1. Introduction
Maize is a crop of considerable economic importance, used as raw material for ethanol, silage, and other derivatives [1]. According to Campos et al. [2], it is the staple food of more than 1.2 billion people in Africa and Latin America.
In the northeast region of Brazil, it serves as the main support for most farmers [3]. However, achieving high levels of productivity requires an adequate water supply throughout its cycle [4,5].
In the northeastern semi-arid region, maize represents approximately 30% of the planted area. This environment is characterized by low rainfall and high potential evapotranspiration, directly affecting its development [6,7,8]. According to Baënziger and Araus [9], maize is more susceptible to drought than other cereals, especially during the reproductive and grain-filling stages, resulting in productivity losses of around 15 to 20% with prolonged exposure [10,11].
There are basically two strategies to mitigate productivity loss: The first involves the development or selection of new cultivars (hybrids with greater heterotic effect) tolerant to water stress. This significantly contributes to sustainable development and the conservation of water resources. The second strategy is nitrogen management to optimize water use [12]. The first strategy is more recommended, while the second, for the semi-arid conditions of the northeast, becomes unfeasible due to the high poverty rate among small farmers in the region, who lack the financial resources to invest in production technologies [13,14,15].
In this context, evaluating and selecting new cultivars developed by breeding programs for recommendation to small- and medium-sized farmers becomes essential for subsistence agriculture. There are dozens of methods for analyzing cultivar adaptability and stability under various environmental conditions, each with its particularities and statistical properties. The breeder should be intimately familiar with them and choose those that best fit their real needs.
The methodology of mixed linear models using restricted maximum likelihood and the best linear unbiased predictor (REML/BLUP), as outlined by Resende [16], can be applied across various environments and for each assessed trait. This approach provides parameters for genotypic adaptability and stability. In simpler terms, utilizing this methodology enables the genetic selection of more productive cultivars under diverse stress conditions. This is achieved through the harmonic means of the relative performance of genotypic values, which are already adjusted for instability and enhanced by adaptability [17,18,19,20].
Furthermore, it is important to note that when subjected to stressed conditions, plants need to acclimate to the unfavorable environment factors, such as climate, soil, and temperature. [21]. In this context, acclimatization refers to inherent changes in gene expression and the production of specific compounds during exposure to stress conditions. Proteomic analysis facilitates the identification of peptides derived from the expression of particular genes activated during stress perception. This enables the development of functional molecular markers that can aid breeding programs in creating, identifying, and recommending new cultivars suited for prolonged drought cultivation [22].
Another significant aspect to highlight is the research development conditions in Brazil. Souza et al. [23] conducted an investigative study and identified several problems within research development. Among the main concerns pointed out by the interviewed researchers were the scarcity of resources allocated to research and the inadequate infrastructure for its execution.
Given the aforementioned points, this study proposes genetic selection and proteomic analysis as a cost-effective strategy for identifying drought-tolerant maize cultivars.
2. Materials and Methods
2.1. Vegetal Material and Water Regimes Used
This experiment was conducted in a greenhouse located at the Center for Agrofood Science and Technology of the Federal University of Campina Grande (CCTA/UFCG) in Pombal, PB (06°46′12″ S, 37°48′07″ W). The region experiences an average annual temperature of 26.7 °C and an average annual rainfall of 800 mm, characterized by irregular distribution [24]. The experiment was initiated in January 2017 and extended over a span of 90 days. Five maize cultivars were evaluated: AG8677P (single hybrid), 30F53YH (single hybrid), 2B688PW (triple hybrid), 2B604PW (triple hybrid), and ROBUSTO (open-pollinated cultivar, employed as a control). Planting was performed in 20 dm3 pots, with three seeds at a depth of 4 cm. The soil used to fill the pots was categorized as Fluvic Neosol [25] and Entisol [26], both exhibiting a sandy-loam texture in the 0–30 cm layer. This soil was sourced from the municipality of Pombal, PB. The soil’s chemical attributes (as presented in Table 1) were determined using the methodologies outlined by Donagema [27].
To determine the water regimes employed in the experiment, three pots were saturated to field capacity with water, and these pots were enveloped in PVC paper to induce water loss solely through drainage.
The field capacity was ascertained by employing weighing lysimeters, following these steps: After filling the pots with the substrate, their initial mass (pot + substrate) was measured. Then, water was gradually added until the initiation of drainage. Subsequently, water addition ceased, and the pot was covered with plastic film to prevent evaporation losses.
Once the drainage was complete, marked by the cessation of water flow from the pot’s holes, a subsequent weighing was conducted, measuring the combined mass of the pot, substrate, and retained water. By subtracting the initial mass from this final value, the amount of water retained by the soil was calculated. This value stood as the field capacity, representing the soil’s water retention capability at 100%. This procedure was executed in three pots.
Consequently, distinct experimental water regimes were established: 30% (stress condition) and 60% of the field capacity. These regimes were maintained through daily irrigation in the late afternoon. Water stress was imposed throughout the plant’s life cycle, encompassing both the vegetative and reproductive stages of the crop. The attributes evaluated at the 90-day mark included the plant height (PH), leaf width (LW), green plant mass (GPM), dry plant mass (DPM), number of grains per ear (NG), and weight of one hundred seeds (GW100 g). These evaluations were conducted in accordance with the recommendations put forth by Alvarenga et al. [28].
2.2. Experimental Design and Statistical Analysis
The experimental design utilized was a randomized complete block design, employing a 2 × 5 × 3 factorial scheme. This scheme incorporated 2 water regimes (R1 = 30% and R2 = 60% of the field capacity), 5 cultivars, and 3 replications, amounting to a total of 30 experimental units. The collected data underwent deviance analysis (ANADEV). Variance components and predicted genetic values (REML/BLUP) were estimated, and harmonic means of relative performance of genetic values (HMRPGV) were also derived. All statistical analyses were conducted using the Selegen software. For a more comprehensive understanding of mixed-model analysis and the Selegen software, readers are directed to Resende [16]. It is important to note that the Selegen software is made available free of charge to the entire international scientific community.
2.3. Total Soluble Protein Extraction
The extraction of total proteins from maize leaves, subjected to various treatments including control and stressed conditions, was conducted using the method outlined by Hurkman and Tanaka [29], with modifications as per Boaretto [30]. The quantification of protein extracts followed the protocol outlined by Bradford [31], using ultrapure water and Bradford reagent as the control solutions. In the case of the samples, 10 μL of the protein extract and 2 mL of the Bradford reagent were added to Eppendorf tubes. Absorbance readings were taken in triplicate using a spectrophotometer (Biochrom WPA Biowave DNA) set at 595 nm.
