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Does Use of Unbordered Plots Affect Estimation of Upland Rice Yield? Kazuki Saito,* Ibnou Dieng, Elke Vandamme, Jean-Martial Johnson, Koichi Futakuchi
Abstract 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 onerow 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 tworow plots in the other experiments improved just 4%. We conclude that unbordered one- or tworow 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).
crop science, vol. 55, january– february 2015
Africa Rice Center (AfricaRice), 01 BP 2031, Cotonou, Benin. Received 5 Mar. 2014. *Corresponding author (
[email protected]). Abbreviations: DAS, days after sowing; HI, harvest index; LSD, least significant difference; TDM, total dry matter.
A
dvances in crop genomics through new high-throughput sequence and genotyping platforms have reduced the cost and increased the speed of delivery of genetic data for many important crops (Bräutigam and Gowik, 2010). More accurate and precise phenotyping strategies are critical for discovering genes and mechanisms underpinning important agronomic traits. Evaluation of large numbers of populations is essential to obtain unbiased estimates of genomic regions associated with a desired trait (Bernier et al., 2007). However, a shortcoming in experiments using large numbers of genotypes is the larger quantity of resources required such as labor and space. Limited resources may encourage field assessment in small, unbordered plots. The challenges using small plots are well known and generally related to competition effects between adjacent plots for light, nutrients, and water (Kempton, 1982; Gomez and Gomez, 1984). The effects may confound the performance of lines. In this study, plot size is defined by row number. For rice (Oryza sativa spp.), numerous studies have used unbordered onerow, or two-row plots for field phenotyping (e.g., Bernier et al., 2007; Venuprasad et al., 2007). However, to our knowledge, few studies have evaluated the effects of row number and genotype ´ row number interaction on grain yield. Gomez (1972) suggested that excluding the two border rows at each site when making observations is required for eliminating competition effects
Published in Crop Sci. 55:255–261 (2015). doi: 10.2135/cropsci2014.03.0182 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. www.crops.org 255
Table 1. Description of crop management, weather data, soil properties and agronomic traits of 14 varieties grown in four experiments in Benin.
Year of study
Exp. 1
Exp. 2
Exp. 3
Exp. 4
2008
2008
2009
2010
Sowing date
28 Apr.
4 Aug.
28 Aug.
27 Apr.
Water management
Rainfed
Supplemental irrigation
Supplemental irrigation
Rainfed
60–13–25
30–13–25
60–13–25
60–13–25
28 Apr. to 18 Aug.
4 Aug. to 31 Oct.
28 Aug. to 3 Dec.
27 Apr. to 4 Aug.
912 No data
407 No data
165 16
834 15
31
30
31
31
22
23
23
23
5.1 7.1
5.4 19.6
5.4 19.6
5.1 9.3
2.2 65
2.2 65
0.76 10
37
37
23
256 672
378 813
241 522
0.31 66 108
0.39 60 117
0.39 66 103
Fertilizer (N–P–K kg ha-1) Weather data Period† Rainfall (mm) Solar radiation (MJ m ) -2
Ma ximum temperature (°C) Minimum temperature (°C) Soil properties (0–15 cm)‡ pH (H2O) Organic carbon (g kg ) -1
0.37 15 Extractable P (mg kg-1) Clay (%) 15 Agronomic traits (average of 14 varieties across three plot sizes) 118 Grain yield (g m-2) -2 349 Total dry matter (g m ) Total N (g kg-1)
Harvest index Days to heading Height at harvest (cm)
0.30 79 88
†
The period from sowing to the last harvesting of longer-duration varieties, except for Exp. 2. The last harvesting date was 17 Nov. in Exp. 2, but no weather data were available for November 2008.
‡
A 1:1 ratio of soil:water for pH, chromic acid for digestion for organic carbon, Kjeldahl digestion, and colorimetric determination for total N, Bray 1 for extractable P, and hydrometer method for clay content determination (IITA, 1982).
