Comparison of Five Nitrogen Dressing Methods to Optimize Rice Growth

Chen, Qingchun; Tian, Yongchao; Yao, Xia; Cao, Weixing; Zhu, Yan

doi:10.1626/pps.17.66

Cited by 21 publications

(12 citation statements)

References 24 publications

(49 reference statements)

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“…Peng et al ( 2007 ) used a chlorophyll meter to monitor rice N status, and develop site-specific N dressing management method for resulting in higher grain yield with lower N dressing rates (Liu et al, 2009 ). However, single SPAD or LAI values cannot fully reflect the canopy dynamics and N status of crops (Wang et al, 2003 ; Zhong et al, 2006 ; Chen et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

Liu

Zhang

et al. 2017

Front. Plant Sci.

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Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R2 = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R2 = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R2 = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield.

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Section: Discussionmentioning

confidence: 99%

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

Liu

Zhang

et al. 2017

Front. Plant Sci.

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“…Likewise, Saberioon et al suggested that N and grain yield were significantly correlated with NDVI (R 2 = 0.78), especially at the panicle initiation and booting stages [14]. Chen et al indicated that the canopy spectrum-based nitrogen optimization algorithm is a good technique for optimizing N application rates in rice, with higher economic benefit, less N input and better applicability under different base-tiller N application rates [15]. So, a strong positive correlation between vegetation canopy bidirectional reflectance factor in the near infrared spectral region and foliar mass-based nitrogen concentration could indicate an additional role for nitrogen in the climate system via its influence on surface albedo and may offer a simple approach for monitoring foliar nitrogen using satellite data.…”

Section: Introductionmentioning

confidence: 99%

Estimating the effect of urease inhibitor on rice yield based on NDVI at key growth stages

Liu¹,

Li²,

Hu³

2014

Front. Agr. Sci. Eng.

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The effect of the urease inhibitor, N-(n-butyl) thiophosphoric triamide (NBPT) at a range of application rates on rice production was examined in a field experiment at Jinxian County, Jiangxi Province, China. The normalized difference vegetation index (NDVI) was measured at key growth stages in both early and late rice. The results showed that the grain yield increased significantly when urea was applied with NBPT, with the highest yield observed at 1.00% NBPT (wt/wt). NDVI differed with the growth stage of rice; it remained steady from the heading to the filling stage. Rice yield could be predicted from the NDVI taken at key rice growing stages, with R 2 ranging from 0.34 to 0.69 in early rice and 0.49 to 0.70 in late rice. The validation test showed that RMSE (t$hm -2 ) values were 0.77 and 0.87 in early and late rice, respectively. Therefore, it was feasible to estimate rice yield for different amounts of urease inhibitor using NDVI.

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“…Through Eq. (), a SPAD‐values‐based NFOA regulation method was established:

normalNSI 4 = normalL 4 / L 4_{normale}

NA = 0.0279 \exp (8.6957 NSI 4)

PGY = 1.137 \exp (2.1153 NSI 4)

GNU = 1.9 PGY / 100

Nr = (GNU - NA) / NUE

where L4 is the SPAD value of the fourth fully expanded leaf from top, and L4 e is the L4 value measured in the N excessive plot (N3, 360 kg N ha −1 ); PGY is the potential grain yield; and NUE was the N use efficiency, set to 0.7 (Chen, Tian, Xia, Cao, & Yan, ).…”

Section: Methodsmentioning

confidence: 99%

“…This study also provided more detail on cost-benefit analyses than previous ones (Chen et al, 2014) by incorporating the costs of fertilizers (N, K, and P), seeding, pesticides, plowing, and field management. The net profit includes all costs and the net benefit reflects the actual financial gain or loss for a farmer, so it is a more appropriate indicator of financial performance than the benefit-fertilizer cost ratio (Li et al, 2012).…”

Section: The Performance Of Nsi4-based Nfoa and Nni Methodsmentioning

confidence: 99%

Chlorophyll meter–based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in‐season fertilization of paddy rice

et al. 2020

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Nitrogen fertilizer optimization algorithm (NFOA) and nitrogen nutrition index (NNI) have been developed to achieve higher yield and N use efficiency. This study was to improve NFOA and NNI strategies for managing rice N nutrition based on portable Soil and Plant Analyzer Development (SPAD) meter. Four field experiments were conducted in 2013–2014 to generate diverse N fertilizer rates in rice cultivars at three eco‐sites. Nitrogen indicators SPAD readings were measured to establish SPAD‐based management strategies. Two experiments were conducted to assess the effectiveness of developed NFOA and NNI algorithm for upward and downward adjustment of N‐fertilizer doses twice within the growing season in 2015. New N‐adjustment strategies optimized N topdressing by upward‐, fine‐, and downward‐ adjustment with an average N‐application rate of 264–272 kg N ha−1 in eastern China. Maximum grain yield (10.5 t ha−1) was obtained when 264 kg N ha−1 was applied. The recovery efficiency of N fertilizer was higher for NFOA and NNI (51–57%) than fixed‐time application of 270 kg N ha−1 (48%). The comparable high net profit (>$1080 ha−1) was achieved with lower N‐adjustment fertilizer input in 270 and 360 kg N ha−1 treatments. SPAD‐based NFOA and NNI methods are practical approaches for reducing excessive use of N fertilizer, achieving slight increase in yield, and net profit was decreased (360 kg N ha−1). More refinements are necessary to ensure that these strategies can be used for yield‐ and income‐enhancing production for farmers.

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Comparison of Five Nitrogen Dressing Methods to Optimize Rice Growth

Cited by 21 publications

References 24 publications

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

Estimating the effect of urease inhibitor on rice yield based on NDVI at key growth stages

Chlorophyll meter–based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in‐season fertilization of paddy rice

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