Next Article in Journal
Sheath Blight of Maize: An Overview and Prospects for Future Research Directions
Previous Article in Journal
Performance Evaluation of a Virtual Test Model of the Frame-Type ROPS for Agricultural Tractors Using FEA
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 13
Issue 10
10.3390/agriculture13102005
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.)

by
Kamila Nowosad
1,*,†,
Jan Bocianowski
2,†,
Farzad Kianersi
3 and
Alireza Pour-Aboughadareh
4
1
Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Grunwaldzki 24A, 53-363 Wrocław, Poland
2
Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
3
School of Enviromental Sciences, University of Guelph, 50 Stone Road East, Guelph, ON N1G 2W1, Canada
4
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj P.O. Box 3158854119, Iran
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2023, 13(10), 2005; https://doi.org/10.3390/agriculture13102005
Submission received: 13 September 2023 / Revised: 9 October 2023 / Accepted: 14 October 2023 / Published: 15 October 2023
(This article belongs to the Section Crop Production)
Browse Figures
Review Reports Versions Notes

Abstract

:
The assessment of 1000-kernel weight holds significant importance in determining maize grain yield, and elucidating its underlying genetic mechanisms is imperative for enhancing its overall performance. The material for the study consisted of 26 doubled-haploid (DH) maize lines obtained from crossing two cultivars with flint kernels. Lines were planted in the northern part of the Lower Silesia voivodship in Poland over ten years (2013–2022). The 1000-kernel weight was assessed. The purposes of the research were as follows: (1) to assess genotype by environment interaction (GEI by the additive main effects and multiplicative interaction (AMMI) model; (2) the selection of stable DH lines and environment-specific lines; and (3) the estimation of parameters related to additive and additive–additive gene interaction (epistasis). The results indicate the significant effects of genotype and environment, as well as the GEI, on the 1000-kernel weight. Estimates of additive gene action effects were statistically significant in every year of the study, except 2022. Estimates of epistasis (total additive-by-additive interaction) effects for 1000-kernel weight were statistically significant in 2013, 2015, and 2017 (positive effects), as well as in 2018 and 2020 (negative effects). The lines KN07 and KN10 are recommended for further inclusion in the breeding program due to their stability and highest average of 1000-kernel weight.

1. Introduction

Maize (Zea mays L.) is one of the most important cultivated plants in the world, playing a crucial role in agriculture, the food industry, and biofuel production [1]. Its versatile applications, high nutritional value, and ease of cultivation make maize a significant staple in people’s lives worldwide [2,3,4]. It serves as a primary source of food for populations in many countries, especially in South and Central America, Africa, and Asia [5]. Due to its ease of cultivation and adaptability to various environmental conditions, maize remains an indispensable source of calories for millions of people across the globe. It is utilized both as human food and animal feed for livestock [6,7,8].
Maize is used in the production of many processed products, such as breakfast cereals, corn chips, tortillas, and popcorn [9,10,11]. It also plays a significant role in biofuel production [12,13,14,15,16,17]. As a high-energy and nutrient-rich feed, maize is an important component of the diet for livestock animals, such as cows, pigs, and poultry [18,19]. Furthermore, it provides essential nutrients to the animals, aiding in their efficient development and growth. Hence, this crop plant plays a significant role in the economy of many countries, especially in agriculture [20,21,22].
Yield, as a quantitative trait, is determined by several factors: genotype, environment, and other traits. The trait that has the greatest impact on maize grain yield is the 1000-kernel weight [23]. The size and weight of maize kernels are closely related to quality, yield, and resistance to biotic and abiotic stresses [24,25]. The 1000-kernel weight can be one of the key predictive indicators of maize yield in the field [26,27,28,29].
The main factors influencing the 1000-kernel weight are genetics and environmental conditions [30,31,32,33]. Weather and soil conditions play a crucial role in yield and kernel weight [34]. Effective agronomy practices, such as proper fertilization, pest and disease control, as well as optimal planting density and timing, can influence kernels’ weight growth [35,36,37,38].
Scientific research indicates that a high amount of 1000-kernel weight in maize can result in higher yields [39,40,41,42,43,44,45,46,47,48,49,50]. The use of mathematical models based on statistical theory and data analysis is another method for predicting the impact of 1000-kernel weight on maize yield [51,52].
In agriculture, the success of maize cultivation is closely linked to the interaction between the genotype of a given variety and the environmental conditions in which it is grown [53,54,55,56]. Understanding the impact of this genotype–environment interaction (GEI) on the 1000-kernel weight of maize is a crucial concern for breeders and farmers who seek to optimize the yield and quality of this important crop [57,58,59,60,61]. The impact of GEI on the 1000-kernel weight of maize poses a challenge for breeders and farmers as it requires selecting suitable varieties for specific growing environments. Striving for maximum yield and crop quality necessitates an understanding of how different varieties respond to various environmental conditions and how to optimize these interactions.
A very important aspect is the evaluation of GEI, as it is an integral source of variation that includes genotype and the influence of an environmental factor. This source of variation (GEI) is needed because selected genotypic or environmental variation is insufficient to provide true genotype or environmental information. GEI can be empirically detected and described in terms of various statistical models, derived from the two-factor analysis of the variance model [62,63,64,65]. Much more difficult is the study of the biological mechanism of conditioning of the interaction of the GEI [66,67,68,69,70]. Traditional statistical methods (such as linear regression, analysis of variance, and principal component analysis) are often ineffective in understanding and estimating complex yield survey data in multiple environments [6]. As opposed to standard statistical analyses, additive main effects and multiplicative interaction (AMMI) allows simple visual interpretation of GEI [71,72]. All IPCAs that are significant can explain the variation in GEI, while other IPCAs that are not significant are added to the residuals. Measuring GEI is very important to determine an optimum breeding strategy for releasing genotypes with an adequate adaptation to target environments [73,74]. The AMMI model was originally developed for analysis in social sciences and physics [75,76,77,78] and later adjusted for research in plant sciences [62,79].
The objectives of this study were as follows: (1) to assess genotype by environment interaction for 1000-kernel weight in doubled-haploid lines of maize (Zea mays L.) grown in the northern part of the Lower Silesia voivodship in Poland by the AMMI model; (2) the selection of DH lines stable through all years of study specific to given environmental conditions; and (3) to estimate the parameters connected with the additive and additive-by-additive interaction (epistasis) gene action.

2. Materials and Methods

2.1. Field Trails

This study was based on a total of 26 doubled-haploid (DH) maize (Zea mays L.) lines derived from a cross of two flint grain type cultivars: ES Palazzo and Anjou 258. All DH lines were developed by the in vivo method described by Prigge and Melchinger [80], in which F1 plants of each cross are pollinated by an inducer line, followed by the identification of haploid seeds using an embryo color marker. Chromosome doubling was promoted by colchicine treatment to produce D0 plants, which were then self-pollinated to produce the D1 generation. All DH lines were of a flint kernel type. Lines were planted in Nowa Wioska in the northern part of the Lower Silesia voivodship in Poland (51°46′58.7″ N, 16°25′43.9″ E) over ten years (2013–2022). The study region is a homogeneous environment with no interaction with specific locations in the region under consideration. Total monthly precipitation and average monthly temperature for the years of the experiment are shown in Table 1 and Table 2, respectively. The experiments were carried out in a randomized block design with four replications each year on typical grey-brown podzolic soil consisting of coarse sandy soil, shallowly deposited on light loam. The same nitrogenium–phosphorus–kalium (NPK) fertilization was applied throughout the experimental field: 100 kg N ha–1 in the form of urea; 80 kg P2O5 ha–1 in the form of granular triple superphosphate 46% P2O5; and 120 kg K2O ha–1 in the form of 60% potassium salt. Each experimental plot consisted of 4 m long rows with 0.70 m inter-row spacing, 25 cm plant-to-plant spacing, and 14 plants per row. The maize sowing in each of the study years was performed during the third decade of April, while the harvest was performed when the maize grain was mature. Standard agronomic practices were followed during the trials. Maize kernels were harvested using a Wintersteiger combine. Twenty cobs were randomly selected from each replicate of each DH line. The 1000-kernel weight was assessed. The characteristics of the 26 DH lines studied in terms of the main agronomic traits (plant height, flowering day, and grain yield) in each year of the study are shown in Table 3.

