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Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study's requirements, with R2 > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a "quick", inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. [ABSTRACT FROM AUTHOR]

This study aimed to explore the potential use of Aleppo pine seed flour as a nutritious ingredient in biscuit production. Aleppo pine seed flour was blended with wheat flour to create biscuits with varying proportions: 15%, 30%, and 45% Aleppo pine seed flour. The analysis of the chemical composition of the biscuits revealed that increasing the proportion of Aleppo pine seed flour led to higher levels of protein, fat, and ash, while the carbohydrate content decreased. Sensory evaluation showed that biscuits with 15% Aleppo pine seed flour and 85% wheat flour had favorable characteristics in terms of color, flavor, crispness, and overall appeal. These findings indicate that incorporating Aleppo pine seed flour into wheat flour can enhance the nutritional profile of biscuits, offering higher protein, fat, and mineral content. The study suggests that a blend containing 15% Aleppo pine seed flour is optimal for producing biscuits with desirable sensory properties, making it a promising alternative ingredient for healthier biscuit formulations. Overall, this research highlights the potential of Aleppo pine seed flour to improve the nutritional quality of biscuits without compromising their sensory qualities, paving the way for its use in health-conscious baking practices. [ABSTRACT FROM AUTHOR]

Researchers are depending more than ever on remote sensing techniques to monitor and assess the agricultural water status, as well as to estimate crop water usage or crop actual evapotranspiration. In the current work, normal and stressed baselines for irrigated wheat genotypes were developed in an arid part of the Sohag governorate, Egypt, using infrared thermometry in conjunction with weather parameters. The experiment was carried out in a randomized complete block design in the normal and drought stress conditions based on three replicates using ten bread wheat genotypes (G1–G10), including five accessions, under drought stress. A standard Class-A-Pan in the experimental field provided the daily evaporation measurements (mm/day), which was multiplied by a pan factor of 0.8 and 0.4 for normal and stressed conditions, respectively. The relationship between the vapor pressure deficit (VPD) and canopy-air temperature differences (Tc − Ta) was plotted under upper (fully stressed) and lower baseline (normal) equations. Accordingly, the crop water stress indexes (CWSIs) for the stressed and normal baselines for wheat genotypes were developed. Additionally, the intercept (b) and the slope (a) of the lower baseline equation were computed for different genotypes. The results indicate that, before applying irrigation water, the CWSI values were high in both growing seasons and under all irrigation regimes. After that, the CWSI values declined. G10 underwent stress treatment, which produced the greatest CWSI (0.975). Conversely, the G6 condition that received well-watered irrigation yielded the lowest result (−0.007). When compared to a well-watered one, the CWSI values indicated a trend toward rising stress. There existed an inverse link between the CWSI and grain yield (GY); that is, a lower CWSI resulted in better plant water conditions and a higher GY. Under standard conditions, the wheat's highest GY was recorded in G2, 8.36 Ton/ha and a WCSI of 0.481. In contrast, the CWSI result for the stress treatment was 0.883, indicating a minimum GY of 5.25 Ton/ha. The Water Use Efficiency (WUE) results demonstrated that the stress irrigation regime produced a greater WUE value than the usual one. This study makes a significant contribution by investigating the techniques that would allow CWSI to be used to estimate irrigation requirements, in addition to determining the irrigation time. [ABSTRACT FROM AUTHOR]

The thrust of the study was to determine the chemical composition of the essential oils extracted from Thymus pallescens de Noé and Cymbogon citratus Stapf. as well as to evaluate their efficacy in controlling Sitophilus zeamais Motschulsky and Tribolium castaneum (Herbst) in either single or combined populations. Carvacrol (56.04%) and geraniol (20.86%) were identified as the major constituents of T. pallescens and C. citratus respectively. The tested essential oils showed pronounced insecticidal activity against the pest species in relation with the applied doses. T. pallescens EO had the highest efficacy and S. zeamais was found to be more susceptible to both individual and combined treatments. With reference to the contact and fumigation assessments, T. pallescens EO effectuated corrected mortality rates ranging from 42.5–100% to 25–100% in S. zeamais with corresponding lethal concentration (LC50) values of 17.7 µl/ml and 15µL/L air respectively. Whereas, the T. pallescens EO exhibited corrected mortality rates of 42.5–100% and 20–100% with corresponding LC50 values of 18.1 µl/ml and 15.5 µL/L air against T. castaneum in contact and fumigation assessments, respectively. The corrected mortality rates increased for both insect species when using combination treatments, with significant increases in the LC50 values, ranging from 8.59 to 49.9% for both pest species. Analysis of energy biomarkers in the treated insects indicate significantly increased protein and carbohydrate contents and decreased lipids levels. The study therefore demonstrated the bio-insecticidal toxicity of the EOs from T. pallescens and C. citratus against two important maize post-harvest pests, concurrently revealing significant positive and negative insecticidal activity gradients in relation to single or combined populations. [ABSTRACT FROM AUTHOR]

