How Does Crop Rotation Influence Soil Moisture, Mineral Nitrogen, and Nitrogen Use Efficiency?

Area of Science:

• Agricultural Science
• Agronomy
• Soil Science

Background:

• Rice-wheat (RW) cropping systems are crucial for global food security but face challenges in China regarding productivity and nitrogen use efficiency (NUE).
• Current management practices are often not optimized for regional and seasonal variations, leading to suboptimal outcomes in winter wheat production within RW systems.

Purpose of the Study:

• To develop optimized nitrogen (N) fertilizer management guides for Chinese rice-wheat (RW) systems.
• To enhance the productivity, nitrogen use efficiency (NUE), and environmental sustainability of winter wheat in RW systems.

Main Methods:

• Conducted 2-year field experiments in the Jianghan Plain's humid climate zones.
• Investigated four N fertilizer rates (0, 135, 180, 225 kg N ha^-1) in RW systems and compared them with soybean/maize dryland wheat (DW) systems.
• Monitored wheat yields, soil moisture, NUE, N accumulation, and soil inorganic N (ammonium and nitrate).

Main Results:

• Wheat yields in RW systems increased by 24% with high N rates (180-225 kg N ha^-1) compared to DW systems, primarily due to more kernels per spike.
• While DW systems showed higher N accumulation in plant tissue, RW systems exhibited greater mean agronomic efficiency of nitrogen (AE_N) and physiological efficiency of nitrogen (PE_N).
• AE_N and PE_N decreased in DW systems with increasing N rates (135-225 kg ha^-1) but remained stable in RW systems. Nitrate leaching significantly increased above 180 kg N ha^-1.

Conclusions:

• Recommended tactical N fertilizer application until mid-grain filling for both RW and DW systems.
• Identified 180 kg N ha^-1 as a threshold above which yield responses diminish, and environmental risks (nitrate leaching) increase.
• Highlighted the trade-offs between yield, NUE, and environmental sustainability in N fertilization strategies for RW systems, emphasizing the value of multi-season data.