scholarly journals Influence of Planting Dates and Plant Densities on Photosynthesis Capacity, Grain and Biological Yield of Soybean [Glycine max (L.) Merr.] in Karaj, Iran

2005 ◽  
Vol 4 (3) ◽  
pp. 230-237 ◽  
Author(s):  
Mazahero Daroish ◽  
Zeinali Hassan . ◽  
Madani Ahad .
Keyword(s):  
Glycine Max ◽  
Planting Dates ◽  
Plant Densities ◽  
Glycine Max L ◽  
Biological Yield ◽  
Photosynthesis Capacity

scholarly journals Evaluación de un Modelo de Simulación de Crecimiento Aplicado a Genotipos de Soya (Glycine max. L. Merr) bajo Condiciones Tropicales

1996 ◽  
Vol 1 (1) ◽  
pp. 16
Author(s):  
Leonardo Rey ◽  
Yamel Lopez ◽  
Alejandro Jaramillo ◽  
Jeffery W. White
Keyword(s):  
Glycine Max ◽  
Simulation Model ◽  
Research Centre ◽  
Planting Dates ◽  
Growth Simulation ◽  
Tropical Conditions ◽  
Soybean Genotypes ◽  
Plant Densities ◽  
Glycine Max L ◽  
Crop Growth Simulation

<p>El modelo de simulación de crecimiento y producción de soya (Glycine max. (L) Merr) Soygro V5-42, fué validado a nivel del trópico con datos experimentales de un ensayo en el cual se evaluaron dos genotipos Soyica P-33 e ICA-Ariarii-1, con diferente hábito de crecimiento, bajo dos densidades de plantas, en el Centro de Investigación Palmira de la Corporación Colombiana de Investigación Agropecuaria Corpoica, localizado a 3°32' de latitud norte y 76°17' de longitud oeste, en un Mollisol, clasificado como Isohipertérmico Aquico Hapludoll. Las salidas del modelo fueron sensibles a los ajustes en trece coeficientes genéticos, los cuales se calibraron sistemática e iterativamente. La calibración del modelo presentó una estrecha relación entre lo observado y lo simulado para las principales variables de respuesta. Su validación con base en datos de experimentos de campo anteriores, presentó una relación muy estrecha R&gt; = o.86, entre lo observado y lo simulado; lo cual indicó, que el modelo explica acertadamente la variación en las épocas de siembra y las densidades de plantas. Como resulta do de la simulación del efecto de las épocas de siembra con variaciones de clima, bajo diferentes ambientes (3 localidades), se encontró que el modelo se ajusta a las condiciones de siembra utilizadas por los agricultores en Colombia.</p><p> </p><p><strong>Evaluation of a Growth Simulation Model Applied to Soybean Genotypes (<em>Glycine </em><em>max</em><em>.</em>L. Merr) Under Tropical Conditions</strong></p><p>The crop growth simulation model of soybean ( <em>Glycin</em><em>e </em><em>max </em>(L) Merr) Soygro V5.42, was tested under tropical conditions, using experimenta l data from a field experiment in which two genotypes (Soyica P-33 and Soyica Ariari-1) with different growth h abit, were grown under two plant densities, at the Palmira Research Centre of the Corporación Colombiana de Investigación Agrop ecua ria, located at 3°32' north latitude and 76°17' west longitude, on a Mollisol soil classified as Isohyperterthermic Aquic Hapludoll. Thirtheen genetic coefficients were calibrated and showed sensitivity to the model. The validation of the model was made using previous experimental data from the same genotypes. The narrow line observed vs predicted seed yield was close to the 1:1 slope, indicating that, the simulating was not skewed. Thus, the model explained well the variation due to planting dates and plant spatial distribution in the field. The simulation of planting dates effects in relation to climatic variation at three different soybean production regions, is in agreement with the traditional planting dates used by the soybean growers of Colombia.</p>

Biomass and nutritive value of forage soybean lines [Glycine max L. (Merr.)] in northwestern Puerto Rico.

1969 ◽  
Vol 99 (1) ◽  
pp. 19-36
Author(s):  
Alfredo Aponte ◽  
Elide Valencia-Chin ◽  
James Beaver
Keyword(s):  
Glycine Max ◽  
Chemical Composition ◽  
Nutritive Value ◽  
Neutral Detergent Fiber ◽  
Soybean Line ◽  
Planting Dates ◽  
Temperature And Precipitation ◽  
Two Factors ◽  
Principal Factors ◽  
Glycine Max L

