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Pre-harvest sprouting (PHS), promoted by rainfall during crop maturity, is a high problem in many wheat-producing regions of the world. Considering its importance in Brazil, 36 national and international varieties and advanced lines of wheat were evaluated for their tolerance to PHS. For this purpose, two experiments were conducted over three years. Seed pericarp rupture was used as an indicator of the beginning of germination. The data were analyzed using analysis of variance, the Scott-Knott test, and the Lin and Binns method. The wide range of germination percentage values allowed the genotypes to be classified as tolerant (in experiment 1 - ND 674 and Grandin*2/RL 4137 and experiment 2 - Frontana and Grandin) and moderately tolerant (Alsen, CD 114, and Milan/3/Attila//Fang 69/CIMMYT 3 in Experiment 1; Avante, BRS 177, IAC 5-Maringá, Onix, OR 1, RL 4137, and Rubi in Experiment 2). Because tolerance to PHS is under genetic control and can be improved through breeding programs, the challenge for wheat breeders is to combine increased PHS tolerance with other requirements to meet market demands.
Pre-harvest sprouting (PHS), promoted by rainfall during crop maturity, is a high problem in many wheat-producing regions of the world. Considering its importance in Brazil, 36 national and international varieties and advanced lines of wheat were evaluated for their tolerance to PHS. For this purpose, two experiments were conducted over three years. Seed pericarp rupture was used as an indicator of the beginning of germination. The data were analyzed using analysis of variance, the Scott-Knott test, and the Lin and Binns method. The wide range of germination percentage values allowed the genotypes to be classified as tolerant (in experiment 1 - ND 674 and Grandin*2/RL 4137 and experiment 2 - Frontana and Grandin) and moderately tolerant (Alsen, CD 114, and Milan/3/Attila//Fang 69/CIMMYT 3 in Experiment 1; Avante, BRS 177, IAC 5-Maringá, Onix, OR 1, RL 4137, and Rubi in Experiment 2). Because tolerance to PHS is under genetic control and can be improved through breeding programs, the challenge for wheat breeders is to combine increased PHS tolerance with other requirements to meet market demands.
A study was performed to evaluate the effects of environment (E) and genotype (G) × E interactions (GGE) for quality traits and grain yield in bread wheat (Triticum aestivum L.). ANOVA and GGE biplots were used to assess quality performance and stability in 36 genotypes of bread wheat through a three‐location trial over two cropping seasons in the Thrace Region of Turkey. The 36 wheat genotypes used were classified into eight different groups, including landraces, 1960s, 1970s, 1980s, 1990s, 2000s, and advanced lines. The data showed a wide range of variation for milling quality parameters with regard to gluten quantity and quality, as well as for grain yield. Genotypes, location, and year, in this order, contributed to this diversity. Remarkably, all the traits except grain protein content showed a larger genotype effect than location (L) effect; the interaction G × L was usually more important than the G × Y. Genotypic and location components markedly exceeded G × L and Y × L for all variables, with the exception of grain yield, in which the G × Y effect was greater than locations according to ANOVA analysis. Noticeable continuous progress (53%) was observed in genetic grain yield during the last 60 yr without limiting wheat quality. GGE biplot analysis found that, on average, the biplots accounted for 80 to 85% of G and G × E variation present. Krasunia and Sagittario were the best genotypes (best performance and higher stability) of the study based on the traits evaluated.
Âèÿâèòè åôåêòèâí³ñòü âèêîðèñòàííÿ êîíòðàñòíèõ ñòðîê³â ñ³âáè ï³ñëÿ ð³çíèõ ïîïåðåäíèê³â äëÿ îö³íþâàííÿ ãåíîòèï³â ïøåíèö³ ì'ÿêî¿ îçèìî¿ çà âðîaeàéí³ñòþ òà ñòàá³ëüí³ñòþ. Ìåòîäè. Ïîëüîâ³, ëàáîðàòîðí³, ñòàòèñòè÷í³. Ðåçóëüòàòè. Óñòàíîâëåíî ð³çíèé, àëå äîñòîâ³ðíèé ð³âåíü âïëèâó íà âðîaeàéí³ñòü ãåíîòèï³â ïøåíèö³ ì'ÿêî¿ îçèìî¿ òàêèõ ÷èííèê³â, ÿê óìîâè ðîêó âèðîùóâàííÿ (66,2%), ïîïåðåäíèêè (12,5%), ñòðîêè ñ³âáè (6,1%) òà ãåíîòèï (1,7%). ³äçíà÷åíî äîñòîâ³ðí³ â³äì³òíîñò³ â ðåàêö³¿ äîñë³äaeåíèõ ãåíîòèï³â íà ñòðîêè ñ³âáè ï³ñëÿ ð³çíèõ ïîïåðåäíèê³â. Âèÿâëåíî â³äíîñíî ìåíøèé âïëèâ ïîïåðåäíèê³â íà âðîaeàéí³ñòü ñîðò³â 'Åñòàôåòà ìèðîí³âñüêà' òà 'Âåaeà ìèðîí³âñüêà', á³ëüøèé-'Ì²Ï Äàðóíîê', 'Ì²Ï Êíÿaeíà' òà 'Ì²Ï Âèøèâàíêà'. Ñòðîêè ñ³âáè ìåíøå âïëèâàëè íà âðîaeàéí³ñòü ñîðò³â 'Ì²Ï Ôîðòóíà', 'Ì²Ï Âèøèâàíêà' òà 'Òðóä³âíèöÿ ìèðîí³âñüêà', çíà÷íî-ñîðòó 'Ì²Ï Äàðóíîê'. Óñòàíîâëåíî çàãàëüíó òåíäåíö³þ çìåíøåííÿ ñåðåäíüî¿ âðîaeàéíîñò³ â äîñë³ä³ ç³ çì³ùåííÿì ñòðîêó ñ³âáè â³ä 26 âåðåñíÿ äî 16 aeîâòíÿ. Îäíàê, äëÿ íèçêè ãåíîòèï³â ï³ñëÿ ïåâíèõ ïîïåðåäíèê³â îïòèìàëüíèì áóâ ñòðîê ñ³âáè 5 aeîâòíÿ: ï³ñëÿ ïîïåðåäíèêà ñèäåðàëüíèé ïàð-äëÿ ñîðò³â 'Òðóä³âíèöÿ ìèðîí³âñüêà', 'Ì²Ï Àññîëü' òà 'Ì²Ï Äí³ïðÿíêà', ï³ñëÿ ã³ð÷èö³-'Âåaeà ìèðîí³âñüêà', ï³ñëÿ ñîíÿøíèêó-'Ì²Ï Ôîðòóíà', ï³ñëÿ êóêóðóäçè-'Ì²Ï Ôîðòóíà' òà 'Ïîäîëÿíêà'. Ó ðîçð³ç³ ñòðîê³â ñ³âáè âñòàíîâëåíî íàéìåíøå âàð³þâàííÿ âðîaeàéíîñò³ ï³ñëÿ ïîïåðåäíèê³â ñèäåðàëüíèé ïàð, ã³ð÷èöÿ òà êóêóðóäçà ñîðò³â 'Ì²Ï Âèøèâàíêà', 'Áàëàäà ìèðîí³âñüêà', 'Ì²Ï Êíÿaeíà', 'Åñòàôåòà ìèðîí³âñüêà'. Ç âèêîðèñòàííÿì GGE biplot âèÿâëåíî, ùî íàáëèaeåíèì äî «³äåàëüíîãî ñåðåäîâèùà» äëÿ ðåàë³çàö³¿ ð³âíÿ âðîaeàéíîñò³ á³ëüøîñò³ ãåíîòèï³â áóâ äðóãèé ñòðîê ñ³âáè ï³ñëÿ ïîïåðåäíèêà ñèäåðàëüíèé ïàð. Çà ð³çíèìè ñòðîêàìè ñ³âáè é ïîïåðåäíèêàìè â ñåðåäíüîìó çà òðè ðîêè íàéîïòèìàëüí³øå ïîºäíàííÿ ð³âíÿ âðîaeàéíîñò³ ³ ñòàá³ëüíîñò³ â³äçíà÷åíî äëÿ ñîðò³â 'Òðóä³âíèöÿ ìèðîí³âñüêà', 'Ì²Ï Â³äçíàêà', 'Ì²Ï Àññîëü', 'Åñòàôåòà ìèðîí³âñüêà', 'Ì²Ï Âàëåíñ³ÿ'. Âèñíîâêè. Âèêîðèñòàííÿ ð³çíèõ ñòðîê³â ñ³âáè ï³ñëÿ ð³çíèõ ïîïåðåäíèê³â º åôåêòèâíèì ï³äõîäîì îðãàí³çàö³¿ ãåíîòèï-ñåðåäîâèùíèõ âèïðîáóâàíü. ³í äຠçìîãó ³äåíòèô³êóâàòè ÿê ñïåöèô³÷íî àäàïòîâàí³ äî ïåâíèõ óìîâ (ïîïåðåäíèê³â òà ñòðîê³â ñ³âáè) ãåíîòèïè, òàê ³ ãåíîòèïè ç â³äíîñíî âèùèì ð³âíåì ñòàá³ëüíîñò³ çà ñ³âáè ï³ñëÿ ð³çíèõ ïîïåðåäíèê³â òà â ð³çí³ ñòðîêè. Òàêèé ï³äõ³ä ìîaeå áóòè âèêîðèñòàíèé ÿê íà çàâåðøàëüíîìó åòàï³ ñåëåêö³¿ äëÿ äèôåðåíö³þâàííÿ ñåëåêö³éíèõ ë³í³é çà âðîaeàéí³ñòþ òà ñòàá³ëüí³ñòþ, òàê ³ ï³ä ÷àñ ðîçðîáëåííÿ áàçîâèõ åëåìåíò³â òåõíîëî㳿 âèðîùóâàííÿ íîâîñòâîðåíèõ ñîðò³â. Êëþ÷îâ³ ñëîâà: ïøåíèöÿ ì'ÿêà îçèìà; âðîaeàéí³ñòü; ñòàá³ëüí³ñòü; ð³ê âèðîùóâàííÿ; ñòðîê ñ³âáè; ïîïåðåäíèê; êîåô³ö³ºíò âàð³àö³¿; ANOVA; GGE biplot.
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