Maize Populations Research Articles

Morphological and phenological variability of Guaraní maize germplasm from Northeastern Argentina (NEA)

Abstract Morphological and phenological variability of Guaraní maize germplasm from Northeastern Argentina (NEA) are potential sources of genetic resources, constituting valuable repositories of alleles associated with tolerance and resistance to biotic and abiotic stresses. Here, we studied the morphological and phenological variability in maize populations of 14 Guaraní landraces from NEA and analysed the relationship among them. These landraces were characterised according to the endosperm consistency of grain as Popcorn (Pc), Floury (F) and Floury with corneal periphery (F-Pc). There were inter-population differences in phenological characters as time from germination to tassel emergence, from germination to spike emergence and from germination to anthesis (length of vegetative cycle -LVC). The morphological and phenological data here reported were used to carry out a cluster analysis, in which the F and Pc populations appeared as two separate groups and the F-Pc populations were included in the F group. Thus, the well-defined F (including F-Pc) and Pc genetic pools should be prioritised within the framework of breeding plans as well as ex situ and in situ conservation programs. For these purposes it is essential the morphological, phenological and genetic characterisation the maize Guaraní landraces.

Development of homozygous maize lines differing in oil and zein content using in-vivo maternal haploid technique

This study was carried out in order to develop homozygous lines that differ in grain quality from the local maize population. Twelve different local maize landraces were used as donor materials in the study. These populations were subjected to induction crossing under greenhouse conditions in September 2020 with the ADAIL-I inducer line. In September 2021, a total of twelve haploid lines were grown in greenhouse conditions. Some plant traits and some grain quality characteristics were examined. Zein protein fractions were also analyzed with SDS-PAGE analysis. The haploid induction rates (HIR) of donor materials ranged from 6.08% to 11.71%. The average HIR value of the ADAIL-I inducer line was determined as 8.20%. The average value of plant height of developed lines varied between 123 cm and 250 cm; first ear height between 54 cm and 120 cm; stem diameter between 0.7 cm and 1.2 cm; crude oil content between 2.39%, and 7.54%; oleic acid content between 15.34% and %30.98; linoleic acid content between 50.4% and 67.8%; protein content between 6.75% and 13.74%; and zein content between 4.58%, and 5.04%. Some the homozygous lines carry the desired protein bands in terms of zein fractions.

Genomic prediction of tocochromanols in exotic-derived maize.

Tocochromanols (vitamin E) are an essential part of the human diet. Plant products, including maize (Zea mays L.) grain, are the major dietary source of tocochromanols; therefore, breeding maize with higher vitamin content (biofortification) could improve human nutrition. Incorporating exotic germplasm in maize breeding for trait improvement including biofortification is a promising approach and an important research topic. However, information about genomic prediction of exotic-derived lines using available training data from adapted germplasm is limited. In this study, genomic prediction was systematically investigated for nine tocochromanol traits within both an adapted (Ames Diversity Panel [AP]) and an exotic-derived (Backcrossed Germplasm Enhancement of Maize [BGEM]) maize population. Although prediction accuracies up to 0.79 were achieved using genomic best linear unbiased prediction (gBLUP) when predicting within each population, genomic prediction of BGEM based on an AP training set resulted in low prediction accuracies. Optimal training population (OTP) design methods fast and unique representative subset selection (FURS), maximization of connectedness and diversity (MaxCD), and partitioning around medoids (PAM) were adapted for inbreds and, along with the methods mean coefficient of determination (CDmean) and mean prediction error variance (PEVmean), often improved prediction accuracies compared with random training sets of the same size. When applied to the combined population, OTP designs enabled successful prediction of the rest of the exotic-derived population. Our findings highlight the importance of leveraging genotype data in training set design to efficiently incorporate new exotic germplasm into a plant breeding program.

A pollen expressed PME gene at Tcb1 locus confers maize unilateral cross-incompatibility.

A pollen expressed PME gene at Tcb1 locus confers maize unilateral cross-incompatibility.