Ten lines of forage soybean [Glycine max L. (Merr.)] were evaluated using two planting dates (PD) in September and January; and two crop harvest physiological stages (CHPS), R2 (full bloom) and R5.4 (most pods 51 to 75% full), a total area of 496 m2 with an Oxisol soil, under conditions of temperature and precipitation not limiting crop growth. The experimental design was one of split plots in randomized complete blocks, PD constituting the complete plots, CHPS the subplots, and soybean lines the sub-subplots. Dry matter yields (DMY) were higher (P menor que 0.01) for R5.4 than R2 (6,452 vs. 5,436 kg/ha), fluctuated (P menor que 0.058) from 5,300 to 6,573 kg/ha among the soybean lines and did not differ between the two PD. Plant populations were about 260,000 kg/ha and were not affected by the three principal factors. Plant height varied (P menor que 0.01) from 84.2 to 93.2 cm among the soybean lines; differed (P menor que 0.05) between the two PD (89.2 cm, September vs. 87.5 cm, January) and (P menor que 0.01) between the two CHPS (72.4 cm, R2 vs. 104.3 cm, R5.4); and was also affected by all of the double and the triple interactions of the three factors. The proportion of leaf in the total DM favored (P menor que 0.01) R2 over R5.4 (44.1 vs. 41.1%) and involved an interaction (P menor que 0.05) of CHPS x soybean line. Regarding the chemical composition of the forage, crude protein (CP) varied among the soybean lines (P menor que 0.05) and between the CHPS (P menor que 0.01) and was affected (P menor que 0.01) by an interaction of these two factors (29.6 to 33.6, R2 and 23.1 to 26.7, R5.4). Acid detergent fiber (ADF) behaved like a CP with respect to significance of the independent variables (21.0 to 25.6, R2 and 26.2 to 33.3, R5.4); whereas neutral detergent fiber (NDF) did not differ among soybean lines, but was higher (P menor que 0.01) for R5.4 than for R2 (41.0 vs. 33.5), and involved an interaction (P menor que 0.01) of CHPS x soybean line. It is concluded that, according to both agronomic characteristics and chemical composition, all the soybean lines evaluated performed adequately, but SF-6, SF-24, SF-50, SF-88 and SF-110 outperformed SF-1, SF-2, SF-22, SF-57 and SF-72; harvest at the R5.4 stage was advantageous in achieving greater DMY without unduly sacrificing the excellent chemical composition of R2 forage; and the magnitude of PD effects was not large.

scholarly journals Effect of Urea, NPK and Compost on Growth and Yield of Soybean (Glycine max L.), in Semi-Arid Region of Sudan

ISRN Agronomy ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Samia Osman Yagoub ◽  
Wigdan Mohamed Ali Ahmed ◽  
A. A. Mariod
Keyword(s):  
Glycine Max ◽  
Block Design ◽  
Growth And Yield ◽  
Straw Yield ◽  
Significant Difference ◽  
Randomized Complete Block Design ◽  
Glycine Max L ◽  
Biological Yield ◽  
Semi Arid Region ◽  
Semi Arid

A field experiment was conducted for two consecutive seasons (2009/2010 and 2010/2011) on the Demonstration Farm of the College of Agricultural Studies, Sudan University of Science and Technology at Shambat, to study the effect of some fertilizers on growth and yield of soybean (Glycine max L. merril). The experiment was laid out in a randomized complete block design (RCBD) with four replicates. The fertilizers treatments consisted of three types of fertilizers: urea (180 kg/ha), NPK (361 kg/ha), compost (%) and the control. The results showed that fertilizers treatments in first season had significant difference on number of pods/plant, economic yield, harvest index. Mean while, highly significant difference on green, biological and straw yield. In second season fertilizers treatments had significant difference on plant height at 30 days, leaf area at 45 and 60 days, green yield, biological yield and straw yield.

TECHNIQUE FOR MEASURING VEGETATIVE VOLUME AND SEED YIELD IN SOYBEANS

1977 ◽  
Vol 57 (2) ◽  
pp. 613-614 ◽  
Author(s):  
CHARLES D. HEINZMAN Jr. ◽  
GARY L. EILRICH
Keyword(s):  
Glycine Max ◽  
Seed Yield ◽  
Glycine Max L ◽  
Biological Yield ◽  
Non Destructive

A non-destructive volume displacement technique for determining vegetative volume as an estimate of biological yield (where the intact shoot of a plant is placed in a cylinder of water and the water displaced by the shoot is measured) was developed which allows soybean (Glycine max (L.) Merr.) plants to be carried to maturity for seed yield.

scholarly journals An Assessment of Soybean (Glycine max, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria

2015 ◽  
Vol 48 (3-4) ◽  
pp. 82-90 ◽  
Author(s):  
Richmond Emuohwo Edugbo ◽  
Godson Emeka Nwofia ◽  
Lawrence Stephen Fayeun
Keyword(s):  
Glycine Max ◽  
Grain Yield ◽  
Positive Interactions ◽  
Gge Biplot ◽  
Planting Dates ◽  
Genotype By Environment ◽  
Main Effect ◽  
Glycine Max L ◽  
Superior Genotypes ◽  
Target Set

