Abstract
The area-of-influence (AOI) approach to quantifying crop/weed competition involves measuring the effect of individual weed plants on crop growth and yield at specified distances away from the weed plant. AOI experiments are often analyzed using classical statistical techniques based on the assumption that successive observations on crop response are independent in spite of their distribution in space. However, as the distance varies along the row, the competitive ability will vary spatially so that observations located nearby are expected to be more alike than those separated by large distances. Analyses based on spatial dependencies will therefore provide a more comprehensive understanding of factors influencing crop yield reductions. A spatial statistical approach for analyzing AOI experiments is presented and applications are demonstrated using data from a field experiment in South Central Idaho designed to determine the interference of three broadleaf weed species in sugarbeets.
Keywords
Competition, spatial dependence, modeling, variogram, sugarbeet.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Shafii, Bahman; Price, William J.; and Morishita, Don W.
(1993).
"SPATIAL STATISTICAL ANALYSIS FOR THE AREA-OF-INFLUENCE EXPERIMENTS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1372
SPATIAL STATISTICAL ANALYSIS FOR THE AREA-OF-INFLUENCE EXPERIMENTS
The area-of-influence (AOI) approach to quantifying crop/weed competition involves measuring the effect of individual weed plants on crop growth and yield at specified distances away from the weed plant. AOI experiments are often analyzed using classical statistical techniques based on the assumption that successive observations on crop response are independent in spite of their distribution in space. However, as the distance varies along the row, the competitive ability will vary spatially so that observations located nearby are expected to be more alike than those separated by large distances. Analyses based on spatial dependencies will therefore provide a more comprehensive understanding of factors influencing crop yield reductions. A spatial statistical approach for analyzing AOI experiments is presented and applications are demonstrated using data from a field experiment in South Central Idaho designed to determine the interference of three broadleaf weed species in sugarbeets.
