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Abstract Soybean-wheat sequence, one of the most vital cropping systems for farmers, has been suffering for productivity stagnation and decline due to several factors. Strategic management of the inputs particularly the nutrients could aid the crops achieve optimum growth and yield. Keeping this in mind, four years of field experiment was conducted to study the effect of combining inorganic as well as organic nutrient sources using soil-test-crop-response (STCR) approach in a randomized block design having ten treatments including control, 100% Recommended Dose of Fertilizers (RDF), 50% RDF, 100% RDF + 5 kg Zn ha−1 (100% RDF + Zn), 100% RDF + 5 t farmyard manure ha−1 (100% RDF + FYM), 50% RDF + 5 t farmyard manure ha−1 (50% RDF + FYM), STCR inorganic with target yield-I (STCRI TY-I), STCR inorganic with TY-II, STCR integrated with TY-I (STCRO TY-I) and STCR integrated with TY-II (STCRO TY-II) with each treatment replicated thrice. The STCR equations were tailored for the experimental location and used to calculate the fertilizer requirements. Plant growth, yield, system yield, sustainability and economics, changes in soil parameters were analyzed during the study. The STCRO TY-II treatment showed 122.4 and 73.3% increase in yield of soybean and wheat over control, respectively and those were at par with the highest values recorded from 100% RDF + FYM treatment. Despite having low gross and net returns, decrease in benefit cost ratio was marginal (0.86%) in STCRO TY-II as compared to 100% RDF + FYM which indicated better utilization of resources with comparatively lower investment. In addition to that, it also performed reasonably well in improving soil available nutrient status (positive balance in available N, P, K and organic carbon), partial factor productivity of nutrients and sustainable yield index (2.43 times over control). The study elucidated significance of integrated nutrient management specially STCRO TY-II in improving soil health, optimizing yield and assuring economic sustainability in soybean-wheat cropping system.

PurposeThis Paper investigate how e-commerce stores adapted the business model of e-fulfilment customers. In addition, it will be explored current logistics model and environment obligations involved. Moreover, Saudi Arabia have considered the importance of the environment with following vision 2030. However, transportation has rely on heavily in recent years as result of e-commerce booming in Saudi market, which have some point of green logistics to investigate.Findings and OriginalityAfter Pandemic firms have occurred of various of changeable factors. Strategy plan, logistics and supply chain. However, Saudi vision 2030 support the environment with building mega projects such as NEOM and the Red Sea and the Line. Therefore, this study will focus on green logistics from B2C point of view in particular in the context of Saudi Arabia market.Research ImpactThis study contributes theoretically to our knowledge in both e-stores retailer in different cities with different category. In addition, to stand the immediacy of the obstacles and approached that adapted in Saudi Arabia context comparing to previous study results.Practical ImpactAs well as the contribution to practical fields will be emerged with new result and model approach. Furthermore, investigate the difference of reasons behind assigned such model. Finally, it might give insight from foreign investors to understand the model adapted and help to customised and adapted.

Background: The study evaluated the levels of academic stress in medical students and investigated its association with eating habits and bowel symptoms. Methods: Two hundred and ninety seven medical students participated in the study. Medical students from all five years of study were recruited using a stratified random sampling technique. A well-structured questionnaire containing three sections on stress levels, eating habits, and bowel symptoms was self-administered. Student t-test and ANOVA were used to quantify the association between variables. Results: The medical students showed high levels of stress accounting for low (2%), moderate (72%), and high-stress levels (26%) during exams. Men showed higher stress levels than women. The levels of stress were similar in distribution across all years of study. Poor dietary habits were noted among the medical students. Around 66% reported skipping breakfast, and 69% consumed frequent unhealthy snacks. Reduced consumption of fruits less than three times per week (61.5%) and drinking water less than 2 L per day (82.3%) was noted. Mean dietary scores worsened as the stress levels increased (p = 0.007). Similarly, higher PAC-SYM scores were associated with higher stress levels (p

Climate change poses serious threats to agriculture and food security, and extreme weather events have reduced crop productivity worldwide. Future projections predict that the average global temperature will rise by 2.0 to 6.4 °C and the increase in sea level will be 59 cm by the end of 21st century. The unprecedented rise in temperature has led to an increase in the incidence of heat waves, droughts, floods, and irregular patterns of precipitation. These changes have a dramatic impact on prevailing agricultural cropping systems, productivity, and food security of people regionally and globally. The change in climatic parameters have substantial effects on weeds, diseases, insect, and pests in different ways, and can result in an increase of their geographical distribution, number of generations, and survival during winter. Thus, to sustain the crop production on the eve of climate change is the main challenge. Therefore, adaptation measures are prerequisites to reduce the effects of climatic changes on production of agricultural crops. In this review, a brief insight has been given in the impact of climate change on agriculture and, the future challenges of climate change on the production of crops. In addition, integrated approaches, or recent developments for the improvement of crops such as breeding, transgenic approaches to biotechnology, and functional genomics, agronomic practices, cultivation of climate resilient crops, and nanotechnology for abiotic stress such as drought stress, temperature, heat, and salinity tolerance have also been discussed.