Abstract The yield of four soybean (Glycine max, L. Merrill) genotypes under six planting dates in two years was assessed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and Genotype-by-Environment biplot models. The results of combined analysis of variance for grain yield of the four genotypes of soybean grown in 12 environments showed that soybean grain yield was significantly (P < 0.01) affected by environments (E), genotypes (G) and genotype by environment interactions (GE). Genotypes and environments accounted for about 6.56% and 47.66% of the variation, respectively, while the GE explained 14.47% of the variation, which is more than double of the genotypic effects of the total variation. AMMI biplot indicated genotype TGx1485-1D and the early July 2012 environment were above average for grain yield and had positive specific interactions with each other. However, TGx1485-1D had negative interactions with the other environments while genotypesTGx14482E, TGx1987-10F and TGx1835-10E had positive interactions with all the environments except E5. In the differential yield ranking of genotypes across the twelve environments TGx1485-1D had the highest yield in seven out of the twelve environments. TGx1835-10E was the highest yielding genotype in three environments, while TGx1448-2E gave the greatest yield in two environments. Although TGx1485-1D exhibited high GEI, in the GGE biplot it was ranked as the most desirable genotype. GGE biplot identified early July 2012(E5) as the best environment. The result showed that application of AMMI and GGE biplots facilitates visual comparison and identified superior genotypes for each target set of environments.

Effect of weed management and sulphur on weed dynamics, soybean (Glycine max) yield and associated quality traits

2017 ◽  
Author(s):  
K. K. Meena ◽  
V. Nepalia ◽  
Dilip Singh ◽  
Mahendra Sharma ◽  
B. Upadhyay
Keyword(s):  
Glycine Max ◽  
Weed Control ◽  
Weed Management ◽  
Dry Matter ◽  
Yield And Quality ◽  
Glycine Max L ◽  
Biological Yield ◽  
Main Plot ◽  
Weed Dynamics ◽  
Quizalofop Ethyl

A field experiment was conducted during rainy seasons of 2011 and 2012 at Udaipur to evaluate the effect of weed control and sulphur on yield and quality of soybean [Glycine max (L.) Merrill]. The twenty eight treatment combinations comprising 7 weed management treatments (weedy check, pendimethalin 1.0 kg ha-1 pre- emergence, quizalofop-ethyl 50 g ha-1 and imazethapyr 100 g ha-1 post-emergence at 15 DAS, pendimethalin, quizalofop-ethyl and imazethapyr followed by hoeing and weeding at 30 DAS) in main plot and 4 rates of sulphur application (00, 20, 40 and 60 kg ha1) in sub plot were laid out in split plot design with three replications. All weed control treatments reduced weed dry matter at harvest during both the years. Pendimethalin followed by hoeing and weeding at 30 DAS was significantly superior in reducing weed dry matter of broad-leaved, narrow-leaved and total weeds at harvest over rest of the treatments. All weed control treatments significantly enhanced straw and biological yield of soybean over weedy check. Pendimethalin followed by hoeing and weeding at 30 DAS produced significantly higher pooled seed (2168 kg ha-1), straw (3167 kg ha-1) and biological (5335 kg ha-1) yields compared to other treatments. Sulphur rates had no significant effect on weed dry matter at harvest. Across the years, increasing level of S application up to 40 kg ha-1 resulted in significantly increased straw yield, biological yield and harvest index of soybean. Increasing rate of sulphur application up to 40 kg ha-1 tended to significantly increase protein and oil yield.

THE EFFECT OF DIFFERENT ROW SPACINGS AND PLANT ARRANGEMENTS ON SOYBEANS

1980 ◽  
Vol 60 (1) ◽  
pp. 227-231 ◽  
Author(s):  
OSEI SAFO-KANTANKA ◽  
NORMAN C. LAWSON
Keyword(s):  
Glycine Max ◽  
Seed Yield ◽  
Significant Response ◽  
Row Spacing ◽  
Row Width ◽  
Plant Densities ◽  
Glycine Max L ◽  
Significant Yield ◽  
Short Season

Two short-season soybean (Glycine max (L.) Merrill) cultivars, Altona and Clay, were tested in narrow rows (10, 15, 20 and 30 cm), and at four rectangularities (1:1, 1:2, 1:3, and 1:4) giving plant densities that ranged from 11 000 to 4 000 000 plants per hectare. Significant yield differences were demonstrated by narrowing row width, but not by changing rectangularity. In a second experiment, the same cultivars were grown in rows 15, 30, 45, and 60 cm apart, but with the density held constant at 670 000 plants/ha. This resulted in respective rectangularities of 1:1.5, 1:6, 1:13.6 and 1:24. Neither seed yield nor its components showed any significant response to row spacing in the second experiment.

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