Univariate Distribution Analysis to Evaluate Variable Rate Fertilization

Abstract

Technological advances in precision fertilization such as yield monitors and remote sensing are increasing the density of samples collected and decreasing the scale inputs can be managed in the field. A compounding problem is that fertilizer applications can often be made at a much smaller scale than yield data can be collected. Analytical tools such as ANOVA and geostatistics can be used on high-density data sets; however, these analytical tools do not provide all the information required to test research ideas. An alternative to solve this problem is the use of statistics not traditionally applied to precision agricultural experiments. The objective of this study was to determine if univariate distribution (population statistics) analysis is useful in the study of wheat (Triticum aestivum L.) yield response to variable-rate N fertilization strategies using active optical sensors in red and nearinfrared bands (NDVI [normalized difference vegetative index] sensors). Measurements of NDVI and fertilizer applications were on a 0.56-m 2 basis, while wheat yield data were collected at a 2-m 2 scale. Classical ANOVA was conducted to compare treatment effects. Analysis of univariate distributions for NDVI and wheat yield monitor data sets was used to further evaluate the effect of the treatments. In addition to a significant effect on the mean NDVI and yield, fetilizer staregies affected the normality, median, mode, skewness, and kurtosis of the resulting NDVI and yield distributions. Unlike ANOVA, the analyses of univariate distributions provided an insight on those portions of the NDVI and yield populations responsible for changes in the mean.

Effect of Nitrogen and Endophytic Bacteria on Biophysical and Spectral Parameters of Wheat Canopy

“…According to Freitas (2000), the practical use of bacteria as inoculants requires more experiments because the effects of bacteria on yield and N assimilation of wheat are not well understood. An increase in the N concentration of wheat produces spectral reflectance changes that may be detected by remote sensing instruments, as demonstrated by several researchers (Serrano et al, 2000; Mullen et al, 2003; Pena‐Yewtukhiw et al, 2006; Shou et al, 2007; Sripada et al, 2007; Tilling et al, 2007; Feng et al, 2008). If bacteria treatment affects yield and N assimilation of wheat, it may affect their relationships with spectral parameters.…”

A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application

“…They found NDVI_GS measurements from growth stages V5-V9 were the most useful and appropriate for making in-season management decisions for corn production. Pena-Yewtukhiwa et al (2006) stressed the importance of the statistical distribution and summary statistics in the study of processes generating NDVI data. The summary statistics are important in the design of algorithms for fertilizer application.…”

“…The summary statistics are important in the design of algorithms for fertilizer application. The algorithms divide the data into classes, and then treat each class with a particular rate of fertilizer N. Uniform N rates do not change existing crop variability patterns (Pena-Yewtukhiwa et al 2006). Welsh et al (2003aWelsh et al ( , 2003b found that the most effective variable-rate N strategy for winter barley and wheat was to apply additional N to areas of low shoot density and to reduce N applications to areas of high density.…”

Spatial Analysis of Early Wheat Canopy Normalized Difference Vegetative Index: Determining Appropriate Observation Scale

“…Each sensor's NDVI readings were individually evaluated. In 2004, univariate statistical analysis (Pena‐Yewtukhiw et al, 2006) found seven significantly different sensor groups (Table 2), with sensor 8 consistently giving the highest NDVI readings and sensor 5 the smallest. The origin and consistency of the observed difference in sensor output in 2004 was not clear.…”