Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions.

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.

Enhancing Wheat Growth, Physiology, Yield, and Water Use Efficiency under Deficit Irrigation by Integrating Foliar Application of Salicylic Acid and Nutrients at Critical Growth Stages.

Transitioning from full to deficit irrigation (DI) has become a key strategy in arid regions to combat water scarcity and enhance irrigation water use efficiency (IWUE). However, implementing DI requires additional approaches to counter its negative effects on wheat production. One effective approach is the foliar application of salicylic acid (SA), micronutrients (Mic; zinc and manganese), and macronutrients (Mac; nitrogen, phosphorus, and potassium). However, there is a lack of knowledge on the optimal combinations and timing of foliar application for these components to maximize their benefits under arid conditions, which is the primary focus of this study. A two-year field study was conducted to assess the impact of the foliar application of SA alone and in combination with Mic (SA + Mic) or Mic and Mac (SA + Mic + Mac) at various critical growth stages on wheat growth, physiology, productivity, and IWUE under DI conditions. Our result demonstrated that the foliar application of different components, the timing of application, and their interaction had significant effects on all investigated wheat parameters with few exceptions. Applying different components through foliar application at multiple growth stages, such as tillering and heading or tillering, heading, and grain filling, led to significant enhancements in various wheat parameters. The improvements ranged from 7.7% to 23.2% for growth parameters, 8.7% to 24.0% for physiological traits, 1.4% to 21.0% for yield and yield components, and 14.8% to 19.0% for IWUE compared to applying the components only at the tillering stage. Plants treated with different components (SA, Mic, Mac) exhibited enhanced growth, production, and IWUE in wheat compared to untreated plants. The most effective treatment was SA + Mic, followed by SA alone and SA + Mic + Mac. The foliar application of SA, SA + Mic, and SA + Mic + Mac improved growth parameters by 1.2-50.8%, 2.7-54.6%, and 2.5-43.9%, respectively. Yield parameters were also enhanced by 1.3-33.0%, 2.4-37.2%, and 3.0-26.6% while IWUE increased by 28.6%, 33.0%, and 18.5% compared to untreated plants. A heatmap analysis revealed that the foliar application of SA + Mic at multiple growth stages resulted in the highest values for all parameters, followed by SA alone and SA + Mic + Mac applications at multiple growth stages. The lowest values were observed in untreated plants and with the foliar application of different components only at the tillering stage. Thus, this study suggested that the foliar application of SA + Mic at various growth stages can help sustain wheat production in arid regions with limited water resources.

Ability of Different Growth Indicators to Detect Salt Tolerance of Advanced Spring Wheat Lines Grown in Real Field Conditions.

Plant growth indicators (GIs) are important for evaluating how different genotypes respond to normal and stress conditions separately. They consider both the morphological and physiological components of plants between two successive growth stages. Despite their significance, GIs are not commonly used as screening criteria for detecting salt tolerance of genotypes. In this study, 36 recombinant inbred lines (RILs) along with four genotypes differing in their salt tolerance were grown under normal and 150 mM NaCl in a two-year field trial. The performance and salt tolerance of these germplasms were assessed through various GIs. The analysis of variance showed highly significant variation between salinity levels, genotypes, and their interaction for all GIs and other traits in each year and combined data for two years, with a few exceptions. All traits and GIs were significantly reduced by salinity stress, except for relative growth rate (RGR), net assimilation rate (NAR), and specific leaf weight (SLW), which increased under salinity conditions. Traits and GIs were more correlated with each other under salinity than under normal conditions. Principal component analysis organized traits and GIs into three main groups under both conditions, with RGR, NAR, and specific leaf area (SLA) closely associated with grain yield (GY) and harvest index, while leaf area duration (LAD) was closely associated with green leaf area (GLA), plant dry weight (PDW), and leaf area index (LAI). A hierarchical clustering heatmap based on GIs and traits organized germplasms into three and four groups under normal and salinity conditions, respectively. Based on the values of traits and GIs for each group, the germplasms varied from high- to low-performing groups under normal conditions and from salt-tolerant to salt-sensitive groups under salinity conditions. RGR, NAR, and LAD were important factors determining genotypic variation in GY of high- and low-performing groups, while all GIs, except leaf area duration (LAR), were major factors describing genotypic variation in GY of salt-tolerant and salt-sensitive groups. In conclusion, different GIs that reveal the relationship between the morphological and physiological components of genotypes could serve as valuable selection criteria for evaluating the performance of genotypes under normal conditions and their salt tolerance under salinity stress conditions.

