Yield mapping of arabic coffee and their relationship with plant nutritional status
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TLDR
The yield variables and leaf nutrients that were found related showed spatial dependence without random distribution and Nutritional imbalance was detected in the studied coffee crop expressed by the deficiency or excess of some nutrients in the plant tissue.Abstract:
The aim of this study was to model the spatial variability of the nutritional status of arabic coffee using leaf macro and micronutrient contents and relate it to drop in bean yield, bark percentage and crop yield. The experiment was conducted in a plantation of arabic coffee variety Catuai located in the Zona da Mata of Minas Gerais State. Leaf nutrient contents, cherry coffee production, drop in bean yield, yield of benefited coffee and bark percentage were determined. Data were analyzed using classical statistical methods to find the relationship between nutrients and yield variables and then examined by geostatistical analysis. The yield variables and leaf nutrients that were found related showed spatial dependence without random distribution. Nutritional imbalance was detected in the studied coffee crop expressed by the deficiency or excess of some nutrients in the plant tissue. Ca provided the smallest drop in bean yield while the leaf contents of B and Zn had an opposite effect on the production and yield of coffee.read more
Citations
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Michele Kremer Sott,Leonardo Bertolin Furstenau,Liane Mahlmann Kipper,Fáber D. Giraldo,José-Ricardo López-Robles,Manuel Cobo,Adnan Zahid,Qammer H. Abbasi,Muhammad Imran +8 more
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Modeling land suitability for Coffea arabica L. in Central America
TL;DR: The results show that even without the use of coffee maps as input, ALECA accurately scores the suitability of actual coffee areas for coffee production as higher than that non-coffee areas, and can accurately predict the known order of quality of coffee reference zones in Central America.
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Mapping the potential beverage quality of coffee produced in the Zona da Mata, Minas Gerais, Brazil.
Samuel de Assis Silva,Daniel Marçal de Queiroz,Williams Pinto Marques Ferreira,Paulo Cesar Corrêa,José Luis dos Santos Rufino +4 more
TL;DR: The production site's position and altitude are the primary variables that influenced the coffee quality and these areas have the potential for producing specialty coffees.
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DRIS and geostatistics indices for nutritional diagnosis and enhanced yield of fertirrigated acai palm
Felipe Oliveira Ribeiro,Antonio Rodrigues Fernandes,Jessivaldo Rodrigues Galvão,Gilson Sergio Bastos de Matos,Marcelo Morita Lindolfo,Cassio Rafael Costa dos Santos,Mauro Junior Borges Pacheco +6 more
TL;DR: Assessment of the nutritional status of fertigated Açaí palm by the Index called Diagnosis and Recommendation Integrated System (DRIS), as well as the spatial variability of this Index and its productivity, found that N and S were well balanced, whereas Mn, Ca and B were the nutrients with the highest frequency of deficiency.
Journal ArticleDOI
Correlation Between Altitude, Soil Chemical Properties, and Physical Quality of Arabica Coffee Beans in Highland Areas of Garut
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References
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Field-scale variability of soil properties in central iowa soils
Cynthia A. Cambardella,Thomas B. Moorman,Timothy B. Parkin,Douglas L. Karlen,Jeffrey M. Novak,Ronald F. Turco,Allan Konopka +6 more
TL;DR: In this paper, field-scale distributions and spatial trends for 28 different soil parameters at two sites within a watershed in central Iowa were investigated using semivariograms and the ratio of nugget to total semivariance, expressed as a percentage, was used to classify spatial dependence.
Book
Geostatistics for Environmental Scientists
TL;DR: In this article, the Covariance and Variogram were used to model the spatial process of spatial processes and predict local estimation or prediction in the presence of trend and factorial Kriging.
Journal ArticleDOI
Geostatistics for Environmental Scientists
TL;DR: In this paper, the Covariance and Variogram were used to model the spatial process of spatial processes and predict local estimation or prediction in the presence of trend and factorial Kriging.
Manual de métodos de análise de solo.
TL;DR: Analises fisicas, Analises quimicas, analises da materia orgânica; Analise mineralogicas; analise micromorfologicas as discussed by the authors.
Manual de métodos de análise de solo.
TL;DR: Analises fisicas, Preparo da amostra, Terra fina, cascalho e calhaus; Umidade atual, Umidades residual e fator "f"; Umidde obtida no aparelho extrator de Richards; Umendade obtined com a mesa de tensao; Densidade aparente; Porosidade total; Microporosidade (Metodo Mesa de Tensao); Macroporosideade; Analise granulometrica (Dispersao Total);
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