Genotype by environment interactions across diverse rainfed lowland rice environments

Field Crops Research 64 (1999) 35±50

Genotype by environment interactions across diverse rainfed lowland rice environments L.J. Wadea,*, C.G. McLarena, L. Quintanaa, D. Harnpichitvitayab, S. Rajatasereekulb, A.K. Sarawgic, A. Kumarc, H.U. Ahmedd, Sarwotoe, A.K. Singhf, R. Rodrigueza, J. Siopongcoa, S. Sarkarunga a

International Rice Research Institute, Box 3127 MCPO, Makati City 1271, Philippines b Ubon Rice Research Center, Rice Research Institute, Thailand c Indira Gandhi Agricultural University, Raipur, MP, India d Rajshahi Research Station, Bangladesh Rice Research Institute, Bangladesh e Jakenan Experiment Station, Central Research Institute for Food Crops, Indonesia f Narendra Deva University of Agriculture and Technology, Faizabad, UP, India Accepted 3 September 1999

Abstract The nature of genotype by environment (G  E) interactions in rainfed lowland rice was examined using data for 37 genotypes across 36 environments in India, Bangladesh, Thailand, Indonesia and the Philippines from 1994 to 1997. G  E interaction accounted for 32% of the total sum of squares, with environment and genotype responsible for 63% and 5%, respectively. More than 47% of the G  E sum of squares was captured by a nine genotype group by nine environment group summary. Sites with similar characteristics were tightly grouped, as were related genotypes. Environment groups included some with favourable water supply, and others with early drought, late drought, rapid-onset late drought, and submergence. Groupings of genotypes could be explained by their performance in relation to these conditions. PSBRc14, IR36 and IR64 had high yield potential and performed well over most environments, while CT9897-55-2-M-3-M and the F1 hybrids IR64615H and IR68877H also had high yield potential but only performed well with adequate water supply and where standing water remained shallow. Groups including Mahsuri, and IR62266-42-6-1 and IR57514-PMI-5-B-1-2, were stable across environments. NSG19 was preferentially adapted to environments with rapid-onset late drought, and Sabita and KDML105 to environments favouring late maturity or recovery after drought. Implications of these results for choice and management of testing sites, and identi®cation of suitable reference lines for the breeding program were discussed. A probe set of six lines was identi®ed to include Sabita or KDML105, NSG19, Mahsuri, IR57514-PMI-5-B-1-2 or IR62266-42-6-1, PSBRc14 and CT9897-55-2-M-3-M, which represent broad and speci®c adaptations to the major target subecosystems in rainfed lowland rice systems. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Genotype by environment interactions; Rice; Rainfed lowland; Reference lines

* Corresponding author. E-mail address: [email protected] (L.J. Wade)

0378-4290/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 4 2 9 0 ( 9 9 ) 0 0 0 4 9 - 0

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L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

1. Introduction Rainfed lowland ecosystems are de®ned as areas where rice is grown in levelled and bunded ®elds without irrigation (Mackill et al., 1996). This ecosystem covers almost 40 million ha in South and Southeast Asia (IRRI, 1993). Rainfed lowland rice encounters an environment more complex than most other rainfed crops. Because it is grown in bunded ®elds without water control, hydrological conditions may ¯uctuate and plants may suffer from submergence and/or drought stress, with major consequences for root growth, nutrient availability, and weed competition (Garrity et al., 1986). The soils of rainfed lowland areas are often infertile, and may be acidic or saline. Soil problems, combined with the problem of uncertain water supply, are major limitations for yield improvement in the rainfed lowland ecosystem (Wade et al., 1999a, b). This study examined genotype by environment (G  E) interactions for grain yield of rainfed lowland rice over ®ve countries in which rainfed lowland rice is important (IRRI, 1993). Experiments were conducted across multi-locations in India, Bangladesh, Indonesia, Thailand and the Philippines from 1994 to 1997 in association with key sites of the Rainfed Lowland Rice Research Consortium (Zeigler and Puckridge, 1995). India has the largest area under rice production in the world (47 million ha). Rice environments in India are extremely diverse: 45% irrigated, 33% rainfed lowland, 15% rainfed upland, and 7% ¯ood-prone, with rainfed lowland being predominant across 15 million ha in eastern India (Madhya Pradesh, Uttar Pradesh, Bihar, Orissa, West Bengal and Assam). In Bangladesh, 43% is rainfed lowland, 24% is irrigated, 24% is ¯ood-prone and 9% is upland. Drought and ¯ooding occur annually, but cause serious damage only in about one or two years in 10 (IRRI, 1993). Rainfed lowland is the major rice ecosystem in Thailand where it occupies more than 6.6 million ha or around 75% of rice land but produces only 60% of the total production because the average yield is only 1.75 t/ha. The Philippines has an estimated 1.32 million ha of rainfed lowland rice with an average yield of 2.0 t/ha. This represents about half the rice area of the country. Rice is also grown in a wide range of environments in Indonesia. About 72% of the area is

irrigated, 7% rainfed lowland, 10% ¯ood-prone and 11% upland. In the rainfed lowland area in Central Java, farmers grow two crops of rice per year: dryseeded rice during the wet season (gogorancah) followed by minimum-till transplanted rice in the postrainy season (walik jerami). Conditions encountered by rainfed lowland rice vary across countries, and are in¯uenced by different combinations of rainfall, soil type and toposequence position, and therefore, different agrohydrology and soil fertility (Wade et al., 1999a, b). The tall traditional rice cultivars commonly grown in the rainfed lowlands provide stability of production against ¯ooding and soils-related problems, but are less able to respond to better seasons or higher inputs (Mackill et al., 1996). Rice farmers in this ecosystem need improved genotypes that are higher yielding, resistant to important insect pests and diseases, tolerant of abiotic stresses, and responsive to better management, but still have stable yields across environments. Rice breeders, however, experience dif®culty in the ef®cient selection of improved genotypes because of large G  E interactions for grain yield in rainfed lowland (Cooper and Somrith, 1997; Wade et al., 1997; Cooper et al., 1999). Problems include dif®culty in de®ning the target population of environments, in choosing suitable test locations representative of the target population, and in effectively characterising the conditions prevailing at each test location in each year, to determine whether it represents the desired target (Wade et al., 1996). Where responses of particular genotypes are known, their performance may be used as a simple index of environmental conditions (Cooper and Fox, 1996). For example, two genotypes differing in sensitivity to a speci®c problem (e.g. root-lesion nematode in wheat) could be used as a probe set for that environmental factor. Alternatively, a set of genotypes known to differ in patterns of response over environments may be used as a reference set for classifying environmental conditions. This paper examines the nature of the G  E interactions across 36 rainfed lowland test environments in the Philippines, Thailand, India, Indonesia, and Bangladesh, from 1994 to 1997. The objectives of the study are to quantify G  E interactions, examine the basis of responses by genotype groups across environment groups, and to consider the implications for

