scholarly journals Reference sample size for multiple regression in corn

2020 ◽  
Vol 55 ◽  
Author(s):  
Alberto Cargnelutti Filho ◽  
Marcos Toebe
Keyword(s):  
Grain Yield ◽  
Sample Size ◽  
Multiple Regression ◽  
Reference Sample ◽  
Multiple Regression Model ◽  
Coefficient Of Determination ◽  
Size Number ◽  
Corn Grain ◽  
Double Cross ◽  
Corn Grain Yield

Abstract: The objective of this work was to determine the number of plants required to model corn grain yield (Y) as a function of ear length (X1) and ear diameter (X2), using the multiple regression model Y = β0 + β1X1 + β2X2. The Y, X1, and X2 traits were measured in 361, 373, and 416 plants, respectively, of single-, three-way, and double-cross hybrids in the 2008/2009 crop year; and in 1,777, 1,693, and 1,720 plants, respectively, of single-, three-way, and double-cross hybrids in the 2009/2010 crop year, totaling 6,340 plants. Descriptive statistics were calculated, and frequency histograms and scatterplots were created. The sample size (number of plants) for the estimate of the β0, β1, and β2 parameters, of the residual standard error, the coefficient of determination, the variance inflation factor, and the condition number between the explanatory traits of the model (X1 and X2) were determined by resampling with replacement. Measuring 260 plants is sufficient to adjust precise multiple regression models of corn grain yield as a function of ear length and ear diameter. The Y = -229.76 + 0.54X1 + 6.16X2 model is a reference for estimating corn grain yield.

Response of Corn Grain Yield to Spatial and Temporal Variability in Emergence

Crop Science ◽  
2004 ◽  
Vol 44 (3) ◽  
pp. 847 ◽  
Author(s):  
Weidong Liu ◽  
Matthijs Tollenaar ◽  
Greg Stewart ◽  
William Deen
Keyword(s):  
Grain Yield ◽  
Temporal Variability ◽  
Spatial And Temporal Variability ◽  
Corn Grain ◽  
Corn Grain Yield

Assessing the impact of agricultural field traffic on corn grain yield using remote sensing and machine learning

2021 ◽  
Vol 208 ◽  
pp. 104880
Author(s):  
Sami Khanal ◽  
Andrew Klopfenstein ◽  
Kushal KC ◽  
Venkatesh Ramarao ◽  
John Fulton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Grain Yield ◽  
Agricultural Field ◽  
The Impact ◽  
Corn Grain ◽  
Corn Grain Yield

GRAIN YIELD COMPARISON OF PURE STANDS AND MIXTURES OF DIFFERENT PROPORTIONS FOR TWO HYBRIDS OF MAIZE

1985 ◽  
Vol 65 (3) ◽  
pp. 481-485 ◽  
Author(s):  
G. J. HOEKSTRA ◽  
L. W. KANNENBERG ◽  
B. R. CHRISTIE
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Zea Mays L ◽  
Pure Stand ◽  
Higher Plant ◽  
Data Support ◽  
Plant Densities ◽  
The Difference ◽  
Corn Grain ◽  
Corn Grain Yield

The objective of this study was to determine the effects on grain yield of growing cultivars in mixtures of different proportions. Two maize (Zea mays L.) hybrids, Pride 116 and United 106, were grown for 2 yr in pure stand and in seven mixtures of different proportions (7:1, 6:2, 5:3, 4:4, 3:5, 2:6, 1:7) at plant densities of 61 500, 99 400, and 136 000 plants per hectare. The total number of mixture combinations was 42, i.e. 2 years × three densities × seven proportions. All but one mixture yielded as expected based on the yield of component hybrids in pure stand. The higher yielding hybrid (United 106) yielded significantly less grain per plant in mixtures than in pure stand. The lower yielding hybrid (Pride 116) yielded more in mixtures than in pure stand, although the difference was not significant. These data support previous observations that the ability of a hybrid to yield in pure stands is not necessarily related to its ability to yield in mixtures. High plant densities appear to enhance the likelihood of interactions occurring among hybrids. For United 106, the number of proportions yielding less grain per plant than in pure stand was highly significant at the two higher plant densities. For Pride 116, the number of proportions yielding more than in pure stand was highly significant at the highest plant density.Key words: Corn, grain yield, mixtures of different proportions, high plant densities, Zea mays

scholarly journals Interactions of Nitrogen Source and Rate and Weed Removal Timing Relative to Nitrogen Content in Corn and Weeds and Corn Grain Yield

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Alexandra M. Knight ◽  
Wesley J. Everman ◽  
David L. Jordan ◽  
Ronnie W. Heiniger ◽  
T. Jot Smyth
Keyword(s):  
Grain Yield ◽  
Weed Management ◽  
Management Strategies ◽  
Field Studies ◽  
Weed Species ◽  
Weed Interference ◽  
Coated Urea ◽  
N Fertility ◽  
Corn Grain ◽  
Corn Grain Yield

