Agronomic and molecular performance of rice lines carrying spikelet number and days to heading loci

Rice panicle architecture is an important factor affecting yield potential. Korean rice cultivars have a narrow genetic background for panicle architecture. To enhance the yield potential of Korean rice cultivars, we developed and characterized rice lines with new panicle architecture. Rice with improved panicle architecture has clustered spikelets and dense panicles (CD type). CD rice was derived from a cross between “Binhae Col.#1” carrying dense panicles, and “ARC10319” that has the clustered spikelets gene (Cl). CD rice lines had short and semi-erect panicles with two to five high density spikelets clustered at the tips of primary and secondary rachis branches. CD rice lines had dramatically increased numbers of spikelets; almost twice as many as those of Korean rice cultivars. The increase in spikelet number was mainly caused by the increased spikelets and branches on secondary rachises compared to those on primary rachises. The increase in spikelet number was expected to enhance the yield of CD rice by expanding sink capacity. However, the yield of selected lines; CD9, CD27, CD34, and CD39, did not reach the level of the Korean high-yielding cultivars “Boramchan” and “Hanareum2,” due to the reduction in panicle number and grain weight, and poor ripening. Although no substantial yield increase was observed in CD rice, the panicle architecture of CD rice, clustered spikelets, and dense panicles could be new genetic resources as breeding material for diversifying panicle architecture and enhancing yield potential.

Wheat is an important cereal crop that helps to meet the food grain needs of people all over the world. Heat stress is one of the most significant abiotic stresses that wheat crops face during terminal growth stages in the wheat growing regions like India. It is very important to identify heat tolerant genotypes to be used as donors for breeding tolerant varieties. Thirty-six wheat genotypes were evaluated under different sowing dates viz., Timely sown (TS), Late sown (LS) and very late sown (VLS), and the fourth was sown in the Temperature controlled phenotyping facility (TCPF) across two years. Genotypes were planted following lattice square design with two replications. Data was recorded for yield and yield contributing traits and analysed using selection indices as well AMMI and GGE biplot stability models. Heat stress affected all the traits under different heat environments which ranged from 1.6% (Spikelet number) to 37.2% (grain yield). Regression analysis indicated that the thousand grains weight (R2 = 0.50) contributed significantly towards grain yield under heat stress. Stress susceptibility index (SSI) found genotypes GW322, RAJ3765, Raj4037and MACS6145 as heat tolerant whereas, Stress Tolerance Index (STI) identified C306, HD2967, WH1080, WH730, DBW90, HD2932, DBW17, RAJ3765 as heat tolerant and high yielding. AMMI biplot analysis indicated stable genotypes DBW90, WH730, RAJ4083, CBW38, HD2932, NI5439, WR544, whereas GGE biplot analysis revealed stable genotypes NIAW34, NI5439, RAJ4083, DBW90, PBW590, Raj3765, HUW 510, WH730, HD2967 and UP2382. Heat stress affects significantly all yield contributing traits. Thousand grain weight was the most important trait that can be used as a selection criterion for selecting tolerant lines. Based on selection indices and both AMMI and GGE analysis, genotype RAJ3765 was identified to be highly heat tolerant with good grain yield.

