Dataset Information

Nutrient Diagnosis of Fertigated "Prata" and "Cavendish" Banana (Musa spp.) at Plot-Scale.

ABSTRACT: Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to customize nutrient diagnoses of fertigated banana at the plot scale. We documented 940 "Prata" and "Cavendish" plot units for tissue and soil tests, environmental and managerial features, and fruit yield. A Neural Network informed by soil tests, tissue tests and other features was the most proficient learner (AUC up to 0.827). Tissue nutrients were shown to have the greatest impact on model accuracy. Regional nutrient standards were elaborated as centered log ratio means and standard deviations of high-yield and nutritionally balanced specimens. Plot-scale diagnosis was customized using the closest successful factor-specific tissue compositions identified by the smallest Euclidean distance from the diagnosed composition using centered or isometric log ratios. Nutrient imbalance differed between regional and plot-scale diagnoses, indicating the profound influence of local factors on plant nutrition. However, plot-scale diagnoses require large, reliable datasets to customize nutrient management using ML and CoDa models.

SUBMITTER: Lima Neto AJ

PROVIDER: S-EPMC7692714 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Nutrient Diagnosis of Fertigated "Prata" and "Cavendish" Banana (<i>Musa</i> spp.) at Plot-Scale.

Lima Neto Antonio João de AJ Deus José Aridiano Lima de JAL Rodrigues Filho Vagner Alves VA Natale William W Parent Léon E LE

Plants (Basel, Switzerland) 20201030 11

Fertigation management of banana plantations at a plot scale is expanding rapidly in Brazil. To guide nutrient management at such a small scale, genetic, environmental and managerial features should be well understood. Machine learning and compositional data analysis (CoDa) methods can measure the effects of feature combinations on banana yield and rank nutrients in the order of their limitation. Our objectives are to review ML and CoDa models for application at regional and local scales, and to ...[more]

PMID: 33143268

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Project description:The "Cavendish" and "Prata" subgroups represent respectively 47% and 24% of the world banana production. Compared to world average progressing from 10.6 to 20.6 t ha-1 between 1961 and 2016, and despite sustained domestic demand and the introduction of new cultivars, banana yield in Brazil has stagnated around 14.5 t ha-1 mainly due to nutrient and water mismanagement. "Prata" is now the dominant subgroup in N-E Brazil and is fertigated at high costs. Nutrient balances computed as isometric log-ratios (ilr) provide a comprehensive understanding of nutrient relationships in the diagnostic leaf at high yield level by combining raw concentration data. Although the most appropriate method for multivariate analysis of compositional balances may be less efficient due to non-normal data distribution and limited nutrient mobility in the plant, robustness of the nutrient balance approach could be improved using Box-Cox exponents assigned to raw foliar concentrations. Our objective was to evaluate the accuracy of nutrient balances to diagnose fertigated "Prata" orchards. The dataset comprised 609 observations on fruit yields and leaf tissue compositions collected from 2010 to 2016 in Ceará state, N-E Brazil. Raw nutrient concentration ranges were ineffective as diagnostic tool due to considerable overlapping of concentration ranges for low- and high-yielding subpopulations at cutoff yield of 40 Mg ha-1. Nutrient concentrations were combined into isometric log-ratios (ilr) and normalized by Box-Cox corrections between 0 and 1 which may also account for restricted nutrient transfer from leaf to fruit. Despite reduced ilr skewness, Box-Cox coefficients did not improve model robustness measured as the accuracy of the Cate-Nelson partition between yield and the multivariate distance across ilr values. Sensitivity was 94%, indicating that low yields are attributable primarily to nutrient imbalance. There were 148 false-positive specimens (high yield despite nutrient imbalance) likely due to suboptimal nutrition, contamination, or luxury consumption. The profitability of "Prata" orchards could be enhanced by rebalancing nutrients using ilr standards with no need for Box-Cox correction.

