Variabilidade Espacial Da Produtividade De Sorgo E De Atributos Do Solo Na Região Do Ecótono Cerrado-Pantanal, MS

Montanari, Rafael; Panachuki, Elói; Lovera, Lenon Henrique; Corrêa, Adriany Rodrigues; Oliveira, Israel Souza; Queiroz, Hernandes Andrade; Tomaz, Pâmela Kerlyane

doi:10.1590/01000683rbcs20140215

Cited by 15 publications

(16 citation statements)

References 14 publications

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“…In the geostatistics scope, the study of the technique helps the computational software used in precision agriculture; That is, the data generated and adjusted from simple data interpolation (kriging) and cross-interpolation (co-kriging) among plant attributes versus another attribute serve as a basis for estimating the spatial variability of a given trait by means of another with ease of determination (MONTANARI et al, 2015). Co-kriging may be more accurate than the kriging of a single attribute when the cross-semivariogram shows dependence among any two phenological indices (TRANGMAR et al, 1985).…”

Section: Introductionmentioning

confidence: 99%

“…In the mapping of phenological indices of the common bean plant, geostatistics has been used mainly in the mapping of yield, such as observed in the works carried out by Montanari et al (2015) and Silva et al (2011). The numbers of grain per plant, pod per plant, grain per pod, mass of one hundred grains and yield of the common bean plant were mapped by Silva et al (2011).…”

Section: Introductionmentioning

confidence: 99%

“…Ferraz et al (2017 mapped the length and diameter of the stem of a coffee plant. Montanari et al (2015) studied the amount of plant dry matter. The objective of this study was to characterize the structure and magnitude of the spatial distribution of phenological indices of the common bean crop and to map the phenological indices aiming at visualizing the spatial distribution and to evaluate the spatial correlation among common bean yield and plant traits.…”

Section: Introductionmentioning

confidence: 99%

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Space variability of phenological indicators of common bean crop

Oliveira¹,

Carvalho²,

Roque³

et al. 2018

Biosci. J.

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ABSTRACT:Common bean (Phaseolus vulgaris L.) has a representative agricultural holding, not only for the economic value of its production, but also for the large area of growing in Brazil. In the harvest 2016/17, this work was conducted in a Quartzarenic Neosol in the municipality of Cassilândia, MS. The objective of this work was to characterize the structure and magnitude of the spatial distribution of phenological indices of the common bean crop and to map the phenological indices in order to visualize the spatial distribution and to evaluate the spatial correlation among common bean yield and plant variables: grain yield (YIE), mass of one hundred grains (MHG), number of grains per plant (NG), number of grains per pod (NGP), number of pods per plant (NP), dry matter (DM), plant length (PL) and stem diameter (SD), sampled in a grid of 117 georeferenced points (81 points of base grid and 36 points of higher density grid). Analysis of these data through statistical and geostatistical techniques made it possible to verify that the production and yield components presented spatial dependence. There was a positive spatial correlation among common bean yield and the mass of one hundred grains, number of grains per pod and plant length, demonstrating that they have a strong spatial dependence.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Space variability of phenological indicators of common bean crop

Oliveira¹,

Carvalho²,

Roque³

et al. 2018

Biosci. J.

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show abstract

“…In the scope of geostatistics, the study of the technique helps the computational programs used in precision agriculture; that is, data generated and adjusted from simple data interpolation (kriging) and cross-interpolation (cokriging) between plant traits vs. any other characteristic serve as a basis for estimating the spatial variability of a particular variable by means of another easily measured (Montanari et al, 2015).…”

mentioning

confidence: 99%

Spatial Variability of Irrigated Garlic (Allium sativum L.) Production Components

Oliveira¹,

Oliveira²,

Oliveira³

et al. 2020

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Among the crops that are usually grown under irrigation, one can mention garlic, which is a product with high demand in Brazil and the world, it is highly valued in the cuisine of several countries, and is an aggregated crop with high economic value. In 2018, this work was conducted in Yellow Red Latosol. The objective was to characterize the structure and magnitude of the spatial distribution of garlic production components and to map the productive components to visualize spatial distribution and to evaluate the spatial correlation between garlic bulb yield (BY) and other variables of the crop: total plant mass (TPM), number of leaves (NL), floral tassel length (FTL), leaf length (LL), leaf width (LW), pseudostem diameter (PD), shoot wet mass (SWM), shoot dry mass (SDM), number of cloves per bulb (NCB), clove mass (CM), root dry mass (RDM), and irrigation (IRR). All these traits were sampled in a 90-point grid georeferenced. Data analysis using statistical and geostatistical techniques made it possible to verify that the production components and BY, TPM, NL, FTL, LL, LW, PD, SWM, SDM, CM, and IRR presented special dependence. The spatial correlation between BY and TPM, LW, and CM showed a moderate spatial dependence.

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“…O sorgo apresentou elevada produtividade de matéria verde (106.832,0 kg ha -1 ) e matéria seca da forragem (34.521,0 kg ha -1 ) com alta variabilidade (Montanari et al, 2015). Os teores de N, P, K, Ca e Mg no tecido foliar do sorgo foram maiores no plantio direto, com doses de potássio resultando em maior produção de MSPA, com redução de 40% do adubo potássico aplicado no plantio direto em relação ao plantio convencional (Galvão et al, 2015).…”

Section: Fundamentação Teóricaunclassified