Effects of Salicylic Acid and Macro- and Micronutrients through Foliar and Soil Applications on the Agronomic Performance, Physiological Attributes, and Water Productivity of Wheat under Normal and Limited Irrigation in Dry Climatic Conditions.

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.

Identification of Wheat Ideotype under Multiple Abiotic Stresses and Complex Environmental Interplays by Multivariate Analysis Techniques.

Multiple abiotic stresses negatively impact wheat production all over the world. We need to increase productivity by 60% to provide food security to the world population of 9.6 billion by 2050; it is surely time to develop stress-tolerant genotypes with a thorough comprehension of the genetic basis and the plant's capacity to tolerate these stresses and complex environmental reactions. To approach these goals, we used multivariate analysis techniques, the additive main effects and multiplicative interaction (AMMI) model for prediction, linear discriminant analysis (LDA) to enhance the reliability of the classification, multi-trait genotype-ideotype distance index (MGIDI) to detect the ideotype, and the weighted average of absolute scores (WAASB) index to recognize genotypes with stability that are highly productive. Six tolerance multi-indices were used to test twenty wheat genotypes grown under multiple abiotic stresses. The AMMI model showed varying differences with performance indices, which disagreed with the trait and genotype differences used. The G01, G12, G16, and G02 were selected as the appropriate and stable genotypes using the MGIDI with the six tolerance multi-indices. The biplot features the genotypes (G01, G03, G11, G16, G17, G18, and G20) that were most stable and had high tolerance across the environments. The pooled analyses (LDA, MGIDI, and WAASB) showed genotype G01 as the most stable candidate. The genotype (G01) is considered a novel genetic resource for improving productivity and stabilizing wheat programs under multiple abiotic stresses. Hence, these techniques, if used in an integrated manner, strongly support the plant breeders in multi-environment trials.

Micromorphology, Ultrastructure and Histochemistry of Commelina benghalensis L. Leaves and Stems.

Commelina benghalensis L. is used as a traditional medicine in treating numerous ailments and diseases such as infertility in women, conjunctivitis, gonorrhea, and jaundice. This study used light and electron microscopy coupled with histochemistry to investigate the micromorphology, ultrastructure and histochemical properties of C. benghalensis leaves and stems. Stereo and scanning electron microscopy revealed dense non-glandular trichomes on the leaves and stems and trichome density was greater in emergent leaves than in the young and mature. Three morphologically different non-glandular trichomes were observed including simple multicellular, simple bicellular and simple multicellular hooked. The simple bicellular trichomes were less common than the multicellular and hooked. Transmission electron micrographs showed mitochondria, vesicles and vacuoles in the trichome. The leaf section contained chloroplasts with plastoglobuli and starch grains. Histochemical analysis revealed various pharmacologically important compounds such as phenols, alkaloids, proteins and polysaccharides. The micromorphological and ultrastructural investigations suggest that Commelina benghalensis L. is an economically important medicinal plant due to bioactive compounds present in the leaves and stems.

Combining Genetic and Multidimensional Analyses to Identify Interpretive Traits Related to Water Shortage Tolerance as an Indirect Selection Tool for Detecting Genotypes of Drought Tolerance in Wheat Breeding.

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.

Potential Use of Hyperspectral Reflectance as a High-Throughput Nondestructive Phenotyping Tool for Assessing Salt Tolerance in Advanced Spring Wheat Lines under Field Conditions.

