Space variability of phenological indicators of common bean crop

Oliveira, Job Teixeira de; Carvalho, Morel de Passos e; Roque, Cassiano Garcia; Baio, Fábio Henrique Rojo; Kamimura, Karina Marie; Ribeiro, Isabela da Silva; Teodoro, Paulo Eduardo

doi:10.14393/bj-v34n2a2018-39659

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…The production components BY, TPM, NL, FTL, LL, PD, SWM, SDM, NCB, CM and RDM were significant at 5% probability by the normality test of Shapiro and Wilk (1965), because their respective probabilities were of 0.6587; 0.6919; 0.6683, 0.7137, 0.8068, 0.9815, 0.4264; 0.7159, 0.0956, 0.6641, and 0.0923. Similar results for the grain yield and stem diameter in the study with common beans obtained normal frequency distribution (Oliveira et al, 2018). Montanari et al (2013) found the frequency distribution of the type tending to normal for brachiaria dry matter.…”

Section: Resultssupporting

confidence: 75%

“…These results show a direct relationship between BY and the studied production components, i.e., taking the CM as an example, there is an increase in yield. Oliveira et al (2018) found in the cokriging of bean yield as a function of grain mass, strong SDE.…”

Section: Resultsmentioning

confidence: 87%

“…The range values for semivariograms are of considerable importance in determining the spatial dependence limit (Supplemental Fig. 3), which also may be indicative of the interval between soil mapping units (Trangmar et al 1985), and also of the production components related to crops (Oliveira et al, 2018). The range found by the variables ranged from 4.0 m (PD) to 25.0 m (CM).…”

Section: Resultsmentioning

confidence: 99%

“…In this way, the farmer can take advantage of the historical information from the area from previous mappings to make the decisions necessary for the good progress of the crop. Identifying regions that have a greater or lesser need for intervention are in the soil or the crop (Oliveira et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…With the advent of precision agriculture in Brazil, the geostatistical study of crop production components related to crop yield has intensified (Oliveira et al, 2018). Ferraz et al (2012) defined precision agriculture as a set of techniques and technologies capable of assisting the farmers to manage their crops, aiming to increase production and income, as well as improving the efficiency in fertilization, harvesting, and spraying techniques, contributing, therefore, to improve the final quality of the marketed product.…”

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confidence: 99%

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

Oliveira¹,

Oliveira²,

Oliveira³

et al. 2020

horts

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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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Section: Resultssupporting

confidence: 75%

Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Oliveira¹,

Oliveira²,

Oliveira³

et al. 2020

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Spatial correlation of soybean yield with the chemical attributes of an Oxisol

Rodrigues,

Castro,

Roque

et al. 2023

Agron. Colomb.

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Soybean is currently the most cultivated oilseed in the world. The objective of this study was to analyze the chemical attributes of Oxisol correlated with soybean productivity. The attributes evaluated were grain productivity, soil properties of organic matter contents, phosphorous, pH, potassium, calcium, magnesium, hydrogen + aluminum, aluminum, sum of bases, cation exchange capacity (CEC), base saturation, liming requirement; penetration resistance at 0.0-0.10 m, 0.10-0.20 m, 0.20-0.30 m, and 0.30-0.40 m. Soil chemical attributes were submitted to a descriptive classic analysis with the help of the RBio software. In the semivariogram analysis, the attributes calcium, magnesium, aluminum, sum of bases, base saturation, liming requirement, and penetration resistance at 0.30-0.40 m showed a pure nugget effect, meaning no spatial dependence. In conclusion, CEC was the attribute that best described the soybean yield data in dystrophic Red Latosols. Attributes of grain productivity, soil organic matter, phosphorus, pH, potassium, hydrogen + aluminum, CEC, penetration resistance at 0.00 to 0.30 m showed spatial dependence.

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Spatial Variability of Irrigated Common Bean Yield Correlated With the Fertility of a Sandy Soil

et al. 2020

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The common bean is widely cultivated in Brazil. In the 2016-2017 growing season, an experiment was carried out in a Typic Quartzipsamment (or Neossolo Quartzarênico) soil in the municipality of Cassilândia, MS, Brazil, with the aim of characterizing specific aspects of soil management, using a linear and spatial correlation between the irrigated yield of common beans and the chemical properties of the soil. Soil samples were collected from layers at depths 0.00-0.10 and 0.10-0.20 m, within a grid of 117 georeferenced points. A data analysis was carried out using statistical and geostatistical techniques. A multiple regression analysis showed that about 31% of the grain yield variability of the irrigated common bean crop could be attributed to the spatial variability in the chemical properties of the soil. These properties have a spatial dependence that is classified as moderate and strong, with a predominantly Gaussian-type semivariogram model. Soil properties such as pH(1), pH(2), S(2) and V%(2) can be considered potential indicators of the grain yield of an irrigated common bean crop when cultivated in a sandy soil under a no-tillage system.

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Space variability of phenological indicators of common bean crop

Cited by 7 publications

References 13 publications

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

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

Spatial correlation of soybean yield with the chemical attributes of an Oxisol

Spatial Variability of Irrigated Common Bean Yield Correlated With the Fertility of a Sandy Soil

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