Estimation of in-season nitrogen requirement for drip-irrigated spring maize without or with straw incorporation using critical nitrogen dilution curve

Guo, Ru; Qian, Rui; Naseer, Muhammad Asad; Han, Fei; Zhang, Peng; Jia, Zhikuan; Chen, Xiaoli; Ren, Xiaolong

doi:10.1007/s11104-023-06099-z

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Preprint1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Straw-derived biochar optimizes water consumption, shoot and root characteristics to improve water productivity of maize under reduced nitrogen

Guo,

Qian,

et al. 2024

Agricultural Water Management

View full text Add to dashboard Cite

Straw-derived biochar optimizes water consumption, shoot and root characteristics to improve water productivity of maize under reduced nitrogen

Guo,

Qian,

et al. 2024

Agricultural Water Management

View full text Add to dashboard Cite

Predictive Modeling Study on the Critical Nitrogen Concentration and Nitrogen Accumulation in Cut Chrysanthemum Based on the Cumulative Photo-Thermal Effect

Liu,

Wu,

et al. 2024

Preprint

View full text Add to dashboard Cite

Background and aims Critical nitrogen (N) concentration (Nc) and critical accumulation (Na) are essential for N status diagnosis and precise N fertilization in crops. However, efficient prediction models for Nc and Na in cut Chrysanthemum remains scarce, limiting precision N management. Methods Five experiments with varying N gradients were conducted from May 2021 to August 2022 using the ‘Nannong Xiaojinxing’ cultivar. We developed and validated dry matter prediction models with various growth and developmental driver variables, established Nc and Na models using dry matter as model driving variable, and created Nc and Na models using optimal driving variable identified from dry matter predictions. Results Among the dry matter prediction models for cut Chrysanthemum, the model incorporating cumulative photo-thermal effect (PTE) demonstrated superior accuracy and stability. We established the Nc and Na models using dry matter as the driving variable. When the above-ground dry matter was 1 g·plant− 1, the Nc and Na were 4.5295% and 45.30 mg·plant− 1, respectively. At the flower picking stage, the Na reached 236.50 mg·plant− 1. The PTE-driven Nc and Na prediction models demonstrated high accuracy, with R2 at 0.9687 and 1.0019, RMSEs at 0.2105% and 17.47 mg·plant− 1, and n-RMSEs at 7.31% and 12.72%, respectively. Conclusions These models can dynamically predict Nc and Na based on light and temperature factors, providing a scientific basis for efficient N diagnostics and precise N fertilizer management for cut chrysanthemum. Moreover, the methodology developed herein could be extrapolated to other crops, contributing to sustainable agriculture and mitigating excessive N fertilizer application.

show abstract

Estimation of in-season nitrogen requirement for drip-irrigated spring maize without or with straw incorporation using critical nitrogen dilution curve

Cited by 2 publications

References 51 publications

Straw-derived biochar optimizes water consumption, shoot and root characteristics to improve water productivity of maize under reduced nitrogen

Straw-derived biochar optimizes water consumption, shoot and root characteristics to improve water productivity of maize under reduced nitrogen

Predictive Modeling Study on the Critical Nitrogen Concentration and Nitrogen Accumulation in Cut Chrysanthemum Based on the Cumulative Photo-Thermal Effect

Contact Info

Product

Resources

About