2.4. SDS-PAGE
To assess protein integrity, denaturing polyacrylamide gel electrophoresis (SDS-PAGE) with a 10% acrylamide composition was conducted in a 20 × 20 cm configuration. The running gel solution consisted of 30% acrylamide, 4× running Tris solution, 10% ammonium persulfate (APS), and TEMED in ultrapure water. The packing gel solution contained 30% acrylamide, 4× packing Tris solution, 10% ammonium persulfate (APS), and TEMED in ultrapure water. After polymerization, 6 μL of molecular marker and 50 μg of proteins from both treatments, in triplicate, were applied. The electrophoresis system was assembled, and 1× Tris-glycine running buffer (250 mM Tris, 192 mM glycine, and 1% SDS) was introduced. Protein migration took place for 2 h and 30 min with a constant current of 34 mA and a voltage of 300 V. To visualize the protein bands, the gel was immersed in a fixing solution (10% acetic acid, 40% ethanol, and 50% distilled water) for 30 min with agitation. Subsequently, the gel was submerged in a Coomassie Blue staining solution (0.1% Coomassie Blue G-250, 8% ammonium sulfate, 1.6% phosphoric acid, and 20% methanol) for a duration of 20 h. Following this incubation, the gel underwent three 10 min rinses with distilled water, and then it was decolorized for 1 h using a 1% acetic acid solution. Finally, the gel was preserved in a solution of 5% acetic acid.
2.5. Image Analysis and Mass Spectrometry
The gels were scanned, and the images were processed using the photo editing software PhotoScape (Mooii Tech. Version 3.7). The chosen bands were excised from the gels, subjected to trypsin digestion, and the resulting peptides were then analyzed via mass spectrometry (AutoFlex III MALDI-ToF-ToF; Bruker Daltonics, Billerica, MA, USA). The acquired spectra were assessed using the Mascot program against the Swissprot database within the Viridiplantae taxonomic unit. Following protein identification, the corresponding accessions were submitted to the Uniprot database (
3. Results
The outcomes concerning deviance analysis, variance components (REML), and estimates of genetic parameters are showcased in Table 2.
The deviance analysis revealed significant differences among cultivars for all evaluated traits. It was also observed that the genotypic variance values were higher than the cultivar χ environment interaction variance (due to water regimes) for all traits, except for GW100g, where the genotypic variance was also higher than the residual variance. Broad-sense heritability was notably high for GW100g. The coefficient of determination of the cultivar χ environment interaction indicated a limited contribution of the cultivar χ environment interaction variance to the phenotypic variance of cultivars. The genotypic coefficient of variation was notably high, exceeding ten for the traits GPM, DPM, NG, and GW100g, which are essential components of productivity. The mean heritability displayed a moderate magnitude for PH and NG, and a high magnitude for the other traits. Concerning the genotypic correlation coefficient, high values were observed for all traits, demonstrating the accuracy of cultivar selection. Finally, the predicted genetic gains (individual BLUP) in relation to the performance of the evaluated cultivars under the two water regimes were satisfactory (as shown in Table 3).
Genetic gains were exclusively observed for the AG8677P cultivar in relation to the PH trait. Regarding LW, genetic gains were attained for the 2B688PW, 30F53YH, and AG8677P cultivars. For the GPM and DPM traits, genetic gains were recorded for the ROBUSTO and 30F53YH cultivars. Substantial genetic gains were achieved for the NG trait in the AG8677P, 2B688PW, and 2B604PW cultivars. Lastly, genetic gains for the GW100g trait were secured for the AG8677P, 30F53YH, and 2B688PW cultivars. The harmonic mean of the relative performance of genotypic values concerning the PH and NG traits among the five evaluated maize cultivars under water stress conditions is illustrated in Figure 1.
The highest harmonic mean values for NG were achieved by the AG8677P, 2B688PW, and 2B604PW cultivars. Due to the superior performance of the AG8677P genotype under water stress conditions compared to the control cultivar, it was chosen for proteomic analysis.
Notwithstanding the limitations associated with the 2D gel, the SDS-PAGE gel facilitated the identification of distinct bands in both treatments. From these bands, the 10 with the highest intensity were selected for protein identification through mass spectrometry (as depicted in Figure 2).
It seems that the AG8677P cultivar, when exposed to the water stress condition, exhibited a distinct response by expressing specific genes, resulting in a higher number of differentially expressed proteins (as shown in Figure 3).
These proteins have previously been identified in studies involving maize, sugarcane, and other cultivated species. They are recognized for their association with water deficit stress (Table 4).
The identification of proteins within each band enabled their association with diverse biological processes, including photosynthesis, defense mechanisms, and carbohydrate metabolism, among others.
4. Discussion
The significant differences revealed through the deviance analysis indicate the presence of genetic variability among the cultivars for the assessed traits. This circumstance is highly advantageous for breeding, as it allows the selection and recommendation of cultivars that express superior phenotypic traits even under water stress conditions. In a similar vein, Melo et al. [32] detected genetic variability and selected materials with enhanced productivity while evaluating the agronomic performance of maize cultivars under water stress conditions.
The substantial values of genotypic variance in relation to genotype–environment interaction variance (due to water regimes) imply a stronger genetic rather than environmental influence on the expression of these traits [33]. This implies that even in adverse conditions, such as water deficit, the cultivars exhibited considerable genetic potential. As for GW100g, the genotypic variance surpassed both the cultivar χ environment interaction variance and the residual variance. This outcome is of paramount significance. While acknowledging that quantitative traits are greatly influenced by the environment, the present study predominantly observed the genetic component’s influence. Consequently, the chosen cultivars are likely to consistently demonstrate their performance when cultivated by farmers in this region.
The presence of high-magnitude genotypic correlations suggests the possibility of identifying cultivars whose phenotypic expression of the mentioned traits, with a predominant genetic component, remained stable under the assessed water regimes. Silva et al. [34] propose that this stability is attributed to the consistent repetition of these traits. In the context of this study, which encompasses the evaluation of maize cultivars under two distinct water regimes, it can be inferred that the consistent repetition of traits occurred under the 30% water regime, thereby consistently simulating water deficit stress conditions.
The low coefficients of determination for the cultivar χ environment interaction indicate minimal involvement of the cultivar χ environment interaction variance (due to water regimes) in the phenotypic variance of the maize cultivars. This observation is corroborated by the heritability coefficients. With an average heritability of high magnitude—values surpassing 0.46 for all traits—the cultivars display substantial selection potential and a promising outlook for attaining significant genetic gains [35].
Nevertheless, the broad-sense individual heritability exhibited a low magnitude for AP and NG. In this scenario, the cultivars displayed divergent performances concerning these attributes under the two water regimes. In simpler terms, the cultivars’ performance regarding these traits was influenced by the water regimes. Consequently, it becomes crucial to calculate adaptability and stability parameters for these traits in order to pinpoint the cultivars that exhibit the most noteworthy performance.
Genotypic coefficients of variation exceeding 10 are regarded as high, as defined by Oliveira et al. [36]. According to Carvalho et al. [37], this implies that a substantial portion of the genotypic variance was extracted from the overall phenotypic variation. This highlights the need to prioritize selection based on these traits.
