Balance nutricional y número de hojas como variables de predicción del rendimiento del plátano Hartón

Rodríguez, V.; Silva, Ana da; Rodríguez, Orlando

doi:10.1590/s0100-204x2005000200011

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…In these conditions, the three biometric variables associated with the PI used (circumference of the pseudo-stem of the mother plant, number of hands per bunch, and the height of the succession plant) confirmed the prior designation of each sampled area in different categories according to the production of each of those spots, indicating its validity as proxies of yield. This is in line with previous studies which also have established good accuracy in the estimation of banana productivity using biometric characteristics of the crop (Sumner 1990;Rodríguez et al 2004Rodríguez et al , 2005Segura et al 2015), but not in the regions covered by our study. We have demonstrated that it is possible to develop different models to characterize the PI in banana plantations based on soil characteristics and provided guidance for selecting the best choice from a stakeholder's perspective.…”

Section: Discussionsupporting

confidence: 94%

Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela

Olivares

Araya-Almán

Acevedo-Opazo

et al. 2020

J Soil Sci Plant Nutr

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To identify the main edaphic variables most correlated to banana productivity in Venezuela and explore the development of an empirical correlation model to predict this productivity based on soil characteristics. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were in large farms (≥ 50 ha) with four productivity levels in "Gran Nain" bananas, with an area of 4 ha for each of four productive levels: High-High, High-Low, Low-High, and Low-Low. Sixty sampling points were used to characterize the soils under study. Additionally, a Productivity Index (PI) based on three different biometric data on plant productivity was proposed. Through hierarchical statistical analysis, the first 16 soil variables that best explained the PI were selected. Thus, five multiple linear regression models were estimated, using the stepwise regression method. Subsequently, a performance analysis was used to compare the prediction quality range and the error associated with the number of soil variables selected for the proposed models. The selected model included the following soil variables: Mg, penetration resistance, total microbial respiration, bulk density, and omnivorous free-living nematodes. These variables explain the PI with an R 2 of 0.55, the mean absolute error (MAE) of 0.8, and the root of the mean squared error (RMSE) of 1.0. The five selected variables are proposed to characterize the soil Productivity Index in banana and could be used in a site-specific soil management program for the banana areas of Venezuela.

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Section: Discussionsupporting

confidence: 94%

Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela

Olivares

Araya-Almán

Acevedo-Opazo

et al. 2020

J Soil Sci Plant Nutr

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“…It is recognised that the impact of defoliation on the qualitative and quantitative development of bananas is variable and highly dependent on (a) when the defoliation is performed (Arcila et al, 1995), (b) the intensity of defoliation (Robinson & Anderson, 1990;Israeli et al, 1995;Rodriguez et al, 2005) and (c) how defoliation is performed: mechanically or through the action of pathogens (Robinson et al, 1992). Here, mechanical defoliation appears not to have been sufficiently early and/or severe to influence the processes determining fruit susceptibility to crown rot.…”

Section: Discussionmentioning

confidence: 99%

Hand position on the bunch and source–sink ratio influence the banana fruit susceptibility to crown rot disease

Lassois

Bastiaanse

Chillet

et al. 2010

Annals of Applied Biology

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Keywords AbstractThe postharvest development of crown rot of bananas depends notably on the fruit susceptibility to this disease at harvest. It has been shown that fruit susceptibility to crown rot is variable and it was suggested that this depends on environmental preharvest factors. However, little is known about the preharvest factors influencing this susceptibility. The aim of this work was to evaluate the extent to which fruit filling characteristics during growth and the fruit development stage influence the banana susceptibility to crown rot. This involved evaluating the influence of (a) the fruit position at different levels of the banana bunch (hands) and (b) changing the source-sink ratio (So-Si ratio), on the fruit susceptibility to crown rot. The fruit susceptibility was determined by measuring the internal necrotic surface (INS) after artificial inoculation of Colletotrichum musae. A linear correlation (r = −0.95) was found between the hand position on the bunch and the INS. The So-Si ratio was found to influence the pomological characteristics of the fruits and their susceptibility to crown rot. Fruits of bunches from which six hands were removed (two hands remaining on the bunch) proved to be significantly less susceptible to crown rot (INS = 138.3 mm 2 ) than those from bunches with eight hands (INS = 237.9 mm 2 ). The banana susceptibility to crown rot is thus likely to be influenced by the fruit development stage and filling characteristics. The present results highlight the importance of standardising hand sampling on a bunch when testing fruit susceptibility to crown rot. They also show that hand removal in the field has advantages in the context of integrated pest management, making it possible to reduce fruit susceptibility to crown rot while increasing fruit size.