Drought stress is a group of abiotic stresses that affects plant growth and yield production. A field experiment over two successive seasons (2021–2022 and 2022–2023) in sand soil was conducted to investigate the integration effect of deficit irrigation, soil amendment "hundzsoil", and the spraying of proline on the water use efficiency (WUE), yield, and fruit quality of 8-year-old Crimson seedless table grapes. Four application rates of soil amendment (0, 2, 4, and 6 kg hundzsoil /vine) were added during the dormancy period, and four irrigation levels at 125, 100, 75, and 60% of the field capacity were applied just before flowering until harvest. Proline at two levels (0 and 500 ppm) was applied as a foliar spray. Parameters such as bud fertility, weight of 100 berries, juice volume, and cluster number were positively affected by irrigation at 75% FC along with applying hundzsoil at 2 and 4 kg/vine under proline spray in both seasons. Irrigation at the 125% FC level with a 6 kg hundzsoil application under proline spray resulted in the highest yield, berries number, cluster length, cluster weight, and total anthocyanin in both seasons. The TSS/acidity ratio was significantly and positively affected by deficit irrigation (60% FC level) under hundzsoil at a rate of 4 kg alongside proline spray. Reducing irrigation to 60% FC without hundzsoil and proline spray negatively affected numerous growth parameters and the yield. However, irrigation at 60% FC alongside 6 kg of hundzsoil and proline showed the highest IWUE in both seasons. Proline spray was a key factor in conserving water used for irrigation. This study recommends using deficit irrigation alongside hundzsoil application under proline spray as an adequate strategy for water use efficiency and improving the yield and fruit quality of Crimson seedless grapevines cultivated in sand soil. [ABSTRACT FROM AUTHOR]

Abiotic stresses pose significant challenges to wheat farming, yet exploiting the genetic variability within germplasm collections offers an opportunity to effectively address these challenges. In this study, we investigated the genetic diversity of key agronomic traits among twenty durum wheat cultivars, with the intention to pinpoint those better suited to semi-arid conditions. Field trials were conducted at the ITGC-FDPS Institute, Setif, Algeria, during the winter season of 2021/22. A completely randomized design was used with three replicates. Statistical analyses revealed significant variation among the genotypes for most of the studied traits, with some cultivars exhibiting a superior performance in a stressful environment. Notably, traits like the number of grains per spike (NGS) and the grain yield (GY) displayed high genotypic coefficients of variation (CVg). Except for membrane thermostability (MT) and biological yield (BY), the majority of the assessed traits exhibited moderate-to-high heritability estimates. Genotypic and phenotypic correlation studies have confirmed the importance of many yield-related traits in the expression of GY. The harvest index (HI) underscored the highest genotypic direct effect on GY, followed closely by spike number (SN), serving as consistent pathways through which most of the measured traits indirectly influenced GY. The cluster analysis categorized the durum wheat cultivars into seven distinct clusters. The largest inter-cluster distance was observed between clusters G3 and G4 (D2 = 6145.86), reflecting maximum dissimilarity between the individuals of these clusters. Hybridizing divergent clusters may benefit future breeding programs aiming to develop potential durum wheat varieties through cross combinations. This study's findings contribute to sustainable agriculture efforts by facilitating the selection of genotypes with enhanced resilience and productivity, particularly for cultivation in challenging semi-arid regions. [ABSTRACT FROM AUTHOR]

Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints. [ABSTRACT FROM AUTHOR]