Humic acid as foliar and soil application improve the growth, yield and quality of coffee (cv. <scp>C × R</scp>) in <scp>W</scp>estern <scp>G</scp>hats of <scp>I</scp>ndia

Mote, Kishor; Jayakumar, M.; Gokavi, Nagaraj; Mukharib, D. S.; Raghuramulu, Y.; Pillai, Surendran Udayar

doi:10.1002/jsfa.10848

Cited by 11 publications

(6 citation statements)

References 33 publications

(39 reference statements)

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“…Pramanik and Sanmay (2016) indicated that increases in the total number of nodes/branches, number of bearing nodes/branches, number of fruits/branches and fruit set percentage resulted in improvements in yield. These results agree with the results of Mote et al (2020) and Gokavi et al (2020), who observed a positive and significant correlation between the yield parameters and coffee yield.…”

Section: Discussionsupporting

confidence: 93%

Effect of fertigation levels on fertilizer use efficiency, yield, quality and economics of Robusta coffee

Mote¹,

Gokavi²,

Babou³

et al. 2023

Irrigation and Drainage

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Robusta coffee production largely depends upon irrigation and nutrition management practices. These factors determine the increase in productivity and quality of coffee. Therefore, restrictions of Robusta coffee production due to soil nutrient deficiencies can be removed by applying the right quantities of fertilizers at the right time with the right method. A long‐term experiment was conducted to study the impact of different fertigation levels on the yield, nutrient use efficiency and quality of Robusta coffee. The present experiment was conducted on a randomized block design consisting of six treatments with four replications over a period of 5 years from 2017 to 2021 at the Central Coffee Research Institute (CCRI), Balehonnuru, India. The results revealed that among different fertigation levels, fertigation with 75% and 100% of the recommended dose of fertilizer resulted in significantly higher yields (2117 kg ha−, 1821 kg ha−1) and yield attributing parameters, namely, total number of nodes/branches (49.1, 44.1), number of bearing nodes/branches (35.0, 29.4) and number of fruits/branches (758, 679) in Robusta coffee, based on the pooled mean (2017–2021). Therefore, adoption of the fertigation level with 75% of the recommended dose of fertilizers not only increased the productivity of Robusta coffee but also ensured a higher efficiency of nutrients (25%–30%) and cost–benefit ratio (1:2.48).

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Section: Discussionsupporting

confidence: 93%

Effect of fertigation levels on fertilizer use efficiency, yield, quality and economics of Robusta coffee

Mote¹,

Gokavi²,

Babou³

et al. 2023

Irrigation and Drainage

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“…Although foliar-applied sedimentary humic acid significantly increased shoot and root growth, it had no effect on the levels of nutrients in the leaves. Kishor et al. (2021) and Hernandez et al.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the molecular composition of humic substances and their effects on physiological metabolism in maize based on untargeted metabolomics

Wang

et al. 2023

Front. Plant Sci.

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IntroductionHumic substances (HSs), components of plant biostimulants, are known to influence plant physiological processes, nutrient uptake and plant growth, thereby increasing crop yield. However, few studies have focused on the impact of HS on overall plant metabolism, and there is still debate over the connection between HS’ structural characteristics and their stimulatory actions.MethodsIn this study, two different HSs (AHA, Aojia humic acid and SHA, Shandong humic acid) screened in a previous experiment were chosen for foliar spraying, and plant samples were collected on the tenth day after spraying (62 days after germination) to investigate the effects of different HSs on photosynthesis, dry matter accumulation, carbon and nitrogen metabolism and overall metabolism in maize leaf.Results and discussionThe results showed different molecular compositions for AHA and SHA and a total of 510 small molecules with significant differences were screened using an ESI-OPLC-MS techno. AHA and SHA exerted different effects on maize growth, with the AHA inducing more effective stimulation than the SHA doing. Untargeted metabolomic analysis revealed that the phospholipid components of maize leaves treated by SHA generally increased significantly than that in the AHA and control treatments. Additionally, both HS-treated maize leaves exhibited different levels of accumulation of trans-zeatin, but SHA treatment significantly decreased the accumulation of zeatin riboside. Compared to CK treatment, AHA treatment resulted in the reorganization of four metabolic pathways: starch and sucrose metabolism, TCA cycle, stilbenes, diarylheptanes, and curcumin biosynthesis, and ABC transport, SHA treatment modified starch and sucrose metabolism and unsaturated fatty acid biosynthesis. These results demonstrate that HSs exert their function through a multifaceted mechanism of action, partially connected to their hormone-like activity but also involving hormoneindependent signaling pathways.

