes in plant architecture and yield traits, including decreased tiller number and increased kernel size and weight. Three TaSPL14-7A haplotypes were identified in Chinese wheat core collection, and haplotype-based association analysis showed that TaSPL14-7A-Hap1/2 were significantly correlated with fewer tillers, larger kernels and higher kernel weights in modern cultivars. The haplotype effect resulted from a difference in TaSPL14- 7A expression levels among genotypes, with TaSPL14-7A-Hap1/2 leading to higher expression levels than TaSPL14-7A-Hap3. As favorable haplotypes, TaSPL14-7A-Hap1/2 underwent positive selection during global wheat breeding over the last century. Together, the findings of our study provide insight into the function and genetic effects of TaSPL14 and provide a useful molecular marker for wheat breeding. [ABSTRACT FROM AUTHOR]

Competing risk data appear widely in modern biomedical research. In the past two decades, cause-specific hazard models are often used to deal with competing risk data. There is no current study on the kernel likelihood method for the cause-specific hazard model with time-varying coefficients. We propose to use the local partial log-likelihood approach for nonparametric time-varying coefficient estimation. Simulation studies demonstrate that our proposed nonparametric kernel estimator performs well under assumed finite sample settings. And we also compare the local kernel estimator with the penalized spline estimator. Finally, we apply the proposed method to analyze a diabetes dialysis study with competing death causes. [ABSTRACT FROM AUTHOR]

Distance metric learning aims to learn a data-dependent similarity measure, which is widely employed in machine learning. Recently, metric learning algorithms that incorporate multiple kernel learning have shown promising outcomes for classification tasks. However, the multiple kernel learning part of the existing metric learning with multiple kernel just uses a linear combination form of different kernel functions, where each kernel shares the same weight in the entire input space, thus the potential local structure of samples located at different locations in the input space is ignored. To address the aforementioned issues, in this paper, we propose a distance metric learning approach with local multiple kernel embedding (DMLLMK) for small datasets. The weight of each kernel function in DMLLMK is assigned locally, so that there are many different values of weight in each kernel space. This local weight method enables metric learning to capture more information in the data. Our proposed DMLLMK adjusts the kernel weight by using a gating function; moreover, the kernel weight locally depends on the input data. The metric of metric learning and the parameters of the gating function are optimized simultaneously by an alternating learning process. The DMLLMK makes metric learning applicable to small datasets by constructing constraints on the set of similar pairs and dissimilar pairs such that some data are reused, and they produce different constraints on the model. In addition, regularization techniques are used to keep DMLLMK more conservative and prevent overfitting on small data. The experimental results of our proposed method when compared with other metric learning methods on the benchmark dataset show that our proposed DMLLMK is effective. [ABSTRACT FROM AUTHOR]

Summary: In highlands, the increase in altitude results in a drastic decrease in temperature (T) that delays phenological development of maize, decreasing light interception during the cycle. This could be partially overcome by increasing plant density, but information is scarce for designing specific management options. The objective of this work was to describe changes in canopy development, photosynthetic performance, biomass and yield of maize grown at contrasting plant densities (5.7 plants m−2, locally used, and 8.7 plants m−2, 50% higher). Three experiments were carried out in two high-altitude environments within the Argentinean Andean region, Hornillos (HOR, 2380 masl, 2019–20 and 2020–21) and El Rosal (ERO, 3350 masl, 2019–20), and complementary data were obtained from samplings in 8 farmer's fields (from 2400 to 3400 masl, 2022–23). In the experiments, mean T during the first 150 days of the cycle was 33% lower at ERO, which implied 39 extra days but 25% shorter thermal time to achieve silking. The higher plant density significantly increased leaf area index and light interception at ERO, whereas at HOR, this was only evident during the second season. At the leaf level, plants grown at ERO had thicker leaves with higher chlorophyll (+36%) and nitrogen (40%) content. Photosynthetic electron transport rate at full irradiance was +20% higher at ERO but significantly varied throughout the day with lowest values in the morning, which was not observed at HOR and was not related to light intensity or stomatal conductance. At HOR, the increase in plant density did not improve light interception, nor yield in 2019–20 (with average yields of 6356 kg ha−1) but it did improve both in 2020–21 when generally lower yields were attained (4821 kg ha−1). Across farmer's fields, increasing densities consistently reduced yield per plant (r 2 = 0.57***) but improved yield per area basis, which was maximised at 10 pl m−2 as a result of a steady increase in kernel number m−2 (up to 15 pl m−2). Thus, in these high-altitude environments, increasing plant density beyond recommended (6 pl m−2) is a promising approach for improving yield, with major penalties of supra-optimum densities being related to kernel weight. Further work is needed to explore the effect of different factors limiting kernel growth, over plant density responses. [ABSTRACT FROM AUTHOR]

