Investigating Mineral Accumulation and Seed Vigor Potential in Bottle Gourd (Lagenaria siceraria) through Crossbreeding Timing.

Bottle gourd (Lagenaria siceraria) is a well-known cucurbit with an active functional ingredient. A two-year field experiment was carried out at the Research Farm of Seed Science and Technology, CCS HAU, Hisar, in a randomized block design during the Kharif season (Kharif is one of the two major cropping seasons in India and other South Asian countries, heavily reliant on monsoon rains with the other being Rabi) and the summer season. Five different crossing periods (CP), viz. CP1, CP2, CP3, CP4, and CP5, were considered to illustrate the effects of agro-climatic conditions on the quality and biochemical components of two bottle gourd parental lines and one hybrid, HBGH-35. The average mean temperature for the Kharif season in 2017 was 31.7 °C, and for the summer season, it was 40.1 °C. Flowers were tagged weekly from the start of the crossing period until the end and harvested separately at different times. The fruits harvested from different crossing periods under different environmental conditions influenced the bottle gourd's qualitative and biochemical traits and showed significant variations among the five crossing period environments. A positive significance and correlation were observed between weather variables and different biochemical characteristics. Henceforth, the CP4 crossing period at a temperature of 31.7 °C retained high-quality seed development, which may be essential in enhancing agricultural productivity and the national economy.

Probing the Phytochemical Composition and Antioxidant Activity of Moringa oleifera under Ideal Germination Conditions.

Moringa oleifera is a rich source of polyphenols whose contents and profile may vary according to environmental conditions, harvest season, and plant tissue. The present study aimed to characterize the profile of phenolic compounds in different tissues of M. oleifera grown under different temperatures (25, 30, and 35 °C), using HPLC/MS, as well as their constituent phytochemicals and in vitro antioxidant activities. The in vitro antioxidant activity of the extracts was evaluated using the 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-azino-bis-3-ethylenebenzothiozoline-6-sulfonicacid (ABTS), and ferric-reducing antioxidant power (FRAP) methods. The polyphenolic compounds were mainly found in the leaves at 30 °C. UPLC/QTOF-MS allowed for the identification of 34 polyphenolic components in seedlings, primarily consisting of glucosides, phenols, flavonoids, and methoxy flavones. At 30 °C, the specific activities of antioxidative enzymes were the highest in leaves, followed by seedlings and then seeds. The leaf and seed extracts also exhibited a greater accumulation of proline, glycine betaine, and antioxidants, such as ascorbic acid, and carotenoids, as measured by the inhibition of ROS production. We found that changes in the expression levels of the validated candidate genes Cu/Zn-SOD, APX, GPP, and TPS lead to significant differences in the germination rate and biochemical changes. These findings demonstrate that M. oleifera plants have high concentrations of phytochemicals and antioxidants, making them an excellent choice for further research to determine their use as health-promoting dietary supplements.

Deciphering the biochemical and functional characterization of rice straw cultivars for industrial applications.

As an agricultural state, Haryana (India) produces about six million metric tons (mt) of rice straw every year from rice cultivation. Currently, rice straw is either burned or ploughed into the field without being turned into a functional product. Burning of paddy straw release green house gases and particulate matter (2.5 and 10 µm), which leads to air pollution and considerable loss of soil property viz. nutrients, organic matter, productivity and biodiversity, and on and off-farm humans and animals' health. The biochemically and functionally specified potential for optimal alternative use of the rice straw of 13 most widely produced rice varieties from Haryana's eastern and western agro-climate zones was undertaken. Pusa-1401 variety had the highest cellulose (46.55%) and silica content (13.70%), while Pusa-1718 had hemicellulose (28.25%) and lignin (11.60%), respectively. Maximum nitrogen (0.81%), phosphorus (0.32%) and potassium (2.78%) were found in rice variety Pusa-1509, Pusa-1401 and Rice-6129. The findings seemed to be statistically significant (p < 0.05). The biochemical profiles of rice straw cultivars were classified into distinct structural groups (C-H alkalanes, O-H alcohol, C[bond, double bond]O, C-H alkanes) based on the FTIR spectrum in order to find the best alternative possibilities for bioethanol and compost production. According to the study, these rice straw varieties could be used to make lucrative industrial products.

Development and Optimization of Label-Free Quantitative Proteomics under Different Crossing Periods of Bottle Gourd.