To successfully enhance the salt tolerance of genotypes, it is crucial to conduct field-based trials, establish effective screening criteria and analysis tools, evaluate salt tolerance at various growth stages, and integrate phenotypic assessment-based traits with molecular markers. This study aimed to assess the salt tolerance of 16 F8 recombinant inbred lines (RILs) and eight genotypes by analyzing 13 agro-morpho-physiological traits using various analysis tools and SSR markers under both control and high salinity levels (15 dS m−1) in real field conditions. Analysis of variance (ANOVA), comparison of mean values, calculation of reduction percentage, and multivariate analysis were used to compare the assessed traits among genotypes and identify which traits are the most effective ones in describing the salt tolerance of these genotypes. A heatmap cluster analysis (HMCA) was also employed to categorize the salt tolerance of genotypes into different clusters based on the stress tolerance index (STI) for all traits. The ANOVA results revealed significant statistical differences (p ≤ 0.05) between the genotypes and salinity levels for all assessed traits in each season and their combined data. Moreover, the 150 mM NaCl treatment led to decreases in the assessed traits by 10.2% to 36.9% when compared to the control treatments. Furthermore, the mean values of assessed traits for certain genotypes were approximately one to three times greater than those of other genotypes. Principal component analysis has identified plant dry weight, green leaf area, leaf area index, and grain yield per hectare as effective screening criteria for explaining the substantial variation observed among the genotypes. The HMCA successfully grouped genotypes into three distinct clusters and distinguished the salt-tolerant genotypes from the salt-sensitive and intermediate ones. The 24 genotypes/RILs were classified into three main groups according to the allelic data of 40 SSRs associated with salt-tolerant genes. A weak yet significant correlation was observed between the similarity coefficients of agro-morpho-physiological traits and SSR markers, as determined by the Mantel test (r = 0.13, p < 0.03, and alpha = 0.05). In conclusion, this study has successfully identified several traits, particularly those associated with SSR markers, that greatly contribute to our understanding of the phenotypic and genotypic basis influencing the salt tolerance of wheat genotypes in real field conditions. Consequently, assessing these traits for a large number of wheat plant materials in a rapid and cost-effective manner will be greatly importance in breeding programs aimed at improving salt stress tolerance in this vital food crop. This will be the main focus of our forthcoming research.

Ensuring food security with severe shortages of freshwater and drastic changes in climatic conditions in arid countries requires the urgent development of feasible and user-friendly strategies. Relatively little is known regarding the impacts of the co-application (Co-A) of salicylic acid (SA), macronutrients (Mac), and micronutrients (Mic) through foliar (F) and soil (S) application strategies on field crops under arid and semiarid climatic conditions. A two-year field experiment was designed to compare the impacts of seven (Co-A) treatments of this strategy, including a control, FSA + Mic, FSA + Mac, SSA + FMic, SSA + FSA + Mic, SSA + Mic + FSA, and SSA + Mic + FMac + Mic on the agronomic performance, physiological attributes, and water productivity (WP) of wheat under normal (NI) and limited (LMI) irrigation conditions. The results reveal that the LMI treatment caused a significant reduction in various traits related to the growth (plant height, tiller and green leaf numbers, leaf area index, and shoot dry weight), physiology (relative water content and chlorophyll pigments), and yield components (spike length, grain weight and grain numbers per spike, thousand-grain weight, and harvest index) of wheat by 11.4–47.8%, 21.8–39.8%, and 16.4–42.3%, respectively, while WP increased by 13.3% compared to the NI treatment. The different Co-A treatments have shown a 0.2–23.7%, 3.6–26.7%, 2.3–21.6%, and 12.2–25.0% increase in various traits related to growth, physiology, yield, and WP, respectively, in comparison to the control treatment. The SSA+ FSA + Mic was determined as the best treatment that achieved the best results for all studied traits under both irrigation conditions, followed by FSA + Mic and SSA + Mic + FSA under LMI in addition to FSA + Mac under NI conditions. It can be concluded that the Co-A of essential plant nutrients along with SA accomplished a feasible, profitable, and easy-to-use strategy to attenuate the negative impacts of deficit irrigation stress, along with the further improvement in the growth and production of wheat under NI conditions.

Seizure and anesthesia is a topic necessitating more studies to understand its mechanism. Some anesthetic agents triggers seizures, while others can control it. We are here reporting a case of apparently healthy young adult patient who underwent diagnostic cystoscopy and urethral dilatation under general anesthesia and who developed seizure immediately after admission to the postanaesthetic care unit.

