Optimization of Within‐Row Plant Spacing Increases Nutritional Status and Corn Yield: A Comparative Study

Abstract

Uniform within‐row plant spacing is a key crop management strategy to achieve high corn (Zea mays L.) yield. A new precision planting concept is emerging based on the use of modern devices in planters. The objective of this study was to investigate the impact of optimizing within‐row plant spacing to enhance plant nutritional status and corn yield. The treatments investigated were (a) traditional planter with mechanical horizontal plate metering system (TP), (b) precision planter with vSet (Precision Planting, Tremont, IL) vacuum meter system (PP) and (c) PP pulled by a tractor equipped with an real time kinematic (RTK)‐based auto‐steering system (APP). The experimental design was a randomized block with three replications, and the plant evaluations were determined based on a main plot and a plant‐to‐plant study. Two types of vegetation indexes (VI and normalized difference, NDVI) were used for assessing plant nutritional status. A high standard of uniform plant spacing (CV < 10%) was required to achieve the highest corn yield. Optimizing the within‐row plant spacing resulted in higher VI and NDVI. Based on the average of two experiments, PP improved the uniformity of plant spacing (CV = 22.5%) compared to TP (CV = 38.7%). This optimization of within‐row plant spacing increased the corn yield by 10.7%. Only in the year with higher corn yield potential did APP result in a yield increase of 6.9% in relation to PP. Although the precision planters investigated had decreased the error in plant distribution, no one reached the plant spacing uniformity required to achieve higher corn yields.Core Ideas For every 10 percentage points increase in CV, yield decreased 1.22 Mg ha−1. A higher plant spacing uniformity is necessary to achieve high yields. Different planter types produced distinct within‐row plant spacing uniformity.

Plant‐to‐plant biomass and yield variability in corn–soybean rotations under three tillage regimes

“…This association was present in all years except 2011, which provided the best growing conditions and lowest variability. This finding supports the conclusion that corn yields are maximized when plant‐to‐plant yield variability is lowest (Andrade & Abbate, 2005; Horbe et al., 2016; Martin et al., 2005). Notably, the trend of increasing CV and decreasing biomass in corn was also significant at both V6 and VT ( p < .01; data not shown), but it was a very weak relationship in both instances ( r 2 = .03 and r 2 = .04, respectively).…”

“…Because it is essentially a relativized standard deviation, CV is a powerful tool for comparing the relative variability of groups with different absolute scales and is frequently used in describing plant‐to‐plant variability (Andrade & Abbate, 2005; Caviglia & Melchiori, 2011; Horbe et al., 2016; Kovacs & Vyn, 2014; Martin et al., 2005). The CVs were assessed using a mixed‐effects model with sampling period, tillage, and year as the main effects and replicate as the random effect.…”

“…One way to assess how plant‐to‐plant variability affects crop production is to identify the relationship between the variability of grain production per plant (hereafter referred to as grain weight) with the variability of grain production per unit area (hereafter referred to as grain yield). In some cases, increasing variability of grain weight has been linked to decreased grain yields (Andrade & Abbate, 2005; Horbe et al., 2016; Martin et al., 2005; Tollenar & Wu, 1999); however, this relationship has been inconsistent among crops, regions, management, and weather conditions (Boomsma et al., 2010; Liu et al., 2004; Vega, Sadras, Andrade, & Uhart, 2000). These inconsistencies, likely driven by differences in environmental factors and agronomic management, demonstrate the need for additional research to elucidate this relationship in a variety of field conditions.…”

“…Similarly, the influence of agronomic management on plant‐to‐plant variability in corn ( Zea mays L.)–soybean [ Glycine max (Merr.) L.] rotations have been investigated with respect to plant populations (Boomsma, Santini, Tollenaar, & Vyn, 2009), row spacing (Horbe et al., 2016), planting method and speed (Nielsen, 1995), fertilizer management (Martin et al., 2005), seed vigor (Vaz Mondo, Cicero, Dourado‐Neto, Pupim, & Dias, 2013), residue management (Govaerts et al., 2007), and tillage practices (Liu, Tollenaar, Stewart, & Deen, 2004). All these factors can contribute to plant‐to‐plant variability and may have the potential to affect crop production potentials across space and time.…”

Corn yield components can be stabilized via tillering in sub-optimal plant densities

“…This result is not surprising, as kernel number is known to be key to corn yield determination (Andrade et al, 1999). In general, situational tiller expression could be associated with non-uniform field features, which is a yield-negating factor for intensively managed corn (Hörbe et al, 2016). Tillering increased corn kernel numbers for shoots with high growth rates in field studies conducted by Rotili et al (2022).…”