Improving Nitrogen Management in California Rice Systems: A Synergy Between Remote Sensing Technology and Fundamental Agronomy

Improving nitrogen (N) management is critical to maximizing the productivity and sustainability of our agroecosystems. Improving N management requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. However, given the lack of tools currently available to accurately assess crop N status, farmers continuously face the challenge of determining whether their crops require additional N fertilizer. The recent emergence of remote sensing technology has provided a promising alternative that can provide farmers the information they need in an accurate and timely manner. Several studies have demonstrated the potential of remote sensing technology to accurately assess the health and vigor of vegetation at the landscape scale, however few have explored how this technology can be utilized to inform sustainable crop management at the farm scale. This knowledge gap is what inspired this research and led us to investigate how remote sensing technology can be utilized to improve in-season N management in California rice systems.In California, where more than 200,000 ha of flooded rice (Oryza sativa) is cultivated annually, the recommended N management strategy is for farmers to apply the average seasonal N fertilizer requirement prior to flooding and planting as aqua-ammonia injected into the soil. On-farm studies have reported that N fertilizer applied in this manner is efficiently utilized by the crop as it remains protected from denitrification and ammonia volatilization losses until the crop needs it. At panicle initiation (PI), it is recommended to assess crop N status to determine if additional N fertilizer inputs are required as top-dress. The current tools available to assess rice N status include the SPAD chlorophyll meter and Leaf Color Chart, but these tools are not often used as they are time consuming and subjective. Thus, most top-dress N applications take place without evaluati