Freshwater shortage and inadequate nutrient management are the two major challenges for sustainable wheat production in arid agro-ecosystems. Relatively little is known about the positive roles of the application methods for the combination of salicylic acid (SA) and plant nutrients in sustaining wheat production under arid climatic conditions. A two-year field study was undertaken to assess the impact of seven treatments for the integrated application of SA, macronutrients, and micronutrients on the morpho-physiological traits, yield, and irrigation water use efficiency (IWUE) of wheat subjected to full (FL) and limited (LM) irrigation regimes. The results showed that the LM regime caused a significant reduction in different plant growth traits, relative water content, chlorophyll pigments, yield components, and yield, while a significant increase was observed in IWUE. The sole application of SA or co-application with micronutrients through soil did not significantly affect the studied traits under the FL regime, while they achieved some improvement over untreated plants under the LM regime. Based on the different multivariate analyses, the soil and foliar applications for the combinations of SA and micronutrients, as well as a foliar application for the combinations of SA, macronutrients, and micronutrients were identified as an efficient option for mitigating the negative impacts of water deficit stress and enhancing the growth and production of wheat under normal conditions. In conclusion, the results obtained herein indicated that the co-application of SA and macro- and micronutrients is an effective option to greatly enhance and improve the growth and production of wheat crops in water-scarce countries of arid regions, such as Saudi Arabia, while an appropriate application method for this combination was required for positive effects.

Increasing atmospheric temperature can significantly reduce global wheat productivity; despite a mounting demand for wheat grain supplies. Developing genotypes with superior performance under current and future hot climates is a key challenge for wheat breeders. Multidimensional tools have supported plant breeders in increasing the genetic stability rate of agro-physiological indices that influence wheat productivity. We used 25 agro-physiological indices to classify 20 bread wheat genotypes for their heat stress tolerance. Agro-physiological indices and multidimensional analyses to identify differences in genetic and phenotypic were used, combining these analyses to reach selection criteria of accurate and credible. The 25 studied indices reflected high genotypic and environmental variations. We used 16 indices, which have brought together high heritability and genetic gain as indicators for screening heat-tolerant genotypes. Based on the seven principal comprehensive indices of (D value), wheat genotypes were classified into three highly heat-tolerant, four heat-tolerant, six moderately heat-tolerant, five heat-sensitive, and two highly heat-sensitive wheat genotypes. Based on four critical indices [grain yield (GY), grain-filling duration (GFD), spike length (SL) and canopy temperature (CT)] obtained from stepwise multiple linear regression (SMLR), the genotypes were grouped as four genotypes highly heat-tolerant, six heat-tolerant, two moderately heat-tolerant, four heat-sensitive and four highly heat-sensitive. The classification D value and SMLR distances were significantly correlated based on the Mantel test, with a perfect match in nine genotypes. SMLR indicated that a mathematical equation for the evaluation of wheat heat tolerance was established: GY = 0.670 + 0.504 × GFD + 0.334 × SL − 0.466 × CT (R2 = 0.739; average prediction accuracy of 94.12%). SMLR-based classification of wheat genotypes for heat tolerance was further verified through discriminant analysis, which showed that prior and posterior classification was identical in eighteen genotypes. Cross-validation showed that prior and posterior classification was identical in thirteen genotypes. Based on this study, we can recommend tolerated new wheat lines (DHL25, DHL05, DHL23 and DHL08) and cultivar Pavone-76 as a promising genetic source for heat-tolerant breeding programs.

Successfully evaluating and improving the salt tolerance of genotypes requires an appropriate analysis tool to allow simultaneous analysis of multiple traits and to facilitate the ranking of genotypes across different growth stages and salinity levels. In this study, we evaluate the salt tolerance of 56 recombinant inbred lines (RILs) in the presence of salt-tolerant and salt-sensitive control genotypes using multivariate analysis of plant dry weight, measured at 75 (PDW-75) and 90 (PDW-90) days from sowing, biological yield (BY), grain yield (GY), and their salt tolerance indices (STIs). All RILs and genotypes were evaluated under the control and 15 dS m−1 for two consecutive years (2019/2020 and 2020/2021). Results showed significant main effects of salinity and genotype as well as their interactions on four plant traits. Significant genotypic differences were also found for all calculated STIs. STIs exhibited moderate to strong relationships with the four plant traits when measured under either the control or salinity conditions and between each other. The principal component analysis (PCA) showed that the most variation among all analyzed variables was explained by the first two PCs, with the PC1 and PC2 explained at 61.8–71.8% and at 28.0–38.2% of the total variation, respectively. The PC1 had positive and strong correlations with the four plant traits measured under salinity conditions and STI, YI, REI, SWPI, MRPI, MPI, GMPI, and HMPI. The PC2 had strong correlations with BY and GY measured under the control conditions and SSI, TOL, RSE, and YSI. The PC1 was able to identify the salt-tolerant genotypes, while the PC2 was able to isolate the salt-sensitive ones. Cluster analysis based on multiple traits organized 64 genotypes into four groups varied from salt-tolerant to salt-sensitive genotypes, with the salt-tolerant group attaining higher value for plant traits under salinity conditions and the STIs related to the PC1. In conclusion, the use of multivariate analysis together with the STIs that evaluated the performance of genotypes under contrasting environmental conditions will help breeders to distinguish salt-tolerant genotypes from salt-sensitive ones, even at the early growth stages of plant development.

