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

Bijay‐Singh,; Ali, Ali M.

doi:10.3390/s20041127

Cited by 47 publications

(21 citation statements)

References 119 publications

(185 reference statements)

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“…Optimum rates of N are dependent on yearly environmental conditions that are random and cannot be predicted in advance (Raun et al, 2019). Further, the standard general fertilizer N recommendations in a region are always associated with risks of low profits and environmental degradation (Fageria and Baligar, 2005;Ladha et al, 2005;Bijay-Singh, 2018). Ali et al (2017) reported that fertilizer N recovery efficiency in wheat in some parts of Egypt was in the range of 35.6-51.1% by following the standard recommendation.…”

Section: Introductionmentioning

confidence: 99%

Fixed-time corrective dose fertilizer nitrogen management in wheat using atLeaf meter and leaf colour chart

et al. 2021

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Fertilizer nitrogen (N) management in any region following standard general recommendations discount the fact that crop response to N varies between sites and seasons. To devise field-specific N management in wheat at jointing stage (Feekes 6 growth stage) using atLeaf meter and leaf colour chart (LCC), eight field experiments were conducted in three wheat seasons during 2017–2020 in the West Delta of Egypt. In the first two seasons, four experiments consisted of treatments with a range of fertilizer N application levels from 0 to 320 kg N ha−1. Monitoring atLeaf and LCC measurements at Feekes 6 growth stage in plots with different yield potentials allowed formulation of different criteria to apply field-specific and crop need-based fertilizer N doses. In the four experiments conducted in the third season in 2019/20, different field-specific N management strategies formulated in 2017/18 and 2018/19 wheat seasons were evaluated. In the atLeaf-based fertilizer N management experiment, prescriptive application of 40 kg N ha−1 at 10 days after seeding (DAS) and 60 kg N ha−1 at 30 DAS followed by application of an adjustable dose at Feekes 6 stage computed by multiplying the difference of atLeaf measurements of the test plot and the N-sufficient plot with 42.25 (as derived from the functional model developed in this study), resulted in grain yield similar or higher to that obtained by following the standard treatment. The LCC-based strategy to apply field-specific fertilizer N at Feekes 6 stage consisted of applying 150, 100 or 0 kg N ha−1 based on LCC shade equal to or less than 4, between 4 and 5 or equal to or more than 5, respectively. Both atLeaf- and LCC-based fertilizer N management strategies not only recorded the highest grain yield levels but also resulted in higher use efficiency with 57–60 kg N ha−1 in average less fertilizer use than the standard treatment.

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Section: Introductionmentioning

confidence: 99%

Fixed-time corrective dose fertilizer nitrogen management in wheat using atLeaf meter and leaf colour chart

et al. 2021

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“…[32,33]. Nitrogen fertilizer management using proximal sensors was extensively studied and also practiced in cereals [34,35], vegetables [36] etc. Remote sensing methods using spectral and thermal approaches have potentiality to schedule nitrogen fertilization after rapid identification of nitrogen status in crop canopy across large areas [37].…”

Section: Soil-cum-plant Analysis-based Ssnmmentioning

confidence: 99%

“…@ 30 kg ha -1 as basal, 60 kg ha -1 at CRI stage and Greenseeker guided nitrogen application @ 40 kg ha -1 at 45 days after sowing (DAS) during second irrigation and 10 kg ha -1 at 65 DAS (third irrigation) showed highest benefit cost ratio in wheat production at Kanke, Ranchi, India [66]. Hand-held chlorophyll meters (SPAD meter and atLeaf) and canopy reflectance sensors (GreenSeeker and Crop Circle) can be effectively utilized by small land holding farmers for fertilizer nitrogen management in cereals (rice, wheat and maize) in developing countries [34]. They observed that sensor based nitrogen management had reduced the dose of fertilizer nitrogen application, increased crop productivity and enhanced nitrogen use efficiency.…”