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

choice and management of test locations and identifying reference lines for the breeding program. 2. Materials and methods 2.1. Locations The experiments were conducted at a total of 36 site-year combinations (environments Ð E) during 1994±1997, including 11 in Ubon Ratchathani±Chum Phae, Northeast Thailand (168N, 103±1058E), 10 in Raipur±Jagdalpur±Bilaspur, Madhya Pradesh, India (19±228N, 828E), three in Faizabad, Uttar Pradesh, India (278N, 828E), ®ve in Masalasa, central Luzon, Philippines (158N, 1218E), four in Rajshahi, Northwest Bangladesh (248N, 888E), and three in Jakenan, Central Java, Indonesia (48N, 1118E). Where different locations are named within a region, they may be separated by several hundred kilometers, e.g. the set of ®ve locations chosen in Northeast Thailand: Ubon Ratchathani, Chum Phae, Phimai, Sakhon Nakhon and Udon Thani; or the set of three locations in Madhya Pradesh, India: Raipur, Bilaspur and Jagdalpur. At each named location, individual sites were chosen within a few kilometers of each other on soils differing in texture or hydrology. Table 1 shows the method and date of establishment, the ¯owering period, hydrologic conditions during pre-¯owering, ¯owering and post-¯owering, and the yield ranking for each of the 36 environments. For each environment, water regime is characterised by dates of last rain in each period, together with the maximum number of consecutive days without rain in the period. Most crops were grown during the wet seasons of 1994±1997, so were generally established in July and August, but also in June in the Philippines. Direct dry-seeding was used in Thailand, the Philippines and four of six sites in Raipur, with transplanting used in Faizabad, Rajshahi, Bilaspur and Jagdalpur. The exception was Jakenan, Indonesia, where two rice crops per year were grown, with the timings offset from all other locations. At Jakenan, two sites were dry-seeded in October 1996 and December 1997 (the gogorancah or wet season), and one site was transplanted in April 1997 (the walik jerami or post-wet season). Percentages of sand, silt and clay, and soil pH are shown for each of the sites in Fig. 1. In Thailand, soils

37

at Ubon Ratchathani and Sakhon Nakhon were acidic with pH 4 and sand contents of 75%, while at Udon Thani, Chum Phae and Phimai, soils had more clay (20±40%) and pH was between 5 and 6. In the Philippines, pH ranged from 6 to 7 and soils were 20± 47% clay, with the Guimba 1995 site not ponding water readily. In Bangladesh, Rajshahi soils had about 40% clay and a pH of about 6. At Jakenan in Indonesia, soils were 62% silt with a pH 6. In India, Faizabad soils had a pH of about 7 with 50% silt, while at Raipur, pH was about 7 with 30±50% clay. Fertiliser applications were adjusted for each site, as indicated for nitrogen, phosphorus and potassium (NPK) dressings in Wade et al. (1999a). 2.2. Experimental design and data analysis This paper considered a common set of 37 genotypes (lines, cultivars and F1 hybrids) grown in 36 environments from 1994 to 1997 (Table 2; Fig. 2). The genotypes included 27 advanced breeding lines; eight cultivars (IR20, NSG19, Sabita, KDML105, Mahsuri, PSBRc14, IR64, IR36); and two F1 hybrids (IR64615H and IR68877H). Lines 1±5 (IR20, NSG19, Sabita, KDML105, Mahsuri), which were commonly used as reference genotypes in the breeding program (Sarkarung et al., 1995), were repeated three times in each replicate to ensure precise observations and con®rm repeatability. The cultivars were chosen because of their popularity in different areas of the rainfed lowlands (Wade et al., 1999a). The genotypes and their pedigrees are listed in Table 2. In 1994, the experiment was conducted at four sites using 10  10 triple lattice designs (Wade et al., 1995), and in subsequent years, using 7  7 triple lattice designs (Wade et al., 1997). The reference genotypes (1±5) were included in embedded Latin square designs within each replicate in 1994, and in embedded randomised complete block designs in subsequent years. Data were collected on time to ¯owering, plant height, the proportion of ®lled spikelets, 1000 grain weight and grain yield. The mean grain yields for the common set of 37 genotypes over 36 environments were extracted from appropriate single-site analyses for each site. Analysis of variance was used to quantify sources of variation in the data set. G  E interactions were analysed using pattern analysis (DeLacy et al., 1996). This method

38

Table 1 Characteristics of the 36 test sites (environments) in Bangladesh, India, Indonesia, Philippines and Thailand from 1994 to 1997 Siteb

BA BB BC BD FA FB FC IA IB IC ID IE IG IH II IJ IK OA OB OC PA PB PC PD PE TA TB TC TD TE TF TG TH TI TJ TK

Rajshahi (RAJ) Rajshahi (RAJ) Rajshahi (RAJ Rajshahi (RAJ) Faizabad (FBD) Faizabad (FBD) Faizabad (FBD) Raipur (RPR) Raipur-1 (RPR) Raipur-2 (RPR) Raipur-3 (RPR) Raipur (RPR) Jagdalpur (JDP) Raipur-1 (RPR) Raipur-2 (RPR) Bilaspur (BLP) Jagdalpur (JDP) Jakenan-1 (JKN) Jakenan-2 (JKN) Jakenan (JKN) Masalasa (MSL) Guimba (GMB) Masalasa (MSL) MunÄoz (MNZ) Masalasa (MSL) Ubon (UBN) Ubon (UBN) ChumPhae (CPA) Udorn (UDN) Phimai (PMI) Ubon (UBN) ChumPhae (CPA) Sakhon (SKN) Nakhon Ubon (UBN) ChumPhae (CPA) Phimai (PMI) a

Country

Bangladesh Bangladesh Bangladesh Bangladesh India India India India India India India India India India India India India Indonesia Indonesia Indonesia Philippines Philippines Philippines Philippines Philippines Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand

Planting date

n.a. 11 Aug. 95 10 Aug. 96 9 Aug. 97 28 Jul. 95 2 Jul. 96 31 Jul. 97 3 Jul. 94 5 Jul. 95 13 Jul. 95 14 Aug. 95 10 Jul. 96 23 Jul. 96 10 Jul. 97 19 Aug. 97 25 Jun. 97 2 Jul. 97 12 Oct. 96 13 Apr. 97 4 Dec. 97 12 Jun. 94 8 Jun. 95 16 Jun. 95 24 Jun. 95 12 Jun. 96 14 Jul. 94 14 Jul. 95 3 Jul. 95 14 Jul. 95 25 Jul. 95 25 Jun. 96 3 Jul. 96 28 Jun. 96 9 Jul. 97 8 Jul. 97 3 Jul. 97

Planting methodc

Flowering period

TPR TPR TPR TPR TPR TPR TPR TPR DSR DSR TPR DSR TPR DSR TPR TPR TPR DSR TPR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR DSR

n.a. n.a. 7 Nov.±6 Dec. 3 Nov.±6 Dec. n.a. n.a. n.a. 5±27 Oct. 23 Sep.±1 Nov. 23 Sep.±5 Nov. 31 Oct.±8 Dec. 22 Sep.±22 Nov. 17 Oct.±20 Nov. 17 Oct.±20 Nov. n.a. 11 Sep.±30 Oct. 3 Oct.±22 Nov. 1997 n.a. n.a. n.a. 2 Sep.±3 Nov. 12 Sep.±31 Oct. 12 Sep.±31 Oct. 21 Sep.±2 Nov. 8 Sep.±20 Nov. 23 Sep.±8 Nov. 2 Oct.±18 Nov. n.a. 3±31 Oct. 13 Oct.±8 Dec. 11 Sep.±23 Oct. 1 Oct.±18 Nov. 27 Sep.±5 Nov. 24 Sep.±4 Nov. 26 Sep.±9 Nov. 1 Oct.±4 Nov.

