Materials and methods

Plant materials

Three hundreds and ninety two rice lines developed for the irrigated lowland ecosystem were used in this study to have a large genetic diversity. About 16% are released cultivars, while the rest are advanced breeding lines. Majority of the lines were from IRRI (223). The number of lines from PhilRice, CIAT, China and Vietnam were more than ten. The rest of the lines were from programs in Bangladesh, Colombia, Indonesia, Nepal, Africa Rice Center, Egypt and Pakinstan, India, Italy, Repubilic of Korea, Sri Lanka, Suriname, Taiwan and Turkey. The seeds were obtained from the International Network for Genetic Evaluation of Rice (INGER) and IRRI breeders (Appendix Table S1.). Since the present study is part of the population improvement project initiated in IRRI to broaden the genetic dveristy of IRRI’s irrigated rice breeding populations, some of the lines did not perform well in the tropical environments (Philippines) and subtropical environments (China) and were removed from the GEI analysis (see the data analysis section below).

Testing environments

Jiangxi (JX) and Sichuan (SC) of China and IRRI headquarters (Los Baños, Philippines) were the three testing locations. JX and SC are two major rice production provinces in China with distinct soil and climatic characteristics. Genotypes perform well in JX and/or SC tend to have good adpation in other indica rice production areas in China as well. IRRI headquarters has been the major breeding site of IRRI’s irrigated breeding program for more than 40 years (Table 3.1). The experiments in JX and SC were for one crop season in 2012. At IRRI, the experiment was carried in the dry season (DS) and wet season (WS) of 2012. Three nitrogen fertilizer application rates, no nitrogen, low (90 kg ha-1) and high (180 kg ha-1), were used to create three artificial environments in the DS, designated as DS1, DS2 and DS3, respectively.

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Similarly, three nitrogen ferilizer application rates, no nitrogen, low (45 kg ha-1) and high (90 kg ha-1), were used to create three artificial environments in the WS, designated as WS1, WS2 and WS3, respectively. The use of no nitrogen application and releatively high nitrogen application (180 kg ha-1) was to allow testing the nitrogen responses beyond the nomral nitrogen rate used in Philippines and other countries. Farmers in China ususally apply more than 180 kg ha-1nitrogen while famers in many South-east Asia countries hardly apply any nitrogen. It has been suggested that IRRI should increase nitrogen rate in its DS trials to allow identifying breeding lines with higher yield potential (Dr. Akim Doberman, Personal communication). The nitrogen ferilizer application rate in the two Chinse locations was 150 kg ha-1.

Trial description

A row-column design (28 × 14) with two replications was used for all the six environments at IRRI with different randomizations. Each plot was 2.56 m2 consisting of 64 plants (8 × 8) with 20 cm spacing between rows and no vacant rows between plots. In the DS of 2012, the genotypes were seeded in a seedling bed on 22 and 23 November, 2011 and transplanted with a single plant per hill on 15 December, 2011 in the field of IRRI campus (Table 3.1). Two days before transplanting, Furadan was applied to control the golden snail and basal nitrogen was applied accordingly. Three weeks later after transplanting, the plants infected by tungro disease were removed by hand from the field to prevent disease spread. Nitrogen fertilizer was top dressed at 14 and 40 days after transplanting. Hand weeding and pesticide application was done as needed. Bird scaring practices were applied from anthesis to harvest to prevent grain losses. Rodents were controlled by setting traps. In the WS, entries were seeded on 12 June and transplanted on 6 July 2012 (Table 3.1). The management practices followed were the same as in the DS. Harvest was done in accordance with the maturity of each variety and the earliest batch started from 24 September 2012 and lasted until 26 October 2012.

In China, the experiment design used was a randomized complete block design with two replications. In JX, the plot was 2.56 m2 consisting of 64 plants (8 × 8) spaced at 0.2 m × 0.2 m, while the plots in SC was 0.96 m2 consisting of 24 plants (4 × 6) plants spaced at 0.2 m × 0.2 m. Plots were managed conventionally following the established local normal practices.

The plot size used in the present study is much smaller than that recommended for yield testing by IRRI (IRRI, 2013). Adopting the recommended plot size is impractical for

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trials with a large number of lines, because the cost and the chance to introduce extraneous errors are high. Whether the GY measured using a small plot had a strong correlation with that measured in large plot has never been systematically studied. However, when the objective is not to select the very best genotypes, which is the case of the present study, multi-row small plot is likely to be appropriate. Indeed, small plot size was adopted by most (if not all) of the reported studies involving more than a hundred genotypes. Small multi-row plot is also widely used in yield testing by Chinese rice breeders, although the last stage of the Chinese National Variety Test uses large plot.