Abstract Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

Background: In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results: We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). Conclusion: Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future. [ABSTRACT FROM AUTHOR]

Dehydration of maize grains in the field can reduce costs associated with drying after harvest. A delayed harvest approach after physiological maturity, in which plants stand in the field to allow the stems to dry, has been widely adopted in maize production. However, it remains unclear how harvesting at different time points during the dehydration stage may affect grain yield and quality. In the present study, experiments were conducted in the Ningxia Irrigation Area of northwest China from 2019 to 2022, we continuously observed and used a linear-plateau model to analyze the changes in grain weight and quality traits (such as bulk density and levels of starch, protein, oil, fiber, and free fatty acids) during the field dehydration period of maize hybrids with differing maturity times. Harvesting at a grain moisture content of >31.0% was shown to affect grain weight, whereas harvesting at a moisture content of

Sulfur fertilizers play an important role in increasing the yield and improving the dough quality of bread wheat, but their regulatory mechanism remains unclear. In this study, 0 kg·ha-1 (S0) and 60 kg·ha-1 (S60) of sulfur were applied on the anthesis date; subsequently, immature wheat grains at 8, 13, and 18 days post-anthesis (DPA) were subjected to integrated transcriptomic and metabolomic analyses to investigate the changes in the gene/metabolite activity in a typical strong-gluten wheat, Gaoyou2018 (GY2018). Our data show that the S60 treatment could significantly increase the grain yield and grain protein content by 13.2 and 3.6%, respectively. The transcriptomic analysis revealed that 10,694 differentially expressed genes (DEGs) were induced by S60 from 8 to 18 DPA when compared with their corresponding no-sulfur controls, and most DEGs were mainly involved in lipid metabolism and amino acid metabolism pathways. Ninety-seven MYB transcription factors (TFs) were identified as responsive to the S60 treatment; of these, 66 showed significantly differential expression at 13 DPA, and MYB118 might participate in the process of sulfur metabolism by regulating glucosinolate synthesis. In total, 542 significantly enriched differentially expressed (DE) metabolites (DEMs) were identified following the S60 treatment, which mainly included secondary metabolites, carbohydrates, and amino acids. Several metabolites (e.g., glutathione, sucrose, GDP-alpha-D-glucose, and amino acids) exhibited altered abundances following the S60 treatment. The combination of transcriptomic and metabolomic analyses highlighted the important role of amino acid metabolism (especially cysteine, methionine, and glutathione metabolism) and starch and sucrose metabolism pathways after S60 application. Our results provide valuable information enhancing our understanding of the molecular mechanism of the response to sulfur and provide useful clues for grain protein quality formation and yield improvement in bread wheat. [ABSTRACT FROM AUTHOR]

In order to evaluate the effects of climatic conditions on maize grain yield (GY), kernel weight (KW), and kernel filling and identify the optimal climatic factors for GY and KW, 2-year field experiments in three seasons, i.e., spring (SPM), summer (SUM), and autumn (AUM), on maize were conducted in Central China. The results showed that SUM had more growing degree days (GDDs) than SPM and AUM due to the higher mean temperature (MT), and also resulted in higher temperature stress (killing degree days (KDDs)) in maize growth duration. Meanwhile, after silking, SPM and SUM had more GDDs and KDDs than AUM because of the higher MT, and the accumulated solar radiation (Ra) for SUM was significantly higher than for SPM and AUM. The GY of SPM was significantly higher than that of SUM and AUM, while SUM's GY was always the lowest, because the GDDGD, MTGD, and KDDGD played significantly negative roles on GY. The final KW for SUM was always the lowest, with GDD, MT, KDD, and Ra causing significantly negative effects, and M△T and precipitation having significant positive effects, resulting in a lower kernel filling rate during the linear kernel filling period (KFRlkf) and a lower GDD at the maximum kernel filling rate (GDDKFRmax). Maize KFRlkf has significant negative linear dependences on GDD, MT, and Ra. In summary, because of the higher MT, KDD, and GDD during maize growth and kernel filling duration negatively affecting the maize kernel filling rate, the GY and KW for SPM were the highest, and for SUM, they were the lowest; therefore, farmers should plant SPM first and then AUM in Central China. [ABSTRACT FROM AUTHOR]