Rhizobacteria from desert plants can alleviate biotic stress and suppress plant diseases, and consequently can enhance plant growth. Therefore, the current study was performed to isolate and identify Prosopis glandulosa-associating rhizobacteria based on their antagonistic activity against Fusarium species and plant growth-promoting properties. Three bacterial isolates were identified as Bacillus subtilis: LDA-1, LDA-2, and LDA-3. The molecular analysis suggests the biosynthesis of the bacteriocins subtilisin and subtilosin, as well as the lipopeptide iturin, by these strains. In addition, the antagonistic study by dual-culture assay showed a high efficacy of all B. subtilis strains against phytopathogenic fungi (Fusarium nygamai, F. equisseti, F. solani, F. solani ICADL1, and F. oxysporum ICADL2) with inhibition percentages ranging from 43.3 to 83.5% in comparison to the control. Moreover, atomic force microscopy (AFM) analysis showed significant differences in the cell wall topography of the F. solani ICADL1 among the treated mycelia and untreated control. As a result, these three B. subtilis strains were used as bioinoculants for cotton seedlings infected by F. solani ICADL1 in pot trials, and the results revealed that the bacterial inoculations as an individual or combined with F. solani ICADL1 significantly improved cotton root and stem length, lateral roots, indole acetic acid (IAA), and gibberellic acid (GA3) contents, as well as increased antioxidants, flavonoids, and phenols in comparison to those obtained from healthy and infected control plants. In conclusion, the three bacterial strains of B. subtilis (i.e., LDA-1, LDA-2, and LDA-3) are considered promising tools as biocontrol agents for F. solani and cotton growth promoters, and consequently can be used as bio-ertilizer in sustainable agriculture systems.

Innovations in water-saving cultivation strategies are urgently needed to achieve high yield and elevated water use efficiency (WUE) simultaneously in arid regions with limited water resources. Here, we conducted a two-year field study to compare the impacts of eight combinations of planting patterns (PPs) and mulching on the soil water content (SWC) in the top 60 cm soil layer, the growth, the yield, and the WUE of wheat under two irrigation rates (1.00 and 0.50 ET). These combinations included three conventional flat planting (CF) patterns, including CF without mulch (CFNM), with plastic film (CFPM), and with wheat straw mulch (CFSM); three raised-bed planting (RB) patterns, including RB without mulch (RBNM), with plastic film (RBPM), and wheat straw (RBSM) mulch; and two ridge–furrow planting (RF) patterns, including RF without mulch (RFNM) and with plastic film mulch (RFPM). The results showed that the tested treatments affected the SWC at different depths under both irrigation rates. Compared with the two non-mulched treatments under 0.50 ET, the SWC of the three PPs with plastic film and the two PPs with wheat straw mulching were significantly higher before irrigation by 14.4–22.0% and 6.9–17.2% at 0–20 cm soil depth, 16.4–29.0% and 6.6–14.9% at 20–40 cm soil depth, and 3.3–34.8% and 3.4–14.5% at 40–60 cm soil depth, respectively. All measured wheat parameters, except harvest index, were significantly affected by the interaction between irrigation rate and PPs. The highest values for plant dry weight (PDW), yield components, grain yield (GY), and WUE under 1.00 ET were obtained in the two PPs with wheat straw mulch, while the three PPs with plastic film showed the highest values of these parameters under 0.50 ET. The yield response factor (Ky) based on PDW was acceptable for all PPs mulched with plastic film and wheat straw as well as for RFNM, while Ky based on GY was acceptable only for the PPs mulched with plastic film and for RFNM, as the Ky values of these PPs were less than 1 under 0.50 ET. The SWC at different depths exhibited quadratic and nonsignificant relationships with all parameters under 1.00 ET, while these relationships were linear and strong under 0.50 ET, with a few exceptions. Overall, we conclude that combining any PPs with plastic film mulching could be used as a feasible and effective strategy for obtaining high wheat yield and WUE in the irrigated and arid agroecosystem.

