Planning and implementing experiments and analyzing experimental data in vegetable crops: problems and solutions

Abstract

The statistical interpretation of experimental results is inherent to the research process. Therefore, every researcher is expected to have basic understanding on the subject. In vegetable crops, the planning, implementing and data gathering is more complex due to specific aspects related to this group of plants, such as intensive management and high labor requirement to carry out the experiments, uneven fruit maturation and heterogeneity of the experimental area. Since all these factors are sources of variability within the experiment, circumventing them in the experiment planning and implementing phases is fundamental to reduce the experimental error. Furthermore, the knowledge of statistical tests and the assumptions for their use is equally critical to make the research statistically valid. The present work presents the problems of unwanted variability within an experiment with vegetables and the possibilities to reduce and manage it. We discuss alternatives to reduce the variability due to uncontrolled effects within an experiment; the most common experimental designs; recommendation of appropriate statistical tests for each type of treatment; and techniques for the diagnosis of residues. We expect to contribute with researchers dealing with vegetable crops, offering subsidies to aid researchers in the planning and implementation of experiments and in the analysis and interpretation of experimental results.

Identification of core locations for soybean breeding in Southern Brazil

“…These characteristics minimize the effects of the interactions involving the year factor (Yan, 2016). Therefore, the success of any breeding effort greatly depends on the high representativeness and potential for the selection of superior genotypes of each core location, as well as on the homogeneity of the experimental area and on an efficient crop management practice (Lucio & Sari, 2017).…”

Estimation of the Optimum Size of Plots for Soybean Radiometer Experiments

“…Experimental planning for the proper choice of plot size is an indispensable procedure for performing tests with experimental precision of acceptable magnitude (SCHWERTNER et al, 2015). When the experimental area is not a limiting factor, it is better to increase the number of repetitions than the plot size (LÚCIO and SARI, 2017). Saving human and financial resources, without losing experimental precision, is considered an important factor in the design of experiments (MICHELS et al, 2015;LÚCIO and SARI, 2017).…”

Statistical prediction of interactions between low concentrations of inhibitors on yeast cells responses added to the SD-medium at low pH values

“…The selection of a minimum number of experiments is an essential factor for the good progress of the research [19]. Statistical interpretation of the experimental results is inherent in the research process and the control of the experimental variables can reduce the experimental error.…”

“…Statistical interpretation of the experimental results is inherent in the research process and the control of the experimental variables can reduce the experimental error. The knowledge of statistical tests and assumptions are equally critical to make the research statistically valid [19].…”