Exploring the symbiosis between plants and plant-growth-promoting bacteria (PGPB) is a new challenge for sustainable agriculture. Even though many works have reported the beneficial effects of PGPB in increasing plant resilience for several stresses, its potential is not yet widely explored. One of the many reasons is the differential symbiosis performance depending on the host genotype. This opens doors to plant breeding programs to explore the genetic variability and develop new cultivars with higher responses to PGPB interaction and, therefore, have higher resilience to stress. Hence, we aimed to study the genetic architecture of the symbiosis between PGPB and tropical maize germplasm, using a public association panel and its impact on plant resilience. Our findings reveal that the synthetic PGPB population can modulate and impact root architecture traits, improve resilience to nitrogen stress, and 37 regions were significant for controlling the symbiosis between PGPB and tropical maize. In addition, we found two overlapping SNPs in the GWAS analysis indicating strong candidates for further investigations. Furthermore, genomic prediction analysis with genomic relationship matrix computed using only significant SNPs obtained from GWAS analysis substantially increased the predictive ability for several traits endorsing the importance of these genomic regions for the response of PGPB. Finally, the public tropical panel reveals a significant genetic variability to the symbiosis with the PGPB and can be a source of alleles to improve plant resilience.

Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24-0.47) and between HTP-based plant height and manually measured plant height (0.55-0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth.

Recebido para publicação em 24/02/2015 Aceito para publicação em 03/09/2015 RESUMO: O manjericão (Ocimum basilicum L.) atualmente encontra-se distribuído por todos os continentes e foi introduzido no Brasil com a chegada da colonização italiana. As principais partes utilizadas para comercialização no Brasil são as folhas frescas ou secas. O presente trabalho teve como objetivo avaliar o efeito de doses da adubação nitrogenada, durante o cultivo na primavera e outono, nas características produtivas e na produtividade de manjericão, Alfavaca basilicão vermelho, em casa de vegetação. O delineamento utilizado foi em blocos casualizados, em esquema fatorial 6 x 2, com cinco repetições, onde o primeiro fator foi constituído de cinco doses de nitrogênio e uma testemunha (0,0; 45,0; 90,0; 135,0; 180,0 e 225,0 kg ha -1 de nitrogênio) e o segundo, pelas épocas de cultivo (primavera e outono). Para as características produtivas da cultivar de manjericão Alfavaca Basilicão vermelho, verificou-se que as doses de nitrogênio entre 90,0 a 135,0 kg ha -1 foram mais adequadas, elevando todas as características produtivas avaliadas, do cultivo de primavera. Quando observado o cultivo no outono, teve-se ajuste apenas para a projeção de copa, massa fresca de parte aérea e área foliar. Quando se obteve ajuste significativo, a dose de aproximadamente 110,0 kg ha -1 de nitrogênio foi a que promoveu maior desenvolvimento da cultura. Em relação às épocas de cultivo na primavera recomenda-se o uso de adubação mineral nitrogenada, porém quando esse cultivo é realizado outono, a adubação nitrogenada não é responsiva. Palavras-chaves:Fertilização, características produtivas, cultivo protegido.ABSTRACT: Effect of nitrogen fertilization on the production and productivity of Basil, red basil (Ocimum basilicum L.) in spring and autumn seasons. The basil (Ocimum basilicum L.) currently is distributed to all continents and was introduced in Brazil with the arrival of Italian colonization. The main parts used for commercialization in Brazil are the fresh or dried leaves. This study aimed to evaluate the effect of doses of nitrogen fertilization for cultivation in spring and fall, the yield characteristics and the productivity of basil, Basil red in the greenhouse. The experimental design was a randomized block, in a factorial 2 x 6 with five replications, where the first factor constituent of five nitrogen doses and one control (0.0; 45.0; 90.0; 135.0; 180.0 and 225.0 kg ha -1 of nitrogen) and by the second, the growing seasons (Spring and Autumn). For the productive characteristics of the cultivar red Basil, it was found that the nitrogen rates between 90.0 to 135.0 kg ha -1 were better, bringing all tested productive characteristics, the spring crop. When observed growing in the autumn, was only fit for the crown projection, fresh weight of shoot and leaf area. When there was significant adjustment, the dose of approximately 110.0 kg ha -1 of nitrogen was that promoted further development of culture. Regarding the growing seasons in the spring we...

Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize (Zea mays L.) lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1,021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies.

Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize (Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.

The objective of this study was to evaluate the asexual propagation of Dovyalis, through the use of substrates and cuttings. Three experiments were conducted in Marechal Cândido Rondon, Paraná State, Brazil, and the first experiment consisted of three removal positions of the cuttings (apical, middle and basal) x 4 Indole butyric acid (IBA) concentrations (0 mg L-1, 1000 mg L-1, 2000 mg L-1 and 3000 mg L-1); the second experiment evaluated the number of leaves in the cuttings (2, 4 and 6) x 3 cutting sizes (10, 15 and 20 cm); and the third experiment evaluated four substrates for rooting. The experiment was carried out in a randomized blocks design, with the first in a 3x4 and the second in a 3x3 factorial, respectively, containing 4 repetitions of 15 cuttings. The third experiment consisted of 5 repetitions of 15 cuttings. After the IBA treatment, the cuttings were taken to rooting in sandy beds during 70 days. The agronomical variables were evaluated. Basal and middle cuttings resulted in root systems with better development. The use of 1666 mg L-1 of IBA favors the Dovyalis cuttings rooting. Cuttings with 15 and 20 cm with four leaves favors the Dovyalis vegetative propagation. Dovyalis cuttings develop better on substrates containing latosol and vermiculite.

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Usually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models.Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-α). The CV-α was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-α, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV-α results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV-α method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV-α can be considered a more precise validation methodology for model selection.