Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Bijay-Singh

PROVIDER: S-EPMC7070990 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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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 Ali M AM

Sensors (Basel, Switzerland) 20200219 4

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 t ...[more]

PMID: 32092989

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Project description:Intensive vegetable production is commonly associated with excessive nitrogen (N) fertilization and associated environmental problems. Monitoring of crop N status can enhance crop N management. Chlorophyll meters (CMs) could be used to monitor crop N status because leaf chlorophyll (chl) content is strongly related to crop N status. To monitor crop N status, relationships between CM measurements and leaf chl content require evaluation, particularly when excessive N is supplied. The SPAD-502 meter, atLEAF+ sensor, MC-100 Chlorophyll Concentration Meter, and Multiplex sensor were evaluated in sweet pepper with different N supply, throughout the crop, ranging from very deficient to very excessive. CM measurements of all sensors and indices were strongly and positively related to leaf chlorophyll a + b content with curvilinear relationships over the entire range of chl measured (∼0-80 μg cm-2). Measurements with the SPAD-502, and atLEAF+, and of the Multiplex's simple fluorescence ratio index (SFR) had asymptotic responses to increasing leaf chl. In contrast, the MC-100's chlorophyll content index (CCI) had a progressively increasing response. At higher chlorophyll a + b contents (e.g., >40 μg cm-2), SPAD-502, atLEAF+ and SFR measurements tended to saturate, which did not occur with CCI. Leaf chl content was most accurately estimated by CCI (R 2 = 0.87), followed by the SPAD-502 meter (R 2 = 0.85). The atLEAF+ sensor was the least accurate (R 2 = 0.76). For leaf chl estimation, CCI measured with the MC-100 meter was the most effective of the four sensors examined because it: (1) most accurately estimated leaf chl content, and (2) had no saturation response at higher leaf chl content. For non-saturating leaf chl content (∼0-40 μg cm-2), all indices were sensitive indicators. As excessive applications of N are frequent in intensive vegetable crop production, the capacity of measuring high leaf chl contents without a saturation response is an important consideration for the practical use of chlorophyll meters.

Project description:BACKGROUND:Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. RESULTS:This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. CONCLUSIONS:We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.

Dataset Information

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

Publications

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

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