Last rainy dayd Pre-Fl.

Flower

Post-Fl.

n.a. n.a. 29 Oct. 23 Oct. n.a. n.a. n.a. ok 20 Sep. 20 Sep. 21 Oct. 15 Sep. 6 Oct. 6 Oct. n.a. 8 Sep. 29 Sep. n.a. n.a. n.a. 31 Aug. ok ok ok 6 Sep. (9) 18 Sep. ok n.a. 1 Oct. Nil 7 Sep. 25 Sep. (21) 25 Sep. 21 Sep. ok 29 Sep. (10)

n.a. n.a. Nil (30) 1 Dec. (12) n.a. n.a. n.a. 23 Oct. (18) 21 Oct. (11) 21 Oct. (15) 10 Nov. (28) 23 Oct. (30) 12 Nov. (22) 12 Nov. n.a. 26 Oct. (5) 20 Nov. n.a. n.a. n.a. 25 Oct. (9) ok ok ok ok (6) 12 Oct. (26) 3 Nov. (15) n.a. 18 Oct. (13) Nil ok (12) 13 Nov. (12) ok (19) 26 Oct. 4 Nov. (33) 14 Oct. (17)

n.a. n.a. Nil (24) 14 Dec. (21) n.a. n.a. n.a. 3 Nov. (24) 10 Nov. (21) 10 Nov. (25) Nil (30) Nil (30) Nil (25) Nil n.a. 4 Nov. (24) 26 Dec. n.a. n.a. n.a. 14 Nov. (24) 15 Nov. (15) 15 Nov. (15) 20 Nov. (10) 28 Nov. (20) 30 Nov. (30) Nil (31) n.a. 3 Nov. (27) Nil 12 Nov. (18) Nil (30) 14 Nov. (24) Nil (32) 16 Nov. (23) 3 Nov. (27)

Yield ranke 16 20 5 19 8 9 7 29 25 11 34 33 13 36 4 12 10 1 23 3 2 22 18 14 31 26 17 35 21 32 30 28 27 15 24 6

Environment code provides a two letter symbol for each environment, which is used in Figs. 1 and 3. It comprises a first letter indicating a country (e.g. B: Bangladesh), or a site (e.g. F: Faizabad), where there are more than one site per country. The second letter is provided sequentially for successive planting dates and years. b Names used for individual sites are shown, together with their three digit codes used in Fig. 1 as location code. c TPR: transplanted rice; DSR: dry-seeded rice. d For each environment, date of last rain is shown for each of these periods: pre-flower, flower and post-flower, relative to the flowering period of the 37 genotypes indicated in the previous column. Where applicable, number of consecutive days without rain is shown in brackets. For pre-flowering and flowering, days without rain precede the date shown. For post-flowering, days without rain follow the date indicated (i.e. terminal drought). The sole exception was for site TG, where there were 21 days without rain at the start of the flowering period. e Ranking of mean grain yield for each environment.

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

Environment codea

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

39

Fig. 1. Environment groups at the nine-group truncation level of Ward's agglomerative clustering algorithm applied to standardised yield data over 37 genotypes. The dendrogram shows fusion levels at which the groups join. The fusion level is proportional to the increase in pooled within group SS at each fusion. Environment groups are identified by environment codes (CODE), locations (LOC) and years (YR) for each of the 36 sites as indicated in Table 1. The mean grain yield (t/ha) for each site is also presented, along with the percentage of sand, silt and clay and pH of the soil at that site. For each group, the mean values are shown in the shaded section to the right of the dendrogram.

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L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

Table 2 Pedigrees of the 37 genotypes grown across 36 environments in Bangladesh, Indonesia, India, Philippines and Thailand from 1994 to 1997 Designationa

Crossb

Yield rankc

01 02 03 04 05 08 09 10 11 12 15 16 17 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 42 62

IR 262-24-3/TKM 6 Unknown: selection from a landrace Unknown: selection from a landrace Unknown: selection from a landrace TAICHUNG 65/2*MAYANG EBOS 80 IR64/C 1064-13 IR5657-33-2-1/IR 2061-465-1-5-5 IR2042/CR 94-13 CT6241-2-2-1-3/CT 9395 P3084-F4-56-2-2/CT 9355 MAHSURI/KDM 65-G4 U-4-492//IR 21848-65-3-2 HAWM DAW/IR 13610-72-2-2E-P1//5173 HAWM DONG/IR 13146-45-2-3 IR43581-57-3-3-6/KDML 105//IR 21836-90-3 IR43581-57-3-3-6/IR 26940-20-3-3-3-1//KDML 105 B4259-48-1-1-3/IR 4563-52-1-3-6//IR 43511-150-2-2-5/IR 41183-57-5-2-1 BR51-74-6-J1/IR 26760-27-1-3-2-1//IR 41389-20-1-5 IR47310-94-4-3-1/IR 44962-7-6-2-2 IR38547-B-B-10-1-3/IR 40473-B-B-B-13-2-3 IR33238-25-2-3-2/CNA 4130//IR 33380-7-2-1-3 R262-2-1-1-3/2*IR 41431-68-1-2-3 IR8192-200-3-3-1-1/IR 31238-474-3-P1//IR 43342-10-1-1-3-3 IR8192-200-3-3-1-1/IR 31238-474-3-P1//IR 43342-10-1-1-3-3 IR8192-200-3-3-1-1/IR 31238-474-3-P1//IR 43342-10-1-1-3-3 IR42241-76-2-3-3/IR 52533-52-2-1-2-1-B-2-3 IR42241-76-2-3-3/IR 52533-52-2-1-2-1-B-2-3 IR42241-76-2-3-3/IR 52533-52-2-1-2-1-B-2-3 IR42241-76-2-3-3/IR 52533-52-2-1-2-1-B-2-3 IR43342-10-1-1-3-3/IR 53508-B2-4-1-3-3 IR48063-SRN-37-1-1-1-3/IR 53508-B2-4-1-3-3 IR48063-SRN-37-1-1-1-3/IR 53508-B2-4-1-3-3 IR48063-SRN-37-1-1-1-3/IR 53508-B2-4-1-3-3 IR48063-SRN-37-1-1-1-3/IR 53508-B2-4-1-3-3 IR55722-B-B-6-2-2/IR 43522-37-3-3-3 IR58025 A/IR 29723-143-3-2-1R IR58025 A/IR 21567-18-3R IR43559-25-5-3-2/IR 32429-47-3-2-2

29 18 22 39 24 1 5 4 47 6 36 11 13 9 2 46 45 12 8 41 31 44 35 33 26 24 18 32 10 20 42 43 37 27 7 3 15

