A framework for refining nitrogen management in dry direct-seeded rice using GreenSeeker™ optical sensor

Ali, Amjed; Thind, H. S.; Varinderpal-Singh,; Bijay‐Singh,

doi:10.1016/j.compag.2014.10.021

Cited by 52 publications

(37 citation statements)

References 24 publications

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“…Nitrogen recovery efficiency increased by more than 12% by applying sensor-guided fertilizer N dose as compared to when fertilizer N was managed as per standard recommendation Ali et al [132] Sanjiang Plain (China), rice, GreenSeeker and Crop Circle At the stem elongation stage, both GreenSeeker as well as Crop Circle ACS-470 sensors performed similarly for guiding fertilizer N. However, at booting and heading stages, significantly better fertilizer N recommendation were obtained by the Crop Circle sensor.…”

Section: Site Crop and Sensor Salient Findings Referencementioning

confidence: 98%

“…Total fertilizer N application was in the range of 75-97 kg N ha −1 under sensor-based N management. Working with dry direct-seeded rice in north-western India, Ali et al [132] found that panicle initiation stage was the appropriate stage for applying sensor-guided fertilizer N dose. Application of 60 to 90 kg N ha −1 in 2 or 3 split doses before applying the sensor-guided N dose produced rice grain yield at par with that produced by following general recommendation.…”

Section: Canopy Reflectance Sensor-based Fertilizer Nitrogen Managemementioning

confidence: 99%

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Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries

Bijay‐Singh

Ali

2020

Sensors

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To produce enough food, smallholder farmers in developing countries apply fertilizer nitrogen (N) to cereals, sometimes even more than the local recommendations. During the last two decades, hand-held chlorophyll meters and canopy reflectance sensors, which can detect the N needs of the crop based on transmission and reflectance properties of leaves through proximal sensing, have been studied as tools for optimizing crop N status in cereals in developing countries. This review aims to describe the outcome of these studies. Chlorophyll meters are used to manage fertilizer N to maintain a threshold leaf chlorophyll content throughout the cropping season. Despite greater reliability of the sufficiency index approach, the fixed threshold chlorophyll content approach has been investigated more for using chlorophyll meters in rice and wheat. GreenSeeker and Crop Circle crop reflectance sensors take into account both N status and biomass of the crop to estimate additional fertilizer N requirement but only a few studies have been carried out in developing countries to develop N management strategies in rice, wheat and maize. Both chlorophyll meters and canopy reflectance sensors can increase fertilizer N use efficiency by reduction of N rates. Dedicated economic analysis of the proximal sensing strategies for managing fertilizer N in cereals in developing countries is not adequately available.

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Section: Site Crop and Sensor Salient Findings Referencementioning

confidence: 98%

Section: Canopy Reflectance Sensor-based Fertilizer Nitrogen Managemementioning

confidence: 99%

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries

Bijay‐Singh

Ali

2020

Sensors

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“…Xu et al (2010) suggested appropriate amount of N application for bud development would be needed in 10-15 d after heading of main rice. In rice cropping system, plant N accumulation could be estimated by GreenSeeker optical sensor (Xue et al, 2014;Ali et al, 2015). And NDVI had been used in N fertilizer management in many sites (Bajwa et al, 2010;Bijay-Singh et al, 2012;Yao et al, 2012;Bijay-Singh et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice

Liu

2015

Chilean J. Agric. Res.

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The growth of ratoon rice (Oryza sativa L.) is affected by growth of main rice, so it could be evaluated by determining growing condition of main rice. Objective of the study was to find regression model for predicting ratoon rice growth rhythm through establishing the quantitative relationship between growth rhythm in ratoon rice and normalized difference vegetation index (NDVI) after heading of main rice. NDVI at key growth stages of main rice was measured using active spectrometry for 12 varieties in 2008 and for 23 varieties in 2009, and compared the physiological indexes of main and ratoon rice. There was an exponential correlation (P < 0.05) between the NDVI after heading of the main rice and biomass, N concentration of green leaves and stems or leaf area index (LAI) after heading of the ratoon rice for all the 12 varieties in 2008. The regression models from the rice variety experiment in 2008 were used to calculate predicted values with NDVI after heading of main rice in 2009. The results showed that the predicted values of biomass, N uptake, and LAI in ratoon rice were significantly different to measured values. However, there were exponential and significantly positive correlation (p < 0.05) between biomass and N uptake and LAI in ratoon rice and NDVI after heading of main rice in 2009. Therefore, it suggested that regression models were not perfect, and need to be improved adding more parameters about spectral characteristic of main and ratoon rice.

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“…3). NDVI variations depending on the level of N fertilization are usually fitted by linear or quadratic equations [1,11]. Although wheat shoot dry matter and grain yields at physiological maturation were not affected by the level of N fertilization at sowing, the NDVI values showed that N fertilization at sowing favored the early growth of wheat plants.…”

Section: Effect Of Sensor Height On Wheat Spectral Reflectance Measurmentioning

confidence: 99%

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

2020

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A field experiment examined the wheat response following soybean due to urea-N fertilization at sowing (0, 20, 40, and 60 kg N ha −1) and top dressing (0, 30, 60, and 90 kg N ha −1). Normalized difference vegetation index (NDVI) measurements using a GreenSeeker active sensor were taken at different growth stages, at various sensor heights above the canopy, and at different times of day. Both the shoot dry matter yield and the grain yield were not affected by N fertilization at sowing and increased with increasing N rates at top dressing. A maximum economic yield was obtained at 55 kg N ha −1 in top dressing, causing a 33% increase in grain yield and an economic return of US$ 189.50 ha −1. Differences in NDVI were found before and after top-dressing N fertilization, indicating that GreenSeeker was efficient in monitoring wheat N nutrition. NDVI varied depending on the measurement distance, stabilizing from 0.30 to 1.20 m above the canopy. Because of the influence of incident radiation, higher NDVI values were obtained at the beginning and end of the day. The results suggest that for the wheat cultivar Quartzo following soybean under minimum soil disturbance, while there is no need to apply N at sowing, an important increase in grain yield can be obtained with top-dressing N fertilization. In addition, to improve the accuracy in developing N fertilizer recommendation models using GreenSeeker, a consistent protocol for spectral reflectance readings, mainly regarding the time of day, is required.

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A framework for refining nitrogen management in dry direct-seeded rice using GreenSeeker™ optical sensor

Cited by 52 publications

References 24 publications

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries

Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen Management in Cereals in Small Farms in Developing Countries

Predicting ratoon rice growth rhythm based on NDVI at key growth stages of main rice

Nitrogen fertilization for wheat following soybean and interfering factors on spectral reflectance readings

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