Step-wise selection using high throughput phenotyping platform (HTTP) and stress tolerance indices as an approach for improving drought tolerance in groundnut (Arachis hypogaea L.)

Step-wise selection using high throughput phenotyping platform (HTTP) and stress tolerance indices as an approach for improving drought tolerance in groundnut (Arachis hypogaea L.) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Step-wise selection using high throughput phenotyping platform (HTTP) and stress tolerance indices as an approach for improving drought tolerance in groundnut ( Arachis hypogaea L.) Ankush Purushottam Wankhadea, Ashutosh Purohit, Seltene Abady, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5503687/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Drought stress is a major production constraint of groundnut in Africa and Asia where it is largely grown as rainfed crop. The experiments aim to design an early testing approach for drought tolerance in the groundnut breeding pipeline to ensure sustainable production. A population of 600 multi parent advanced generation inter-cross (MAGIC) lines (MLs) (F 8/9 generation) and 100 advance breeding lines (ABLs) were studied in LeasyScan, a high throughput phenotyping platform (HTPP) to assess early canopy growth, and under a managed stress environment (MSE). MSE ensures uniform water application in well-watered and water-stressed plots, while intermittent drought is imposed in water-stressed plots from 1000 0 cumulative thermal time (CTT) during pod-filling stage. Digital biomass, leaf area 3D and plant height measured under HTPP recorded high heritability along with high genetic gain and were identified for use as selection criteria for early canopy vigour. The second selection criteria is Mean Score Index (MSI) (1 to 10 scale), which accounts for both resilience and productivity capacity indices (RCI and PCI), with the MSI ranging from 1.4 to 8.4. Based on results, a two-step selection approach is proposed for selection of traits required for adaption under drought stress. The approach involves HTPP (LeasyScan) to select early canopy vigour followed by selection based on MSI under MSE. MSE is field based and expensive, hence screening of a large number of selection candidates under HTTP helps to select a relatively small subset of early vigour lines for screening under MSE for agronomic performance. Hydrology Drought tolerance early canopy vigour groundnut HTPP LeasyScan managed stress environment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Groundnut or peanut ( Arachis hypogaea L.) is an important crop used for food, oil, feed, fodder, confectionery and industrial purposes and grown in 116 countries across the world, and ~ 90% of the total groundnut production in the world comes from Africa and Asia (FAOSTAT 2022). The major production constraints of groundnut in the developing countries are abiotic stresses, lack of efficient seed systems, low input usage, and socio-economic issues (Variath and Janila, 2017 ). Among the stresses, drought stress is most important constraint in groundnut production across the globe. Drought stress has a significant impact on crop production, which further elevates the other abiotic and biotic stresses (Ceccarelli et al., 2020), and more abiotic stress events are predicted in the future as a consequence of changing climate (Cooper et al., 2023). Thus, the groundnut breeding programs in different countries of Asia, Africa, Americas’ and Australia focus on genetic enhancement for drought tolerance. Groundnut is predominantly grown under rainfed conditions of arid (below 400 mm rainfall) and semi-arid regions (400–800 mm rainfall) of Asia and Africa where drought occurs frequently, affecting the critical stages of pod development, and causing major penalty in yield (Nigam et al., 2005 ). Besides, drought stress predisposes the pods to pre-harvest Aspergillus fungus infection, which produces a potent carcinogen, aflatoxin (Sibakwe et al., 2017 ). Drought stress during mid-growth, especially during flowering and pegging can reduce the pod yield by up to 44% (Abady et al., 2021 a). Drought stress causes poor filling of pods resulting in lower shelling outturn (kernel weight from a unit weight of pods expressed as percentage) and therefore, several researchers have suggested that selection for high shelling outturn or pod and kernel yield can be rewarding in groundnut breeding to improve drought tolerance (Abady et al., 2021 b). In a given groundnut growing agro-ecology, the frequency and intensity of drought varies over a given period of time, and therefore not all years are drought years. For such scenarios, that are most common for groundnut growing agro-ecologies, groundnut varieties that produce superior yield in normal years, and minimize the yield loss during drought years are needed. This requires a selection strategy in groundnut breeding that must ensure the development of varieties that have good production capacity under normal condition, and resilience capacity during stress to minimize the yield loss during drought years. Environmental characterization identified seven homogenous production units (HPUs) for groundnut in India, and drought is a major production constraint in most of the HPUs, particularly in HPU6 comprising the southern states of India, Andhra Pradesh and Karnataka (Hajjarpoor et al., 2021 ). While in HPU1 comprising of Rajasthan, the supplemental irrigation overcomes the effect of drought that could otherwise reduce the pod yield by up to 73%. The conventional method of selection depending on yield may not be screening better performing genotypes under stress (Blum, 2005 ) unless the component traits of adaptation to drought are considered in selection. On the other hand, selections in groundnut performed based on the water-saving physiological traits without considering yield may not select the high-yielding lines under either normal or drought stress condition (Nigam et al., 2005 ). Therefore, a dynamic selection strategy supported by advanced high throughput technologies and knowledge of physiological aspects of adaptation to reduce the gap between yield under potential condition and drought stress condition in groundnut is needed. Drought tolerance, a complex trait is governed by several genes in groundnut. Therefore, genotype by environment interaction often comes in the way of selection for drought tolerance to develop tolerant cultivars (Ravi et al., 2011 ; Bacharou et al., 2019) and thus requires extensive multi-environment testing (MET) in a target population of environment (TPE). On the other hand, in the groundnut breeding programs, the selection for drought tolerance is often delayed to screen a small number of lines in a TPE. This hampers the progress of genetic enhancement for drought tolerance, as the number of selection candidates subjected to screening for drought tolerance are limited, and there is a possibility of rejecting the desirable segregants in early generations as advancement in early generation were based on yield performance under normal conditions. While selection in a managed stress environment (MSE) is a good option, it is resource intensive hence used for late generation testing in the breeding programs. Most of the breeding programs do not test in the MSE. Based on the yield data recorded on the genotypes in the well-watered (WW) and water-stressed (WS) plots of MSE, the genetic differences for drought tolerance can be assessed by different scoring and index methods. Stress tolerance index (STI) identifies the genotypes that produce superior yield in both, WW and WS conditions (Fernandez, 1992), and geometric mean productivity (GMP) measures the relative performance as the intensity of drought stress varies in severity (Rosielle and Hamblin 1981 ). Thiry et al., ( 2016 ) proposed a method based on the scoring scale to measure Productivity Capacity Index (PCI) and Resilience Capacity Index (RCI) to allow selection of genotypes that combine high production capacity and resilience to the stress. Previous research has indicated the ability of groundnuts to withstand drought depends on their water uptake capacity and transpiration efficiency (Ratnakumar et al., 2009 ). Water uptake capacity is largely influenced by the functional characteristics of the roots, while transpiration efficiency is influenced by canopy dynamics. According to Vadez et al., (2016), there is a strong positive correlation between groundnut biomass and transpiration efficiency under drought stress conditions. This phenomenon is related to the water extraction capacity of the genotypes and the mobility of water from the soil for stem elongation and biomass production. In a transgenic event in wild-type JL 24, where the "rd29A:DREB1A" gene was overexpressed under stress, a close association was established between root length density, higher root-to-shoot ratio, and water uptake, resulting in increased biomass accumulation in the roots and pods (Shridhar et al., 2012). Drought stress affects various leaf morphological and anatomical features in groundnuts, and drought tolerance in groundnuts has been linked to early canopy traits and stomatal closure (Sinclair et al., 2019), decreased leaf area (Reddy et al., 2003 ), maintenance of vegetative growth (Tardieu and Tuberosa, 2010 ), and photosynthesis (Zhang et al., 2021 ). According to Ratnakumar et al., ( 2009 ), drought-adapted groundnut genotypes under water stress conditions maintain a high harvest index and small leaf canopy, meaning that their reproductive processes are less affected and less water is used during the dry spell. The crop growth rate (CGR) during the critical pod formation stage affects the dry matter accumulation in pods (Oteng-Frimpong et al., 2019 ). With the advent of high-throughput 3D imaging platforms, such as LeasyScan, which captures leaf area development continuously, it is possible to measure plant canopy traits associated with water use, such as leaf area, leaf area index, and transpiration (Vadez et al., 2016). The study attempts to develop a ‘selection strategy’ for drought tolerance breeding in groundnut that identifies genotypes with early canopy vigour, combined with high production capacity as well as resilience. To develop a strategy, groundnut populations that includes a multi-parent advanced generation inter-cross (MAGIC) population (600 F 9/10 lines, and advanced breeding lines (260) were tested under a high-throughput phenotyping platform (HTPP) “LeasyScan” ( https://gems.icrisat.org/leasyscan ) for early canopy traits, and under an optimally managed MSE assessed. Based on the results obtained, a ‘selection strategy’ is proposed for use in groundnut breeding programs to achieve genetic enhancement for drought tolerance. 2. Materials and methods 2.1. Plant material A MAGIC population comprised of 600 F 9/10 MLs, eight founder parents, and 12 checks; and two sets of advanced breeding lines (ABLs) viz., ABLs-1 (100) ABLs (160). The ABLs-2 used in the study have high oleic acid content (> 75%), tolerance to foliar fungal diseases (rust and late leaf spot), and high yield potential. The founder parents of MAGIC population are, ICGVs 91114, 06040, 00440, 00308, 05155, and 88145, GPBD 4, and 55–437 (Wankhade et al., 2023 ) and the MAGIC population was developed using a 8-way crossing schema. 2.2. Spatio-temporal canopy traits We utilized a powerful high-throughput crop phenotyping platform called the LeasyScan system ( http://gems.icrisat.org/leasyscan/ ; Vadez et al., 2015 ) established at ICRISAT, Patancheru, India, (17_300N, 78_160E; altitude 549 m. a. s. l.). LeasyScan (Phenospex, The Netherlands; https://phenospex.com/ ) is a unique multispectral based continuous plant monitoring and phenotyping system that generates 3D point clouds in every 2 h on each of the 4800 sectors. Currently, the algorithms have been validated to process 3D point clouds for 9 canopy dynamics traits i.e. leaf area [mm²], plant height [mm], light penetration depth [mm], projected leaf area (unshaded leaf area; mm²), height max (mm), digital biomass (mm³), leaf angle (°), leaf inclination (mm²/mm²) and leaf area index (mm²/mm²). One of the key applications of the LeasyScan phenotyping platform is that it allows for the precise spatio-temporal monitoring and automated measurements of plant canopy to generate a number of traits related to canopy development pattern, early vigour and growth rate in the course of plant growth (Vadez et al., 2015 ). Most importantly, it offers the opportunity to phenotype large population on same time under outdoor natural plants under uncontrolled growing environment similar to the field conditions. Plants were grown under well-watered conditions in a sector area of 40 × 60 × 30 cm, i.e. approximately a quarter square meter called mini-plots. Each mini-plot contains 65 kg of soil (Vertisol) collected from the ICRISAT farm. The MAGIC population of 620 genotypes and 100 ABLs along with elite checks planted in alpha lattice design in 4 replications was tested on platform from 21 June to 18 July 2019. The average temperature and relative humidity varied between T 320.20/22.87°C and RH%-62.15/86.4% day/night during the experimentation. Two weeks after sowing, plants were thinned to maintain a uniform plant count of 4 plants per sector. Plant count was recorded after the final thinning. Plants were watered either early in the morning or late in the afternoon. Top dressing was done with di-ammonium phosphate (300 mg kg − 1 of soil). Plants were harvested after 6 weeks of sowing. 2.3. Field screening in a managed stress environment (MSE) The MAGIC population was evaluated in MSE for two seasons, PR 2018-19 and PR 2021-22, where the population size was 620 and 574, respectively. ABLs-2 (160) were screened under a managed stress condition. The experiments were conducted in alpha lattice design with two replications each under WS and WW condition with a plot size of 2m×2 rows. Phenotypic data for days to 50% flowering (DFF), SPAD (soil plant analysis development) chlorophyll meter reading (SCMR), dry pod yield (Yield), shelling percent (SP) and hundred kernel weight (HKW) were recorded. The pod yield data for MAGIC population was recorded as dry pod yield per plant, which was calculated by dividing the yield obtained by the number of plants, whereas in PR 2021 pod yield per plot was recorded in MAGIC populations and ABLs-2. 