IR20 Nam SaGui 19 (NSG19) Sabita Khao Dawk MaLi 105 (KDML105) Mahsuri PSBRc14 IR64 IR36 CT9993-5-10-1-M (CT9993) CT9897-55-2-M-3-M (CT9897) IR52561-UBN-1-1-2 (IR52561) IR54071-UBN-1-1-3-1> IR54977-UBN-6-1-3-3> IR57514-PMI-5-B-1-2 (IR57514) IR57515-PMI-8-1-1-S> IR57546-PMI-1-B-2-2 IR58821-23-1-3-1 (IR58821) IR62266-42-6-1 (IR62266) IR63429-23-1-3-3 (IR63429) IR66469-17-5-B IR66506-5-1-B IR66516-11-3-B IR66516-24-3-B IR66516-37-7-B IR66879-19-1-B IR66879-2-2-B IR66879-20-2-B IR66879-8-1-B IR66882-4-4-B IR66883-11-1-B IR66883-18-2-B IR66883-18-3-B IR66883-44-3-B IR66893-5-2-B IR64615H IR68877H IR58307-210-1-2-3-3>

a Genotype number and name for each of 37 genotypes; numbers 1±5 were included three times in each environment, with numbers 01±05, 1A±5A, 1B±5B, contracted names for some entries are shown in parenthesis. b Pedigrees for each of the 37 genotypes. c Ranking of mean grain yield for each genotype across the 36 test locations.

involved the joint application of cluster analysis and ordination to a transformed G  E matrix. Since the objective was exploratory analysis to understand interaction and adaptation (Cooper, 1999), the G  E matrix was transformed by double centring of row and column means (mean polish), resulting in a matrix of residuals from the two-way additive, main effects model. Hence, in this case, the ordination component

of pattern analysis corresponded with the additive main effects and multiplicative interaction (AMMI) analysis (Gauch, 1992). For both genotypes and environments, clustering of the transformed data was computed using an agglomerative hierarchical algorithm based on minimising incremental sum of squares (Ward, 1963). The numbers of genotype and environment groups retained in the cluster analysis was based

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

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Fig. 2. Genotype groups at the nine-group truncation level of Ward's agglomerative clustering algorithm applied to standardised yield data over 36 environments. The dendrogram shows fusion levels at which the groups join. The fusion level is proportional to the increase in withingroup SS at each fusion. Genotype groups are identified by genotype number (code) and name (designation) for each of the 37 genotypes as indicated in Table 2. The mean grain yield (t/ha), plant height (cm), days to flowering, percentage filled spikelets and 1000 grain weight (g) are shown for each genotype, with mean values for the groups in the shaded areas.

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L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

Fig. 3. Biplot of the first two ordination scores for standardised grain yield data for 37 genotypes in 36 environments. Scores on the first axis (IPCA1) account for 32% of G  E-SS, and scores on the second axis (IPCA2) account for 14% of G  E-SS. Environment points are at the end of spikes, and are identified by the two letter codes from Table 1. Symbols for genotype points and ovals indicate genotype groups defined by cluster analysis, with genotypes identified by genotype numbers used in Table 2. Four groups (84, 79, 76 and 81) plotted close to the origin.

on a requirement to retain a reasonable amount of the GE interaction sum of squares among group means and to select the pair of group numbers that had the smallest pooled within-group mean squares of all groupings which just achieve this minimum (Wade et al., 1997). Scores for both genotypes and environments from the two-component interaction principal component model (IPCA) were computed for each component (IPCA1 and IPCA2) and plotted as a biplot (Fig. 3), with environment points at the end of spokes with labels from Table 1, and genotype points as symbols labelled with the two-letter codes indicated in Table 2. Ovals and plot symbols indicate groupings from the cluster analysis. Genotype points which plot close together indicate genotypes with similar patterns of interaction over environments. The response plots (Fig. 4) indicate the nature of genotype by environ-

ment interactions with the main effects of genotypes and environments removed. Thus, the values plotted for each genotype group by environment group are deviations from additive main effects predictions of genotype and environment effects. The larger the deviation, the greater was the interaction of the genotype group with the environment group. The interaction may be positive or negative, depending upon whether or not the genotype group yielded more or less than the main effects expectation. 3. Results 3.1. Characterisation of sites Across the 36 sites, conditions varied from those producing almost no yield at Raipur-1 in India in 1997

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

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Fig. 4. Response plots for nine genotype groups and nine environment groups (averages of mean polish transformed data).

(IH) to those producing an average of 4.62 t/ha at Jakenan-1 in Indonesia in 1996 (OA) (Table 1). In Raipur-1 1997 (IH), yields averaging only 0.11 t/ha resulted from severe water stress during grain ®lling, after rains ceased in mid-¯owering (Table 1). In contrast, water supply was favourable in the 1996 gogorancah season in Indonesia. Between these extremes, site conditions varied in timing and severity of water limitation. In the Philippines, rains continued until after ¯owering, so water de®cit was of little concern at those ®ve sites (PA±PE), nor in comparable circumstances such as Bilaspur 1997 (IJ) and Jagdalpur 1997 (IK). Symptoms of early drought were observed at several sites in Northeast Thailand, including Ubon 1994 (TA) and 1995 (TB), Chum Phae 1995 (TC) and 1996 (TG), and Sakhon Nakhon 1996 (TH). Early drought did not reduce yield

severely, except in Chum Phae 1995 (TC), where drought continued throughout the season, and water was rarely ponded. Where soils could store enough water due to higher silt and clay contents (Rajshahi 1996 (BC), Jagdalpur 1996 (IG)), yields were not reduced by absence of rains in grain ®lling. But where rains ceased before or during ¯owering on soils which were light in texture, yields were severely reduced by late season drought (Raipur-3 in 1995 (ID), Raipur 1996 (IE), Raipur-1 in 1997 (IH), Jakenan-2 in walik jerami 1997 (OB), Chum Phae 1995 (TC) and Ubon 1997 (TI)). The crops were submerged for a short period near ¯owering in Faizabad 1995 (FA) and Phimai 1995 (TE), the latter site being affected by ¯oodwater from the river despite the absence of local rains. The crop at Phimai 1996 was killed by prolonged submergence, and was not included. Bilaspur

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L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

