Sound irrigation and nitrogen management strategies are necessary to achieve sustainable yield and water use efficiency of winter wheat in the North China Plain (NCP). The coupled effects of irrigation scheduling and the nitrogen application mode (NAM) on winter wheat growth, yield and water use efficiency under drip irrigation were evaluated with a two-year field experiment, which consisted of three irrigation scheduling levels (ISLs) (irrigating when soil water consumption (SWC) reached 20, 35 and 50 mm, referred as I20, I35 and I50, respectively) and three nitrogen application modes (NAMs) (ratio of basal application and topdressing as 50:50, 25:75 and 0:100, referred as N50:50, N25:75 and N0:100, respectively). The experimental results showed that irrigating winter wheat at ISL I35 substantially (p < 0.05) improved the grain yield by 15.89%, 3.32% and 14.82%, 4.31% and water use efficiency (WUE) by 5.23%, 16.03% and 5.26%, 12.36%, compared with those at ISL I20 and I50 in 2017–2018 and 2018–2019 growing seasons, respectively. NAM N25:75 appeared very beneficial in terms of grain yield, yield components and WUE as compared to other NAM levels. The maximum grain yield (8.62 and 9.40 t ha⁻¹) and water use efficiency (1.88 and 2.09 kg m⁻³) were achieved in treatment I35N25:75 in two growing seasons over those in other treatments. The results in this study may deliver a scientific basis for irrigation and nitrogen fertilization management of the drip-irrigated winter wheat production in the NCP.

The North China Plain is an important irrigated agricultural area in China. However, the effects of irrigation management on carbon emission are not well documented in this region. Due to the uneven seasonal distribution of rainfall, irrigation is mainly concentrated in the winter wheat growing season in the North China Plain. In this study, we estimated CO₂ emission and soil CH₄ uptake from winter wheat fields with different irrigation methods and scheduling treatments using the static chamber-gas chromatography method from April to May 2017 and 2018. Treatments included three irrigation methods (surface drip, sprinkler, and border) and three irrigation scheduling levels that initiated as soon as the soil moisture drained to 50%, 60%, and 70% of the field capacity for a 0–100 cm soil profile were tested. The results showed that both the irrigation methods and scheduling significantly influenced (p < 0.05) the cumulative CO₂ and CH₄ emission, grain yield, global warming potential (GWP), GWP Intensity (GWPI), GWPI per unit irrigation applied, and water use efficiency (WUE). Compared to 60% and 70% FC, 50% FC irrigation scheduling de-creased accumulated CH₄ uptake 26.8–30.3% and 17.8–25.4%, and reduced accumulated CO₂ emissions 7.0–15.3% and 12.6–19.4%, respectively. Conversely, 50% FC reduced GWP 6.5–13.3% and 12.5–19.4% and lower grain yield 10.4–19.7% and 8.5–16.6% compared to 60% and 70% FC irrigation scheduling in 2017 and 2018, respectively. Compared to sprinkler irrigation and border irrigation, drip irrigation at 60% FC increased the accumulated CH₄ uptake 11.3–12.1% and 1.9–5.5%, while reduced the accumulated CO₂ emissions from 7.5–8.8% and 10.1–12.1% in 2017 and 2018, respectively. Moreover, drip irrigation at 60% FC increased grain yield 5.2–7.5% and 6.3–6.8%, WUE 0.9–5.4% and 5.7–7.4%, and lowered GWP 8.0–9.8% and 10.1–12.0% compared to sprinkler and border irrigation in 2017 and 2018, respectively. The interaction of irrigation scheduling and irrigation methods significantly impacted accumulated CH₄ uptake, cumulative CO₂ amount, and GWP in 2018 only while grain yield and WUE in the entire study. Overall, drip irrigation at 60% FC is the optimal choice in terms of higher grain yield, WUE, and mitigating GWP and GWPI from winter wheat fields in North China Plain.

The North China Plain (NCP) produces about half of the winter wheat yield in China; therefore, it is essential to improve winter wheat grain yield, biomass, and water productivity (WP) under current water shortage conditions in this area. In this study, the AquaCrop model was used for calibrating and validating crop canopy cover, grain yield, biomass, soil water content, crop evapotranspiration (ET_C), and crop WP under an irrigation scheduling of 50%, 60%, and 70% field capacities with sprinkler irrigation, drip irrigation, and flood irrigation methods for winter wheat crop. The model was calibrated employing experimental data for the 2016–2017 winter wheat season and, subsequently, validated with using data from 2017–2018. The model performance was analyzed using root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), the coefficient of determination (R²), and Willmott’s index of agreement (d). The prediction error between the simulated and observed values for grain yield, biomass, soil water content, ET_C, and WP were the minimum at a 60% field capacity and the maximum at a 50% field capacity irrigation scheduling. The model simulation was satisfactory under the 60% and 70% field capacity irrigation scheduling, while the model performance was relatively low under the 50% field capacity irrigation scheduling. Irrigation to 4–5 times the 30 mm depth (total 120–150 mm) by drip irrigation and sprinkler irrigation was the most effective irrigation schedule to obtain the optimum grain yield, biomass, and WP on the NCP. Our findings suggest that the AquaCrop model could be a feasible tool for precisely simulating the canopy cover, grain yield, biomass, soil water content, ET_C, and WP of winter wheat under different irrigation schedules and irrigation methods on the NCP with higher certainty than under current practices.

Developing accurate, non-destructive, and automated methods for monitoring the phenotypic traits of rapeseed is crucial for improving yield and quality in modern agriculture. We used a line laser binocular stereo vision technology system to obtain the three-dimensional (3D) point cloud data of different rapeseed varieties (namely Qinyou 7, Zheyouza 108, and Huyou 039) at the seedling stage, and the phenotypic traits of rapeseed were extracted from those point clouds. After pre-processing the rapeseed point clouds with denoising and segmentation, the plant height, leaf length, leaf width, and leaf area of the rapeseed in the seedling stage were extracted by a series of algorithms and were evaluated for accuracy with the manually measured values. The following results were obtained: the R² values for plant height data between the extracted values of the 3D point cloud and the manually measured values reached 0.934, and the RMSE was 0.351 cm. Similarly, the R² values for leaf length of the three kinds of rapeseed were all greater than 0.95, and the RMSEs for Qinyou 7, Zheyouza 108, and Huyou 039 were 0.134 cm, 0.131 cm, and 0.139 cm, respectively. Regarding leaf width, R² was greater than 0.92, and the RMSEs were 0.151 cm, 0.189 cm, and 0.150 cm, respectively. Further, the R² values for leaf area were all greater than 0.98 with RMSEs of 0.296 cm², 0.231 cm² and 0.259 cm², respectively. The results extracted from the 3D point cloud are reliable and have high accuracy. These results demonstrate the potential of 3D point cloud technology for automated, non-destructive phenotypic analysis in rapeseed breeding programs, which can accelerate the development of improved varieties.