Comparing Spatial Sampling Designs for Estimating Effectively Maize Crop Traits in Experimental Plots

Abstract

The current study investigates the performance of various sampling designs in providing accurate estimates for crucial maize yield traits (intended for silage) including plant height, fresh/dry/ear weight, number of maize ears per plant, and total ear weight per plant, using spatial maize data. The experiment took place in an experimental field area at Aristotle University (AUTH) farm during the 2016 growing season. Nine sampling designs were statistically analyzed and compared with spatial data from an Italian maize hybrid (AGN720) to identify the most suitable and effective sampling design for dependable maize yield estimates. The study’s results indicate that, among the different sampling techniques, Stratified Random Sampling is the most effective and reliable method for obtaining accurate maize yield estimates. This new approach not only provides precise estimates but also requires fewer measurements, making it suitable for experiments where not all plants have emerged. These findings suggest that Stratified Random Sampling can be employed effectively as an alternative to harvesting the entire plot for effectively estimating maize crop traits in experimental plots.