Análise do N-terminal da proteína capsidial de SCMV infectando milho e sorgo no Brasil

Os objetivos do presente estudo foram (i) identificar por meio do sequenciamento da proteína capsidial a espécie de potyvírus causando sintomas de mosaico em milho e sorgo no Brasil, e (ii) analisar a sequência de aminoácidos (aa) do N-terminal do gene da proteína capsidial.Fil: Souza, Isabel Regina Prazeres de. Embrapa Milho e Sorgo, Sete Lagoas; BrasilFil: Carneiro, Newton Portilho. Embrapa Milho e Sorgo, Sete Lagoas; BrasilFil: Giolitti, Fabian. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Lenardon, Sergio Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Patología Vegetal; ArgentinaFil: Oliveira Sabato, Elizabeth de. Embrapa Milho e Sorgo, Sete Lagoas; BrasilFil: Gomes, Eliane Aparecida. Embrapa Milho e Sorgo, Sete Lagoas; BrasilFil: Noda, Roberto Willians. Embrapa Milho e Sorgo, Sete Lagoas; BrasilFil: Souza, Francisco Adriano de. Embrapa Milho e Sorgo, Sete Lagoas; Brasi

Hitos tecnológicos que cambiaron el rol de Brasil en la producción de maíz: 30 años de crecimiento para convertirse en importante actor del escenario mundial, una revisión

Corn has experienced a true revolution in Brazil in the last 30 years (agricultural harvests from 1991/92 to 2021/2022). Currently, the country has established itself as the third producer and second exporter worldwide of this cereal, with a production of more than 100 million tons of this grain per agricultural year. In this period, soybean cultivation stands out as the great driver of technological advances, leading corn and other crops to more prominent positions and transforming grain production systems; previously monoculture or rotation, to more intensified systems, with two (or more) agricultural crops per year in the same area. The Brazilian Cerrado region, previously considered unsuitable for agriculture, is today the great barn of grain production in Brazil. In these three decades of escalation in corn production, some legal and technological milestones stand out, such as the Law for the Protection of Cultivars and its regulations (since 1997), the direct sowing system, the cultivation of corn in the second harvest (after soybean), and the use of biotechnologies. These factors were decisive for the growth of maize production to exceed by more than 3.6 times the volume of the 1991/92 agricultural season, while the area devoted to maize cultivation increased only 1.5 times. Increases in productivity are linked to technologies and knowledge applied to the management of production systems, soybean-corn, and not only in an isolated crop; allowing greater advances in the gross production of both grains (recent yields in the corn harvest are about 2.5 times higher than 30 years ago). This article shows data and facts that allowed Brazil to get out of a position of vulnerability, in terms of corn supply, to become an important player in the production and marketing of this cereal worldwide.En los últimos 30 años (cosechas agrícolas de 1991/92 a 2021/2022), el maíz ha vivido una verdadera revolución en Brasil. Actualmente, el país se ha consolidado como el tercer productor y segundo exportador de este cereal, con una producción de más de 100 millones de toneladas de este grano por año agrícola. En este período, el cultivo de soja se destaca como el gran impulsor de los avances tecnológicos, llevando al maíz y a otros cultivos a posiciones más destacadas y transformando los sistemas de producción de granos; que antes eran de monocultivo o rotación, a sistemas más intensificados, con dos (o más) cultivos agrícolas por año en la misma área. La región del Cerrado brasileño, antes considerada no apta para la agricultura, es hoy el gran granero de la producción de granos de Brasil. En estas tres décadas de escalada en la producción de maíz, se destacan algunos hitos legales y tecnológicos, como la Ley de Protección de Cultivares y su reglamento (desde 1997), el Sistema de Siembra Directa, el cultivo de maíz en segunda cosecha o “safrinha” (después de la soja) y el uso de biotecnologías. Estos factores fueron determinantes para que el crecimiento de la producción de maíz superara en más de 3,6 veces el volumen de la campaña agrícola 1991/92, mientras que el área destinada al cultivo de maíz aumentó sólo 1,5 veces. Los incrementos en la productividad están ligados a tecnologías y conocimientos aplicados a la gestión de los sistemas productivos, en el binomio soja-maíz, y no solo en un cultivo aislado; permitiendo mayores avances en la producción bruta de ambos granos (rendimientos recientes en la cosecha de maíz son unas 2,5 veces mayores que hace 30 años). Este trabajo presenta datos y hechos que permitieron a Brasil salir de una posición de vulnerabilidad, en cuanto a la oferta de grano de maíz, para convertirse en un actor importante en la producción y comercialización de este cereal a nivel mundial

