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- Author or Editor: Walter W. Stroup x
Abstract
First, we thank Littell, Sanders, Milliken, and Nelson for their excellent and highly relevant contributions. It is obvious from the size of the audience at the 1987 ASHS Annual Meeting at Kissimmee and the length and energy of the question-and-answer period that these were subjects of great interest to horticultural researchers.
Hydrangeas are sold as a potted florist plant during the spring, usually around Mothers Day and Easter. They are considered “heavy feeders” because of their high requirement for nitrogen. Two experiments were conducted to determine if the addition of sulfur (S) would allow lower rates of nitrogen (N) to be applied without sacrificing plant color and quality. Hydrangea macrophylla `Blue Danube' were fertilized with four levels of N (50, 100, 200, and 450 ppm) in combination with six levels of S (0, 6, 12, 24, 48, and 96 ppm) during a typical forcing program. The experimental design was a randomized complete block with a complete factorial treatment design. Data collected included visual observations (using the Royal Horticultural Society Color Chart) on leaf color and uniformity of flower color as well as flower shape. Quantitative data included flower diameter, floret diameter, height, and N an S leaf concentrations. Soil pH was monitored throoughout the experiment and remained fairly constant (range of 5.0–6.0). Additional sulfur seemed to have no effect on leaf color at the higher levels of N. Lower concentrations of N produced more true blue flower color. Also, at lower N concentrations, higher S resulted in larger flowers with larger florets.
Three cultivars of poinsettia, Freedom Red, Lilo and Red Sails, were grown in a peat:perlite:vermiculite mix according to a commercial production schedule. Twelve selected nitrogen–sulfur fertilizer combinations were applied (125, 150, 175 ppm N with either 12.5, 25, or 37.5 ppm S, 225 and 275 ppm N with either 37.5 or 75 ppm S). The experimental design was a split plot with cultivars as the whole plot and fertilizer levels as the split-plot factor. Mix samples were taken initially, at production week 7 and at the end of the experiment. Nitrate-nitrogen, sulfate-sulfur and total nitrogen were determined. Data were analyzed using SAS PROC MIXED. Visually all cultivars responded similarly to all treatments and were salable. Thus, levels of N as low as 125 or 150 with 12.5 ppm S produced quality plants. Sulfate-S tended to accumulate in the mix while nitrate-N and total N did not. Both nitrate-N and sulfate-S concentrations were affected by an interaction between the cultivar and the amount of S applied with `Freedom' better able to utilize available sulfur. `Lilo' removed more nitrate-N and total N from the mix than `Freedom' which removed more than `Red Sails', but only at specific levels of sulfur. There was no cultivar by nitrogen interaction for any variable measured.
Windbreaks can increase crop growth and improve crop quality. The effects of shelter on vegetable production varies with crop, location, and farming practices. While the advantages of minimizing wind stress on vegetable production is well-known, little research documents the specific response of vegetables to microclimate modification through the use of shelterbelts.
During the summer, 1991, a preliminary experiment was conducted on the effects of tree windbreaks (shelterbelts) on muskmelon plant growth, yield, and fruit quality. A split-plot design was used with shelter and exposed areas as main treatments with 3 replications. Subtreatments were 7 combinations of anti-transpirant and time of application. Leaf growth was measured 4 and 6 weeks after planting. Muskmelon fruit were harvested over a 6 week period at 2 day intervals. Muskmelon yield, fruit and cavity diameter, fruit color, and total sugar content were obtained.
The use of anti-transpirant did not significantly affect total yield, fruit or cavity diameter, total sugar content, or early leaf growth. The effect of shelter varied with the measured variable.
A key characteristic of scientific research is that the entire experiment (or series of experiments), including the data analyses, is reproducible. This aspect of science is increasingly emphasized. The Materials and Methods section of a scientific paper typically contains the necessary information for the research to be replicated and expanded on by other scientists. Important components are descriptions of the study design, data collection, and statistical analysis of those data, including the software used. In the Results section, statistical analyses are presented; these are usually best absorbed from figures. Model parameter estimates (including variances) and effect sizes should also be included in this section, not just results of significance tests, because they are needed for subsequent power and meta-analyses. In this article, we give key components to include in the descriptions of study design and analysis, and discuss data interpretation and presentation with examples from the horticultural sciences.
Response surface methods refer to a set of experimental design and analysis methods to study the effect of quantitative treatments on a response of interest. In theory, these methods have a broad range of applicability. While they have gained widespread acceptance and application in manufacturing and quality improvement research, they have never caught on in the agricultural sciences. We propose that this is because there has not been specific research demonstrating their usage. In this paper, two 34 factorial experiments were performed using 100 poinsettia plants (Euphorbia pulcherrima Willd. ex Klotzsch) to measure nutrient element concentrations in leaves at three rates each of nitrogen (N), sulfur (S), iron (Fe), and manganese (Mn). Three different methods of analysis were compared—the standard analysis of variance with no regression model, the quadratic regression model commonly assumed for most standard response surface methods and the Hoerl model regression, a nonlinear alternative to quadratic response. Actual nutrient element values were compared with the values predicted by each regression model and then also evaluated to see if the visual symptomology of yellowing related to those nutrient concentrations in leaves. The Hoerl model demonstrated better ability to detect biologically relevant nonlinear two-, three-, and four-way nutrient interactions. Though there was minimal replication this model characterized the treatment effects while keeping the size of the experiment manageable both in terms of time (labor) and cost of plant analyses. Additionally, it was shown that when S, Fe, and/or Mn were deficient along with N, their visual deficiency symptoms were masked by the overall yellowing associated with N deficiency. This model is recommended as the initial experiment in a series where scientists are looking to expand information already determined for two factors. Other treatment systems that this can be used with include: levels of irrigation, pesticides, and plant growth regulators.
