Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming

Cao, Qiang; Cui, Zhenling; Chen, Xinping; Khosla, Raj; Dao, Thanh H.; Miao, Yuxin

doi:10.1007/s11119-011-9244-3

Cited by 69 publications

(41 citation statements)

References 42 publications

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“…Due to the complex interrelationship between many environmental factors, such as soil heterogeneity, cultivation and land surface, the parameters described above show high spatial and temporal variability so that a high measurement density would be needed to reflect their spatial patterns within the field [16]. It has been shown that site-specific management strategies in the context of precision farming increase management efficiency [4].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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Abstract:Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system-three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px −1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m 2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R 2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R 2 values with the best model obtained at flowering (R 2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.

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Section: Introductionmentioning

confidence: 99%

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

et al. 2016

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“…Therefore, there is an increasing interest in using proximal and remote sensing technologies to non-destructively estimate the crop NNI [10][11][12][13]. Several researchers have successfully used chlorophyll meter (CM) data to estimate the NNI of wheat (Triticum aestivum L.) [14][15][16][17] and maize (Zea mays L.) [18]. However, CM data are point measurements at the leaf level and unsuitable for precision N management across large areas [19].…”

Section: Introductionmentioning

confidence: 99%

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

et al. 2015

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Rice farming in Northeast China is crucially important for China's food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N OPEN ACCESS Remote Sens. 2015, 7 10647 application at the stem elongation stage in Northeast China. Five farmers' fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R 2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

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“…In addition to these point measurement tools, there is increasing interest in developing active crop canopy sensor-based precision N management strategies because they are more efficient and suitable for large area applications (Xue and Yang 2008;Harrell et al 2011;Yao et al 2012;Cao et al 2013). A commonly used active crop canopy sensor for precision N management is the GreenSeeker handheld sensor (Trimble Navigation Limited, Sunnyvale, CA, USA) with two fixed wavebands (red and NIR) (Raun et al 2002;Li et al 2009;Bijay-Singh et al 2011;Cao et al 2012). This sensor provides two vegetation indices, normalized difference vegetation index (NDVI) and ratio vegetation index (RVI).…”

Section: Introductionmentioning

confidence: 99%

Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

et al. 2015

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In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP 0 ) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP 0 and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP 0 and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R 2 = 0.66 and 0.70, respectively) and Crop Circle (best R 2 = 0.71 and 0.77, respectively) sensors worked well for estimating YP 0 and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R 2 = 0.82) and green ratio vegetation index (R 2 = 0.73) explained 26 and 22 % more variability in YP 0 and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP 0 or RI estimation, while Crop Circle REOSAVI and normalized green index could still explain more than 70 % of YP 0 and RI variability. It is concluded that both sensors performed similarly at the stem elongation stage, but significantly better results were obtained by the Crop Circle sensor at the booting and heading stages. Furthermore, the results revealed that Crop Circle green band-based vegetation indices performed well for RI estimation while the red edge-based vegetation indices were the best for estimating YP 0 at later growth stages.

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Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming

Cited by 69 publications

References 42 publications

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

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