2.4. Managed stress environment (MSE) The MSE comprises of a WS and WW plots in the same precision experimental field with same soil physical and chemical properties, and nutrient status established at ICRISAT, India (17.3850° N, 78.4867° E and 545 meter above mean sea level. The WS plots were exposed to mid-season moisture stress by with-holding 2–3 irrigations from 1000 growing degree days (GDD) corresponding to mid-season, which was 82 days after planting (DAP) during 2018-19 and 76 DAP during 2021-22. In order to avoid complete wilting, a single irrigation was given to WS plots during the stress period and no further irrigations were provided till harvesting. While WW plots were normally irrigated till harvest. Decision on irrigation in WS plots was taken by referring to weather and soil moisture data. Visual wilting symptoms and permanent wilting point (PWP), (which was determined to be 8.94% for alfisol, using a pressure plate extractor (klute et al., 1986) were used as an indicator to resume irrigation in WS plots. Neutron probes (Neutron Probe Smart503, ICT international) and time domain reflectometry probes (TRIME®-FM) were used for the measurement of soil moisture. Optimal crop stand was ensured using sprinkler irrigation for an initial period of 20–25 DAP. Thereafter, irrigations till harvest were given using drip lines that allowed to have better control over supply of water and check the seepage from WW to WS plots. Irrigations in terms of time and quantity were measured during each irrigation. 2.5. Quantification of imposed stress This study uses a one-dimensional water balance model ‘Water Impact Calculator’ (WIC) developed by ICRISAT to analyse the water balance components (Garg et al., 2016 ). The WIC is a generic decision-making tool which could be applied to any land use and cropping system by providing minimum sets of biophysical (soil, weather and crop growth) and crop management inputs (Garg et al., 2016 ). The model calculates the daily water balance as: R + I = DP + ET + ΔS Where R = rainfall (mm), I = irrigation (mm), DP = Deep percolation (mm), ET = evapotranspiration (mm) and ΔS = change in soil moisture storage (mm). Deep percolation, evaporation and transpiration are considered as factors in WIC. The crop water requirement (CWR) for a given day is calculated as: CWR = Kc*ETo Where Kc = crop coefficient and ETo (mm/day) = reference crop evapotranspiration. The root zone depth is a dynamic variable and is controlled by crop growth stage (days after sowing) as defined by Allen et al. , (1998). Inputs of water in the experimental plots were measured by measuring irrigation hours and discharge rates of the sprinkler and drip system (litres per min). Sowing and harvesting dates along with irrigation amount were provided in the model to estimate water budget component at daily time scale. Model was run for i) auto-irrigation set up; ii) with stressed imposed plots and iii) without stressed imposed plots. Auto irrigation is a situation in which model considers no-stress condition and automatically apply required irrigation to the crop when available residue moisture of the root zone is not able to meet crop water requirements. 2.6. Selection indices A novel scoring scale has been adopted which takes in to account productivity capacity index (PCI) – the mean yield of different genotypes in both WS and WW environment, and resilience capacity index (RCI) – yield penalty in WS condition compared to WW condition. Genotypes are categorized into different ranks based on a scale on 1–10, where genotypes with a score of 10 are considered as superior for drought adaptation (Thiry et al., 2016 ). 2.7. Data analysis Combined and treatment-wise analysis of variance was performed to test the significance of genotype, treatment, and genotype x treatment effects for LeasyScan and managed stressed environment using SASv9.4 Mixed model (SAS Institute Inc. 2018) procedure. The treatment effect was considered as the fixed while genotype, replication, and block were consider as random effect. The individual treatment variances were estimated and modelled to error distribution using Residual Maximum Likelihood Estimate (REML) method. Best Linear Unbiased Predictors (BLUPs) were estimated for the main and interaction effects of treatment and genotype from the combined and treatment-wise analysis. Using Cullis (Cullis et al., 2006 ) method, Broad-sense heritability was calculated for treatment-wise analysis. Treatment-wise Broad sense heritability (H 2 ) $$\:{{H}^{2}}_{Cullis}=1-\frac{{{\stackrel{-}{v}}_{\varDelta\:}}^{Blup}}{2\text{*}{{\sigma\:}_{g}}^{2}}$$ Broad sense heritability for pooled analysis $$\:{H}^{2}={{\sigma\:}_{g}}^{2}+\raisebox{1ex}{${{\sigma\:}_{gt}}^{2}$}\!\left/\:\!\raisebox{-1ex}{$t$}\right.+\raisebox{1ex}{${{\sigma\:}_{e}}^{2}$}\!\left/\:\!\raisebox{-1ex}{$rt$}\right.$$ Where r and t are number of treatments and number of replications respectively, \(\:{{\sigma\:}_{g}}^{2}\) , \(\:{{\sigma\:}_{gt}}^{2}\) and \(\:{{\sigma\:}_{e}}^{2}\:\) are variance components of genotype, genotype x treatment and error respectively. Karl Pearson’s correlation coefficients between early canopy traits were calculated using SAS Proc corr (SAS Institute Inc. 2018) procedure. 3. Results 3.1. Variability and distribution for early canopy traits Variance components for plant height (PH), leaf area 3D (LA3D), digital biomass (DBM), leaf area index (LAI), projected leaf area (PLA), light penetration depth (LPD), leaf angle (LA) and leaf inclination (LI) measured under HTTP showed significant differences (p 90%), DBM, LA3D, LAI, PLA, and LPD also had high genetic advance (> 20%) as percent of mean (Table 1 a and Table 1 b). Table 1 a Analysis of variance (ANOVA) and genetic parameters of early canopy traits under Leasyscan for MAGIC population Population Effect DBM [mm³] LAI [mm²/mm²] PH [mm] PLA [mm²] LA [°] LI [mm²/mm²] LA3D [mm²] LPD [mm] MAGIC Replication (Rep) 0 0 0.44 0 0 0 0 0.26 Block (Rep) 1.69E + 11 0.00 7.05 9880023 0.23 0.00 13230448 3.03 Genotype 3.40E + 12** 0.00** 163.46** 47796431** 0.38** 0.00** 85339728** 60.19** Residual 6.79E + 10 0.00 1.10 2984478 0.12 0.00 4137411 0.61 H 2 (%) 99.00 99.00 99.00 98.00 92.00 96.00 99.00 99.00 Range 3621581–14731453 0.19–0.41 72.35-144.68 33250–76645 41.79–45.94 1.39–1.49 49608–106654 52.65-100.57 Mean 8752184.29 0.29 109.27 52592.47 43.76 1.44 76067.85 75.86 CV % 2.98 2.86 0.96 3.28 0.80 0.45 2.67 1.03 GA % of Mean 43.58 25.37 24.14 27.46 3.10 2.32 25.29 21.12 DBM, digital biomass; LAI, leaf area index; PH, plant height; PLA, projected leaf area; LA, leaf angle; LI, leaf inclination; LA3D, leaf area 3D; LPD, light penetration depth; H 2 (%), broad sense heritability; CV %, coefficient of variance; GA % of Mean, genetic advance as percentage of mean; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Table 1 b Analysis of variance (ANOVA) and genetic parameters of early canopy traits under Leasy scan for ABLs_1 Population Effect DBM [mm³] LAI [mm²/mm²] PH [mm] PLA [mm²] LA [°] LPD [mm] ABLs_1 Replication (Rep) 4.09E + 12** 0.00853** 321.1559** 1.79E + 08** 4.67E + 08** 238.11** Genotype 1.08E + 12** 0.00** 192.65** 60110363** 1.41E + 08** 129.22** Residual 2.93E + 10 0.00 4.51 2260234 5769052 2.99 H 2 (%) 89.96 77.37 91.25 86.48 85.42 91.34 Range 657610–3469740 0.04–0.17 41–83 7378–30171 11413–47438 26.7–61.3 Mean 1974772 0.1 61.45 19274.07 29469.04 44 CV % 8.66 10.83 3.45 7.8 8.15 3.92 GA % of Mean 50.70 37.55 21.96 37.79 37.56 25.13 DBM, digital biomass; LAI, leaf area index; PH, plant height; PLA, projected leaf area; LA, leaf angle; LI, leaf inclination; LPD, light penetration depth; H 2 (%), broad sense heritability; CV %, coefficient of variance; GA % of Mean, genetic advance as percentage of mean; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. All the early canopy traits recorded in ABLs-1 (Fig. 1 ) and MLs (Fig. 2 ) measured under HTPP were normally distributed and showed continuous variations. Variation was observed in MLs and ABLs-1 for the early canopy traits, viz. , DBM (3,621,581 to 14,731,453 mm³ in MLs and 657,610 to 3,469,740 mm³ in ABLs), LAI (0.19 to 0.41 mm²/mm² in MLs and 0.04 to 0.17 mm²/mm² ABLs), PH (72 to 145 mm in MLs and 41 to 83 mm in ABLs), PLA (33,250 to 76,645 mm² in MLs and 7,378 to 30,171 mm² ABLs), LA (38.75 to 45.94 ° across MLs and ABLs), LA3D (49,608 to 106,654 mm² in MLs and 14,529 to 42,327 mm² ABLs) and LPD (52.65 to 100.57 mm in MLs and 26.7 to 61.3 mm in ABLs) and LI(1.39 to 1.49 in MLs mm²/mm² and not recorded in ABLs) (Table 1 a and Table 1 b). 3.2. Correlation between early canopy traits recorded in MAGIC population and ABLs_1 The person’s correlation coefficients calculated between the eight early canopy traits in a MAGIC population (Upper diagonal in Table 2 ) and ABLs_1 (Lower diagonal in Table 2 ) were used to select independent early canopy trait that can be used in groundnut breeding as selection criteria. Digital biomass, plant height, leaf area 3D, leaf area index, projected leaf area and light penetration depth were strongly correlated with each other with the correlation coefficient (r 2 > 0.5) and non-correlated or weakly correlated with leaf angle and leaf inclination (r 2 ≤ 0.2 or -0.2) in both MAGIC population and ABLs_1. Whereas, leaf angle had significant negative correlation with leaf inclination (r 2 =-0.91). Table 2 Correlation among early canopy traits in MAGIC population and ABLs_1 MAGIC Trait DBM [mm³] PH [mm] LA [°] LA3D [mm²] LAI [mm²/ mm²] PLA [mm²] LI [mm²/mm²] LPD [mm] ABLs_1 DBM [mm³] 1 0.87 ** 0.09 * 0.87 ** 0.86 ** 0.86 ** -0.09 * 0.79 ** PH [mm] 0.83** 1 0.03 0.63 ** 0.63 ** 0.61 ** -0.03 0.91 ** LA [°] 0.12 0.15 1 0.16 ** 0.15 ** 0.22 ** -0.91 ** 0.03 LA3D [mm²] 0.92** 0.69** 0.12 1 0.99 ** 0.97 ** -0.15 ** 0.57 ** LAI [mm²/ mm²] 0.85** 0.64** 0.14 0.94** 1 0.97 ** -0.16 ** 0.57 ** PLA [mm²] 0.91** 0.67** 0.13 0.99** 0.94** 1 -0.22** 0.54 ** LI [mm²/mm²] - - - - - - 1 -0.04 LPD [mm] 0.81** 0.98** 0.17 0.67** 0.62** 0.64** - 1 DBM, digital biomass; PH, plant height; LA, leaf angle; LA3D, leaf area 3D; LAI, leaf area index; PLA, projected leaf area; LI, leaf inclination; LPD, light penetration depth; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Above-diagonal – correlation among early canopy traits in MAGIC population, whereas, below-diagonal – correlation among early canopy traits in ABLs_1 population. 3.3. Evaluation of phenological, physiological and yield attributing traits in MAGIC population and ABLs under a managed stress environment Year-wise variance components of SCMR, PY, SP, and HKW were estimated for MAGIC population (PRs 2018-19 and 2021-22) and ABLs_2 (PR 2021-22) under WW and WS regimes. Both MLs and ABLs_2 showed significant (p < 0.01) differences among the genotypes for SCMR, PY, SP and HKW under both WW and WS conditions during PRs 2018-19 and 2021-22 (Table 3 a, Table 3 b, Table 4 a, Table 4 b, Table 5 a and Table 5 b). High broad sense heritability (H 2 > 60%) was recorded for SCMR, PY, SP, and HKW in MAGIC (PRs 2018-19 and 2021-22) and ABLs-2 (PR 2021-22) under both WW and WS condition except SP under WW condition during PR 2021-22 (Table 3 , Table 4 and Table 5 ). Pooled ANOVA for MAGIC population across water regimes showed a significant effect of drought stress (genotype by environment) on the genotypes for yield, SP and HKW during 2018-19 (Table 3 c) and yield and HKW in MAGIC population during 2021-22 (Table 4 c). ABLs-1 (100 genotypes) were evaluated during 2018/19 and 2019/20 post-rainy seasons at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. Analysis of variance for phenotypic traits under drought-stress and non-stressed conditions suggested significant (p < 0.05) difference among genotypes for plant height (cm), SCMR, shelling percentage, and highly significant differences(p < 0.001) for days to 50% flowering, number of primary branches, leaf relative water content, haulm weight (g plant − 1 ), hundred seed weight (g), pod weight (g plant − 1 ) (Seltene et al., 2021). Table 3 a Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2018-19 under WW condition. Effect SCMR PY SP HKW Replication 0 0.06 0.01 0.04 Block (Replication) 0 0 0.12 0 Genotype 10.86** 7.34** 34.52** 21.93** Residual - WW 3.51 0.83 8.90 5.66 H 2 (%) 85.76 94.05 88.97 88.56 Range 25.32–54.07 4.18–20.63 43.94–83.26 18.86–50.53 Mean 42.93 9.83 65.38 31.36 LSD 4.36 1.75 5.56 4.49 SCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H 2 (%), broad sense heritability; LSD, least significant difference; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Table 3 b Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2018-19 under WS condition. Effect SCMR PY SP HKW Replication 0 0.06 0.12 0.10 Block (Replication) 0.04 0.02 0 0 Genotype 43.31** 3.30** 58.64** 18.29** Residual - WS 4.84 0.64 8.47 5.95 H 2 (%) 94.51 91.15 93.26 86.01 Range 23.33–52.46 2.13–13.15 36.95–75.70 17.28–45.65 Mean 38.38 7.20 58.10 26.72 LSD 4.28 1.53 5.56 4.49 SCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H 2 (%), broad sense heritability; LSD, least significant difference; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Table 4 a Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2021-22 under WW condition Effect SCMR PY SP HKW Replication 0.05 23.14 6.32** 0.76 Block (Rep) 0.82 35.98 0.00 0.61 Genotype 26.44** 3724.21** 21.66** 23.43** Residual - WW 12.01 722.29 32.61 8.76 H 2 (%) 78.55 86.84 51.62 78.82 Range 24.03–49.50 27.67–347.40 45.48–64.33 15.05–41.71 Mean 38.28 143.52 57.14 27.75 LSD 6.61 61.44 8.98 6.18 SCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H 2 (%), broad sense heritability; LSD, least significant difference; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Table 4 b Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2021-22 under WS condition Effect SCMR PY SP HKW Replication 0.12 0.00 0.69 0.00 Block (Rep) 1.28 0.00 0.00 0.00 Genotype 14.85** 424.86** 247.34** 32.93** Residual - WS 5.79 79.78 69.06 10.03 H 2 (%) 72.23 83.64 78.37 79.47 Range 26.51–49.91 3.67-120.42 7.20–83.50 2.86–35.43 Mean 40.69 32.14 43.67 17.82 LSD 5.64 23.14 20.30 7.22 SCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H 2 (%), broad sense heritability; LSD, least significant difference; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Table 5 a Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for ABLs_2 during 2021-22 under WW Effect SCMR PY SP HKW Replication 0.32 0.00 3.01* 0.62 Block (Rep) 0.00 36.62 2.61 0.22 Genotype 21.68** 5193.05** 30.18** 50.83** Residual -WW 17.41 1423.82 40.88 24.48 H 2 (%) 67.26 86.87 57.09 79.23 Range 26.57–43.07 64.05-384.33 34.94–63.73 18.64–47.68 Mean 33.50 226.90 53.72 30.02 LSD 7.41 72.65 10.02 9.04 SCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H 2 (%), broad sense heritability; LSD, least significant difference; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Table 5 b Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for ABLs_2 during 2021-22 under WS Effect SCMR PY SP HKW Replication 0.07 1.48 0.00 0.00 Block (Rep) 0.26 2.07 0.00 0.72* Genotype 1.79 556.75** 74.62** 16.09** Residual - WS 11.73 128.28 15.67 4.25 H 2 (%) 20.91 87.73 88.09 85.38 Range 45.39–48.77 11.68-102.42 3.86–75.62 5.27–25.59 Mean 47.10 62.47 16.10 11.72 LSD - 23.01 8.30 4.27 SCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H 2 (%), broad sense heritability; LSD, least significant difference; Significance level: *P < 0.05, **P < 0.01, ***P < 0.001. Significant differences in the range for pod yield and yield attributes were observed in MAGIC population (For season PRs 2018-19 and 2021-22) (Fig. 3 ) and ABLs_2 (for season PR 2021) under managed stress conditions. In PR 2021-22, range for pod yield per plot recorded in WS for MLs (3.67 to 120.42g) and ABLs_2 (11.68 to 102.42 g) was much lower than the yield per plot recorded in WW for MLs (27.67 to 347.40 g) and ABLs_2 (64.05 to 384.33 g). The range recorded for yield attributes viz. , SP (MLs: 45.48 to 64.33%; ABLs: 34.94 to 63.73%) HKW (MLs: 15.05 to 41.71 g; ABLs: 18.64 to 47.68 g) in WW; and SP (MLs: 7.20 to 83.50%; ABLs: 3.86 to 75.62%), HKW (MLs: 2.86 to 35.43 g; ABLs: 5.27 to 25.59 g) in WS. Due to imposition of stress embryo of few genotypes were aborted, which in turn affected kernel development and resulted in highly shrivelled kernels. That is why few genotypes recorded vey low shelling percentage and hundred kernel weight. 