Kernel weight (KW), together with kernel number per unit area, determines yield of cereal crops. Here, two barley recombinant inbred lines (RILs) populations with a shared parent were used to identify loci controlling KW. One is Baudin/AWCS276 (BA) for which a linkage map was available. Several large-effect QTL controlling KW were detected in this population. Another is Morex/AWCS276 (MA). A linkage map with 5273 makers formed 1454 clusters, was constructed by the genotyping by sequence (GBS) data of 201 RILs from this population. A single marker was selected to represent each of the clusters and the linkage map constructed with these markers covers a total length of 1022.4 cM with an average interval of approximately 0.7 cM between loci. Three of the large-effect loci controlling KW (located on 2HL, 6HL, and 7HL, respectively) identified from the BA population were also detected in the MA population under different environments. The locus on 6HL was detected in each of the experiments conducted for both populations thus was selected for developing near isogenic lines (NILs). Apart from KW, the two isolines for each pair of the putative NILs obtained showed no significant difference for any of the morphological characteristics assessed. The average difference in KW between the isolines for all the NILs obtained was about 15% based on assessments under both glasshouse and field conditions. Taken advantage that high quality genome assemblies for both Morex and AWCS276 are available, we identified candidate genes underlying two of the three loci based on an orthologous analysis. The NILs developed and the candidate genes identified in this study should facilitate the cloning and functional analysis of genes regulating KW in barley.

Chickpea is the second-most-cultivated legume globally, with India and Australia being the two largest producers. In both of these locations, the crop is sown on residual summer soil moisture and left to grow on progressively depleting water content, finally maturing under terminal drought conditions. The metabolic profile of plants is commonly, correlatively associated with performance or stress responses, e.g., the accumulation of osmoprotective metabolites during cold stress. In animals and humans, metabolites are also prognostically used to predict the likelihood of an event (usually a disease) before it occurs, e.g., blood cholesterol and heart disease. We sought to discover metabolic biomarkers in chickpea that could be used to predict grain yield traits under terminal drought, from the leaf tissue of young, watered, healthy plants. The metabolic profile (GC-MS and enzyme assays) of field-grown chickpea leaves was analysed over two growing seasons, and then predictive modelling was applied to associate the most strongly correlated metabolites with the final seed number plant−1. Pinitol (negatively), sucrose (negatively) and GABA (positively) were significantly correlated with seed number in both years of study. The feature selection algorithm of the model selected a larger range of metabolites including carbohydrates, sugar alcohols and GABA. The correlation between the predicted seed number and actual seed number was R2 adj = 0.62, demonstrating that the metabolic profile could be used to predict a complex trait with a high degree of accuracy. A previously unknown association between D-pinitol and hundred-kernel weight was also discovered and may provide a single metabolic marker with which to predict large seeded chickpea varieties from new crosses. The use of metabolic biomarkers could be used by breeders to identify superior-performing genotypes before maturity is reached.

The frequency and magnitude of drought and heat events are predicted to increase, exerting adverse effects on maize yield. Repeated stress events may lead to critical stage or process shifts in maize yield response, but few studies document such shifts. In this experiment, maize plants were exposed to drought at the six-leaf period (V6) to investigate the priming effects on maize yield formation under control (CK), drought stress (DS), heat stress (HS), and combined drought and heat stress (DHS) occurring around flowering (AF). The stressors was significantly reduced maize yield by 10.0%–41.4% compared with T1 (V6 CK +AF CK), with the exception of the T5 (V6 DS +AF CK) treatment. Fortunately, compared with T1 (V6 CK +AF CK), T2 (V6 CK +AF DS), T3 (V6 CK +AF HS), and T4 (V6 CK +AF DHS) treatments around flowering, exposure to drought at V6 improved yield by 6.4%, 11.1%, 2.6%, and 33.4%, respectively. The increased yield in stressors around flowering was attributed to the enhancement of kernel numbers (KNS) and kernel weight (KW). However, KNS and KW showed variable responses to treatments. Drought exposure at V6 relieved KNS loss without stressors around flowering, as the fertilized florets increased by 2.7%. The KNS loss in DS and DHS treatment around flowering was reversed via seed setting, increased by 6.0% and 7.4%, resulting from strengthened sugar-starch conversion. Furthermore, the reduced KW in HS and DHS treatment around flowering was improved due to increased starch synthesis ability during kernel filling. Consequently, drought priming at the V6 stage increased seed setting, sugar-starch conversion, and starch synthesis ability, improving KNS and KW, offering a method to relieve yield loss under stressors around flowering. • Priming DS at V6 stage improved maize yield under stressors around flowering (AF). • Yield loss under stressors at AF was relieved through the enhancement of KNS and KW. • KNS loss in the DS and DHS at AF was reversed through seed setting. • KW loss in the HS and DHS at AF was improved by increasing starch synthesis ability. [ABSTRACT FROM AUTHOR]

Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize. [ABSTRACT FROM AUTHOR]

Appropriate genotype selection and management can impact wheat (Triticum aestivum L.) yield in dryland environments, but their impact on yield components and their role in yield modulation are not well understood. Our objectives were to evaluate the yield response of commercial winter wheat genotypes to different management practices reflecting a stepwise increase in management intensity (including a reduction in crop density under high input), and to quantify how the different yield components modulate wheat yield. A factorial experiment evaluated six management (M) intensities ["farmer practice" (FP), "enhanced fertility" (EF), "ecological intensification" (EI), "increased foliar protection" (IFP), "water-limited yield" (Yw), and "increased plant productivity" (IPP)] and four winter wheat genotypes (G) in four Kansas environments (E). Average grain yield was 4.9 Mg ha–1 and ranged from 2.0 to 7.4 Mg ha–1, with significant two-way interactions (E × M and E × G). The EF usually maximized yields in dry environments, while EI, which consisted of EF plus one fungicide application, maximized yields in environments with greater water availability. Across all sources of variation, kernels m–2 and aboveground biomass were the strongest modulators of yield as compared to kernel weight and harvest index, while spikes m–2 and kernels spike–1 modulated yields at a similar magnitude. Foliar fungicides improved yield through increased green canopy cover duration and greater radiation intercepted during grain filling. When crop density was reduced from 2.7 to 1.1 million plants per hectare in an otherwise high-input system, plants produced more productive tillers (with genotype-specific response); however, reduced green canopy cover at anthesis and reduced cumulative solar radiation intercepted during grain filling limited wheat yield—although large differences in canopy cover or intercepted radiation were needed to cause modest changes in yield. Treatments more intensive than EI were not warranted as EF or EI maximized yields at all environments, and practices that promote biomass and kernels m–2 are to be targeted for future increases in wheat yield. [ABSTRACT FROM AUTHOR]

To provide food security for a growing world population, it will be necessary to increase yields of staple crops such as wheat (Triticum aestivum L.). Yield is a complex, polygenic trait influenced by grain weight and number, which are negatively correlated with one another. Spikelet number is an important determinant of grain number, but allelic variants impacting its expression are often associated with heading date, constraining their use in wheat germplasm that must be adapted for specific environments. Identification and characterization of genetic variants affecting spikelet number will increase selection efficiency through their deployment in breeding programs. In this study, a quantitative trait locus (QTL) on chromosome arm 6BL for spikelet number was identified and validated using an association mapping panel, a recombinant inbred line population, and seven derived heterogeneous inbred families. The superior allele, QSn.csu-6Bb, was associated with an increase of 0.248 to 0.808 spikelets per spike across multiple environments that varied for mean spikelet number. Despite epistatic interactions between QSn.csu-6B and three other loci (WAPO-A1, VRN-D3, and PPD-B1), genotypes with a greater number of superior alleles at these loci consistently exhibit higher spikelet number. The frequency of superior alleles at these loci varies among winter wheat varieties adapted to different latitudes of the US Great Plains, revealing opportunities for breeders to select for increased spikelet number using simple molecular markers. This work lays the foundation for the positional cloning of the genetic variant underlying the QSn.csu-6B QTL to strengthen our understanding of spikelet number determination in wheat. [ABSTRACT FROM AUTHOR]