Section: Canopy Reflectance Sensormentioning

confidence: 99%

Site-specific Crop Nutrient Management for Precision Agriculture – A Review

Gorai

Yadav

Choudhary

et al. 2021

CJAST

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Present irrational crop and nutrient management practices have raised several concerns of high merit. The concerns include low factor productivity or nutrient use efficiency (NUE), declining crop productivity, farmer’s profitability, impaired soil health and ecological contamination. Site-specific nutrient management (SSNM), after considering indigenous nutrient supplying capacity of soil using plant and soil analysis, can feed the crop in synchrony with its nutrient requirement in different physiological growth stages. Besides, several modern geospatial techniques viz. remote sensing techniques, geographic information system (GIS), global positioning system (GPS), proximal sensing; information and communication technologies (ICTs) including decision support system, smartphone apps and web services can also assist in diagnosis of soil and crop nutrient status, fertilizer recommendation and its dissemination to users. Optical and thermal remote sensing can effectively detect crop stress including nitrogen (N) deficiency through several vegetation indices especially normalized difference vegetation index (NDVI). GIS techniques with spatial data acquired by GPS, can create spatial variability map and management zone (MZ) for precise farm operations including variable rate fertilization. Proximal crop sensors viz. chlorophyll meter and Green Seeker can also recognize crop nitrogen status and promote fertilizer N use efficiency by synchronizing fertilizer N supply with crop requirement. Even proximal soil sensing using electromagnetic radiation and contact electrode can estimate soil properties like soil pH, electrical conductivity, major and micronutrient content. Several decision support systems such as QUEFTS based model, crop manager, nutrient expert® and smartphone apps like ‘crop doctor’ can suggest for precise application of agro-inputs to rural youths and farmers. Yield monitoring and mapping tool can generate historical GIS database for spatial variability of crop yield under farmers’ crop management practices and assessment of nutrient uptake. Variable rate machinery based on variability map and sensor technologies can also be used for fertilization under different management zones. Therefore, SSNM technologies can enhance NUE; improve and sustain crop productivity, profitability; avoid nutrient wastage; maintain good soil health and environmental safety.

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“…GreenSeeker takes into account the N status and the biomass of the crop to access additional N fertilizer recommendations. It showed a high correlation with in-season N status in wheat (Németh et al, 2007;Bijay and Ali, 2020). However, chlorophyll is the main pigment of plant leaves and responsible for their greenness.…”

Section: Introductionmentioning

confidence: 98%

“…Therefore, N management is crucial as it can volatilize or leach out of the soil and the probability that its requirement is the same in different growing seasons is only about 1% (Zheng et al, 2015). In aditition, the standard N fertilizer references and recommendations cannot include the spatial and in-season variability in soil N supplying capacity during the crop cycle (Gabriel et al, 2017;Bijay and Ali, 2020). Therefore, a split application method is seen as the best way to increase fertilizer use efficiency under potato production.…”

Section: Introductionmentioning

confidence: 99%

Use of GreenSeeker and CM-100 as manual tools for nitrogen management and yield prediction in irrigated potato (Solanum tuberosum) production

Satognon

Lelei

Owido

2021

Arch. Agric. Environ. Sci.

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This study evaluated the possibility of the use of GreenSeeker sensor and CM-100 chlorophyll content meter for in-season N and yield prediction in order to promote timely split N application in potato production in Kenya. Four N-fertilization rates; N0 (0), N1 (60), N2 (90) and N3 (130 kg N/ha) were led out in a Randomized Complete Block Design (RCBD) in a Greenhouse for two seasons. The results showed that % N leaf content was significantly affected by N rates. The % N leaf content and potato leaf chlorophyll content decreased as the season continued whereas the Normalized Difference Vegetation Index (NDVI) increased as the season continued. CM-100 values were significantly correlated with % N leaf content at vegetative (r=0.86***) and tuber initiation (r=0.74***) growth stages of the crop whereas the NDVI values were only significantly correlated with % N leaf at tuber initiation (r=0.82***). A significant relationship was found between CM-100 values taken at different potato stages (end of vegetative, tuber initiation, bulking and maturation stages) and tuber yield (r=0.90***, 0.82***, 0.47* and 0.41*). The NDVI values at end of vegetative growth, tuber initiation and maturation of potato were also significantly correlated with tuber yield (r=0.81***, 0.43* and 0.54*), except at bulking stage (r=0.33). For efficient in-season N management and yield prediction, CM-100 and GreenSeeker are recommended at an early stage of the crop. Further research in the different potato growing areas in Kenya to establish the different thresholds at different potato growth stages is recommended.

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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

Cited by 47 publications

References 119 publications

Fixed-time corrective dose fertilizer nitrogen management in wheat using atLeaf meter and leaf colour chart

Fixed-time corrective dose fertilizer nitrogen management in wheat using atLeaf meter and leaf colour chart

Site-specific Crop Nutrient Management for Precision Agriculture – A Review

Use of GreenSeeker and CM-100 as manual tools for nitrogen management and yield prediction in irrigated potato (Solanum tuberosum) production

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