Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat.

Mengjiao Yang,Awais Rasheed,Zhonghu He,Chengyan Zheng,Xiuliang Jin,Muhammad Adeel Hassan,Yong Zhang,Yonggui Xiao,Xianchun Xia,Kaijie Xu

doi:10.3389/fpls.2020.00927

Abstract

Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha–1, T2 = 180 kg ha–1, and T3 = 240 kg ha–1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha–1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.

Highlights

Low water and nutrient uptake efficiency of crops is one of the most detrimental limitation in agriculture productivity (Chuan et al, 2016; Lesk et al, 2016)
All multispectral traits i.e., red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge (NDRE), red-edge chlorophyll index (RECI), and normalized green red difference index (NGRDI) mainly surrogate to green biomass, water content and chlorophyll level of plants were positively correlated with manually measured indicators of water status i.e., biomass, temporally at heading (r = 0.17 to 0.87), flowering (r = 0.15 to 0.77) and maturity (r = 0.15 to 0.90) in control and all water-supply treatments (Figure 2)
Water use efficiency calculated from grain yield (GY) (WUE.GY) was strongly correlated with RNDVI (r = 0.51 to 0.65), GNDVI (r = 0.47 to 0.64), NDRE (r = 0.53 to 0.65), RECI (r = 0.40 to 0.66), and NGRDI (r = 0.56 to 0.64)

Summary

Introduction

Low water and nutrient uptake efficiency of crops is one of the most detrimental limitation in agriculture productivity (Chuan et al, 2016; Lesk et al, 2016). Efficient irrigation and nitrogen (N) supply according to the plant requirement is a key regulator for resource-efficient crop yield (Christopher et al, 2016; Chuan et al, 2016; Nehe et al, 2018; Thorp et al, 2018). In China, the agricultural system generally relies on the high-to-excessive N inputs and the total average application of N for winter wheat has increased more than 500 kg N/ha, while the nitrogen-use efficiency (NUE) in wheat system remains lowest as compared with other crops like maize and rice (Cui et al, 2018). Establishing substantial regulations and breeding new varieties for high water and N acquisition could provide an effective approach to improve WUE, NUE, and yield potential (Hu and Xiong, 2014; Zhang et al, 2017)

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