Oussama Hnizil, Aziz Baidani, Ilham Khlila, N. Nsarellah, A. Amamou
{"title":"评估氮肥、品种选择、年份及其相互作用对小麦产量和产量成分的影响","authors":"Oussama Hnizil, Aziz Baidani, Ilham Khlila, N. Nsarellah, A. Amamou","doi":"10.3390/nitrogen5020018","DOIUrl":null,"url":null,"abstract":"This five-year study (2016–2021) in Morocco’s Mediterranean climate investigated the effect of nitrogen fertilization and genotypic selection on wheat yield and associated components. Utilizing a split-plot design, the study assessed five wheat genotypes—’Faraj’, ‘Luiza’, ‘Itri’, ‘Karim’ and ‘Nassira’—under three nitrogen application rates (120, 60 and 0 kg/ha) across thirty plots with two replicates. Interactions between nitrogen and year showed marked significance in yield (p = 0.001), biomass (p = 0.002), TKW (p = 0.003) and Spk/m2 (p = 0.001), underscoring the variability in optimal nitrogen application rates across different years. Additionally, significant interactions between variety and year were observed for biomass (p = 0.001) and G/m2 (p = 0.001), indicating variability in the performance of different varieties across years. The ‘Itri’ genotype showed the highest yield in 2017, while ‘Luiza’ was pre-eminent in 2018, with ‘Itri’ producing the most biomass. ‘Faraj’ demonstrated consistent superiority in yield and biomass during 2019 and 2020. Our integrated principal component analysis and quadratic models elucidated that an intermediate nitrogen rate of 60 kg/ha (N2) was particularly advantageous for the ‘Faraj’ and ‘Karim’ genotypes. These findings highlight the substantial impact of informed nitrogen level adjustment and genotypic selection on yield optimization.","PeriodicalId":19365,"journal":{"name":"Nitrogen","volume":"135 47","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the Impact of Nitrogen Fertilization, Variety Selection, Year and Their Interaction on Wheat Yield and Yield Components\",\"authors\":\"Oussama Hnizil, Aziz Baidani, Ilham Khlila, N. Nsarellah, A. Amamou\",\"doi\":\"10.3390/nitrogen5020018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This five-year study (2016–2021) in Morocco’s Mediterranean climate investigated the effect of nitrogen fertilization and genotypic selection on wheat yield and associated components. Utilizing a split-plot design, the study assessed five wheat genotypes—’Faraj’, ‘Luiza’, ‘Itri’, ‘Karim’ and ‘Nassira’—under three nitrogen application rates (120, 60 and 0 kg/ha) across thirty plots with two replicates. Interactions between nitrogen and year showed marked significance in yield (p = 0.001), biomass (p = 0.002), TKW (p = 0.003) and Spk/m2 (p = 0.001), underscoring the variability in optimal nitrogen application rates across different years. Additionally, significant interactions between variety and year were observed for biomass (p = 0.001) and G/m2 (p = 0.001), indicating variability in the performance of different varieties across years. The ‘Itri’ genotype showed the highest yield in 2017, while ‘Luiza’ was pre-eminent in 2018, with ‘Itri’ producing the most biomass. ‘Faraj’ demonstrated consistent superiority in yield and biomass during 2019 and 2020. Our integrated principal component analysis and quadratic models elucidated that an intermediate nitrogen rate of 60 kg/ha (N2) was particularly advantageous for the ‘Faraj’ and ‘Karim’ genotypes. These findings highlight the substantial impact of informed nitrogen level adjustment and genotypic selection on yield optimization.\",\"PeriodicalId\":19365,\"journal\":{\"name\":\"Nitrogen\",\"volume\":\"135 47\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nitrogen\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/nitrogen5020018\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nitrogen","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/nitrogen5020018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessing the Impact of Nitrogen Fertilization, Variety Selection, Year and Their Interaction on Wheat Yield and Yield Components
This five-year study (2016–2021) in Morocco’s Mediterranean climate investigated the effect of nitrogen fertilization and genotypic selection on wheat yield and associated components. Utilizing a split-plot design, the study assessed five wheat genotypes—’Faraj’, ‘Luiza’, ‘Itri’, ‘Karim’ and ‘Nassira’—under three nitrogen application rates (120, 60 and 0 kg/ha) across thirty plots with two replicates. Interactions between nitrogen and year showed marked significance in yield (p = 0.001), biomass (p = 0.002), TKW (p = 0.003) and Spk/m2 (p = 0.001), underscoring the variability in optimal nitrogen application rates across different years. Additionally, significant interactions between variety and year were observed for biomass (p = 0.001) and G/m2 (p = 0.001), indicating variability in the performance of different varieties across years. The ‘Itri’ genotype showed the highest yield in 2017, while ‘Luiza’ was pre-eminent in 2018, with ‘Itri’ producing the most biomass. ‘Faraj’ demonstrated consistent superiority in yield and biomass during 2019 and 2020. Our integrated principal component analysis and quadratic models elucidated that an intermediate nitrogen rate of 60 kg/ha (N2) was particularly advantageous for the ‘Faraj’ and ‘Karim’ genotypes. These findings highlight the substantial impact of informed nitrogen level adjustment and genotypic selection on yield optimization.