Using wastewater in agriculture is a desirable alternative source of irrigation and is gaining attraction worldwide. Therefore, this study was designed to assess the effect of treated municipal wastewater (TWW) and groundwater (GW), along with half and full doses of the recommended NPK dose on the plant growth, total biomass, gross energy, and macro- and trace element content and uptake of safflower (Carthamus tinctorius L.), canola (Brassica napus L.), and triticale (X Triticosecale Wittmack) grown in old and virgin soil as potential bioenergy crops. The results showed that crops planted in old or virgin soil irrigated with TWW had higher values of plant height, leaf area per plant, total chlorophyll content, total biomass, and gross and net energy contents compared to those irrigated with GW grown in virgin soil. Similarly, crops grown in old soil irrigated with TWW showed higher concentrations in dry matter and uptake for both macronutrients (N, P, and K) and trace elements (B, Zn, Mn, Cu, Cd, Pb, and Ni) compared to those planted in virgin soil and irrigated with GW. Furthermore, the application of the recommended half dose of NPK in old and virgin soil irrigated with TWW showed occasionally comparable results to that of a full recommended dose of NPK for most of the measured parameters. Importantly, the recommended half dose applied to old soil irrigated with TWW resulted in a significant improvement in all measured parameters compared to virgin soil irrigated with GW, along with a full recommended dose of NPK. Briefly, TWW can be used to irrigate crops grown for bioenergy purposes, since it did not pose any harmful effect for energy crops. In addition, it provides additional nutrients to soil and thus decreases the required rate of synthetic fertilizer by up to 50% without any significant decreases in the final production of crops.

Hybrid performance during wheat breeding can be improved by analyzing genetic distance (GD) among wheat genotypes and determining its correlation with heterosis. This study evaluated the GD between 16 wheat genotypes by using 60 simple sequence repeat (SSR) markers to classify them according to their relationships and select those with greater genetic diversity, evaluate the correlation of the SSR marker distance with heterotic performance and specific combining ability (SCA) for heat stress tolerance, and identify traits that most influence grain yield (GY). Eight parental genotypes with greater genetic diversity and their 28 F1 hybrids generated using diallel crossing were evaluated for 12 measured traits in two seasons. The GD varied from 0.235 to 0.911 across the 16 genotypes. Cluster analysis based on the GD estimated using SSRs classified the genotypes into three major groups and six sub-groups, almost consistent with the results of principal coordinate analysis. The combined data indicated that five hybrids showed 20% greater yield than mid-parent or better-parent. Two hybrids (P2 × P4) and (P2 × P5), which showed the highest performance of days to heading (DH), grain filling duration (GFD), and GY, and had large genetic diversity among themselves (0.883 and 0.911, respectively), were deemed as promising heat-tolerant hybrids. They showed the best mid-parent heterosis and better-parent heterosis (BPH) for DH (-11.57 and -7.65%; -13.39 and -8.36%, respectively), GFD (12.74 and 12.17%; 12.09 and 10.59%, respectively), and GY (36.04 and 20.04%; 44.06 and 37.73%, respectively). Correlation between GD and each of BPH and SCA effects based on SSR markers was significantly positive for GFD, hundred kernel weight, number of kernels per spike, harvest index, GY, and grain filling rate and was significantly negative for DH. These correlations indicate that the performance of wheat hybrids with high GY and earliness could be predicted by determining the GD of the parents by using SSR markers. Multivariate analysis (stepwise regression and path coefficient) suggested that GFD, hundred kernel weight, days to maturity, and number of kernels per spike had the highest influence on GY.

The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.

Water shortages have direct adverse effects on wheat productivity and growth worldwide, vertically and horizontally. Productivity may be promoted using water shortage-tolerant wheat genotypes. High-throughput tools have supported plant breeders in increasing the rate of stability of the genetic gain of interpretive traits for wheat productivity through multidimensional technical methods. We used 27 agrophysiological interpretive traits for grain yield (GY) of 25 bread wheat genotypes under water shortage stress conditions for two seasons. Genetic parameters and multidimensional analyses were used to identify genetic and phenotypic variations of the wheat genotypes used, combining these strategies effectively to achieve a balance. Considerable high genotypic variations were observed for 27 traits. Eleven interpretive traits related to GY had combined high heritability (h2 > 60%) and genetic gain (>20%), compared to GY, which showed moderate values both for heritability (57.60%) and genetic gain (16.89%). It was determined that six out of eleven traits (dry leaf weight (DLW), canopy temperature (CT), relative water content (RWC), flag leaf area (FLA), green leaves area (GLA) and leaf area index (LAI)) loaded the highest onto PC1 and PC2 (with scores of >0.27), and five of them had a positive trend with GY, while the CT trait had a negative correlation determined by principal component analysis (PCA). Genetic parameters and multidimensional analyses (PCA, stepwise regression, and path coefficient) showed that CT, RWC, GLA, and LAI were the most important interpretive traits for GY. Selection based on these four interpretive traits might improve genetic gain for GY in environments that are vulnerable to water shortages. The membership index and clustering analysis based on these four traits were significantly correlated, with some deviation, and classified genotypes into five groups. Highly tolerant, tolerant, intermediate, sensitive and highly sensitive clusters represented six, eight, two, three and six genotypes, respectively. The conclusions drawn from the membership index and clustering analysis, signifying that there were clear separations between the water shortage tolerance groups, were confirmed through discriminant analysis. MANOVA indicated that there were considerable variations between the five water shortage tolerance groups. The tolerated genotypes (DHL02, DHL30, DHL26, Misr1, Pavone-76 and DHL08) can be recommended as interesting new genetic sources for water shortage-tolerant wheat breeding programs.