1995 (IF) which was killed by drought was also excluded from the analysis. 3.2. Characterisation of genotypes The genotypes included diverse genetic backgrounds (Table 2), and a range of plant heights, days to ¯owering, percent ®lled spikelets, and 1000 grain weights (Fig. 2). Some entries were tall and photoperiod-sensitive (KDML105, Sabita, NSG19), while others were short and insensitive (IR20, IR36, IR64). 3.3. Analysis of variance and pattern analysis Mean grain yields were computed for each environment using the appropriate analysis for each trial. These means were extracted for a subset of 36 environments (Table 1) and 37 genotypes (Table 2) according to the criterion that yield responses were available for each genotype in at least 27 of the 37 environments. The resulting 37  36 G  E table for yield had fewer than 10% missing values. For grain yield, genotype main-effects accounted for only 5% of the total sum of squares, with environments responsible for 63% and GE interactions for 32%. Thus, the sum of squares for G  E (G  E-SS) was more than six times that for G. Cluster analysis on the mean polish residuals was used to identify nine environment groups (Fig. 1) and nine genotype groups (Fig. 2). This combination preserved 47% of the G  E-SS among groups. Hence, about half of the G  E-SS was associated with high dimensional interaction which was assumed to be error. The consistent grouping in Fig. 2 of repeated cultivars (i.e. KDML105, Sabita in genotype group 82; NSG19 in genotype group 52; Mahsuri in genotype group 79; IR20 in genotype group 81) and sister genotypes (e.g. IR66516 lines in genotype group 78 and the IR66883 lines in genotype group 79) indicated a strong component of repeatable interaction represented in the results of the cluster analysis. The ordination analysis of the mean polish residuals, formally equivalent to an AMMI analysis, indicated two strong interaction principal component axes IPCA1 and IPCA2, accounting for 32% and 14% of the G  E-SS, respectively (Fig. 3). Hence, the ®rst two axes preserved a similar proportion of interaction

variability to the 9  9 grouping identi®ed by cluster analysis. In Fig. 1, with the exception of the three singleton groups, the environment groups are presented in order of their mean IPCA1 scores from the ordination biplot (Fig. 3). Hence, the cluster analysis has exploited the same pattern information in the G  E matrix as the ordination, and the ordering of the environments on IPCA1 corresponds with the grouping identi®ed by cluster analysis. Separation of Raipur-2 in 1995 (code IC, group 10) and Bilaspur 1997 (code IJ, group 16) from the remaining groups corresponds with IPCA2 (Fig. 3), and of Masalasa 1994 (code PA, group 21) with IPCA3 (IPCA3 data not shown). In Fig. 2, the genotype group order has also been arranged to correspond with mean IPCA1 scores, with the exception of NSG19 (group 52). Genotype groups are clearly de®ned, with duplicate probe lines always grouping together at very low fusion levels. Similarly, the sister lines of IR66516, IR66879 and IR66883 always grouped as sets, except for one line of IR66883 which went into the neighbouring group. IPCA2 was dominated by only two environments, Raipur-2 in 1995 (code IC, group 10) and Bilaspur 1997 (code IJ, group 16), in the environment ordination. Genotype groups were separated on IPCA2 at all levels of IPCA1 scores: genotype group 82 from 78 at low IPCA1 scores, 52 from others at median IPCA1 scores and 74 from 85 at high scores (Fig. 3). 3.4. Grain yields of nine genotype groups in nine environment groups Mean grain yields for the genotype groups ranged from 1.78 t/ha for group 78 to 2.63 t/ha for group 74 (Tables 3 and 4). Five genotype groups, which comprised of advanced breeding lines and late-maturing photoperiod-sensitive cultivars (82, 52, 78, 79 and 84), were generally lower yielding on an average than the other four groups which comprised early-maturing semi-dwarf cultivars (76, 74 and 85, but not 81). Group 74 (PSBRc14, IR36 and IR64) was highest yielding in ®ve of nine environment groups, producing low yields only in environment group 63, where ¯ooding and rat damage adversely affected shortstatured early-maturing genotypes. While the yield potential of group 85 (CT9897-55-2-M-3-M and the hybrids IR64615H and IR66877H) was higher in some

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

45

Table 3 Grain yield (t/ha) of nine genotype groups across nine environment groups Environment group

Genotype group

Mean

82

52

78

79

84

81

76

74

85

63 46 62 57 60 21 10 16 61

3.27 2.21 1.85 1.17 3.24 2.24 2.89 2.75 0.80

2.31 1.90 1.84 2.13 4.62 5.31 3.94 3.47 1.17

2.70 2.86 1.65 1.01 3.59 4.53 0.00 1.59 1.28

2.39 2.66 1.76 1.26 3.42 4.94 2.91 2.11 1.51

2.32 2.67 2.02 1.63 4.21 3.39 1.93 1.56 1.92

1.85 1.64 1.80 1.48 3.46 3.79 2.65 2.63 2.29

1.61 3.31 2.00 2.03 4.16 3.99 3.46 3.00 2.39

1.28 3.60 2.06 2.54 5.05 ± 3.47 5.10 3.00

1.27 2.00 1.71 2.34 5.99 4.78 2.74 2.60 3.59

Mean

1.88

2.19

1.78

1.95

2.15

1.95

2.30

2.63

2.49

environment groups (60 and 21), performance of this group was much lower in other environments (16, 10, 46). Genotype groups 76 (IR57514-PMI-5-B-1-2 group) and 81 (IR20 group) were more consistent in yield across environments, with group 76 producing slightly higher yields than group 81, especially in environment group 46. Among the later maturing lines, group 79 (including Mahsuri) was quite consistent across environments, never being highest nor lowest yielding at a site. The Sabita-KDML105 group (82) generally yielded well at sites favouring late maturing genotypes, especially in environment group 63. The NSG19 group (52) was well adapted to conditions in the singleton sites (10, 16 and 21), while genotype groups 84 and 78 (including IR52561-UBN-1-1-2 and IR58821-23-1-3-3) were poorly adapted at 10 and 16. Genotype groups 82 and 78 did poorly in environments where there was terminal drought (i.e. environment group 57). For the environment groups, mean grain yields ranged from 1.54 t/ha for group 57 to 4.06 t/ha for group 60 (Table 3). For different reasons, three environment groups seemed to favour later-maturing cultivars: damage to early cultivars from submergence and rats in environment group 63, damage to early cultivars from early or intermittent drought in environment group 62, and late rains in environment group 46. Among the remaining six groups, the Philippine sites (Guimba 1995, Masalasa 1995, Munoz 1995 and Masalasa 1996 in groups 61 and Masalasa 1994 in group 21) had favourable water regimes. Late drought was important for environment group 57, and seemed

2.25 2.35 1.93 1.54 4.06 4.00 2.55 2.51 1.81

to reduce yield potentials of later genotypes in the gogorancah sites in Indonesia (environment group 60). At Raipur-2 in 1995 and Bilaspur in 1997 (groups 10 and 16), observations suggest crops were on track to very high yields, when severe drought stress with rapid onset terminated grain ®lling over a short period. 3.5. Interaction patterns from biplots and response plots Genotype groups were identi®ed which were reasonably stable across all environments, including group 79 (Mahsuri (05) and the IR66883 lines (36± 38)), group 81 (including IR20 (01) and CT9993-510-1-M (11)) and group 76 (including IR62266-42-61 (23) and IR57514-PMI-5-B-1-2 (20)) with mean yields of 1.95, 1.95 and 2.30 t/ha, respectively. These groups mapped to the centre of the biplot (Fig. 3) and showed minimal deviations in the response plots (Fig. 4). Genotype group 79 was separated from group 84 (IR52561-UBN-1-1-2 (15) and the IR66879 lines (30± 33)) by IPCA3 (not shown), with group 79 doing well in Masalasa 1994 (group 21) where waterlogging was observed. Five genotype groups showed speci®c adaptations to particular environment groups by mapping far from the centre of the biplot. NSG19 (group 52), with positive deviations for environment groups 10 and 16 in the response plots, mapped close to those environments (Raipur-2 in 1995 (IC) and Bilaspur 1997 (IJ)) in the biplot. NSG19 also did well in Masalasa 1994. Genotype group 78 (including