The experimental coefficient of variation exhibited a remarkably high value for the GW100g and NG traits, a value akin to that observed in other studies conducted with maize crops [38]. It is noteworthy that the methodology of mixed linear models, as outlined by Resende [16], inherently addresses data heterogeneity and imbalances, resulting in more accurate estimations. The author further asserts that these procedures enable the handling of intricate data structures that traditional ANOVA cannot accommodate. Mendes et al. [39] argue that achieving high accuracy in mixed models reflects strong experimental quality. Within the scope of this study, significant levels of accuracy are apparent across all traits. Pereira et al. [40] contend that accuracy relies on trait heritability and pertains to the correlation between predicted genetic values and the actual genetic values of individuals. Consequently, it can be deduced that effective selection for water stress conditions is attainable among the assessed cultivars [41].
Upon observing the predicted genetic values for selection under water stress conditions, it becomes evident that in the case of LW, the cultivar 2B688PW exhibited a higher mean, unaffected by interaction effects. This aspect positions it as a secure choice for both forage and animal feed. As a simple hybrid, Mendes et al. [39] contend that it stands as a suitable cultivation alternative for less technically advanced regions. This choice enables storage and replanting, thereby circumventing the necessity for annual seed purchases.
Similarly, the cultivar ROBUSTO proved to be an excellent option for animal feed due to its higher mean for GPM and DPM. Nonetheless, for overall productivity (NG and GW100g), it registered lower means, possibly due to its classification as an open-pollinated variety. For NG and GW100g, cultivars AG8677P and 2B688PW exhibited superior means, predicted genetic values, and genetic gains, effectively displaying their superiority over the other cultivars. The heightened productivity of these cultivars can be attributed to their status as simple hybrids, which manifests heterosis.
In relation to adaptability and stability parameters, employing the method of highest genotypic values (HMRPPGV) entails a simultaneous consideration of adaptability, stability, and productivity [42]. Consequently, for the traits PH and NG, the cultivar AG8677P stands out as the most stable, exhibiting superior productivity and demonstrating a heightened adaptation to water stress conditions. As previously emphasized, the mixed linear models methodology (REML/BLUP), as outlined by Resende [16], offers the advantage of applicability across diverse environments, in contrast to other methods employed for adaptability and stability analyses. For instance, the approach advocated by Eberhart and Russel [43], based on simple linear regression, is widely utilized; however, its utilization is discouraged in scenarios with a limited number of environments, as it may lead to the non-rejection of null hypotheses [44].
Based on the findings presented, it can be deduced that the mixed linear models methodology has demonstrated efficacy by identifying the cultivar AG8677P as being tolerant to water stress.
In the realm of conventional genetic improvement, the strategy of indirect selection has been utilized to develop maize cultivars resilient to water stress, as exemplified by the works of Bernini et al. [45] and Crosa et al. [46]. This methodology centers around selecting cultivars within stress conditions, a process in which the choice is informed by the phenotypic assessment of physiological or productivity traits, with subsequent data analysis conducted using traditional ANOVA methods.
In contrast, the mixed linear models methodology offers a more comprehensive and efficient approach. In addition to facilitating deviance analysis decomposition, enabling the acquisition of vital genetic parameters such as heritability, this approach allows for the computation of selective accuracy—a feat not attainable through conventional ANOVA techniques. As emphasized earlier, this accuracy refers to the correlation between predicted genetic values and the actual genetic values of the cultivars being assessed under stress conditions. Such an enhancement considerably elevates the effectiveness of the selection process [47].
With the integration of modern biotechnological techniques into conventional breeding, the opportunity arises to implement direct selection based on molecular data. In this scenario, marker-assisted selection has been harnessed to acquire cultivars resilient to abiotic stresses, relying on the selection driven by QTLs (Quantitative Trait Loci) [48]. As outlined by Resende et al. [49], for marker-assisted selection to be effective, it must encompass a substantial fraction of the genetic variation pertaining to a quantitative trait, which is governed by numerous alleles/loci of slight effects. Given that these QTLs are significantly influenced by environmental factors, only a limited number have been identified, constraining the efficacy of marker-assisted selection and yielding modest gains [50].
Conversely, proteomic analysis enables the extensive assessment of gene expression at the translational level, facilitating the identification of peptides associated with abiotic stress tolerance mechanisms. In the present study, peptides linked to the mechanism of water stress tolerance were discerned as maize cultivars experienced shifts in gene expression in response to the specific stress.
Despite the limitation of SDS-PAGE gel not separating proteins as efficiently as the 2D gel—specifically, in terms of size and electric charge—it can still be employed, coupled with a robust statistical methodology, in this case, linear mixed models, to foster greater confidence in identifying water-stress-tolerant cultivars. Moreover, due to its cost-effectiveness, it can find utility in laboratories and research centers grappling with financial resource constraints for their research endeavors.
The analysis of gene expression in the AG8677P cultivar unveiled significant proteins associated with the mechanism of water deficit stress tolerance, contributing to heightened productivity.
A pivotal mechanism for plants enduring water stress conditions involves regulating water loss, evading chronic dehydration, and promoting efficient, swift, and energetically favorable water uptake. The plant cell wall comprises cellulose, which interacts with hemicellulose—consisting of various simple carbohydrates. Cell wall expansion necessitates the separation of these components, facilitating their movement and enabling water absorption by the cell. Expansin B6 (spot E6) functions analogously by facilitating the separation of these layers [51], highlighting a plant strategy to judiciously utilize the available water.
Glutaredoxin-C13 (spot C1) exhibited visibly higher band intensity in the control group, and it engages with the glutathione-disulfide oxidoreductase system, binding to disulfide ions. Glutaredoxin-C13 also plays a role in Arabidopsis root growth, and silencing Glutaredoxin-C13 has been shown to directly reduce plant root growth by approximately 25% [52]. This observation implies a potential adaptation to water stress based on the quantity of Glutaredoxin-C13 present in the plant cell, which is absent in the AG8677P cultivar subjected to stress conditions.
A mechanism to mitigate water loss under adverse conditions like water stress involves the plant’s regulation of stomatal openings. However, this mechanism directly impacts photosynthesis by curtailing CO2 assimilation. The protein harboring the PsbP domain (spot E3), crucial in the photosynthetic process for water hydrolysis [53], exhibited increased band intensity under stress, suggesting a possible compensatory mechanism for the photosynthetic apparatus [54]. Among the proteins linked to stomatal opening is Calcium-dependent protein kinase 6 (spot E2; E4), a signaling protein that governs abscisic acid (ABA), vital for diverse cellular processes including gene expression modulation. It was identified under water stress, implying potential mechanisms for curbing water loss, whether through inhibiting stomatal openings or modulating genes essential for plant responsiveness under such conditions [55].
Another protein linked to ABA synthesis is Molybdopterin synthase (spot E1; E6), an indispensable component in the synthesis of the molybdenum cofactor, which was found to exhibit higher intensity under stress conditions. Molybdenum stands as an essential element in plant growth, and this cofactor actively participates in the biogeochemical cycles of carbon, nitrogen, and sulfur and the synthesis of select plant hormones [55]. This observation suggests a potential mechanism to safeguard against potential damage arising from water deficiency, albeit potentially at the cost of developmental processes and the assimilation of crucial nutrients for the plant.