Article Details: Received: 2020-11-30 | Accepted: 2020-12-09 | Available online: 2021-06-30 https://doi.org/10.15414/afz.2021.24.02.117-123 Multi-environment trials were conducted in two locations (Algiers and Setif) during two crop seasons in order to assess the responses of 17 genotype of barley (Hordeum vulgare L.) by evaluation of genotype-by-environment interactions (GEI) on grain yield and determine the stable genotypes. Results showed significant (p <0.001) effects of environment and genotypes and their interaction on grain yield. The genotypes had different behavior conducting to yield variation in the tested locations. So, selection could consider a specific adaptation of the genotypes and their yield stability. The Additive main effects and multiplicative interaction analysis is a useful tool allowing to explore important information on the obtained results; it revealed that 'Plaisant/ charan01' is the most stable genotype followed by 'Barberousse' and 'Barberousse/Chorokhod', while 'Begonia' and 'Plaisant' were unstable with specific adaptation to Setif location during 2018/19. the cultivar 'Express' presented a high productivity. Keywords: AMMI analysis, barley, genotype by environment interaction, grain yield, stability References Abdipur, M. & Vaezi, B. (2014). Analysis of the genotype-by-environment interaction of winter barley tested in the rain-fed regions of Iran by AMMi adjustment. Bulgarian Journal of Agricultural Science, 20(2), 421–427. https://www.agrojournal.org/20/02-27.html Chalak, L. et al. (2015). Performance of 50 Lebanese barley landraces (Hordeum vulgare L. subsp. vulgare) in two locations under rainfed conditions. Annals of Agricultural Sciences, 60(2), 325–334. http://dx.doi.org/10.1016/j.aoas.2015.11.005 Alfian, F. H. & Halimatus, S. (2016). On The Development of Statistical Modeling in Plant Breeding: An Approach of Row-Column Interaction Models (RCIM) For Generalized AMMI Models with Deviance Analysis. Agriculture and Agricultural Science Procedia, 9(1), 134–145. https://doi.org/10.1016/j.aaspro.2016.02.108 Bouzerzour, H. & Dekhili, M. (1995). Heritabilities, gains from selection and genetic correlations for grain yield of barley grown in two contrasting environments. Field Crops Research, 41(3), 173–178. http://dx.doi.org/10.1016/0378-4290(95)00005-B De Mendiburu, F. (2017). Agricolae: Statistical procedures for agricultural research. R package version, 1.2-8. Retrieved November 14, 2020 from https://tarwi.lamolina.edu.pe/~fmendiburu/ Dogan, Y. et al. (2016). Identifying of relationship between traits and grain yield in spring barley by GGE biplot analysis. Agriculture and Forestry, 62(4), 239–252. http://dx.doi.org/10.17707/AgricultForest.62.4.25 Farshadfar, E. et al. (2011). AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (Triticum aestivum L.). Australian Journal of Crop Science, 5(13), 1837–1844. http://www.cropj.com/farshadfar_5_13_2011_1837_1844.pdf Farshadfar, E. et al. (2012). GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Australian Journal of Crop Science, 6(6), 1074–1079. http://www.cropj.com/farshadfar_6_6_2012_1074_1079.pdf Gauch, H.G. (1988). Model selection and validation for yield trials with interaction. Biometrics, 44(3), 705–715. http://dx.doi.org/10.2307/2531585 Gauch, H.G. et al. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48(3), 866–889. https://doi.org/10.2135/cropsci2007.09.0513 Halimatus, S. & Alfian, F. H. (2016). AMMI Model for Yield Estimation in Multi-Environment Trials: A Comparison to BLUP. Agriculture and Agricultural Science Procedia, 9(1), 163–169. https://doi.org/10.1016/j.aaspro.2016.02.113 Vishnu, K. et al. (2016). AMMI, GGE biplots and regression analysis to comprehend the G × E interaction in multi-environment barley trials. Indian Journal of Genetics and Plant Breeding, 76(2), 202–204. https://dx.doi.org/10.5958/0975-6906.2016.00033.X Mirosavljevic, M. et al. (2014). Analysis of new experimental barley genotype performance for grain yield using AMMI biplot. Selekcija I semenarstvo, 20(1), 27–36. In Bosnian. http://dx.doi.org/10.5937/SelSem1401027M Peyman, S. et al. (2017). Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Science, 24(3), 173–180. https://doi.org/10.1016/j.rsci.2017.02.001 Purchase, J.L. et al. (2000). Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil, 17(3), 101–107. http://dx.doi.org/10.1080/02571862.2000.10634878 Rodrigues, P.C. et al. (2016). A robust AMMI model for the analysis of genotype-by-environment data. Bioinformatics, 32(1), 58–66. http://dx.doi.org/10.1093/bioinformatics/btv533 Romagosa, I. & Fox, P.N. (1993). Genotype X environment interaction and adaption. In Hayward, M.D. et al. (eds.) Plant breeding principles and prospects. Plant Breeding Series. Dordrecht: Springer (pp. 373–390). https://doi.org/10.1007/978-94-011-1524-7_23 Temesgen, B. et al. (2015). Genotype X Environment Interaction and Yield Stability of Bread Wheat (Triticum aestivum L.) Genotype in Ethiopia using the Ammi Analysis. Journal of Biology, Agriculture and Healthcare, 5(11), 129–139. https:// www.iiste.org/Journals/index.php/JBAH/article/view/23245 Yan, W. et al. (2007). GGE biplot vs. AMMI analysis of genotype by environment data. Crop science, 47(2), 643–653. http://dx.doi.org/10.2135/cropsci2006.06.0374 Zadoks, J.C. et al. (1974). A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. http://dx.doi.org/10.1111/j.1365-3180.1974.tb01084.x Zobel, R.W. et al. (1988). Statistical analysis of a yield trial. Agronomy Journal, 80(3), 388–393. http://dx.doi.org/10.2134/agronj1988.00021962008000030002x

