灌水和旱作方式对北方小麦营养指数标准化差异的影响及其对粮食产量的影响

Tumaini Erasto Robert Mazengo, Zhongying Guo, Xiaoying Liu, Yingnan Wu, Yuzhong Li, Catherine Gwandu
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摘要

摘要采用归一化营养指数(NDVI)对5个冬小麦基因型进行了评价。在2017 - 2019年连续两个种植季,采用30个处理组合的随机完全区组设计,采用分畦布置。利用NDVI评价了灌溉和雨养对小麦基因型生长的影响。结果表明,NDVI值在各营养阶段均存在差异,且差异显著(p <3月底至5月中旬孕穗期和灌浆期各基因型NDVI指数差异显著(0.05)。生理成熟后,这些指标立即开始下降。灌水组中麦-36基因型NDVI最大,为0.82,对应的籽粒产量为8.05 mg ha - 1。雨养组中麦36的NDVI最大值为0.78,对应的产量为7.28 mg ha−1。本研究表明,在其他4个基因型中,中麦-36可以在不影响产量的条件下,在有限的灌溉条件下优先选用。由于NDVI、叶面积指数(LAI)与GY在整个生育期均呈正相关,因此可用于小麦生长季节需水量的实时监测和籽粒产量模拟。这些信息可被农业利益相关者和决策者用于小麦生产力的粮食安全预警。
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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
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.
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