Future investigations could be pursued to establish these molecules as functional molecular markers, thereby aiding maize breeding programs in the early selection of cultivars best suited for the edaphoclimatic conditions of the semiarid region of Paraíba.
5. Conclusions
Genetic selection, coupled with SDS-PAGE gel proteomic analysis, has demonstrated its efficiency even in the face of limited resources and a small number of treatments.
Proteins associated with diverse biologically crucial processes essential to the mechanism of drought stress tolerance were successfully identified.
The AG8677P cultivar exhibited exceptional productivity under drought stress conditions.
The proteins identified in the cultivar AG8677P subjected to drought stress conditions show promise for the development of functional molecular markers for the identification and development of new genotypes tolerant to drought stress.
Conceptualization, J.d.A.D.F. and T.C.J.; methodology, M.C.P., O.H.d.O. and C.R.d.S.; software, J.M.d.V., F.G.-S., G.R.M. and M.P.S.d.F.; validation, L.S.S., R.G.d.L. and A.d.S.S.; formal analysis, M.C.P., O.H.d.O., C.R.d.S., A.R.d.O.G. and R.R.d.A.L.; investigation, A.C.F.P., M.L.L.d.C., and M.R.P.d.F.; resources, L.S.S., R.G.d.L., A.d.S.S., and F.A.C.S.; data curation, F.C.G.A., L.d.S.A., D.G.N. and F.G.-S.; writing—original draft preparation, D.G.N., A.R.d.O.G., O.H.d.O. and A.E.P.S.; writing—review and editing, M.B.A.d.O., B.G.P.A., A.C.F.P., T.C.J. and F.A.C.S.; visualization, J.M.d.V., B.G.P.A., G.R.M. and M.R.P.d.F.; supervision, L.C.d.F. and T.C.J.; project administration, J.d.A.D.F. All authors have read and agreed to the published version of the manuscript.
Data sharing not applicable.
The authors acknowledge the National Council for Scientific and Technological Development (CNPq) for granting the scholarship.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Relative performance of genotypic values of the evaluated traits in five maize (Zea mays) cultivars under water stress conditions in the municipality of Pombal—PB (2017). In (A), HMRPGV is the Harmonic Mean of Relative Performance of Genotypic Values. In (B), HMRPGV (GM) is the Harmonic Mean of Relative Performance of Genotypic Values multiplied by the overall average of the environments.
Enlarge this image.Figure 2. SDS-PAGE gel of maize subjected to the control treatment with 60% soil water content and water stress treatment with 30% soil water content. Low molecular weight protein marker (GE Low Marker) was used.
Enlarge this image.Figure 3. Venn diagram representing differentially expressed proteins in cultivar AG8677P. In A, proteins expressed in the control treatment (AG8677P 60%). In B, proteins expressed under conditions of severe water stress (AG8677P 30%). C refers to the common protein in both treatments.
Chemical attributes of the soil sample employed as the substrate in the experiment.
| pH | P | K+ | Na+ | Ca2+ | Mg2+ | H+ + Al3+ | CEC | MO | PST |
|---|---|---|---|---|---|---|---|---|---|
| H2O | mg dm−3 | -------------------Cmolcdm−3---------------- | g kg−1 | % | |||||
| 7.1 | 868 | 0.34 | 0.02 | 5.0 | 2.7 | 0.0 | 8.06 | 5.71 | <1 |
CEC—cation exchange capacity. MO—organic matter: wet digestion using the Walkley–Black method; Ca2+ and Mg2+ extracted with 1 mol L−1 KCl at pH 7.0; Na+ and K+ extracted using NH4OAc 1 mol L−1 a pH 7.0; H+ and Al3+ extracted using 0.5 mol L−1 calcium acetate at pH 7.0; PST—exchangeable sodium percentage.
Deviance analysis, variance components (REML), and genetic parameter estimates for the evaluated traits in five maize (Zea mays L.) cultivars in the city of Pombal—PB (2017).
| Traits | ||||||
|---|---|---|---|---|---|---|
| PH | LW | GPM | DPM | NG | GW100 g | |
| Gv | 113.11 | 0.39 | 1485.7 | 356.56 | 2608.6 | 28.74 |
| VC × E | 1.77 | 0.00 | 86.93 | 6.65 | 25.16 | 1.60 |
| RV | 475.57 | 0.28 | 825.93 | 356.79 | 5411.0 | 7.62 |
| PV | 590.45 | 0.67 | 2398.5 | 719.80 | 8044.8 | 37.98 |
| h2 g | 0.19 | 0.58 | 0.62 | 0.50 | 0.32 | 0.76 |
| h2 m | 0.59 | 0.89 | 0.89 | 0.85 | 0.74 | 0.93 |
| CAG | 0.77 | 0.94 | 0.94 | 0.92 | 0.86 | 0.97 |
| R2C × E | 0.00 | 0.00 | 0.03 | 0.00 | 0.00 | 0.04 |
| rG × L | 0.98 | 0.98 | 0.94 | 0.98 | 0.99 | 0.95 |
| CVg (%) | 6.66 | 7.87 | 13.70 | 17.00 | 23.45 | 46.53 |
| CVe (%) | 13.65 | 6.63 | 10.21 | 17.01 | 33.78 | 23.96 |
| Mean | 159.77 | 7.92 | 281.40 | 111.03 | 217.74 | 11.52 |
| Effect | Likelihood ratio test (LRT) | |||||
| Genotype | 185.18 ** | 12.03 ** | 205.92 ** | 182.73 ** | 185.31 ** | 75.52 ** |
LRT values. ** Significant by the chi-square test at 1% probability, with 1 degree of freedom. GV: genotypic variance; VC χ E: cultivar χ environment interaction variance (water regimes); RV: residual variance; PV: phenotypic variance; h2 g: broad-sense heritability; h2 m: mean heritability; CAG: cultivar selection accuracy; R2C χ E: coefficient of determination of the cultivar χ environment interaction effects; rG χ L: genotypic correlation of cultivar performance in different environments (water regimes); CVg (%): genotypic coefficient of variation; CVe (%): residual coefficient of variation.
