Maize Populations Research Articles

Assessment of yield performance and stability of hybrids and populations of tropical maize across multiple environments in Southeastern Brazil

AbstractThe identification of stable genotypes with high yield in diverse multiple‐stress environments is important to increase maize (Zea mays L.) grain yield under tropical environments. Our objective was to assess the yield performance and stability of experimental hybrids and broad‐based populations of tropical maize across diverse environments in Southeastern Brazil. We evaluated two sets of maize genotypes for grain yield: 190 experimental hybrids along with 6 commercial hybrids and 45 population hybrids along with their 10 parental populations across 8 environments in Southeastern Brazil. Multiple statistical methods were used and compared in the analyses. Combined analysis of variance indicated that genotypic main effect (G), environmental main effect (E), and genotype by environment interaction were highly significant (p < 0.0001) for grain yield. The E accounted for 42% of the total variation for both hybrids and populations, and they were more similar within the growing season than between seasons, mainly for populations. Low nitrogen (N) stress was a key factor in hybrid evaluation and recommendation, particularly under drought stress conditions. Among the environment classification methods, genotype main effect plus genotype × environment interaction (GGE) biplot provided more accurate information about environments grouping and selection of the genotypes than the Eberhart and Russell method. Harmonic mean of the relative performance of the predicted genetic values (HMRPGV) based on mixed models ranked the hybrids and populations according to mean grain yield and stability, penalizing hybrids, and populations with lower stability. Therefore, we recommend the GGE biplot and HMRPGV for genotype evaluation based on multi‐environment trials data. These methods identified 92V2144 and 92V2033 as the most promising hybrids for favorable and 92V2141, 92V2153, and 92V2137 as the most promising for unfavorable environments. 92VX033 and 92VX043 were identified as broadly adapted and stable populations across multiple environments in Southeastern Brazil.

Combination of suitable planting density and nitrogen rate for high yield maize and their source-sink relationship in Northwest China.

Increasing crop yield per unit area by increasing planting density is essential to ensure food security. However, the optimal combination of planting density and N application for high-yielding maize and its source-sink characteristics need to be more clearly understood. A two-year field experiment was conducted combining three planting densities (D1: 70,000 plants ha-1 ；D2: 100,000 plants ha-1 ；D3: 130,000 plants ha-1 ) and three nitrogen rates (N1: 150 kg N hm-2 , N2: 350 kg N hm-2 , N3: 450 kg N hm-2 ). The results showed that increasing planting density significantly increased leaf area index and grain yield, but negatively affected ear traits. The Richards model was used to fit the dynamic changes of dry matter accumulation of maize under different treatments, and the fitting results were good. Increasing planting density increased population yield while limiting the development of individual plants, bringing the period of rapid dry matter accumulation to an early end and accelerating leaf senescence. Appropriate N rate could prolong the period of rapid accumulation of dry matter in maize, increase the 100-kernel weight. Increasing planting density enhanced post-silking dry matter accumulation to a lesser extent, and the source-sink relationship of the maize population gradually developed from sink limitation to source limitation with increasing planting density. The decrease in yield due to the insufficient source strength to meet the sink demand at too high densities was the reason that limited further improvement of the optimal planting density. Appropriate N rate facilitated the realization of yield potential at high density. This article is protected by copyright. All rights reserved.

Divergent selection of KNR6 maximizes grain production by balancing the flowering-time adaptation and ear size in maize.

Divergent selection of KNR6 maximizes grain production by balancing the flowering-time adaptation and ear size in maize.

Evaluating Founding Landraces of Maize Population PI 674097 for Resistance to Leaf-Feeding Spodoptera frugiperda1

A maize (Zea mays L.) pest native to the Americas, the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is a recent invasive of Africa, Asia, and Oceania. Resistant to several insecticide classes and toxins of Btmaize, the insect damages maize by feeding on leaf, tassel, and ear tissue. Using native resistance in maize along with existing tactics could improve control outcomes. The objective for the study was to evaluate resistance to leaf-feeding fall armyworm in maize population PI 674097 and founding landraces used to develop this germplasm. Plants grown in the field and artificially infested at the seven/eight-leaf stage were visually scored for leaf-feeding damage at 7 and 14 days post infestation. All scores for NSL 283507, PI 498583, PI 583912, and PI 674097 were less (i.e., more resistant) than the susceptible check, GEMN-0131. In 2022, 14-day scores for PI 674097 were not different than the resistant check, Mp708 (3.6 ± 0.5 and 2.7 ± 0.5, respectively). PI 674097 was variable in its response to fall armyworm leaf feeding with individual plants receiving a range of 14-day scores from 2 (resistant) to 7 (susceptible) across both years of testing. This variability might be useful when selecting for greater resistance to the pest.