46

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

Table 4 Plant height, days to flowering and percent filled spikelets for nine genotype groups across nine environment groups Environment group 63

IJ

PA

Means

123.2 83.4 101.6 112.7 107.0 93.7 122.6 105.8 90.5

62

57

60

61

Average plant height (cm) Ggp-52 137.9 116.6 Ggp-74 90.6 84.4 Ggp-76 107.5 101.8 Ggp-78 124.2 114.5 Ggp-79 115.9 102.8 Ggp-81 99.8 89.0 Ggp-82 139.2 117.1 Ggp-84 114.1 104.2 Ggp-85 99.8 84.5

116.4 85.0 101.4 110.6 104.3 91.9 116.3 104.5 89.0

118.2 76.4 96.4 102.8 100.3 88.5 115.1 99.0 84.5

128.5 72.3 91.5 103.8 104.4 86.2 127.2 98.1 80.9

131.2 83.7 102.5 119.7 112.1 98.6 131.7 110.0 96.6

106.4 87.5 104.1 106.4 103.6 106.0 106.4 96.2 84.6

158.1 109.3 125.8 136.2 134.0 110.9 154.4 136.7 109.4

146.2

Means SE

103.0 9.81

99.7

100.9

111.7

102.4

132.4

120.9

115.7 3.53

46

IC

104.3 LSD5%

a

120.5 133.0 126.8 111.2 138.0 114.7 93.5

Days to flowering (days) Ggp-82 110 100 Ggp-52 94 90 Ggp-78 100 106 Ggp-79 111 105 Ggp-84 104 100 Ggp-81 103 101 Ggp-76 98 100 Ggp-74 96 89 Ggp-85 96 95

110 97 112 109 106 105 104 99 103

104 91 107 104 97 96 91 82 86

114 96 109 107 102 100 97 75 90

121 105 121 117 111 109 109 93 99

108 103 109 107 103 100 91 81 91

124 98 116 108 104 104 97 88 92

137 109 111 113 107 111 108

Means SE

104 2.9

107 8.0

98

102

112

103

106

111

Percent filled Ggp-82 Ggp-52 Ggp-78 Ggp-79 Ggp-84 Ggp-81 Ggp-76 Ggp-74 Ggp-85

spikelets (%) 69.5 66.4 69.0 68.9 68.7 60.8 72.4 65.7 68.5 64.9 70.0 66.4 70.7 71.2 70.0 70.8 70.6 65.3

Means SE a

70.0 3.113

100 LSD5%

66.2 LSD5%

66.9 66.8 60.1 65.4 64.8 64.9 65.6 68.8 63.9

58.7 64.9 54.3 62.4 53.5 59.5 59.2 73.0 60.4

59.6 61.9 52.0 50.6 55.5 53.1 63.3 69.0 60.4

59.3 60.0 56.9 64.8 60.8 64.1 69.2 61.4 62.7

a

65.1 8.69

59.4

57.3

62.0

63.0

a a a a a a a a

a

91

59.1 63.2 48.1 51.8 46.7 51.1 58.0 70.0 59.5

29.8 73.1 62.0 58.5 49.5 54.8 57.1

54.5

53.7

a

58.1

111 96 111 109 104 103 100 91 96

63.0 65.4 58.7 64.0 60.7 63.0 65.0 68.8 63.4

Not available.

IR58821-23-1-3-3) had positive deviations in environment groups 63 and 46, but negative in environment groups 10 and 16, while genotype group 82 (KDML105 and Sabita) was positive in all four of those environment groups. Genotype group 85 (including CT9897-55-2-M-3-M and the hybrids) showed positive interaction in environment groups

60 and 61, but negative in environment groups 10 and 16, while group 74 (PSBRc14, IR36 and IR64) was positive in all of those environments. These relationships are re¯ected in the biplot by the proximity of the genotype groups to the environment groups with their most positive deviations in the response plots.

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

4. Discussion In this study, G  E interaction accounted for 32% of the total sum of squares for grain yield, more than six times the magnitude of variation due to the main effect of genotype. This dominant contribution of G  E relative to genotypic variation is consistent with other studies in rainfed lowland rice (Cooper and Somrith, 1997; Cooper et al., 1999). Thus, selection of better adapted genotypes for the rainfed lowland ecosystem is complex because of large G  E interactions, as discussed by Cooper (1999). 4.1. Target population of environments Much of the complexity in selecting better genotypes of rainfed lowland rice is due to the complexity in the target population of environments. The environment groups identi®ed in this paper suggest those targets include environments of favourable water supply, those prone to early drought, to late drought, to fast-onset late drought, and to submergence, although the last group was not well sampled here. In some cases, an environment group did comprise a set of environments which were in close geographic proximity (e.g. the Philippine sites in group 61), or which were representative of a particular cropping system (e.g. the gogorancah seasons from Jakenan, Indonesia in group 60). More commonly, however, an environmental condition such as late drought did not recur universally at a particular site, and this was consistent with other studies (Cooper et al., 1999). For example, in Raipur and Ubon, the severity of late drought varied with season and planting time, so the groups to which individual experiments were allocated varied, depending on the conditions encountered. Nevertheless, Raipur and Ubon had a higher probability of encountering late drought than other sites. But because of seasonal variability, it is dif®cult to ensure a test environment chosen for its likelihood of exposure to drought stress would actually encounter drought at the desired growth stage and intensity. These results suggest choice of coarse-textured soils in drought-prone areas, together with delayed planting, may increase the probability of exposure to late drought. Conversely, early dry-seeding may increase the likelihood of exposure to early drought.

47

Use of such selection hot-spots is a common breeding technique, but to increase the likelihood of obtaining the desired screen, it may be necessary to use other forms of environmental management, such as rainout shelters or out of season plantings (Fukai and Cooper, 1995; Fukai et al., 1999). How representative the test environment is to the target population of environments is critical for success in evaluating a set of lines or in selecting lines with improved adaptation to those conditions. While dry season plantings may reliably expose the crop to drought, conditions may be quite unrepresentative of the target, if radiation is high, humidity low, and water loss rapid in the dry season. Coarse-textured soils may enhance the probability of drought stress, but their exclusive use may favour selection of lines adapted to low pH conditions, for example. It is important that the test environments chosen are relevant to the desired target population of environments, and if necessary, breeders should adjust selection strategies according to whether appropriate conditions occurred at a site or not. Effective and rapid characterisation of each site is critical in determining its relevance to the target population of environments, and in understanding the adaptations and trait combination strategies that contribute to high yield by different genotypes. Time of disappearance of ponded water relative to time of ¯owering is a simple measure of exposure to late drought (Jearakongman et al., 1995). Maximum water depth and duration of submergence may be used to characterise waterlogging and submergence in ¯oodprone environments (Mackill et al., 1996). Greater efforts are needed to effectively characterise test environments, even though collection of adequate data is often dif®cult in widely separated locations. Where responses of probe genotypes are known for speci®ed conditions, their performance may be used as a simple index of environmental conditions (Cooper and Fox, 1996), and any genotype with a similar response may be considered to have similar adaptive characteristics. These issues and their consequences are discussed below. 4.2. Adaptation of genotype groups This study identi®ed genotype groups with speci®c positive adaptations to the target subecosystems iden-