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. If scientists ignore the importance of plot size, the possible confounding effects of interplot competition are likely to result in biased selection of germplasm and the identification of genomic regions that are not relevant to the target trait and target environment (Rebetzke et al., 2013). Likely, such bias is high under both highyielding conditions where neighboring plants compete for light and low-yielding conditions where neighboring plants compete for nutrients and/or water. It has been proposed to use statistical models to correct for competition effect (e.g., Goldringer et al., 1994; Foucteau et al., 2000). This study considers a covariate model that includes plant characteristics associated with the competition as one or more covariates (Kempton et al., 1984; Goldringer et al., 1994). The covariates used equaled the difference between the value of a certain agronomic trait in the plot and the mean of that trait in the adjacent plots. Previous studies on wheat (Triticum aestivum 256
L.) showed that the covariate model based on height could improve the estimation of grain yield in unbordered plots (Goldringer et al., 1994; Clarke et al., 1999; Foucteau et al., 2000). It is not known whether such statistical models can improve the estimation of grain yield of upland rice grown in unbordered plots. The current study examines variety ´ row number interaction effect on rice yield using 14 diverse upland rice varieties grown in unbordered one- or two-row plots and in self-bordered four-row plots under conditions differing in yield level. The objectives of the study were to investigate whether the use of unbordered plots significantly affects experimental error and grain yields of varieties, and whether these effects are related to yield level, and to examine if statistical models for correcting competition effect can improve the estimation of the yield in unbordered plots.
Materials and methods Description of Experimental Design and Crop Management We conducted four upland rice experiments over 3 yr (2008– 2010) at the Africa Rice Center (AfricaRice) experimental farm in Cotonou, Benin (2°20¢ E, 6°25¢ N) (Table 1). Daily climate data were collected from the research farm’s weather station. This site is located in the southern Guinea Savanna zone
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Table 2. Description of the 14 varieties evaluated, including information on germplasm group, least-square means for days to heading, panicle number, and plant height across four experiments and grain yield for each experiment. Grain yield Variety name Aus 257 IR 74371-3-1-1 IR 79971-B-450-B-2 IR 79913-B-20-B-2 NERICA 1 IR 71525-19-1-1 NERICA 6 CG 14 IR 80508-B-57-3-B NERICA 8 Yumenohatamochi WAB56–50 IR 78877-163-B-1-1 IR 76569-259-1-2-1
Germplasm group
Days to heading
Indica Indica Indica Indica Interspecific progeny Tropical japonica Interspecific progeny Oryza glaberrima Indica Interspecific progeny Temperate japonica Tropical japonica Indica Tropical japonica
67 71 64 72 69 74 70 70 73 62 67 64 70 57
Panicle number Height at per hill harvest Exp. 1† 9 8 8 8 6 7 6 12 9 6 8 6 7 5
122 101 110 103 93 107 108 113 119 91 87 96 98 111
CV(%) 5% LSD
Exp. 3‡ Exp. 2
†
29.6 71.2
†
Mean grain yield across three plot sizes, as plot size ´ variety interaction on grain yield was not significant (see Table 3).
‡
Mean grain yield in each plot size, as plot size ´ variety interaction on grain yield was significant (see Table 3).