3.4. Resilience Capacity Index (RCI), Productivity Capacity Index (PCI) and Mean Score Index (MSI) based selection of drought tolerant genotypes using early canopy traits from HTPP as filter Digital biomass, leaf area 3D and plant height recorded using LeasyScan platform were used as a filter to narrow down number of entries to be screened under managed stress condition in the field in the same order. The selected 240 MLs (40% of the screened MLs) recorded the RCI (1.5–9.5), PCI (-7.5-4.5) and MSI (3.4–8.2) in 2018 and RCI (1.5–6.5), PCI (-7.5-7.5) and MSI (3.8-7.0) in 2021. The 40% selected ABLs recorded the RCI (1.0–9.0), PCI (-6.0 to 6.5) and MSI (2.4 to 8.5) in 2018 and RCI (1 to 8.5), PCI (-7.5 to 5.5) and MSI (2.4 to 8.6) in 2019. Among the filtered 40% MLs the number of lines recorded RCI, PCI and MSI greater than or equal to 5 was 143, 0 and 149 out of 240, in 2018, and 6, 5, and 80 out of 230 in 2021, respectively. Among the filtered 40% ABLs, the number of lines recorded RCI, PCI and MSI greater than or equal to 5 was 19, 1, and 29 out of 40, in 2018, and 16, 1, and 24 out of 40 in 2021, respectively. The drought tolerant parent ICGV 02266 recorded RCI (4.5), PCI (-1) and MSI (5.4) in MAGIC population and RCI (4.5 to 6), PCI (0 to1) and MSI (5.4 to 6.6) in ABLs. A second drought tolerant founder parent, 55–437, which was one among the eight-founder parent recorded RCI (2 to 4.5), PCI (-2 to -1) and MSI (4 to 6) in different seasons for MAGIC population. 3.5. Water Budgeting Total crop water requirement for the crop period is estimated to be 421 mm in PR 2018-19 (November 2018 to March 2019) and 510 mm in PR 2021-22 (December 2021 to April 2022). Available moisture during sowing was about 57–60 mm in both seasons. Amount of irrigation provided was 1166 mm (PR 2018-19) and 763 mm (PR 2021-22) in WW plot and 780 mm (PR 2018-19) and 463 mm (PR 2021-22) in WS plots (Table 6 ). Significant amount of water was partitioned into deep percolation both in WS and WW plots. Simulation results further suggested that crop in WS experienced water stress during 14th, 15th, 17th and 18th weeks for 18 days in PR 2018-19, and 14th ,15th, 16th, 19th and 20th week for 45 days (Fig. 4 a and 4 b). Further Fig. 5 shows the daily crop water requirements with actual ET under moisture stressed plot and non-stressed plots. Daily crop water requirement for groundnut MAGIC population varied from 1 mm to 3 mm in PR 2018-19 depending on the growth stage. It is to be noted that 14-20th weeks are critical in terms of crop water requirements as crop demands significant amount of water from flowering to pod formation stage. Daily crop water requirements vary from 5 to 7 mm/day during this period. Table 6 Water budget components of managed stress experiments Water budget components in mm Auto-irrigation simulation (in mm) With stress (in mm) Well water (in mm) (2018-19) Moisture available at beginning of sowing 63 63 63 Irrigation applied 429 780 1166 ET actual 421 360 437 Deep percolation 51 484 793 Balance 20 -1 -1 Number of stress days 0 18 days 0 (2021-22) Moisture available at beginning of sowing 57 57 57 Irrigation applied 500 463 763 ET actual 510 298 525 Deep percolation 30 190 310 Balance 17 32 -15 Number of stress days 0 45 days 0 4. Discussion Groundnut, mostly grown as rainfed crop in semi-arid often experiences drought stress during mid- and end-season of the crop growth that coincides with pod filling thus affecting the pod and kernel yield and quality. Heat and intermittent moisture stress, which frequently occur together, can reduce groundnut pod yield up to 72% (Hamidou et al., 2013 ). Despite setting drought tolerance as the main objective of many groundnut breeding programs across the globe, progress has been slow given the quantitative and complex nature of drought tolerance trait and the challenges associated with screening for drought tolerance for selection. Managed stress environment or screening in drought target sites offer an ideal field screening platform for selecting drought tolerant lines in a breeding program. However, screening for drought tolerance at the drought target site is a challenge, given the variable frequency and intensity of occurrence of drought at the sites of testing. While establishing a managed stress environment and screening large number of breeding lines in MSE requires huge resources. Consequently, the groundnut breeding programs delay the selection for drought tolerance to later generations when the number of lines are relatively small to handle. The slow progress for drought tolerance in groundnut can be attributed to screening of a limited number of selection candidates. Besides, the selection based on yield and yield attributes of the selection candidates may have resulted in the loss of alleles contributing to drought tolerance in the selected candidates. The primary focus of groundnut breeding programs across the world in the past was on increasing yield and addressing the yield-limiting factors, such as, diseases and drought, however the progress on drought tolerance has been slow. The scientific work on drought tolerance in groundnut at the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) began during 1976, which works for groundnut improvement for the target countries in Sub-Saharan Africa and Asia. The efforts for improvement of drought tolerance started with the identification of sensitive stages of growth for drought stress. The studies identified that the pegging and pod filling as the most sensitive stages for drought stress in groundnut (Stirling et al., 1989 ; Patil and Gangavani, 1990; Meisner et al., 1991; Ramachandrappa et al., 1992 ; Prasad et al., 2010 ). The tips of the pegs are sensitive and when they touch the dry soil surface, they abort drastically affecting pegging and consequently the number of pods and yield. The drought stress during the pod development stage affects the filling of the pods, consequently ill filled pods, also referred as pops are formed resulting in reduced pod yield and shelling outturn. At ICRISAT, progress has been made over the last four decades in selecting for yield under water-stressed conditions, and some drought tolerant varieties have been released for cultivation in Asia and Africa (Monyo et al., 2016; Desmae et al., 2019 ). Use of trait-based approaches need understanding of key traits contributing to drought adaptation that aid in selection for identifying and breeding drought-tolerant cultivars. Separate selections performed on same set of crosses, one based on yield and yield parameters (empirical approach) and that other based on drought tolerance surrogate traits such as carbon isotope discrimination, WUE, and specific leaf area were found inconsistent in selecting high yielding peanut genotypes (Nigam et al., 2005 ). Nonetheless, selection of physiological traits or their surrogates in the breeding schema will be advantageous. However, measurement of the traits such as specific leaf area (SLA), carbon isotope discrimination (d13C) and SPAD chlorophyll meter reading (SCMR) is difficult, laborious, and costly even if they help in selections (Chen et al., 2013 ). Breeding for drought adaptation using yield alone as a selection criterion is generally inefficient since yield is an integration of complicated mechanisms at different stages of organisation affected by many elements of the phenotype and the environment interacting in a complex and often unknown ways (Chimungu et al., 2014 ). Rapid progress in plant phenomics enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits (Furbank et al., 2019 ). Integration of plant structure traits measured on a high-throughput phenotyping platform (HTPP) as a filter ahead of actual field screening under managed stress environment can speed up the selections in early generations and can enhance the genetic gain (Watt et al., 2020 ). In the present study, an imaging based HTPP platform, LeasyScan measured water use related early canopy traits viz. , digital biomass, plant height, leaf area 3D, leaf area index, projected leaf area, leaf angle and light penetration depth was used together with stress tolerance indices developed by Thiry et al., 2016 . The leaf area 3D measured using LeasyScan and actual leaf area has recorded 0.94 coefficient of determination (R 2 ) (Vadez et al., 2015 ). Sivasakthi et al . 2018 used LeasyScan platform to measure canopy traits in chickpea to map plant vigour traits. In a groundnut MAGIC population (600) and a set of advanced breeding lines (ABLs-1) (100), significant genotypic differences among genotypes were observed for the eight traits measured under the LeasyScan platform as well as for DFF, SCMR, yield, SP and HKW measured under WW and WS conditions of a managed stress environment. High heritability coupled with high genetic advance as percent of mean was recorded for digital biomass, plant height, leaf area 3D, leaf area index, projected leaf area, and light penetration depth. Genetic advance is a measure of gain achieved in a trait under a certain selection pressure (Ogunniyan et al . 2014), and high genetic advance coupled with high heritability estimates in the study suggest the involvement of an additive component in the traits and indicate possible gain through selections for the traits. For selecting genotypes under moisture stress SCMR (Nigam et al., 2005 ; Varshney et al., 2008; Upadhyaya et al., 2011 ; Janila et al., 2015 ; Shaibu et al., 2020 ; Bacharou et al., 2019) trait has been widely used as one of the surrogates of transpiration efficiency. SCMR measures the light-transmittance characteristics of the leaf which is dependent on the leaf chlorophyll content (Richardson et al., 2002 ). However, the H 2 for SCMR has been recorded low (31%) by Janila et al., ( 2015 ) and moderate (55%) by Shaibu et al., ( 2020 ) in different populations revealing less to moderate contribution of genetic effect into phenotype. Although in the managed stress condition in the present study high H 2 was recorded for SCMR under both WW and WS conditions which explain high impact of genotype on phenotype, however, its use in large breeding programs is limited as the SCMR recording cannot be completed in a specified time interval. Most groundnut breeding programs follow an empirical approach for screening against moisture-deficit stress tolerance, which is largely based on pod and kernel yield under moisture-deficit stress (Nigam et al., 2005 ; Janila et al., 2016). Often the breeding program employ a selection strategy where early generation selection is based on yield under normal growing conditions, followed by screening under drought stress when the number of lines are small. Such a breeding schema, that delays the screening for drought stress to later generations limits the progress for drought tolerance as a small set of lines are subjected to screening. Besides selection primarily for yield alone has also contributed to slow progress in drought tolerance in groundnut. Results from the present study standardised a ‘selection strategy’ and integrated into the breeding schema wherein early generation materials (F 2 to F 3 , F 3 to F 4 ) are advanced in a rapid generation advancement (RGA) facility using single seed decent followed by raising the F 4 single plants and F 5 plant progenies in the field from which F 6 plant progeny rows are harvested. F 6 progenies are screened in a 3D imaging based high throughput phenotyping platform, LeasyScan. Early canopy traits like digital biomass, leaf area 3D and plant height from HTPP screening are utilized to narrow down the number of F 6 progenies to be tested under managed stress environment. Around 40% of population is advanced for screening under managed stress environment. About 10 seeds from F 6 progeny are used for HTTP screening and the remnants seeds of selected progenies are grown in progeny rows to harvest sufficient seed for field screening in a managed stress environment. Resilience and productivity indices measured under managed stress environment are used as a selection criterion to advance up to 20% of the progenies for multi-environment testing (Fig. 6 ). New selection indices proposed by Thiry et al., ( 2016 ) are used to identify genotypes with high productivity, resilience and both resilience and productivity. The proposed breeding schema integrate early generation selection to fix the early canopy vigour traits for drought adaptation using HTPP followed by screening of selected lines under a managed stress environment using new selection indices to speed up drought tolerance selections and to enhance the genetic gain. To have better control over error while screening under a managed stress environment, superior lines (~ 30–40%) for early canopy traits such as digital biomass, leaf area 3D and plant height can be filtered for screening in managed stress environments. Digital biomass and leaf area 3D index are derived traits, with high heritability and associated with other directly measured traits in LeasyScan. Biomass production is often used as a criterion to select drought adaptive genotypes in peanut. Leaf area index is a measure to quantify amount of foliage in the crop canopy. It is frequently used by physiologist and plant breeders for indirect selection of drought adaptive genotypes in groundnut (Schubert and Reed, 2005; Nigam and Aruna, 2007; Arunyanark, et al., 2008). Plant counter moisture stress by reducing the length of main shoot and side branches, thereby causing a reduction in plant height (Reddy and Anbumozhi, 2003). Traits like leaf angle and leaf inclination were not used in selection due to lack of understanding of their role in drought adaptation in groundnuts. 