Persian walnut (Juglans regia L.) is an economically important species of edible nuts. In the present study, northwest regions of Iran were explored to find the spring frost resistant Persian walnut genotypes. Pomological traits related to the fruits of selected genotypes and physiochemical properties were measured and analyzed. Moreover, an optimized model using supervised machine learning models to predict the tolerant and sensitive genotypes based on pomological and physiochemical traits was developed. In the current study, we identified 24 superior genotypes with ideal values. Nut weight in our identified genotypes varied from 5.26 to 20.6 g, kernel weight ranged from 0.35 to 8.42 g, and kernel percentage varied from 6.65 to 73.83%. Significant positive correlation was observed between nut dimensions and kernel percentage. Results showed that the tolerant genotypes had significant lower injury index than sensitive genotypes. In comparison to the sensitive genotypes, higher levels of proline, protein and lower level of H2O2 content were measured in cold-tolerant genotypes. Moreover, water content had significant correlation with proline, malonyldialdehyde, H2O2, and relative electrolyte leakage. The best performances of the model based on pomological and physiochemical traits gained when the tree was induced by the decision tree with Information Gain ratio and decision tree with Information Gain criteria, respectiveley. In conclusion, identified genotypes can be cultivated in frost occurrence regions or can be used for germplasm conservation programs. Moreover, the optimized machine learning-based model presented here opens a new avenue for future studies in this field. [ABSTRACT FROM AUTHOR]

As a major abiotic stress, drought causes instability and deterioration of malt barley quality. There is distinct difference among barley cultivars in the responses of the main malt quality traits to drought stress. In the previous study, we identified some Tibetan wild barley accessions with relatively less change of malt quality traits under drought. In this study, we examined the impact of drought stress during grain filling stage on grain weight and several important malt quality traits, including total protein content, β-glucan content, limit dextrinase activity, β-amylase activity, and protein fractions in four barley genotypes (two Tibetan wild accessions and two cultivars). Drought treatment reduced grain weight, β-glucan content, and increased total protein content, β-amylase activity. These changes differed among barley genotypes and treatments, and are closely associated with grain filling process and kernel weight. All the results indicated Tibetan wild barley had great potential for developing drought tolerant barley cultivars. Relatively stable kernel weight or filling process under water stress should be highlighted in malt barley breeding in order to reduce the effect of water stress on malt barley quality.

Introduction: Maize ( Zea mays L.) is one of the most important crops worldwide, the kernel size-related traits are the major components of maize grain yield., Methods: To dissect the genetic architecture of four kernel-related traits of 100-kernel weight, kernel length, kernel width, and kernel diameter, a genome-wide association study (GWAS) was conducted in the waxy and sweet maize panel comprising of 447 maize inbred lines re-sequenced at the 5× coverage depth. GWAS analysis was carried out with the mixed linear model using 1,684,029 high-quality SNP markers., Results: In total, 49 SNPs significantly associated with the four kernel-related traits were identified, including 46 SNPs on chromosome 3, two SNPs on chromosome 4, and one SNP on chromosome 7. Haplotype regression analysis identified 338 haplotypes that significantly affected these four kernel-related traits. Genomic selection (GS) results revealed that a set of 10,000 SNPs and a training population size of 30% are sufficient for the application of GS in waxy and sweet maize breeding for kernel weight and kernel size. Forty candidate genes associated with the four kernel-related traits were identified, including both Zm00001d000707 and Zm00001d044139 expressed in the kernel development tissues and stages with unknown functions., Discussion: These significant SNPs and important haplotypes provide valuable information for developing functional markers for the implementation of marker-assisted selection in breeding. The molecular mechanism of Zm00001d000707 and Zm00001d044139 regulating these kernel-related traits needs to be investigated further., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Qu, Yu, Gu, Khalid, Kuang, Dang, Wang, Prasanna, Zhang, Zhang, Zheng and Guan.)

We propose using the local kernel regression method to estimate the conditional mean function of a panel count model with time-varying coefficients. A partial log-likelihood with a local polynomial is used for the estimation. Under some regularity conditions, strong uniform consistency rates are obtained for the local estimator. For a fixed time point, we show that the local estimator converges in distribution to the normal distribution. Moreover, the Breslow-type estimation of the baseline mean function is shown to be consistent. Simulation studies show that the time-varying coefficient estimator is close to the true value, and that the empirical coverage probability of the confidence interval is close to the nominal level. Finally, we demonstrate the proposed method by applying it to analyze a clinical data set on childhood wheezing. [ABSTRACT FROM AUTHOR]

Kernel size and kernel weight are important yield attributing traits in maize (Zea mays L.). Though yield hascomplex inheritance, understanding and improvement of yield per se becomes relatively easy, when maize breedingis targeted for genetic enhancement of yield component traits. In the present investigation, a set of 45 tropical fieldcorn inbred lines were evaluated under three environments and at different location for kernel length, kernel thicknessand kernel weight traits. In a given location, environmental influence on the expression of these traits were negligibleas it was evident by exhibition of high heritability (broad sense) for the traits under study, however pooled effect ofenvironments showed some interactions. Based on the AMMI stability value, the inbred lines AI 04 followed by AI 37,AI 18, AI 25 and AI 35 were selected as highly stable genotypes for its yield per se. Inbred lines were characterizedusing gene-based markers linked to kernel traits. It was observed that molecular markers rightly classified the inbredlines into different groups based on their trait means. Furthermore, the makers, umc1890 and umc1120 were putativelylinked to kernel weight and kernel thickness respectively. These markers may be utilized for identification of suitabledonor and genetic improvement of kernel traits driven maize improvement program.

C-terminal encoded peptides (CEPs) are peptide hormones which act as mobile signals coordinating important developmental programs. Previous studies have unraveled that CEPs are able to regulate plant growth and abiotic stress via cell-to-cell communication in Arabidopsis and rice; however, little is known about their roles in maize. Here, we examined the spatiotemporal expression pattern of ZmCEP1 and showed that ZmCEP1 is highly expressed in young ears and tassels of maize, particularly in the vascular bundles of ears. Heterologous expression of ZmCEP1 in Arabidopsis results in smaller plants and seed size. Similarly, overexpression of ZmCEP1 in maize decreased the plant and ear height, ear length, kernel size, and 100-kernel weight. Consistently, exogenous application of the synthesized ZmCEP1 peptide to the roots of Arabidopsis and maize inhibited root elongation. Knock-out of ZmCEP1 through CRISPR/Cas9 significantly increased plant and ear height, kernel size and 100-kernel weight. Transcriptome analysis revealed that knock-out of ZmCEP1 up-regulated a subset of genes involved in nitrogen metabolism, nitrate transport, sugar transport and auxin response. Thus, these results provide new insights into the genetic and molecular function of ZmCEP1 in regulating kernel development and plant growth, providing novel opportunities for maize breeding. [ABSTRACT FROM AUTHOR]

Before the introduction of genetically modified insect-tolerant maize (Zea mays L.) in 1997, most of the production of this staple in Argentina was concentrated in humid and sub-humid temperate regions. Early spring sowings minimized the risk of water deficit around flowering and yield reduction due to pests. Use of genetically modified maize allowed optimization of sowing dates to synchronize critical periods for kernel set determination with the times of the year when water deficits are less likely, reducing large interannual variations in grain yield. This change in sowing date did not start until 2009, after the occurrence of two successive dry phases of the El Niño–Southern Oscillation phenomenon. The area of land cropped to maize in Argentina has expanded dramatically since then, particularly beyond the humid areas. Currently, maize is sown in an almost 50%/50% distribution between early and late sowings, including double cropping. Changes in agronomic practices such as sowing date and production area can lead to changes in the timing and intensity of water deficits along the maize growth cycle. This review provides an overview of new patterns of water deficit across humid, sub-humid, and semi-arid mid-latitude environments of Argentina, and their effects on grain yield and yield components. [ABSTRACT FROM AUTHOR]

In order to evaluate the effects of climatic conditions on maize grain yield (GY), kernel weight (KW), and kernel filling and identify the optimal climatic factors for GY and KW, 2-year field experiments in three seasons, i.e., spring (SPM), summer (SUM), and autumn (AUM), on maize were conducted in Central China. The results showed that SUM had more growing degree days (GDDs) than SPM and AUM due to the higher mean temperature (MT), and also resulted in higher temperature stress (killing degree days (KDDs)) in maize growth duration. Meanwhile, after silking, SPM and SUM had more GDDs and KDDs than AUM because of the higher MT, and the accumulated solar radiation (Ra) for SUM was significantly higher than for SPM and AUM. The GY of SPM was significantly higher than that of SUM and AUM, while SUM’s GY was always the lowest, because the GDDGD, MTGD, and KDDGD played significantly negative roles on GY. The final KW for SUM was always the lowest, with GDD, MT, KDD, and Ra causing significantly negative effects, and M△T and precipitation having significant positive effects, resulting in a lower kernel filling rate during the linear kernel filling period (KFRlkf) and a lower GDD at the maximum kernel filling rate (GDDKFRmax). Maize KFRlkf has significant negative linear dependences on GDD, MT, and Ra. In summary, because of the higher MT, KDD, and GDD during maize growth and kernel filling duration negatively affecting the maize kernel filling rate, the GY and KW for SPM were the highest, and for SUM, they were the lowest; therefore, farmers should plant SPM first and then AUM in Central China.

