Importance of Silicon and Mechanisms of Biosilica Formation in Plants

Silicon (Si) is one of the most prevalent macroelements, performing an essential function in healing plants in response to environmental stresses. The purpose of using Si is to induce resistance to distinct stresses, diseases, and pathogens. Additionally, Si can improve the condition of soils, which contain toxic levels of heavy metals along with other chemical elements. Silicon minimizes toxicity of Fe, Al, and Mn, increases the availability of P, and enhances drought along with salt tolerance in plants through the formation of silicified tissues in plants. However, the concentration of Si depends on the plants genotype and organisms. Hence, the physiological mechanisms and metabolic activities of plants may be affected by Si application. Peptides as well as amino acids can effectively create polysilicic species through interactions with different species of silicate inside solution. The carboxylic acid and the alcohol groups of serine and asparagine tend not to engage in any significant role in polysilicates formation, but the hydroxyl group side chain can be involved in the formation of hydrogen bond with Si(OH)4. The mechanisms and trend of Si absorption are different between plant species. Furthermore, the transportation of Si requires an energy mechanism; thus, low temperatures and metabolic repressors inhibit Si transportation

Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer

Leaf nutrients are needed for oil palm growth and production, and the nutrient contents of oil palm leaves can be determined by the chemical analyses of the number 9 and 17 leaves for young and adult palms, respectively. However, the accurate selection of the proper leaf for sampling is problematic. Remote sensing techniques based on the reflectance values of leaves may easily monitor leaf nutrients in oil palm plantations. We studied leaf nutrient contents using spectral reflectance data to determine suitable wavelengths for predicting the contents of the most important leaf nutrients: nitrogen, phosphorus, potassium, calcium, magnesium, boron, copper, and zinc. The samples were taken from one oil palm plantation in Pundu, Central Kalimantan, Indonesia. The proposed vegetative indices, several common vegetative indices, and a stepwise regression that continued with a principal component regression were used to build models for predicting leaf nutrient contents. The proposed vegetative indices performed better than the common vegetative indices. For each of the leaf nutrients, models that included all of the significant variables from the stepwise regression and continued with principal component regression from the ultraviolet A and green to far red wavelength groups had better performance levels than models that included individually selected variables selected from each wavelength group. For total leaf nutrient content predictions, variables from the green wavelength group were always selected and contributed more to the models than any other group. Thus, our proposed vegetative indices and multivariate model may be used to predict leaf nutrient contents in oil palm plantations