Information Processing in Agriculture (Dec 2017)
Leaf chlorophyll and nitrogen dynamics and their relationship to lowland rice yield for site-specific paddy management
Abstract
The optimum rate and application timing of Nitrogen (N) fertilizer are crucial in achieving a high yield in rice cultivation; however, conventional laboratory testing of plant nutrients is time-consuming and expensive. To develop a site-specific spatial variable rate application method to overcome the limitations of traditional techniques, especially in fields under a double-cropping system, this study focused on the relationship between Soil Plant Analysis Development (SPAD) chlorophyll meter readings and N content in leaves during different growth stages to introduce the most suitable stage for assessment of crop N and prediction of rice yield. The SPAD readings and leaf N content were measured on the uppermost fully expanded leaf at panicle formation and booting stages. Grain yield was also measured at the end of the season. The analysis of variance, variogram, and kriging were calculated to determine the variability of attributes and their relationship, and finally, variability maps were created. Significant linear relationships were observed between attributes, with the same trends in different sampling dates; however, accuracy of semivariance estimation reduces with the growth stage. Results of the study also implied that there was a better relationship between rice leaf N content (R2Â =Â 0.93), as well as yield (R2Â =Â 0.81), with SPAD readings at the panicle formation stage. Therefore, the SPAD-based evaluation of N status and prediction of rice yield is more reliable on this stage rather than at the booting stage. This study proved that the application of SPAD chlorophyll meter paves the way for real-time paddy N management and grain yield estimation. It can be reliably exploited in precision agriculture of paddy fields under double-cropping cultivation to understand and control spatial variations. Keywords: Spatial variability, Non-invasive measurement, Precision farming, Decision support