SEQUENCE DIVERSITY IN THE COAT PROTEIN OF SCMV INFECTING MAIZE AND SORGHUM IN BRAZIL

The “maize common mosaic”, caused by potyvirus, is among the major virus diseases of this crop inBrazil. Although there were evidences indicating Sugarcane mosaic virus (SCMV) as the most common potyvirusspecies in maize (Zea mays L.) in Brazil, information about those species that infect sorghum plants [Sorghum bicolor(L.) Moench] are few. Leaves showing characteristic mosaic symptoms were collected from maize and sorghum andused in serological and sequencing analysis of the coat protein (CP) gene for potyvirus species identification. Amino acid(aa) analysis of the CP N-terminal sequence of our samples showed a different repeated sequence, a higher content of thedipeptide GT, and a 15 aa longer than the majority of the SCMV sequences used for comparisons. The Brazilian maizeand sorghum potyviruses formed a monophyletic group, suggesting that they can be classified within a new SCMV strain.Studies using potyvirus CP gene sequencing from Brazilian sorghum potyvirus have been reported for the first time

Boxplot analysis showing the distribution of agro-industrial traits according to each cluster identified through molecular and phenotypic diversity analysis.

<p>The upper, median, and lower quartiles of gray boxes represent the 75^th, 50^th, and 25^th percentiles of the clusters, respectively. The vertical lines represent the variation of the clusters. Dots represent outliers. CEL: cellulose; EXT: juice extraction; FBY: fresh biomass yield; FLOW: days to flowering; HEM: hemicellulose; LIG: lignin; PH: plant height; POL: sucrose concentration in juice; RSJ: reducing sugars in the juice and TSS: total soluble solids.</p

Neighbor-Joining tree using SNP data.

<p>Genetic distances between the sweet sorghum accessions were calculated using the identity-by-state (IBS) coefficient. The colors of the branches correspond to the six subpopulations defined according to the genealogy and the historic background of the sweet sorghum lines. I-M, II-M, III-M, IV-M, V-M and VI-M correspond to the clusters identified through the Neighbor-Joining method. LIS: Landrace World Collection—ICRISAT sorghum collection; LMN: Landrace Meridian Mississippi—USDA sorghum collection; LSSM: Landrace Sorghum Seed Montpelier—CIRAD sorghum collection; ML: Modern Line; ML—EMBRAPA: Modern Line EMBRAPA; and HL: Historical Line. The scale-bar (0–0.1) represents the coefficient of dissimilarity.</p

Principal component analysis using SNP data.

<p>Plotting the first two principal components (PC1 and PC2) using SNP data. The colors of the genotypes correspond to the six subpopulations of sweet sorghum according to the genealogy and the historic background. LIS: Landrace World Collection—ICRISAT sorghum collection; LMN: Landrace Meridian Mississippi—USDA sorghum collection; LSSM: Landrace Sorghum Seed Montpelier—CIRAD sorghum collection; ML: Modern Line; ML—EMBRAPA: Modern Line EMBRAPA; and HL: Historical Line.</p

Fixed and random effects, heritability, average, minimum and maximum phenotypic values for the agro-industrial traits.

<p>Fixed and random effects, heritability, average, minimum and maximum phenotypic values for the agro-industrial traits.</p

Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials

Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids’ genotypes were inferred based on their parents’ genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids