Molecular characterization of γ gliadin from durum wheat (Triticum turgidum L. Subsp. Durum ((Desf.) Husn.)

The gluten quality is one of the main factors affecting the quality of durum wheat. It depends primarily on its storage proteins composition (glutenins and gliadins). In order to set up and initiate a technological quality improvement program of durum wheat we have conducted a prospection of the different protein sequences of gliadin in different databases for Triticum, then the filtration steps and assembly by appropriate software have been conducted to reduce the number of redundant sequences. On the other hand, we have isolated a gene from Iride "Gli-A1" encoding a γ-gliadin protein associated with gluten strength and viscoelasticity of the dough, we performed an in silico molecular and structural analysis in order to define its putative functional properties. The latter could be a valuable candidate as molecular marker for selecting high nutritive value of durum wheat and/or for genetic improvement of durum wheat quality.Keywords: in silico; Storage Proteins; Gliadin; Triticum ; wheat; technological qualit

The application of ground-based and satellite remote sensing for estimation of bio-physiological parameters of wheat grown under different water regimes

Remote sensing technologies have been widely studied for the estimation of crop biometric and physiological parameters. The number of sensors and data acquisition methods have been increasing, and their evaluation is becoming a necessity. The aim of this study was to assess the performance of two remote sensing data for describing the variations of biometric and physiological parameters of durum wheat grown under different water regimes (rainfed, 50% and 100% of irrigation requirements). The experimentation was carried out in Policoro (Southern Italy) for two growing seasons. The Landsat 8 and Sentinel-2 images and radiometric ground-based data were acquired regularly during the growing season with plant biometric (leaf area index and dry aboveground biomass) and physiological (stomatal conductance, net assimilation, and transpiration rate) parameters. Water deficit index was closely related to plant water status and crop physiological parameters. The enhanced vegetation index showed slightly better performance than the normalized difference vegetation index when plotted against the leaf area index with R2 = 0.73. The overall results indicated that the ground-based vegetation indices were in good agreement with the satellite-based indices. The main constraint for effective application of satellite-based indices remains the presence of clouds during the acquisition time, which is particularly relevant for winter-spring crops. Therefore, the integration of remote sensing and field data might be needed to optimize plant response under specific growing conditions and to enhance agricultural production

Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, and vegetation indices were tested over the experimental area to estimate both soil and leaf EC using Sentinel-2 imagery on the Google Earth Engine platform. Subsequently, estimation models of soil and leaf EC were calibrated by employing machine learning (ML) techniques using 12 spectral bands of Sentinel-2 images. The prediction accuracy of the EC estimation was assessed by using k-fold cross-validation and computing statistical metrics. The results of the study revealed that machine learning algorithms, together with multispectral data, could advance the mapping and monitoring of soil and leaf electrical conductivity

Hibiscus cannabinus L. – Kenaf : A Review Paper

Kenaf (Hibiscus cannabinus L.) is a valuable fiber and medicinal plant from the Malvaceae family. It is an alternative crop that may be a feasible source of cellulose which is economically viable and ecologically friendly. This plant is cultivated for its fiber although its leaves and seeds have also been used in traditional medicine in India and Africa for the treatment of various disease conditions. Kenaf fibers are commonly used for paper pulp and cordage, but it is also a promising lignocellulosic feedstock for bioenergy production. The kenaf seed oil can be used for cooking and in different industrial applications. The present paper is an overview on its ethnobotanical and phytochemical properties reported in the literature that we have investigated and its great potential as a valuable multipurpose crop due to numerous uses