The availability of suitable water is an important factor for increasing the cultivated areas and sustainability in arid (i.e., less than 200 mm precipitation per year) and semiarid regions (i.e., 200–700 mm precipitation per year). Therefore, this study aimed to analyze the impact of treated wastewater (TWW) and groundwater (GW) as well as synthetic fertilizers (50% and 100% of the recommended NPK dose; 150–150–60 kg N–P2O5–K2O ha−1) on the growth, biomass, energy traits, and macro and trace elements of maize (Zea mays L.), sorghum (Sorghum bicolor L.), and pearl millet (Pennisetum glaucum L) grown in old cultivated (first location; L1) and virgin soil (L2 and L3) as potential bioenergy crops. The soil in L1 has been irrigated with treated wastewater for the last 15 years and continued to be irrigated with treated wastewater in this investigation. The virgin soil was divided into two parts: the first part was irrigated with TWW, and the second part was irrigated with GW. The experiments were laid out in a split-plot with a randomized complete block design with water treatments (TWW in old and virgin soil, and GW in virgin soil) in main plots, and the two treatments of fertilization (50% and 100% of the recommended NPK dose) were distributed randomly in subplots. Compared with the crops irrigated with GW, the crops irrigated with TWW, whether grown on old or virgin soil, showed higher plant height, total chlorophyll content, leaf area per plant, total biomass, energy content, and gross energy with low ash. They also contained higher (but lower than permissible limits) concentrations of macro-elements (NPK) and trace elements (Fe, Mn, Cu, Zn, Cd, Pb, Ni, and Co). In addition, the application of a 50% recommended dose of NPK with TWW showed equivalent results to a 100% recommended dose of NPK on all measured parameters with few exceptions. In conclusion, the TWW can be used to irrigate field crops allocated for bioenergy production in arid regions because it does not harm the plants and environment. In addition, the 50% recommended dose of NPK fertilizer exerted no negative effects on the growth and energy production of field crops, thereby protecting the environment and reducing the leaching of excessive fertilizers into GW.

Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.

The application of proximal hyperspectral sensing, using simple vegetation indices, offers an easy, fast, and non-destructive approach for assessing various plant variables related to salinity tolerance. Because most existing indices are site- and species-specific, published indices must be further validated when they are applied to other conditions and abiotic stress. This study compared the performance of various published and newly constructed indices, which differ in algorithm forms and wavelength combinations, for remotely assessing the shoot dry weight (SDW) as well as chlorophyll a (Chla), chlorophyll b (Chlb), and chlorophyll a+b (Chlt) content of two wheat genotypes exposed to three salinity levels. Stepwise multiple linear regression (SMLR) was used to extract the most influential indices within each spectral reflectance index (SRI) type. Linear regression based on influential indices was applied to predict plant variables in distinct conditions (genotypes, salinity levels, and seasons). The results show that salinity levels, genotypes, and their interaction had significant effects (p ≤ 0.05 and 0.01) on all plant variables and nearly all indices. Almost all indices within each SRI type performed favorably in estimating the plant variables under both salinity levels (6.0 and 12.0 dS m−1) and for the salt-sensitive genotype Sakha 61. The most effective indices extracted from each SRI type by SMLR explained 60%–81% of the total variability in four plant variables. The various predictive models provided a more accurate estimation of Chla and Chlt content than of SDW and Chlb under both salinity levels. They also provided a more accurate estimation of SDW than of Chl content for salt-tolerant genotype Sakha 93, exhibited strong performance for predicting the four variables for Sakha 61, and failed to predict any variables under control and Chlb for Sakha 93. The overall results indicate that the simple form of indices can be used in practice to remotely assess the growth and chlorophyll content of distinct wheat genotypes under saline field conditions.