Integrated QTL Mapping, Meta-Analysis, and RNA-Sequencing Reveal Candidate Genes for Maize Deep-Sowing Tolerance.

Synergetic elongation of mesocotyl and coleoptile are crucial in governing maize seedlings emergence, especially for the maize sown in deep soil. Studying the genomic regions controlling maize deep-sowing tolerance would aid the development of new varieties that are resistant to harsh conditions, such as drought and low temperature during seed germination. Using 346 F2:3 maize population families from W64A × K12 cross at three sowing depths, we identified 33 quantitative trait loci (QTLs) for the emergence rate, mesocotyl, coleoptile, and seedling lengths via composite interval mapping (CIM). These loci explained 2.89% to 14.17% of phenotypic variation in a single environment, while 12 of 13 major QTLs were identified at two or more sowing environments. Among those, four major QTLs in Bin 1.09, Bin 4.08, Bin 6.01, and Bin 7.02 supported pleiotropy for multiple deep-sowing tolerant traits. Meta-analysis identified 17 meta-QTLs (MQTLs) based on 130 original QTLs from present and previous studies. RNA-Sequencing of mesocotyl and coleoptile in both parents (W64A and K12) at 3 cm and 20 cm sowing environments identified 50 candidate genes expressed differentially in all major QTLs and MQTLs regions: six involved in the circadian clock, 27 associated with phytohormones biosynthesis and signal transduction, seven controlled lignin biosynthesis, five regulated cell wall organization formation and stabilization, three were responsible for sucrose and starch metabolism, and two in the antioxidant enzyme system. These genes with highly interconnected networks may form a complex molecular mechanism of maize deep-sowing tolerance. Findings of this study will facilitate the construction of molecular modules for deep-sowing tolerance in maize. The major QTLs and MQTLs identified could be used in marker-assisted breeding to develop elite maize varieties.

Elucidating the patterns of pleiotropy and its biological relevance in maize.

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.

The maize ZmVPS23-like protein relocates the nucleotide-binding leucine-rich repeat protein Rp1-D21 to endosomes and suppresses the defense response.

Plants often utilize nucleotide-binding leucine-rich repeat (NLR) proteins to perceive pathogen infections and trigger a hypersensitive response (HR). The endosomal sorting complex required for transport (ESCRT) machinery is a conserved multisubunit complex that is essential for the biogenesis of multivesicular bodies and cargo protein sorting. VPS23 is a key component of ESCRT-I and plays important roles in plant development and abiotic stresses. ZmVPS23L, a homolog of VPS23-like in maize (Zea mays), was previously identified as a candidate gene in modulating HR mediated by the autoactive NLR protein Rp1-D21 in different maize populations. Here, we demonstrate that ZmVPS23L suppresses Rp1-D21-mediated HR in maize and Nicotiana benthamiana. Variation in the suppressive effect of HR by different ZmVPS23L alleles was correlated with variation in their expression levels. ZmVPS23 also suppressed Rp1-D21-mediated HR. ZmVPS23L and ZmVPS23 predominantly localized to endosomes, and they physically interacted with the coiled-coil domain of Rp1-D21 and mediated the relocation of Rp1-D21 from the nucleo-cytoplasm to endosomes. In summary, we demonstrate that ZmVPS23L and ZmVPS23 are negative regulators of Rp1-D21-mediated HR, likely by sequestrating Rp1-D21 in endosomes via physical interaction. Our findings reveal the role of ESCRT components in controlling plant NLR-mediated defense responses.

Embracing diversity: a genetic marker dataset with increased marker density facilitates association studies in maize

Embracing diversity: a genetic marker dataset with increased marker density facilitates association studies in maize

Genome-wide association analysis of kernel nutritional quality in two natural maize populations.

The online version contains supplementary material available at 10.1007/s11032-023-01360-w.

EVALUACIÓN DE DAÑO POR Spodoptera frugiperda (J.E. SMITH) (LEPIDOPTERA: NOCTUIDAE) Y CARACTERIZACIÓN DE LOS RASGOS MORFOLÓGICOS FOLIARES EN POBLACIONES CRIOLLAS DE MAÍZ (Zea mays L.)

