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Low radiation reduces wheat grain yield in tree‐crop intercropping systems in the major wheat planting area of China. Here, two winter wheat (Triticum aestivum L) cultivars, Yangmai 158 (shading tolerant) and Yangmai 11 (shading sensitive), were shaded from jointing to maturity to evaluate the impact of low radiation on crop growth, photosynthesis and yield. Grain yield losses and leaf area index (LAI) reduction were less than the reduction in solar radiation under both shading treatment in both cultivars. Compared with the control (S0), grain yield only reduced 6.4 % and 9.9 % under 22 % shading treatment (S1), while 16.2 % and 25.8 % under 33 % shading (S2) in Yangmai 158 and Yangmai 11 respectively. The reduction in LAI was 6.0 % and 9.2 % (S1), and 18.2 % and 22.2 % (S2) in Yangmai 158 and Yangmai 11 respectively. However, decline in canopy apparent photosynthetic rate (CAP) was 15.0–22.9 % (S1) and 29.5–49.6 % (S2), which was consistent with the reduction in radiation. The reduction in LAI was partially compensated by increases in the fraction of the top and bottom leaf area to the total leaf area, which facilitated to intercept more solar radiation by the canopy. The decrease in photosynthetic rate (Pn) of flag leaf was partially compensated by the increase in Pn of the third leaf from the top. In addition, an inconsistency between the low Pn and the high Chl content in flag leaf was observed at 30 DAA. This could be explained that more excitation energy was dispersed via the non‐photochemical approaches in the photosystem II (PSII) of flag leaf after long‐term shading.

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Background Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF). Results Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy ( R 2 = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics. Conclusions Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.

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