Background: Jack bean is an under-exploited legume species, a source of food, medicine and cover crop. By virtue of its adaptive nature to low fertility soils, it is one of the few pulses that grow well on highly leached, nutrient depleted, lowland tropical soils. But, in India, crop improvement work is very little done. Stability of yield is a major criterion for farmer’s acceptability of any variety and there are several methods to estimate the stability and G x E interaction effects of a genotype across seasons. Among these, AMMI analysis is the most recent and widely exploited in different crops for the identification of stable genotypes. In this context, yield stability of 10 accessions of jack bean is studied to identify the stable genotypes. Methods: The experiment was conducted with 10 Jack bean genotypes in RCBD with two replications under rain fed conditions during 2017-2020 in Kharif for four seasons. The data was subjected to analysis of variance and then taken for AMMI and GGE analysis for identification of stable genotypes. Result: The combined analysis of variance revealed that there was highly significant variation (p less than 0.01) in grain yield and environments and genotype interaction among the genotypes. The average bean yield of the genotypes was 533.1 grams per plant. The highest and the lowest mean yield was recorded in PSR-12202 and CHMJB-02 respectively which was corroborated by the AMMI bi-plot as well. Similar to the AMMI bi-plot, the GGE bi-plot also confirmed that PSR-12202 was the stable genotype across the environments, whereas, G1, G2, G3, G4, G6, G7 and G8 were the other genotypes with low yields in some or all the environments. Kharif, 2018 and Kharif, 2020 are discriminating environments and are declared as the most representative than Kharif, 2017 and Kharif, 2019. Generally, PSR-12202 was the ideal genotype with higher mean yield and relatively good stability; G5 was the moderately good yielding genotype and the most unstable genotype; Whereas, G1, G2, G3, G4, G6, G7 and G8 were the poorly yielding and unstable genotypes. Both AMMI and GGE bi-plot are able to establish the genotypic stability and these models can be exploited for judging the genotypes for their GEI in other crops as well.