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“…Na literatura, há trabalhos que contemplam o DRIS em fruteiras, e citam-se: citros (MOURÃO FILHO; AZEVEDO, 2005;SANTANA et al, 2008), uva (TERRA et al, 2007); maçã ; banana (TEIXEIRA et al, 2007;RODRÍGUEZ et al, 2005); cupuaçu (DIAS et al, 2010); coqueiroanão ; manga (POLITI et al, 2013), e para goiabeira, cita-se o trabalho de Hundal et al (2007) na Índia; no entanto, para condições brasileiras, ou mesmo para o Estado de São Paulo, que é um dos maiores produtores de goiabas do País, não há normas desenvolvidas.…”

Section: Introductionunclassified

Normas preliminares dris e faixas de suficiência para goiabeira 'Paluma'

Souza

Rozane²,

Amorim³

et al. 2013

Rev. Bras. Frutic.

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RESUMO-O diagnóstico nutricional a partir da análise do tecido foliar é um instrumento eficiente para detectar desequilíbrios e auxiliar no processo de recomendação de adubação. Assim, objetivou-se avaliar a relação entre os índices DRIS e os teores foliares de nutrientes, estabelecer teores foliares adequados de nutrientes com o DRIS e validar normas DRIS para a goiabeira 'Paluma'. Avaliou-se um pomar comercial nas condições de cultivo do Estado de São Paulo, durante seis safras de produção de frutos, provenientes de ensaios de adubação orgânica e mineral, totalizando 168 amostras. A ordem dos nutrientes limitantes por falta em ordem decrescente foram: Fe>K>Mn>Ca>B>N>P>Cu>S>Mg>Zn, e limitantes por excesso em ordem decrescente foram: Cu>Fe>P>Mn=Mg>Zn>Ca>B>K>S>N. As faixas adequadas, provenientes dos índices DRIS, 1,7;[15][16][17][8][9][10][11] 1,,5 e 2,5-2,9 (g kg -1 ) para os macronutrientes N, P, K, Ca, Mg e S, respectivamente, e 26-38; 5-57; 54-112; 53-101 e 13-126 (mg kg -1 ) para os micronutrientes B, Cu, Fe, Mn e Zn, respectivamente. A produtividade das goiabeiras esteve associada ao estado nutricional. Termos para indexação: Psidium guajava; estado nutricional; diagnóstico nutricional. PRELIMINARY DRIS NORMS AND SUFFICIENCY RANGEFOR 'PALUMA' GUAVA TREE ABSTRACT -The nutritional diagnosis based on the analysis of leaf tissue is an efficient tool for detecting imbalances and help the process of fertilization recommendation. Thus, it was evaluated to establish preliminary DRIS norms, and to derive critical levels and sufficiency ranges of nutrients in the leaf tissue to Paluma guava cultivar in commercial orchards growing conditions in the State of São Paulo. It was evaluated six harvests of fruit production, from organic and mineral fertilizer, totaling 168 samples. The order of limiting nutrients for failure in descending order were: Fe> K> Mn> Ca> B> N> P> Cu> S> Mg> Zn, and limiting excess in descending order were: Cu> Fe> P> Mn = Mg> Zn> Ca> B> K> S> N. The tracks from the appropriate DRIS indices are: 18-21, 1.5 to 1.7, 15-17, 8-11, 1.8 to 2.5 and 2.5 to 2.9 (g kg -1 ) for the macronutrients N, P, K, Ca, Mg and S, respectively, and 26-38, 5-57, 54-112, 53-101 and 13-126 (mg kg -1 ) for the micronutrients B, Cu, Fe, Mn and Zn, respectively. The yield of guava was related to nutritional status. Index terms: Psidium guajava; nutritional state; nutritional diagnosis. INTRODUÇÃODentre várias metodologias existentes atualmente, capazes de avaliar o estado nutricional das plantas, tem-se o Sistema Integrado de Diagnose e Recomendação (DRIS), como alternativa viável, científica e capaz de inferir respostas rápidas e práticas no decorrer do desenvolvimento das lavouras, pois é considerado um método de interpretação holístico, menos afetado por alguns fatores que influenciam no teor de nutrientes nas plantas e, além disso, é capaz de ordenar os nutrientes em sua ordem de limitação e importância naquele momento (CRESTE, 2008).No DRIS, faz-se a comparação das relações entre nutrientes com as encontradas nas folha...

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Balance nutricional y número de hojas como variables de predicción del rendimiento del plátano Hartón

Cited by 4 publications

References 3 publications

Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela

Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela

Hand position on the bunch and source–sink ratio influence the banana fruit susceptibility to crown rot disease

Normas preliminares dris e faixas de suficiência para goiabeira 'Paluma'

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