September 23, 2020 at 10:27 pm | Updated March 15, 2022 at 11:47 am | 5 min read
Leaf spectroscopy is used to indirectly get information on several morphological and physiological features of leaves and the plant as a whole. These can help in detecting stress. Though there are several means to measure stress using leaf spectral data, scientists continue looking for species-specific approaches to develop vegetatioin indices that can accurately predict stress and its effect on crop yield.
Detecting Stress with Leaf Spectral Data
Heat stress is a common type of stress that is gaining importance due to the effects of climate change. Temperatures are expected to rise. Summers will be longer, as each spring ends earlier, and there will be less rain, which will lead to a longer and more intense drought.
These changes in environmental conditions have to be considered by scientists who are trying to resolve the challenge of growing enough food for expanding populations.
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Wheat is a major source of calories and proteins for more than two billion people living in developing countries. Wheat productivity will need to be increased by a hundred percent to be able to satisfy demand by 2050.
The sensitivity of crops to heat could mean a reduction of current levels of yield. In the case of grains, scientists estimate there could be a 6% reduction of yield for every degree of increase in global mean temperature. At a time when society needs to increase food production, there isn’t much area available to expand agriculture.
Hence, scientists are concentrating on increasing the productivity of plants. They are trying to develop new cultivars that can withstand increased heat stress and yield more.
Researchers are looking beyond the conventional methods of improving yield by trying to manipulate minor morphological and physiological features of plants. Species vary in sensitivity to stress and use various adaptations to cope, and scientists want to capitalize on these adaptations.
Plant Adaptations Against Heat Stress
Plants get warm when the solar radiation falls on the leaves, and their warmth increases when they are exposed to longer periods of light. To cool down, leaves increase evapotranspiration. This, however, leads to more water uptake, which dries up the soil quickly while simultaneously reducing the water use efficiency of plants, as they are unable to retain water for their own growth.
Plants have, therefore, developed an adaption that reduces the amount of light absorbed by the leaves. These adaptations could be hairs or a thick coating of wax on the leaves, which also increases the reflection of light or albedo. As a result, the leaves avoid getting too warm and losing unnecessary water.
Increased wax on the cuticle or epicuticular wax of leaves would be able to save 31,000 liters of water per acre or 1/3 inch of irrigation per day.
However, the conventional methods of quantifying epicuticular wax thickness are slow, inefficient, and use a lot of chemicals.
Leaf spectral information is the result of the interaction of light with the compounds that make up a leaf. It can be measured as the amount of light absorbed, reflected, and transmitted, and it varies according to the characteristics of the leaves.
So, differences in leaf structure and chemical composition that enhance adaptations against heat stress will alter the interaction of light with the leaves, resulting in varying spectral signatures.
Hence, leaf spectral information collected on the field can provide accurate, rapid, and non-destructive analysis of epicuticular wax thickness.
Measuring Epicuticular Wax with Leaf Spectral Data
A team of scientists from Texas and Mexico, Camarillo-Castillo et al., decided to test how accurate leaf spectral data were in the estimation of differences or phenotyping of epicuticular wax (EW) in wheat and in predicting plant heat stress and its effect on yield.
In the experiment, 24 lines bred from a cross of the heat-tolerant cultivar “Helbred” and heat susceptible “Len” were used. Plants from each group were separated into two groups, ten days after pollination.
Light interaction was measured for plants in the first group, called EW-change, before and after the cuticle wax was removed with HPL chloroform.
The light absorbed, reflected, and transmitted by the abaxial (lower side) and adaxial (upper side) of the leaves was measured with a CI-710 Miniature Leaf Spectrometer from CID Bio-Science Inc. The spectrometer is a light, handheld device that can be easily carried to the field. It rapidly takes non-destructive readings and measures a wide range of wavelengths, covering visible and near-infrared light. It is made to test optical properties related to thickness of films. A built-in software analyzes the measurements even of discrete peaks. The data can be stored in the device or used in real-time to calculate accepted vegetation indices.
In the second group, EW-content, only light reflectance was measured by the CI-710, and the leaves were subsequently used to estimate wax quantities by colorimetry.
The scientists found that the EW had a major effect on the light interaction with leaves. Light reflection was significantly higher in leaves with EW versus leaves with EW removed. Moreover, the lower of the abaxial surface showed the biggest difference; therefore, more wax is accumulated here than on the upper leaf surface.
Derivative vegetation indices were developed by using combinations of one to three spectral bands in eleven mathematical forms of indices. After statistical analyses of the indices, sixteen were selected, as they detected about 65% of the EW variation.
These 16 empirical indices were tested in the field for the prediction of heat stress and yield in wheat. The field tests were extensive and used hundreds of cultivars.
In Sonora, seven spring wheat populations were planted 80 days late, to subject plants to higher than normal temperatures. Canopy reflectance was recorded by a handheld spectroradiometer and calibrated with reflectance by tarpaulins of known (8, 16, 32, and 48%) light reflectance.
In another field test in Chillicothe and Bushland, 296 wheat genotypes were tested without giving them irrigation. Canopy reflectance was recorded by aerial hyperspectral photos and analyzed in ERDGAS.
EW content was estimated for all varieties using the colorimetry method.
The scientists tested the sixteen empirical indices they had developed to analyze the canopy reflectance to see how well they correlated with EW content under varying heat stress and wheat yield conditions.
Of all the sixteen indices tested, the scientists found most were not applicable for airborne estimation of wax. Most indices were sensitive only to changes in lower EW levels but were unable to estimate variation when EW content was higher.
Only two empirical vegetation indices were consistently correlated with EW levels across all environments and cultivars for wheat. One used a broad spectrum of light wavelengths (red and blue) and the other was based on a narrow band of red that lay between 694-625 nm and is associated with chlorophyll.
Use of Precision Tools
Using precision tools like the CI-710 Leaf Spectrometer, the scientists could make accurate collection and analysis of data that, up until now, was difficult to measure. Based on the leaf spectral data, they could develop indices that can analyze aerial imagery and be applied in precision and smart farming to monitor heat and drought stress effects. Using this kind of technology, it is possible to develop new cultivars that can assure sustainable yet better grain supply for humanity.
Science Writer, CID Bio-Science
Ph.D. Ecology and Environmental Science, B.Sc Agriculture
Feature image courtesy of Brad Higham
Camarillo-Castillo, F., Tattaris, M., Hays, D.B., & Reynolds, M.P. (2016). Prediction accuracy of high-resolution spectral information for nondestructive phenotyping of epicuticular wax in wheat. Proceedings of the 3rd International TRIGO Wheat Yield Potential.
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