Introduction: Rice is one of the most valuable nutrients for the food security in the world. Gowing population and changes in diet habits in the world, it is necessary to increase rice production. Using hybrid rice technology to increase grain yield can be an effective step in achieving food security (Shabestari & Mojtahedi, 2008), Therefore, it is important to examine the different characteristics and indices of parents and the resulting hybrids. Yield increase in hybrid rice is due to plant growth period and high harvest index. Increasing of number of grains and panicle weight increases grain yield in hybrid rice. Limitation of suitable cultivars to restorer, low number of effective lines and their genetic basis and cooking quality have always been the main problems of hybrid rice production in the Iran. The purpose of this experiment was identify of restorer lines in crosses with the sterile cytoplasmic line of Neda A to finally identify suitable parent lines and use them for hybrid breeding programs. Materials and Methods: This experiment was conducted in two years )2018-2019 (in the Genetics and Agricultural Biotechnology Institute of Tabarestan. Plant materials included 26 restorer lines sent by the International Rice Research Inistitute (IRRI) as the male parent, and the Cytoplasmic male sterile (CMS) lines of Neda A as the female parent with Shirodi and Neda (control varietirs). The traits such as number of days to 50% flowering, number of days to maturity, plant height (cm), number of fertile tillers, length of spikelet (cm), number of floret in spikelet, number of spikelet per panicle, pollen grain sterility percentage, spikelet fertility percentage, grain length (mm), grain width (mm), weight of 1000-grains (gr), and grain yield (gram per square meter) were measured. Finally, crosses with the desired pollen grain fertility, spikelet fertility, and good yield were selected and introduced. The analysis of data was performed using SAS9.1 software. Cluster analysis of data was done with Ward method (using PAST software) and the dendrogram cuts were performed based on the formula proposed by (Darvish Kajouri, 2009). Results and Discussion: Among the parental lines of fertility restorer, IR68078-15-2-2-2-2-2 R and R4842-2-3-2-1R lines had the highest number of tillers and the highest percentage of spikelet fertility were observed in the line MILYANG 46. The IRi347 line had a higher yield than all the restorer lines due to its high number of fertile spikelet and low percentage of pollen grains fertility. Cluster analysis of the genotypes placed them in five separate clusters. The mean heterosis of the hybrids for the yield trait as compared to the parents, the superior parent, and the control variety showed a variation from -94.54 to 57.46% for the parent, from -94.73 to 35.45% for superior parent, and from -2.94 to 42.52% for the control. Conclusions: According to the result of the current research, the hybrids with optimal yield and high percentage of pollen as well as high spikelet fertility included the crosses of Neda A with IR68078-15-2-1-2-2-R, IR 65912-90-1-6-3-2-2R, IR36, MILYANG 54, and IR 56 hybrids which produced yields of 7112, 8708.3, 6340.3, 7253.3, and 6828.8 kg.m2 with standard heterosis values of 16.40, 42.52, 3.77, 18.74 and 8.49 respectively. Among the superior hybrids, NedaA/ MILIANG 54 hybrid had the highest milling and suitable amylose content, and NedaA/IR68078-15-2-1-2-2- R hybrid had a favorable aroma. These hybrids also exhibited good heterosis and were recognized as the best hybrids in terms of agronomic traits, which can be used for yield improvement. Since the lines studied are imported, it is necessary to make attempts to adapt them and also use them in crosses, which can lead to the production and release of promising new rice hybrids (Baloch Zehi et al., 2016). Keywords: Genotype, hybrid, restorer, rice and sterile line.

Crop yield is determined by the acquisition and allocation of photoassimilates in sink organs. Therefore, genetic modification of sink size is essential for understanding the complex signaling network regulating sink strength and source activities. Sink size in wheat depends on the number of spikelets per spike, floret/grain number per spikelet as well as the grain weight or dry matter accumulation. Hence, increasing spikelet number and improving sink size are targets for wheat breeding. The main objective of the present work was to genetically modify the wheat spike architecture, i.e., the sink size by introgressing the ‘Miracle wheat’ or the bht-A1 allele into an elite durum wheat cv. Floradur. After four generations of backcrossing to the recurrent parent, Floradur (FL), we have successfully developed Near Isogenic Lines (NILs) with a modified spikelet arrangement thereby increasing spikelet and grain number per spike. Genotyping of bht-A1 NILs using the Genotyping-By-Sequencing approach revealed that the size of the introgressed donor segments carrying bht-A1 ranged from 2.3 to 38 cM. The size of the shortest donor segment introgressed into bht-A1 NILs was estimated to be 9.8 mega base pairs (Mbp). Phenotypic analysis showed that FL-bht-A1-NILs (BC3F2 and BC3F3) carry up to seven additional spikelets per spike, leading to up to 29% increase in spike dry weight at harvest (SDWh). The increased SDWh was accompanied by up to 23% more grains per spike. More interestingly, thousand kernel weight (TKW) did not show significant differences between FL-bht-A1-NILs and Floradur, suggesting that besides increasing spikelet number, bht-A1 could also be targeted for increasing grain yield in wheat. Our study suggests that the genetic modification of spikelet number in wheat can be an entry point for improving grain yield, most interestingly and also unexpectedly without the trade-off effects on TKW. Hence, FL-bht-A1-NILs are not only essential for increasing grain number, but also for understanding the molecular and genetic mechanism of the source–sink interaction for a clearer picture of the complex signaling network regulating sink strength and source activities.