2.2. Statistical Analysis

Obtained data for the 1000-kernel weight were analyzed using the additive main effects and multiplicative interaction (AMMI) model [76,81] by:
y g e = μ + α g + β e + ∑ n = 1 N λ n γ g n δ e n + Q g e ,
where y g e is the value of the mean of 1000-kernel weight of genotype g in year e; μ is the general mean; α g is the genotypic mean deviations; β e is the year mean deviations; N is the number of PCA axis retained in the adjusted model; λ n is the eigenvalue of the PCA axis n; γ g n is the genotype score for PCA axis n; δ e n is the coordinate of the eigenvector for the environmental score for PCA axis n; Q g e is the residual, including the AMMI noise and pooled experimental error. The significance level of PCA analysis was tested using an F-test according to Gollob [78]. The stability of the DH lines was assessed by the AMMI stability value (ASV) [82]:
A S V = S S I P C A 1 S S I P C A 2 I P C A 1 2 + I P C A 2 2 ,
where S S I P C A 1 and S S I P C A 2 are the sums of squares for IPCA1 and IPCA2, respectively, and the IPCA1 and IPCA2 scores are the genotypic scores in the AMMI model. ASV with the smallest possible values indicated a more stable genotype.
The genotype selection index (GSI) was calculated for each genotype that incorporates both the mean of trait and the ASV index in single criteria ( G S I i ) as follows [83]:
G S I i = R M i + R A i ,
where R M i is rank of the trait mean (from maximal to minimal) for the i-th genotype, and R A i is the rank of the ASV for the i-th genotype.
The relationships between the 1000-kernel weight observed in different years of the study were assessed by Pearson correlation coefficients based on genotypic averages.
Estimation of the additive gene effect and additive-by-additive interaction of homozygous loci (epistasis) effect on the basis of phenotypic observations requires identification of groups of extreme DH lines, i.e., lines with the minimal and maximal expression of the observed trait [84]. The group of minimal (maximal) lines consists of the lines which contain, theoretically, only alleles reducing (increasing) the value of the trait. The groups of extreme lines were identified by the quantile method. The total additive effect, a, of all genes controlling the trait and the total additive-by-additive interaction effect, aa, may be estimated by the following formulas [85,86]:
a ^ = 1 2 L ¯ m a x − L ¯ m i n
and
a a ^ = 1 2 L ¯ m a x + L ¯ m i n − L ¯ ,
where L ¯ m a x and L ¯ m i n are the means for the extreme groups of DH lines, respectively, and L ¯ is the mean value for all DH lines. Test statistics for verifying hypotheses about the significance of genetic parameters are given by the following:
F a = M S a M S e   a n d   F a a = M S a a M S e ,  
where M S a is the mean square for parameter a, M S a a is the mean square for epistasis aa, and M S e is the mean square for residual.
All analyses were performed using the GenStat v. 23 statistical software package [87].

3. Results

All three sources of variation were highly significant. The sum of squares for GEI represented 33.79% of the total, and this factor had the highest effect on 1000-kernel weight (Table 4). The effect of environments (years) explained 14.10%, while the effect of genotypes explained 9.69% of the total 1000-kernel weight variation. The values of the first three principal components were also highly significant. Together, the three principal components of GEI accounted for 68.59% of the total effect it had on the variation of 1000-kernel weight. The first principal component (IPCA 1) accounted for 33.11% of the variation caused by interaction, while IPCA 2 and IPCA 3 accounted for 19.06 and 16.42%, respectively (Figure 1, Table 4).
The results of the field trials demonstrated the impact of weather conditions, environment (year), and genotypes on the 1000-kernel weight of the maize doubled-haploid lines. The 1000-kernel weight of the tested DH lines varied from 275.1 g (for KN13 in 2013) to 433.0 g (for KN04 in 2015) throughout the ten years, with an average of 343.7 g (Table 5, Figure 2). The average 1000-kernel weight per genotype varied from 330.9 g (for KN08) to 356.5 g (for KN06) (Table 5, Figure 3). The relationships between the DH lines are presented in the form of a dendrogram (Figure 3), in which three clusters were clearly distinguished. The first cluster contains five DH lines: KN22, KN23, KN24, KN25, and KN26; the second (eight DH lines) cluster contains: KN01, KN10, KN11, KN12, KN13, KN14, KN15, and KN17; and the third cluster contains the other 13 DH lines (Figure 3). The average 1000-kernel weight per year also varied from 322.0 g in 2017 to 360.0 g in 2015 (Table 5, Figure 4). The relationships among years of study are presented in the form of a dendrogram (Figure 4), in which three clusters were clearly distinguished. The first cluster contains two years, namely 2019 and 2020; the second (three years) cluster contains 2013, 2014, and 2017; and the third cluster contains the other five years of study, namely 2015, 2016, 2018, 2021, and 2022 (Figure 4). The highest variability (the coefficient of variation is equal to 15.50%) in the 1000-kernel weight of maize was observed in the first year of conducting the study (2013). In contrast, the lowest variability in the 1000-kernel weight was observed in 2020 and 2022 (7.27% and 7.65%, respectively) (Figure 5).
The AMMI2 biplot (Figure 1) shows the stability of genotypes (lines) and environments (years), as well as specific GEIs. Among the tested DH lines, the line KN12 had the highest IPCA 1 value of 4.431, while the lowest value of IPCA 1 was –7.709 for KN24 (Table 5, Figure 1 and Figure 6). IPCA 1 values for years (IPCA e1) varied from –8.782 (in 2013) to 5.339 (in 2015). IPCA 2 values for genotypes (IPCA g2) varied from –5.229 (for line KN04) to 4.722 (for line 7) (Table 5, Figure 1 and Figure 7). Meanwhile, IPCA g3 varied from –4.964 (for line KN23) to 4.438 (for line KN17) (Table 5). IPCA e2 values varied from –8.047 (in 2015) to 4.354 (in 2017) (Table 5, Figure 1 and Figure 7). However, IPCA e3 values varied from –5213 (in 2015) to 6.259 (in 2016) (Table 5). The stability of the tested lines can be assessed by the biplot for 1000-kernel weight (Figure 2). The DH lines responded differently to climatic conditions during the test years. The lines KN03 and KN04 adapted in environments with similar conditions to those in 2015 (a very dry year) and did not adapt in 2017. Line KN09 adapted in environments with similar conditions to these in 2018 (a very hot year) and did not adapt in 2013. Line KN21 adapted in environments with similar conditions to those in years 2014 and 2020 (the rainiest years among those considered in the experiment) and did not adapt in 2015 (Figure 1 and Figure 6). ASV revealed variations in 1000-kernel weight stability among the 26 DH lines (Table 5). According to Purchase et al. [82], a stable line is defined as one with an ASV close to zero. Consequently, the lines KN16 with an ASV of 0.544, KN01 with an ASV of 0.617, KN15 with an ASV of 0.967, and KN10 with an ASV of 0.998 were the most stable (Table 5, Figure 1 and Figure 6), while the lines KN24 (ASV equal to 13.419) and KN26 (ASV equal to 11.011) were the least stable (Table 5, Figure 1 and Figure 6). Lines KN07 and KN10, with a high average 1000-kernel weight (354.1 g and 346.6 g, respectively) and the ASV equal to 1.142 and 0.998, respectively, had the best genotype selection indices (7 and 13, respectively) (Table 5). However, the lines KN08 and KN25 had the worst genotype selection indices (43 and 40, respectively) (Table 5).
The negatively statistically significant correlation coefficients for 1000-kernel weight were observed for 2016 and 2017 (r = –0.65), as well as for 2020 and 2021 (r = –0.44) (Figure 8).
Gene additive effects were significant in every year of the study, except 2022 (Table 6). Epistasis (total additive-by-additive interaction) effects for 1000-kernel weight was significant in 2013, 2015, and 2017 (positive effects), as well as in 2018 and 2020 (negative effects) (Table 6).

