Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R (2)) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.
There are still few studies on the role that rock fragments (RFs) have in the change in soil structure based on direct observation of the soil‐pore system. Physically degraded soil is of particular interest because small RFs might be considered a factor in its remediation. In our laboratory experiment we mixed five different proportions of 4–8‐mm sized RFs with a Luvisol and a Regosol that have poor ability to self‐structure and were characterized by a massive structure in the field. Nine wetting and drying cycles were applied to repacked samples (15 cm in diameter and height) of soil–RF mixtures to facilitate the formation of soil structure. Image analysis was used to quantify development of the pore system at varying RF contents. The physically degraded soils studied in this research initially showed a decrease and then an increase in porosity with increasing amounts of RFs in the soil–RF mixtures. The results indicated that the Regosol responded more than the Luvisol to RF content. Threshold values of RF content at which the mechanism of pore formation prevails over that of pore reduction depended upon the soil type and can be attributed reasonably to small differences in the coefficient of linear extensibility (COLE). We also identified a mechanism for the propagation of porosity downwards from the soil surface with increasing RF content, together with a vertical homogenization effect on porosity. Our results contribute to understanding the mechanisms by which small rock fragments embedded in physically degraded topsoil induce changes in the pore system and confirm the potential of rock fragments to protect soil structure in soil susceptible to compaction.
Highlights
Physical interaction between rock fragments and fine earth in degraded soil.
Soil porosity examined by combined experimental laboratory approach with image analysis.
Soil porosity first decreases then increases with increasing content of rock fragments.
Coexistence of two opposing mechanisms: one of porosity reduction and one of formation of new pores.
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