48

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

ti®ed above, or with broad adaptation across those environment groups. Such genotype groupings and patterns of response may also be used to infer the adaptations of breeding lines for which less information is available. For example, the genotype groups with Mahsuri (79), IR20 (81) and IR62266 (76) were stable across environments. The Mahsuri group (79) was stable despite being longer in duration (111 days to ¯owering) than the IR20 (81) and IR62266 (76) groups (100±103 days to ¯owering). Mahsuri was originally released for irrigated conditions in Malaysia, is widely grown rainfed in India, and is known to be strongly competitive with weeds (Bastiaans et al., 1997). The IR66883 lines which grouped with Mahsuri may be of interest as new sources of germplasm possessing these traits. Comparing the two earlier maturing groups, which were stable in yield across environments, the IR62266 group (76) was higher yielding overall and especially in environment groups where drought stress was a concern (e.g. environment groups 57, 10, 16). This group included IR63429-231-3-3 which was reported to have more roots at depth (Ahmed et al., 1995), IR62266-42-6-1 which was reported to have a higher capacity for osmotic adjustment (Lilley and Ludlow, 1996), and IR57514-PMI-5B-1-2, which was reported to be higher yielding in drought-prone Northeast Thailand and Laos (Romyen et al., 1998). Thus, early maturity and drought escape conferred yield stability to groups 81 and 76, with the greater drought tolerance of group 76 an advantage. It is interesting to note that lines within group 76 possessed different combinations of traits for drought tolerance, but nevertheless grouped together in yield response across environments. Two early maturing genotype groups (74±85) produced the highest yields overall, but differed in their patterns of response. Relative to the high and stable yielding PSBRc14, IR36 and IR64 (group 74), CT9897-55-2-M-3-M and the hybrids IR64615H and IR68877H (group 85) were more sensitive to early drought and more adversely affected by rapid-onset late drought. PSBRc14, IR36 and IR64 are already widely grown in favourable shallow rainfed areas in the Philippines and Indonesia. The hybrids and CT9897-55-2-M-3-M demonstrated a potential for yields of 5.0 t/ha or more in situations with little risk of drought, and where standing water depths remained shallow.

Sabita and KDML105 (group 82) produced lower yields in locations where their late maturity was a disadvantage, such as the Philippines and the gogorancah season in Indonesia. The ability of KDML105 to maintain some leaf area in drought and recover on rewatering (Wade et al., 1999c) may have been advantageous in sites with rapid-onset late drought. While both NSG19 (group 52) and IR58821-23-1-3-1 (group 78) have demonstrated an ability to increase the proportional allocation of dry matter to deep roots under drought (Azhiri-Sigari et al., 1999), this capacity may have been more advantageous for the earlier maturing NSG19 (96 days to ¯owering) than the later maturing IR58821-23-1-3-1 (121 days to ¯owering) in rapid-onset late season drought. Groups with Mahsuri and IR58821-23-1-3-1 (groups 79 and 78) were similar in response in most environments, except the later IR58821-23-1-3-1 (121 compared with 110 days to ¯owering for Mahsuri) failed in Raipur-2 in 1995 (IC). Both lines have been reported to be able to develop roots and extract water rapidly from deeper soil layers in late season drought (Samson and Wade, 1998; Kamoshita et al., 1999). This outcome supports the contention that a greater capacity to extract water from deeper layers may not necessarily be advantageous, if supplies run out before grain ®lling is completed. Trait combinations must be matched with phenology appropriate to the characteristics of the target subecosystem. These interpretations of genotype group responses based on published evidence from other studies need to be examined more closely, to establish whether those traits are in fact responsible for the observed patterns of adaptation discussed here. Such understandings should be of great bene®t in further clarifying plant type requirements and breeding objectives for target subecosystems of rainfed lowland rice. 4.3. Reference lines for the breeding program These genotype groupings may also be used to identify an improved set of genotypes for use as reference lines in rainfed lowland breeding programs in the future. It is interesting to note that the current set of reference lines (Sarkarung et al., 1995) includes representatives from four groups identi®ed in this study: Sabita and KDML105 (group 82), NSG19 (group 52), Mahsuri (group 79), and IR20 (group 81). Since Sabita and KDML105 had similar

L.J. Wade et al. / Field Crops Research 64 (1999) 35±50

responses and grouped together, there is probably no need to include both as reference lines in future. While late maturing and photoperiod sensitive groups are well represented, only IR20 represents the early maturing lines. Our results suggest IR20 should be replaced by three early maturing reference lines: PSBRc14, CT9897-55-2-M-3-M and either IR6226642-6-1 or IR57514-PMI-5-B-1-2; to represent high yield potential with general adaptation, high yield potential in favourable environments, and broadly adapted, respectively. Likewise, for the later maturing groups, NSG19, KDML105 or Sabita, and Mahsuri represent speci®c adaptation to rapid-onset late drought, situations favouring late maturity or drought recovery, and broadly adapted, respectively. Subject to their suitability for other traits not considered here (e.g. grain quality, disease resistance), these lines, or any advanced breeding lines from the same group which outperform them, may be considered for recommendation in their target subecosystems. 5. Conclusions These results clearly indicate differences in broad and speci®c adaptation within the rainfed lowland rice germplasm. For breeding progress to be made, these differing adaptive strategies should be considered. While broad adaptation would provide stability against the variability inherent in the rainfed lowland ecosystem, speci®c adaptations may provide signi®cant yield advantage in particular environments. By considering the characteristics of the target subecosystem and using the reference lines suggested, breeders may be better able to identify genotypes capable of performing more reliably in their target population of environments. Our ability to structure this evidence and identify genotypes with broad and speci®c adaptation was enhanced by the extent of environment sampling made possible by international collaboration. Such partnership permitted characteristics of different regions to be seen in a larger context, and the capacity for genotypes from other regions which perform well in a different region to be recognised. The effective linkage of international and national systems was seen as bene®cial, as analytical skills from outside could be merged with local understanding for the bene®t of all.

