Identifying maize inbred lines that are more efficient in nitrogen (N) use is an important strategy and a necessity in the context of environmental and economic impacts attributed to the excessive N fertilization. N-uptake efficiency (NUpE) and N-utilization efficiency (NUtE) are components of N-use efficiency (NUE). Despite the most maize breeding data have a multi-trait structure, they are often analyzed under a single-trait framework. We aimed to estimate the genetic parameters for NUpE and NUtE in contrasting N levels, in order to identify superior maize inbred lines, and to propose a Bayesian multi-trait multi-environment (MTME) model. Sixty-four tropical maize inbred lines were evaluated in two experiments: at high (HN) and low N (LN) levels. The MTME model was compared to single-trait multi-environment (STME) models. Based on deviance information criteria (DIC), both multi- and single-trait models revealed genotypes x environments (G x E) interaction. In the MTME model, NUpE was found to be weakly heritable with posterior modes of heritability of 0.016 and 0.023 under HN and LN, respectively. NUtE at HN was found to be highly heritable (0.490), whereas under LN condition it was moderately heritable (0.215). We adopted the MTME model, since combined analysis often presents more accurate breeding values than single models. Superior inbred lines for NUpE and NUtE were identified and this information can be used to plan crosses to obtain maize hybrids that have superior nitrogen use efficiency.

This study was undertaken to obtain information about the behavior of sulfentrazone in soil by evaluating the sorption and desorption of the herbicide in different Brazilian soils. Batch equilibrium method was used and the samples were analyzed by high performance liquid chromatography. Based on the results obtained from the values of Freundlich constants (Kf), we determined the order of sorption (Haplic Planosol < Red-Yellow Latosol < Red Argisol < Humic Cambisol < Regolitic Neosol) and desorption (Regolitic Neosol < Red Argisol < Humic Cambisol < Haplic Planosol < Red-Yellow Latosol) of sulfentrazone in the soils. The process of pesticide sorption in soils was dependent on the levels of organic matter and clay, while desorption was influenced by the organic matter content and soil pH. Thus, the use of sulfentrazone in soils with low clay content and organic matter (low sorption) increases the probability of contaminating future crops.

RESUMO -Objetivou-se com este trabalho avaliar o potencial de sorção do sulfentrazone em cinco diferentes tipos de solo, por meio da técnica do bioensaio. O comportamento do herbicida foi estudado nos seguintes tipos de solo: Planossolo Háplico, Argissolo Vermelho, Cambissolo Húmico, Neossolo Regolítico e Latossolo Vermelho-Amarelo, além de um controle, apenas com areia lavada. O experimento foi realizado no delineamento inteiramente casualizado, e os tratamentos foram constituídos de sete doses crescentes do sulfentrazone em cada um dos tipos de solo. Aos 21 dias após emergência, realizou-se a colheita da planta indicadora e foi determinada a massa da matéria seca, bem como a dose do herbicida capaz de reduzir em 50% o acúmulo de massa da matéria seca das plantas indicadoras (C 50 ). Com esses dados calculou-se a relação de sorção (RS) do sulfentrazone, por meio da comparação da relação dos resultados da C 50 de cada solo com a C 50 obtida na areia lavada. Os valores de RS diferiram para os diferentes solos, apresentando a seguinte ordem crescente: Planossolo Háplico < Latossolo Vermelho-Amarelo < Argissolo Vermelho < Cambissolo Húmico < Neossolo Regolítico; os solos com maiores teores de matéria orgânica apresentaram os maiores RS e pH de cada solo. Conclui-se que a sorção do sulfentrazone é influenciada pelo teor de matéria orgânica e pH dos solos.Palavras-chave: herbicida, planta indicadora, ensaio biológico. ABSTRACT -The objective of this study was to

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Cassava improvement using traditional breeding strategies is slow due to the species’ long breeding cycle. However, the use of genomic selection can lead to a shorter breeding cycle. This study aimed to estimate genetic parameters for productive traits based on pedigree (pedigree and phenotypic information) and genomic (markers and phenotypic information) analyses using biparental crosses at different stages of selection. A total of 290 clones were genotyped and phenotyped for fresh root yield (FRY), dry matter content (DMC), dry yield (DY), fresh shoot yield (FSY) and harvest index (HI). The clones were evaluated in clonal evaluation trials (CET), preliminary yield trials (PYT), advanced yield trials (AYT) and uniform yield trials (UYT), from 2013 to 2018 in ten locations. The breeding stages were analyzed as follows: one stage (CET), two stages (CET and PYT), three stages (CET, PYT and AYT) and four stages (CET, PYT, AYT and UYT). The genomic predictions were analyzed via k -fold cross-validation based on the genomic best linear unbiased prediction (GBLUP) considering a model with genetic additive effects and genotype × location interactions. Genomic and pedigree accuracies were moderate to high (0.56–0.72 and 0.62–0.78, respectively) for important starch-related traits such as DY and FRY; when considering one breeding stage (CET) with the aim of early selection, the genomic accuracies ranged from 0.60 (DMC) to 0.71 (HI). Moreover, the correlations between the genomic estimation breeding values of one-stage genomic analysis and the estimated breeding values of the four-stage (full data set) pedigree analysis were high for all traits as well as for a selection index including all traits. The results indicate great possibilities for genomic selection in cassava, especially for selection early in the breeding cycle (saving time and effort).

The objectives of this study were to use a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes belonging to the flood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100-grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h2: 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated.

The objectives of this study were to use a bayesian multi-trait model, estimate genetic parameters, and select ood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-ve rice genotypes belonging to the ood-irrigated rice improvement program were evaluated. The grain yields, grain length, width and thickness, grain length, and grain width and weight of 100-grains in the agricultural year 2016/2017. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: h 2 : 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a low correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from ρ= 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of ood-irrigated.

Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava’s products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm’s of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs’ estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers’ effects that were trained with data from other research institutes/country’s germplasm to estimate their clones’ GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8–0.85) compared to the prediction accuracy of either germplasm’s sources individually (0.51–0.67). CC GP proved to have potential use under the present study’s scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa’s germplasm, whereas for IITA’s it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.