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A field experiment was conducted during two seasons 2019-2020 and 2020-2021 at the Grdarasha Research Station of the Agricultural Engineering Sciences College-Salahaddin University-Erbil. The experiment was designed as factorial within randomized complete block design (RCBD), with three replicates. The first factor included three target seeding rates of Triticale (×Triticosecale wittmack, variety Admiral (200,250,and300seeds.m-2), while the second factor was three grain size grades whole, thousand kernels weight (TKW) 47.32 g, heavy seeds, TKW 54.04 and light seeds TKW 40.18 g. The results revealed to no significant influence of seeds weight or seeding rates and their interaction on this trait excluding whole sample seed (ungraded) at the rate target of 300 seeds.m-2, which was significantly reduced flag leaf area. Leaf area index was significantly varied among seed weight and target seeding rates or by their interactions. The plant height of triticale although it was higher for heavy seeds and higher target seeding rates, but it was not reached significant level, either by seed grade or target seed rate and their interactions in both. Plant height ranges from 88.00 for heavy seeds at rate 300 seeds.m-2 to 83.00 for light seeds at rate 200 seeds.m-2 for the season 2019-2020. The highest tillers number was recorded for seed rate 300.m-2 with whole ungraded seeds (176.00).

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.

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.

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.

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.

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 CO2 ASSIMILATION 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. HSP90.2 mediates the targeted sorting of nucleus‐encoded photosystem preproteins along actin microfilaments to chloroplasts to promote the CO2 assimilation rate and yield in wheat.

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.

Background The future productivity of wheat ( T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated. Results Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767–602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture. Conclusions These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.

Progressive N assimilation by maize kernels may constrain dry matter (DM) accumulation and final kernel weights (KW). We sought to better understand whole-plant and kernel N mechanisms associated with incremental DM and N accumulation patterns in kernels during grain fill. Maize was grown with multiple fertilizer N rates and N timings or plant densities to achieve a wide N availability gradient. Whole-plant DM and N sampling enabled determination of apparent N nutrition sufficiency at flowering (NNIR1) and when linear-fill began (NNIR3). Linear-plateau, mixed-effects models were fitted to kernel DM and N accumulation data collected weekly from early R3. Higher N supply, regardless of application timing or plant density, increased grain-fill duration (GFD) and, more inconsistently, effective grain-filling rate (EGFR). Kernels accumulated DM and N for similar durations. Both final KW and kernel N content increased consistently with N availability mostly because of higher kernel N accumulation rates (KNAR) and duration (KNAD). Both NNIR1 and NNIR3 were positively associated with KNAD and KNAR, and less strongly with EGFR. These results confirm the direct role of kernel N accumulation, in addition to prior NNI, in limiting KW gain rates and duration during grain filling.

Although most of the durum wheat produced in the Canadian prairies in 2017 and 2018 met the test weight (TW) requirements for the top grades of Canada Western Amber Durum (CWAD), some samples of top grades were inferior in milling quality. To understand the abnormality, this study was conducted to investigate TW, thousand kernel weight (TKW) and kernel size distribution (KSD) in relation to durum milling potential, semolina composition and pasta quality. With reduction of kernel size, semolina and total milling yields decreased progressively, and kernels passing through no.6 slotted sieve had detrimental impact on milling. The overall relationship between TW and milling yields appeared to be genotype dependent. At similar TW, variety showed lower milling yields had greater proportion of smaller kernels. By account for the difference in KSD, greater relationships (R2 > 0.91, p < 0.001) were found for TKW and proportion of kernels passing No.6 slotted sieve with milling yields than TW (R2 = 0.75, p < 0.001). This infers potential use of small kernels (passing No.6 slotted sieve) as a new objective grading factor for rapid prediction of milling quality of CWAD. Although small kernels exhibited much higher yellow pigment than the larger ones, pasta made from small kernels was duller, redder and less yellow, likely due to the higher semolina ash and protein contents, which adversely affected pasta color.