Bottle gourd, a common vegetable in the human diet, has been valued for its medicinal and energetic properties. In this experiment, the time-resolved analysis of the changes in the proteins' electrophoretic patterning of the seed development at different crossing periods was studied in bottle gourd using label-free quantitative proteomics. Hybrid HBGH-35 had the highest observed protein levels at the 4th week of the crossing period (F4) compared to the parental lines, viz. G-2 (M) and Pusa Naveen (F). The crossing period is significantly correlated with grain filling and reserve accumulation. The observed protein expression profile after storage was related to seed maturation and grain filling in bottle gourds. A total of 2517 proteins were identified in differentially treated bottle gourd fruits, and 372 proteins were differentially expressed between different crossing periods. Proteins related to carbohydrate and energy metabolism, anthocyanin biosynthesis, cell stress response, and fruit firmness were characterized and quantified. Some proteins were involved in the development, while others were engaged in desiccation and the early grain-filling stage. F4 was distinguished by an increase in the accumulation of low molecular weight proteins and enzymes such as amylase, a serine protease, and trypsin inhibitors. The seed vigor also followed similar patterns of differential expression of seed storage proteins. Our findings defined a new window during seed production, which showed that at F4, maximum photosynthetic assimilates accumulated, resulting in an enhanced source-sink relationship and improved seed production. Our study attempts to observe the protein expression profiling pattern under different crossing periods using label-free quantitative proteomics in bottle gourd. It will facilitate future detailed investigation of the protein associated with quality traits and the agronomic importance of bottle gourd through selective breeding programs.

Assessment of future water demand and supply using WEAP model in Dhasan River Basin, Madhya Pradesh, India.

Understanding the available resources and the needs of those who use them is necessary for the evaluation and allocation of water resources. The main sectors utilizing the basin water resources are agriculture, drinking water, animal husbandry, and industries, and the efficient and rational management of water resources to be distributed among those different sectors of activity is vital. This study attempts to develop an integrated water resource management system for the Dhasan River Basin (DRB) by employing a scenario analysis approach in conjunction with Water Evaluation and Planning Model (WEAP) to analyze trends in water use and anticipated demand between 2015 and 2050, simulating five possible scenarios (I, II, III, IV, and V) as for external driving factors. For the WEAP modeling framework, 2015 was chosen as a current (base) year for which all available information and input data were given to the model and the future demand situation was analyzed for the period 2016-2050 (forecasting period). From the findings, it was observed that for the forecasting period, total water demand, unmet demand, and streamflow were 185.29 Bm3, 117.35 Bm3, and 58.26 Bm3, respectively, in the case of scenario I; 232.34 Bm3, 162.17 Bm3, and 59.87 Bm3 in case of scenario II; 139.40 Bm3, 84.37 Bm3, and 58.15 Bm3 in case of scenario III; 186.15 Bm3, 118.76 Bm3, and 56.98 Bm3 in case of scenario IV; and 181.89 Bm3, 96.87 Bm3, and 53.11 Bm3 in case of scenario V. Results of the study indicated that by 2050, increasing population growth, industrial development, and an increase in the agricultural area will rise the water demand dramatically, posing threats to the environment and humans. Therefore, implementing improved irrigation technologies, advancing agricultural practices on farms, and constructing water conservation and retaining structures could significantly reduce the unmet demands and shortfalls in DRB. Overall findings reveal that the pressure on the Dhasan water resources would increase in the future, and thus several suggestions have been provided to assist decision-makers in sustainable planning and management of water resources to meet future demands.

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh.

The rising salinity trend in the country's coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl- (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling.

Machines learning models have recently been proposed for predicting rivers water temperature (Tw) using only air temperature (Ta). The proposed models relied on a nonlinear relationship between the Tw and Ta and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (VMD) contributed to the improvement of machines learning performances for river Tw modelling. Measured data collected at five stations located in Poland from 1987 to 2014 were acquired and used for the analysis. Six machines learning models were used and compared namely, K-nearest neighbor's regression (KNNR), least square support vector machine (LSSVM), generalized regression neural network (GRNN), cascade correlation artificial neural networks (CCNN), relevance vector machine (RVM), and locally weighted polynomials regression (LWPR). The six models were developed according to three scenarios. First, the models were calibrated using only the Ta as input and obtained results show that the models were able to predict consistently water temperature, showing a high determination coefficient (R2) and Nash-Sutcliffe efficiency (NSE) with values near or above 0.910 and 0.915, respectively, and in overall the six models worked equally without clear superiority of one above another. Second, the air temperature was combined with the periodicity (i.e., day, month and year number) as input variable and a significant improvement was achieved. Both models show their ability to accurately predict river Tw with an overall accuracy of 0.956 for R2 and 0.955 for NSE values, but the LSSVM2 have some advantages such as a small errors metrics, and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction accuracy, exhibiting an improvement of about 40.50% and 39.12% in terms of RMSE and MAE between the first and the third scenarios, however, some models, i.e., GRNN and KNNR have not benefited from the VMD. This research has demonstrated the high capability of the VMD algorithm as a preprocessing approach in improving the accuracies of the machine learning models for river water temperature prediction.

Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India.

Prediction of soil temperature (ST) at multiple depths is important for maintaining the physical, chemical, and biological activities in soil for various scientific aspects. The present study was conducted in a semi-arid region of Punjab to predict the daily ST at 5-cm (ST5), 15-cm (ST15), and 30-cm (ST30) soil depths by employing the three-hybrid machine learning (ML) paradigms, i.e. support vector machine (SVM), multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS) optimized with slime mould algorithm (SMA), particle swarm optimization (PSO), and spotted hyena optimizer (SHO) algorithms. Five scenarios with different input variables were constructed using daily meteorological parameters, and the optimal one was extracted by exploiting the GT (gamma test). The feasibility of the proposed hybrid SVM, MLP, and ANFIS models was inspected based on performance metrics and visual interpretation. According to the results, the SVM-SMA model yields better estimates than other models at 5-cm, 15-cm, and 30-cm soil depths, respectively, for scenario 5 in the validation phase. Furthermore, conferring to the results, the SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering (Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index of agreement) and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.

Selection of the gridded temperature dataset for assessment of thermal bioclimatic environmental changes in Amu Darya River basin.

Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest. Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

Superabsorbent Polymers as a Soil Amendment for Increasing Agriculture Production with Reducing Water Losses under Water Stress Condition.

With an increasing population, world agriculture is facing many challenges, such as climate change, urbanization, the use of natural resources in a sustainable manner, runoff losses, and the accumulation of pesticides and fertilizers. The global water shortage is a crisis for agriculture, because drought is one of the natural disasters that affect the farmers as well as their country's social, economic, and environmental status. The application of soil amendments is a strategy to mitigate the adverse impact of drought stress. The development of agronomic strategies enabling the reduction in drought stress in cultivated crops is, therefore, a crucial priority. Superabsorbent polymers (SAPs) can be used as an amendment for soil health improvement, ultimately improving water holding capacity and plant available water. These are eco-friendly and non-toxic materials, which have incredible water absorption ability and water holding capacity in the soil because of their unique biochemical and structural properties. Polymers can retain water more than their weight in water and achieve approximately 95% water release. SAP improve the soil like porosity (0.26-6.91%), water holding capacity (5.68-17.90%), and reduce nitrogen leaching losses from soil by up to 45%. This review focuses on the economic assessment of the adoption of superabsorbent polymers and brings out the discrepancies associated with the influence of SAPs application in the context of different textured soil, presence of drought, and their adoption by farmers.

Ascorbate-Glutathione Oxidant Scavengers, Metabolome Analysis and Adaptation Mechanisms of Ion Exclusion in Sorghum under Salt Stress.

Salt stress is one of the major significant restrictions that hamper plant development and agriculture ecosystems worldwide. Novel climate-adapted cultivars and stress tolerance-enhancing molecules are increasingly appreciated to mitigate the detrimental impacts of adverse stressful conditions. Sorghum is a valuable source of food and a potential model for exploring and understanding salt stress dynamics in cereals and for gaining a better understanding of their physiological pathways. Herein, we evaluate the antioxidant scavengers, photosynthetic regulation, and molecular mechanism of ion exclusion transporters in sorghum genotypes under saline conditions. A pot experiment was conducted in two sorghum genotypes viz. SSG 59-3 and PC-5 in a climate-controlled greenhouse under different salt concentrations (60, 80, 100, and 120 mM NaCl). Salinity drastically affected the photosynthetic machinery by reducing the accumulation of chlorophyll pigments and carotenoids. SSG 59-3 alleviated the adverse effects of salinity by suppressing oxidative stress (H2O2) and stimulating enzymatic and non-enzymatic antioxidant activities (SOD, APX, CAT, POD, GR, GST, DHAR, MDHAR, GSH, ASC, proline, GB), as well as protecting cell membrane integrity (MDA, electrolyte leakage). Salinity also influenced Na+ ion efflux and maintained a lower cytosolic Na+/K+ ratio via the concomitant upregulation of SbSOS1, SbSOS2, and SbNHX-2 and SbV-Ppase-II ion transporter genes in sorghum genotypes. Overall, these results suggest that Na+ ions were retained and detoxified, and less stress impact was observed in mature and younger leaves. Based on the above, we deciphered that SSG 59-3 performed better by retaining higher plant water status, photosynthetic assimilates and antioxidant potential, and the upregulation of ion transporter genes and may be utilized in the development of resistant sorghum lines in saline regions.

