Abstract
RapidSCAN is a portable active canopy sensor with red, red-edge, and near infrared spectral bands. The objective of this study is to develop and evaluate a RapidSCAN sensor-based precision nitrogen (N) management (PNM) strategy for high-yielding rice in Northeast China. Six rice N rate experiments were conducted from 2014 to 2016 at Jiansanjiang Experiment Station of China Agricultural University in Northeast China. The results indicated that the sensor performed well for estimating rice yield potential (YP0) and yield response to additional N application (RIHarvest) at the stem elongation stage using normalized difference vegetation index (NDVI) (R2 = 0.60–0.77 and relative error (REr) = 6.2–8.0%) and at the heading stage using normalized difference red edge (NDRE) (R2 = 0.70–0.82 and REr = 7.3–8.7%). A new RapidSCAN sensor-based PNM strategy was developed that would make N recommendations at both stem elongation and heading growth stages, in contrast to previously developed strategy making N recommendation only at the stem elongation stage. This new PNM strategy could save 24% N fertilizers, and increase N use efficiencies by 29–35% as compared to Farmer N Management, without significantly affecting the rice grain yield and economic returns. Compared with regional optimum N management, the new PNM strategy increased 4% grain yield, 3–10% N use efficiencies and 148 $ ha−1 economic returns across years and varieties. It is concluded that the new RapidSCAN sensor-based PNM strategy with two in-season N recommendations using NDVI and NDRE is suitable for guiding in-season N management in high-yield rice management systems. Future studies are needed to evaluate this RapidSCAN sensor-based PNM strategy under diverse on-farm conditions, as well as to integrate it into high-yield rice management systems for food security and sustainable development.
Highlights
As one of the major cereal crops in the world, more than half of the world’s population takes rice (Oryza sativa L.) as the staple food [1]
Rice grain yield was significantly affected by the factors of N rates, varieties, and years, and RapidSCAN-based normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) showed similar results (Table 3)
The performance of the RI_VI calculated with NDVI (RI_NDVI), NDRE (RI_NDRE), and best performing vegetation indices (VI) to estimate rice RIHarvest varied with growth stages across N rate treatments, sites, and years (Table 5 and Figure 4)
Summary
As one of the major cereal crops in the world, more than half of the world’s population takes rice (Oryza sativa L.) as the staple food [1]. Northeast China is a major rice production region in China and the abovementioned management problems are common [4,5,6] Facing these challenges, Chinese agricultural scientists have developed regional optimum N management (RONM) systems, aiming to obtain higher yields with less resources and N losses suitable for different regions [4,6,7]. Precision N management (PNM) strategies consider both spatial and temporal variability in soil N supply and crop N demand. They have the potential to further improve NUE over the RONM strategy [8]
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