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This research aims to evaluate the efficacy of biochar in alleviating cadmium stress in maize via spectral modeling of chlorophyll content.

July 8, 2026AgronomyOpen Access

A Study on Spectral Inversion Modeling of Biochar Regulation on SPAD Values in Cadmium-Contaminated Maize Leaves

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Randomized trial evaluates biochar's role in improving chlorophyll content in maize under cadmium stress, indicating potential for crop recovery.

This research aims to evaluate the efficacy of biochar in alleviating cadmium stress in maize via spectral modeling of chlorophyll content.
Zhengdan 958 maize seedlings were grown in Cd-contaminated soil with varying biochar application rates (0, 10, 30, 50, and 70 g/pot).
SPAD values were analyzed alongside cadmium accumulation in plant parts using spectral detection models and optimization methods.
Partial least squares regression and support vector regression models were used to predict SPAD values based on spectral data preprocessing.
Biochar application significantly boosted SPAD values in maize leaves (r = 0.879) and lowered bioavailable Cd in soil, particularly at 70 g/pot (30% reduction).
GA-SVR model achieved optimal performance for predicting SPAD values (R2c = 0.95, RMSEC = 0.24; R2p = 0.75, RMSEP = 1.63).
Detrended spectra provided the best predictive accuracy (R2c = 0.94, RMSEC = 0.82) for assessing physiological status.

Gao et al. (2026) studied this question.

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July 8, 2026AgronomyOpen Access

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Authors

Randomized trial evaluates biochar's role in improving chlorophyll content in maize under cadmium stress, indicating potential for crop recovery.

This research aims to evaluate the efficacy of biochar in alleviating cadmium stress in maize via spectral modeling of chlorophyll content.
Zhengdan 958 maize seedlings were grown in Cd-contaminated soil with varying biochar application rates (0, 10, 30, 50, and 70 g/pot).
SPAD values were analyzed alongside cadmium accumulation in plant parts using spectral detection models and optimization methods.
Partial least squares regression and support vector regression models were used to predict SPAD values based on spectral data preprocessing.
Biochar application significantly boosted SPAD values in maize leaves (r = 0.879) and lowered bioavailable Cd in soil, particularly at 70 g/pot (30% reduction).
GA-SVR model achieved optimal performance for predicting SPAD values (R2c = 0.95, RMSEC = 0.24; R2p = 0.75, RMSEP = 1.63).
Detrended spectra provided the best predictive accuracy (R2c = 0.94, RMSEC = 0.82) for assessing physiological status.

Gao et al. (2026) studied this question.

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