The incorporation of stress tolerance indices (STIs) with the early estimation of grain yield (GY) in an expeditious and nondestructive manner can enable breeders for ensuring the success of genotype development for a wide range of environmental conditions. In this study, the relative performance of GY for sixty-four spring wheat germplasm under the control and 15.0 dS m-1 NaCl were compared through different STIs, and the ability of a hyperspectral reflectance tool for the early estimation of GY and STIs was assessed using twenty spectral reflectance indices (SRIs; 10 vegetation SRIs and 10 water SRIs). The results showed that salinity treatments, genotypes, and their interactions had significant effects on the GY and nearly all SRIs. Significant genotypic variations were also observed for all STIs. Based on the GY under the control (GYc) and salinity (GYs) conditions and all STIs, the tested genotypes were classified into three salinity tolerance groups (salt-tolerant, salt-sensitive, and moderately salt-tolerant groups). Most vegetation and water SRIs showed strong relationships with the GYc, stress tolerance index (STI), and geometric mean productivity (GMP); moderate relationships with GYs and sometimes with the tolerance index (TOL); and weak relationships with the yield stability index (YSI) and stress susceptibility index (SSI). Obvious differences in the spectral reflectance curves were found among the three salinity tolerance groups under the control and salinity conditions. Stepwise multiple linear regressions identified three SRIs from each vegetation and water SRI as the most influential indices that contributed the most variation in the GY. These SRIs were much more effective in estimating the GYc (R2 = 0.64 - 0.79) than GYs (R2 = 0.38 - 0.47). They also provided a much accurate estimation of the GYc and GYs for the moderately salt-tolerant genotype group; YSI, SSI, and TOL for the salt-sensitive genotypes group; and STI and GMP for all the three salinity tolerance groups. Overall, the results of this study highlight the potential of using a hyperspectral reflectance tool in breeding programs for phenotyping a sufficient number of genotypes under a wide range of environmental conditions in a cost-effective, noninvasive, and expeditious manner. This will aid in accelerating the development of genotypes for salinity conditions in breeding programs.

Use of Hyperspectral Reflectance Sensing for Assessing Growth and Chlorophyll Content of Spring Wheat Grown under Simulated Saline Field Conditions.

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.

Major Bioactive Alkaloids and Biological Activities of Tabernaemontana Species (Apocynaceae).

Several species belonging to the genus Tabernaemontana have been well researched and utilized for their wide-ranging biological activities. A few of the most prominent species include Tabernaemontana divaricata, Tabernaemontana catharinensis, Tabernaemontana crassa, and Tabernaemontana elegans. These species and many others within the genus often display pharmacological importance, which is habitually related to their chemical constituents. The secondary metabolites within the genus have demonstrated huge medicinal potential for the treatment of infections, pain, injuries, and various diseases. Regardless of the indispensable reports and properties displayed by Tabernaemontana spp., there remains a wide variety of plants that are yet to be considered or examined. Thus, an additional inclusive study on species within this genus is essential. The current review aimed to extensively analyze, collate, and describe an updated report of the current literature related to the major alkaloidal components and biological activities of species within the genus Tabernaemontana.

Drought Stress Impacts on Plants and Different Approaches to Alleviate Its Adverse Effects.

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.

Performance of optimized hyperspectral reflectance indices and partial least squares regression for estimating the chlorophyll fluorescence and grain yield of wheat grown in simulated saline field conditions.

To overcome the salinity threats to crop production in arid conditions, wheat cultivars should be developed with better performance with regard to key physiological traits. Although different chlorophyll fluorescence (ChlF) parameters, such as maximum quantum PSII photochemical efficiency (Fv/Fm), quantum yield of PSII (ΦPSII), and non-photochemical quenching (NPQ) have been proven to be key physiological traits to improve salt tolerance, their evaluation is time-consuming. In this study, hyperspectral canopy reflectance was used to assess ChlF parameters and grain yield (GY) of two wheat cultivars growing in simulated saline field conditions and exposed to three salinity levels (control, 6.0 dS m-1, and 12.0 dS m-1). Different spectral reflectance indices (SRIs) were formulated as ratios based on contour maps and tested for their relationship with ChlF parameters. The performance of individual SRIs and partial least squares regression (PLSR) models based on ChlF parameters, all examined SRIs, or data fusion of combined ChlF and SRIs to estimate the GY was considered. All examined SRIs failed to assess ΦPSII and NPQ under control condition, but most of them showed a moderate to strong relationship with both parameters under the salinity levels of 6.0 and 12.0 dS m-1. The examined SRIs showed a moderate and strong relationship with Fv/Fm under conditions of 6.0 and 12.0 dS m-1, respectively. Most SRIs correlated better with the three ChlF parameters for the salt-sensitive cultivar Sakha 61 than for the salt-tolerant cultivar Sakha 93. Several SRIs exhibited strong relationships with GY under the salinity levels of 6.0 and 12.0 dS m-1 and for both cultivars. Overall, the PLSR models exhibited additional improvements for estimating and predicting GY in both calibration and validation datasets over that using individual SRIs. The PLSR model based on data fusion was the best model to accurately estimate GY in the validation model even under control conditions. This study, of a type rarely conducted in simulated saline field conditions, indicates that the ChlF parameters could be linked to hyperspectral reflectance data for the rapid and non-destructive assessment of photosynthetic status and prediction of wheat production under salt stress field conditions.

