2019African Journal of Agricultural ResearchOpen access

Dynamics of weed biomass on yield and yield component stability of maize under various weed management strategies

Babasola Daniel Adewale, A. Adesile Badmus

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Abstract

Weed is a very significant enemy of crop production. Its density, diversity and the interaction complexes on the yield and yield component of maize cv “Quality protein” were investigated in the present study. The experiment was laid out in randomized complete block design (RCBD) containing five weed management strategies and a weedy check as treatments in 2015 and 2016. Data were collected on weed density, yield and yield components of maize for the two years. For the years and the treatments, a mixed model factorial in RCBD was employed for the analysis of variance of the data. Significant (P≤0.05) variation exists among the two years; the six treatments and their interaction for the grain yield and its components. The use of Pendimenthalin (330 EC) at 3.0 kg a.i.ha-1 supported the highest grain yield (2.4 tons ha-1); hoe weeding and mulching was next with significantly (P≤0.05) lower grain yield of 2.2 tons ha-1. The weedy check had the lowest grain yield of 1.2 tons/ha. An average yield loss of 42% was obtained by comparing the weed control methods with each of the weedy check. By Shukla variance estimate, maize-soybean intercrop gave the most stable grain yield for the two years. Year 2016 significantly (P≤0.05) favoured grain yield, its components and weed density. The proportion of weed categories in the study was: Broadleaves (52.38%), grasses (33.33%) and sedges (14.28%). Broad leaves and grasses density measured at interval displayed a significant linear trend. The sixth week after planting was most critical for grain yield determination in the tested maize cultivar. Key words: Weed management strategies, maize, grain yield, interaction complexes.

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Weed is a very significant enemy of crop production. Its density, diversity and the interaction complexes on the yield and yield component of maize cv “Quality protein” were investigated in the present study. The experiment was laid out in randomized complete block design (RCBD) containing five weed management strategies and a weedy check as treatments in 2015 and 2016. Data were collected on weed density, yield and yield components of maize for the two years. For the years and the treatments, a mixed model factorial in RCBD was employed for the analysis of variance of the data. Significant (P≤0.05) variation exists among the two years; the six treatments and their interaction for the grain yield and its components. The use of Pendimenthalin (330 EC) at 3.0 kg a.i.ha-1 supported the highest grain yield (2.4 tons ha-1); hoe weeding and mulching was next with significantly (P≤0.05) lower grain yield of 2.2 tons ha-1. The weedy check had the lowest grain yield of 1.2 tons/ha. An average yield loss of 42% was obtained by comparing the weed control methods with each of the weedy check. By Shukla variance estimate, maize-soybean intercrop gave the most stable grain yield for the two years. Year 2016 significantly (P≤0.05) favoured grain yield, its components and weed density. The proportion of weed categories in the study was: Broadleaves (52.38%), grasses (33.33%) and sedges (14.28%). Broad leaves and grasses density measured at interval displayed a significant linear trend. The sixth week after planting was most critical for grain yield determination in the tested maize cultivar. Key words: Weed management strategies, maize, grain yield, interaction complexes.

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Available abstract

Weed is a very significant enemy of crop production. Its density, diversity and the interaction complexes on the yield and yield component of maize cv “Quality protein” were investigated in the present study. The experiment was laid out in randomized complete block design (RCBD) containing five weed management strategies and a weedy check as treatments in 2015 and 2016. Data were collected on weed density, yield and yield components of maize for the two years. For the years and the treatments, a mixed model factorial in RCBD was employed for the analysis of variance of the data. Significant (P≤0.05) variation exists among the two years; the six treatments and their interaction for the grain yield and its components. The use of Pendimenthalin (330 EC) at 3.0 kg a.i.ha-1 supported the highest grain yield (2.4 tons ha-1); hoe weeding and mulching was next with significantly (P≤0.05) lower grain yield of 2.2 tons ha-1. The weedy check had the lowest grain yield of 1.2 tons/ha. An average yield loss of 42% was obtained by comparing the weed control methods with each of the weedy check. By Shukla variance estimate, maize-soybean intercrop gave the most stable grain yield for the two years. Year 2016 significantly (P≤0.05) favoured grain yield, its components and weed density. The proportion of weed categories in the study was: Broadleaves (52.38%), grasses (33.33%) and sedges (14.28%). Broad leaves and grasses density measured at interval displayed a significant linear trend. The sixth week after planting was most critical for grain yield determination in the tested maize cultivar. Key words: Weed management strategies, maize, grain yield, interaction complexes.

Key concepts: Weed, Agronomy, Yield (engineering), Weed control, Randomized block design, Mathematics, Grain yield, Biomass (ecology)

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