Description of Rice Varieties and Data Collection The same 14 varieties, selected from different germplasm groups and for their variation in agronomic traits (days to heading, plant height, and panicle number) were used for all the experiments (Table 2). Entries include one Oryza glaberrima variety, one temperate japonica variety, three tropical japonica varieties, three interspecific progenies from the cross between Oryza sativa L. and O. glaberrima Steud. (Saito and Futakuchi, 2009, 2014; Saito et al., crop science, vol. 55, january– february 2015
2 rows
4 rows
Exp. 4†
————————————————— g m-2 ————————————————— 202 325 666 486 490 370 155 432 447 452 478 290 136 324 647 474 386 323 102 344 423 521 448 228 106 309 411 425 508 184 104 308 324 360 431 248 112 223 410 506 392 197 114 357 454 266 273 155 171 232 270 344 326 238 87 167 313 318 320 242 120 203 300 265 304 183 74 120 297 311 319 265 89 148 199 270 309 210 83 88 209 200 255 240 31.5 35.0
(Windmeijer and Andriesse, 1993). Soil properties of the fields used for the four experiments are shown in Table 1. Experiments 2 and 3 were conducted in the same field. Before sowing, the fields were tilled manually or by tractor. Rice was then sown by placing about four seeds into 1- to 2-cm deep holes spaced at 0.2 m ´ 0.2 m. After emergence, plants were thinned to two plants per hill. All crop management was performed uniformly in the entire site, and manual weeding was done when required. In Exp. 2 and 3, the crop was not limited by moisture, as supplementary irrigation was applied by restoring soil moisture to field capacity manually once or twice a day as necessary. Basal fertilizer at a rate of 3N–1.3P–2.5K g m-2 was applied, and a further 3 g N m-2 of fertilizer was applied at 30 d after sowing (DAS), except for Exp. 3 (Table 1). Each experiment was designed with row number (unbordered, one-row; unborderd, two-row; self-bordered, four-row) as the main-plot factor and 14 varieties as the subplot factor in a split-plot design with three replications. The length of each subplot was 3 m, resulting in a subplot size of 0.2 m ´ 3 m for the one-row plots, 0.4 m ´ 3 m for the two-row plots, and 0.8 m ´ 3 m for the four-row plots, respectively, and a total of 15, 30, and 60 hills per subplot in one-row, two-row, and four-row plots, respectively, in case of no missing hills.
1 row
202.6
22.9 117.0
90.7
25.2 57.0
2012), and indica varieties (Atlin et al., 2006; Saito et al., 2007; Asai et al., 2009). CG 14 and Aus 257 are landraces. IR series were developed by the International Rice Research Institute, whereas WAB56-50 and four NERICA varieties were developed by AfricaRice. Yumenohatamochi was developed in Japan. At maturity, plant height and panicle number were measured on six hills in each subplot as well as on six hills in the neighboring rows for unbordered subplots. The latter information was used for obtaining mean height and panicle number of adjacent subplots. Height was measured from the above-ground stem base to the tip of the tallest panicle. Grain yield and straw dry weight were measured for all hills (grain yields are reported at 14% moisture content), excluding one hill on the end of each row. In the four-row plots, plant height, panicle number, and grain yield measurements were performed only in the two central rows.
Statistical Analyses Grain yield was analyzed using the restricted maximum likelihood (REML) mixed procedure of SAS (SAS, 2008). We considered variety, row number, and the interaction between variety and row number as fixed effects and block and the interaction between block and row number as random effects. 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.
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We fit this reference model, M0, which does not consider interplot competition, for one-row, two-row, and four-row plots for the four experiments. The following analysis was done as a randomized complete block design, and the phenotypic observation on variety i in the jth plot was modeled as: Yij = μ + vi + bj + eij where μ is the overall mean, vi the fixed effect of variety i, bj a fixed effect for the block j, and ejj a random error effect. We also estimated variance components, and considered the varietal effect as random (Scheffe, 1959). They are denoted by Vg and Ve, respectively. Hence, the broad sense heritability (h2) at the level of the trial was estimated by
h2 =
Vg Vg +
Ve k
where k is the number of replications. Two other types of models were fitted to correct for interplot competition effects. The first type, the models M1 included only one covariate derived from neighboring plot values. Hence the response Yij was described as
æ ö 1 Yij = + gi + b j + a ççt ij - (t i-1, j + t i +1, j )÷÷÷ + eij çè ø 2 where tij is the associate trait measured on the ith plot of block j and α is the regression coefficient for the covariate. Plant height, panicle number, and days to heading were respectively considered as covariates for M1 models: M1–height, M1–panicle number, and M1–days to heading. Besides, two M 2 models including each of two covariates were computed: M 2–height and days to heading and M 2–panicle number and days to heading. For each model, two criteria were computed: (i) the residual mean square as model fitness; and (ii) the relative selection 2 efficiency estimated by = r / h4-row where r is the correlation coefficient between corrected one-row or two-row varietal 2 means and four-row varietal means and h4-row is the broad sense heritability in the four-row trial (Goldringer et al., 1994). Results of the M1 and M 2 were shown, only when residual mean squares in these models are lower than in M0 and P values of effects of agronomic traits as covariates are statistically significant (P < 0.05). Yield evaluation under one-row or two-row plots is preferable to evaluation in four-row plots when relative selection efficiency is close to or greater than 1.