Drought stress, being the inevitable factor that exists in various environments without recognizing borders and no clear warning thereby hampering plant biomass production, quality, and energy. It is the key important environmental stress that occurs due to temperature dynamics, light intensity, and low rainfall. Despite this, its cumulative, not obvious impact and multidimensional nature severely affects the plant morphological, physiological, biochemical and molecular attributes with adverse impact on photosynthetic capacity. Coping with water scarcity, plants evolve various complex resistance and adaptation mechanisms including physiological and biochemical responses, which differ with species level. The sophisticated adaptation mechanisms and regularity network that improves the water stress tolerance and adaptation in plants are briefly discussed. Growth pattern and structural dynamics, reduction in transpiration loss through altering stomatal conductance and distribution, leaf rolling, root to shoot ratio dynamics, root length increment, accumulation of compatible solutes, enhancement in transpiration efficiency, osmotic and hormonal regulation, and delayed senescence are the strategies that are adopted by plants under water deficit. Approaches for drought stress alleviations are breeding strategies, molecular and genomics perspectives with special emphasis on the omics technology alteration i.e., metabolomics, proteomics, genomics, transcriptomics, glyomics and phenomics that improve the stress tolerance in plants. For drought stress induction, seed priming, growth hormones, osmoprotectants, silicon (Si), selenium (Se) and potassium application are worth using under drought stress conditions in plants. In addition, drought adaptation through microbes, hydrogel, nanoparticles applications and metabolic engineering techniques that regulate the antioxidant enzymes activity for adaptation to drought stress in plants, enhancing plant tolerance through maintenance in cell homeostasis and ameliorates the adverse effects of water stress are of great potential in agriculture.

There is a need for a more innovative fertilizer approach that can increase the productivity of agricultural systems and be more environmentally friendly than synthetic fertilizers. In this article, we reviewed the recent development and potential benefits derived from the use of nanofertilizers (NFs) in modern agriculture. NFs have the potential to promote sustainable agriculture and increase overall crop productivity, mainly by increasing the nutrient use efficiency (NUE) of field and greenhouse crops. NFs can release their nutrients at a slow and steady pace, either when applied alone or in combination with synthetic or organic fertilizers. They can release their nutrients in 40–50 days, while synthetic fertilizers do the same in 4–10 days. Moreover, NFs can increase the tolerance of plants against biotic and abiotic stresses. Here, the advantages of NFs over synthetic fertilizers, as well as the different types of macro and micro NFs, are discussed in detail. Furthermore, the application of NFs in smart sustainable agriculture and the role of NFs in the mitigation of biotic and abiotic stress on plants is presented. Though NF applications may have many benefits for sustainable agriculture, there are some concerns related to the release of nanoparticles (NPs) from NFs into the environment, with the subsequent detrimental effects that this could have on both human and animal health. Future research should explore green synthesized and biosynthesized NFs, their safe use, bioavailability, and toxicity concerns.

Salinity is one of the major issues that limits field crop productivity in an arid and semiarid environment. Therefore, two field trials were carried out over two seasons of 2018 and 2019 to investigate the enhancement of different methods of potassium application (i.e., recommended soil amendment (control; K2O), seed soaking (SS) and foliar spray (FS) in the form of potassium sulfate (K2SO4, 6 mM)) on antioxidant protection, physio-biochemical, yield and quality traits of soybean (cv. Giza 22) grown in normal (electrical conductivity; EC = 2.68 dS m−1) and saline soil (EC = 7.46 dS m−1). Physio-biochemical attributes (total chlorophyll, carotenoids, K+ and K+/Na+ ratios, performance index and catalase (CAT) activity), growth traits (i.e., shoot length, number and area of leaves plant−1 and shoot dry weight), yield and its components and seed quality (number of pods plant−1, 100-seed weight, seed yield ha−1 and seed protein and oil contents) were significantly decreased when soybean plants were grown in saline soil compared with those grown in normal soil. In contrast, activity of enzymatic antioxidants (i.e., superoxide dismutase (SOD), ascorbate peroxidase (APX) and glutathione peroxidase (GPX)), contents of non-enzymatic antioxidants and osmoprotectants (i.e., total soluble sugars, free proline, ascorbic acid and α-tocopherol), Na+, Cl−, H2O2 and malondialdehyde (MDA) were increased in soybean plants grown in saline soil compared with normal soil. However, under salt-stressed conditions, potassium applied through SS or FS significantly enhanced all soybean growth, photosynthetic efficiency, K+ content, ratio of K+/Na+ and activity of CAT, SOD, APX and GPX as well as improved yield and quality traits, while potassium application did not affect the contents of non-enzymatic antioxidants and osmoprotectants. For instance, foliar potassium application (FS) increased seed yield ha−1 by 92.31% and protein content by 63.19% compared with the control under the salt stress condition. In addition, both applications of potassium significantly reduced Na+, Cl−, H2O2 and MDA contents in soybean plants compared with those obtained from control treatments. Exogenous application of K2SO4 was more effective than SS at improving soybean physio-biochemical attributes, yield and seed quality traits under soil-salinity stress.

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.