Correction: Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices.

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

Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis.

The timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350-2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R2) in both the calibration and validation datasets (0.30-0.95), but that this range of R2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth- and photosynthetic-related trait responses to salinity stress.

Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices.

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.

Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes.

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.

Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region.

Simultaneous indirect assessment of multiple and diverse plant parameters in an exact and expeditious manner is becoming imperative in irrigated arid regions, with a view toward creating drought-tolerant genotypes or for the management of precision irrigation. This study aimed to evaluate whether spectral reflectance indices (SRIs) in three parts of the electromagnetic spectrum ((visible-infrared (VIS), near-infrared (NIR)), and shortwave-infrared (SWIR)) could be used to track changes in morphophysiological parameters of wheat cultivars exposed to 1.00, 0.75, and 0.50 of the estimated evapotranspiration (ETc). Significant differences were found in the parameters of growth and photosynthetic efficiency, and canopy spectral reflectance among the three cultivars subjected to different irrigation rates. All parameters were highly and significantly correlated with each other particularly under the 0.50 ETc treatment. The VIS/VIS- and NIR/VIS-based indices were sufficient and suitable for assessing the growth and photosynthetic properties of wheat cultivars similar to those indices based on NIR/NIR, SWIR/NIR, or SWIR/SWIR. Almost all tested SRIs proved to assess growth and photosynthetic parameters, including transpiration rate, more efficiently when regressions were analyzed for each water irrigation rate individually. This study, the type of which has rarely been conducted in irrigated arid regions, indicates that spectral reflectance data can be used as a rapid and non-destructive alternative method for assessment of the growth and photosynthetic efficiency of wheat under a range of water irrigation rates.

Comparative Performance of Multivariable Agro-Physiological Parameters for Detecting Salt Tolerance of Wheat Cultivars under Simulated Saline Field Growing Conditions.

Field-based trials are crucial for successfully achieving the goals of plant breeding programs aiming to screen and improve the salt tolerance of crop genotypes. In this study, simulated saline field growing conditions were designed using the subsurface water retention technique (SWRT) and three saline irrigation levels (control, 60, and 120 mM NaCl) to accurately appraise the suitability of a set of agro-physiological parameters including shoot biomass, grain yield, leaf water relations, gas exchange, chlorophyll fluorescence, and ion accumulation as screening criteria to establish the salt tolerance of the salt-tolerant (Sakha 93) and salt-sensitive (Sakha 61) wheat cultivars. Shoot dry weight and grain yield per hectare were substantially reduced by salinity, but the reduction was more pronounced in Sakha 61 than in Sakha 93. Increasing salinity stress caused a significant decrease in the net photosynthesis rate and stomatal conductance of both cultivars, although their leaf turgor pressure increased. The accumulation of toxic ions (Na+ and Cl-) was higher in Sakha 61, but the accumulation of essential cations (K+ and Ca2+) was higher in Sakha 93, which could be the reason for the observed maintenance of the higher leaf turgor of both cultivars in the salt treatments. The maximum quantum PSII photochemical efficiency (Fv/Fm) and the PSII quantum yield (ΦPSII) decreased with increasing salinity levels in Sakha 61, but they only started to decline at the moderate salinity condition in Sakha 93. The principle component analysis successfully identified the interrelationships between all parameters. The parameters of leaf water relations and toxic ion concentrations were significantly related to each other and could identify Sakha 61 at mild and moderate salinity levels, and, to a lesser extent, Sakha 93 at the moderate salinity level. Both cultivars under the control treatment and Sakha 93 at the mild salinity level were identified by most of the other parameters. The variability in the angle between the vectors of parameters explained which parameters could be used as individual, interchangeable, or supplementary screening criteria for evaluating wheat salt tolerance under simulated field conditions.