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Kernel weight is a key determinant of yield in wheat (Triticum aestivum L.). Starch consists of amylose and amylopectin and is the major constituent of mature grain. Therefore, starch metabolism in the endosperm during grain filling can influence kernel weight. In this study, we sequenced 87 genes involved in starch metabolism from 300 wheat accessions and detected 8,141 polymorphic sites. We also characterized yield-related traits across different years in these accessions. Although the starch contents fluctuated, thousand kernel weight (TKW) showed little variation. Polymorphisms in six genes were significantly associated with TKW. These genes were located on chromosomes 2A, 2B, 4A, and 7A; none were associated with starch content or amylose content. Variations of 15 genes on chromosomes 1A and 7A formed haplotype blocks in 26 accessions. Notably, accessions with higher TKWs had more of the favorable haplotypes. We thus conclude that these haplotypes contribute additive effects to TKW.

Wheat growing regions and seasons are diverse, mandating different varietal adaptation and management practices. Grain yield is the primary target for soft-red winter (SRW) wheat, due to lower protein content requirements. The growing season for SRW wheat in the eastern United States takes up to 9 months under variable environments, highlighting the importance of variety and management. In this study, we present the results of a 2-year field-based investigation of yield response of 30 wheat lines to different nitrogen treatments by dissecting yield to its components. For 5 out of the 30 lines, we performed in-tissue nitrogen analysis. Spring nitrogen (N) treatments were two levels of 0 kg N ha–1 (low N) and 112 kg N ha–1 (high N). On average, application of 112 kg N in the spring, in addition to fall N fertilizer, increased phytomass by 22% at maturity, enhanced fertile tiller numbers by 16%, and increased grain yield by 18% that coincided with a 26% increase in grain number per unit area. N in the grains, or the nitrogen harvest index, was lower (36% of total) in high N than in low N (40% of total) treatment, which indicated plants did not increase the in-grain utilization of N. The 18% higher grain yield with 112 kg N treatment occurred without considerable change in grain N content. However, lines with greater biomass produced greater yields in low N. Therefore, increasing tiller numbers and grain numbers for SRW wheat are the targeted traits for improving grain yield under N management, with less emphasis on the utilization of N in grains because N content is not critically influential for the marketability of soft wheat grains.

Starch is a major component of wheat (Triticum aestivum L.) endosperm and is an important part of the human diet. The functions of many starch synthesis genes have been elucidated. However, little is known about their regulatory mechanisms in wheat. Here, we identified a novel NAC transcription factor, TaNAC019-A1 (TraesCS3A02G077900), that negatively regulates starch synthesis in wheat and rice (Oryza sativa L.) endosperms. TaNAC019-A1 was highly expressed in the endosperm of developing grains and encoded a nucleus-localized transcriptional repressor. Overexpression of TaNAC019-A1 in rice and wheat led to significantly reduced starch content, kernel weight, and kernel width. The TaNAC019-A1 -overexpression wheat lines had smaller A-type starch granules and fewer B-type starch granules than wild-type. Moreover, TaNAC019-A1 could directly bind to the 'ACGCAG' motif in the promoter regions of ADP-glucose pyrophosphorylase small subunit 1 (TaAGPS1-A1 , TraesCS7A02G287400) and TaAGPS1-B1 (TraesCS7B02G183300) and repress their expression, thereby inhibiting starch synthesis in wheat endosperm. One haplotype of TaNAC019-B1 (TaNAC019-B1-Hap2 , TraesCS3B02G092800) was positively associated with thousand-kernel weight and underwent positive selection during the Chinese wheat breeding process. Our data demonstrate that TaNAC019-A1 is a negative regulator of starch synthesis in wheat endosperm and provide novel insight into wheat yield improvement. [ABSTRACT FROM AUTHOR]

Kernel weight is a key determinant of yield in wheat (Triticum aestivum L.). Starch consists of amylose and amylopectin and is the major constituent of mature grain. Therefore, starch metabolism in the endosperm during grain filling can influence kernel weight. In this study, we sequenced 87 genes involved in starch metabolism from 300 wheat accessions and detected 8,141 polymorphic sites. We also characterized yield-related traits across different years in these accessions. Although the starch contents fluctuated, thousand kernel weight (TKW) showed little variation. Polymorphisms in six genes were significantly associated with TKW. These genes were located on chromosomes 2A, 2B, 4A, and 7A; none were associated with starch content or amylose content. Variations of 15 genes on chromosomes 1A and 7A formed haplotype blocks in 26 accessions. Notably, accessions with higher TKWs had more of the favorable haplotypes. We thus conclude that these haplotypes contribute additive effects to TKW. [ABSTRACT FROM AUTHOR]

