DRIS norms and limiting nutrients in banana cultivation in the South of Ecuador

Villaseñor, Diego; Prado, Renato de Mello; Silva, Gilmara Pereira da; Carrillo, Manuel Alvarado; Durango, Wuellins

doi:10.1080/01904167.2020.1793183

Cited by 34 publications

(33 citation statements)

References 18 publications

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“…Common tissue nutrient diagnostic methods for banana crops are the "Diagnosis and Recommendation Integrated System" (DRIS) and the "Compositional Nutrient Diagnosis" (CND). Regional DRIS and CND tissue diagnostic standards have been elaborated for rainfed "Cavendish" in East Africa [11,12], irrigated "Cavendish" in Ecuador [13] and Brazil [14], and irrigated "Prata" in Brazil [15,16], using yield thresholds or boundary lines as yield separators. The latter approaches are unable to separate true negative (high yielding, nutritionally balanced) from false positive (high yielding, nutritionally imbalanced, due to luxury consumption or contamination) specimens.…”

Section: Introductionmentioning

confidence: 99%

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

Neto

Deus

Filho³

et al. 2020

Plants

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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.

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Section: Introductionmentioning

confidence: 99%

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

Neto

Deus

Filho³

et al. 2020

Plants

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“…The variation observed in yield is a result of the variability of leaf nutrient content and reflects the nutritional management adopted in each plot, in addition to other factors involved that act on the dynamics of nutrients in the soil-plant continuum, as evidenced by Teixeira et al (2007) and Villaseñor et al (2020), who found smaller and greater variations for N and Mn, in banana trees of the Cavendish subgroup in Brazil and Ecuador, respectively. Raghupathi et al (2002), Naik andBhatt (2017), andVillaseñor et al (2020) also observed lower variation for macronutrient contents in banana in India, mango in India, and banana in Ecuador, respectively. This wide variation in the yield and contents of nutrients in the leaf tissue are necessary conditions for establishing nutritional reference values based on field data (Camacho et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…DRIS standards have been established for 'Cavendish' banana in Africa, Ecuador, and Brazil, respectively (Wairegi and Van Asten 2011;Villaseñor et al 2020;Teixeira et al 2007). CND standards have already been established for 'Robusta' and 'Ney Poovan' banana in India (Raghupathi et al 2002) and 'Cavendish' banana in Africa (Wairegi and Van Asten 2011;Ndabamenye et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Establishment of DRIS and CND Standards for Fertigated ‘Prata’ Banana in the Northeast, Brazil

Neto

Natale

Rozane

et al. 2021

J Soil Sci Plant Nutr

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The objective of this study was to establish nutritional standards for fertigated 'Prata' banana in the Northeast, Brazil, using the Diagnosis and Recommendation Integrated System (DRIS) and Compositional Nutrient Diagnosis (CND). The study was carried out in partnership with the company Sítio Barreiras Fruticultura LTDA., located in the municipality of Missão Velha, Ceará, Brazil. A database containing information on fruit yield and contents of nutrients, sodium, and aluminum in leaves, comprising the period from 2010 to 2018, was used. The results show a relationship of the mean nutrient balance index (NBI m ) and the nutrient imbalance index (CND-r 2 ) with yield. The DRIS and CND methods were effective in diagnosing the nutritional status of the banana tree; however, the CND method proved to be superior in explaining the variation in yield as a function of nutritional balance. The sufficiency ranges established from the DRIS and CND indices are similar to each other and differ from those obtained in other countries and regions. The adoption of these nutritional norms obtained in this work allows the rational use of applied fertilizers and higher yields.

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“…Micronutrient applications affect the growth of leaves (Souza et al, 2016), roots (Liu et al, 2019), and pseudostems (Kumar et al, 2020) and banana production (Robinson and Galán, 2012). The contents of Fe, Mn, Zn, Cu, and B in plant organs estimate the plant nutrient status with the DRIS method (Villaseñor et al, 2020). The concentrations of Mn and B in banana leaves may correlate with chlorophyll contents (Arantes et al, 2016), and micronutrient allocation to fruits is required for bunch development (Soto, 2001).…”

mentioning

confidence: 99%

“…The concentrations of Mn and B in banana leaves may correlate with chlorophyll contents (Arantes et al, 2016), and micronutrient allocation to fruits is required for bunch development (Soto, 2001). However, excess Fe, Mn, and Cl in coastal tropical soils could negatively affect yields (Osorio, 2014;Villaseñor et al, 2020).…”

mentioning

confidence: 99%

Accumulation and distribution of micronutrients in banana cv. Williams (Musa AAA Simmonds) with different doses of nitrogen

Torres-Bazurto¹,

Magnitskiy²,

Sánchez³

2021

Rev. Colomb. Cienc. Hortic.

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This research evaluated the effect of nitrogen fertilization (0, 161, 321.8, or 483 kg ha-1) on the accumulation of Fe, Cu, Zn, Mn, and B in banana plants cv. Williams in two production cycles in Uraba, Colombia. The micronutrient accumulation models for the plants were obtained with a multivariate approach for differences between cycles, using a randomized complete block design with repeated measurements over time. The N doses with greater accumulation of Cu, Fe, Mn, Zn and B in the plants with fruits were 321.8 and 483 kg ha-1. The average sequence of micronutrient extraction by whole plants was Fe>Mn≥Zn>B>Cu in the first cycle and Fe>Cu>Mn≥Zn>B in the second cycle. The micronutrient accumulation was organ-specific and varied depending on the stage of development. Fe was the major micronutrient extracted by the corm, pseudostem, and bunch. The leaves had the greatest accumulation of Mn. The higher fertilizer doses generated a major response in terms of micronutrient extraction by the banana plants.

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DRIS norms and limiting nutrients in banana cultivation in the South of Ecuador

Cited by 34 publications

References 18 publications

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

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

Establishment of DRIS and CND Standards for Fertigated ‘Prata’ Banana in the Northeast, Brazil

Accumulation and distribution of micronutrients in banana cv. Williams (Musa AAA Simmonds) with different doses of nitrogen

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