Predicted genetic gain estimates for the evaluated traits in five maize (Zea mays) cultivars under two water regimes in the municipality of Pombal—PB (2017).
| Traits | ||||||||
|---|---|---|---|---|---|---|---|---|
| Plant Height (PH)/Accuracy: 77% | ||||||||
| Cultivars | g + ge | u + g + ge | Genetic gain | Mean | ||||
| 30% | 60% | 30% | 60% | 30% | 60% | 30% | 60% | |
| AG8677P | 13.93 | 14.06 | 153.67 | 193.86 | 13.93 | 14.06 | 153.67 | 193.86 |
| 30F53YH | −1.08 | −0.99 | 138.65 | 178.80 | 6.42 | 6.53 | 146.16 | 186.33 |
| 2B688PW | −2.16 | −2.27 | 137.57 | 177.52 | 3.56 | 3.59 | 143.30 | 183.39 |
| 2B604PW | −3.16 | −3.22 | 136.55 | 176.57 | 1.87 | 1.88 | 141.61 | 181.68 |
| ROBUSTO | −7.50 | −7.55 | 132.23 | 172.24 | 0.00 | 0.00 | 139.73 | 179.80 |
| Leaf width (LW)/Accuracy: 94% | ||||||||
| 2B688PW | 0.75 | 0.75 | 8.50 | 8.84 | 0.75 | 0.75 | 8.50 | 8.84 |
| 30F53YH | 0.34 | 0.30 | 8.09 | 8.39 | 0.54 | 0.52 | 8.30 | 8.62 |
| AG8677P | 0.07 | 0.08 | 7.83 | 8.18 | 0.39 | 0.38 | 8.14 | 8.47 |
| ROBUSTO | −0.42 | −0.41 | 7.32 | 7.67 | 0.18 | 0.18 | 7.94 | 8.27 |
| 2B604PW | −0.74 | −0.72 | 7.00 | 7.36 | 0.00 | 0.00 | 7.75 | 8.09 |
| Green plant mass (GPM)/Accuracy: 94% | ||||||||
| ROBUSTO | 53.1 | 59.77 | 301.9 | 373.86 | 53.18 | 59.77 | 301.93 | 373.80 |
| 30F53YH | 21.0 | 11.65 | 269.8 | 325.71 | 37.12 | 35.71 | 285.87 | 349.70 |
| 2B688PW | −11.0 | −6.38 | 237.7 | 307.67 | 21.07 | 21.68 | 269.82 | 335.70 |
| AG8677P | −29.4 | −28.03 | 219.2 | 286.01 | 8.43 | 9.25 | 257.18 | 323.85 |
| 2B604PW | −33.7 | −37.01 | 215.0 | 277.03 | 0.00 | 0.00 | 248.75 | 314.05 |
| Dry plant mass (DPM)/Accuracy: 92% | ||||||||
| ROBUSTO | 22.78 | 23.69 | 120.29 | 148.26 | 22.78 | 23.69 | 120.29 | 148.25 |
| 30F53YH | 13.18 | 12.81 | 110.69 | 137.38 | 17.98 | 18.25 | 115.49 | 142.81 |
| 2B688PW | −5.06 | −4.35 | 92.45 | 120.21 | 10.30 | 10.72 | 107.81 | 135.28 |
| AG8677P | −13.8 | −14.03 | 83.65 | 110.52 | 4.26 | 4.53 | 101.77 | 129.09 |
| 2B604PW | −17.0 | −18.03 | 80.45 | 106.42 | 0.00 | 0.00 | 97.50 | 124.56 |
| Number of grains per ear (NG)/Accuracy: 86% | ||||||||
| AG8677P | 50.18 | 50.64 | 229.30 | 307.01 | 50.18 | 50.64 | 229.29 | 307.01 |
| 2B688PW | 18.90 | 18.93 | 198.02 | 275.29 | 34.54 | 34.79 | 213.65 | 291.15 |
| 2B604PW | 3.68 | 3.68 | 182.80 | 260.04 | 24.25 | 24.42 | 203.37 | 280.78 |
| 30F53YH | −10.3 | −10.33 | 168.79 | 246.02 | 15.61 | 15.73 | 194.73 | 272.09 |
| ROBUSTO | −62.4 | −62.93 | 116.67 | 193.42 | 0.00 | 0.00 | 179.12 | 256.36 |
| Weight of one hundred seeds (GW100g)/Accuracy: 97% | ||||||||
| AG8677P | 4.11 | 4.66 | 14.87 | 16.94 | 4.11 | 4.66 | 14.87 | 16.94 |
| 30F53YH | 2.82 | 4.08 | 13.58 | 16.36 | 3.47 | 4.37 | 14.22 | 16.65 |
| 2B688PW | 2.44 | 1.03 | 13.20 | 13.21 | 3.13 | 3.25 | 13.88 | 15.54 |
| 2B604PW | −0.75 | −1.02 | 10.00 | 11.25 | 2.15 | 2.18 | 12.91 | 14.47 |
| ROBUSTO | −8.64 | −8.75 | 2.11 | 3.52 | 0.00 | 0.00 | 10.75 | 12.28 |
Accuracy refers to the correlation between the predicted genetic values and the real genetic values of the evaluated cultivars. It is noteworthy that for the trait weight of one hundred seeds, the accuracy reached 97%, which means very high efficiency in the selection and 3% probability of error when practicing the selection based on this trait, which is the most important component of productivity.
Presumptive identification of maize leaf proteins subjected to control (AG8677P 60%) and stress (AG8677P 30%) treatments using the Mascot program.
| Treatment | Spot ID | Access | Protein | Score | Organism | Function |
|---|---|---|---|---|---|---|
| Control | 1.1_C | Q0IRB0 | Glutaredoxin-C13 | 47 | Oryza sativa | Electron transport |
| 2.3_C | Q0DNU1 | Transcription factor GATA 19 | 49 | Oryza sativa | Cell differentiation | |
| 2.8_C | Q38872 | Kinase 6 | 38 | Arabidopsis thaliana | Catalytic activity | |
| 2.10_C | Q7XA07 | Cytoplasmic Dynein 1 of Intermediate Light Chain 2 | 40 | Chlamydomonas reinhardtii | Development protein | |
| Stress | 1.2_E | O22827 | Molybdopterin synthase | 39 | Arabidopsis thaliana | Catalytic activity |
| 1.4_E | Q38872 | Kinase 6 | 36 | Arabidopsis thaliana | Catalityc activity | |
| 1.5_E | O49292 | Domain PsbP | 41 | Arabidopsis thaliana | Photosynthesis | |
| 1.6_E | Q38872 | Kinase 6 | 43 | Arabidopsis thaliana | Catalityc activity | |
| 1.7_E | Q6IVU7 | Expansin-B6 | 49 | Arabidopsis thaliana | Reproduction |
References
1. Deminicis, B.B.; Vieira, H.D.; Jardim, J.G.; do Carmo Araújo, S.A.; Neto, A.C.; de Oliveira, V.C.; da Silva Lima, E. Silagem de milho-Características agronômicas e considerações. Redvet Rev. Electron. Vet.; 2009; 10, pp. 1-6.