<p><strong>Background.</strong> The fall armyworm <em>Spodoptera frugiperda </em>is one of the most damaging pests of maize in tropical areas. <strong>Objective.</strong> To evaluate the damage caused by <em>S. frugiperda</em> and the leaf morphological traits in 15 populations of maize landrace. <strong>Methodology.</strong> The leaf damage caused by <em>S. frugiperda</em> and four-leaf morphological traits (leaf blade thickness, leaf blade hardness, specific leaf area and trichome density) were evaluated at 20, 30 and 40 days after emergence (DAE) in two seasons, dry (March and April) and rainy (June and July). Spearman correlation between the degree of leaf damage and each leaf morphological trait was also evaluated. <strong>Results.</strong> Maize populations showed differential response to the damage caused by <em>S. frugiperda</em> among seasons and evaluating date. The leaf morphological traits were different between seasons and among evaluating dates. The trichome density had intermediate negative correlation with the level of leaf damage in rainy season (<em>r<sub>s</sub></em> = − 0.32,<em> p</em> < 0.001). <strong>Implications.</strong> The lower leaf damage may be indicator of resistance against <em>S. frugiperda</em>. <strong>Conclusion.</strong> The population 2020−M62 had low susceptibility to <em>S. frugiperda</em>, this population may be candidate for maize breeding program.</p>

A common resequencing-based genetic marker data set for global maize diversity.

Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366million segregating variants and approximately 46million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.

Optimizing Row Spacing Increases Stalk Lodging Resistance by Improving Light Distribution in Dense Maize Populations

Dense planting effectively increases maize yield while increasing stalk lodging risk. Appropriate row spacing can improve the maize population structure and stalk lodging resistance, but its physiological ecological mechanisms and interaction with planting density are unclear. Here, a two-year field experiment to determine the joint effects of row spacing and planting density on maize stem characteristics and the quantitative relationship of the light condition within a maize population with stalk lodging resistance indicated that the stalk mechanical strength showed a quadratic function relationship with photosynthetically active radiation (PAR), whereas the lodging rate showed an exponential function relationship with basal light transmittance (LT). Further, the basal LT was significantly positively correlated with basal internode thickness, dry weight per unit stem length (DWUL), mechanical and cortical tissue thickness, and lignin and cellulose contents. Increasing the planting density decreased the basal LT and PAR; correspondingly decreased the basal internode thickness, DWUL, mechanical and cortical tissue thickness, lignin and cellulose contents, and stalk mechanical strength; and increased the lodging rate, while increasing row spacing did the opposite. Thus, optimizing the row spacing enhanced the lodging resistance through LT and PAR improvement of the lower part of the population and further increased the grain yield by optimizing the yield components. The appropriate row spacing varied with the planting density. The proper strategy for high stalk lodging resistance and grain yielding under this experimental condition was 67,500 plants ha−1 density with 60 + 60 cm equal row spacing.

Combating iron and zinc malnutrition through mineral biofortification in maize through plant growth promoting Bacillus and Paenibacillus species.

The burgeoning population of the world is causing food insecurity not only by less food availability but also by the malnutrition of essential nutrients and vitamins. Malnutrition is mostly linked with food having micronutrients lower than the optimal concentration of that specific food commodity and becoming an emerging challenge over the globe. Microbial biofortification in agriculture ensures nutritional security through microbial nitrogen fixation, and improved phosphate and zinc solubilization, which increase the uptake of these nutrients. The present study evaluates the novel plant growth-promoting rhizobacteria (PGPR) to biofortify maize gain. For this purpose, a pot and two field experiments for maize were conducted. PGPRs were applied alone and in combination for a better understanding of the biofortification potential of these strains. At physiological maturity, the growth parameters, and at harvest, the yield, microbial population, and nutritional status of maize were determined. Results revealed that the consortium (ZM27+ZM63+S10) has caused the maximum increase in growth under pot studies like plant height (31%), shoot fresh weight (28%), shoot dry weight (27%), root fresh (33%) and dry weights (29%), and microbial count (21%) in the maize rhizosphere. The mineral analysis of the pot trial also revealed that consortium of ZM27+ZM63+S10 has caused 28, 16, 20, 11 and 11% increases in P, N, K, Fe, and Zn contents in maize, respectively, as compared to un-inoculated treatment in pot studies. A similar trend of results was also observed in both field trials as the consortium of ZM27+ZM63+S10 caused the maximum increase in not only growth and biological properties but also caused maximum biofortification of mineral nutrients in maize grains. The grain yield and 1000-grain weight were also found significantly higher 17 and 12%, respectively, under consortium application as compared to control. So, it can be concluded from these significant results obtained from the PGPR consortium application that microbial inoculants play a significant role in enhancing the growth, yield, and quality of the maize. However, the extensive evaluation of the consortium may help in the formulation of a biofertilizer for sustainable production and biofortification of maize to cope with nutritional security.

Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor

Automatic plant phenotype measurement technology based on the rapid and accurate reconstruction of maize structures at the seedling stage is essential for the early variety selection, cultivation, and scientific management of maize. Manual measurement is time-consuming, laborious, and error-prone. The lack of mobility of large equipment in the field make the high-throughput detection of maize plant phenotypes challenging. Therefore, a global 3D reconstruction algorithm was proposed for the high-throughput detection of maize phenotypic traits. First, a self-propelled mobile platform was used to automatically collect three-dimensional point clouds of maize seedling populations from multiple measurement points and perspectives. Second, the Harris corner detection algorithm and singular value decomposition (SVD) were used for the pre-calibration single measurement point multi-view alignment matrix. Finally, the multi-view registration algorithm and iterative nearest point algorithm (ICP) were used for the global 3D reconstruction of the maize seedling population. The results showed that the R2 of the plant height and maximum width measured by the global 3D reconstruction of the seedling maize population were 0.98 and 0.99 with RMSE of 1.39 cm and 1.45 cm and mean absolute percentage errors (MAPEs) of 1.92% and 2.29%, respectively. For the standard sphere, the percentage of the Hausdorff distance set of reconstruction point clouds less than 0.5 cm was 55.26%, and the percentage was 76.88% for those less than 0.8 cm. The method proposed in this study provides a reference for the global reconstruction and phenotypic measurement of crop populations at the seedling stage, which aids in the early management of maize with precision and intelligence.

Complex genetic architecture underlying the plasticity of maize agronomic traits

Phenotypic plasticity is the ability of a given genotype to produce multiple phenotypes in response to changing environmental conditions. Understanding the genetic basis of phenotypic plasticity and establishing a predictive model is highly relevant to future agriculture under a changing climate. Here we report findings on the genetic basis of phenotypic plasticity for 23 complex traits using a diverse maize population planted at five sites with distinct environmental conditions. We found that latitude-related environmental factors were the main drivers of across-site variation in flowering time traits but not in plant architecture or yield traits. For the 23 traits, we detected 109 quantitative trait loci (QTLs), 29 for mean values, 66 for plasticity, and 14 for both parameters, and 80% of the QTLs interacted with latitude. The effects of several QTLs changed in magnitude or sign, driving variation in phenotypic plasticity. We experimentally validated one plastic gene, ZmTPS14.1, whose effect was likely mediated by the compensation effect of ZmSPL6 from a downstream pathway. By integrating genetic diversity, environmental variation, and their interaction into a joint model, we could provide site-specific predictions with increased accuracy by as much as 9.9%, 2.2%, and 2.6% for days to tassel, plant height, and ear weight, respectively. This study revealed a complex genetic architecture involving multiple alleles, pleiotropy, and genotype-by-environment interaction that underlies variation in the mean and plasticity of maize complex traits. It provides novel insights into the dynamic genetic architecture of agronomic traits in response to changing environments, paving a practical way toward precision agriculture.

Modeling the influence of phenotypic plasticity on maize hybrid performance

Phenotypic plasticity, the ability of an individual to alter its phenotype in response to changes in the environment, has been proposed as a target for breeding crop varieties with high environmental fitness. Here, we used phenotypic and genotypic data from multiple maize (Zea mays L.) populations to mathematically model phenotypic plasticity in response to the environment (PPRE) in inbred and hybrid lines. PPRE can be simply described by a linear model in which the two main parameters, intercept a and slope b, reflect two classes of genes responsive to endogenous (class A) and exogenous (class B) signals that coordinate plant development. Together, class A and class B genes contribute to the phenotypic plasticity of an individual in response to the environment. We also made connections between phenotypic plasticity and hybrid performance or general combining ability (GCA) of yield using 30 F1 hybrid populations generated by crossing the same maternal line with 30 paternal lines from different maize heterotic groups. We show that the parameters a and b from two given parental lines must be concordant to reach an ideal GCA of F1 yield. We hypothesize that coordinated regulation of the two classes of genes in the F1 hybrid genome is the basis for high GCA. Based on this theory, we built a series of predictive models to evaluate GCA in silico between parental lines of different heterotic groups.