Results Mean grain yield across all the varieties ranged from 118 to 378 g m-2 among the four experiments, with the highest mean yield in Exp. 3 and the lowest in Exp. 1 (Table 1). This large variation in mean grain yield can be explained by differences in soil fertility, fertilizer application rate, and water management. Soil organic carbon, total N, extractable P, and clay contents were highest in the field used for Exp. 2 258
and 3, and hence Exp. 2 and 3 can be considered to represent relatively high soil fertility conditions in this study. In both Exp. 2 and 3, rice was grown under high soil fertility conditions with supplemental irrigation. However, the N fertilizer application rate was smaller in Exp. 2 than in Exp. 3. The lowest yield in Exp. 1 was the rice grown under poor soil fertility and rainfed conditions. Aus 257 and IR 743713-1-1 generally showed high grain yields across four trials, comfirming previous studies in Benin (Saito et al., 2012, 2014). Varieties differed widely in days to heading (57 to 74 DAS), height at harvest (87 to 122 cm), and panicle number per hill (5 to 12) (Table 2). The effect of variety ´ row number interaction on grain yield did not reach the significance level at p = 0.05 except for Exp. 3, where mean yield was highest (Tables 1 and 3). In this experiment, the variety ´ row number interaction was significant between one-row and four-row plots, but not between two-row and four-row plots. In Exp. 3, grain yields of Aus 257, IR 79971-B-450-B-2, and CG 14 were >150 g m-2 higher in one-row plots than in four-row plots and can be considered highly competitive varieties (Table 2). While Aus 257 was the tallest variety among all, CG 14 had the largest panicle number. The good performance of Aus 257 and CG 14 in terms of vigorousness is consistent with previous results from Caton et al. (2003), Saito et al. (2010), and Saito and Futakuchi (2014). Genetic variance estimates were larger in unbordered plots than in self-bordered four-row plots in all the experiments except for two-row plots in Exp. 2 (Table 3). Residual variance of grain yield in one-row and two-row plots was higher than in self-bordered four-row plots in three and two of four experiments, respectively (Table 3). Furthermore, the difference in residual variance between one-row and four-row plots was larger in the high-yielding experiment (Exp. 3) than others. Heritability in this study was beyond 0.75 in all the experiments except for four-row plots in Exp. 1 and 2-row plots in Exp. 4. Heritability was lower in one-row plots than in four-row plots except for Exp. 4, where mean yield was the lowest. The low heritability in four-row plots in Exp. 1 was reflected by higher residual variance and extremely low genetic variance in four-row plots. As this result from Exp. 1 was largely different from other experiments, we did not proceed with further analysis using covariate models for Exp. 1. The result of heritability was not consistent between two-row and four-row plots, and heritability was lower in two-row plots in two of four experiments. Relative selection efficiency using the reference model M0 for one-row and two-row plots ranged from 0.67 to 0.88 and from 0.56 to 0.96 (Table 4). Two of three experiments showed >0.8 of relative selection intensity for both unbordered plots, suggesting that selection based on unadjusted means in unbordered plots would be effective. Table 4 also shows the neighboring covariate models M1 and M2 that had
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Table 3. P value from the analysis of variance, genetic and residual variance components, and heritability for grain yield of 14 varieties evaluated in three types of plots with different row numbers in four experiments. Exp. 1
Exp. 2
Exp. 3
Exp. 4
1-row 2-row 4-row
—————————— P value—————————— 0.70 0.65 0.87 0.67