5. Conclusion Selection for yield alone is not an efficient strategy for improving drought tolerance in groundnut. The proposed ‘selection strategy’ is efficient for genetic enhancement of drought tolerance as it fixes the early canopy vigour traits before advancing the lines to yield evaluation under managed stress environment. Compared to traditional selection approaches, the breeding scheme that employs the proposed ‘selection strategy’ for drought tolerance can save the time and money needed for screening by 20 to 30%. Selection for early canopy traits can retain the favourable alleles for water-saving traits. Superior lines selected using the proposed ‘selection strategy’ carry alleles for water-saving traits and high yield potential under normal as well as drought conditions can be recycled as parents in the breeding pipelines to accumulate the favourable alleles for drought tolerance. Declarations Funding The research was financially supported by CRP-Grain Legumes and Dryland Cereals (CRP-GLDC) and OPEC Fund for International Development (OFID) with grant number 13161. CRediT authorship contribution statement Ankush Purushottam Wankhade: Investigation, Writing-original draft, Data curation. Ashutosh Purohit: Investigation, Writing-original draft, Data curation, Validation, Visualization, Software. Seltene Abady: Investigation, Writing-review and editing, Data curation. Vivek Pandurang Chimote: Writing-review and editing. Anilkumar Vemula: Formal analysis, software,Writing-review and editing . Kaushal Garg: Formal analysis, Writing-review and editing . Sunita Choudhary: Conceptualization, Resources,Writing-review and editing. Jana Kholova: Conceptualization, Resources,Writing-review and editing. Graeme C. Wright : Writing-review and editing. Devraj Lenka: Writing-review and editing . Janila Pasupuleti: Conceptualization, Methodology, Project administration, Funding acquisition, Resources, Supervision, Writing-review and editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgements The authors are thankful to Surendra Singh Manohar, Sunil Choudhari, B. Rekha, Gopi Potupureddi, and K. Sivasakthi for their support in conducting the experiments. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version. 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The peanut genome. 7–26. https://doi.org/10.1007/978-3-319-63935-2_2 Varshney RK, Bertioli DJ, Moretzsohn MDC, Vadez V, Krishnamurthy L, Aruna R, Nigam SN, Moss BJ, Seetha K, Ravi K, He G (2009) The first SSR-based genetic linkage map for cultivated groundnut (Arachis hypogaea L). 118:729–739. https://doi.org/10.1007/s00122-008-0933-x Wankhade AP, Chimote VP, Viswanatha KP, Yadaru Shashidhar, Deshmukh DB, Gattu S, Sudini HK, Deshmukh MP, Shinde VS, Vemula A, Pasupuleti J (2023) Genome-wide association mapping for LLS resistance in a MAGIC population of groundnut (Arachis hypogaea L.). Theor. Appl Genet. https://doi.org/10.1007/s00122-023-04256-7 Wankhade AP, Kadirimangalam SR, Viswanatha KP, Deshmukh MP, Shinde VS, Deshmukh DB, Pasupuleti J (2021) Variability and trait association studies for late leaf spot resistance in a groundnut MAGIC population. Agronomy 11:2193. https://doi.org/10.3390/agronomy11112193 Watt M, Fiorani F, Usadel B, Rascher U, Muller O, Schurr U (2020) Phenotyping: new windows into the plant for breeders. Annu Rev Plant Biol 71:689–712. https://doi.org/10.1146/annurev-arplant-042916-041124 Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC, Boca Raton, FL, USA Zhang J, Wang Q, Xia G, Wu Q, Chi D (2021) Continuous regulated deficit irrigation enhances peanut water use efficiency and drought resistance. Agric Water Manag 255:106997. https://doi.org/10.1016/j.agwat.2021.106997 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryDatasets.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5503687","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381430118,"identity":"88acf9fe-8fac-48a7-9dea-031b0c43541c","order_by":0,"name":"Ankush Purushottam Wankhadea","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Ankush","middleName":"Purushottam","lastName":"Wankhadea","suffix":""},{"id":381430119,"identity":"6ee18fa9-4a22-46c3-8b3c-fa1a84bb6b91","order_by":1,"name":"Ashutosh Purohit","email":"","orcid":"https://orcid.org/0009-0005-3138-7214","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Ashutosh","middleName":"","lastName":"Purohit","suffix":""},{"id":381430120,"identity":"508a0e52-99d4-41e4-8ce0-ea1173a9faa6","order_by":2,"name":"Seltene Abady","email":"","orcid":"","institution":"School of Plant Sciences, Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Seltene","middleName":"","lastName":"Abady","suffix":""},{"id":381430121,"identity":"62063528-99ad-4152-83c4-f8849de90afa","order_by":3,"name":"Vivek Pandurang Chimote","email":"","orcid":"","institution":"Mahatma Phule Krishi Vidyapeeth (MPKV)","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"Pandurang","lastName":"Chimote","suffix":""},{"id":381430122,"identity":"698e7bb3-eba1-4ca4-b9df-08cc7851d670","order_by":4,"name":"Anilkumar Vemula","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Anilkumar","middleName":"","lastName":"Vemula","suffix":""},{"id":381430123,"identity":"68ecde77-90ed-47da-b79f-c9a569e856e4","order_by":5,"name":"Kaushal Garg","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Kaushal","middleName":"","lastName":"Garg","suffix":""},{"id":381430124,"identity":"1291f027-2482-44e6-b894-558a4e732c16","order_by":6,"name":"Sunita Choudhary","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Sunita","middleName":"","lastName":"Choudhary","suffix":""},{"id":381430125,"identity":"f91b61d4-60f0-4f3d-8bab-18a41342fecd","order_by":7,"name":"Jana Kholová","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Jana","middleName":"","lastName":"Kholová","suffix":""},{"id":381430126,"identity":"dc7ff503-d823-45f2-97a1-5179c229e4e7","order_by":8,"name":"Graeme C. Wright","email":"","orcid":"","institution":"Peanut Company of Australia","correspondingAuthor":false,"prefix":"","firstName":"Graeme","middleName":"C.","lastName":"Wright","suffix":""},{"id":381430127,"identity":"c073ca0d-3924-4b52-9977-992d01b88f6c","order_by":9,"name":"Devraj Lenka","email":"","orcid":"","institution":"Odisha University of Agriculture and Technology (OUAT)","correspondingAuthor":false,"prefix":"","firstName":"Devraj","middleName":"","lastName":"Lenka","suffix":""},{"id":381430128,"identity":"517f303c-d522-4fd4-b091-029577dad84e","order_by":10,"name":"Janila Pasupuleti","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2583-9630","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":true,"prefix":"","firstName":"Janila","middleName":"","lastName":"Pasupuleti","suffix":""}],"badges":[],"createdAt":"2024-11-22 10:09:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5503687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5503687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69808340,"identity":"c4031955-3a7e-41d8-a14b-280f004e05fe","added_by":"auto","created_at":"2024-11-25 12:22:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":634477,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of early canopy traits recorded in ABLs_1 under HTPP\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/1c6430f5be1b89efad40c5d7.jpg"},{"id":69808349,"identity":"c3c2561d-100d-459c-9e4b-598e9887a86c","added_by":"auto","created_at":"2024-11-25 12:22:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":592762,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of early canopy traits recorded in MAGIC population under HTPP\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/a5e549f25ad23576b864b570.jpg"},{"id":69809575,"identity":"03e73be8-b644-42e9-8e88-37cb96903ef1","added_by":"auto","created_at":"2024-11-25 12:30:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312174,"visible":true,"origin":"","legend":"\u003cp\u003eVariation for yield attributing traits in MAGIC population 2018-19 (a) and 2021-22 (b) under water stress (PW_WS) and well water (PW_WW) condition\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/f83ee1b087c38d27db29d77b.jpg"},{"id":69809576,"identity":"abd16f62-6f6d-4503-be20-90dc1002ce89","added_by":"auto","created_at":"2024-11-25 12:30:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":390717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eEstimated ET under stressed and without stressed plots along with actual requirements during 2018-19\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003eEstimated ET under stressed and without stressed plots along with actual requirements during 2020-21\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/8fcc8f8003b296f4e2e2f2bc.png"},{"id":69808342,"identity":"58a1cb9c-36e0-42cf-907d-7a67ad178a2d","added_by":"auto","created_at":"2024-11-25 12:22:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":549354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003e Daily variations in crop water requirements along with ET with stressed and without stressed plots (year 2018-19)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003e Daily variations in crop water requirements along with ET with stressed and without stressed plots (year 2020-21)\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/c00d428b7774aebb1ac498fe.png"},{"id":69809578,"identity":"bc2c2822-0d20-4cfc-bd16-27b41e6cad33","added_by":"auto","created_at":"2024-11-25 12:30:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":211788,"visible":true,"origin":"","legend":"\u003cp\u003eBreeding schema integrating HTPP and managed stress in groundnut breeding for drought tolerance\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/adf02884321c745d80b3ebf9.jpg"},{"id":69812104,"identity":"0b58dfbd-ddbe-47cb-9155-29e025fe80d9","added_by":"auto","created_at":"2024-11-25 12:54:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3800585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/09203253-7792-4fee-b6f4-28fd3aea1914.pdf"},{"id":69811453,"identity":"b6e4b49c-496e-46c6-a737-255bbcb0d410","added_by":"auto","created_at":"2024-11-25 12:46:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":432175,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDatasets.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5503687/v1/d835e017d9d298da94fe662d.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eStep-wise selection using high throughput phenotyping platform (HTTP) and stress tolerance indices as an approach for improving drought tolerance in groundnut (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eArachis hypogaea\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e L.)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGroundnut or peanut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) is an important crop used for food, oil, feed, fodder, confectionery and industrial purposes and grown in 116 countries across the world, and ~\u0026thinsp;90% of the total groundnut production in the world comes from Africa and Asia (FAOSTAT 2022). The major production constraints of groundnut in the developing countries are abiotic stresses, lack of efficient seed systems, low input usage, and socio-economic issues (Variath and Janila, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among the stresses, drought stress is most important constraint in groundnut production across the globe. Drought stress has a significant impact on crop production, which further elevates the other abiotic and biotic stresses (Ceccarelli et al., 2020), and more abiotic stress events are predicted in the future as a consequence of changing climate (Cooper et al., 2023). Thus, the groundnut breeding programs in different countries of Asia, Africa, Americas\u0026rsquo; and Australia focus on genetic enhancement for drought tolerance.\u003c/p\u003e \u003cp\u003eGroundnut is predominantly grown under rainfed conditions of arid (below 400 mm rainfall) and semi-arid regions (400\u0026ndash;800 mm rainfall) of Asia and Africa where drought occurs frequently, affecting the critical stages of pod development, and causing major penalty in yield (Nigam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Besides, drought stress predisposes the pods to pre-harvest \u003cem\u003eAspergillus\u003c/em\u003e fungus infection, which produces a potent carcinogen, aflatoxin (Sibakwe et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Drought stress during mid-growth, especially during flowering and pegging can reduce the pod yield by up to 44% (Abady et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003ea). Drought stress causes poor filling of pods resulting in lower shelling outturn (kernel weight from a unit weight of pods expressed as percentage) and therefore, several researchers have suggested that selection for high shelling outturn or pod and kernel yield can be rewarding in groundnut breeding to improve drought tolerance (Abady et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eIn a given groundnut growing agro-ecology, the frequency and intensity of drought varies over a given period of time, and therefore not all years are drought years. For such scenarios, that are most common for groundnut growing agro-ecologies, groundnut varieties that produce superior yield in normal years, and minimize the yield loss during drought years are needed. This requires a selection strategy in groundnut breeding that must ensure the development of varieties that have good production capacity under normal condition, and resilience capacity during stress to minimize the yield loss during drought years. Environmental characterization identified seven homogenous production units (HPUs) for groundnut in India, and drought is a major production constraint in most of the HPUs, particularly in HPU6 comprising the southern states of India, Andhra Pradesh and Karnataka (Hajjarpoor et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While in HPU1 comprising of Rajasthan, the supplemental irrigation overcomes the effect of drought that could otherwise reduce the pod yield by up to 73%. The conventional method of selection depending on yield may not be screening better performing genotypes under stress (Blum, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) unless the component traits of adaptation to drought are considered in selection. On the other hand, selections in groundnut performed based on the water-saving physiological traits without considering yield may not select the high-yielding lines under either normal or drought stress condition (Nigam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Therefore, a dynamic selection strategy supported by advanced high throughput technologies and knowledge of physiological aspects of adaptation to reduce the gap between yield under potential condition and drought stress condition in groundnut is needed.