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Premise of the Study: In crop breeding programs, breeders use yield performance in optimal and stressful environments as key indicator for screening the most tolerant genotypes. Over four decades, several yield-based indices have been suggested for evaluating stress tolerance in crops. Despite the well-established use of these indices in agronomy a...
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Context 1
... we offer the first user-friendly online software that meets this need, the Plant Abiotic Stress Index Calculator (iPASTIC). Table 1 shows the mathematical formulas and selection pattern for each index. iPASTIC is written in the JavaScript programming language on the browser-side and PHP on the server-side, and is available as a web application (https ://mohse nyous efian.com/ipast ...Context 2
... we offer the first user-friendly online software that meets this need, the Plant Abiotic Stress Index Calculator (iPASTIC). Table 1 shows the mathematical formulas and selection pattern for each index. iPASTIC is written in the JavaScript programming language on the browser-side and PHP on the server-side, and is available as a web application (https ://mohse nyous efian.com/ipast ...Context 3
... we offer the first user-friendly online software that meets this need, the Plant Abiotic Stress Index Calculator (iPASTIC). Table 1 shows the mathematical formulas and selection pattern for each index. iPASTIC is written in the JavaScript programming language on the browser-side and PHP on the server-side, and is available as a web application (https ://mohse nyous efian.com/ipast ...Citations
... One of the challenges that face plant breeders is the presence of many selection indices. It was reported that combining many selection indices provides more accurate results (Thiry et al. 2016;Pour-Aboughadareh et al. 2019). In the current study, the best ten genotypes were selected based on the average sum of ranks (ASR). ...
... In the current study, the best ten genotypes were selected based on the average sum of ranks (ASR). ASR was reported to provide more understanding of the response of the evaluated genotypes and has been used widely in different breeding programs to select superior genotypes (Pour-Aboughadareh et al. 2019;Belay et al. 2021). Furthermore, the selection was done based on TKW as it is the most important yield trait in wheat. ...
Background Alkaline-saline (AS) stress threats crop development and productivity. Understanding the genetic control of AS tolerance in wheat is important to produce wheat cultivars that outstand such a severe stress condition.
Methods A set of 48 cultivars were tested under controlled and AS stress conditions at seedling and maturity stages. The effect of AS on seedlings and kernel traits was measured to select tolerant and high-yielding genotypes. Single-marker-analysis (SMA) and gene enrichment were conducted to understand the genetic control of AS tolerance in both growth stages.
Results AS stress decreased all kernel traits and most of the seedling traits. High correlations were found between the studied traits in each growth stage. The correlation between the traits related to both stages was non-significant. SMA identified a total of 292 and 52 markers significantly associated with the studied traits under controlled and AS stress conditions. Seven and 20 gene models were identified to control AS tolerance in each stage. Gene enrichment analysis identified one and six networks that control AS tolerance. Four genotypes were selected as superior genotypes.
Conclusion The genetic control of the studied traits differ under control and AS conditions. Two genetic systems control AS tolerance in each growth stage. This study is the first one that unlocked the genetic control of AS tolerance in seedling and mature growth stages and identified the biological process that lead to this tolerance. Four genotypes were selected for crossing in future breeding programs to improve AS tolerance in spring wheat.
... STIs are used extensively in breeding programs (Sardouie-Nasab et al., 2014;Sabouri et al., 2022), and several applications compute them, such as iPASTIC (Pour-Aboughadareh et al., 2019). This online software generates several selection parameters, e.g., tolerance index (TOL) (Rosielle and Hamblin, 1981), mean productivity stress (MP) (Rosielle and Hamblin, 1981), STI (Fernandez, 1992), geometric mean productivity (GMP) (Fernandez, 1992), harmonic mean (HM) (Bidinger et al., 1987), stress susceptibility index (SSI) (Fischer and Maurer, 1978), yield index (YI), yield stability index (YSI) (Bouslama and Schapaugh, 1984), and relative stress index (Fischer and Wood, 1979). ...
Introduction
Salinity is the abiotic obstacle that diminishes food production globally. Salinization causes by natural conditions, such as climate change, or human activities, e.g., irrigation and derange misuse. To cope with the salinity problem, improve the crop environment or utilize crop/wheat breeding (by phenotyping), specifically in spread field conditions. For example, about 33 % of the cropping area in Egypt is affected by salinity.