Measurement of traits

Grain yield and related traits were evaluated following IRRI’s standard evaluation system for rice (IRRI, 1996). Flowering date was the date when more than half panicles of each plot were flowering. Days to flowering (DTF) was the sum of days from seeding to flowering date. Plant height (PH) was measured as the average height of five plants (three plants from second row and two plants from third row) in cm from the soil surface to the tip of the tallest panicle (awns excluded). Tiller number (TN) was counted as the average of the five plants which were measured for the PH. The five plants harvested at maturity separately from the middle of each plot were used for measuring the following agronomic traits. (1) Panicle number per plant (PN): the average number of panicles per plant; (2) Spikelet number per panicle (SN): the average number of the filled grains and unfilled grains, measured using three panicles per plant; (3) Grain number per panicle (GN): the average of filled grain number, measured using three panicles per plant; (4) Seed setting rate (SR): the ratio of GN to SN; (5) Number of primary branches per panicle (PB) was the average number of primary branches of three panicles from each plant; (6) Number of secondary branches per panicle (SB) was the average number of secondary branches of 3 panicles from each plant. Thousand grain weight (TGW): average weight of 1, 000 filled grain, measured in grams, average over two samples of 100 grains taken from the bulk harvested grains from each plot. All the materials were threshed and dried in the oven for two days at 55 °C and then stored in cool room for 2 months. This allowed the moisture content of all samples to uniformly reach around 14%. Grain yield per plot (GY) was the sum grain weight of the bulk harvested plants and the five plants harvested sperately for data collection (totally 36 plants).

All traits were measured for the six experiments at IRRI. GY, DTF, GN, PH, PN, TGW and SR were measured in SC, while GY, DTF, GN, PH and SR were measured in JX.

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Data analysis

Due to photoperiod sensitivity, insect or rat damage, some of the lines couldn’t give any production in some environments. Although we aimed at indica breeding lines, 24 lines were later confirmed to be japonica and removed from the data analysis. A few lines were found to be outliers using a model-based population structure analysis implemented in STRUCTURE (Pritchard et al., 2000b) and multi-dimensional scaling and cluster analysis implemented in the R packages AWclust (Gao and Starmer, 2008) based on 50 SSR markers evenly distributed among all chromosomes and removed as well (results not shown). Finally, 303 lines had GY data in all testing environments were used in multi-site analysis. The number of lines used for analysis for different traits varied slightly.

All trials were separately analyzed by fitting an appropriate spatial model with rows and columns using PBTools (bbi.irri.org) and R (R Core Team, 2015). The field plot row and column positions were used as fixed covariates to partially adjust for the possible local field trend. The best linear unbiased estimations (BLUE) from the best-fit model were used as raw data for all subsequent analyses. Classification of genotypes was performed using an agglomerative hierarchical clustering procedure with squared Euclidean distance as the dissimilarity measure (Williams, 1976) and Ward’s method, which uses incremental sums of squares as the clustering strategy (Ward, 1963).

PBTools (bbi.irri.org) and R were also used to perform the two-stage combined analysis to estimate variance components of different sources. For GY, the GEI was also decomposed into genotype-by-season, genotype-by-nitrogen and genotype-by-season-by- nitrogen interaction. The AMMI model (Gauch, 1988) was used in analyzing the GEI for GY. The AMMI model is a combination of analysis of variance (ANOVA) and principal component analysis (PCA). ANOVA is used to analyze the main effects while PCA decomposes the interaction into PCA axes.

The analytical model can be written as Yge = µ + αg + βe + Σn λn γgn δen + ρge + εger

Where Yge is the trait of genotype g in environment e; µ is the grand mean, αg is the genotypes deviation from grand mean and the environment deviation βe, λn is the eigenvalue of PCA axis n; γgn and δen are the genotype and environment PCA scores for PCA axis n; ρge is the residual of AMMI model and εger is the random error.

Biplot was used to visualize the AMMI results. Genotype group performance plot was constructed by plotting mean grain yields for genotype groups against environment groups based on the untransformed mean yield.

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