Progress in high-throughput tools has enabled plant breeders to increase the rate of genetic gain through multidimensional assessment of previously intractable traits in a fast and nondestructive manner. This study investigates the potential use of spectral reflectance indices (SRIs; 15 vegetation-SRIs; 15 water-SRIs) as alternative selection tools for destructively measured traits in wheat breeding programs. The genetic variability, heritability (h2), genetic gain (GG), and expected genetic advances (GA) of these indices were compared with those of destructively measured traits in 43 F7-8 recombinant inbred lines (RILs) grown under limited water conditions. The performance of SRIs to estimate the destructively measured traits directly was also evaluated using the partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) models. Most vegetation-SRIs exhibited high genotypic variation, similar to the measured traits, and phenotypic correlations with these traits, compared with the water-SRIs. Most vegetation-SRIs presented comparable values for h2 (>60%) and GG (>20%) as intermediate traits, while about half of water-SRIs exhibited a high h2 (>60%), but low GG (2 in calibration (0.53–0.75) and validation (0.46–0.72) datasets, with strong relationships between observed and predicted values of measured traits. The SMLR models identified four and three SRIs from vegetation-SRIs and water-SRIs, respectively, to explain 63–86% of the total variability in measured traits among genotypes. These results demonstrated that vegetation-SRIs can be used individually or combined with water-SRIs as alternative breeding tools to increase genetic gains and selection accuracy in spring wheat breeding.

[This corrects the article DOI: 10.1371/journal.pone.0212294.].

Manipulating plant densities under different irrigation rates can have a significant impact on grain yield and water use efficiency by exerting positive or negative effects on ET. Whereas traditional spectral reflectance indices (SRIs) have been used to assess biophysical parameters and yield, the potential of multivariate models has little been investigated to estimate these parameters under multiple agronomic practices. Therefore, both simple indices and multivariate models (partial least square regression (PLSR) and support vector machines (SVR)) obtained from hyperspectral reflectance data were compared for their applicability for assessing the biophysical parameters in a field experiment involving different combinations of three irrigation rates (1.00, 0.75, and 0.50 ET) and five plant densities (D1: 150, D2: 250, D3: 350, D4: 450, and D5: 550 seeds m-2) in order to improve productivity and water use efficiency of wheat. Results show that the highest values for green leaf area, aboveground biomass, and grain yield were obtained from the combination of D3 or D4 with 1.00 ET, while the combination of 0.75 ET and D3 was the best treatment for achieving the highest values for water use efficiency. Wheat yield response factor (ky) was acceptable when the 0.75 ET was combined with D2, D3, or D4 or when the 0.50 ET was combined with D2 or D3, as the ky values of these combinations were less than or around one. The production function indicated that about 75% grain yield variation could be attributed to the variation in seasonal ET. Results also show that the performance of the SRIs fluctuated when regressions were analyzed for each irrigation rate or plant density specifically, or when the data of all irrigation rates or plant densities were combined. Most of the SRIs failed to assess biophysical parameters under specific irrigation rates and some specific plant densities, but performance improved substantially for combined data of irrigation rates and some specific plant densities. PLSR and SVR produced more accurate estimations of biophysical parameters than SRIs under specific irrigation rates and plant densities. In conclusion, hyperspectral data are useful for predicting and monitoring yield and water productivity of spring wheat across multiple agronomic practices.

Hyperspectral sensing offers a quick and non-destructive alternative for assessing phenotypic parameters of plant physiological status and salt stress tolerance. This study compares the performance of published and modified spectral reflectance indices (SRIs) for estimating and predicting the growth and photosynthetic efficiency of two wheat cultivars exposed to three salinity levels (control, 6.0, and 12.0 dS m−1). Results show that individual SRIs based on visible- and near-infrared (VIS/VIS, NIR/VIS, and NIR/NIR) estimate and predict measured parameters considerably more efficiently than those based on shortwave-infrared (SWIR/VIS and SWIR/NIR), with the exception of some modified indices (the water balance index (WABI-1(1550, 482), WABI-2(1640, 482), and WABI-3(1650, 531)), normalized difference moisture index (NDMI(1660, 1742)), and dry matter content index (DMCI(1550, 2305)), which show moderate to strong relationships with measured parameters. Overall results indicate that modified SRIs can serve as rapid and non-destructive high-throughput alternative approaches for tracking growth and photosynthetic efficiency of wheat under salt stress field conditions.

Colorectal cancer (CRC), which has high prevalence in Saudi Arabia and worldwide, needs better understanding by exploiting the latest available cytogenetic microarrays. We used biopsy tissue from consenting colorectal cancer patients to extract DNA and carry out microarray analysis using a CytoScan HD platform from Affymetrix. Patient specific comparisons of tumor–normal pairs were carried out. To find out the high probability key players, we performed Genomic Identification of Significant Targets in Cancer analysis and found 144 genes to form the list of driver genes. Of these, 24 genes attained high GISTIC scores and suggest being significantly associated with CRC. Loss of heterozygosity and uniparental disomy were found to affect 9 genes and suggest different mechanisms associated with CRC in every patient. Here we present the details of the methods used in carrying out the above analyses. Also, we provide some additional data on biomarker analysis that would complement the findings.