A data mining approach gives insights of causes related to the ongoing transgene presence in Mexican native maize populations

ABSTRACT Mexico is the center of origin of maize and one of the phenomena that imperil maize conservation is transgene presence. Although previous studies have documented transgene presence in Mexican landraces, to date there is no countrywide transgene monitoring protocol, nor systematic analyses assessing which factors could be related with transgene presence and dispersal. In this work, we propose a geographically representative sampling protocol and present empirical data from three sampled states: Mexico City, Oaxaca and Chiapas. To further investigate which environmental and social variables could be associated with transgene presence, we carried out a data mining approach. To assess transgene presence in collected maize samples, we used Real-Time PCR, finding that transgenes were widely distributed across sampled localities: 33% of the localities in Chiapas, 25% in Mexico City and 11% in Oaxaca. The data mining approach allowed us to identify state-specific spatial associations in Chiapas and Oaxaca. In Chiapas, a higher probability of transgene presence appeared related to the coexistence of industrialized maize agriculture, while in Oaxaca it was related with seed exchange. We discuss the importance of implementing a national biomonitoring protocol to increase our understanding of the sources that enable transgene presence and dispersal.

ZmCCT10-relayed photoperiod sensitivity regulates natural variation in the arithmetical formation of male germinal cells in maize.

Extensive mutational screening studies have documented genes regulating anther and pollen development. Knowledge concerning how formation of male germinal cell is arithmetically controlled in natural populations, under different environmental conditions, is lacking. We counted pollen number within a single anther and a maize-teosinte BC2 S3 recombinant inbred line population to identify ZmCCT10 as a major determinant of pollen number variation. ZmCCT10 was originally identified as a photoperiod-sensitive negative regulator of flowering. ZmCCT10 inactivation, after transposon insertion within its promoter, is proposed to have accelerated maize spread toward higher latitudes, thus allowing temperate maize to flower under long-day conditions. We showed that the active ZmCCT10 allele decreased pollen formation. As different active and inactive ZmCCT10 alleles have been found in natural maize populations, this represents the first report of a gene controlling pollen number in a crop natural population. These findings suggest that higher pollen number, which provides a competitive advantage in open-pollinated populations, may have been one of the major driving forces for the selection of an inactive ZmCCT10 allele during tropical maize domestication. We provide evidence that ZmCCT10 has opposite effects on cell proliferation of archesporial and tapetum cells and it modulates expression of key regulators during early anther development.

QTL and genomic prediction accuracy for grain yield and secondary traits in a maize population under heat and heat-drought stresses

ABSTRACT Heat and drought stresses negatively affect maize (Zea mays L.) productivity. We aimed to identify the genetic basis of tolerance to heat stress (HS) and combined heat and drought stress (HS+DS) and compare how QTL and whole genome selection (GS) could be leveraged to improve tolerance to both stresses. A set of 97 testcross hybrids derived from a maize bi-parental doubled-haploid population was evaluated during the summer seasons of 2014, 2015, and 2016 in Ciudad Obregon, Sonora, Mexico, under HS and HS+DS. Grain yield (GY) reached 5.7 t ha−1 under HS and 3.0 t ha−1 under HS+DS. Twenty-six QTL were detected across six environments, with LOD scores ranging from 2.03 to 3.86; the QTL explained 8.6% to 18.6% of the observed phenotypic variation. Hyperspectral biomass and structural index (HBSI) had higher genetic correlation with GY for HS (r = 0.97) and HS+DS (r = 0.74), relative to the correlation with crop water mass or greenness indices. Genetic correlations between GY and canopy temperature for HS (r = −0.89) and HS+DS (r = −0.75) or vegetation indices, along with clusters of QTL in bins 1.02, 1.05, and 2.05, underline the importance of these genomic areas for secondary traits associated with general vigor and greenness. Prediction accuracy of the model used for GS had values below those found in previous studies. We found a high-yielding hybrid that was tolerant to HS and HS+DS.

