Effect of Spacing, Planting Methods and Nitrogen on Maize Grain Yield

Fornah, Alimamy; Aula, Lawrence; Omara, Peter; Oyebiyi, Fikayo; Dhillon, Jagmandeep; Raun, W. R.

doi:10.1080/00103624.2020.1789163

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…The HP and FP treatments did not receive 0-N rates. Additional details of the experiment are summarized in Oyebiyi et al (2019) and Fornah et al (2020).…”

Section: Example Datamentioning

confidence: 99%

A Bayesian approach for analyzing crop yield response data with limited treatments

Cho

Lambert

Brorsen

et al. 2023

Agrosystems Geosci & Env

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This paper proposes a Bayesian multilevel modeling approach to incorporate response parameters from published studies into crop yield response estimation procedures when nonlimiting or limiting treatment levels are omitted or limited in agronomic experiments. Such circumstances may be encountered when data are from farmer‐led research, which may use nonstandardized experimental designs. The paper's focus is on maize yield response to nitrogen fertilizer, but the procedure is flexible enough to accommodate other factors that could affect crop yield response. A proof‐of‐concept Monte Carlo (MC) exercise supplements an empirical application. The MC simulation investigates the small sample properties of the proposed procedure. The empirical example uses field trial data for a maize planter experiment under different nitrogen (N) fertilizer rates. The planter trial compared mechanical planting methods to methods used in developing countries with limited access to mechanized planter technology. Some experiments had no check plots and all experiments lacked nonlimiting fertilizer rates. Linear and quadratic response functions with plateaus are used in the MC simulation and empirical application. MC results suggest that estimates were closest to true parameter values when priors for optimal N rates from published sources were used.

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“…The HP and FP treatments did not receive 0-N rates. Additional details of the experiment are summarized in Oyebiyi et al (2019) and Fornah et al (2020).…”

Section: Example Datamentioning

confidence: 99%

A Bayesian approach for analyzing crop yield response data with limited treatments

Cho

Lambert

Brorsen

et al. 2023

Agrosystems Geosci & Env

Self Cite

View full text Add to dashboard Cite

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“…Corn growth depends on various environmental and management factors, and they significantly affect grain quality (Fornah et al, 2020;Sharma et al, 2023;Oglesby et al, 2023). Oil, protein, and starch are the major quality parameters in corn that determine the overall economic and nutritious value of the crop (Hilliard & Daynard, 1974).…”

Section: Introductionmentioning

confidence: 99%

Assessing the effect of cultural practices on Mississippi corn production: 2. Grain composition

Gajula,

Dew,

Sharma

et al. 2024

Crop Forage & Turfgrass Mgmt

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Global demand for corn (Zea mays L.) is increasing and it remains one of the most consumed crops by both humans and animals due to its high calorie content. However, corn grain quality research is sparse and often focused only on a few selected influencing factors. Therefore, two side‐by‐side studies (Addition and Deletion) were conducted in 2020 and 2021 in Mississippi, assessing the grain composition including protein, starch, oil, and moisture of corn under several management practices. A randomized complete block design was implemented in both experiments involving a complete factorial of three factors including two plant populations (32 and 40K seed acre−1), two‐row configurations (single and twin), and six combinations of nutrients plus fungicide application (NF). The trials differed based on the manner of NF applications. In trial termed Addition, all NF treatments were added incrementally, whereas in the Deletion trial, they were withheld in a stepwise manner. Conditional inference tree (CIT) analysis was conducted to examine interaction effects among the three factors over site‐years. Corn protein content ranged between 8.2 and 9.8% across all years and locations. All three factors and certain interactions significantly influenced both protein and starch content. Specifically, single row, 40K seeds acre−1, and higher rates of N resulted in higher protein content. Contrarily, the starch content was positively influenced by twin row, 32K seeds acre−1 and only N application. Single row configuration resulted in higher oil than twin rows. This study determined that different management factors have the potential to positively influence protein, starch, and oil. These management strategies could extend farmers profitability and provide superior products for industrial purposes with additional implications for livestock feed supplements.This article is protected by copyright. All rights reserved

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“…The reduction in plant spacing can positively affect crop production, as seen in other crops (Jiang et al, 2013;Fornah et al, 2020). However, it can also alter metabolism, cycle length, plant size, canopy architecture, and plant photosynthesis capacity (Kavut et al, 2014), lower leaf shading, stimulate growth, internode elongation, decrease leaf area, and reduce the number of ramifications (Taiz & Zeiger, 2017).…”

Section: Introductionmentioning

confidence: 99%

Production and fruit quality of watermelon hybrids under different plant spacing

Lemos

Charlo

Barreto

et al. 2022

Rev. bras. eng. agríc. ambient.

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Plant spacing management in watermelon alters plant competition for water, light, and nutrients, influencing fruit yield, morphology, and quality. The study aimed to evaluate the spacing management between plants of two watermelon hybrids in terms of morphology and fruit yield and quality. Two watermelon hybrids (NUN 21613 and NUN 21901) and three plant spacings (0.60, 0.80 and 1.0 m with 2,5 m between rows) were studied in a randomized block design arranged in a 2 × 3 factorial scheme with five replications. Fruit firmness, the content of total soluble solids, visual, and sensorial notes at 0, 7 and 14 days after harvest, length of the main branch of the plant, fresh fruit mass, number of fruits per plant and per hectare, and fruit yield were evaluated from August to December 2018. The greater planting spacing provided better vegetative development, higher yield, and increased resistance of the watermelon fruit to deterioration after harvest. The physicochemical and sensory characteristics of the fruits were not affected by the spacing and hybrids of watermelon in this study.

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Effect of Spacing, Planting Methods and Nitrogen on Maize Grain Yield

Cited by 4 publications

References 24 publications

A Bayesian approach for analyzing crop yield response data with limited treatments

A Bayesian approach for analyzing crop yield response data with limited treatments

Assessing the effect of cultural practices on Mississippi corn production: 2. Grain composition

Production and fruit quality of watermelon hybrids under different plant spacing

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