\u003c/p\u003e \u003cp\u003eDrought tolerance, a complex trait is governed by several genes in groundnut. Therefore, genotype by environment interaction often comes in the way of selection for drought tolerance to develop tolerant cultivars (Ravi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bacharou et al., 2019) and thus requires extensive multi-environment testing (MET) in a target population of environment (TPE). On the other hand, in the groundnut breeding programs, the selection for drought tolerance is often delayed to screen a small number of lines in a TPE. This hampers the progress of genetic enhancement for drought tolerance, as the number of selection candidates subjected to screening for drought tolerance are limited, and there is a possibility of rejecting the desirable segregants in early generations as advancement in early generation were based on yield performance under normal conditions. While selection in a managed stress environment (MSE) is a good option, it is resource intensive hence used for late generation testing in the breeding programs. Most of the breeding programs do not test in the MSE. Based on the yield data recorded on the genotypes in the well-watered (WW) and water-stressed (WS) plots of MSE, the genetic differences for drought tolerance can be assessed by different scoring and index methods. Stress tolerance index (STI) identifies the genotypes that produce superior yield in both, WW and WS conditions (Fernandez, 1992), and geometric mean productivity (GMP) measures the relative performance as the intensity of drought stress varies in severity (Rosielle and Hamblin \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Thiry et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) proposed a method based on the scoring scale to measure Productivity Capacity Index (PCI) and Resilience Capacity Index (RCI) to allow selection of genotypes that combine high production capacity and resilience to the stress.\u003c/p\u003e \u003cp\u003ePrevious research has indicated the ability of groundnuts to withstand drought depends on their water uptake capacity and transpiration efficiency (Ratnakumar et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Water uptake capacity is largely influenced by the functional characteristics of the roots, while transpiration efficiency is influenced by canopy dynamics. According to Vadez et al., (2016), there is a strong positive correlation between groundnut biomass and transpiration efficiency under drought stress conditions. This phenomenon is related to the water extraction capacity of the genotypes and the mobility of water from the soil for stem elongation and biomass production. In a transgenic event in wild-type JL 24, where the \"rd29A:DREB1A\" gene was overexpressed under stress, a close association was established between root length density, higher root-to-shoot ratio, and water uptake, resulting in increased biomass accumulation in the roots and pods (Shridhar et al., 2012). Drought stress affects various leaf morphological and anatomical features in groundnuts, and drought tolerance in groundnuts has been linked to early canopy traits and stomatal closure (Sinclair et al., 2019), decreased leaf area (Reddy et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), maintenance of vegetative growth (Tardieu and Tuberosa, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and photosynthesis (Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to Ratnakumar et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), drought-adapted groundnut genotypes under water stress conditions maintain a high harvest index and small leaf canopy, meaning that their reproductive processes are less affected and less water is used during the dry spell. The crop growth rate (CGR) during the critical pod formation stage affects the dry matter accumulation in pods (Oteng-Frimpong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the advent of high-throughput 3D imaging platforms, such as LeasyScan, which captures leaf area development continuously, it is possible to measure plant canopy traits associated with water use, such as leaf area, leaf area index, and transpiration (Vadez et al., 2016). The study attempts to develop a \u0026lsquo;selection strategy\u0026rsquo; for drought tolerance breeding in groundnut that identifies genotypes with early canopy vigour, combined with high production capacity as well as resilience. To develop a strategy, groundnut populations that includes a multi-parent advanced generation inter-cross (MAGIC) population (600 F\u003csub\u003e9/10\u003c/sub\u003e lines, and advanced breeding lines (260) were tested under a high-throughput phenotyping platform (HTPP) \u0026ldquo;LeasyScan\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gems.icrisat.org/leasyscan\u003c/span\u003e\u003cspan address=\"https://gems.icrisat.org/leasyscan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for early canopy traits, and under an optimally managed MSE assessed. Based on the results obtained, a \u0026lsquo;selection strategy\u0026rsquo; is proposed for use in groundnut breeding programs to achieve genetic enhancement for drought tolerance.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Plant material\u003c/h2\u003e \u003cp\u003eA MAGIC population comprised of 600 F\u003csub\u003e9/10\u003c/sub\u003e MLs, eight founder parents, and 12 checks; and two sets of advanced breeding lines (ABLs) viz., ABLs-1 (100) ABLs (160). The ABLs-2 used in the study have high oleic acid content (\u0026gt;\u0026thinsp;75%), tolerance to foliar fungal diseases (rust and late leaf spot), and high yield potential. The founder parents of MAGIC population are, ICGVs 91114, 06040, 00440, 00308, 05155, and 88145, GPBD 4, and 55\u0026ndash;437 (Wankhade et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the MAGIC population was developed using a 8-way crossing schema.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Spatio-temporal canopy traits\u003c/h2\u003e \u003cp\u003eWe utilized a powerful high-throughput crop phenotyping platform called the LeasyScan system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gems.icrisat.org/leasyscan/\u003c/span\u003e\u003cspan address=\"http://gems.icrisat.org/leasyscan/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Vadez et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) established at ICRISAT, Patancheru, India, (17_300N, 78_160E; altitude 549 m. a. s. l.). LeasyScan (Phenospex, The Netherlands; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phenospex.com/\u003c/span\u003e\u003cspan address=\"https://phenospex.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a unique multispectral based continuous plant monitoring and phenotyping system that generates 3D point clouds in every 2 h on each of the 4800 sectors. Currently, the algorithms have been validated to process 3D point clouds for 9 canopy dynamics traits \u003cem\u003ei.e.\u003c/em\u003e leaf area [mm\u0026Acirc;\u0026sup2;], plant height [mm], light penetration depth [mm], projected leaf area (unshaded leaf area; mm\u0026Acirc;\u0026sup2;), height max (mm), digital biomass (mm\u0026Acirc;\u0026sup3;), leaf angle (\u0026Acirc;\u0026deg;), leaf inclination (mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;) and leaf area index (mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;). One of the key applications of the LeasyScan phenotyping platform is that it allows for the precise spatio-temporal monitoring and automated measurements of plant canopy to generate a number of traits related to canopy development pattern, early vigour and growth rate in the course of plant growth (Vadez et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Most importantly, it offers the opportunity to phenotype large population on same time under outdoor natural plants under uncontrolled growing environment similar to the field conditions. Plants were grown under well-watered conditions in a sector area of 40 \u0026times; 60 \u0026times; 30 cm, \u003cem\u003ei.e.\u003c/em\u003e approximately a quarter square meter called mini-plots. Each mini-plot contains 65 kg of soil (Vertisol) collected from the ICRISAT farm. The MAGIC population of 620 genotypes and 100 ABLs along with elite checks planted in alpha lattice design in 4 replications was tested on platform from 21 June to 18 July 2019. The average temperature and relative humidity varied between T 320.20/22.87\u0026deg;C and RH%-62.15/86.4% day/night during the experimentation. Two weeks after sowing, plants were thinned to maintain a uniform plant count of 4 plants per sector. Plant count was recorded after the final thinning. Plants were watered either early in the morning or late in the afternoon. Top dressing was done with di-ammonium phosphate (300 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of soil). Plants were harvested after 6 weeks of sowing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Field screening in a managed stress environment (MSE)\u003c/h2\u003e \u003cp\u003eThe MAGIC population was evaluated in MSE for two seasons, PR 2018-19 and PR 2021-22, where the population size was 620 and 574, respectively. ABLs-2 (160) were screened under a managed stress condition. The experiments were conducted in alpha lattice design with two replications each under WS and WW condition with a plot size of 2m\u0026times;2 rows. Phenotypic data for days to 50% flowering (DFF), SPAD (soil plant analysis development) chlorophyll meter reading (SCMR), dry pod yield (Yield), shelling percent (SP) and hundred kernel weight (HKW) were recorded. The pod yield data for MAGIC population was recorded as dry pod yield per plant, which was calculated by dividing the yield obtained by the number of plants, whereas in PR 2021 pod yield per plot was recorded in MAGIC populations and ABLs-2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Managed stress environment (MSE)\u003c/h2\u003e \u003cp\u003eThe MSE comprises of a WS and WW plots in the same precision experimental field with same soil physical and chemical properties, and nutrient status established at ICRISAT, India (17.3850\u0026deg; N, 78.4867\u0026deg; E and 545 meter above mean sea level. The WS plots were exposed to mid-season moisture stress by with-holding 2\u0026ndash;3 irrigations from 1000 growing degree days (GDD) corresponding to mid-season, which was 82 days after planting (DAP) during 2018-19 and 76 DAP during 2021-22. In order to avoid complete wilting, a single irrigation was given to WS plots during the stress period and no further irrigations were provided till harvesting. While WW plots were normally irrigated till harvest. Decision on irrigation in WS plots was taken by referring to weather and soil moisture data. Visual wilting symptoms and permanent wilting point (PWP), (which was determined to be 8.94% for alfisol, using a pressure plate extractor (klute et al., 1986) were used as an indicator to resume irrigation in WS plots. Neutron probes (Neutron Probe Smart503, ICT international) and time domain reflectometry probes (TRIME\u0026reg;-FM) were used for the measurement of soil moisture. Optimal crop stand was ensured using sprinkler irrigation for an initial period of 20\u0026ndash;25 DAP. Thereafter, irrigations till harvest were given using drip lines that allowed to have better control over supply of water and check the seepage from WW to WS plots. Irrigations in terms of time and quantity were measured during each irrigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Quantification of imposed stress\u003c/h2\u003e \u003cp\u003eThis study uses a one-dimensional water balance model \u0026lsquo;Water Impact Calculator\u0026rsquo; (WIC) developed by ICRISAT to analyse the water balance components (Garg et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The WIC is a generic decision-making tool which could be applied to any land use and cropping system by providing minimum sets of biophysical (soil, weather and crop growth) and crop management inputs (Garg et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The model calculates the daily water balance as: R\u0026thinsp;+\u0026thinsp;I\u0026thinsp;=\u0026thinsp;DP\u0026thinsp;+\u0026thinsp;ET\u0026thinsp;+\u0026thinsp;ΔS\u003c/p\u003e \u003cp\u003eWhere R\u0026thinsp;=\u0026thinsp;rainfall (mm), I\u0026thinsp;=\u0026thinsp;irrigation (mm), DP\u0026thinsp;=\u0026thinsp;Deep percolation (mm), ET\u0026thinsp;=\u0026thinsp;evapotranspiration (mm) and ΔS\u0026thinsp;=\u0026thinsp;change in soil moisture storage (mm).\u003c/p\u003e \u003cp\u003eDeep percolation, evaporation and transpiration are considered as factors in WIC. The crop water requirement (CWR) for a given day is calculated as:\u003c/p\u003e \u003cp\u003eCWR\u0026thinsp;=\u0026thinsp;Kc*ETo\u003c/p\u003e \u003cp\u003eWhere Kc\u0026thinsp;=\u0026thinsp;crop coefficient and ETo (mm/day)\u0026thinsp;=\u0026thinsp;reference crop evapotranspiration.\u003c/p\u003e \u003cp\u003eThe root zone depth is a dynamic variable and is controlled by crop growth stage (days after sowing) as defined by Allen \u003cem\u003eet al.