Methods
Therefore, this study evaluated forty bread wheat genotypes under contrasting salinity field conditions across seasons 2019/20 and 2020/21 at Sakha research station in the north of Egypt. To identify the tolerance genotypes, performing physiological parameters, e.g., Fv/Fm, CCI, Na+, and K+, spectral reflectance indices (SRIs), such as NDVI, MCARI, and SR, and estimated salinity tolerance indices based on grain yield in non-saline soil and saline soil sites over the tested years. These traits (parameters) and grain yield are simultaneously performed for generating GYT biplots.
Results
The results presented significant differences (P≤0.01) among the environments, genotypes, and their interaction for grain yield (GY) evaluated in the four environments. And the first season for traits, grain yield (GY), plant height (PH), harvest index (HI), chlorophyll content index (CCI), chlorophyll fluorescence parameter Fv/Fm, normalized difference vegetation index (NDVI) in contrasting salinity environments. Additionally, significant differences were detected among environments, genotypes, and their interaction for grain yield along with spectral reflectance indices (SRIs), e.g., Blue/Green index (BIG2), curvature index (CI), normalized difference vegetation index (NDVI), Modified simple ratio (MSR). Relying on the genotype plus genotype by environment (GGE) approach, genotypes 34 and 1 are the best for salinity sites. Genotypes 1 and 29 are the best from the genotype by stress tolerance indices (GSTI) biplot and genotype 34. Genotype 1 is the best from the genotype by yield*trait (GYT) method with spectral reflectance indices.
Discussion
Therefore, we can identify genotype 1 as salinity tolerant based on the results of GSTI and GYT of SRIs and recommend involvement in the salinity breeding program in salt-affected soils. In conclusion, spectral reflectance indices were efficiently identifying genotypic variance.
... Stress tolerance index (Fernandez, 1992) of the seedling biomass trait, was computed using iPASTIC online tool kit (Pour-Aboughadareh et al., 2019) to identify the best performing treatments. All recorded data were analyzed by using the statistical tool (Statistix 8.1 v2.0.1) by calculating Analysis of Variance (ANOVA) and significant difference among the treatment means was determined using least significant differences (LSD) test at 5% probability level (p ≤ 0.05). ...
Heat stress caused due to increasing warming climate has become a severe threat to global food production including rice. Silicon plays a major role in improving growth and productivity of rice by aiding in alleviating heat stress in rice. Soil silicon is only sparingly available to the crops can be made available by silicate solubilizing and plant-growth-promoting bacteria that possess the capacity to solubilize insoluble silicates can increase the availability of soluble silicates in the soil. In addition, plant growth promoting bacteria are known to enhance the tolerance to abiotic stresses of plants, by affecting the biochemical and physiological characteristics of plants. The present study is intended to understand the role of beneficial bacteria viz. Rhizobium sp. IIRR N1 a silicate solublizer and Gluconacetobacter diazotrophicus , a plant growth promoting bacteria and their interaction with insoluble silicate sources on morpho-physiological and molecular attributes of rice ( Oryza sativa L.) seedlings after exposure to heat stress in a controlled hydroponic system. Joint inoculation of silicates and both the bacteria increased silicon content in rice tissue, root and shoot biomass, significantly increased the antioxidant enzyme activities (viz. superoxidase dismutase, catalase and ascorbate peroxidase) compared to other treatments with sole application of either silicon or bacteria. The physiological traits (viz. chlorophyll content, relative water content) were also found to be significantly enhanced in presence of silicates and both the bacteria after exposure to heat stress conditions. Expression profiling of shoot and root tissues of rice seedlings revealed that seedlings grown in the presence of silicates and both the bacteria exhibited higher expression of heat shock proteins (HSPs viz., OsHsp90 , OsHsp100 and 60 kDa chaperonin ), hormone-related genes ( OsIAA6 ) and silicon transporters ( OsLsi1 and OsLsi2 ) as compared to seedlings treated with either silicates or with the bacteria alone. The results thus reveal the interactive effect of combined application of silicates along with bacteria Rhizobium sp. IIRR N1, G. diazotrophicus inoculation not only led to augmented silicon uptake by rice seedlings but also influenced the plant biomass and elicited higher expression of HSPs, hormone-related and silicon transporter genes leading to improved tolerance of seedling to heat stress.