49

Acknowledgements The Rainfed Lowland Rice Research Consortium received support from the Asian Development Bank, Philippines, and the Directorate General for International Cooperation, Netherlands, with additional support to the International Rice Research Institute from the Department for International Development, UK. References Ahmed, H.U., Ali, M.L., Zaman, S.K., Kabir, M.A., Miah, N.M., 1995. Varietal characteristics and soil management to reduce drought stresses. In: Proceedings of the Third Technical Meeting, Rainfed Lowland Rice Research Consortium, Lucknow, India, March 1994, pp. 150±167. Azhiri-Sigari, T., Yamauchi, A., Kamoshita, A., Wade, L.J., 1999. Genotypic variation in response of rainfed lowland rice to drought and rewatering. 2. Root growth. Plant Prod. Sci., in press. Bastiaans, L., Kropff, M.J., Kempuchetty, N., Rajan, A., Migo, T.R., 1997. Can simulation models help design rice cultivars that are more competitive against weeds?. Field Crops Res. 51, 101±111. Cooper, M., 1999. Concepts and strategies for plant adaptation research in rainfed lowland rice. Field Crops Res. 64, 13±34. Cooper, M., Fox, P.N., 1996. Environmental characterisation based on probe and reference genotypes. In: Cooper, M., Hammer, G.L. (Eds.), Plant Adaptation and Crop Improvement. CAB International, Wallingford, pp. 529±547. Cooper, M., Rajatasereekul, S., Immark, S., Fukai, S., Basnayake, J., 1999. Rainfed lowland rice breeding strategies for Northeast Thailand. I. Genotypic variation and genotype  environment interactions for grain yield. Field Crops Res. 64, 131±151. Cooper, M., Somrith, B., 1997. Implications of genotype-byenvironment interactions for yield adaptation of rainfed lowland rice: influence of flowering date on yield variation. In: Fukai, S., Cooper, M., Salisbury, J. (Eds.), Breeding Strategies for Rainfed Lowland Rice in Drought-prone Environments. Proceedings of an International Workshop held at Ubon Ratchathani, Thailand, 5±8 November 1996. Australian Centre for International Agricultural Research, Canberra, Australia, pp. 104±114. DeLacy, I.H., Basford, K.E., Cooper, M., Bull, J.K., McLaren, C.G., 1996. Analysis of multi-environment trials Ð an historical perspective. In: Cooper, M., Hammer, G.L. (Eds.), Plant Adaptation and Crop Improvement. CAB International, Wallingford. pp. 39±124. Fukai, S., Cooper, M., 1995. Development of drought resistant cultivars using physio-morphological traits in rice. Field Crops Res. 40, 67±86. Fukai, S., Pantuwan, G., Jongdee, B., Cooper, M., 1999. Screening for drought resistance in rainfed lowland rice. Field Crops Res. 64, 61±74.

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Garrity, D.P., Oldeman, L.R., Morris, R.A., 1986. Rainfed lowland rice ecosystems: characterisation and distribution. Progress in Rainfed Lowland Rice. International Rice Research Institute, Los Banos, Philippines, pp. 3±23. Gauch, H.G., 1992. Statistical Analysis of Regional Yield Trials. Elsevier, Amsterdam. IRRI, 1993. 1993±1995 IRRI Rice Almanac. International Rice Research Institute, Los Banos, Philippines. Jearakongman, S., Rajatasereekul, S., Nahlang, K., Romyen, P., Fukai, S., Shulkhu, E., Jumpaket, B., Nathbutr, K., 1995. Growth and grain yield of contrasting rice cultivars grown under different conditions of water availability. Field Crops Res. 44, 139±150. Kamoshita, A., Wade, L.J., Yamauchi, A., 1999. Genotypic variation in response of rainfed lowland rice to drought and rewatering. 3. Water extraction during the drought period. Plant Prod. Sci., in press. Lilley, J.M., Ludlow, M.M., 1996. Expression of osmotic adjustment and dehydration tolerance in diverse rice lines. Field Crops Res. 48, 185±197. Mackill, D.J., Coffman, W.R., Garrity, D.P., 1996. Rainfed lowland rice improvement. International Rice Research Institute, MCPO Box 3127, Makati City 1271, Philippines. Romyen, P., Hanviriyapant, P., Rajatasereekul, S., Khunthasuvon, S., Fukai, S., Basnayake, J., Skulhku, E., 1998. Lowland rice improvement in northern and northeastern Thailand. 2. Cultivar differences. Field Crops Res. 59, 109±119. Samson, B.K., Wade, L.J., 1998. Soil physical constraints affecting root growth, water extraction and nutrient uptake in rainfed lowland rice. In: Ladha, J.K., Wade, L.J., Dobermann, A., Reichardt, W., Kirk, G., Piggin, C. (Eds.), Rainfed Lowland Rice: Advances in Nutrient Management Research, IRRI, MCPO Box 3127, Makati City 1271, Philippines, pp. 231± 244. Sarkarung, S., Singh, O.N., Roy, J.K., Vanavichit, A., Bhekasut, P., 1995. Breeding strategies for rainfed lowland ecosystem. In: Bennett, J., Courtois, B., Khush, G.S., Senadhira, D. (Eds.), Fragile Lives in Fragile Ecosystems: Proceedings of the International Rice Research Conference, 3±17 February 1995, IRRI, PO Box 933, Manila, Philippines. pp. 709±720.

Wade, L.J., Amarante, S.T., Olea, A., Harnpichitvitaya, D., Naklang, K., Wihardjaka, A., Sengar, S.S., Mazid, M.A., Singh, G., McLaren, C.G., 1999a. Nutrient requirements in rainfed lowland rice, Field Crops Res. 64, 91±107. Wade, L.J., Fukai, S., Samson, B.K., Ali, A., Mazid, M.A., 1999b. Rainfed lowland rice: Physical environment and cultivar requirements. Field Crops Res., 64, 3±12. Wade, L.J., Kamoshita, A., Yamauchi, A., Azhiri-Sigari, T., 1999c. Genotypic variation in response of rainfed lowland rice to drought and rewatering. 1. Growth and water use. Plant Prod. Sci., in press. Wade, L.J., McLaren, C.G., Criseno, L., Amarante, S.T., Sarawgi, A.K., Kumar, R., Bhamri, M.C., Singh, O.N., Ahmed, H.U., Rajatasereekul, S., Pornuraisanit, P., Boonwite, C., Harnpichitvitaya, D., Sarkarung, S., 1997. Genotype-by-environment interactions: RLRRC experience. In: Fukai, S., Cooper, M., Salisbury, J. (Eds.), Breeding Strategies for Rainfed Lowland Rice in Drought-prone Environments. Proceedings of an International Workshop held at Ubon Ratchathani, Thailand, 5±8 November 1996. Australian Centre for International Agricultural Research, Canberra, Australia, pp. 115±124. Wade, L.J., McLaren, C.G., Guhey A., Quander, B., Boonvite, C., Amarante, S.T., Sarawgi, A.K., Haque, A., Harnpichitvitaya, D., Pamplona, A., Bhambri, M.C., 1995. Genotype by environment interaction and selection methods for identifying improved rainfed lowland rice genotypes. In: Bennett, J., Courtois, B., Khush, G.S., Senadhira, D. (Eds.), Fragile Lives in Fragile Ecosystems: Proceedings of the International Rice Research Conference, 3±17 February 1995. IRRI, PO Box 933, Manila, Philippines, pp. 885±900. Wade, L.J., McLaren, C.G., Samson, B.K., Regmi, K.R., Sarkarung, S., 1996. The importance of site characterization for understanding genotype by environment interactions. In: Cooper, M., Hammer, G.L. (Eds.), Plant Adaptation and Crop Improvement. CAB International, Wallingford, UK, pp. 549±562. Ward, J.H., 1963. Hierarchical grouping to optimise an objective function. J. Am. Stat. Assoc. 58, 236±244. Zeigler, R.S., Puckridge, D.W., 1995. Improving sustainable productivity in rice-based rainfed lowland systems of south and southeast Asia. Geojournal 35, 307±324.