Integration effect of vetiver grass strips with maize population density on soil erosion under two contrasting slopes of rainforest agroecology

Water erosion is a global concern that threatens sustainable agriculture. Using vetiver grass strip (VGS) and maize population density (MPD) separately to control soil erosion has been documented. Complementing VGS with high MPD could enhance crop yield and erosion control efficiency on sloping lands but there is no quantitative study on the combined effects of VGS with MPD on soil nutrient erosion globally. Thus, a 2-year field experiment was conducted to assess the effectiveness of VGS + MPD in controlling soil nutrient erosion and improving maize yields on two contrasting slopes under rainforest agroecology. Five treatments [VGS established at 5 m intervals with MPD of 40,000 stands ha−1 (VGS5 + MPD40); VGS established at 5 m intervals with MPD of 53,333 stands ha−1 (VGS5 + MPD53); VGS established at 10 intervals with MPD of 40,000 stands ha−1 (VGS10 + MPD40); VGS established at 10 intervals with MPD of 53,333 stands ha−1 (VGS10 + MPD53) and plot planted to MPD of 53,333 stands ha−1 only serves as control (MPD53)] were imposed on 5 % and 10 % slopes. Runoff was higher in control (MPD53) than other treatments by a range of 92.2 % to 73.8 % on a 5 % slope and 73.3 % to 52.2 % on a 10 % slope. Similarly, the relative reductions in soil loss compared to the control ranged from 89.9 % to 70.2 % on a 5 % slope and 90.8 % to 84.2 % on a 10 % slope. Amongst treatments, VGS5 + MPD53 had the lowest nutrient volume discharge on both slopes due to complimentary impacts of higher MPD to provide a full vegetation cover to prevent splash erosion while closely spaced VGS helps to trap transportable fine particles down the slope. Runoff and soil loss from the 10 % slope exceeded the 5 % slope by 61.2 % and 77.4 % respectively. Average maize yields for VGS5 + MPD53 on both slopes were significantly (p ≤ 0.05) higher than other treatments and maize yield on the 5 % slope exceeded the 10 % slope by 30.9 %. Slope gradient is a major driving force for water erosion and integration of VGS5 with MPD53 is the most effective in controlling soil nutrient erosion and improving maize yield on 5–10 % slopes under rainforest agroecology.

Development of the maize 5.5K loci panel for genomic prediction through genotyping by target sequencing.

Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement.

Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm.

Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately. In order to address the problems of the low efficiency of the manual counting method and the stability problem of traditional image processing methods in the field complex background environment, a deep-learning-based method for counting maize plants was proposed. Image datasets of the maize field were collected by a low-altitude UAV with a camera onboard firstly. Then a real-time detection model of maize plants was trained based on the object detection model YOLOV5. Finally, the tracking and counting method of maize plants was realized through Hungarian matching and Kalman filtering algorithms. The detection model developed in this study had an average precision mAP@0.5 of 90.66% on the test dataset, demonstrating the effectiveness of the SE-YOLOV5m model for maize plant detection. Application of the model to maize plant count trials showed that maize plant count results from test videos collected at multiple locations were highly correlated with manual count results (R2 = 0.92), illustrating the accuracy and validity of the counting method. Therefore, the maize plant identification and counting method proposed in this study can better achieve the detection and counting of maize plants in complex backgrounds and provides a research basis and theoretical basis for the rapid acquisition of maize plant population density.

Genetic Diversity and Population Structure of Normal Maize Germplasm Collected in South Sudan Revealed by SSR Markers.

Maize is one of the leading global cereals, and in South Sudan maize cultivation occurs in nearly all of the country’s agro-ecological zones. Despite its widespread cultivation, farmers in South Sudan depend on undeveloped varieties, which results in very low yields in the field. In the current study, 27 simple sequence repeat (SSR) markers were used to investigate genetic diversity and population structures among 37 landrace maize accessions collected from farmers’ fields in South Sudan. In total, 200 alleles were revealed with an average of 7.4 alleles per locus and a range from 3.0 to 13.0 alleles per locus. The observed heterozygosity values ranged from 0.06 to 0.91 with an average of 0.35. High polymorphic information content (PIC) values were identified with a mean of 0.69, which indicates the informativeness of the chosen SSR loci. Genetic structure analysis revealed a moderate genetic differentiation among the maize populations with a fixation index of 0.16, while there was very high genetic differentiation within the groups of populations of three regions with a mean fixation index (F) of 0.37. An unweighted pair group method with an arithmetic mean (UPGMA) dendrogram clustered the 37 maize accessions into three groups with 43% genetic similarity. The clustering pattern of the maize accessions was moderately consistent with their collection area. The findings of this study will provide maize breeders with a better understanding of maize diversification as well as a reserve of genetic resources for use in the selection of advantageous and useful resources for the development of maize varieties in South Sudan.