... Wheat screening methods provided the mathematical computation to compare grain yield of water-stressed and non-water-stressed environments. The iPASTIC [32] online tool kit available calculated many STI parameters. For example, the desired value was minimum in the Tolerance Index (TOL) [33]. ...
... The grain yield means of the fifty genotypes in both well-irrigated/seasons (Yp) and water-stressed/seasons (Ys) subjected to calculate the stress tolerance/sensitive indices (STI) using iPASTIC online software [32]. The name, abbreviation, stress tolerance/sensitive indices, equations, and selected value Table 2. ...
... These graphical approaches relied on physiological parameters, hyperspectral reflectance measurements, and stress tolerance indices (STI) concurrently with agronomic traits. In this investigation, we performed the genotype by trait (GT) biplots to identify the waterdeficit tolerant genotypes after calculating the STI from the iPASTIC online software [32]. Hence, the normalized STI data were performed to depict the biplots (GSTI). ...
Drought is an environmental abiotic stress that diminishes wheat production worldwide. In the present study, we evaluated fifty bread wheat genotypes (arranged in alpha lattice design) under two main water regimes, water-deficit (two surface irrigations) and well-watered (four irrigations), at different sites in two consecutive cropping seasons, 2019/20 and 2020/21. To identify the drought-tolerant genotypes, utilized several selection/phenotyping criteria, including agronomic traits, e.g., grain yield (GY) and yield components (SM); physiological parameters such as canopy temperature (CT), leaf transpiration rate (TRN), intercellular CO2 concentration (INCO); spectral reflectance indices, e.g., Leaf Chlorophyll Index (LCI), curvature index (CI), and normalized difference vegetation index (NDVI); and stress tolerance indices (STI) were determined concurrently with the grain yield. The results revealed significant differences (p ≤ 0.01) among the environments, genotypes, and their interaction for grain yield (GY), days to heading (DH), days to maturity (DM), grain filling period (GFP), grain filling rate (GFR), Normalized difference vegetation index (NDVI), plant height (PH), and spikes per square meter (SM). The genotype plus genotype by environment (GGE) and genotype by yield*trait (GYT) biplot techniques indicated that Genotype 37 (Sakha 95) and Genotype 45 performed best under well-watered and water-deficit environments. Furthermore, the same genotypes were the best from the genotype by stress tolerance indices (GSTI) approach view. Genotype 37 (Sakha 95) was superior to the GYT selection method, with physiological parameters and spectral reflectance indices. Likewise, we can identify this genotype as low-water-tolerant based on GSTI, GYT, and SRI results and recommend involving it in the drought breeding program. Citation: Darwish, M.A.; Elkot, A.F.; Elfanah, A.M.S.; Selim, A.I.; Yassin, M.M.M.; Abomarzoka, E.A.; El-Maghraby, M.A.; Rebouh, N.Y.; Ali, A.M.
... The criteria for selecting the best accessions for all traits were introduced using the stress tolerance index (STI) and the average number of ranks (ASRs). The best accessions for this assessment had the highest STI and lowest ASR values [23,28]. ...