Deciphering Reserve Mobilization, Antioxidant Potential, and Expression Analysis of Starch Synthesis in Sorghum Seedlings under Salt Stress.

Salt stress is one of the major constraints affecting plant growth and agricultural productivity worldwide. Sorghum is a valuable food source and a potential model for studying and better understanding the salt stress mechanics in the cereals and obtaining a more comprehensive knowledge of their cellular responses. Herein, we examined the effects of salinity on reserve mobilization, antioxidant potential, and expression analysis of starch synthesis genes. Our findings show that germination percentage is adversely affected by all salinity levels, more remarkably at 120 mM (36% reduction) and 140 mM NaCl (46% reduction) than in the control. Lipid peroxidation increased in salt-susceptible genotypes (PC-5: 2.88 and CSV 44F: 2.93 nmloe/g.FW), but not in tolerant genotypes. SSG 59-3 increased activities of α-amylase, and protease enzymes corroborated decreased starch and protein content, respectively. SSG 59-3 alleviated adverse effects of salinity by suppressing oxidative stress (H2O2) and stimulating enzymatic and non-enzymatic antioxidant activities (SOD, APX, CAT, POD, GR, and GPX), as well as protecting cell membrane integrity (MDA, electrolyte leakage). A significant increase (p ≤ 0.05) was also observed in SSG 59-3 with proline, ascorbic acid, and total carbohydrates. Among inorganic cations and anions, Na+, Cl-, and SO42- increased, whereas K+, Mg2+, and Ca2+ decreased significantly. SSG 59-3 had a less pronounced effect of excess Na+ ions on the gene expression of starch synthesis. Salinity also influenced Na+ ion efflux and maintained a lower cytosolic Na+/K+ ratio via concomitant upregulation of SbNHX-1 and SbVPPase-I ion transporter genes. Thus, we have highlighted that salinity physiologically and biochemically affect sorghum seedling growth. Based on these findings, we highlighted that SSG 59-3 performed better by retaining higher plant water status, antioxidant potential, and upregulation of ion transporter genes and starch synthesis, thereby alleviating stress, which may be augmented as genetic resources to establish sorghum cultivars with improved quality in saline soils.

Genome-Wide Transcriptome Profiling, Characterization, and Functional Identification of NAC Transcription Factors in Sorghum under Salt Stress.

Salinity stress has become a significant concern to global food security. Revealing the mechanisms that enable plants to survive under salinity has immense significance. Sorghum has increasingly attracted researchers interested in understanding the survival and adaptation strategies to high salinity. However, systematic analysis of the DEGs (differentially expressed genes) and their relative expression has not been reported in sorghum under salt stress. The de novo transcriptomic analysis of sorghum under different salinity levels from 60 to 120 mM NaCl was generated using Illumina HiSeq. Approximately 323.49 million high-quality reads, with an average contig length of 1145 bp, were assembled de novo. On average, 62% of unigenes were functionally annotated to known proteins. These DEGs were mainly involved in several important metabolic processes, such as carbohydrate and lipid metabolism, cell wall biogenesis, photosynthesis, and hormone signaling. SSG 59-3 alleviated the adverse effects of salinity by suppressing oxidative stress (H2O2) and stimulating enzymatic and non-enzymatic antioxidant activities (SOD, APX, CAT, APX, POX, GR, GSH, ASC, proline, and GB), as well as protecting cell membrane integrity (MDA and electrolyte leakage). Significant up-regulation of transcripts encoding the NAC, MYB, and WRYK families, NHX transporters, the aquaporin protein family, photosynthetic genes, antioxidants, and compatible osmolyte proteins were observed. The tolerant line (SSG 59-3) engaged highly efficient machinery in response to elevated salinity, especially during the transport and influx of K+ ions, signal transduction, and osmotic homeostasis. Our data provide insights into the evolution of the NAC TFs gene family and further support the hypothesis that these genes are essential for plant responses to salinity. The findings may provide a molecular foundation for further exploring the potential functions of NAC TFs in developing salt-resistant sorghum lines.

On the investigation of COVID-19 lockdown influence on air pollution concentration: regional investigation over eighteen provinces in Iraq.

At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.

Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms.

Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.

Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm.

For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14 years (2000-2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), root-mean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVR-WOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin, Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study region.

