OPTIMUM SAMPLE SIZE FOR MAIZE SEEDLING EVALUATION
Linear measurements of seedling parts are laborious and time-consuming, thereby limiting the number of treatments that can be handled at any time. A possible solution is to collect data only on the minimum number of seedlings that is sufficiently representative of a population. From investigations i...
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| Những tác giả chính: | , |
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
The Faculty of Agriculture Obafemi Awolowo University, Ile-Ife, Nigeria.
2020
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| Những chủ đề: | |
| Truy cập trực tuyến: | https://ija.oauife.edu.ng/index.php/ija/article/view/387 |
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| Tóm tắt: | Linear measurements of seedling parts are laborious and time-consuming, thereby limiting the number of treatments that can be handled at any time. A possible solution is to collect data only on the minimum number of seedlings that is sufficiently representative of a population. From investigations involving different maize genotypes x soil compaction, maturity stages x duration of storage, seed testing substrata x sterilization treatments, individual measurements of root and shoot length as well as number of primary roots of 8,300 seedlings were used to estimate the optimum sample size for maize seedling evaluation. Using data on each seedling only once, the data were randomly grouped into different sample sizes ranging from 5 to 20 plants in increments of five. Mean, standard deviation and skewness were calculated for each sample size and analysis of variance was done. Sample size had no significant effect (P>0.05) on mean of all traits in the combined analysis and its interaction with treatment effects was also negligible (P>0.05). But despite significant effect of sample size on deviations and skewness, its contribution to observed variability was less than 1%. A similar pattern was observed when data were analyzed experiment by experiment. In studies like those considered here, there is no statistical and economic advantage in collecting data on more than 15 seedlings per replication when the mean value is the criterion for comparison and decision-making. |
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