Soybean morphophysiology and yield response to seeding systems and plant populations

Souza, Raniele Tadeu Guimarães; Teixeira, Itamar Rosa; Reis, Elton Fialho dos; Silva, Alessandro Guerra da

doi:10.4067/s0718-58392016000100001

Cited by 30 publications

(32 citation statements)

References 22 publications

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“…In the present study, the plant height varied from 66.70 cm to 112.80 cm, with an average plant height of 89.192 cm. This result was in agreement with those reported by Öz (2002), Çopur et al (2009) and Souza et al (2016), but disagreement with those found by Karasu et al (2002) and Yetkin and Arioglu (2009) and higher than those determined by Yari et al (2013) and Naidu et al (2017). These differences are attributable to the differences in varieties and different planting dates and climatic and growing conditions.…”

Section: Discussionsupporting

confidence: 90%

Description of the Relationships Between Different Plant Characteristics in Soybean Using Multivariate Adaptive Regression Splines (Mars) Algorithm

2020

THE JAPS

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The aim of this study was to reveal the relationships between several morphological characteristics of the soybean (Glycine max (L.) Merr.) plants in the year 2014. For this aim, plant height (PH), first pod height (FPH), branch number (BN), number of nodes (NN), pod number per plant (PNP), seed number per pod (SNP), 1000-seed weight (1000SW), yield per decare (YD) and harvest index (HI) were measured. Five different MARS models were developed for the plant height, first pod height, pod number, harvest index and yield per decare characteristics. The constructed models were evaluated based on the criteria of minimum generalized cross-validation (GCV), SDratio, RMSE, AIC, AICc and maximum coefficient of determination (R 2) in predictive performance. The R 2 values of the MARS models were determined to be 0.902, 0.924, 0.949, 0.987 and 0.998, respectively. For the prediction of PH, FPH and HI, the second degree interaction model was determined to be the most suitable model. For predicting PNP and YD, the third degree interaction MARS model was determined to be the best model. The dependent variables considered here was predicted with a high accuracy by all models established with the MARS algorithm. As a result, application of the MARS algorithm may allow plant breeders to obtain influential clues in selecting promising soybean varieties.

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Section: Discussionsupporting

confidence: 90%

Description of the Relationships Between Different Plant Characteristics in Soybean Using Multivariate Adaptive Regression Splines (Mars) Algorithm

2020

THE JAPS

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“…This will affect grape composition, in particular with respect to its aroma composition (van Leeuwen and Darriet, 2016). Due to this, it has shown that some viticultural practices such as defoliation or foliar N applications can improve aromatic composition in grapes or wines (Ancín-Azpilicueta et al, 2013;Pascual et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Improvement of wine volatile composition through foliar nitrogen applications to 'Cabernet Sauvignon' grapevines in a warm climate

Gutiérrez‐Gamboa

Garde‐Cerdán

Carrasco-Quiroz

et al. 2018

Chil. j. agric. res.

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Volatile compounds play a key role on wine quality due to their importance in wine aroma. The improvement of these compounds in Cabernet Sauvignon wines may be of oenological relevance because these are usually defined by strong character and somewhat herbaceous. The effect of different foliar N applications on 'Cabernet Sauvignon' grapevines (Vitis vinifera L.) was studied in order to improve wine volatile composition. Five treatments were applied: urea (Ur), urea plus sulfur (Ur+S), arginine (Arg), and two commercial products: Nutrimyr Thiols (NT) and Basfoliar Algae (BA). Volatile compounds were analyzed by GC-MS. Results showed that wines elaborated with grapes treated with arginine (Arg) exhibited the lowest content of total higher alcohols (322.29 mg L -1 ). Wine concentrations of ethyl hexanoate, octanoate, and decanoate from Ur+S and BA treatments were the highest (385.28, 590.46, 214.14, and 416.00, 600.50, 227.96 μg L -1 , respectively). No effects were found on vanillin derivatives content in wines by foliar treatments. Total terpenes content in wines from Ur and Arg grapevine treatments were higher than in control (17.87 and 20.36 μg L -1 than 14.99 μg L -1 ). Thus, N foliar applications can improve wine volatile composition, mainly by Ur+S and BA treatments. The importance of this study is to add knowledge about the effects of foliar N applications on Cabernet Sauvignon wine volatile composition in warm climates.

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“…Therefore, it is important to investigate the effects of biochar on plant root growth. Root morphology changes with different soil conditions, such as soil fertility, soil bulk density and soil water content (Souza et al, 2016;Li et al, 2017). Biochar has special properties, and its existence in soil could influence plant growth.…”

Section: Introductionmentioning

confidence: 99%

Effects of biochar on seedling root growth of soybeans

Zhu¹,

Kong²,

Xie³

et al. 2018

Chil. j. agric. res.

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Biochar application is an effective method for agriculture production. In order to study the biochar effect on soybean (Glycine max [L.] Merr.) seedling root growth, this study was conducted in a sand culture experiment using two soybean cultivars with different P efficiencies. Soybean was pot-grown at four biochar rates (0%, 0.15%, 0.75% and 1.5%, w/v). The results showed no difference in Cultivar × Biochar interaction on root morphologic characteristics. At 7 d after germination (DAG), biochar slightly enhanced root growth; at 10 DAG, biochar significantly increased total root length (TRL) and total root surface area (TRSA) highest by 48.4% and 27.4% (P < 0.05) at 1.5% biochar rate, respectively, compared to the control. The positive effects on root morphology by biochar were especially concentrated on fine roots (< 0.5 mm). In addition, root vitality and leaf soluble sugar content were significantly increased by both 0.75% and 1.5% biochar; shoot biomass increased maximally by 65.6% at 1.5% biochar rate compared to the control; inversely, root/shoot ratio significantly reduced by 32.3% and 23.5% at 1.5% biochar rate, respectively, at 7 and 10 DAG. We suggest that biochar had positive effects on soybean seedling growth through improving root morphology and root vitality, regardless of different P efficient cultivars.

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Soybean morphophysiology and yield response to seeding systems and plant populations

Abstract: -1 corresponding to an approximate 8% increase compared with conventional sowing using equidistant lines with 0.5 m spacing.

Cited by 30 publications

References 22 publications

Description of the Relationships Between Different Plant Characteristics in Soybean Using Multivariate Adaptive Regression Splines (Mars) Algorithm

Description of the Relationships Between Different Plant Characteristics in Soybean Using Multivariate Adaptive Regression Splines (Mars) Algorithm

Improvement of wine volatile composition through foliar nitrogen applications to 'Cabernet Sauvignon' grapevines in a warm climate

Effects of biochar on seedling root growth of soybeans

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