Effects of irrigation and rainfed practices on Normalized Difference Vegetative Index of Wheat (Triticum aestivum L.) and its Implications on Grain Yield in Northern China
{"title":"Effects of irrigation and rainfed practices on Normalized Difference Vegetative Index of Wheat (Triticum aestivum L.) and its Implications on Grain Yield in Northern China","authors":"Tumaini Erasto Robert Mazengo, Zhongying Guo, Xiaoying Liu, Yingnan Wu, Yuzhong Li, Catherine Gwandu","doi":"10.1186/s40068-023-00303-w","DOIUrl":null,"url":null,"abstract":"Abstract Five (5) winter wheat genotypes were evaluated based on the Normalized Difference Vegetative Index (NDVI) under irrigation and rainfed conditions. A randomized complete block design in a split-plot arrangement was used with 30 treatment combinations during the two consecutive cropping seasons, from 2017 to 2019. The NDVI was used to evaluate the differences in wheat genotypes growth from the effects of irrigation and rainfed. The results indicated that NDVI values varied at all vegetative stages and that there were significant differences (p < 0.05) in NDVI indices among genotypes throughout the growth period, especially at the booting and grain-filling stages from the end of March to mid-May. However the indices started to decrease immediately after physiological maturity. In the entire study, the maximum NDVI was 0.82 for the Zhongmai-36 genotype, corresponding to a grain yield of 8.05 mg ha −1 and was obtained in irrigation group. The maximum NDVI in rainfed group was 0.78 from Zhongmai-36 and corresponded to the grain yield of 7.28 mg ha −1 . This study suggests that among the other four genotypes, Zhongmai-36 could be prioritized under limited irrigation without compromising grain yield (GY). Since the NDVI, leaf area index (LAI) and GY related positively during the entire growth period therefore, can be used for the real time monitoring of wheat growth seasonal water requirements and grain yield simulation. This information could be used by agricultural stakeholders and decision-makers in early warning of food security concerning wheat productivity.","PeriodicalId":12037,"journal":{"name":"Environmental Systems Research","volume":"41 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Systems Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40068-023-00303-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Five (5) winter wheat genotypes were evaluated based on the Normalized Difference Vegetative Index (NDVI) under irrigation and rainfed conditions. A randomized complete block design in a split-plot arrangement was used with 30 treatment combinations during the two consecutive cropping seasons, from 2017 to 2019. The NDVI was used to evaluate the differences in wheat genotypes growth from the effects of irrigation and rainfed. The results indicated that NDVI values varied at all vegetative stages and that there were significant differences (p < 0.05) in NDVI indices among genotypes throughout the growth period, especially at the booting and grain-filling stages from the end of March to mid-May. However the indices started to decrease immediately after physiological maturity. In the entire study, the maximum NDVI was 0.82 for the Zhongmai-36 genotype, corresponding to a grain yield of 8.05 mg ha −1 and was obtained in irrigation group. The maximum NDVI in rainfed group was 0.78 from Zhongmai-36 and corresponded to the grain yield of 7.28 mg ha −1 . This study suggests that among the other four genotypes, Zhongmai-36 could be prioritized under limited irrigation without compromising grain yield (GY). Since the NDVI, leaf area index (LAI) and GY related positively during the entire growth period therefore, can be used for the real time monitoring of wheat growth seasonal water requirements and grain yield simulation. This information could be used by agricultural stakeholders and decision-makers in early warning of food security concerning wheat productivity.