In-Season Nitrogen Fertilizer Management in Irrigated Wheat Based on Available Decision Support Tools

Efficient management of nitrogen (N) fertilizer, i.e., minimizing N loses from a rice-wheat cropping system is a major challenge. To identify the efficient and appropriate decision tools to increase N use efficiency (NUE) in wheat crop (cv, Vijaya), a field experiment was conducted at Regional Agricultural Research Station, Khajura, Banke, Nepal during 2017-2019. Eight N fertilizer treatments from combination of application methods and other available decision support tools were tested in a randomized complete block design with three replicates. The treatments consist control (0 kg N ha-1), decision support tools for N management such as use of optical sensor, SPAD meter, leaf color chart (LCC) in combination with basal N fertilization and conventional broadcast application at 100 kg N ha-1. Application of N fertilizer based on decision support tools had significant (p<0.05) effects on grain yields. The highest grain yield (3.84 ton ha1 , average across two years) was recorded in the conventional broadcast method, which was at par with optical sensor and LCC in combination with 25 kg N ha-1 as basal application. Results suggest that use of optical sensor could reduce N use as much as by 50 kg ha-1.Similarly, use of optical sensor and LCC in combination with 25 kg N ha-1 as basal application could save N by 37.5 kg N ha-1 and 29.2 kg N ha-1, respectively. The highest agronomic NUE (37.7 kg grain kg-1 N) and partial factor productivity of N (PFPN) of 58.7 kg kg-1 N was observed in optical sensor guided N management with 25 kg N as basal application. These results indicated that optical sensor and LCC could save N significantly without compromising grain yields. There were significant correlation between NDVI value, SPAD value and LCC value measured at 45 DAS to 75 DAS and grain yield. This indicated that measurement of NDVI, SPAD value and LCC value allow a practical window for in-season N management in wheat.
Optical sensors, Leaf