4. Discussion

The measurement known as the 1000-kernel weight serves as a significant indicator of seed size. Seed dimensions, along with the 1000-kernel weight, exhibit notable variability across different crops, among various varieties of the same crop, and even within identical varieties from year to year or from one field to another. This inherent diversity in seed size directly impacts the number of seeds contained within a pound or a bushel of seed, leading to substantial fluctuations in plant density. In light of this variability, producers can employ the 1000-kernel weight as a pivotal parameter. Its application facilitates the precise adjustment of seeding rates, the calibration of seed drills, and the estimation of losses incurred due to shattering and combine operations, thus enhancing the efficiency and accuracy of agricultural practices. In agricultural practice, regular monitoring of maize development and appropriate adjustment of agro-technical practices based on observed conditions are important. The impact of 1000-kernel weight on maize yield is a complex process that depends on numerous factors; hence, farmers should be aware and responsible for optimal management of their crops.
In the present investigation, analysis was undertaken to assess the 1000-kernel weight within a population of doubled-haploid lines of maize under varying environmental conditions. The findings elucidated a marked phenotypic diversity in the 1000-kernel weight among the doubled-haploid lines. Notably, within this study, the statistical analysis indicated the profound significance of three distinct sources of variation in the observed 1000-kernel weight data. Similar results were obtained by Wang et al. [1] and Peng et al. [88]. Wang et al. [1] analyzed 400 recombinant inbred lines; however, Peng et al. [88] analyzed yield components and kernel-related traits in two F2:3 populations of maize in six environments. Similar, significant genotype and environmental effects, as well as GEI effect with respect to seed yield, were obtained by Bocianowski et al. [89], analyzing 32 maize genotypes (13 hybrids and their 19 parental lines) evaluated in four environments (two locations in two years) in Western Poland.
In the present investigation, the source of variation with the greatest impact on 1000-kernel weight was GEI (33.79%). Similarly, the greatest effect of GEI on 1000-kernel weight was obtained by Mousavi et al. [59] studying corn hybrids. Different results were obtained by Mousavi et al. [52,90]. In their study of maize hybrids, the GEI had the smallest (but statistically significant) effect on the 1000-kernel weight. In these studies, DH lines showed the smallest percentage of explained variation (3.34%) of the observed trait. Similar results were obtained by Matin et al. [91] studying 23 genotypes (twenty-one single cross hybrids and two check hybrids) of maize.
The AMMI model was not only used to assess GEI. In maize, the AMMI model was used to analyze the response of dry matter of plant yield and harvest index for four soil tillage methods over 12 years of research. In addition, the AMMI model was used to evaluate the four NP fertilizer placement depths through six-year interaction on the number of maize plants after emergence.
The AMMI model is very widely used to analyze GEI in many plant species, including barley [92], pea [93], oilseed rape, sugar beet, timothy, triticale, and wheat.
Within the realm of plant breeding, a critical facet revolves around the categorization of studied genotypes concerning their stability and adaptability to particular environmental parameters. The identification of genotypes ideally suited for specific environmental contexts becomes feasible through meticulous AMMI analyses, enabling the precise estimation of the interaction effects of a given genotype within each distinct environment. In this research, four DH-specific lines were selected that adapted in environments with specific conditions. The lines are as follows: KN03, KN04 (in 2015—a very dry year), KN09 (in 2018—a very hot year), and KN21 (in 2014 and 2020—the rainiest years among those considered in the experiment). The lack of a significant correlation between the 1000-kernel weight observed in different years of the study does not facilitate decision making when selecting the tested DH lines for further breeding studies. In only two of the forty-five cases were significant correlation coefficients observed. It should be emphasized that the negative correlation is the result of GEI. High genotype stability is linked with the AMMI stability value. The results obtained made it possible to distinguish four stable lines: KN16 (ASV = 0.544), KN01 (ASV = 0.617), KN15 (ASV = 0.967), and KN10 (ASV = 0.998). Two DH lines (KN24 and KN26) were not stable and were not adapted in environments with specific conditions, which disqualifies them from inclusion in any breeding programs. The lines KN07 and KN10 are recommended for further inclusion in the breeding program because of their stability and because they had the highest average of 1000-kernel weight.
Assessment of genetic parameters has a considerable role in maize breeding [94,95,96,97]. The result reveals the importance of additive gene effects for the weight of 1000 kernels in nine of the ten years of the study. In contrast, epistatic gene interaction effects were significant in five of the ten years of the study. The existence of epistasis is important in any breeding program. The presence of epistasis exhibited a pronounced significance, concomitant with an additive effect that lacked statistical significance, observed across multiple years (2013, 2015, 2017, 2018, and 2020) with regard to the 1000-kernel weight. This observation suggests that the manifestation of this trait is likely under the control of genes characterized by subtle individual impacts yet exerts robust interactions in a gene–gene context. The encompassing epistatic effects elucidated by these models serve to underscore the intricate and polygenic genetic underpinnings inherent in these traits [98]. Epistatic phenomena have been substantiated across a spectrum of traits within diverse species, including barley, sugar beet, rice [99], rapeseed, and common wheat [100].

5. Conclusions

The 1000-kernel weight wields a direct and discernible influence upon the overall yield of this pivotal crop. Within the realm of modern agricultural practices, a comprehensive acknowledgment of these intricate interrelationships becomes imperative to not only bolster crop output but also to judiciously steward finite natural resources, all while efficaciously satiating the escalating global populace. It is herein underscored that the relentless pursuit of scientific inquiry coupled with the continual evolution of agrarian technologies stands as the linchpin towards realizing this overarching aspiration. A poignant repository of insights can be gleaned from an incisive examination into the intricate tapestry of genotype–environment interaction, together with the meticulous dissection of genetic parameters. The present work demonstrated that the lines KN07 and KN10 are recommended for further inclusion in the breeding program because their stability and the highest averages of 1000-kernel weight. Four DH lines are recommended for breeding programs in specific weather conditions. The KN03 and KN04 lines have very good 1000-kernel weight values under water-deficient conditions; the same can be said for the KN09 line under drought conditions and the KN21 line under conditions characterized by high humidity. The study of DH lines is characterized by significant parameters related to the additive effect and epistasis, which indicates the consolidation of the observed feature in the subsequent years of the study. These results, meticulously harvested from rigorous scientific investigations, are pivotal in orchestrating improvements in maize yield dynamics, thereby engendering steadfast constancy in the realm of sustenance production. Such perspicacious exploration, poised on the precipice of technological advancement, is poised to engender a holistic comprehension that will invariably underpin the incisive development of heartier, resilient, and prodigiously fecund strains of maize. It is within the crucible of these emerging advancements that the global exigencies for nourishment shall discover their harmonious resolution.