\u003c/em\u003e, (1998). Inputs of water in the experimental plots were measured by measuring irrigation hours and discharge rates of the sprinkler and drip system (litres per min). Sowing and harvesting dates along with irrigation amount were provided in the model to estimate water budget component at daily time scale. Model was run for i) auto-irrigation set up; ii) with stressed imposed plots and iii) without stressed imposed plots. Auto irrigation is a situation in which model considers no-stress condition and automatically apply required irrigation to the crop when available residue moisture of the root zone is not able to meet crop water requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Selection indices\u003c/h2\u003e \u003cp\u003eA novel scoring scale has been adopted which takes in to account productivity capacity index (PCI) \u003cb\u003e\u0026ndash;\u003c/b\u003e the mean yield of different genotypes in both WS and WW environment, and resilience capacity index (RCI) \u0026ndash; yield penalty in WS condition compared to WW condition. Genotypes are categorized into different ranks based on a scale on 1\u0026ndash;10, where genotypes with a score of 10 are considered as superior for drought adaptation (Thiry et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Data analysis\u003c/h2\u003e \u003cp\u003eCombined and treatment-wise analysis of variance was performed to test the significance of genotype, treatment, and genotype x treatment effects for LeasyScan and managed stressed environment using SASv9.4 Mixed model (SAS Institute Inc. 2018) procedure. The treatment effect was considered as the fixed while genotype, replication, and block were consider as random effect. The individual treatment variances were estimated and modelled to error distribution using Residual Maximum Likelihood Estimate (REML) method. Best Linear Unbiased Predictors (BLUPs) were estimated for the main and interaction effects of treatment and genotype from the combined and treatment-wise analysis. Using Cullis (Cullis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) method, Broad-sense heritability was calculated for treatment-wise analysis.\u003c/p\u003e \u003cp\u003eTreatment-wise Broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e)\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{{H}^{2}}_{Cullis}=1-\\frac{{{\\stackrel{-}{v}}_{\\varDelta\\:}}^{Blup}}{2\\text{*}{{\\sigma\\:}_{g}}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBroad sense heritability for pooled analysis\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{H}^{2}={{\\sigma\\:}_{g}}^{2}+\\raisebox{1ex}{${{\\sigma\\:}_{gt}}^{2}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$t$}\\right.+\\raisebox{1ex}{${{\\sigma\\:}_{e}}^{2}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$rt$}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere r and t are number of treatments and number of replications respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}_{g}}^{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}_{gt}}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\sigma\\:}_{e}}^{2}\\:\\)\u003c/span\u003e\u003c/span\u003eare variance components of genotype, genotype x treatment and error respectively.\u003c/p\u003e \u003cp\u003eKarl Pearson\u0026rsquo;s correlation coefficients between early canopy traits were calculated using SAS Proc corr (SAS Institute Inc. 2018) procedure.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Variability and distribution for early canopy traits\u003c/h2\u003e\n \u003cp\u003eVariance components for plant height (PH), leaf area 3D (LA3D), digital biomass (DBM), leaf area index (LAI), projected leaf area (PLA), light penetration depth (LPD), leaf angle (LA) and leaf inclination (LI) measured under HTTP showed significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) among genotypes in a groundnut MAGIC population in both the seasons, and ABLs-1 (100). While all these traits recorded high broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;90%), DBM, LA3D, LAI, PLA, and LPD also had high genetic advance (\u0026gt;\u0026thinsp;20%) as percent of mean (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters of early canopy traits under Leasyscan for MAGIC population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDBM [mm\u0026Acirc;\u0026sup3;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLAI [mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePH [mm]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLA [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLA [\u0026Acirc;\u0026deg;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLI [mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLA3D [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLPD [mm]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" align=\"left\"\u003e\n \u003cp\u003eMAGIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9880023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13230448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40E\u0026thinsp;+\u0026thinsp;12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163.46**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47796431**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85339728**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.19**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2984478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4137411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3621581\u0026ndash;14731453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u0026ndash;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.35-144.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33250\u0026ndash;76645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.79\u0026ndash;45.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u0026ndash;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49608\u0026ndash;106654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.65-100.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8752184.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52592.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76067.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA % of Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eDBM, digital biomass; LAI, leaf area index; PH, plant height; PLA, projected leaf area; LA, leaf angle; LI, leaf inclination; LA3D, leaf area 3D; LPD, light penetration depth; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; CV %, coefficient of variance; GA % of Mean, genetic advance as percentage of mean; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters of early canopy traits under Leasy scan for ABLs_1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDBM [mm\u0026Acirc;\u0026sup3;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLAI [mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePH [mm]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLA [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLA [\u0026Acirc;\u0026deg;]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLPD [mm]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eABLs_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.09E\u0026thinsp;+\u0026thinsp;12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00853**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.1559**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79E\u0026thinsp;+\u0026thinsp;08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67E\u0026thinsp;+\u0026thinsp;08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238.11**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08E\u0026thinsp;+\u0026thinsp;12**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60110363**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41E\u0026thinsp;+\u0026thinsp;08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93E\u0026thinsp;+\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2260234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5769052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e657610\u0026ndash;3469740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u0026ndash;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026ndash;83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7378\u0026ndash;30171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11413\u0026ndash;47438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7\u0026ndash;61.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1974772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19274.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29469.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCV %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGA % of Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eDBM, digital biomass; LAI, leaf area index; PH, plant height; PLA, projected leaf area; LA, leaf angle; LI, leaf inclination; LPD, light penetration depth; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; CV %, coefficient of variance; GA % of Mean, genetic advance as percentage of mean; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAll the early canopy traits recorded in ABLs-1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and MLs (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) measured under HTPP were normally distributed and showed continuous variations. Variation was observed in MLs and ABLs-1 for the early canopy traits, \u003cem\u003eviz.\u003c/em\u003e, DBM (3,621,581 to 14,731,453 mm\u0026Acirc;\u0026sup3; in MLs and 657,610 to 3,469,740 mm\u0026Acirc;\u0026sup3; in ABLs), LAI (0.19 to 0.41 mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2; in MLs and 0.04 to 0.17 mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2; ABLs), PH (72 to 145 mm in MLs and 41 to 83 mm in ABLs), PLA (33,250 to 76,645 mm\u0026Acirc;\u0026sup2; in MLs and 7,378 to 30,171 mm\u0026Acirc;\u0026sup2; ABLs), LA (38.75 to 45.94 \u0026Acirc;\u0026deg; across MLs and ABLs), LA3D (49,608 to 106,654 mm\u0026Acirc;\u0026sup2; in MLs and 14,529 to 42,327 mm\u0026Acirc;\u0026sup2; ABLs) and LPD (52.65 to 100.57 mm in MLs and 26.7 to 61.3 mm in ABLs) and LI(1.39 to 1.49 in MLs mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2; and not recorded in ABLs) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Correlation between early canopy traits recorded in MAGIC population and ABLs_1\u003c/h2\u003e\n \u003cp\u003eThe person\u0026rsquo;s correlation coefficients calculated between the eight early canopy traits in a MAGIC population (Upper diagonal in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and ABLs_1 (Lower diagonal in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) were used to select independent early canopy trait that can be used in groundnut breeding as selection criteria. Digital biomass, plant height, leaf area 3D, leaf area index, projected leaf area and light penetration depth were strongly correlated with each other with the correlation coefficient (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.5) and non-correlated or weakly correlated with leaf angle and leaf inclination (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.2 or -0.2) in both MAGIC population and ABLs_1. Whereas, leaf angle had significant negative correlation with leaf inclination (r\u003csup\u003e2\u003c/sup\u003e =-0.91).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation among early canopy traits in MAGIC population and ABLs_1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003eMAGIC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBM [mm\u0026Acirc;\u0026sup3;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH [mm]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLA [\u0026Acirc;\u0026deg;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLA3D [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAI\u003c/p\u003e\n \u003cp\u003e[mm\u0026Acirc;\u0026sup2;/ mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLA [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLI [mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPD [mm]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eABLs_1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBM [mm\u0026Acirc;\u0026sup3;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH [mm]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLA [\u0026Acirc;\u0026deg;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.91\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLA3D [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAI [mm\u0026Acirc;\u0026sup2;/ mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLA [mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.22**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLI [mm\u0026Acirc;\u0026sup2;/mm\u0026Acirc;\u0026sup2;]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPD [mm]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eDBM, digital biomass; PH, plant height; LA, leaf angle; LA3D, leaf area 3D; LAI, leaf area index; PLA, projected leaf area; LI, leaf inclination; LPD, light penetration depth; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Above-diagonal \u0026ndash; correlation among early canopy traits in MAGIC population, whereas, below-diagonal \u0026ndash; correlation among early canopy traits in ABLs_1 population.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003e3.3. Evaluation of phenological, physiological and yield attributing traits in MAGIC population and ABLs under a managed stress environment\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eYear-wise variance components of SCMR, PY, SP, and HKW were estimated for MAGIC population (PRs 2018-19 and 2021-22) and ABLs_2 (PR 2021-22) under WW and WS regimes. Both MLs and ABLs_2 showed significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) differences among the genotypes for SCMR, PY, SP and HKW under both WW and WS conditions during PRs 2018-19 and 2021-22 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb). High broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;60%) was recorded for SCMR, PY, SP, and HKW in MAGIC (PRs 2018-19 and 2021-22) and ABLs-2 (PR 2021-22) under both WW and WS condition except SP under WW condition during PR 2021-22 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Pooled ANOVA for MAGIC population across water regimes showed a significant effect of drought stress (genotype by environment) on the genotypes for yield, SP and HKW during 2018-19 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec) and yield and HKW in MAGIC population during 2021-22 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec). ABLs-1 (100 genotypes) were evaluated during 2018/19 and 2019/20 post-rainy seasons at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. Analysis of variance for phenotypic traits under drought-stress and non-stressed conditions suggested significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) difference among genotypes for plant height (cm), SCMR, shelling percentage, and highly significant differences(p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for days to 50% flowering, number of primary branches, leaf relative water content, haulm weight (g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), hundred seed weight (g), pod weight (g plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Seltene et al., 2021).