A decision tree for nitrogen application based on a low cost radiometry

Rodriguez-Moreno, F.; Llera-Cid, F.

doi:10.1007/s11119-012-9272-7

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The data confirmed that using VRA N has a potential to promote plant growth because N is applied on the part of field with the potential for its use and the applied N can be taken up by plants (Rodriguez-Moreno & Cid, 2012). This leads to the increased potential of plants to use N more efficiently than with the flat rate application (Charf et al, 2005).…”

Section: Discussionsupporting

confidence: 54%

Comparisons of Uniform and Variable Rate Nitrogen Fertilizer Applications in Real Conditions - Evaluation of Potential Impact on the Yield of Wheat Available for Use in Animal Feed

Elbl¹,

Mezera²,

Kintl³

et al. 2021

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The presented research deals with the variable application of N fertilizers (VRA NF) in the stand of winter wheat and with possibilities for evaluating the effect of such application on grain yield. The effect of VRA NF was assessed in close cooperation between Spearhead Czech Ltd. and Mendel University in Brno within an operational experiment. In 2018 and 2019, three experimental plots were always chosen on which VRA NF was implemented by conventional technique according to application maps. Each application map included control strips with the uniform NF application. The application maps were prepared based on the spectral analysis of satellite images. The individual plots were divided into three zones: Zone 1 with the lowest yield potential, Zone 2 with the medium yield potential, Zone 3 with the highest yield potential. The highest dose of N was at all times applied in Zone 3, and conversely the lowest dose was applied in Zone 1. In 2018 and 2019, the experimental fields were harvested by harvester New Holland CX 8080 which was equipped with the technology for the monitoring of grain yields. The main goal of data processing was to remove error data at first and then to re-calibrate them using the information about the weight of harvested grain. The expected benefit, i.e. a yield increase by min. 3% was found only in 2018, when the benefit was about 5%. In the following year, the difference between the conventional application of N and VRA technologies was minimal. However, this condition was probably caused by drought which negatively affected all stands. In particular, measured values of grain yield do not indicate the negative effect of VRA NF on grain yield. This research further showed the applicability of yield data acquired by harvest technology; however, the elimination of error data is necessary as well as their re-calibration according to total yield ascertained by weighing the total production of grain from a specific plot.

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Section: Discussionsupporting

confidence: 54%

Comparisons of Uniform and Variable Rate Nitrogen Fertilizer Applications in Real Conditions - Evaluation of Potential Impact on the Yield of Wheat Available for Use in Animal Feed

Elbl¹,

Mezera²,

Kintl³

et al. 2021

Acta Univ. Agric. Silvic. Mendelianae Brun.

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“…Non-destructive monitoring technologies based on feature identification through reflectance spectra have been widely used in crop-growth monitoring, which promotes research into, and application of, crop-growth monitors based on spectral characteristics [ 36 , 37 , 38 ]. Some research institutes in China, and abroad, have studied the correlation between canopy reflectance spectra and nutrient levels in crops using commercial ground-based object spectrometers such as the hyper-spectrometer ASD-FieldSpec3 and multispectral Cropscan spectrometer [ 39 , 40 , 41 ]. These have broad wave bands and offer high-resolution data.…”

Section: Discussionmentioning

confidence: 99%

Development of an Apparatus for Crop-Growth Monitoring and Diagnosis

Zhang

et al. 2018

Sensors

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To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.

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“…Moreover, several studies by Wu et al 38 and Zhang et al 39 have demonstrated the ability of this index to assess vegetation water content, not only from space as proposed by Gao 30 , but also from ground measurements. Also, Chen et al 31 studied NDWI to estimate vegetation water content in different crops, selecting bands centered in the NIR at around 860 nm and in the SWIR at 2,130 nm Besides spectral indices, multivariate methods have also been explored to monitor various kind of stress in vegetation [40][41][42] . Among the different methods, Principal Component Analysis (PCA) 43 has been employed as an unsupervised analysis of the data 40,42 , Linear Discriminant Analysis (LDA) 43 has been employed as a supervised classification method 42 , and Principal Component Regression (PCR) and Partial Least Square (PLS) regression 44 have been employed for multivariate regression in retrieval applications 45 .…”

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confidence: 99%

Effects of water stress on spectral reflectance of bermudagrass

Caturegli

Matteoli

Gaetani

et al. 2020

Sci Rep

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In the south-central Italy, during summer rainfall does not supply a sufficient amount of water. Therefore, irrigation management during dry periods is important for maintaining turf quality. The hybrid bermudagrass (Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt–Davy) is known to represent the dominant warm-season turfgrass in warm to temperate climatic regions and its drought tolerance make bermudagrass a competitive turfgrass. A greenhouse experiment was conducted using uniform cores of hybrid bermudagrass, which were secured in a polyvinyl chloride cylinders and watered by constant sub-irrigation. The objectives of the present research were to measure the spectral reflectance with a new generation handheld spectroradiometer on hybrid bermudagrass and to explore various vegetation indices to be used as future detecting tool to study water stress in bermudagrass. Moreover, the potential uses of multivariate processing techniques for discriminating different water stress conditions in turfgrass has been investigated. Besides spectral indices, multivariate methods, although performed on a data set limited in terms of sample size, have shown a great potential for water stress monitoring in turfgrass and surely deserve further investigations. There are different indices that use distinct water absorption features independent of chlorophyll concentration, such as water index (WI = R900/R970) that has been reported to be a robust index of canopy water content and is used as an active indicator of changes in Leaf Relative Water Content (LRWC). Also, the ratio of WI with NDVI (WI/NDVI = (R900/R970)/((R800 − R680)/(R800 + R680)]) was found to be an effective indicator of water stress. Another vegetation index to detect water features is normalized difference water index (NDWI), designed to maximize reflectance of water by using green wavelengths. In our trial in bermudagrass the relationships studied, suggest that WI (900/970) and WI/NDVI, among the indices studied, are the more effective indicators of water stress. In fact, lower values of WI indicate higher water stress, while higher values of WI/NDVI indicate higher water stress levels.

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A decision tree for nitrogen application based on a low cost radiometry

Cited by 5 publications

References 30 publications

Comparisons of Uniform and Variable Rate Nitrogen Fertilizer Applications in Real Conditions - Evaluation of Potential Impact on the Yield of Wheat Available for Use in Animal Feed

Comparisons of Uniform and Variable Rate Nitrogen Fertilizer Applications in Real Conditions - Evaluation of Potential Impact on the Yield of Wheat Available for Use in Animal Feed

Development of an Apparatus for Crop-Growth Monitoring and Diagnosis

Effects of water stress on spectral reflectance of bermudagrass

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