Author Contributions

Conceptualization, K.N. and J.B.; methodology, K.N. and J.B.; software, J.B.; validation, K.N. and J.B.; formal analysis, K.N. and J.B.; investigation, K.N. and J.B.; resources, K.N. and J.B.; data curation, K.N.; writing—original draft preparation, K.N. and J.B.; writing—review and editing, K.N., J.B., F.K. and A.P.-A.; visualization, K.N. and J.B.; supervision, K.N.; project administration, J.B.; funding acquisition, K.N. and J.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data in this manuscript are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, Y.; Bi, Y.; Jiang, F.; Shaw, R.K.; Sun, J.; Hu, C.; Guo, R.; Fan, X. Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate–Tropical Introgression Lines of Maize (Zea mays L.). Curr. Issues Mol. Biol. 2023, 45, 4416–4430. [Google Scholar] [CrossRef] [PubMed]
  2. Garg, M.; Sharma, N.; Sharma, S.; Kapoor, P.; Kumar, A.; Chunduri, V.; Arora, P. Biofortified Crops Generated by Breeding, Agronomy, and Transgenic Approaches Are Improving Lives of Millions of People around the World. Front. Nutr. 2018, 5, 12. [Google Scholar] [CrossRef] [PubMed]
  3. Farooq, A.; Farooq, N.; Akbar, H.; Hassan, Z.U.; Gheewala, S.H. A Critical Review of Climate Change Impact at a Global Scale on Cereal Crop Production. Agronomy 2023, 13, 162. [Google Scholar] [CrossRef]
  4. Albahri, G.; Alyamani, A.A.; Badran, A.; Hijazi, A.; Nasser, M.; Maresca, M.; Baydoun, E. Enhancing Essential Grains Yield for Sustainable Food Security and Bio-Safe Agriculture through Latest Innovative Approaches. Agronomy 2023, 13, 1709. [Google Scholar] [CrossRef]
  5. 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, 19–34. [Google Scholar] [CrossRef]
  6. Ranum, P.; Peña-Rosas, J.P.; Garcia-Casal, M.N. Global maize production, utilization, and consumption. Ann. N.Y. Acad. Sci. 2014, 1312, 105–112. [Google Scholar] [CrossRef]
  7. Erenstein, O.; Jaleta, M.; Sonder, K.; Mottaleb, K.; Prasanna, B.M. Global maize production, consumption and trade: Trends and R&D implications. Food Sec. 2022, 14, 1295–1319. [Google Scholar] [CrossRef]
  8. Kashyap, S.; Kumar, R.; Ram, H.; Kumar, A.; Basak, N.; Sheoran, P.; Bhatacharjee, S.; Biswal, B.; Ali, G.; Kumar, B.; et al. Quantitative and Qualitative Response of Fodder Maize to Use of Bulk and Nano-fertilizers in North Western Plains of India. Agronomy 2023, 13, 1889. [Google Scholar] [CrossRef]
  9. Patel, S.; Hazel, C.M.; Winterton, A.G.M.; Gleadle, A.E. Surveillance of fumonisins in UK maize-based foods and other cereals. Food Addit. Contam. 1997, 14, 187–191. [Google Scholar] [CrossRef]
  10. Yadav, V.K.; Supriya, P. Value Addition in Maize. In Maize: Nutrition Dynamics and Novel Uses; Chaudhary, D., Kumar, S., Langyan, S., Eds.; Springer: New Delhi, India, 2014. [Google Scholar] [CrossRef]
  11. Kumar, K.; Singh, J.; Singh, B.R.; Chandra, S.; Chauhan, N.; Yadav, M.K.; Kumar, P. Consumption and processing patterns of maize (Zea mays): A review. Pharma Innov. J. 2022, 11, 51–57. [Google Scholar]
  12. Patzek, T.W. Thermodynamics of the Corn-Ethanol Biofuel Cycle. Crit. Rev. Plant Sci. 2004, 23, 519–567. [Google Scholar] [CrossRef]
  13. Groom, M.J.; Gray, E.M.; Townsend, P.A. Biofuels and Biodiversity: Principles for Creating Better Policies for Biofuel Production. Conserv. Biol. 2008, 22, 602–609. [Google Scholar] [CrossRef] [PubMed]
  14. Quintero, J.A.; Montoya, M.I.; Sánchez, O.J.; Giraldo, O.H.; Cardona, C.A. Fuel ethanol production from sugarcane and corn: Comparative analysis for a Colombian case. Energy 2008, 33, 385–399. [Google Scholar] [CrossRef]
  15. Hassan, M.H.; Kalam, A. An Overview of Biofuel as a Renewable Energy Source: Development and Challenges. Procedia Eng. 2013, 56, 39–53. [Google Scholar] [CrossRef]
  16. Ramos, J.L.; Pakuts, B.; Godoy, P.; García-Franco, A.; Duque, E. Addressing the energy crisis: Using microbes to make biofuels. Microb. Biotechnol. 2022, 15, 1026–1030. [Google Scholar] [CrossRef]
  17. Felten, D.; Fröba, N.; Fries, J.; Emmerling, C. Energy balances and greenhouse gas-mitigation potentials of bioenergy cropping systems (Miscanthus, rapeseed, and maize) based on farming conditions in Western Germany. Renew. Energy 2013, 55, 160–174. [Google Scholar] [CrossRef]
  18. Zhang, R.; Ma, S.; Li, L.; Zhang, M.; Tian, S.; Wang, D.; Liu, K.; Liu, H.; Zhu, W.; Wang, X. Comprehensive utilization of corn starch processing by-products: A review. Grain Oil Sci. Technol. 2021, 4, 89–107. [Google Scholar] [CrossRef]
  19. Parisi, G.; Tulli, F.; Fortina, R.; Marino, R.; Bani, P.; Zotte, A.D.; De Angelis, A.; Piccolo, G.; Pinotti, L.; Schiavone, A.; et al. Protein hunger of the feed sector: The alternatives offered by the plant world. Ital. J. Anim. Sci. 2020, 19, 1204–1225. [Google Scholar] [CrossRef]
  20. Suyker, A.E.; Verma, S.B. Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity. Agric. For. Meteorol. 2010, 150, 553–563. [Google Scholar] [CrossRef]
  21. Allen, L.H., Jr.; Kakani, V.G.; Vu, J.C.V.; Boote, K.J. Elevated CO2 increases water use efficiency by sustaining photosynthesis of water-limited maize and sorghum. J. Plant Physiol. 2011, 168, 1909–1918. [Google Scholar] [CrossRef]
  22. Panchasara, H.; Samrat, N.H.; Islam, N. Greenhouse Gas Emissions Trends and Mitigation Measures in Australian Agriculture Sector—A Review. Agriculture 2021, 11, 85. [Google Scholar] [CrossRef]
  23. Zeng, T.; Meng, Z.; Yue, R.; Lu, S.; Li, W.; Li, W.; Meng, H.; Sun, Q. Genome wide association analysis for yield related traits in maize. BMC Plant Biol. 2022, 22, 449. [Google Scholar] [CrossRef] [PubMed]
  24. Chen, J.; Xu, W.; Velten, J.; Xin, Z.; Stout, J. Characterization of maize inbred lines for drought and heat tolerance. JSVC 2012, 67, 354–364. [Google Scholar] [CrossRef]
  25. Ahila Priyadharshini, R.; Arivazhagan, S.; Arun, M.; Mirnalini, A. Maize leaf disease classification using deep convolutional neural networks. Neural. Comput. Applic. 2019, 31, 8887–8895. [Google Scholar] [CrossRef]
  26. Li, X.Y.; Gong, J.D. Effects of different ridge:furrow ratios and supplemental irrigation on crop production in ridge and furrow rainfall harvesting system with mulches. Agric. Water Manag. 2002, 54, 243–254. [Google Scholar] [CrossRef]
  27. Ren, B.; Zhang, J.; Li, X.; Fan, X.; Dong, S.; Liu, P.; Zhao, B. Effects of waterlogging on the yield and growth of summer maize under field conditions. Can. J. Plant Sci. 2014, 94, 23–31. [Google Scholar] [CrossRef]