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“…También se han usado datos oficiales por provincia del rendimiento del cultivo de café, para ajustar parámetros de modelos calculados que entregan como respuesta dicha variable objetivo, en kg/ha o ton/ha (39), es decir, en cada variación de un atributo, los rendimientos pronosticados se compararon con los observados hasta que se encontraron las mejores combinaciones (48), en otras palabras, el modelo cuyos resultados entregó menos errores. Además, se ejecutó una evaluación visual para verificar qué tan bien el modelo simula la variabilidad del rendimiento interanual observada.…”

Section: Variable Objetivounclassified

“…Variables obtenidas satelitalmenteQuímicas (ver tabla 10): Otro de los componentes más relevantes en la productividad de los cultivos de café es su mantenimiento químico; la variación de la cantidad de nutrientes que recibe la tierra es crucial para las cosechas futuras (44)(41), algunos de los elementos más utilizados, medidos comúnmente en mol/l, son: magnesio, hierro, calcio, boro, nitrógeno, fosforo y potasio, junto con los niveles de pH en el suelo y los nutrientes del suelo evaluados por los niveles de carbono orgánico presentes en la muestra (37) (47)(35). Se observa como en la mayoría de los artículos evaluados, el rendimiento del café Robusta está principalmente limitado por la disponibilidad de nitrógeno (29), también algunas combinaciones optimizan la producción(38), así como la implementación de insumos de nutrientes orgánicos como la hojarasca y sintéticos: acido húmico (kg / acre) y urea(48). Otros análisis más detallados se presentan en (44) donde a algunas de las variables descritas antes se le suman: Ca…”

unclassified

Predicción del rendimiento de cultivos de café: un mapeo sistemático

Muñoz Ordoñez,

Cobos Lozada,

Muñoz Ordóñez

2023

inycomp

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El café es uno de los productos agrícolas más comercializados internacionalmente y en Colombia, es el primer producto de exportación no minero-energético. En este contexto, la predicción del rendimiento de los cultivos de café es vital para el sector, ya que permite a los caficultores establecer estrategias de manejo del cultivo, maximizando sus ganancias o reduciendo posibles pérdidas. En este artículo, se abordan aspectos cruciales de la predicción del rendimiento de los cultivos de café mediante una revisión sistemática de literatura de documentos consultados en Scopus, ACM, Taylor & Francis y Nature. Estos documentos se sometieron a un proceso de filtrado y evaluación para responder cinco preguntas clave: variables predictoras, variable objetivo, técnicas y algoritmos empleados, métricas para evaluar la calidad de la predicción y tipos de café reportados. Los resultados revelan distintos grupos de variables predictoras que incluyen factores atmosféricos, químicos, obtenidos vía satélite, relacionados con fertilizantes, suelo, manejo del cultivo y sombras. La variable objetivo más recurrente es el rendimiento medido en peso de granos por hectárea u otras medidas, con un caso que considera el área foliar. Entre las técnicas predominantes para predecir el rendimiento se encuentran la regresión lineal, los bosques aleatorizados, el análisis de componentes principales, la regresión por conglomerados, las redes neuronales, los árboles de clasificación y regresión y las máquinas de aprendizaje extremo, entre otras. Las métricas más comunes para evaluar la calidad de los modelos predictivos incluyen R², RMSE, MAE, MAPE, MRE, error estándar, coeficiente de correlación de Pearson y desviación estándar. Por último, las variedades de café más estudiadas son robusta, arábica, racemosa y zanguebariae.

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Humic acid as foliar and soil application improve the growth, yield and quality of coffee (cv. C × R) in Western Ghats of India

Cited by 11 publications

References 33 publications

Effect of fertigation levels on fertilizer use efficiency, yield, quality and economics of Robusta coffee

Effect of fertigation levels on fertilizer use efficiency, yield, quality and economics of Robusta coffee

Analysis of the molecular composition of humic substances and their effects on physiological metabolism in maize based on untargeted metabolomics

Predicción del rendimiento de cultivos de café: un mapeo sistemático

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