Genomic prediction of drought tolerance during seedling stage in maize using low-cost molecular markers

Drought tolerance in maize is a complex and polygenic trait, especially in the seedling stage. In plant breeding, complex genetic traits can be improved by genomic selection (GS), which has become a practical and effective breeding tool. In the present study, a natural maize population named Northeast China core population (NCCP) consisting of 379 inbred lines were genotyped with diversity arrays technology (DArT) and genotyping-by-sequencing (GBS) platforms. Target traits of seedling emergence rate (ER), seedling plant height (SPH), and grain yield (GY) were evaluated under two natural drought stress environments in northeast China. Adequate genetic variations were observed for all the target traits, but they were divergent across environments. Similarly, the heritability of the target trait also varied across years and environments, the heritabilities in 2019 (0.88, 0.82, 0.85 for ER, SPH, GY) were higher than those in 2020 (0.65, 0.53, 0.33) and cross-2-years (0.32, 0.26, 0.33). In total, three marker datasets, 11,865 SilicoDArT markers obtained from the DArT-seq platform, 7837 SNPs obtained from the DArT-seq platform, and 91,003 SNPs obtained from the GBS platform, were used for GS analysis after quality control. The results of phylogenetic trees showed that broad genetic diversity existed in the NCCP population. Genomic prediction results showed that the average prediction accuracies estimated using the DArT SNP dataset under the two-fold cross-validation scheme were 0.27, 0.19, and 0.33, for ER, SPH, and GY, respectively. The result of SilicoDArT is close to the SNPs from DArT-seq, those were 0.26, 0.22, and 0.33. For the trait with lower heritability, the prediction accuracy can be improved using the dataset filtered by linkage disequilibrium. For the same trait, the prediction accuracies estimated with two DArT marker datasets were consistently higher than that estimated with the GBS SNP dataset under the same genotyping cost. The prediction accuracy was improved by controlling population structure and marker quality, even though the marker density was reduced. The prediction accuracies were improved by more than 30% using the significant-associated SNPs. Due to the complexity of drought tolerance under the natural stress environments, multiple years of data need to be accumulated to improve prediction accuracy by reducing genotype-by-environment interaction. Modeling genotype-by-environment interaction into genomic prediction needs to be further developed for improving drought tolerance in maize. The results obtained from the present study provides valuable pathway for improving drought tolerance in maize using GS.

A teosinte-derived allele of an HKT1 family sodium transporter improves salt tolerance in maize.

The sodium cation (Na+ ) is the predominant cation with deleterious effects on crops in salt-affected agricultural areas. Salt tolerance of crop can be improved by increasing shoot Na+ exclusion. Therefore, it is crucial to identify and use genetic variants of various crops that promote shoot Na+ exclusion. Here, we show that a HKT1 family gene ZmNC3 (Zea mays L. Na+ Content 3; designated ZmHKT1;2) confers natural variability in shoot-Na+ accumulation and salt tolerance in maize. ZmHKT1;2 encodes a Na+ -preferential transporter localized in the plasma membrane, which mediates shoot Na+ exclusion, likely by withdrawing Na+ from the root xylem flow. A naturally occurring nonsynonymous SNP (SNP947-G) increases the Na+ transport activity of ZmHKT1;2, promoting shoot Na+ exclusion and salt tolerance in maize. SNP947-G first occurred in the wild grass teosinte (at a allele frequency of 43%) and has become a minor allele in the maize population (allele frequency 6.1%), suggesting that SNP947-G is derived from teosinte and that the genomic region flanking SNP947 likely has undergone selection during domestication or post-domestication dispersal of maize. Moreover, we demonstrate that introgression of the SNP947-G ZmHKT1;2 allele into elite maize germplasms reduces shoot Na+ content by up to 80% and promotes salt tolerance. Taken together, ZmNC3/ZmHKT1;2 was identified as an important QTL promoting shoot Na+ exclusion, and its favourable allele provides an effective tool for developing salt-tolerant maize varieties.