  28. Fang, H.; Niu, M.; Wang, X.; Zhang, Q. Effects of reduced chemical application by mechanical-chemical synergistic weeding on maize growth and yield in East China. Front. Plant Sci. 2022, 13, 1024249. [Google Scholar] [CrossRef]
  29. Han, X.; Dong, L.; Cao, Y.; Lyu, Y.; Shao, X.; Wang, Y.; Wang, L. Adaptation to Climate Change Effects by Cultivar and Sowing Date Selection for Maize in the Northeast China Plain. Agronomy 2022, 12, 984. [Google Scholar] [CrossRef]
  30. Tokatlidis, I.S.; Koutroubas, S.D. A review of maize hybrids’ dependence on high plant populations and its implications for crop yield stability. Field Crops Res. 2004, 88, 103–114. [Google Scholar] [CrossRef]
  31. Duvick, D.N. The Contribution of Breeding to Yield Advances in maize (Zea mays L.). Adv. Agron. 2005, 86, 83–145. [Google Scholar] [CrossRef]
  32. McMillen, M.S.; Mahama, A.A.; Sibiya, J.; Lübberstedt, T.; Suza, W.P. Improving drought tolerance in maize: Tools and techniques. Front. Genet. 2022, 13, 1001001. [Google Scholar] [CrossRef]
  33. Santos, T.d.O.; Amaral Junior, A.T.d.; Moulin, M.M. Maize Breeding for Low Nitrogen Inputs in Agriculture: Mechanisms Underlying the Tolerance to the Abiotic Stress. Stresses 2023, 3, 136–152. [Google Scholar] [CrossRef]
  34. Butts-Wilmsmeyer, C.J.; Seebauer, J.R.; Singleton, L.; Below, F.E. Weather during Key Growth Stages Explains Grain Quality and Yield of Maize. Agronomy 2019, 9, 16. [Google Scholar] [CrossRef]
  35. Altieri, M.A.; Nicholls, C.I. Soil fertility management and insect pests: Harmonizing soil and plant health in agroecosystems. Soil Tillage Res. 2003, 72, 203–211. [Google Scholar] [CrossRef]
  36. Munkvold, G. Crop Management Practices to Minimize the Risk of Mycotoxins Contamination in Temperate-Zone Maize. In Mycotoxin Reduction in Grain Chains; Leslie, J.F., Logrieco, A.F., Eds.; John Wiley & Sons: Hoboken, NJ, USA, 2014. [Google Scholar] [CrossRef]
  37. Rajcan, I.; Swanton, C.J. Understanding maize–weed competition: Resource competition, light quality and the whole plant. Field Crops Res. 2001, 71, 139–150. [Google Scholar] [CrossRef]
  38. Pierre, J.F.; Latournerie-Moreno, L.; Garruña, R.; Jacobsen, K.L.; Laboski, C.A.M.; Us-Santamaría, R.; Ruiz-Sánchez, E. Effect of Maize–Legume Intercropping on Maize Physio-Agronomic Parameters and Beneficial Insect Abundance. Sustainability 2022, 14, 12385. [Google Scholar] [CrossRef]
  39. Zhang, Q.; Zhang, L.; Evers, J.; van der Werf, W.; Zhang, W.; Duan, L. Maize yield and quality in response to plant density and application of a novel plant growth regulator. Field Crops Res. 2014, 164, 82–89. [Google Scholar] [CrossRef]
  40. Jilo, T.; Tulu, L. Association and path coefficient analysis among grain yield and related traits in Ethiopian maize (Zea mays L.) inbred lines. Afr. J. Plant Sci. 2019, 13, 264–272. [Google Scholar] [CrossRef]
  41. Liu, D.-Y.; Zhang, W.; Liu, Y.-M.; Chen, X.-P.; Zou, C.-Q. Soil Application of Zinc Fertilizer Increases Maize Yield by Enhancing the Kernel Number and Kernel Weight of Inferior Grains. Front. Plant Sci. 2020, 11, 188. [Google Scholar] [CrossRef]
  42. Wang, F.; Xie, R.; Ming, B.; Wang, K.; Hou, P.; Chen, J.; Liu, G.; Zhang, G.; Xue, J.; Li, S. Dry matter accumulation after silking and kernel weight are the key factors for increasing maize yield and water use efficiency. Agric. Water Manag. 2021, 254, 106938. [Google Scholar] [CrossRef]
  43. Nurgia, N.; Tanab, T.; Dechassac, N.; Alemayehuc, Y.; Tessoc, B. Effects of planting density and variety on productivity of maize-faba bean intercropping system. Heliyon 2023, 9, e12967. [Google Scholar] [CrossRef] [PubMed]
  44. Wu, Y.; Messing, J. Proteome balancing of the maize seed for higher nutritional value. Front. Plant Sci. 2014, 5, 240. [Google Scholar] [CrossRef] [PubMed]
  45. Shiferaw, B.; Prasanna, B.M.; Hellin, J.; Bänziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Sec. 2011, 3, 307–327. [Google Scholar] [CrossRef]
  46. Yan, Y.; Hou, P.; Duan, F.; Niu, L.; Dai, T.; Wang, K.; Zhao, M.; Li, S.; Zhou, W. Improving photosynthesis to increase grain yield potential: An analysis of maize hybrids released in different years in China. Photosynth. Res. 2021, 150, 295–311. [Google Scholar] [CrossRef]
  47. Mogesse, W.; Zeleke, H. Estimates of Combining Ability and Association among Morpho-Agronomic Traits of Single Cross Maize (Zea mays L.) Hybrids. J. Agric. Prod. 2022, 3, 78–87. [Google Scholar] [CrossRef]
  48. Ren, H.; Liu, M.; Zhang, J.; Liu, P.; Liu, C. Effects of agronomic traits and climatic factors on yield and yield stability of summer maize (Zea mays L) in the Huang-Huai-Hai Plain in China. Front. Plant Sci. 2022, 13, 1050064. [Google Scholar] [CrossRef]
  49. Mosisa, W.; Dechassa, N.; Kibret, K.; Zeleke, H.; Bekeko, Z. Response of Maize (Zea mays L.) Varieties to Rates of Mineral Nitrogen Fertilizer Application in Haramaya District, Eastern Highlands of Ethiopia. East Afr. J. Sci. 2023, 17, 1–18. [Google Scholar]
  50. Liu, Y.; Hou, P.; Zhang, W.; Xing, J.; Lv, T.; Zhang, C.; Wang, R.; Zhao, J. Drought resistance of nine maize cultivars released from the 1970s through the 2010s in China. Field Crops Res. 2023, 302, 109065. [Google Scholar] [CrossRef]
  51. Karayel, D.; Barut, Z.B.; Özmerzi, A. Mathematical Modelling of Vacuum Pressure on a Precision Seeder. Biosyst. Eng. 2004, 87, 437–444. [Google Scholar] [CrossRef]
  52. Mousavi, S.M.N.; Illés, Á.; Bojtor, C.; Nagy, J. The Impact of Different Nutritional Treatments on Maize Hybrids Morphological Traits Based on Stability Statistical Methods. Emir. J. Food Agric. 2020, 32, 666–672. [Google Scholar] [CrossRef]
  53. Kang, M.S.; Gorman, D.P. Genotype × Environment Interaction in Maize. Agron. J. 1989, 81, 662–664. [Google Scholar] [CrossRef]
  54. Crossa, J.; Vargas, M.; van Eeuwijk, F.; Jiang, C.; Edmeades, G.O.; Hoisington, D. Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables. Theor. Appl. Genet. 1999, 99, 611–625. [Google Scholar] [CrossRef]
  55. Signor, C.E.-L.; Dousse, S.; Lorgeou, J.; Denis, J.-B.; Bonhomme, R.; Carolo, P.; Charcosset, A. Interpretation of Genotype × Environment Interactions for Early Maize Hybrids over 12 Years. Crop Sci. 2001, 41, 663–669. [Google Scholar] [CrossRef]