2. Campos, H.; Cooper, M.; Habben, J.E.; Edmeades, G.O.; Schussler, J.R. Improving drought tolerance in maize: A view from industry. Field Crops Res.; 2004; 90, pp. 19-34. [DOI: https://dx.doi.org/10.1016/j.fcr.2004.07.003]
3. Carvalho, H.W.L.D.; Leal, M.D.L.D.S.; Santos, M.X.D.; Monteiro, A.A.T.; Cardoso, M.J.; Carvalho, B.C.L.D. Estabilidade de cultivares de milho em três ecossistemas do Nordeste brasileiro. Pesqui. Agropecu. Bras.; 2000; 35, pp. 1773-1781. [DOI: https://dx.doi.org/10.1590/S0100-204X2000000900010]
4. Gouesnard, B.; Zanetto, A.; Welcker, C. Identification of adaptation traits to drought in collections of maize landraces from southern Europe and temperate regions. Euphytica; 2016; 209, pp. 565-584. [DOI: https://dx.doi.org/10.1007/s10681-015-1624-8]
5. Nagore, M.L.; Della Maggiora, A.; Andrade, F.H.; Echarte, L. Water use efficiency for grain yield in an old and two more recent maize hybrids. Field Crops Res.; 2017; 214, pp. 185-193. [DOI: https://dx.doi.org/10.1016/j.fcr.2017.09.013]
6. Kagoda, F.; Hearne, S.; Adewuyi, O.; Coyne, D.L. Response of drought tolerant maize inbreds to water stress under nematode infested conditions. Euphytica; 2015; 206, pp. 77-87. [DOI: https://dx.doi.org/10.1007/s10681-015-1474-4]
7. Lemos, M.C.; Finan, T.J.; Fox, R.W.; Nelson, D.R.; Tucker, J. The use of seasonal climate forecasting in policymaking: Lessons from Northeast Brazil. Clim. Chang.; 2002; 55, pp. 479-507. [DOI: https://dx.doi.org/10.1023/A:1020785826029]
8. Nascimento, M.; Tabosa, J.N.; Tavares Filho, J.J. Avaliação de cultivares de milho no agreste semi-árido de Pernambuco. Rev. Bras. Eng. Agrícola Ambient.; 2003; 7, pp. 53-56. [DOI: https://dx.doi.org/10.1590/S1415-43662003000100009]
9. Banziger, M.; Araus, J.L. Recent advances in breeding maize for drought and salinity stress tolerance. Advances in Molecular Breeding toward Drought and Salt Tolerant Crops; Jenks, M.A.; Hasegawa, P.M.; Jain, S.M. Springer: Dordrecht, The Netherlands, 2012; Volume 1, pp. 587-601.
10. Chen, J.; Xu, W.; Velten, J.; Xin, Z.; Stout, J. Characterization of maize inbred lines for drought and heat tolerance. J. Soil Water Conserv.; 2012; 67, pp. 354-364. [DOI: https://dx.doi.org/10.2489/jswc.67.5.354]
11. Santos, M.X.; Andrade, C.D.L.T.; Oliveria, A.C.; Leite, C.E.P.; Carvalho, H.W.L.; Gama, E.E.G.E.; Parentoni, S.N. Comportamento de híbridos de milho selecionados e não selecionados para ASI sob estresse de água no florescimento e no enchimento de grãos. Rev. Bras. Milho Sorgo; 2003; 2, pp. 71-81. [DOI: https://dx.doi.org/10.18512/1980-6477/rbms.v2n2p71-81]
12. Ertiro, B.T.; Olsen, M.; Das, B.; Gowda, M.; Labuschagne, M. Efficiency of indirect selection for grain yield in maize (Zea mays L.) under low nitrogen conditions through secondary traits under low nitrogen and grain yield under optimum conditions. Euphytica; 2020; 216, 134. [DOI: https://dx.doi.org/10.1007/s10681-020-02668-w]
13. Cardoso, M.J.; de Carvalho, H.W.L.; LEAL, M.D.L.; dos Santos, M.X. Comportamento, adaptabilidade e estabilidade de híbridos de milho no estado do Piauí no ano agrícola de 1998. Rev. Cient. Rural.; 2000; 5, pp. 146-153.
14. Carvalho, H.W.L.D.; Leal, M.D.L.D.S.; Cardoso, M.J.; Santos, M.X.D.; Carvalho, B.C.L.D.; Tabosa, J.N.; Albuquerque, M.M. Adaptabilidade e estabilidade de cultivares e híbridos de milho no Nordeste brasileiro no ano agrícola de 1998. Pesqui. Agropecu. Bras.; 2001; 36, pp. 637-644. [DOI: https://dx.doi.org/10.1590/S0100-204X2001000400006]
15. Carvalho, H.W.L.D.; Leal, M.D.L.D.S.; Santos, M.X.D. Estimativas de parâmetros genéticos na população de milho CMS 35 no Estado de Sergipe. Pesqui. Agropecu. Bras.; 2002; 37, pp. 1399-1405. [DOI: https://dx.doi.org/10.1590/S0100-204X2002001000007]
16. Resende, M.D.V. Sistema Estatístico e Seleção Genética Computadorizada via Modelos Lineares Mistos; Embrapa Florestas: Colombo, Brazil, 2007; pp. 145-156.
17. Borges, V.; Soares, A.A.; Reis, M.S.; Resende, M.D.V.; Maizeélio, V.M.O.; Leite, N.A.; Vieira, A.R. Desempenho genotípico de linhagens de arroz de terras altas utilizando metodologia de modelos mistos. Bragantia; 2010; 69, pp. 833-841. [DOI: https://dx.doi.org/10.1590/S0006-87052010000400008]
18. Bornhofen, E.; Benin, G.; Storck, L.; Woyann, L.G.; Duarte, T.; Stoco, M.G.; Marchioro, S.V. Métodos estatísticos para estudo de adaptabilidade e estabilidade em trigo. Bragantia; 2017; 76, pp. 1-10. [DOI: https://dx.doi.org/10.1590/1678-4499.557]
19. Oliveira, I.J.D.; Atroch, A.L.; Dias, M.C.; Guimarães, L.J.; Guimarães, P.E.D.O. Seleção de cultivares de milho quanto à produtividade, estabilidade e adaptabilidade no Amazonas. Pesqui. Agropecu. Bras.; 2017; 52, pp. 455-463. [DOI: https://dx.doi.org/10.1590/s0100-204x2017000600009]
20. Verissimo, M.A.A.; Oliveira, R.A.D.; Daros, E.; Härter, A. Genetic parameters and performance of sugarcane families under cold stress in the South of Brazil. Pesqui. Agropecu. Bras.; 2018; 53, pp. 583-592. [DOI: https://dx.doi.org/10.1590/s0100-204x2018000500007]
21. Nogueira, R.J.M.C.; Aloufa, M.A.I.; De Albuquerque, M.B. Stomatic behaviour and leaf water potential in young plants of Annona squamosa submitted to saline stress. Fruits; 2004; 59, pp. 209-214.
22. Pacheco, C.M.; Pestana-Calsa, M.C.; Gozzo, F.C.; Mansur Custodio Nogueira, R.J.; Menossi, M.; Calsa Junior, T. Differentially delayed root proteome responses to salt stress in sugar cane varieties. J. Proteome Res.; 2013; 12, pp. 5681-5695. [DOI: https://dx.doi.org/10.1021/pr400654a]
23. Souza, D.L.D.; Zambalde, A.L.; Mesquita, D.L.; Souza, T.A.D.; Silva, N.L.C.D. A perspectiva dos pesquisadores sobre os desafios da pesquisa no Brasil. Educ. Pesqui.; 2020; 46, pp. 1-21. [DOI: https://dx.doi.org/10.1590/s1678-4634202046221628]
24. Alves, M.D.F.A.; Alves, L.R.A.; Sarmento, E.B.; de Lima, G.A.; Crispim, D.L. Análise da precipitação pluvial de Pombal-PB relacionada com sistemas atmosféricos atuantes. Rev. Verde; 2015; 10, pp. 169-175. [DOI: https://dx.doi.org/10.18378/rvads.v10i2.3697]
25. Santos, H.G.; Jacomine, P.K.T.; Dos Anjos, L.H.C.; De Oliveira, V.A.; Lumbreras, J.F.; Coelho, M.R.; Cunha, T.J.F. Sistema Brasileiro de Classificação de Solos; Embrapa: Brasília, Brazil, 2018; pp. 1-356.