Project description:BackgroundBananas (Musa spp.) are a globally significant crop and are severely afflicted by diseases for which there are no effective chemical controls. Banana microbiomes may provide novel solutions to these constraints but are difficult to manage due to their high diversity and variability between locations. Hence 'common core' taxa, which are a subset of the microbiome that frequent all, or most, individuals of a host species, represent logical targets for the development of microbiome management approaches. Here, we first performed a pot experiment to characterise the effects of two factors that are likely to differ between farms (viz. edaphic conditions and host genotype) on bacterial diversity in bulk soil and seven plant compartments. From this experiment, we created shortlisted core 'candidates' that were then refined using a survey of 52 field-grown Musa spp. We confirmed the importance of the core through network analysis and by comparing the sequences of our core taxa with those reported in 22 previous studies.ResultsDiversity was found to differ between plant compartments and soils, but not genotypes. Therefore, we identified populations that were frequent across most plants irrespective of the soil in which they were grown. This led to the selection of 36 'common core' bacteria, that represented 65-95% of the dominant taxa in field-grown plants and were identified as highly interconnected 'hubs' using network analysis - a characteristic shown to be indicative of microbes that influence host fitness in studies of other plants. Lastly, we demonstrated that the core taxa are closely related to banana-associated bacteria observed on five other continents.ConclusionsOur study provides a robust list of common core bacterial taxa for Musa spp. Further research may now focus on how changes in the frequencies and activities of these most persistent taxa influence host fitness. Notably, for several of our core taxa, highly similar populations have already been isolated in previous studies and may be amenable to such experimentation. This contribution should help to accelerate the development of effective Musa spp. microbiome management practices.

Project description:BackgroundIn plants, RNA- based gene silencing mediated by small RNAs functions at the transcriptional or post-transcriptional level to negatively regulate target genes, repetitive sequences, viral RNAs and/or transposon elements. Post-transcriptional gene silencing (PTGS) or the RNA interference (RNAi) approach has been achieved in a wide range of plant species for inhibiting the expression of target genes by generating double-stranded RNA (dsRNA). However, to our knowledge, successful RNAi-application to knock-down endogenous genes has not been reported in the important staple food crop banana.ResultsUsing embryogenic cell suspension (ECS) transformed with ß-glucuronidase (GUS) as a model system, we assessed silencing of gusAINT using three intron-spliced hairpin RNA (ihpRNA) constructs containing gusAINT sequences of 299-nt, 26-nt and 19-nt, respectively. Their silencing potential was analysed in 2 different experimental set-ups. In the first, Agrobacterium-mediated co-transformation of banana ECS with a gusAINT containing vector and an ihpRNA construct resulted in a significantly reduced GUS enzyme activity 6-8 days after co-cultivation with either the 299-nt and 19-nt ihpRNA vectors. In the second approach, these ihpRNA constructs were transferred to stable GUS-expressing ECS and their silencing potential was evaluated in the regenerated in vitro plants. In comparison to control plants, transgenic plants transformed with the 299-nt gusAINT targeting sequence showed a 4.5 fold down-regulated gusA mRNA expression level, while GUS enzyme activity was reduced by 9 fold. Histochemical staining of plant tissues confirmed these findings. Northern blotting used to detect the expression of siRNA in the 299-nt ihpRNA vector transgenic in vitro plants revealed a negative relationship between siRNA expression and GUS enzyme activity. In contrast, no reduction in GUS activity or GUS mRNA expression occurred in the regenerated lines transformed with either of the two gusAINT oligo target sequences (26-nt and 19-nt).ConclusionsRNAi-induced silencing was achieved in banana, both at transient and stable level, resulting in significant reduction of gene expression and enzyme activity. The success of silencing was dependent on the targeted region of the target gene. The successful generation of transgenic ECS for second transformation with (an)other construct(s) can be of value for functional genomics research in banana.