  56. Singamsetti, A.; Shahi, J.P.; Zaidi, P.H.; Seetharam, K.; Vinayan, M.T.; Kumar, M.; Singla, S.; Shikha, K.; Madankar, K. Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes. Field Crops Res. 2021, 270, 108224. [Google Scholar] [CrossRef]
  57. Mahajan, V.S.; Khehra, A.S. Stability analysis of kernel yield and its components in maize (Zea mays L.) in winter and monsoon seasons. Indian J. Genet. Plant Breed. 1992, 52, 63–67. [Google Scholar]
  58. Greveniotis, V.; Bouloumpasi, E.; Zotis, S.; Korkovelos, A.; Ipsilandis, C.G. Estimations on Trait Stability of Maize Genotypes. Agriculture 2021, 11, 952. [Google Scholar] [CrossRef]
  59. Mousavi, S.M.N.; Bojtor, C.; Illés, Á.; Nagy, J. Genotype by Trait Interaction (GT) in Maize Hybrids on Complete Fertilizer. Plants 2021, 10, 2388. [Google Scholar] [CrossRef]
  60. 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, 565–584. [Google Scholar] [CrossRef]
  61. 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, 185–193. [Google Scholar] [CrossRef]
  62. Gauch, H.G. Model selection and validation for yield trials with interaction. Biometrics 1988, 44, 705–715. [Google Scholar] [CrossRef]
  63. Piepho, H.P. Methods for comparing the yield stability of cropping systems-a review. J. Agron. Crop Sci. 1998, 180, 193–213. [Google Scholar] [CrossRef]
  64. Mekbib, F. Yield stability in common bean (Phaseolus vulgaris L.) genotypes. Euphytica 2003, 130, 147–153. [Google Scholar] [CrossRef]
  65. Pacheco, R.M.; Duarte, J.B.; Vencovsky, R.; Pinheiro, J.B.; Oliveira, A.B. Use of supplementary genotypes in AMMI analysis. Theor. Appl. Genet. 2005, 110, 812–818. [Google Scholar] [CrossRef] [PubMed]
  66. Motzo, R.; Giunta, F.; Deidda, M. Factors affecting the genotype × environment interaction in spring triticale grown in a Mediterranean environment. Euphytica 2001, 121, 317–324. [Google Scholar] [CrossRef]
  67. Yan, W.; Hunt, L.A. Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Sci. 2001, 41, 19–25. [Google Scholar] [CrossRef]
  68. Brancourt-Hulmel, M.; Lecomte, C. Effect of environmental varieties on genotype x environment interaction of winter wheat. A comparison of biadditive factorial regression to AMMI. Crop Sci. 2003, 43, 608–617. [Google Scholar] [CrossRef]
  69. Sinebo, W. Trade off between yield increase and yield stability in three decades of barley breeding in a tropical highland environment. Field Crops Res. 2005, 92, 35–52. [Google Scholar] [CrossRef]
  70. Rodriguez, M.; Rau, D.; Papa, R.; Attene, G. Genotype by environment interactions in barley (Hordeum vulgare L.): Different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica 2008, 163, 231–247. [Google Scholar] [CrossRef]
  71. Balestre, M.; Von Pinho, R.G.; Souza, J.C.; Oliveira, R.L. Genotypic stability and adaptability in tropical maize based on AMMI and GGE biplot analysis. Genet. Mol. Res. 2009, 8, 1311–1322. [Google Scholar] [CrossRef]
  72. Pires, L.P.; Garcia Von Pinho, R.; Balestre, M. AMMI Bayesian Models to Study Stability and Adaptability in Maize. Agron. J. 2018, 110, 1765–1776. [Google Scholar] [CrossRef]
  73. Romagosa, I.; Fox, P.N. Genotype × environment interaction and adaptation. In Plant Breeding; Hayward, M.D., Bosemark, N.O., Romagosa, I., Cerezo, M., Eds.; Plant Breeding Series; Springer: Dordrecht, The Netherlands, 1993. [Google Scholar] [CrossRef]
  74. Nowosad, K.; Liersch, A.; Popławska, W.; Bocianowski, J. Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 2016, 208, 187–194. [Google Scholar] [CrossRef]
  75. Mandel, J. A new analysis of variance model for non-additive data. Technometrics 1971, 13, 1–8. [Google Scholar] [CrossRef]
  76. Mandel, J. Non-additivity in two-way analysis of variance. J. Am. Stat. Assoc. 1961, 56, 878–888. [Google Scholar] [CrossRef]
  77. Mandel, J. The partitioning of interaction in analysis of variance. J. Res. Nat. Bureau. Stand. Sect. B 1969, 73, 309–328. [Google Scholar] [CrossRef]
  78. Gollob, H.F. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 1968, 33, 73–155. [Google Scholar] [CrossRef] [PubMed]
  79. Cornelius, P.L. Statistical tests and retention of terms in the additive main effects and multiplicative interaction model for cultivar trials. Crop Sci. 1993, 33, 1186–1193. [Google Scholar] [CrossRef]
  80. Prigge, V.; Melchinger, A.E. Production of haploids and doubled haploids in maize. In Plant Cell Culture Protocols; Loyola-Vargas, V., Ochoa-Alejo, N., Eds.; Methods in Molecular Biology; Humana Press: Totowa, NJ, USA, 2012; Volume 877, pp. 161–172. [Google Scholar] [CrossRef]
  81. Gauch, H.G.; Zobel, R.W. Imputing missing yield trial data. Theor. Appl. Genet. 1990, 79, 753–761. [Google Scholar] [CrossRef]
  82. Purchase, J.L.; Hatting, H.; van Deventer, C.S. Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. S. Afr. J. Plant Soil 2000, 17, 101–107. [Google Scholar] [CrossRef]
  83. Farshadfar, E.; Sutka, J. Locating QTLs controlling adaptation in wheat using AMMI model. Cereal Res. Commun. 2003, 31, 249–256. [Google Scholar] [CrossRef]
  84. Choo, T.M.; Reinbergs, E. Estimation of the number of genes in doubled haploid populations of barley (Hordeum vulgare). Can. J. Genet. Cytol. 1982, 24, 337–341. [Google Scholar] [CrossRef]
  85. Bocianowski, J.; Krajewski, P. Comparison of the genetic additive effect estimators based on phenotypic observations and on molecular marker data. Euphytica 2009, 165, 113–122. [Google Scholar] [CrossRef]
  86. Bocianowski, J. Analytical and numerical comparisons of two methods of estimation of additive × additive interaction of QTL effects. Sci. Agric. 2012, 69, 240–246. [Google Scholar] [CrossRef]
  87. VSN International Genstat for Windows, 23rd ed.; VSN International: Hemel Hempstead, UK, 2023.
  88. Peng, B.; Li, Y.; Wang, Y.; Liu, C.; Liu, Z.; Tan, W.; Zhang, Y.; Wang, D.; Shi, Y.; Sun, B.; et al. QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor. Appl. Genet. 2011, 122, 1305–1320. [Google Scholar] [CrossRef]
  89. Bocianowski, J.; Nowosad, K.; Tomkowiak, A. Genotype—Environment interaction for seed yield of maize hybrids and lines using the AMMI model. Maydica 2019, 64, M13. [Google Scholar]
  90. Mousavi, S.M.N.; Illés, A.; Szabó, A.; Shojaei, S.H.; Demeter, C.; Bakos, Z.; Vad, A.; Széles, A.; Nagy, J.; Bojtor, C. Stability yield indices on different sweet corn hybrids based on AMMI analysis. Braz. J. Biol. 2024, 84, e270680. [Google Scholar] [CrossRef] [PubMed]
