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

Soil properties zoning of agricultural fields based on a climate-driven spatial clustering of remote sensing time series data

The identification of zones within an agricultural field that respond differently to environmental factors and agronomic management is a key requirement for the adoption of more precise and sustainable agricultural practices. Several approaches based on spatial clustering methods applied to different data sources, e.g. yield maps, proximal sensors and soil surveys, have been proposed in the last decades. The current availability of a huge amount of free remote sensing data allows to apply these approaches to agricultural areas where ground or proximal data are not available. However, in order to provide useful agronomic management information, it is essential that the zoning obtained by clustering is linked to the underlying spatial variability of soil properties. In this work we explore the hypothesis that the response of crop vigor to temporal climate variability, assessed by remote sensing data time series, selected to correspond to specific growth phases and seasonal climate patterns, provides indications on the variability of soil properties within agricultural fields, for both herbaceous and tree crops. NDVI time-series for 38 years (1984–2021) were obtained for fourteen non-irrigated herbaceous and tree crop fields in Central Italy, from multispectral satellites data (Landsat 5/7/8, Sentinel 2). The Standardized Precipitation-Evapotranspiration Index (SPEI) was used to classify time series into three climatic classes (dry/normal/wet) for five different periods of the growth season, covering the main phenological phases. K-means clustering was used to identify patterns of crop growth from climatically classified image sets, as well as for all the bulked images for comparison (bulk clustering). Clustering results were compared with soil maps obtained from spatialized ground data, for soil texture (clay, silt and sand), soil organic matter and available soil water (ASW). The agreement between the different clustering results and soil maps was assessed by the Adjusted Rand Index. Agreement with soil maps varied depending on the field, the phenological phase considered and the soil property considered. Climate driven clustering from long, late growth season periods best matched soil properties, both for herbaceous and tree crops, despite being based on a limited number of images. The clustering from images spanning a longer growth period for dry years systematically outpaced the bulk clustering for silt, sand and ASW, while the clustering for normal climatic conditions was the best for organic matter. The performance of the matching between clustering and soil maps increased with soil variability significantly more (P < 0.05) than in the bulk clustering (mean slopes respectively 0.468 ± 0.167; 0.113 ± 0.270). The integration of the SPEI climatic index into the clustering procedure systematically improved the identification of zones with homogeneous soil properties, highlighting that a greater attention should be posed to the climate-crop-field interactions when using remotely sensed images

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