Developing cultivars that are stable in a variety of conditions has been a problem for plant breeders. The environment can have an impact on a cultivar's phenotypic performance, or different environments can have different effects on different cultivars. Variance resulting from a combination of an individual's genetic composition and the environment in which they were raised is referred to as the genotype-environment interaction. Reducing genotype-environment interaction through breeding stable genotypes facilitates selection of stable, high-yielding genotypes. In multi-environment cultivar trials, AMMI and GGE biplot analyses are frequently utilized to explain G×E interactions. In order to assess breeding material effectively, India's pearl millet agriculture has been split into three main zones, A1, A, and B, based on climatic circumstances. Using AMMI and GGE biplot analysis, the current study assessed the G×E interaction in pearl millet genotypes from Zone-B in India. Based on normalized grain yield and ASV indices, a new weighted index (WI) has been developed to assess stable and high-yielding genotypes. For this zone, the three interaction principal component axes (IPCA1, IPCA2, and IPCA3) have been found to be important. The indices YSI and WI have been used to identify both the high-yield and most stable genotypes, while the AMMI Stability Value (ASV) and Stability Index have been used to find the most stable genotypes. Based on WI, the genotypes G24 and G13 have been identified stable and high yielding genotypes for zone-B.

Background: The research focused on investigating the impact of genotype-environment interaction on the yield stability and performance of eight chickpea genotypes across three different locations (Bhatapara, Kawardha and Raipur). Method: The study employed a randomized block design with two replications in each environment in Rabi season during 2021-22. Analysis of variance (ANOVA) revealed significant variations between environments and genotype-environment interaction (G x E) based on AMMI and GGE biplot. Results: To visualize the genotype-environment relationship, a two-dimensional GGE biplot was created using the first two principal components, which accounted for 87.8% and 11.5% of the interaction’s variation. The GGE biplot indicated that genotype g4 (ICCX 161100-B-B-B-B) in environment e2 (Kawardha) and g8 (JG-24) in environment e1 (Bhatapara) exhibited high yield. Genotype g3 (ICCV 201112) in environment e3 (Raipur) also showed high yield, while genotype g5 (JG 2020-10) demonstrated stability across all three environments. Furthermore, the AMMI analysis identified genotype g2 (ICCV 201103) as a superior performer in terms of yield stability across environments. By employing the GGE biplot and AMMI analysis, the study categorized the genotypes into three groups, facilitating simplified visual evaluations. The GGE biplot highlighted that genotype g8 (JG-24) exhibited better seed index in environment e3 (Raipur), while genotype g1 (ICCV 201210) performed well in environment e1 (Bhatapara). Genotype g2 (ICCV 201103) demonstrated a good seed index in all three environments. The AMMI analysis indicated that genotype g3 (ICCV 201112) had a slightly lower seed index but maintained stability across all environments. Overall, the findings of this investigation, as represented by the GGE and AMMI biplots, visually illustrated the relationship between genotypes and environments. The identified genotypes hold potential for improving yield production and further research and breeding efforts can be based on these findings.

&lt;p&gt;Rice lines for increasing grain yield derived from Code variety that have loci associated to the spikelet number and early heading date (qTSN4 and qDTH8 locus, respectively) have been developed. The objectives of this research were to molecularly analyze, to evaluate the yield of Code-qTSN4 and Code-qDTH8 lines in the field, and to obtain the lines with yield potential of at least 10% higher than that shown by Code. The study was conducted in October 2016 to March 2017. The study was divided into two activities: molecular verification of the qTSN4, qDTH8, and Xa7 loci using specific markers and field trials at two locations in West Java, i.e. Sukamandi Experimental Station and Cianjur farmer’s paddy field. The genetic materials used were 56 rice genotypes consisted of 49 lines (Code-qTSN4 and Code-qDTH8) and 7 check varieties. Molecular analysis showed that all rice lines tested contained qTSN4, qDTH8, and Xa7 loci. All of the loci were in homozygous stage indicating that they were pure lines. Field trial results showed that Cianjur location gave much better on yield component variables than that in Sukamandi. The highest increase in spikelet number was shown by B6-4 planted at Cianjur with increase of 30.06% and B12-2 planted at Sukamandi with increase of 25.15% compared to Code. Both lines were classified as Code-qTSN4 line group. The qTSN4 and qDTH8 loci proved to increase yield more than 20% compared to Code. A total of 34 lines resulted from this study can be used for advanced yield trials conducted at several agro-ecologically different locations.&lt;/p&gt;