Summary: Kernel size is an important trait determining cereal yields. In this study, we cloned and characterized TaDA1, a conserved negative regulator of kernel size in wheat (Triticum aestivum). The overexpression of TaDA1 decreased the size and weight of wheat kernels, while its down‐regulation using RNA interference (RNAi) had the opposite effect. Three TaDA1‐A haplotypes were identified in Chinese wheat core collections, and a haplotype association analysis showed that TaDA1‐A‐HapI was significantly correlated with the production of larger kernels and higher kernel weights in modern Chinese cultivars. The haplotype effect resulted from a difference in TaDA1‐A expression levels between genotypes, with TaDA1‐A‐HapI resulting in lower TaDA1‐A expression levels. This favourable haplotype was found having been positively selected during wheat breeding over the last century. Furthermore, we demonstrated that TaDA1‐A physically interacts with TaGW2‐B. The additive effects of TaDA1‐A and TaGW2‐B on kernel weight were confirmed not only by the phenotypic enhancement arising from the simultaneous down‐regulation of TaDA1 and TaGW2 expression, but also by the combinational haplotype effects estimated from multi‐environment field data from 348 wheat cultivars. A comparative proteome analysis of developing transgenic and wild‐type grains indicated that TaDA1 and TaGW2 are involved in partially overlapping but relatively independent protein regulatory networks. Thus, we have identified an important gene controlling kernel size in wheat and determined its interaction with other genes regulating kernel weight, which could have beneficial applications in wheat breeding. [ABSTRACT FROM AUTHOR]

Wheat growing regions and seasons are diverse, mandating different varietal adaptation and management practices. Grain yield is the primary target for soft-red winter (SRW) wheat, due to lower protein content requirements. The growing season for SRW wheat in the eastern United States takes up to 9 months under variable environments, highlighting the importance of variety and management. In this study, we present the results of a 2-year field-based investigation of yield response of 30 wheat lines to different nitrogen treatments by dissecting yield to its components. For 5 out of the 30 lines, we performed in-tissue nitrogen analysis. Spring nitrogen (N) treatments were two levels of 0 kg N ha–1 (low N) and 112 kg N ha–1 (high N). On average, application of 112 kg N in the spring, in addition to fall N fertilizer, increased phytomass by 22% at maturity, enhanced fertile tiller numbers by 16%, and increased grain yield by 18% that coincided with a 26% increase in grain number per unit area. N in the grains, or the nitrogen harvest index, was lower (36% of total) in high N than in low N (40% of total) treatment, which indicated plants did not increase the in-grain utilization of N. The 18% higher grain yield with 112 kg N treatment occurred without considerable change in grain N content. However, lines with greater biomass produced greater yields in low N. Therefore, increasing tiller numbers and grain numbers for SRW wheat are the targeted traits for improving grain yield under N management, with less emphasis on the utilization of N in grains because N content is not critically influential for the marketability of soft wheat grains. [ABSTRACT FROM AUTHOR]

Improving plant net photosynthetic rates and accelerating water-soluble carbohydrate accumulation play an important role in increasing the carbon sources for yield formation of wheat (Triticum aestivum L.). Understanding and quantify the contribution of these traits to grain yield can provide a pathway towards increasing the yield potential of wheat. The objective of this study was to identify kernel weight gap for improving grain yield in 15 winter wheat genotypes grown in Shandong Province, China. A cluster analysis was conducted to classify the 15 wheat genotypes into high yielding (HY) and low yielding (LY) groups based on their performance in grain yield, harvest index, photosynthetic rate, kernels per square meter, and spikes per square meter from two years of field testing. While the grain yield was significantly higher in the HY group, its thousand kernel weight (TKW) was 8.8% lower than that of the LY group (p < 0.05). A structural equation model revealed that 83% of the total variation in grain yield for the HY group could be mainly explained by TKW, the flag leaf photosynthesis rate at the grain filling stage (Pn75), and flag leaf water-soluble carbohydrate content (WSC) at grain filling stage. Their effect values on yield were 0.579, 0.759, and 0.444, respectively. Our results suggest that increase of flag leaf photosynthesis and WSC could improve the TKW, and thus benefit for developing high yielding wheat cultivars.