Proteome dynamics and transcriptome profiling in sorghum [Sorghum bicolor (L.) Moench] under salt stress.

Sorghum is a C4 cereal grain crop which is well adapted to harsh environment. It is a potential model for gaining better understanding of the molecular mechanism due to its wider adaptability to abiotic stresses. In this study, protein extraction was standardized using different methods to study the electrophoretic pattern of sorghum leaves under different salinity levels. The extraction of soluble protein with lysis buffer, followed by its clean-up was found to be the most effective method. The different profiles of salt-responsive proteins were analyzed in G-46 and CSV 44F sorghum genotypes based on their tolerance behavior towards salinity. The kafirin level also changed depending upon the concentration and exposure time to salts suggesting the stored proteins as energy source under stress conditions. The relative expression of salt-responsive genes was studied using Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) which might be used as a molecular screening tool for identification of salt-tolerant genotypes in affected areas. The validated responses were examined in terms of metabolic changes and the expression of stress-induced proteins-viz. heat shock proteins (hsp) via immunoblotting assay. The results showed that the two sorghum genotypes adopted distinct approaches in response to salinity, with G-46 performing better in terms of leaf function. Also, we have standardized different protein extraction methods followed by their clean-up for electrophoretic profiling.

The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt.

Spatial-temporal information of different water resources is essential to rationally manage, sustainably develop, and optimally utilize water. This study focused on simulating future water footprint (WF) of two agronomically important crops (i.e., wheat and maize) using deep neural networks (DNN) method in Nile delta. DNN model was calibrated and validated by using 2006-2014 and 2015-2017 datasets. Moreover, future data (2022-2040) were obtained from three Representative Concentration Pathways (RCP) 2.6, 4.5, and 8.5, and incorporated into DNN prediction set. The findings showed that determination-coefficient between historical-predicted crop evapotranspiration (ETc) varied from 0.92 to 0.97 for two crops. The yield prediction values of wheat-maize deviated within the ranges of -3.21% to 3.47% and -4.93% to 5.88%, respectively. Based on the ensemble of RCP, precipitation was forecasted to decease by 667.40% and 261.73% in winter and summer in western as compared to eastern, respectively, which will ultimately be dropped to 105.02% and 60.87%, respectively parallel to historical. Therefore, the substantial fluctuations in precipitation caused an obvious decrease in green WF of wheat (i.e., 24.96% and 37.44%) in western and eastern, respectively. Additionally, for maize, it induced a 103.93% decrease in western and an 8.96% increase in eastern. Furthermore, increasing ETc by 8.46% and 12.45% gave rise to substantially increasing (i.e., 8.96% and 17.21%) in western for wheat-maize compared to the east, respectively. Likewise, grey wheat-maize WF findings reveals that there was an increase of 3.07% and 5.02% in western as compared to -14.51% and 12.37% in eastern. Hence, our results highly recommend the optimal use of the eastern delta to save blue-water by 16.58% and 40.25% of total requirements for wheat-maize in contrast to others. Overall, the current research framework and results derived from the adopted methodology will help in optimal planning of future water under climate change in the agricultural sector.

Identification and Detection of Bioactive Peptides in Milk and Dairy Products: Remarks about Agro-Foods.

Food-based components represent major sources of functional bioactive compounds. Milk is a rich source of multiple bioactive peptides that not only help to fulfill consumers 'nutritional requirements but also play a significant role in preventing several health disorders. Understanding the chemical composition of milk and its products is critical for producing consistent and high-quality dairy products and functional dairy ingredients. Over the last two decades, peptides have gained significant attention by scientific evidence for its beneficial health impacts besides their established nutrient value. Increasing awareness of essential milk proteins has facilitated the development of novel milk protein products that are progressively required for nutritional benefits. The need to better understand the beneficial effects of milk-protein derived peptides has, therefore, led to the development of analytical approaches for the isolation, separation and identification of bioactive peptides in complex dairy products. Continuous emphasis is on the biological function and nutritional characteristics of milk constituents using several powerful techniques, namely omics, model cell lines, gut microbiome analysis and imaging techniques. This review briefly describes the state-of-the-art approach of peptidomics and lipidomics profiling approaches for the identification and detection of milk-derived bioactive peptides while taking into account recent progress in their analysis and emphasizing the difficulty of analysis of these functional and endogenous peptides.

Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

Accurate estimation of reference evapotranspiration (ETo) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETo-based estimation is a major concern in the hydrological cycle. The estimation of ETo can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETo estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETo on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETo at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tmax and Tmin), solar radiation (Rs), and wind speed (Us) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETo at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.