26. Soil Survey Staff. Available online: https://www.nrcs.usda.gov/sites/default/files/2022-09/The-Soil-Survey-Manual.pdf (accessed on 25 May 2023).
27. Donagema, G.K.; de Campos, D.B.; Calderano, S.B.; Teixeira, W.G.; Viana, J.M. Manual de Métodos de Análise de Solo; Embrapa Solos: Rio de Janeiro, Brazil, 2011; pp. 1-212.
28. Alvarenga, A.A.; Nery, F.C.; Rodrigues, A.M. Experimentação em Fisiologia Vegetal; Editora UFLA: Minas Gerais, Brazil, 2014; pp. 1-171.
29. Boaretto, L.F. Análise do Transcritôma e Proteôma do Colmo de Cana-de-Açúcar Relacionada ao Metabolismo da Sacarose. Doctoral Dissertation; Universidade de São Paulo: São Paulo, Brazil, 2012.
30. Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem.; 1976; 72, pp. 248-254. [DOI: https://dx.doi.org/10.1016/0003-2697(76)90527-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/942051]
31. Hurkman, W.J.; Tanaka, C.K. Solubilization of plant membrane proteins for analysis by two-dimensional gel electrophoresis. Plant Physiol.; 1986; 81, pp. 802-806. [DOI: https://dx.doi.org/10.1104/pp.81.3.802] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16664906]
32. Melo, A.V.; dos Santos, V.M.; Varanda, M.A.F.; Cardoso, D.P.; Dias, M.A.R. Desempenho agronômico de genótipos de milho submetidos ao estresse hídrico no sul do estado do tocantins. Rev. Bras. Milho Sorgo; 2018; 17, pp. 177-189. [DOI: https://dx.doi.org/10.18512/1980-6477/rbms.v17n2p177-189]
33. Souza, P.H.N.; Bastos, G.Q.; de Andrade Dutra Filho, J. Interação cana-de-açúcar x épocas de colheita na Microrregião Centro de Pernambuco via modelos mistos. Res. Soc. Dev.; 2020; 9, e713997700. [DOI: https://dx.doi.org/10.33448/rsd-v9i9.7700]
34. Silva, H.D.C.; Anunciação Filho, C.J.; Bastos, G.Q.; Dutra Filho, J.A.; Simões Neto, D.E. Repeatibility of agroindustrial characters in sugarcane in different harvest cycles. Rev. Ciênc. Agron.; 2018; 49, pp. 275-282. [DOI: https://dx.doi.org/10.5935/1806-6690.20180031]
35. Garcia, C.H.; Nogueira, M.C.S. Utilização da metodologia REML/BLUP na seleção de clones de eucalipto. Sci. For.; 2005; 68, pp. 107-112.
36. Oliveira, R.A.; Daros, E.; Bespalhok Filho, J.C.; Zambon, J.L.C.; Ido, O.T.; Weber, H.; Hugo, Z.E.N.I. Seleção de famílias de cana-de-açúcar via modelos mistos. Sci. Agra.; 2008; 9, pp. 269-274. [DOI: https://dx.doi.org/10.5380/rsa.v9i3.11564]
37. Carvalho, A.D.; Silva, G.O.; Resende, F.V. Adaptabilidade e estabilidade de populações de cenoura pelo método REML/BLUP. Hort. Bras.; 2017; 35, pp. 69-74. [DOI: https://dx.doi.org/10.1590/s0102-053620170111]
38. Costa, J.R.D.; de Pinho, J.L.; Parry, M.M. Produção de matéria seca de cultivares de milho sob diferentes níveis de estresse hídrico. Rev. Bras. Eng. Agríc. Ambient.; 2008; 12, pp. 443-450. [DOI: https://dx.doi.org/10.1590/S1415-43662008000500001]
39. Mendes, F.F.; Guimarães, L.J.M.; Souza, J.C.; Guimarães, P.E.O.; Pacheco, C.A.P.; Machado, J.R.A.; Meirelles, W.F.; Silva, A.R.; Parentoni, S.N. Adaptability and stability of maize varieties using mixed model methodology. Crops Breed. Appl. Biotechnol.; 2012; 12, pp. 111-117. [DOI: https://dx.doi.org/10.1590/S1984-70332012000200003]
40. Pereira, T.B.; Carvalho, J.P.F.; Botelho, C.E.; Resende, M.D.V.D.; Rezende, J.C.D.; Mendes, A.N.G. Eficiência da seleção de progênies de café F4 pela metodologia de modelos mistos (REML/BLUP). Bragantia; 2013; 72, pp. 230-236. [DOI: https://dx.doi.org/10.1590/brag.2013.031]
41. Maia, M.C.C.; de Oliveira, L.C.; Vasconcelos, L.F.L.; Lima Neto, F.P.; Yokomizo, G.K.I.; Araújo, L.D. Repetibilidade de características quantitativas de frutos em seleções elite de manga rosa. Rev. Agro@mbiente Online; 2017; 11, pp. 56-62. [DOI: https://dx.doi.org/10.18227/1982-8470ragro.v11i1.3486]
42. Rosado, A.M.; Rosado, T.B.; Alves, A.A.; Laviola, B.G.; Bhering, L.L. Seleção simultânea de clones de eucalipto de acordo com produtividade, estabilidade e adaptabilidade. Pesqui. Agropecu. Bras.; 2012; 47, pp. 964-971. [DOI: https://dx.doi.org/10.1590/S0100-204X2012000700013]
43. Eberhart, S.T.; Russell, W.A. Stability parameters for comparing varieties 1. Crop Sci.; 1966; 6, pp. 36-40. [DOI: https://dx.doi.org/10.2135/cropsci1966.0011183X000600010011x]
44. Teodoro, P.E.; Barroso, L.M.A.; Nascimento, M.; Torres, F.E.; Sagrilo, E.; Santos, A.D.; Ribeiro, L.P. Artificial neural networks to identify semi-prostrate cowpea genotypes with high phenotypic adaptability and stability. Pesqui. Agropecu. Bras.; 2015; 50, pp. 1054-1060. [DOI: https://dx.doi.org/10.1590/S0100-204X2015001100008]
45. Bernini, C.S.; Santo, F.A.S.; Silva, D.S.; Figueiredo, Z.N. Seleção fenotípica de híbridos de milho para ambientes de baixa altitude e deficit hídrico. Pesqui. Agrárias Ambient.; 2020; 8, pp. 172-177. [DOI: https://dx.doi.org/10.31413/nativa.v8i2.9265]
46. Crosa, C.F.R.; Ortiz, A.C.; Felipez, W. Germinação e desenvolvimento de sementes de dois híbridos de milho sob estesse hídrico. Rev. Científica Rural.; 2021; 23, pp. 110-123. [DOI: https://dx.doi.org/10.30945/rcr-v23i1.3245]
47. Dutra Filho, J.A.; Souto, L.S.; Luna, R.G.; Souza, A.S.; Silva, F.G.; Silva, F.A.C.; Simões Neto, D.E.; Calsa Junior, T. Mixed modeling for fiber yield genetic selection in sugarcane. Rev Fac Cienc Agrar; 2021; 53, pp. 11-19. [DOI: https://dx.doi.org/10.48162/rev.39.034]
48. Messmer, R.; Fracheboud, Y.; Banzinger, M.; Vargas, M.; Stamp, P.; Ribaut, J.M. Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor. Appl. Genet.; 2009; 119, pp. 913-930. [DOI: https://dx.doi.org/10.1007/s00122-009-1099-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19597726]
49. Resende, M.D.V.; Lopes, P.S.; Silva, R.L.; Pires, I.E. Seleção genômica ampla (GWS) e maximização da eficiência do melhoramento genético. Pesq. Flor. Bras.; 2008; 56, pp. 63-77.