Project description:Banana streak virus (BSV) belongs to the members of the genus Badnavirus, family Caulimoviridae. At present, BSV contains nine species in the International Committee on Taxonomy of Viruses (ICTV) classification report (2018b release). Previous study indicated that the viral particles of Banana streak virus Acuminata Yunnan (BSV-Acum) were purified from banana (Cavendish Musa AAA group) leaves in Yunnan Province, China, and its complete genome was obtained. To further determine whether this sample infecting with Banana streak GF virus (BSGFV), the polymerase chain reaction (PCR) cloning and complete genome analysis of the Banana streak GF virus Yunnan isolate (BSGFV-YN) isolate were carried out in this study. The result showed that BSGFV-YN infecting Cavendish Musa AAA group was co-infecting this sample. Its genome contains a total of 7,325 bp in length with 42% GC content. This complete genome sequence was deposited in GenBank under accession number MN296502. Sequence analysis showed that the complete genome of BSGFV-YN was 98.14% sequence similarity to BSGFV Goldfinger, while it was 49.10-57.09% to other BSV species. Two phylogenetic trees based on the complete genome and ORFIII polyprotein indicated that BSGFV-YN and other BSV species clustered into a group, while it was the highest homology with BSGFV Goldfinger. Although BSGFV-YN and BSGFV Goldfinger were highly homologous, their cultivating bananas are different. The former cultivating banana was from Cavendish Musa AAA group, while the latter cultivating banana was from Goldfinger Musa AAAB group. Compared with BSGFV Goldfinger, the genome of BSGFV-YN has an extra multiple repetitive sequences in the intergenetic region between ORFIII and ORFI, suggesting that this region might be related to host selection. In summary, a BSGFV-YN distant from BSV-Acum was identified from the same sample, and its complete genome sequence was determined and analyzed. The study extends the polymorphism of BSVs in China and provides scientific clue for the evolutionary relationship with host selection of badnaviruses.

Project description:Cold-sensitive Cavendish Banana and relatively cold-tolerant Dajiao (Musa spp.) comprise an important part of diets for millions of people around the globe. Low temperature is one of the key environment stresses which greatly affect the global banana production. However, little is known about the changes of global protein phosphorylation in Musa spp. and their regulatory roles in response to cold stress. In this study, we employed a TMT6-plex quantitative analysis to conduct a global phosphoproteome profiling between Cavendish Banana and Dajiao subject to the cold stress for 0 hour and 3 hour. A total of 679 phosphopeptides containing 772 distinct phosphorylated sites from 529 phosphoproteins were identified in Cavendish Banana, 180 phosphorylation sites (belonging to 147 phosphoproteins) were differentially changed after 3 h cold stress. While in Dajiao 241 phosphopeptides with 271 individual phosphosites from 207 phosphoproteins were confidently identified, and 83 phosphorylation sites from 63 phosphoproteins were differentially changed under 3 h cold stress. Bioinformatic analysis of protein interaction network indicated that Mitogen-activated protein kinase kinase 2 (MKK2) was located in the center of the MAPK signaling network along with 7 other members whose phosphorylated site abundances were remarkably differentiated between Cavendish Banana and Dajiao in response to cold stress. Western blotting of MKK2 protein and its T31 phosphorylated site showed the increased expression of MKK2 in the time course of cold stress, with no detectable T31 phosphorylation in Cavendish Banana. On the contrary, the decreased MKK2 expression with increased T31 phosphorylation was consistently observed in Dajiao. These results suggest that the MKK2 interaction network in Dajiao, along with other cold-specific phosphoproteins found in this study, appears to play an important role in the molecular mechanisms of Dajiao being high tolerance to cold stress. The results also provide new evidence that cellular MKK2 phosphorylation as a signaling pathway plays an important role in abiotic stress tolerance that serves as a universal plant cold tolerance mechanism. To the best of our knowledge, this is the first report of MKK2 network involved in the regulatory of the Musa spp. response to cold stress.

Dataset Information

Nutrient Diagnosis of Fertigated "Prata" and "Cavendish" Banana (Musa spp.) at Plot-Scale.

Publications

Nutrient Diagnosis of Fertigated "Prata" and "Cavendish" Banana (<i>Musa</i> spp.) at Plot-Scale.

Similar Datasets

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