Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor

Lu, Junjun; Miao, Yuxin; Chen, Wei; Li, Jingxin; Yuan, Fei

doi:10.1038/s41598-017-14597-1

Cited by 53 publications

(61 citation statements)

References 36 publications

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“…More studies are needed to further evaluate this ACS-based PNM strategy in more farmer fields under diverse on-farm conditions. This strategy can be further improved by using three band sensors like Crop Circle ACS 470 or 430 or RapidSCAN, with red edge band [33,65,66] or by incorporating soil and weather information into the algorithm [26,58].…”

Section: Developing Active Sensor-based Precision N Management Strategymentioning

confidence: 99%

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

et al. 2019

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Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP 0 ), response index to side-dress N based on harvested yield (RI Harvest ), and side-dress N agronomic efficiency (AE NS ). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015)(2016)(2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP 0 (R 2 = 0.44-0.78) than only using NDVI or RVI (R 2 = 0.26-0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AE NS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AE NS . The INFOA-based PNM strategies could increase marginal returns by 212 $ ha −1 and 70 $ ha −1 , reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%-40% and 11%-65% compared with farmer's typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.Sustainability 2019, 11, 706 2 of 26 as climate change and tropospheric ozone pollution, can also negatively affect crop yields [7,8]. N fertilizer related greenhouse gas (GHG) emissions (nitrous oxide under wet conditions and ammonia under hot conditions) have exceeded the corresponding gains in soil carbon related to its effect on increased biomass by 700% in China [9]. Therefore, optimizing N management for maize production to minimize the adverse environmental impacts is crucially important for sustainable development of agriculture [5,10].The optimum N rate depends on crop N demand and soil N supply. The crop N demand is determined by the plant growth status and grain yield potential, while the soil N supply is a net result of mineralization, immobilization and losses of soil N. They are both influenced by many factors such as seasonal temperature, precipitation, physical and biogeochemical soil properties, and management history [11]. The interactions between soil water and N determine the growth, development and yield of maize. Efficient utilization of water and N can only be realized if they are closely matched [12]. Climate conditions can affect optimum N via various processes in soil such as nitrification, denitrification, leaching, and mineralization, which will modula...

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Section: Developing Active Sensor-based Precision N Management Strategymentioning

confidence: 99%

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

et al. 2019

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“…The linear relationships between the single wavelengths derived from CC-470 (R550, R730, and R760) and CC-430 (R670, R730, and R780) at different measurement heights (40,70, and 100 cm) and N status indicators (PNU and NNI) before canopy closure (Feekes 6-7) and after canopy closure (Feekes 9-10) as well as across all the growth stages were established. Consequently, the sensitivities of the single wavelengths of both sensors for estimating N status in winter wheat cultivars were studied (Tables 2).…”

Section: Correlations Between N Status Indicators and Single Wavelengthmentioning

confidence: 99%

“…The linear relationships between the N status indicators (PNU and NNI) and the vegetation indices (NDRE and CI RE ) were derived using CC-470 and CC-430 sensors at three measurement heights (40,70, and 100 cm) and at different crop growth stages (Feekes 6-7, Feekes 9-10, and across all growth stages ( Table 3). The results showed that the values of R 2 for NDRE-PNU and CI RE -PNU relations during the Feekes 6-7 stages at different measurement heights ranged from 0.51 to 0.86 and 0.46 to 0.87 for CC-470, while the corresponding ranges were 0.78-0.86 and 0.79-0.86 for CC-430.…”

Section: Correlations Between N Status Indicators and Vegetation Indicesmentioning

confidence: 99%

Evaluating Two Crop Circle Active Canopy Sensors for In-Season Diagnosis of Winter Wheat Nitrogen Status

