Spatial Variability of Soil Properties, Corn Quality and Yield in Two Illinois, USA Fields: Implications for Precision Corn Management

Abstract

Better understanding of within-field spatial variability of crop quality parameters and yield are needed for precision management of crops. This study was conducted to determine the magnitude of within-field variability in soil properties, corn (Zea mays L.) quality parameters and yield and to characterize their spatial structures. Another objective was to compare the effects of hybrid on corn quality, yield, and the spatial structure of grain quality. Four Pioneer hybrids were planted side-by-side, two in each of the two study fields in eastern Illinois, USA. Coefficients of variation (CV%) for soil properties varied from 6.3 (pH) to 56.8% (soil test P). All the soil properties (except pH at Site 2) displayed well-defined spatial structures, with either strong or moderate spatial dependence. Variability in corn quality and yield (CVs < 10%) was smaller than variability in soil properties. Most quality parameters examined at Site 1 exhibited either moderate or strong spatial dependence, except that corn oil (both hybrids), kernel roundness and weight (hybrid 33Y18) did not show any spatial correlation. Hybrid 33G26 had significantly higher yield and quality for most quality parameters than 33Y18 at Site 1. At Site 2, hybrid 34W67 was significantly lower in oil and protein content, length, roundness and vitreousness than 34K77, but higher in other quality parameters. Significant differences in spatial structures were also observed across hybrids for some corn quality parameters. We conclude that hybrid selection is an important strategy for precision management of corn for optimum yield and quality.

Stretching Soil Sampling to Watershed: Evaluation of Soil‐Test Parameters in a Semi‐arid Tropical Watershed

“…Normally, a large variability in various soil fertility parameters has been reported by several researchers at the plot or field scale (Cambardella et al 1994;Geypens et al 1999;Mallarino and Wittry 2004;Miao, Mulla, and Robert 2006), but the results of this study suggest that the farmers' fields in the Appayapally watershed are uniform in the chemical fertility parameters.…”

Atributos químicos e físicos de latossolos e sua relação com os rendimentos de milho e feijão irrigados

“…A maioria dos modelos de ajuste dos semivariogramas foi o esférico. Este modelo tem sido o mais frequentemente adaptado para descrever semivariogramas de atributos de solo e de planta, conforme proposto por Salviano et al (1998), Cambardella & Karlen (1999), Geypens et al (1999), Silva et al (2003) e Miao et al (2006). Segundo Cambardella et al (1994), os atributos que apresentam forte dependência espacial são mais influenciados por propriedades intrínsecas do solo, notadamente a textura e a mineralogia, enquanto os que apresentam fraca dependência são mais influenciados por fatores externos, como aplicações de fertilizantes e sistemas de preparo e cultura, ou seja, estão relacionados com o manejo do solo.…”

“…A irregularidade da distribuição da precipitação pluviométrica e, consequentemente, da disponibilidade de água em períodos críticos tem sido reconhecida como um dos principais fatores responsáveis pela variabilidade temporal dos rendimentos de milho (Lamb et al, 1997;Bakhsh et al, 2001;Miao et al, 2006) e do feijão-preto (Kluthcouski et al, 2000). A irrigação pode diminuir esta variabilidade, porém a eficiência desta prática em atender plenamente a demanda das plantas está relacionada, entre outros fatores, com a taxa de infiltração, a capacidade de armazenamento no solo e a disponibilidade de água às plantas, sendo, portanto, influenciada pelo estado de compactação.…”

Delineating management zones for precision agriculture applications: a case study on wheat in sub-tropical Brazil

“…To this aim, the adoption of preci-sion agriculture (PA) techniques is seen a valuable approach to combine plant and soil attributes in a joint analysis of their spatial variation in wheat (Diacono et al, 2013). In this frame, Pearson's correlation is one of the simplest and most widely used tools to match soil characteristics with plant traits (Miao et al, 2006). Multiple regressions represent a further step, combining parameters to explain a higher share of a trait's variation.…”