  91. Matin, M.Q.I.; Rasul, M.G.; Islam, A.K.M.M.; Mian, M.A.K.; Ahmed, J.U.; Amiruzzaman, M. Stability analysis for yield and yield contributing characters in hybrid maize (Zea mays L.). Afr. J. Agric. Res. 2017, 12, 2795–2806. [Google Scholar] [CrossRef]
  92. Bocianowski, J.; Tratwal, A.; Nowosad, K. Genotype by environment interaction for area under the disease-progress curve (AUDPC) value in spring barley using additive main effects and multiplicative interaction model. Australas. Plant Pathol. 2020, 49, 525–529. [Google Scholar] [CrossRef]
  93. Bocianowski, J.; Księżak, J.; Nowosad, K. Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model. Euphytica 2019, 215, 191. [Google Scholar] [CrossRef]
  94. Presterl, T.; Seitz, G.; Landbeck, M.; Thiemt, E.M.; Schmidt, W.; Geiger, H.H. Improving Nitrogen-Use Efficiency in European Maize. Crop Sci. 2003, 43, 1259–1265. [Google Scholar] [CrossRef]
  95. Sang, Z.; Wang, H.; Yang, Y.; Zhang, Z.; Liu, X.; Li, Z.; Xu, Y. Epistasis Activation Contributes Substantially to Heterosis in Temperate by Tropical Maize Hybrids. Front. Plant Sci. 2022, 13, 921608. [Google Scholar] [CrossRef]
  96. Cyplik, A.; Sobiech, A.; Tomkowiak, A.; Bocianowski, J. Genetic Parameters for Selected Traits of Inbred Lines of Maize (Zea mays L.). Appl. Sci. 2022, 12, 6961. [Google Scholar] [CrossRef]
  97. Rio, S.; Charcosset, A.; Moreau, L.; Mary-Huard, T. Detecting directional and non-directional epistasis in bi-parental populations using genomic data. Genetics 2023, 224, iyad089. [Google Scholar] [CrossRef] [PubMed]
  98. Crow, J.F. On epistasis: Why it is unimportant in polygenic directional selection. Philos. Trans. R. Soc. B 2010, 365, 1241–1244. [Google Scholar] [CrossRef]
  99. Matsubara, K.; Yamamoto, E.; Mizobuchi, R.; Yonemaru, J.; Yamamoto, T.; Kato, H.; Yano, M. Hybrid breakdown caused by epistasisbased recessive incompatibility in a cross of rice (Oryza sativa L.). J. Hered. 2015, 106, 113–122. [Google Scholar] [CrossRef] [PubMed]
  100. Jaiswal, V.; Gahlaut, V.; Meher, P.K.; Mir, R.R.; Jaiswal, J.P.; Rao, A.R.; Balyan, H.S.; Gupta, R.R. Genome wide single locus single trait, multilocus and multi-trait association mapping for some important agronomic traits in common wheat (T. aestivum L.). PLoS ONE 2016, 11, e0159343. [Google Scholar] [CrossRef] [PubMed]
Agriculture 13 02005 g001
Figure 1. Biplot for genotype by environment interaction of 26 maize genotypes (doubled-haploid lines) in ten years, showing the effects of primary and secondary components (IPC1 and IPC2, respectively).
Figure 1. Biplot for genotype by environment interaction of 26 maize genotypes (doubled-haploid lines) in ten years, showing the effects of primary and secondary components (IPC1 and IPC2, respectively).
Agriculture 13 02005 g001
Agriculture 13 02005 g002
Figure 2. The heatmaps diagram shows the 1000-kernel weight of 26 doubled-haploid lines in ten years of study. Red indicates lower the 1000-kernel weight and white indicates higher the 1000-kernel weight.
Figure 2. The heatmaps diagram shows the 1000-kernel weight of 26 doubled-haploid lines in ten years of study. Red indicates lower the 1000-kernel weight and white indicates higher the 1000-kernel weight.
Agriculture 13 02005 g002
Agriculture 13 02005 g003
Figure 3. The heatmaps diagram of 26 doubled-haploid lines similarity estimated on the basis of 1000-kernel weight using Euclidean similarity. The DH lines were grouped using the unweighted pair group method with arithmetic mean (UPGMA).
Figure 3. The heatmaps diagram of 26 doubled-haploid lines similarity estimated on the basis of 1000-kernel weight using Euclidean similarity. The DH lines were grouped using the unweighted pair group method with arithmetic mean (UPGMA).
Agriculture 13 02005 g003
Agriculture 13 02005 g004
Figure 4. The heatmap diagram of similarity of ten years of research estimated on the basis of 1000-kernel weight using Euclidean similarity. The years were grouped using the unweighted pair group method with arithmetic means (UPGMA).
Figure 4. The heatmap diagram of similarity of ten years of research estimated on the basis of 1000-kernel weight using Euclidean similarity. The years were grouped using the unweighted pair group method with arithmetic means (UPGMA).
Agriculture 13 02005 g004
Agriculture 13 02005 g005
Figure 5. The density of the distribution of observations of the 1000-kernel weight in each year of the study.
Figure 5. The density of the distribution of observations of the 1000-kernel weight in each year of the study.
Agriculture 13 02005 g005
Agriculture 13 02005 g006
Figure 6. Biplot for the primary component of interaction (IPC1) and the average 1000-kernel weight (g). Vertical line at the center of biplot is the general grand mean.
Figure 6. Biplot for the primary component of interaction (IPC1) and the average 1000-kernel weight (g). Vertical line at the center of biplot is the general grand mean.
Agriculture 13 02005 g006
Agriculture 13 02005 g007
Figure 7. Biplot for the secondary component of interaction (IPC2) and the average 1000-kernel weight (g). Vertical line at the center of biplot is the general grand mean.
Figure 7. Biplot for the secondary component of interaction (IPC2) and the average 1000-kernel weight (g). Vertical line at the center of biplot is the general grand mean.
Agriculture 13 02005 g007
Agriculture 13 02005 g008
Figure 8. A heatmap showing the relationships of the 1000-kernel weight between different years of the study (* p < 0.05; *** p < 0.001).
Figure 8. A heatmap showing the relationships of the 1000-kernel weight between different years of the study (* p < 0.05; *** p < 0.001).
Agriculture 13 02005 g008
Table 1. Total monthly rainfall (in mm) recorded in Krościna Mała, the nearest weather station located from the study site.
Table 1. Total monthly rainfall (in mm) recorded in Krościna Mała, the nearest weather station located from the study site.
YearAprilMayJuneJulyAugustSeptemberOctoberNovember
2013328910847421111326
201442863090831284619
20152735584114383954
20163850609725458140
20174727808369707837
2018176281641262348
20191456194440551738
202011751595569728419
202143843910195171029
202242316959123692324
The meteorological data provided from the Research Centre for Cultivar Testing meteorological station located in Krościna Mała.
Table 2. Mean monthly air temperature (in °C) at an altitude of 2 m recorded in Krościna Mała, the nearest meteorological station located from the study site.