Soil contamination by heavy metals such as cadmium (Cd), which is present as a result of agricultural and industrial practices, is a critical problem in many countries around the world. High Cd concentrations in crops during the seedling stage can have a negative impact on performance and growth. The aim of the present study, which involved 59 barley accessions, was to investigate the effects of different Cd concentrations (125, 250, and 500 µM) on the responses of the barley accessions and to identify the biomarker parameters that would aid in the early growth stage selection of the best-performing accession. Barley accessions differed significantly in their morphological and physiochemical characteristics. Compared to the untreated plants, treatments with Cd lowered germination percentages by 1.75–64.28%, 1.67–46.62%, and 1.66–61.90% for concentrations of 125, 250, and 500 μM, respectively. The average of all genotypes showed significant reductions in root length, shoot length, and fresh weight of seedlings, ranging from 37.08% to 77.88%, 18.70% to 44.10%, and 7.69% to 35.87%, respectively. In comparison to untreated plants, the average seed water absorption (WU) increased across all accessions by 42.21% and 20.74%, respectively, under Cd-125 and Cd-250 stress conditions. In contrast, all biochemical measurements increased when Cd concentrations were elevated, with the exception of guaiacol peroxidase (GPA) and catalase (CAT). Across all genotypes, the mean of proline (PC) and sugar (SSC) contents showed the largest increases (123% for PC and 98.63% for SSC) under the Cd-500 stress condition. Three barley accessions: Acsad-14, ABN, and Arabi Aswad, were found to be the most tolerant accessions under all cadmium exposure, whereas the performance of the other tested accessions: Black-Kalar, Bujayl 1-Shaqlawa, and Black-Chiman was inferior. The OMIC analysis identified the biomarker parameters for differentiating the high, moderate, and low tolerant groups as the WU for Cd-125 stress, GPA, WU, CAT, total phenolic content for Cd-250 stress, and all physiochemical traits, with the exception of the CAT feature for Cd-500 treatment. The majority of trait pairings showed significant correlations. Hence, Acsad-14, ABN, and Arabi Aswad barley accessions that had great performance under cadmium conditions can be candidates for selection in a breeding program to improve the growth of plants and output in lands infected by cadmium. It can be concluded that seed water uptake, guaiacol peroxidase, and proline content were biomarker traits that would aid in the early growth stage selection of the best-performing accession under Cd stress conditions.
... To better evaluate and identify stress-tolerant/susceptible genotypes, the following stress indices were calculated by applying the formulas reported in Table 1 (as from [60]): tolerance index (TOL), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HM), stress susceptibility index (SSI), yield index (YI), yield stability index (YSI) and relative stress index (RSI). All the indices are yield-based and commonly used to estimate the tolerance or susceptibility to abiotic stresses (e.g., [60][61][62]). ...
... To better evaluate and identify stress-tolerant/susceptible genotypes, the following stress indices were calculated by applying the formulas reported in Table 1 (as from [60]): tolerance index (TOL), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HM), stress susceptibility index (SSI), yield index (YI), yield stability index (YSI) and relative stress index (RSI). All the indices are yield-based and commonly used to estimate the tolerance or susceptibility to abiotic stresses (e.g., [60][61][62]). The Plant Abiotic Stress Index Calculator (iPASTIC) software [60] was used to calculate all indices, by setting the input model with mean data derived from GYP, obtained in both control (Yp) and stress (Ys) conditions. ...
... All the indices are yield-based and commonly used to estimate the tolerance or susceptibility to abiotic stresses (e.g., [60][61][62]). The Plant Abiotic Stress Index Calculator (iPASTIC) software [60] was used to calculate all indices, by setting the input model with mean data derived from GYP, obtained in both control (Yp) and stress (Ys) conditions. Through the estimation of the average of ranks (AR), based on all indices' values, iPASTIC also allowed for a comprehensive and reliable ranking of genotypes (the lower the AR value, the more stress tolerant the genotype). ...
Abiotic stress occurrence and magnitude are alarmingly intensifying worldwide. In the Mediterranean basin, heat waves and precipitation scarcity heavily affect major crops such as durum wheat (DW). In the search for tolerant genotypes, the identification of genes/QTL in wild wheat relatives, naturally adapted to harsh environments, represents a useful strategy. We tested three DW-Thinopyrum ponticum recombinant lines (R5+, R112+, R23+), their control sibs lacking any alien introgression, and the heat-tolerant cv. Margherita for their physiological, biochemical and yield response to heat stress (HS) application at anthesis, also in combination with water-deficit stress applied from booting until maturity. Under HS, R5+ and R112+ (23%- and 28%-long 7el1L Th. ponticum chromosome segment distally inserted on DW 7AL, respectively) showed remarkable stability of the yield-related traits; in turn, R23+ (40%-long 7el1L segment), despite a decreased grain yield, exhibited a greater spike fertility index and proline content in spike than its control sib. Under water-deficit + HS, R5+ showed the highest increment in water use efficiency and in flag leaf proline content, accompanied by the lowest yield penalty even vs. Margherita. This research confirms the value of harnessing wild gene pools to enhance DW stress tolerance and represents a starting point for elucidating the mechanisms of Thinopyrum spp. contribution to this relevant breeding target.