Genome-Wide Association Study and Genomic Prediction on Plant Architecture Traits in Sweet Corn and Waxy Corn

Sweet corn and waxy corn has a better taste and higher accumulated nutritional value than regular maize, and is widely planted and popularly consumed throughout the world. Plant height (PH), ear height (EH), and tassel branch number (TBN) are key plant architecture traits, which play an important role in improving grain yield in maize. In this study, a genome-wide association study (GWAS) and genomic prediction analysis were conducted on plant architecture traits of PH, EH, and TBN in a fresh edible maize population consisting of 190 sweet corn inbred lines and 287 waxy corn inbred lines. Phenotypic data from two locations showed high heritability for all three traits, with significant differences observed between sweet corn and waxy corn for both PH and EH. The differences between the three subgroups of sweet corn were not obvious for all three traits. Population structure and PCA analysis results divided the whole population into three subgroups, i.e., sweet corn, waxy corn, and the subgroup mixed with sweet and waxy corn. Analysis of GWAS was conducted with 278,592 SNPs obtained from resequencing data; 184, 45, and 68 significantly associated SNPs were detected for PH, EH, and TBN, respectively. The phenotypic variance explained (PVE) values of these significant SNPs ranged from 3.50% to 7.0%. The results of this study lay the foundation for further understanding the genetic basis of plant architecture traits in sweet corn and waxy corn. Genomic selection (GS) is a new approach for improving quantitative traits in large plant breeding populations that uses whole-genome molecular markers. The marker number and marker quality are essential for the application of GS in maize breeding. GWAS can choose the most related markers with the traits, so it can be used to improve the predictive accuracy of GS.

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform.

The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (R2 = 0.98), and the accuracy was higher than only using one source point cloud data (R2 = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.

TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting.

Plant trichomes are epidermal structures with a wide variety of functions in plant development and stress responses. Although the functional importance of trichomes has been realized, the tedious and time-consuming manual phenotyping process greatly limits the research progress of trichome gene cloning. Currently, there are no fully automated methods for identifying maize trichomes. We introduce TrichomeYOLO, an automated trichome counting and measuring method that uses a deep convolutional neural network, to identify the density and length of maize trichomes from scanning electron microscopy images. Our network achieved 92.1% identification accuracy on scanning electron microscopy micrographs of maize leaves, which is much better performed than the other 5 currently mainstream object detection models, Faster R-CNN, YOLOv3, YOLOv5, DETR, and Cascade R-CNN. We applied TrichomeYOLO to investigate trichome variations in a natural population of maize and achieved robust trichome identification. Our method and the pretrained model are open access in Github (https://github.com/yaober/trichomecounter). We believe TrichomeYOLO will help make efficient trichome identification and help facilitate researches on maize trichomes.

Produção integrada de silagem de Urochloabrizantha e silagem de milho ou sorgo: avaliação agronômica, perdas de fermentação e estabilidade aeróbia da silagem

ABSTRACT: The present study evaluated germination, production, and morphological composition of Urochloabrizantha intercropped with corn and sorghum; and silage fermentation losses and aerobic stability of intercrop silage using microbial inoculant. Twenty experimental parcels (5.0 × 3.6 m) were used in a blocked randomized design to evaluate four treatments obtained from a 2 × 2 factorial arrangements: I) crop material (corn vs. sorghum) and II) Brachiaria (U. brizantha) establishment (present vs. absent). Corn- and sorghum-brachiaria integrated systems showed similar brachiaria germination, forage yield, and morphological composition. There was no crop and brachiaria interaction effect on the variables related to corn and sorghum plants and the total productivity. Brachiaria decreased the stem diameter and increased the population of maize and sorghum plants. However, it did not affect systems productivity. Microbial inoculation did not affect corn silage effluent losses and reduced sorghum silage effluent losses. In corn silage, brachiaria did not affect gas losses, while in sorghum silage, brachiaria increased the gas losses. Total losses were higher in sorghum silage than in corn silage, which resulted in a lower DM recovery. The treatments did not affect the pH of the silage after aerobic exposure. However, brachiaria increased silage temperature evaluated at 32 and 40 hours after aerobic exposure. Thus, corn or sorghum consortium has similar brachiaria morphological composition and productivity. Moreover, in intercropped silage, brachiaria increases effluent losses and reduces silage aerobic stability.