50. Dekkers, J.C.M. Prediction of response to marker assisted and genomic selection using selection index theory. J. Anim. Breed. Genet.; 2007; 124, pp. 331-341. [DOI: https://dx.doi.org/10.1111/j.1439-0388.2007.00701.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18076470]
51. Zhu, Y.; Wu, N.; Song, W.; Yin, G.; Qin, Y.; Yan, Y.; Hu, Y. Soybean (Glycine max) expansin gene superfamily origins: Segmental and tandem duplication events followed by divergent selection among subfamilies. BMC Plant Biol.; 2014; 14, 93. [DOI: https://dx.doi.org/10.1186/1471-2229-14-93]
52. Patterson, K.; Walters, L.A.; Cooper, A.M.; Olvera, J.G.; Rosas, M.A.; Rasmusson, A.G.; Escobar, M.A. Nitrate-regulated glutaredoxins control Arabidopsis primary root growth. Plant Physiol.; 2016; 170, pp. 989-999. [DOI: https://dx.doi.org/10.1104/pp.15.01776]
53. Balsera, M.; Arellano, J.B.; Gutiérrez, J.R.; Heredia, P.; Revuelta, J.L.; De Las Rivas, J. Structural analysis of the PsbQ protein of photosystem II by Fourier transform infrared and circular dichroic spectroscopy and by bioinformatic methods. Biochemistry; 2003; 42, pp. 1000-1007. [DOI: https://dx.doi.org/10.1021/bi026575l]
54. Vessal, S.; Arefian, M.; Siddique, K.H. Proteomic responses to progressive dehydration stress in leaves of chickpea seedlings. BMC Genom.; 2020; 21, 523. [DOI: https://dx.doi.org/10.1186/s12864-020-06930-2]
55. Tejada-Jimenez, M.; Chamizo-Ampudia, A.; Calatrava, V.; Galvan, A.; Fernandez, E.; Llamas, A. From eukaryotic molybdenum cofactor biosynthesis to the clandestine enzyme mARC. Molécules; 2018; 23, 3287. [DOI: https://dx.doi.org/10.3390/molecules23123287]
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Maria Beatriz Araújo de Oliveira 3 ; Cleiton Ribeiro da Silva 4 ; Marcela Portela Santos de Figueiredo 5 ; Rômulo Gil de Luna 6 ; Anielson dos Santos Souza 6 ; Lauter Silva Souto 6 ; Ancélio Ricardo de Oliveira Godim 6 ; Rodolfo Rodrigo de Almeida Lacerda 6 ; Andréa Chaves Fiuza Porto 7 ; Gomes-Silva, Frank 8 ; Josimar Mendes de Vasconcelos 8 ; Guilherme Rocha Moreira 8 ; Maria Lindomárcia Leonardo da Costa 9 ; Mércia Regina Pereira de Figueiredo 10 ; Fabiana Aparecida Cavalcante Silva 11
; Francisco Cássio Gomes Alvino 12 ; Amaro Epifânio Pereira Silva 13 ; de Sousa Alves, Leonardo 14 ; Diogo Gonçalves Neder 15 ; Bianca Galúcio Pereira Araújo 16 ; Carvalho de Freitas, Lucas 17 ; Tercilio Calsa Junior 18
; João de Andrade Dutra Filho 19
1 BP Bunge Bioenergia, Highway BR 364, Km 18, Frutal 38200-000, Brazil;
2 Adecoagro, Highway BR 141, Km 10, Ivinhema 79740-000, Brazil;
3 Vitória Academic Center, Federal University of Pernambuco, Recife 50670-901, Brazil;
4 Graduate Program in Biotechnology, Federal University of Pernambuco, Recife 50670-901, Brazil;
5 Graduate Program in Biometrics and Applied Statistics, Federal Rural University of Pernambuco, Recife 54735-000, Brazil;
6 Agri-Food Science and Technology Center, Federal University of Campina Grande, Pombal 58840-000, Brazil;
7 Dom Agostinho Ikas Agricultural College, Federal Rural University of Pernambuco, São Lourenço da Mata 54735-000, Brazil;
8 Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife 52171-900, Brazil;
9 Animal Science Department, Federal University of Paraiba, 12 Rodovia, PB-079, Areia 58397-000, Brazil;
10 Regional Center for Rural Development Center North, Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural, Rodovia, BR 101 Norte, Km 151, Linhares 29915-140, Brazil;
11 Phytosanitary Diagnosis and Fidelity Genetic Laboratory, Northeast Strategic Technologies Center, Avenida Professor Luís Freire, Cidade Universitária, Recife 50740-545, Brazil;
12 Department of Agricultural Engineering, Federal University of Viçosa, Viçosa 36570-900, Brazil;
13 Carpina Sugarcane Experimental Station, Federal Rural University of Permambuco, Rua Ângela Cristina Canto Pessoa de Luna, s/n, Carpina 55810-700, Brazil;
14 Department of Plant Sciences, Federal Rural University of the Semiarid, Mossoró 59625-900, Brazil;
15 Department of Agroecology, Agriculture Center for Agricultural and Environmental Sciences Campina Grande State, University, Sítio Imbaúba, sn, Zona Rural, Lagoa Seca 58429-500, Brazil;
16 Northeast Strategic Technologies Center, Avenida Professor Luís Freire, Cidade Universitária, Recife 50740-545, Brazil
17 Graduate Program Genetics, Federal University of Pernambuco, Recife 50670-901, Brazil
18 Department of Genetics, Biosciences Center, Federal University of Pernambuco, Recife 50670-901, Brazil;
19 Biological Science Nucleus, Vitoria Academic Center, Federal University of Pernambuco, Vitória de Santo Antão 55608-680, Brazil