Due to increasing farmers’ attention to sorghum (Sorghum bicolor (L.) Moench) cultivation in arid and semi-arid regions, it is necessary to introduce new, suitable sorghum cultivars. The present study aimed to evaluate the yield stability of grain sorghum genotypes via AMMI and GGE biplot analyses to identify high-yielding and best adapted genotypes for release in Iran’s warm-temperate regions. Seven promising grain sorghum lines (KGS15, KGS19, KGS23, KGS25, KGS27, KGS32, and KGS36), along with three commercial cultivars (Kimiya, Payam, and Sepideh), were studied in seven regions of Iran (Karaj, Isfahan, Gorgan, Birjand, Shiraz, Zabol, and Hamedan) during the 2019 and 2020 growing seasons. AMMI analysis showed that genotypes G7, G3, and G6 had the lowest AMMI stability values (ASV). GGE biplot analysis showed that genotype G6 had the highest grain yield (GY) in most environments, whereas genotype G1 was specifically adapted to the Hamedan province. According to the average-environment coordination of the GGE biplot, genotype G6 was the most desirable genotype, significantly better than the second and third best genotypes G4 and G3 (G4 was missed by AMMI). This study also indicated that the GGE biplot method was more effective than the AMMI method in analyzing genotype by environment and identifying superior genotypes. Overall, G6 can be introduced as the superior-most genotype for cultivation in Iran’s warm-temperate regions.

Water and nitrogen (N) are two of the most important abiotic factors limiting rice yield. However, a little information is available on why a moderate water and N interaction significantly increase rice biomass, from the point of view of photosynthetic physiology. A pot experiment with three water regimes [continued flood (CF), alternate wetting and moderate drying (WMD), and alternate wetting and severe drying (WSD)] and four N application levels (no nitrogen, N0; 90 kg hm−2, N1; 180 kg hm−2, N2; 270 kg hm−2, N3) was carried out to investigate this problem. Results demonstrated that WSD significantly inhibited rice height, leaf area, chlorophyll content, photosynthesis, and yield at the four different N levels, as compared to that with CF and WMD. However, WMD substantially alleviated these reductions, and their values were not significantly different from those of CF. Contents of leaf soluble protein and total chlorophyll in WMD were increased compared to the WSD, and this mitigating effect was beneficial to the increase of rice photosynthesis and yield development. Photosynthesis in rice leaf was significantly affected by water status but not N level. Analysis of variance demonstrated a significant effect of water on spikelet number, which indicates that the reduction of spikelet number under water stress may be the major reason for its low yield. Therefore, we concluded that WMD could be considered as an effective water management regime to obtain high yield in rice production, and its strengthened drought tolerance was closely associated with the higher dry matter and in the physiological characteristics including an increase in spikelet number, chlorophyll and soluble protein contents, and photosynthetic rate.

The number of rachis nodes (spikelets) on a wheat spike is a component of grain yield that correlates with flowering time. The genetic basis regulating flowering in cereals is well understood, but there are reports that flowering time can be modified at a high frequency by selective breeding, suggesting that it may be regulated by both epigenetic and genetic mechanisms. We investigated the role of DNA methylation in regulating spikelet number and flowering time by treating a semi-spring wheat with the demethylating agent, Zebularine. Three lines with a heritable increase in spikelet number were identified. The molecular basis for increased spikelet number was not determined in 2 lines, but the phenotype showed non-Mendelian inheritance, suggesting that it could have an epigenetic basis. In the remaining line, the increased spikelet phenotype behaved as a Mendelian recessive trait and late flowering was associated with a deletion encompassing the floral promoter, FT-B1. Deletion of FT-B1 delayed the transition to reproductive growth, extended the duration of spike development, and increased spikelet number under different temperature regimes and photoperiod. Transiently disrupting DNA methylation can generate novel flowering behaviour in wheat, but these changes may not be sufficiently stable for use in breeding programs.