GENETIC DIVERSITY AMONG THE PHILIPPINE TRADITIONAL MAIZE (ZEA MAYS L.) POPULATIONS BASED ON SSR MARKERS

This paper reports the first genetic diversity analysis of Philippine traditional maize populations performed through a cost-effective DNA pooling strategy. The diversity among selected 100 traditional maize populations collected from Luzon, Visayas, and Mindanao was evaluated using twenty simple sequence repeats (SSR) markers at the Institute of Plant Breeding, University of the Philippines Los Baños, Laguna, Philippines. A total of 138 bands ranging from two to 12 bands per primer were detected. The average number of polymorphic alleles, polymorphism rate, effective multiplex ratio, marker index, resolving power, and expected heterozygosity are 6.283, 87.17%, 5.798, 4.104, 15.897, and 0.658, respectively. The polymorphism information content (PIC) varied between 0.141 to 0.848, with an average value of 0.620. A dendrogram was constructed with a dissimilarity coefficient ranging from 0.14 to 0.55 and a mean dissimilarity index of 0.425. Cluster analysis revealed 13 groups based on the result of Approximately Unbiased (AU) p-values from 10,000 bootstrap iterations. The cluster analysis enabled the classification of populations with ambiguous places of origin. Analysis of molecular variance (AMOVA) showed higher within-population diversity (70%) than among-population diversity (30%) with PhiPT (pairwise genetic differentiation metric) of 0.298 (P = 0.001). These results revealed the significant diversity of traditional maize populations in the Philippines and the power of SSR markers in diversity and cluster analyses despite the age of this marker technology. These findings will aid plant breeders in developing approaches towards knowledgeable and efficient execution of breeding programs using traditional maize populations.

Genetic variation of morphological and yield-related traits in backcrossed and selfed population of maize

The number of genes controlling the morphological and yield-related traits and their mode of action plays a vital role in selecting traits and favourable progenies in a segregating population. In this study, we analysed the skewness and kurtosis in the backcrossed and selfed populations of SC11-2×UMI1230β1+. Negative skewness refers to redundant gene activity, positive skewness is connected to complementary gene action. Positive kurtosis suggests the existence of gene activity controlled by a smaller number of genes. Negative kurtosis shows a lack of gene activity and is controlled by a large number of genes. The BC2F2 population of plant height and single plant yield showed positive skewness and platykurtic distribution, suggesting that the genes were governed by complementary gene action and controlled by the large number of genes. This implied need for rigorous selection to achieve a genetic gain in later generations. Cob length and kernel weight showed a duplicate gene action which implied that mild selection can fix these traits and accelerate genetic gain.

Genetic diversity of subtropical double‐haploid maize lines selected for high oil content

AbstractThe inclusion of high‐oil maize germplasm into breeding programs may be an excellent alternative for increasing grain nutritional quality. Knowing the germplasm genetic diversity is crucial for assisting breeding programs. Here, the genetic diversity and population structure of four high‐oil‐content maize (Zea mays L.) populations (Bajio yellow population [BYP], northwestern yellow population [NYP], Bajio white population [BWP], and northwestern white population [NWP]) were analyzed by Diversity Arrays Technology sequencing. Three‐hundred ten double‐haploid (DH) lines were genotyped, and 19,078 single‐nucleotide polymorphism (SNP) markers were uniformly detected among populations after filtering by missing data >20% and minor allele frequency ≥0.05. Genetic diversity indexes showed polymorphic information content (PIC) values of 0.346, 0.352, 0.353, and 0.353; observed heterozygosity values of 0.221, 0.194, 0.284, and 0.177; and expected heterozygosity values of 0.188, 0.165, 0.219, and 0.152, for BYP, NYP, BWP, and NWP, respectively. Genetic structure results showed variations in pairwise genetic distance comparisons among the 310 DH lines, ranging from 0.119 to 0.385. Multidimensional scaling analysis and discriminant analysis of principal components grouped the DH lines into three different and five clusters, respectively, based on their origin region and grain color. On the other hand, STRUCTURE analysis revealed the presence of two different groups unrelated to grain color or origin region. The wide genetic variability among the analyzed DH lines highlights their potential to contribute new beneficial alleles into subtropical maize breeding programs and will facilitate the selection of parental lines and the identification of heterotic groups to generate high‐oil maize hybrids.