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2018-19 under WW condition.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHKW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Replication)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.86**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.52**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.93**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual - WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.32\u0026ndash;54.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u0026ndash;20.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.94\u0026ndash;83.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.86\u0026ndash;50.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; LSD, least significant difference; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2018-19 under WS condition.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHKW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Replication)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.64**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.29**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual - WS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.33\u0026ndash;52.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.13\u0026ndash;13.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.95\u0026ndash;75.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.28\u0026ndash;45.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; LSD, least significant difference; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2021-22 under WW condition\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHKW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.44**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3724.21**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.66**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.43**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual - WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e722.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.03\u0026ndash;49.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.67\u0026ndash;347.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48\u0026ndash;64.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.05\u0026ndash;41.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; LSD, least significant difference; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for MAGIC population during 2021-22 under WS condition\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHKW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.85**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e424.86**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.34**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.93**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual - WS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.51\u0026ndash;49.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67-120.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.20\u0026ndash;83.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u0026ndash;35.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; LSD, least significant difference; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for ABLs_2 during 2021-22 under WW\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHKW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.68**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5193.05**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.18**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.83**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual -WW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1423.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.57\u0026ndash;43.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.05-384.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.94\u0026ndash;63.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.64\u0026ndash;47.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; LSD, least significant difference; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Analysis of variance (ANOVA) and genetic parameters for morphological and yield attributing traits under managed stress condition for ABLs_2 during 2021-22 under WS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSCMR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHKW\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (Rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e556.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.62**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual - WS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.39\u0026ndash;48.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.68-102.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.86\u0026ndash;75.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u0026ndash;25.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSCMR, SPAD chlorophyll meter reading; PY, pod yield per plant; SP, shelling percentage; HKW, hundred kernel weight; H\u003csup\u003e2\u003c/sup\u003e (%), broad sense heritability; LSD, least significant difference; Significance level: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003eSignificant differences in the range for pod yield and yield attributes were observed in MAGIC population (For season PRs 2018-19 and 2021-22) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and ABLs_2 (for season PR 2021) under managed stress conditions. In PR 2021-22, range for pod yield per plot recorded in WS for MLs (3.67 to 120.42g) and ABLs_2 (11.68 to 102.42 g) was much lower than the yield per plot recorded in WW for MLs (27.67 to 347.40 g) and ABLs_2 (64.05 to 384.33 g). The range recorded for yield attributes \u003cem\u003eviz.\u003c/em\u003e, SP (MLs: 45.48 to 64.33%; ABLs: 34.94 to 63.73%) HKW (MLs: 15.05 to 41.71 g; ABLs: 18.64 to 47.68 g) in WW; and SP (MLs: 7.20 to 83.50%; ABLs: 3.86 to 75.62%), HKW (MLs: 2.86 to 35.43 g; ABLs: 5.27 to 25.59 g) in WS. Due to imposition of stress embryo of few genotypes were aborted, which in turn affected kernel development and resulted in highly shrivelled kernels. That is why few genotypes recorded vey low shelling percentage and hundred kernel weight.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e3.4. Resilience Capacity Index (RCI), Productivity Capacity Index (PCI) and Mean Score Index (MSI) based selection of drought tolerant genotypes using early canopy traits from HTPP as filter\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eDigital biomass, leaf area 3D and plant height recorded using LeasyScan platform were used as a filter to narrow down number of entries to be screened under managed stress condition in the field in the same order. The selected 240 MLs (40% of the screened MLs) recorded the RCI (1.5\u0026ndash;9.5), PCI (-7.5-4.5) and MSI (3.4\u0026ndash;8.2) in 2018 and RCI (1.5\u0026ndash;6.5), PCI (-7.5-7.5) and MSI (3.8-7.0) in 2021. The 40% selected ABLs recorded the RCI (1.0\u0026ndash;9.0), PCI (-6.0 to 6.5) and MSI (2.4 to 8.5) in 2018 and RCI (1 to 8.5), PCI (-7.5 to 5.5) and MSI (2.4 to 8.6) in 2019.\u003c/p\u003e\n \u003cp\u003eAmong the filtered 40% MLs the number of lines recorded RCI, PCI and MSI greater than or equal to 5 was 143, 0 and 149 out of 240, in 2018, and 6, 5, and 80 out of 230 in 2021, respectively. Among the filtered 40% ABLs, the number of lines recorded RCI, PCI and MSI greater than or equal to 5 was 19, 1, and 29 out of 40, in 2018, and 16, 1, and 24 out of 40 in 2021, respectively. The drought tolerant parent ICGV 02266 recorded RCI (4.5), PCI (-1) and MSI (5.4) in MAGIC population and RCI (4.5 to 6), PCI (0 to1) and MSI (5.4 to 6.6) in ABLs. A second drought tolerant founder parent, 55\u0026ndash;437, which was one among the eight-founder parent recorded RCI (2 to 4.5), PCI (-2 to -1) and MSI (4 to 6) in different seasons for MAGIC population.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Water Budgeting\u003c/h2\u003e\n \u003cp\u003eTotal crop water requirement for the crop period is estimated to be 421 mm in PR 2018-19 (November 2018 to March 2019) and 510 mm in PR 2021-22 (December 2021 to April 2022). Available moisture during sowing was about 57\u0026ndash;60 mm in both seasons. Amount of irrigation provided was 1166 mm (PR 2018-19) and 763 mm (PR 2021-22) in WW plot and 780 mm (PR 2018-19) and 463 mm (PR 2021-22) in WS plots (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Significant amount of water was partitioned into deep percolation both in WS and WW plots. Simulation results further suggested that crop in WS experienced water stress during 14th, 15th, 17th and 18th weeks for 18 days in PR 2018-19, and 14th ,15th, 16th, 19th and 20th week for 45 days (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Further Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the daily crop water requirements with actual ET under moisture stressed plot and non-stressed plots. Daily crop water requirement for groundnut MAGIC population varied from 1 mm to 3 mm in PR 2018-19 depending on the growth stage. It is to be noted that 14-20th weeks are critical in terms of crop water requirements as crop demands significant amount of water from flowering to pod formation stage. Daily crop water requirements vary from 5 to 7 mm/day during this period.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWater budget components of managed stress experiments\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater budget components in mm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuto-irrigation simulation (in mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWith stress (in mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWell water (in mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e(2018-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoisture available at beginning of sowing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eET actual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep percolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBalance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of stress days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" align=\"left\"\u003e\n \u003cp\u003e(2021-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMoisture available at beginning of sowing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrrigation applied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eET actual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep percolation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBalance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of stress days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGroundnut, mostly grown as rainfed crop in semi-arid often experiences drought stress during mid- and end-season of the crop growth that coincides with pod filling thus affecting the pod and kernel yield and quality. Heat and intermittent moisture stress, which frequently occur together, can reduce groundnut pod yield up to 72% (Hamidou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Despite setting drought tolerance as the main objective of many groundnut breeding programs across the globe, progress has been slow given the quantitative and complex nature of drought tolerance trait and the challenges associated with screening for drought tolerance for selection. Managed stress environment or screening in drought target sites offer an ideal field screening platform for selecting drought tolerant lines in a breeding program. However, screening for drought tolerance at the drought target site is a challenge, given the variable frequency and intensity of occurrence of drought at the sites of testing. While establishing a managed stress environment and screening large number of breeding lines in MSE requires huge resources. Consequently, the groundnut breeding programs delay the selection for drought tolerance to later generations when the number of lines are relatively small to handle. The slow progress for drought tolerance in groundnut can be attributed to screening of a limited number of selection candidates. Besides, the selection based on yield and yield attributes of the selection candidates may have resulted in the loss of alleles contributing to drought tolerance in the selected candidates.\u003c/p\u003e \u003cp\u003eThe primary focus of groundnut breeding programs across the world in the past was on increasing yield and addressing the yield-limiting factors, such as, diseases and drought, however the progress on drought tolerance has been slow. The scientific work on drought tolerance in groundnut at the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT) began during 1976, which works for groundnut improvement for the target countries in Sub-Saharan Africa and Asia. The efforts for improvement of drought tolerance started with the identification of sensitive stages of growth for drought stress. The studies identified that the pegging and pod filling as the most sensitive stages for drought stress in groundnut (Stirling et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Patil and Gangavani, 1990; Meisner et al., 1991; Ramachandrappa et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Prasad et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The tips of the pegs are sensitive and when they touch the dry soil surface, they abort drastically affecting pegging and consequently the number of pods and yield. The drought stress during the pod development stage affects the filling of the pods, consequently ill filled pods, also referred as pops are formed resulting in reduced pod yield and shelling outturn. At ICRISAT, progress has been made over the last four decades in selecting for yield under water-stressed conditions, and some drought tolerant varieties have been released for cultivation in Asia and Africa (Monyo et al., 2016; Desmae et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUse of trait-based approaches need understanding of key traits contributing to drought adaptation that aid in selection for identifying and breeding drought-tolerant cultivars. Separate selections performed on same set of crosses, one based on yield and yield parameters (empirical approach) and that other based on drought tolerance surrogate traits such as carbon isotope discrimination, WUE, and specific leaf area were found inconsistent in selecting high yielding peanut genotypes (Nigam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Nonetheless, selection of physiological traits or their surrogates in the breeding schema will be advantageous. However, measurement of the traits such as specific leaf area (SLA), carbon isotope discrimination (d13C) and SPAD chlorophyll meter reading (SCMR) is difficult, laborious, and costly even if they help in selections (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Breeding for drought adaptation using yield alone as a selection criterion is generally inefficient since yield is an integration of complicated mechanisms at different stages of organisation affected by many elements of the phenotype and the environment interacting in a complex and often unknown ways (Chimungu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Rapid progress in plant phenomics enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits (Furbank et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Integration of plant structure traits measured on a high-throughput phenotyping platform (HTPP) as a filter ahead of actual field screening under managed stress environment can speed up the selections in early generations and can enhance the genetic gain (Watt et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, an imaging based HTPP platform, LeasyScan measured water use related early canopy traits \u003cem\u003eviz.