... The combined analysis of variance for grain yield under non-stress and waterlogging stress conditions was performed based on RCBD design by SAS (SAS, 2003). The several stress tolerance indices were computed based on grain yield under non-stress and waterlogging stress using an online toolkit, iPASTIC (Pour-Aboughadareh et al., 2019). In this study, to evaluation of faba bean genotypes for waterlogging tolerance was used nine selection indices including SSI, GMP, MP, HM, TOL, STI, YI, and RSI. ...
... Correlations coefficients analysis were calculated to established interrelationships among grain yield for each irrigation treatment and waterlogging tolerance indices using an online toolkit, iPASTIC (Pour-Aboughadareh et al., 2019). For specifying the waterlogging-tolerant genotypes with high yielding potential in non-stress and stress environments, a three-dimensional graph based on yield in non-stress and waterlogging stress and the best waterlogging-tolerance indices was performed by SAS method. ...
Waterlogging stress is one of the most important abiotic stresses in Mediterranean conditions such as north of Iran. The tolerance of faba bean to waterlogging may vary between genotypes. This study investigated the effects of 10 days of waterlogging on grain yield for 21 faba bean genotypes at two stages (flowering and pod-filling stages) during 2016-2017 and 2017-2018 under farm conditions. A randomized complete block design with three replications was used at three field sites (normal and waterlogging sites). Nine indices of endurance were calculated in normal and waterlogging conditions. The results indicated that waterlogging stress reduced the faba bean grain yield. Also, the negative waterlogging effect at flowering stage is more than pod-filling stage. Correlation coefficients and principal component analysis (PCA) results revealed that mean productivity (MP), geometric mean productivity (GMP), harmonic mean (HM), and stress tolerance index (STI) indices could be effectively used for screening of waterlogging stress tolerant genotypes. Waterlogging was caused to decrease significantly grain yield in all genotypes. According to results of three-dimensional graphs the genotypes G21, G18, G15, G6 and G2 with an average yield 4806, 4815, 4789, 4686 and 4681 kg.ha-1 , respectively, were selected as waterlogging stress tolerance and suitable grain yield under non-stress and waterlogging stress (waterlogging stress in flowering and pod-filling stages) conditions. Therefore, these genotypes can be used as source of genes in faba bean breeding programs to obtain tolerant cultivars and cultivation in the areas under waterlogging stress.
... Te normalized diference vegetative index (NDVI) was measured using a hand-held green seeker optical sensor. Te relative GY, BY, and NDVI readings were calculated by dividing the GY, BY, and NDVI readings of a genotype under low N by the GY, BY, and NDVI readings of the same genotype under optimal N. Te stress tolerance indices were computed as described by [29] as per the following equations: International Journal of Agronomy 3 ...
... Taking absolute grain yield as a screening parameter, ge- notypes 155, 101, 154, 196, 105, 140, 30, 147, 105, 84 121, 158, 191, 142, 27, 10, 80, 164, and 45 were found to be the most desirable genotypes because they were grouped as efcient and responsive to N and produced higher grain yield under both N defciency and sufciency. On the other hand, genotypes 6,22,29,79,171,199,151,193,102,47, and 3 were considered as being among the most inefcient and nonresponsive to N application ( Figure 1 and Table 4) because they produced lower grain yield under both optimum and low N conditions. Similar to these results, [10,44,45] used grain yield to categorize diverse wheat genotypes as efcient and responsive, efcient and nonresponsive, inefcient and responsive, and inefcient and nonresponsive to N, zinc, and manganese, respectively. ...
