Effects of Different Plant Populations on Yield of Different Soybean (Glycine max (L.) Merrill) Varieties in a Smallholder Sector of Zimbabwe

Madanzi, Tendai; Chiduza, C.; Kageler, Richardson S.J.; Muziri, Tavagwisa

doi:10.3923/ja.2012.9.16

Cited by 4 publications

(3 citation statements)

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“…In dry conditions in 2010, there may have been competition for water, which led to low grain yield, indicating that perhaps seed size was decreased in 2010. This is similar to findings of Madanzi et al (2012) in Zimbabwe, who reported that low availability of water was a limiting factor to soybean grain yield at high plant populations beyond 200,000 plants ha −1 . Norsworthy and Frederick (2002) noted that rainfall was more important than seeding rate or variety.…”

Section: Resultssupporting

confidence: 89%

“…Most farmers, however, plant soybean at wider spacing similar to maize ( Zea mays L.) or cowpea, resulting in low populations and subsequently low grain yields. Madanzi et al (2012) listed low plant population as one of the factors limiting soybean production among smallholder farmers in Zimbabwe. Effect of plant densities on soybean yield is not well understood by farmers.…”

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confidence: 99%

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Agronomic Response of Soybean Varieties to Plant Population in the Guinea Savannas of Nigeria

et al. 2014

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e agronomic responses of three contrasting soybean [Glycine max (L.) Merr.] varieties to plant populations were examined in two distinct agro-ecological locations (at Samaru Zaria and Samaru-Kataf), both in the Guinea savanna of northern Nigeria in 2009, 2010, and in 2011 growing seasons. ree soybean varieties: TGx1835-10E, TGx1904-6F, and TGx1448-2E di ering in maturity duration (early, medium, and late maturing, respectively), were evaluated at four plant populations (266,700, 333,300, 533,300, and 666,700 plants ha -1 ) using a split plot arrangement in randomized complete block design with three replications. e plant populations were the main plots, whereas varieties were subplots. e proportion of intercepted photosynthetically active radiation (IPAR) and leaf area index (LAI) increased with increasing plant population at both locations, indicating that high leaf area indices and high degree of canopy closure at higher plant population intercepted more light than the canopy at lower population and subsequently resulted in relatively high grain and fodder yields. At both locations, optimum plant populations ranged from 533,300 to 666,700 plants ha -1 across the years. e northern Guinea savanna location (Samaru Zaria) produced more pods m -2 , grain yield, and fodder at higher plant populations than that at the southern Guinea savanna (Samaru-Kataf). Varieties TGx1448-2E and TGx1904-6F intercepted higher proportion of IPAR had higher LAI and produced a greater number of pods m -2 , seeds m -2 , grain yield, and fodder than TGx1835-10E at both locations in years of good rainfall. ese data suggest that soybean yields in the Guinea savanna of northern Nigeria can be increased using higher plant populations than those currently recommended.

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Section: Resultssupporting

confidence: 89%

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Agronomic Response of Soybean Varieties to Plant Population in the Guinea Savannas of Nigeria

et al. 2014

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“…Matsuo et al (2018) reported that the production of pod mass per square metre is significantly higher at a high plant density than at a lower plant density. Frequently, there is an increase in yield with an increase in plant density, although yields tend to decline across the soybean varieties after the 200 000 plants per hectare mark has been reached (Madanzi et al 2012). In terms of the cowpea, Ahmed et al (2010) reported that reduced plant densities lead to increased cowpea 100 seed weight.…”

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confidence: 99%

The Effect of Plant Density, Zinc Added to Phosphorus Fertilizer Sources and Location on Selected Yield Parameters of Soybean

Gonyane

Sebetha

2021

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Background: Soybean [Glycine max (L.) Merrill] is an annual herbaceous leguminous grain crop which is cultivated mainly for its oil and protein. The objective of the study was to determine the effects of plant density and zinc added to phosphorus fertilizer sources on soybean yield performance under different environmental conditions. Methods: A field trial was executed in two sites during the 2018/19 and 2019/20 summer planting seasons. The experimental design involved a 2 × 2 × 5 factorial fitted into a randomized complete block design (RCBD) with four replications. The experiment consisted of two plant densities, a lower plant density of 66 666 plants/ha and a higher plant density of 111 111 plants/ha. The five different types of fertilizer treatments were single superphosphate, monoammonium phosphate, zinc sulphate added to single superphosphate, zinc sulphate added to monoammonium phosphate and control. Result: The results showed that plant density has a significant effect (P less than 0.05) on soybean number of pods per plant and the field biomass yield. Soybean planted under lower plant density conditions produced a significantly larger number of pods per plant during both planting seasons, whereas higher plant density conditions resulted in a higher soybean field biomass yield during the 2018/19 planting season. It was observed that soybean treated with monoammonium phosphate had higher seed mass.

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Soybean Yield Estimation and Its Components: A Linear Regression Approach

Wei

Molin

2020

Agriculture

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Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1.

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Effects of Different Plant Populations on Yield of Different Soybean (Glycine max (L.) Merrill) Varieties in a Smallholder Sector of Zimbabwe

Cited by 4 publications

References 3 publications

Agronomic Response of Soybean Varieties to Plant Population in the Guinea Savannas of Nigeria

Agronomic Response of Soybean Varieties to Plant Population in the Guinea Savannas of Nigeria

The Effect of Plant Density, Zinc Added to Phosphorus Fertilizer Sources and Location on Selected Yield Parameters of Soybean

Soybean Yield Estimation and Its Components: A Linear Regression Approach

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