To evaluate the effects of elevated CO2 concentration on low-temperature induced sterility, we conducted chamber and FACE (Free-Air CO2 Enrichment) experiments under two levels of CO2 concentration; ambient and elevated (ambient + 200ppmV). In the chamber experiment for three rice seasons from 2001 through 2003, rice (Oryza sativa L., cv. Sasanishiki) plants were submerged up to 25 cm above the soil surface in a water pool. The young panicles near the soil surface were thus subjected to a critical temperature of 19.5 ℃ throughout the susceptible period from panicle initiation to anthesis. The effects of elevated CO2 on floral sterility were inconsistent among the three years, but the relationship between the floral sterility and the total spikelet number was consistent. As the spikelet number increased, the sterility increased, whether the change was caused by CO2 or nitrogen. Similar results were obtained in the FACE experiment in 2003, when summer temperatures were extremely low. The earliest cultivar Kirara397 increased in spikelet number under elevated CO2 but decreased in yield due to floral sterility. These results suggest that elevated CO2 concentration may increase floral sterility under low temperature indirectly via the increase in spikelet number.

The effects of constant photoperiods (8, 9, 10, normal and 24 hr) and of transfer to another photoperiod at floral initiation (from 24 hr to 10 and vice versa) on rate of development and spikelet number per ear were studied in eight wheat cultivars grown at 20°C. The objective was to know what factors related to photoperiod control spikelet number. The lengths of the vegetative, spikelet and ear elongation phases, the numbers of spikelets and leaves, the numbers of phytomers and lengths of the shoot apices at floral initiation increased, but the rate of spikelet initiation decreased, as the photoperiod decreased from 24 to 8 hr. Responses to varying photoperiod for all these parameters were similar in the different cultivars but the sizes of the responses differed. Within a given cultivar, an increase in spikelet number was always associated with longer durations of the vegetative and spikelet phases and longer apices at floral initiation. The results of the transfer treatments suggest that spikelet number is not fully determined by the time of floral initiation, but can be altered significantly by manipulating the environment during the spikelet phase. It was concluded that the main factors determining spikelet number are rate and duration of spikelet initiation.

近年, 中国で育成された穂重型多収性水稲もち系統9004(日本型稲)に対する, 窒素施用時期[分げつ始期(分げつ肥), 穎花分化期(穂肥I), 同退化期(穂肥II)および出穂期(実肥)]の単独時期または分げつ始期を含む2～4時期を組み合わせた時期に施用(施用量は7.5～30gN/m2)し, 籾数および物質生産に着目して収量成立に及ぼす窒素施用の影響について解析した. 1)各処理区の精籾収量は804～1081g/m2で, m2当たり籾数3.14～5.06万粒, 登熱歩合79.6～93.7%, 精籾千粒重25.2～29.0gの範囲にあった. 2)分げつ肥の施用は穂数の, 穂肥IIの施用は穂数と1穂籾数によるm2当たり籾数の増加により増収となった.実肥の施用は出穂前の窒素施用量の少ない場合にのみ登熟歩合および精籾千粒重の増加により増収となった. また, 窒素施用量の増加に伴い収量は増加する傾向がみられたが, 窒素施用量が同一水準での増収程度は, 穂肥I>穂肥II>分げつ肥>実肥の順で認められた. 3)収量はm2当たり籾数と登熟期間の乾物生産量に支配され, m2当たり籾数は穂首分化期～出穂期までの窒素吸収量により決定される1穂籾数と, 登熟期間の乾物生産量は登熟期間の平均葉面積指数と窒素吸収量, m2当たり籾数と密接な関係を示した. 4)以上より, 中国で育成された穂重型多収性品種である9004系統では, 籾数確保のために幼穂分化期～出穂期までの窒素の吸収量の増大が必要であり, さらに多収穫を実現するためには, 出穂期前の乾物蓄積量と出穂期後の乾物生産量を高めて, 籾1粒当たりの乾物分配量を増加することが必要と思われた.