Stability and applicability of the leaf value model for variable nitrogen application based on SPAD value in rice

Abstract

Many fertilization models have been created to scientifically determine the amount of fertilization. With the same purpose, we constructed a nitrogen (N) application model, the leaf value model, which can make N fertilizer decisions in a timely, fast and nondestructive manner during rice planting. However, only one area (A 1 , Jiuzhou Town, Xixiu District, Guizhou Province) and one cultivar (Qyou6) were involved in the construction of the leaf value model. Its stability and applicability could not be well evaluated. Thus, we chose another area (A 2 , Jiuzhou Town, Huangping County, Guizhou Province) in Guizhou Province and carried out the experiment by using four cultivars (Nie5you5399, Qyou6, Yixiangyou2115 and Zhongzheyou8) for the leaf value model construction. Compared with the average value of apparent total N uptake (Nz) obtained in 2 years in the A1 area, that in the Qyou6 leaf value model in the A2 area increased by 12%, reaching 635.72 kg ha −1 , whereas the corresponding target yield changed slightly, reaching 10,999.90 kg ha −1. Simultaneously, the linear relationship between several good SPAD value-derived indexes (Ys) and apparent N supply of the field (Nx) was still significant or extremely significant in the Qyou6 leaf value model. Compared with the A 1 area, it slightly differed, and the R 2 of SPAD L1 was higher than that of SPAD L3×L4/mean. In the leaf value model of the other three cultivars, the relationship between yield and Nx and that between Ys and Nx were significant or extremely significant. The Nz of Yixiangyou2115 and Zhongzheyou8 (618.33 and 617.76 kg ha-1) were close to that of Qyou6 and the corresponding target yields were 10313.36 and 10301.99 kg ha-1 , respectively. The Nz and target yield of Nie5you5399 were lowest at 546.63 and 10680.24 kg ha-1 , respectively. In general, this study showed that relationships used in the construction of leaf value model had certain stability and applicability to difference areas and cultivars. The leaf value model can be considered in N fertilizer decision-making of rice planting management.

Response of root development and nutrient uptake of two chinese cultivars of hybrid rice to nitrogen and phosphorus fertilization in Sichuan Province, China

“…Our results showed that grain yield progressively increased with nitrogen fertilization in both cultivars, although in Y21 the yield difference between N 120 and N 180 was not statistically significant (Table 1). The underperformance of Y21 with high nitrogen fertilization is in concordance with its low nitrogen efficiency as compared to other rice cultivars, as has been previously reported by Li et al [36], and demonstrated further by comparing the nitrogen use efficiency of the two cultivars in this study. Both cultivars performed better at moderate (P 90 ) than at high (P 120 ) phosphorus fertilization (Table 1).…”

“…This work represents a comparative analysis of the effects of high, moderate, low-dosage application and non-application of nitrogen and phosphorus fertilizer on the growth, yield, yield-related traits and expression of selected genes in two Chinese hybrid rice cultivars. The cultivar Deyou4727 (herein shortened to D47) has been previously described as drought-tolerant, with a particularly well-developed root system [35], while Yixiangyou2115 (referred to as Y21) was reported as relatively nitrogen-inefficient compared to other hybrid rice cultivars and restorer lines [36].…”

Regional Monitoring of Leaf ChlorophyII Content of Summer Maize by Integrating Multi-Source Remote Sensing Data

“…Therefore, some scholars proposed the soil and plant analysis development (SPAD) method for nondestructive rapid measurements of the LCC values of leaves by means of light and electricity [10,11]; furthermore, the relative contents of ChlorophyII obtained by Soil and Plant Analyzer Development 502 (SPAD 502) are highly consistent with the results obtained by chemical experiments, supporting the idea of using this approach instead of the traditional chemical measurement method [12]. However, the drawbacks of this method include limited measuring points and unsuitability for measurement involving a large area [13,14].…”

Estimation of chlorophyll and nitrogen in double-cropping rice based on machine learning

“…Leaf chlorophyll content and nitrogen content are two very important indices of rice nutrition and growth status. Real-time and accurate monitoring of chlorophyll content and nitrogen status of crops is of great signi cance to scienti c cultivation of crops, accurate management of eld fertilizers, and improvement of crop yield and quality [6][7][8][9] .…”