Adequate fertility combined with effective weed management is important in maximizing corn (Zea mays L.) grain yield. Corn uptake of nitrogen (N) is dependent upon many factors including weed species and density and the rate and formulation of applied N fertilizer. Understanding interactions among corn, applied N, and weeds is important in developing management strategies. Field studies were conducted in North Carolina to compare corn and weed responses to urea ammonium nitrate (UAN), sulfur-coated urea (SCU), and composted poultry litter (CPL) when a mixture of Palmer amaranth (Amaranthus palmeri S. Wats.) and large crabgrass (Digitaria sanguinalis L.) was removed with herbicides at heights of 8 or 16 cm. These respective removal timings corresponded with 22 and 28 days after corn planting or V2 and V3 stages of growth, respectively. Differences in N content in above-ground biomass of corn were noted early in the season due to weed interference but did not translate into differences in corn grain yield. Interactions of N source and N rate were noted for corn grain yield but these factors did not interact with timing of weed control. These results underscore that timely implementation of control tactics regardless of N fertility management is important to protect corn grain yield.

The Critical Period of Johnsongrass (Sorghum halepense) Control in Field Corn (Zea mays)

Weed Science ◽  
1996 ◽  
Vol 44 (4) ◽  
pp. 944-947 ◽  
Author(s):  
Hani Z. Ghosheh ◽  
David L. Holshouser ◽  
James M. Chandler
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Critical Period ◽  
Sorghum Halepense ◽  
Control Methods ◽  
Field Corn ◽  
Hand Weeding ◽  
Corn Grain ◽  
Corn Grain Yield

Experiments were conducted from 1989 to 1991 to determine the critical period of johnsongrass control in field corn. Maximum weed-infested and weed-free periods of 0 to 20 wk after corn emergence were maintained by either hand weeding or nicosulfuron application. Interference duration effects on corn grain yield were not affected by johnsongrass control methods. The critical period for johnsongrass control was determined to be between 3 and 6.5 wk after corn emergence to avoid losses above 5% of yield produced by full-season weed-free corn.

In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index

2006 ◽  
Vol 98 (6) ◽  
pp. 1488-1494 ◽  
Author(s):  
R. K. Teal ◽  
B. Tubana ◽  
K. Girma ◽  
K. W. Freeman ◽  
D. B. Arnall ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Yield Potential ◽  
Corn Grain ◽  
Corn Grain Yield

Tillage Effects on Soil Temperature, Shoot Dry Matter Accumulation and Corn Grain Yield

1995 ◽  
Vol 5 (1-2) ◽  
pp. 85-99 ◽  
Author(s):  
L. M. Dwyer ◽  
B. L. Ma ◽  
H. N. Hayhoe ◽  
J.L.B. Culley
Keyword(s):  
Soil Temperature ◽  
Grain Yield ◽  
Dry Matter ◽  
Dry Matter Accumulation ◽  
Corn Grain ◽  
Corn Grain Yield

Application of near Infrared Spectroscopy On-Combine in Corn Grain Breeding

2005 ◽  
Vol 13 (2) ◽  
pp. 69-75 ◽  
Author(s):  
Roland Welle ◽  
Willi Greten ◽  
Thomas Müller ◽  
Gary Weber ◽  
Hartwig Wehrmann
Keyword(s):  
Grain Yield ◽  
Dry Matter ◽  
Near Infrared ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Successful Implementation ◽  
Grain Moisture ◽  
Yield Determination ◽  
Corn Grain

Improving maize ( Zea mays L.) grain yield and agronomic properties are major goals for corn breeders in northern Europe. In order to facilitate field grain yield determination we measured corn grain moisture content with near infrared (NIR) spectroscopy directly on a harvesting machine. NIR spectroscopy, in combination with harvesting, significantly improved quality and speed of yield determination within the very narrow harvest time window. Moisture calibrations were developed with 2117 samples from the 2001 to 2003 crop seasons using six diode array spectrometers mounted on combines. These models were derived from databases containing spectra from all instruments. Spectrometer-specific calibrations cannot be used to predict samples measured on other instruments of the same type. Standard error of cross-validation ( SECV) and coefficient of determination ( R2) were 0.56 and 0.99%, respectively. Moisture standard errors of prediction ( SEPs) for the six instruments, using varying independent sample sets from the 2004 harvest, ranged between 0.59% and 0.99% with R2 values between 0.92 to 0.98. The six instruments produced the same dry matter predictions on a common sample set as indicated by high R2 and low biases among them, hence there was no need to apply specific standardisation algorithms. Moisture NIR spectroscopy determinations were significantly more precise than those obtained using the reference method. Analysis of variance revealed low least significant differences and high heritabilities. High precision and heritability demonstrate successful implementation of on-combine NIR spectroscopy for routine dry matter (yield) measurements.

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