QTL mapping and genomic selection for Fusarium ear rot resistance using two F2:3 populations in maize

QTL mapping and genomic selection for Fusarium ear rot resistance using two F2:3 populations in maize

DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits

Bulked segregant analysis (BSA) is a rapid, cost-effective method for mapping mutations and quantitative trait loci (QTLs) in animals and plants based on high-throughput sequencing. However, the algorithms currently used for BSA have not been systematically evaluated and are complex and fallible to operate. We developed a BSA method driven by deep learning, DeepBSA, for QTL mapping and functional gene cloning. DeepBSA is compatible with a variable number of bulked pools and performed well with various simulated and real datasets in both animals and plants. DeepBSA outperformed all other algorithms when comparing absolute bias and signal-to-noise ratio. Moreover, we applied DeepBSA to an F2 segregating maize population of 7160 individuals and uncovered five candidate QTLs, including three well-known plant-height genes. Finally, we developed a user-friendly graphical user interface for DeepBSA, by integrating five widely used BSA algorithms and our two newly developed algorithms, that is easy to operate and can quickly map QTLs and functional genes. The DeepBSA software is freely available to non-commercial users at http://zeasystemsbio.hzau.edu.cn/tools.html and https://github.com/lizhao007/DeepBSA.

Mathematics of the Relationship between Plant Population and Individual Production of Maize (Zea mays L.)

Predominantly, field experiments aim to determine the optimal plant density for a unit area. High spatial variation results in lower attained yield than the genetic potential. To find the highest value of the product of mean individual production and crop density, their mathematical relationship is needed to be known. In this study, the relationship between the population and the weight of the individual fresh, dehusked ear of grain maize (Zea mays L.) was investigated, and a relevant mathematical simulation model was developed. Linear regression was disproved to describe this relationship properly based on the analysis of different mosaic sizes within a plot. The distribution of plant-to-plant distance was found to be binomial. The distribution of the ear weight was found to be lognormal. For the description of the population homogeneity, neither the standard deviation nor the variation coefficient was found to be applicable apart from the scale-independent relative variance. The simulation model, based on the above statistical functions, was validated with a real dataset, and it is recommended for the optimization of the plant population at farm scale.

Plant Tissue Nutrient Concentration and Major Soil Nutrients Influenced by NPSB Fertilizer Rates and Plant Population of Maize (Zea Mays L.) at Bako, Oromia National Regional State

Plant nutrients such as nitrogen, phosphorus, potassium and sulfur are the nutrients frequently limit the yields of maize over the world. Nutrient recommendation should be based on the optimum yield and environmental save that can be through plant tissue analysis and soil test. This experiment was conducted at Bako Agricultural Research Center west region of Ethiopia Gobu Sayo using split plot desighn with three levels of plant population 53.333,66,666 and 76,923 plants per hectare on main plot and five level 0,100,150,200,250 kg/ha of NPSB fertilizer rates on sub-plots. Soil sample was taken from the plots pre-harvesting for composite sample and post-harvest from each plot and analyzed at Bako soil laboratory and Ethiopia water work design Addis Ababa for soil fertility management. Nutrient in plant tissue was analyzed at Holota Agricultural Research Center soil laboratory. Collected data was analyzed using Genstat computer statistical soft-ware package. The result of pre planting soil analysis showed soil of the study area was categorized as clay soil with medium and stable organic matter and low organic carbon and low total nitrogen that shows low soil fertility. The ANOVA result showed addition of fertilizer reduced soil pH from 5.7 to 5.47 and bellow. 150 kg/ha of NPSB which gave economically feasible yield 9460kg/ha moderately acidified the soil. This soil analysis result showed that the level at which fertilizer boost yield of maize. Only percent of phosphorus was changed due to plant population density. Plant tissue concentration at silking was significantly influenced by fertilizer rates. Potassium is at sufficiency range for all level of supplied fertilizer. Sufficient range nitrogen at silkin was recorded from the plot received 100-150 kg/ha which gave economically feasible yield. It is better to use fertilizer rate of 150kg/ha which shows sufficiency range for nutrient concentration of soil and plant tissue in leaf at silking stage. This finding is not the end for the area and further research should be carried out for feature. Keywords: plant tissue, soil, Zea mays L., Nitrogen, Phosphorus, Sulfur, potassium DOI: 10.7176/JBAH/12-13-04 Publication date: July 31 st 2022