Development of low-nitrogen (N) tolerant and N-responsive durum wheat genotypes is required since nitrogen efficiency has emerged as a highly desirable trait from economic and environmental perspectives. Two hundred durum wheat genotypes were evaluated at three locations under optimum (ON) and low (LN) nitrogen conditions to screen genotypes for low-nitrogen tolerance and responsiveness to an optimum N supply. The results showed significant variations among the durum wheat genotypes for low-N tolerance and responsiveness. The average reduction in grain yield under the LN condition was 48.03% across genotypes. Only 17% of the genotypes tested performed well (grain yield reduction <40%) under LN conditions. Based on the absolute grain yield, biomass yield, and normalized difference vegetative index value, on average, 32, 14, 17, and 37% of the tested genotypes were classified as efficient and responsive, efficient and nonresponsive, inefficient and responsive, and inefficient and nonresponsive, respectively. Considering the absolute and relative grain yield, biomass yield, normalized difference vegetative index values, and stress tolerance indices as selection criteria, 17 genotypes were chosen for subsequent breeding. Among the screening indices, geometric mean productivity, stress tolerance index, yield index, and stress susceptibility index exhibited positive and significant correlations with grain yield under both N conditions; hence, either of these traits can be used to select low-N-tolerant genotypes. The common genotypes identified as LN-tolerant and responsive to N application in this study could be used as parental donors for developing N-efficient and responsive durum wheat varieties.
... It could be challenging to isolate tolerant genotypes using just one indication. ASR for all indices may be utilized to identify potentially superior genotypes; the lower the value, the more desirable the genotype (Pour-Aboughadareh et al. 2019). Swarna Sub1 has the lowest ASR value in our sample, followed by Nagina-22. ...
... 5). These indices can be used to select genotypes with high potential yield and drought tolerance, as shown by the highly significant correlations between them and yield under drought conditions (Pour-Aboughadareh et al. 2019). ...
The purpose of the current research was to investigate 6 rice genotypes (Swarna-Sub1, IR-44-Sub1, IR-07-F289-Sub1, Ciherang-Sub1, Nagina-22 and IR-64) under reproductive stage drought stress. Field experiment was laid out with triplicate randomized complete block design in split-plot fashion. The drought stress was applied at booting stage to onwards for 30 days. Results of ANOVA indicated that drought stress significantly reduced the overall performance of all the genotypes under study. Correlation studies showed that grain yield per plant was highly significant and positively correlated with fertile spikelets per panicle, fertility percentage and harvest index while highly significant negatively correlated with panicle length and sterile spikelets per panicle. Nine drought stress indices were also calculated for grain yield per plant. GM, HM, YI, YSI and RSI showed highly significant positive correlation with crop performance under drought conditions thus these indices can be used for the selection of drought tolerant genotypes. The first two principal components (eigenvalues > 1) described 99.29% of the overall variation in yield performance, according to PCA data. Nagina-22 and Swarna Sub1were identified as superior genotypes based on the results of indices and their association and PCA analysis. The information obtained from this study can be exploited in future drought screening and breeding programs.
... Additionally, iPASTIC [54], an online toolset, calculated the stress tolerance index (STI) to screen better-performing genotypes under water stress conditions, and the genotypes with the lowest average sum of rankings (ASR) were considered the most tolerant. ...
... Using iPastic, an online toolbox created by Pour et al. [54], drought stress indices were calculated based on dry matter. According to the STI bar graph (Figure 5 ...
... Using iPastic, an online toolbox created by Pour et al. [54], drought stress indices were calculated based on dry matter. According to the STI bar graph ( ...
Drought tolerance of Brassica crops can be genetically improved by establishing plant ideotypes with improved yield responses associated with agronomic traits and biochemical markers. The objective of this study was to compare 20 Brassica oleracea L. accessions grown under two different water treatments (100% and 35% reintegration of evapotranspiration by irrigation) to select potential tolerant genotypes for organic cultivation based on several agronomic and biochemical parameters measured in response to drought stress. Significant differences were registered for the genotype and the irrigation regime and for their interaction (p < 0.0001 ***). A principal component analysis was performed to summarize the correlations among the analyzed phytochemicals and the stressed and not stressed genotypes and highlighted the importance of the antioxidant compounds as stress biomarkers. The present results showed that drought significantly reduces growth parameters and increases the amount of ascorbic acid and polyphenols compared to the irrigated control. Additionally, the results show that antioxidant metabolism increased by drought in some genotypes while others maintained a good biomass production by increasing the value of growth parameters considered. Based on the average sum of ranks (ASR) of morpho-physiological and biochemical parameters, the genotypes CR, CC, and BH were determined to be the most drought tolerant, whereas CI5, BU, and CV1 were determined to be the most susceptible. Due to the potential of these genotypes, further molecular and cellular research will be carried out to identify the genetic marker associated with the water stress response.