We examined all articles in volume 139 and the first issue of volume 140 of the Journal of the American Society for Horticultural Science (JASHS) for statistical problems. Slightly fewer than half appeared to have problems. This is consistent with what has been found for other biological journals. Problems ranged from inappropriate analyses and statistical procedures to insufficient (or complete lack of) information on how the analyses were performed. A common problem arose from taking many measurements from the same plant, which leads to correlated test results, ignored when declaring significance at P = 0.05 for each test. In this case, experiment-wise error control is lacking. We believe that many of these problems could and should have been caught in the writing or review process; i.e., identifying them did not require an extensive statistics background. This suggests that authors and reviewers have not absorbed nor kept current with many of the statistical basics needed for understanding their own data, for conducting proper statistical analyses, and for communicating their results. For a variety of reasons, graduate training in statistics for horticulture majors appears inadequate; we suggest that researchers in this field actively seek out opportunities to improve and update their statistical knowledge throughout their careers and engage a statistician as a collaborator early when unfamiliar methods are needed to design or analyze a research study. In addition, the ASHS, which publishes three journals, should assist authors, reviewers, and editors by recognizing and supporting the need for continuing education in quantitative literacy.
We examined all articles in volume 139 and the first issue of volume 140 of the Journal of the American Society for Horticultural Science (JASHS) for statistical problems. Slightly fewer than half appeared to have problems. This is consistent with what has been found for other biological journals. Problems ranged from inappropriate analyses and statistical procedures to insufficient (or complete lack of) information on how the analyses were performed. A common problem arose from taking many measurements from the same plant, which leads to correlated test results, ignored when declaring significance at P = 0.05 for each test. In this case, experiment-wise error control is lacking. We believe that many of these problems could and should have been caught in the writing or review process; i.e., identifying them did not require an extensive statistics background. This suggests that authors and reviewers have not absorbed nor kept current with many of the statistical basics needed for understanding their own data, for conducting proper statistical analyses, and for communicating their results. For a variety of reasons, graduate training in statistics for horticulture majors appears inadequate; we suggest that researchers in this field actively seek out opportunities to improve and update their statistical knowledge throughout their careers and engage a statistician as a collaborator early when unfamiliar methods are needed to design or analyze a research study. In addition, the ASHS, which publishes three journals, should assist authors, reviewers, and editors by recognizing and supporting the need for continuing education in quantitative literacy.
We examined all articles in volume 139 and the first issue of volume 140 of the Journal of the American Society for Horticultural Science (JASHS) for statistical problems. Slightly fewer than half appeared to have problems. This is consistent with what has been found for other biological journals. Problems ranged from inappropriate analyses and statistical procedures to insufficient (or complete lack of) information on how the analyses were performed. A common problem arose from taking many measurements from the same plant, which leads to correlated test results, ignored when declaring significance at P = 0.05 for each test. In this case, experiment-wise error control is lacking. We believe that many of these problems could and should have been caught in the writing or review process; i.e., identifying them did not require an extensive statistics background. This suggests that authors and reviewers have not absorbed nor kept current with many of the statistical basics needed for understanding their own data, for conducting proper statistical analyses, and for communicating their results. For a variety of reasons, graduate training in statistics for horticulture majors appears inadequate; we suggest that researchers in this field actively seek out opportunities to improve and update their statistical knowledge throughout their careers and engage a statistician as a collaborator early when unfamiliar methods are needed to design or analyze a research study. In addition, the ASHS, which publishes three journals, should assist authors, reviewers, and editors by recognizing and supporting the need for continuing education in quantitative literacy.
Horticulturists are often interested in evaluating the effect of several treatment factors on plant growth in order to determine optimal growing conditions. Factors could include three or more nutrient elements, or types and rates of irrigation, pesticides or growth regulators, possibly in combination with one another. Two problems with such experiments are how to characterize plant response to treatment combinations and how to design such experiments so that they are manageable. The standard statistical approach is to use linear and quadratic (a.k.a. response surface) regression to characterize treatment effects and to use response surface designs, e.g., central-composite designs. However, these often do a poor job characterizing plant response to treatments. Hence the need for more generally applicable methods. While our goal is to be able to analyze three and higher factor experiments, we started by tweaking two-factor nutrient analysis data. The result was a hybrid model which allows for a given factor to respond linearly or non-linearly. We will show how this was done and our current “in progress” model and analysis for analyzing three quantitative factors.