Effect of Plot Size on Accuracy of Yield Estimation of Rainfed Lowland Rice Genotypes with Different Plant Heights and Grown under Different Soil Fertility Conditions

Jearakongman, Suwat; Immark, S.; Noenplub, Apichart; Fukai, S.; Cooper, Mark

doi:10.1626/pps.6.95

Cited by 12 publications

(13 citation statements)

References 5 publications

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“…Table 4 also shows the neighboring covariate models M 1 and M 2 that had and the covariate models did not consistently improve fit and relative selection intensity over the reference model. These results are in agreement with the study on rainfed lowland rice in Thailand of Jearakongman et al (2003), who reported that yield estimation from two-row plots was reliable under low soil fertility conditions where mean yield was around 300 g m -2 , but not under high fertility conditions, where the mean yield surpassed 400 g m -2 .…”

Section: Resultssupporting

confidence: 92%

“…Whereas increasing row number improves the accuracy of estimation of grain yields of upland rice, experiments with two‐row plots provide estimates of grain yield that are unbiased by competition effects with neighboring plots as there was no variety × row number interaction for grain yield between two‐row and four‐row plots across 4 experiments, and the covariate models did not consistently improve fit and relative selection intensity over the reference model. These results are in agreement with the study on rainfed lowland rice in Thailand of Jearakongman et al (2003), who reported that yield estimation from two‐row plots was reliable under low soil fertility conditions where mean yield was around 300 g m −2 , but not under high fertility conditions, where the mean yield surpassed 400 g m −2 .…”

Section: Discussionsupporting

confidence: 92%

“…The finding of the positive regression coefficient for height in the covariate models is compatible with previous reports for rice (Jennings and de Jesus Jr., 1968; Kawano et al, 1974; Dingkuhn et al, 1999; Jearakongman et al, 2003), which indicated that height was one of the important characteristics for competitiveness when rice was grown with other crops or other rice varieties. Regarding the positive regression coefficients for the covariates based on panicle number, previous studies on weed competitiveness reported that a large number of tillers, which are often closely linked with a large number of panicles, is also an important agronomic trait for competitiveness (Zhao et al, 2006; Saito and Futakuchi, 2009).…”

Section: Discussionsupporting

confidence: 91%

“…When row number ´ variety interaction was significant, the sum of squares for variety ´ row number interaction was partitioned into contrasts using SAS contrast statement for determining if varietal performance in two-row and four-row plots differs from that in one-row plots and for determining if varietal performance in two-row plots differs from that in four-row plots (SAS, 2008). Following a study on rice by Jearakongman et al (2003), we assumed that self-bordered plots provided a control treatment that revealed true varietal performance free from the effects of competition. (Windmeijer and Andriesse, 1993).…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Gomez (1972) suggested that excluding the two border rows at each site when making observations is required for eliminating competition effects between neighboring varieties. Jearakongman et al (2003) reported that unbordered two‐row plots can be used to estimate grain yields provided the grain yield is adjusted according to plant height in the plots where grain yields were measured. However, it has not been examined yet whether the use of unbordered one‐row plots can provide acceptable estimates of grain yields in comparison with bordered plots.…”

mentioning

confidence: 99%

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Does Use of Unbordered Plots Affect Estimation of Upland Rice Yield?

et al. 2015

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Advance in crop genomics have led to a demand for more accurate and precise phenotyping of a large number of genotypes for discovering genes and mechanisms underpinning important agronomic traits. The use of unbordered plots for field phenotyping is one approach for reducing the resources needed, but competition effects between neighboring lines may confound varietal performance. Four field experiments were conducted in Benin to examine whether grain yields of 14 diverse upland rice (Oryza sativa spp.) varieties determined in unbordered one‐row or two‐row plots differ from those measured in self‐bordered four‐row plots and to examine if statistical models including covariates based on plant characteristics (height, panicle number, and days to heading) for correcting competition effect can improve the estimation of the yield in unbordered plots. Mean grain yield across all varieties ranged from 118 to 378 g m−2 in four experiments. There was no significant variety × row number interaction effect on grain yield, except for the highest yielding experiment. In that experiment, the variety × row number interaction was significant for one‐row versus four‐row plots, but not for two‐row versus four‐row plots. In one‐row plots in this high‐yielding experiment, the neighborhood covariate model based on panicle number improved residual mean square by 20%, but relative selection intensity by 3% only. Similarly, the covariate models based on height or panicle number in both one‐ and two‐row plots in the other experiments improved just 4%. We conclude that unbordered one‐ or two‐row plots can provide reasonable estimates of grain yield of upland rice without any bias due to competition effects, except for high‐yielding one‐row plots (>350 g m−2).

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Section: Resultssupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%