\u003c/em\u003e, digital biomass, plant height, leaf area 3D, leaf area index, projected leaf area, leaf angle and light penetration depth was used together with stress tolerance indices developed by Thiry et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e. The leaf area 3D measured using LeasyScan and actual leaf area has recorded 0.94 coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) (Vadez et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sivasakthi \u003cem\u003eet al\u003c/em\u003e. 2018 used LeasyScan platform to measure canopy traits in chickpea to map plant vigour traits. In a groundnut MAGIC population (600) and a set of advanced breeding lines (ABLs-1) (100), significant genotypic differences among genotypes were observed for the eight traits measured under the LeasyScan platform as well as for DFF, SCMR, yield, SP and HKW measured under WW and WS conditions of a managed stress environment. High heritability coupled with high genetic advance as percent of mean was recorded for digital biomass, plant height, leaf area 3D, leaf area index, projected leaf area, and light penetration depth. Genetic advance is a measure of gain achieved in a trait under a certain selection pressure (Ogunniyan \u003cem\u003eet al\u003c/em\u003e. 2014), and high genetic advance coupled with high heritability estimates in the study suggest the involvement of an additive component in the traits and indicate possible gain through selections for the traits. For selecting genotypes under moisture stress SCMR (Nigam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Varshney et al., 2008; Upadhyaya et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Janila et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shaibu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bacharou et al., 2019) trait has been widely used as one of the surrogates of transpiration efficiency. SCMR measures the light-transmittance characteristics of the leaf which is dependent on the leaf chlorophyll content (Richardson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, the H\u003csup\u003e2\u003c/sup\u003e for SCMR has been recorded low (31%) by Janila et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and moderate (55%) by Shaibu et al., (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in different populations revealing less to moderate contribution of genetic effect into phenotype. Although in the managed stress condition in the present study high H\u003csup\u003e2\u003c/sup\u003e was recorded for SCMR under both WW and WS conditions which explain high impact of genotype on phenotype, however, its use in large breeding programs is limited as the SCMR recording cannot be completed in a specified time interval. Most groundnut breeding programs follow an empirical approach for screening against moisture-deficit stress tolerance, which is largely based on pod and kernel yield under moisture-deficit stress (Nigam et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Janila et al., 2016). Often the breeding program employ a selection strategy where early generation selection is based on yield under normal growing conditions, followed by screening under drought stress when the number of lines are small. Such a breeding schema, that delays the screening for drought stress to later generations limits the progress for drought tolerance as a small set of lines are subjected to screening. Besides selection primarily for yield alone has also contributed to slow progress in drought tolerance in groundnut.\u003c/p\u003e \u003cp\u003eResults from the present study standardised a \u0026lsquo;selection strategy\u0026rsquo; and integrated into the breeding schema wherein early generation materials (F\u003csub\u003e2\u003c/sub\u003e to F\u003csub\u003e3\u003c/sub\u003e, F\u003csub\u003e3\u003c/sub\u003e to F\u003csub\u003e4\u003c/sub\u003e) are advanced in a rapid generation advancement (RGA) facility using single seed decent followed by raising the F\u003csub\u003e4\u003c/sub\u003e single plants and F\u003csub\u003e5\u003c/sub\u003e plant progenies in the field from which F\u003csub\u003e6\u003c/sub\u003e plant progeny rows are harvested. F\u003csub\u003e6\u003c/sub\u003e progenies are screened in a 3D imaging based high throughput phenotyping platform, LeasyScan. Early canopy traits like digital biomass, leaf area 3D and plant height from HTPP screening are utilized to narrow down the number of F\u003csub\u003e6\u003c/sub\u003e progenies to be tested under managed stress environment. Around 40% of population is advanced for screening under managed stress environment. About 10 seeds from F\u003csub\u003e6\u003c/sub\u003e progeny are used for HTTP screening and the remnants seeds of selected progenies are grown in progeny rows to harvest sufficient seed for field screening in a managed stress environment. Resilience and productivity indices measured under managed stress environment are used as a selection criterion to advance up to 20% of the progenies for multi-environment testing (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e). New selection indices proposed by Thiry et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) are used to identify genotypes with high productivity, resilience and both resilience and productivity. The proposed breeding schema integrate early generation selection to fix the early canopy vigour traits for drought adaptation using HTPP followed by screening of selected lines under a managed stress environment using new selection indices to speed up drought tolerance selections and to enhance the genetic gain. To have better control over error while screening under a managed stress environment, superior lines (~\u0026thinsp;30\u0026ndash;40%) for early canopy traits such as digital biomass, leaf area 3D and plant height can be filtered for screening in managed stress environments. Digital biomass and leaf area 3D index are derived traits, with high heritability and associated with other directly measured traits in LeasyScan. Biomass production is often used as a criterion to select drought adaptive genotypes in peanut. Leaf area index is a measure to quantify amount of foliage in the crop canopy. It is frequently used by physiologist and plant breeders for indirect selection of drought adaptive genotypes in groundnut (Schubert and Reed, 2005; Nigam and Aruna, 2007; Arunyanark, et al., 2008). Plant counter moisture stress by reducing the length of main shoot and side branches, thereby causing a reduction in plant height (Reddy and Anbumozhi, 2003). Traits like leaf angle and leaf inclination were not used in selection due to lack of understanding of their role in drought adaptation in groundnuts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eSelection for yield alone is not an efficient strategy for improving drought tolerance in groundnut. The proposed \u0026lsquo;selection strategy\u0026rsquo; is efficient for genetic enhancement of drought tolerance as it fixes the early canopy vigour traits before advancing the lines to yield evaluation under managed stress environment. Compared to traditional selection approaches, the breeding scheme that employs the proposed \u0026lsquo;selection strategy\u0026rsquo; for drought tolerance can save the time and money needed for screening by 20 to 30%. Selection for early canopy traits can retain the favourable alleles for water-saving traits. Superior lines selected using the proposed \u0026lsquo;selection strategy\u0026rsquo; carry alleles for water-saving traits and high yield potential under normal as well as drought conditions can be recycled as parents in the breeding pipelines to accumulate the favourable alleles for drought tolerance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was financially supported by CRP-Grain Legumes and Dryland Cereals (CRP-GLDC) and OPEC Fund for International Development (OFID) with grant number 13161.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnkush Purushottam Wankhade:\u003c/strong\u003eInvestigation, Writing-original draft, Data curation.\u003cstrong\u003e\u0026nbsp;Ashutosh Purohit:\u003c/strong\u003e Investigation, Writing-original draft, Data curation, Validation, Visualization, Software.\u0026nbsp;\u003cstrong\u003eSeltene Abady:\u0026nbsp;\u003c/strong\u003eInvestigation, Writing-review and editing, Data curation.\u0026nbsp;\u003cstrong\u003eVivek Pandurang Chimote:\u0026nbsp;\u003c/strong\u003eWriting-review and editing.\u0026nbsp;\u003cstrong\u003eAnilkumar Vemula:\u0026nbsp;\u003c/strong\u003eFormal analysis, software,Writing-review and editing\u003cstrong\u003e. Kaushal Garg:\u0026nbsp;\u003c/strong\u003eFormal analysis, Writing-review and editing\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003eSunita Choudhary:\u003c/strong\u003e Conceptualization, Resources,Writing-review and editing.\u003cstrong\u003e\u0026nbsp;Jana Kholova:\u003c/strong\u003e Conceptualization, Resources,Writing-review and editing.\u003cstrong\u003eGraeme C. Wright\u003c/strong\u003e: Writing-review and editing.\u0026nbsp;\u003cstrong\u003eDevraj Lenka:\u003c/strong\u003e Writing-review and editing\u003cstrong\u003e.\u003c/strong\u003e \u003cstrong\u003eJanila Pasupuleti:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Project administration, Funding acquisition, Resources, Supervision, Writing-review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to Surendra Singh Manohar, Sunil Choudhari, B. Rekha, Gopi Potupureddi, and K. Sivasakthi for their support in conducting the experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix A. Supporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data associated with this article can be found in the online version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbady S, Shimelis H, Janila P, Deshmukh D, Wankhade A, Chaudhari S, Manohar SS (2021) Combining ability analysis of groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) genotypes for yield and related traits under drought-stressed and non-stressed conditions. 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CRC, Boca Raton, FL, USA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Wang Q, Xia G, Wu Q, Chi D (2021) Continuous regulated deficit irrigation enhances peanut water use efficiency and drought resistance. Agric Water Manag 255:106997. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agwat.2021.106997\u003c/span\u003e\u003cspan address=\"10.1016/j.agwat.2021.106997\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"International Crops Research Institute for the Semi-Arid Tropics","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Drought tolerance, early canopy vigour, groundnut, HTPP, LeasyScan, managed stress environment","lastPublishedDoi":"10.21203/rs.3.rs-5503687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5503687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrought stress is a major production constraint of groundnut in Africa and Asia where it is largely grown as rainfed crop. The experiments aim to design an early testing approach for drought tolerance in the groundnut breeding pipeline to ensure sustainable production. A population of 600 multi parent advanced generation inter-cross (MAGIC) lines (MLs) (F\u003csub\u003e8/9\u003c/sub\u003e generation) and 100 advance breeding lines (ABLs) were studied in LeasyScan, a high throughput phenotyping platform (HTPP) to assess early canopy growth, and under a managed stress environment (MSE). MSE ensures uniform water application in well-watered and water-stressed plots, while intermittent drought is imposed in water-stressed plots from 1000\u003csup\u003e0\u003c/sup\u003e cumulative thermal time (CTT) during pod-filling stage. Digital biomass, leaf area 3D and plant height measured under HTPP recorded high heritability along with high genetic gain and were identified for use as selection criteria for early canopy vigour. The second selection criteria is Mean Score Index (MSI) (1 to 10 scale), which accounts for both resilience and productivity capacity indices (RCI and PCI), with the MSI ranging from 1.4 to 8.4. Based on results, a two-step selection approach is proposed for selection of traits required for adaption under drought stress. The approach involves HTPP (LeasyScan) to select early canopy vigour followed by selection based on MSI under MSE. MSE is field based and expensive, hence screening of a large number of selection candidates under HTTP helps to select a relatively small subset of early vigour lines for screening under MSE for agronomic performance.\u003c/p\u003e","manuscriptTitle":"Step-wise selection using high throughput phenotyping platform (HTTP) and stress tolerance indices as an approach for improving drought tolerance in groundnut (Arachis hypogaea L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 12:22:10","doi":"10.21203/rs.3.rs-5503687/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3645d044-390d-42bc-b7d1-7ed8c4fe2fa7","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40627584,"name":"Hydrology"}],"tags":[],"updatedAt":"2024-11-25T12:22:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-25 12:22:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5503687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5503687","identity":"rs-5503687","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}