Study on nitrogen demand model in pakchoi (Brassica campestris ssp. Chinensis L.) based on nitrogen contents and phenotypic characteristics

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Abstract

Introduction: In precision agriculture, the diagnosis of the nitrogen (N) nutrition status based on the plant phenotype, combined effects of soil types, various agricultural practices, and environmental factors which are essential for plant N accumulation. It helps to assess the N supply for plants at the right time and optimal amount to ensure high N use efficiency thereby reducing the N fertilizer applications to minimize environmental pollution. For this purpose, three different experiments were performed. Methods: A critical N content (Nc) model was constructed based on cumulative photothermal effect (LTF), Napplications, and cultivation systems on yield and N uptake in pakchoi. Results and discussion: According to the model, aboveground dry biomass (DW) accumulation was found equal or below to 1.5 t/ha, and the Nc value was observed at a constant of 4.78%. However, when DW accumulation exceeded 1.5 t/ha, Nc declined with the increase in DW accumulation, and the relationship between Nc and DW accumulation developed with the function Nc %=4.78 x DW-0.33. An N demand model was established based on the multi-information fusion method, which integrated multiple factors, including Nc, phenotypical indexes, temperature during the growth period, photosynthetically active radiation, and N applications. Furthermore, the model’s accuracy was verified, and the predicted N contents were found consistent with the measured values (R2 = 0.948 and RMSE = 1.96 mg/plant). At the same time, an N demand model based on N use efficiency was proposed. Conclusions: This study can provide theoretical and technical support for precise N management in pakchoi production.

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Chang, L., Xiong, X., Hameed, M. K., Huang, D., & Niu, Q. (2023). Study on nitrogen demand model in pakchoi (Brassica campestris ssp. Chinensis L.) based on nitrogen contents and phenotypic characteristics. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1111216

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