Corn response to row spacing and plant population in the Mid‐South United States

Williams, John Joseph; Dodds, Darrin M.; Buehring, Normie W.; Dhillon, Jagmandeep; Henry, W. Brien

doi:10.1002/agj2.20817

Cited by 6 publications

(8 citation statements)

References 43 publications

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“…Density (Van Roekel and Coulter, 2011) or population of maize plants (Williams et al, 2021) Relationship between weed density and yield loss (Cousens, 1985)…”

Section: Regression Modelsmentioning

confidence: 99%

“…Experiments that study quantitative factors in agricultural sciences have been reported in several articles such as studies of plant density or population (Van Roekel and Coulter, 2011;Williams et al, 2021), fruit post-harvest quality (Marodin et al, 2016), seed germination (Motsa et al, 2015), weed control (Noel et al, 2018), and growth curves (Lúcio and Sari, 2017). In these studies, polynomial models were predominantly used and, although this is not incorrect, many natural phenomena do not present such behavior but rather specific models (Archontoulis and Miguez, 2015).…”

Section: Introductionmentioning

confidence: 99%

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AgroReg: main regression models in agricultural sciences implemented as an R Package

Shimizu

Gonçalves

2023

Sci. agric. (Piracicaba, Braz.)

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Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental results, mainly because much of the existing software perform this analysis and a lack of knowledge of other models. On the other hand, many of the natural phenomena do not present such behavior; nevertheless, the use of non-linear models is costly and requires advanced knowledge of language programming such as R. Thus, this work presents several regression models found in scientific studies, implementing them in the form of an R package called AgroReg. The package comprises 44 analysis functions with 66 regression models such as polynomial, non-parametric (loess), segmented, logistic, exponential, and logarithmic, among others. The functions provide the coefficient of determination (R 2 ), model coefficients and the respective p-values from the t-test, root mean square error (RMSE), Akaike's information criterion (AIC), Bayesian information criterion (BIC), maximum and minimum predicted values, and the regression plot. Furthermore, other measures of model quality and graphical analysis of residuals are also included. The package can be downloaded from the CRAN repository using the command: install.packages("AgroReg"). AgroReg is a promising analysis tool in agricultural research on account of its user-friendly and straightforward functions that allow for fast and efficient data processing with greater reliability and relevant information.

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“…Density (Van Roekel and Coulter, 2011) or population of maize plants (Williams et al, 2021) Relationship between weed density and yield loss (Cousens, 1985)…”

Section: Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AgroReg: main regression models in agricultural sciences implemented as an R Package

Shimizu

Gonçalves

2023

Sci. agric. (Piracicaba, Braz.)

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“…Lobell et al (2009) and Beza et al (2017) outlined several biophysical factors including nutrient deficiencies, water stress, flooding, planting issues, soil problems, weed pressures, insects, diseases, and lodging, as well as socioeconomic factors such as risk aversion, inexperience, and economic issues. Numerous agronomic strategies exist to improve corn production in Mississippi: substituting crop yield goals (Oglesby et al, 2022) for remote sensing based N management (Dhillon et al, 2020;Sumner et al, 2021), irrigation scheduling using soil moisture sensors (Spencer et al, 2019), crop rotation (Abbas et al, 2012), adopting site-specific optimum plant densities (Williams et al, 2021), proper hybrid selection (Walne & Reddy, 2021;Williams et al, 2020), and optimum planting dates (Williams et al, 2018).…”

Section: Reasons For Yield Gapmentioning

confidence: 99%

Mississippi state and county level yield gap in corn production

Dhillon

Bheemanahalli

et al. 2022

Agricultural & Env Letters

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Continuous corn (Zea mays L.) yield increases are required to promote economic development and support a larger population. Reducing the existing yield gaps is a potential strategy to accomplish this goal. The objective of this study was to evaluate yield trends, and gaps at different production levels in Mississippi using data from 2012 to 2021. Production levels considered were Mississippi yield contest (Yc), Mississippi State University hybrid testing trials under irrigation (Yp) and dryland (Yw), and actual yield (Ya) from USDA National Agricultural Statistics Service. Since 2012, Yc, Yp, and Ya are stagnant, and Yw has a nonsignificant positive trend. Averaged over 10 yr, a yield gap of 5.6 Mg ha−1 between Yc and Ya, 4.1 Mg ha−1 between Yp and Ya, and 2.0 Mg ha−1 between Yw and Ya were noted at state level. Existing yield gap underlines current production limitations and necessitates adoption of improved agronomic practices. Core Ideas In the last 10 yr, contest yield, potential yield, and actual yield in Mississippi have been stagnant. State level yield gap ranges between 2.0 to 5.6 Mg ha‐1 at different levels. Washington County showed the lowest (25.9%) and Lee County (48.8%) the highest yield gap in Mississippi.

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“…Since the 1960s, the average corn plant population has been increasing at a rate of 400 plants acre −1 year −1 , reaching current average of 32,000 plants acre −1 (Bernhard & Below, 2020), with corresponding increase in grain yield (Assefa et al., 2016). Specifically, agronomic optimum plant population of 41,600 and 47,600 plants acre −1 was noted for Mississippi, depending upon site‐year (Williams et al., 2021). Intraspecific competition for nutrients, moisture, and sunlight increases as plant density increases (Assefa et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Assessing the effect of cultural practices on Mississippi corn production: 1. Grain yield

Dew,

Li,

Oglesby

et al. 2024

Crop Forage & Turfgrass Mgmt

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Improved management strategies are needed to increase corn (Zea mays L.) production. This study aimed to determine suitable cultural practices for improved corn production in Mississippi (MS). Two experiments were setup side‐by‐side (addition/deletion) at Verona and Stoneville, MS, from 2020 to 2022. A randomized complete block design was implemented that included two row configurations (single‐ and twin‐row), two plant populations (32,000 and 40,000 plants acre−1), and six combinations of nutrients with or without a fungicide. Nutrients including nitrogen (N) 210 and 280 lb acre−1, phosphorus (P) 40 lb acre−1, potassium (K) 100 lb acre−1, elemental sulfur (S) 20 lb acre−1, zinc (Zn) 10 lb acre−1, and fungicide at (3.72 oz acre−1) were applied. In the addition trial, nutrients plus fungicide were added incrementally, whereas in the deletion trial these were withheld in a stepwise manner. Among the tested factors row configuration impacts were most consistent among all site‐years, specifically, twin‐rows resulted in higher yield compared to single‐row. Additionally, higher plant population under irrigated condition (Stoneville) resulted in greater yield compared to rainfed condition (Verona). Higher rate of N and fungicide application affected grain yield positively, but these agronomic benefits were not economically feasible. This study determined that application of different nutrients can enhance the yield but to a limited extent, and farmers should consider the economic investment of fertilizer and fungicides. Moreover, producers should balance yield and profit by taking soil testing and fertilizer prices into consideration.This article is protected by copyright. All rights reserved

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Corn response to row spacing and plant population in the Mid‐South United States

Abstract: Core ideas (3-5 impact statements, 85 char max for each)  Narrow rows (48 cm vs. 96 cm) increased grain yield by 5% (0.69 Mg ha -1 )  Narrow rows increased stalk diameter and produced two more kernels per row  Agronomic optimum plant populations ranged from 104-119 thousand ha -1

Cited by 6 publications

References 43 publications

AgroReg: main regression models in agricultural sciences implemented as an R Package

AgroReg: main regression models in agricultural sciences implemented as an R Package

Mississippi state and county level yield gap in corn production

Assessing the effect of cultural practices on Mississippi corn production: 1. Grain yield

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