et al. 2018

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Active crop canopy sensors can be used for non-destructive real-time diagnosis of crop nitrogen (N) status and guiding in-season N management. However, limited studies have compared the performances of two commercially available sensors with three different wavebands: Crop Circle ACS-470 (CC-470) and Crop Circle . The objective of this study was to evaluate the performances of CC-470 and CC-430 sensors for estimating winter wheat (Triticum aestivum L.) N status at different measurement heights (40 cm, 70 cm and 100 cm) and growth stages. Results indicated that the canopy reflectance values of CC-470 were more affected by height compared to the CC-430 sensor. The normalized difference red edge (NDRE) and red edge chlorophyll index (CI RE ) of CC-430 were stable at the three different measuring heights. The relationships between these indices and the N status indicators were stronger at the Feekes 9-10 stages than the Feekes 6-7 stages for both sensors; however, the CC-430 sensor-based vegetation indices had higher coefficient of determination (R 2 ) values for both stages. It is concluded that the CC-430 sensor is more reliable than CC-470 for winter wheat N status estimation due to its capability of making height-independent measurements. These results demonstrated the importance of considering the influences of height when using active canopy sensors in field measurements.

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“…We are not sure why this difference between studies, but it may be because Yao et al (2014) conducted all their research at a single location, thus resulting in less variation of AGB during the course of their study. Others have looked at the relationship between NDVI and N CONC and have reported both strong ( r 2 = 0.81) and weak ( r 2 = 0.08) correlations (Zhu et al, 2007; Lu et al, 2017), which may be due to differences in rice varieties or the growth stage when data was collected. In other studies, Gnyp et al (2014) examined the relationship between NDVI and AGB and reported the same correlation ( r 2 = 0.51) as our study; while Li et al (2018) examined the relationship between leaf N UP and NDVI and found a similar correlation ( r 2 = 0.70) to our study with plant N UP .…”

Section: Discussionmentioning

confidence: 99%

“…Comparatively, there have been relatively few such studies in rice. Some have tested the ability of NDVI to assess rice N status (Zhu et al, 2007; Gnyp et al, 2014; Yao et al, 2014; Lu et al, 2017) and few have used NDVI to develop in‐season yield predictions (Harrell et al, 2011; Yao et al, 2012; Cao et al, 2016). However, most of these studies have focused their research on single sites, leaving at question the scalability of their findings to other sites representing different soils and management practices.…”

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confidence: 99%

Use of Normalized Difference Vegetation Index to Assess N Status and Predict Grain Yield in Rice

2019

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Fine tuning N recommendations requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. Recent studies have demonstrated the ability of Normalized Difference Vegetation Index (NDVI) to assess crop N status and predict yield in wheat (Tricticum aestivum L.) and maize (Zea mays L.); however, there has been relatively little such research on rice (Oryza sativa L.). The objectives of this study were to determine how well NDVI measured at the panicle initiation (PI) rice growth stage assesses crop N status and predicts final grain yield. Nitrogen response trials were established over a 4‐yr period (10 site‐years) at various locations throughout the Sacramento Valley rice growing region of California. Additionally, the relationship between NDVI and crop N status was characterized across 28 on‐farm plots representing a range of environmental conditions and management practices. The NDVI at PI was best correlated with total N uptake (NUP, r2 = 0.66), followed by N concentration (NCONC, r2 = 0.54), and aboveground biomass (AGB, r2 = 0.51). The utility of NDVI was greatest at lower values of crop N status, whereas at higher values, NDVI saturated. The NDVI at PI was positively correlated with final grain yield (r2 = 0.58) indicating utility for developing in‐season yield predictions. While NDVI is a potentially useful tool to improve N fertilizer management and develop in‐season yield predictions in rice, alternative indices that do not saturate would likely provide a basis for a better tool. Core Ideas The ability of NDVI to assess rice N status and predict final grain yield was evaluated across 38 sites and four years. NDVI at panicle initiation was most closely related to crop N uptake. At high values of crop N status NDVI had limited utility due to saturation. NDVI at panicle initiation was positively correlated (r2 = 0.58) with final grain yield. NDVI of 0.66 at panicle initiation indicated sufficient crop N uptake to achieve average maximum grain yield.

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Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor

Cited by 53 publications

References 36 publications

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

Evaluating Two Crop Circle Active Canopy Sensors for In-Season Diagnosis of Winter Wheat Nitrogen Status

Use of Normalized Difference Vegetation Index to Assess N Status and Predict Grain Yield in Rice

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