Table 2. Mean monthly air temperature (in °C) at an altitude of 2 m recorded in Krościna Mała, the nearest meteorological station located from the study site.
YearAprilMayJuneJulyAugustSeptemberOctoberNovember
20139.114.617.420.418.912.510.55.2
201410.813.516.421.318.415.911.76.8
20158.813.716.620.720.914.98.47.2
20169.114.918.819.818.316.88.33.9
20178.414.318.719.019.613.611.35.7
201814.016.518.519.921.516.311.75.5
201910.211.821.819.320.814.911.97.2
20209.812.117.718.720.516.011.36.3
20216.812.519.520.418.015.510.75.6
20227.514.919.519.220.313.012.65.4
The meteorological data provided from the Research Centre for Cultivar Testing meteorological station located in Krościna Mała.
Table 3. Characterization of 26 DH lines in terms of the main agronomic traits (plant height, flowering day, and grain yield) in each year of the study presented as a range of mean values for the studied lines.
Table 3. Characterization of 26 DH lines in terms of the main agronomic traits (plant height, flowering day, and grain yield) in each year of the study presented as a range of mean values for the studied lines.
YearPlant Height (cm)Day to Flowering (Days)Grain Yield (t ha–1)
MinMaxMinMaxMinMax
201313027484.592.72.664.21
201414926988.592.54.406.07
201512728185.697.04.596.17
201612527785.194.53.505.97
201715024677.690.52.364.77
201813527388.293.11.665.71
201916126689.393.63.107.73
202014525789.794.44.225.89
202119526490.192.54.235.88
202216626889.594.93.906.01
Table 4. Analysis of variance of main effects and interactions for 1000-kernel weight of 26 maize genotypes (doubled-haploid lines) and variability explained (in %).
Table 4. Analysis of variance of main effects and interactions for 1000-kernel weight of 26 maize genotypes (doubled-haploid lines) and variability explained (in %).
Source of Variationd.f.Sum of SquaresMean SquaresF-StatisticVariability Explained (%)
Total10391,368,7921317
Treatments259652,86325213.38 ***47.70
Genotypes, G25132,65653067.12 ***9.69
Environments, E9192,94521,43828.76 ***14.10
Block/Environment3020,7486920.931.52
GEIs225462,45820552.76 ***33.79
IPCA 133153,12146406.23 ***33.11
IPCA 23188,12928433.81 ***19.06
IPCA 32975,92726183.51 ***16.42
Residuals132145,28111011.48 ***31.41
Error750558,985745
d.f.—the number of degrees of freedom; *** p < 0.001.
Table 5. Average 1000-kernel weight (g), for genotypes and years, principal component analysis values, AMMI stability value (ASV), rank of the AMMI stability value (RA), rank of trait mean (RM), and genotype selection index (GSI) of tested maize genotypes.
Table 5. Average 1000-kernel weight (g), for genotypes and years, principal component analysis values, AMMI stability value (ASV), rank of the AMMI stability value (RA), rank of trait mean (RM), and genotype selection index (GSI) of tested maize genotypes.
GenotypeYearsIPCA g1IPCA g2IPCA g3RMASVRAGSI
2013201420152016201720182019202020212022Mean
KN01328.8339.6348.2327.9321.1359.4322.6312.3362.1343.3336.5−0.2560.4280.347230.617225
KN02345.1336.3340.1363.5299.0371.8329.1337.1352.6346.4342.10.7230.4232.499151.325823
KN03359.7363.8370.1370.3286.3356.0324.5352.4359.7368.5351.11.076−1.8633.04352.639914
KN04349.7353.2433.0333.2306.8356.6344.9337.9334.3363.7351.31.693−5.229−2.69746.0002024
KN05320.8333.0406.2352.2297.7373.1335.1347.1337.8362.0346.53.371−2.367−1.779106.3172333
KN06332.9371.3398.8370.3325.3369.5337.8350.1345.6363.2356.51.946−0.867−0.81713.4911314
KN07335.1374.5377.3355.1329.5363.0349.9366.7335.7354.6354.10.6450.224−1.39021.14257
KN08282.9322.7345.4340.6302.6367.3328.9354.9336.5327.2330.92.8272.468−1.587265.4971743
KN09295.1364.8368.9355.7302.8356.1347.4372.2326.8324.6341.43.0161.383−1.681175.4201633
KN10339.1365.5360.4369.1314.0298.2363.1352.8359.1345.3346.60.153−0.9612.37790.998413
KN11317.6336.2417.7336.7299.2312.1365.7364.3365.0349.6346.42.784−3.895−1.841116.2112233
KN12305.9353.6415.1387.5295.5318.2334.2331.6367.1366.3347.54.431−2.8531.39888.2112432
KN13275.1356.8325.2374.0327.5338.8311.5330.4373.1358.2337.12.0194.7222.115215.8821940
KN14297.6367.1315.6343.8316.3322.8327.2334.2363.6337.6332.60.1223.4411.950253.4481237
KN15334.2380.2339.9336.3296.8317.1322.6328.9368.6341.8336.6−0.556−0.0422.924220.967325
KN16333.4352.2337.6374.6302.1321.0332.1344.9322.3339.7336.00.2890.2092.507240.544125
KN17382.2364.8332.0356.5311.6296.3355.8327.0343.1353.1342.2−2.976−2.0064.348145.5471832
KN18339.0341.6355.2350.4308.7327.4352.6348.6314.7358.1339.6−0.021−1.1770.172181.178725
KN19344.8341.3336.1357.1318.2349.5354.9359.5326.7347.8343.6−0.5070.7660.716131.167619
KN20310.8382.1340.1369.7337.6364.3333.4363.9328.0347.6347.70.5973.753−0.37373.8941421
KN21336.5351.2336.2352.2345.1365.2341.9368.0366.3358.5352.1−0.9142.7760.20133.1991114
KN22329.9348.4354.2305.0359.9331.9353.6357.6342.6334.5341.8−2.4290.893−3.497164.3141531
KN23284.7363.4350.4312.2359.4327.8352.1363.5312.3356.9338.3−0.6342.961−4.964203.1601030
KN24411.0347.7324.9308.9370.7325.3307.2352.8344.1370.0346.3−7.709−0.8280.3071213.4192638
KN25334.0365.4366.2307.6371.8318.7301.4344.7331.6348.6339.0−3.5210.161−3.538196.1202140
KN26410.7368.4363.9322.4365.9323.6318.9359.2337.3339.1350.9−6.169−2.521−0.739611.0112531
Mean332.2355.6360.0347.4322.0339.7336.5348.6344.5350.2343.7
IPCA e1−8.782−1.0565.3395.159−6.8223.2601.989−0.1531.068−0.002
IPCA e2−6.2322.260−8.0471.6554.3543.449−0.2182.0891.207−0.517
IPCA e33.8511.136−5.2136.259−5.027−2.194−0.649−2.8793.9990.718
Table 6. Estimates of the total additive and epistasis (additive-by-additive interaction) effects for 1000-kernel weight.
Table 6. Estimates of the total additive and epistasis (additive-by-additive interaction) effects for 1000-kernel weight.
YearThe Total Additive Effect, aThe Total Additive-by-Additive Interaction Effect, aa
201367.95 ***10.85 ***
201429.70 *–3.20
201558.70 ***14.30 ***
201641.25 *–1.15
201742.75 **7.05 **
201838.40 *–5.00 *
201932.15 *–2.95
202029.95 *–6.35 *
202130.40 *–1.80
202222.70–2.90
* p < 0.05; ** p < 0.01; *** p < 0.001.
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.

Share and Cite

MDPI and ACS Style

Nowosad, K.; Bocianowski, J.; Kianersi, F.; Pour-Aboughadareh, A. Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.). Agriculture 2023, 13, 2005. https://doi.org/10.3390/agriculture13102005

AMA Style

Nowosad K, Bocianowski J, Kianersi F, Pour-Aboughadareh A. Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.). Agriculture. 2023; 13(10):2005. https://doi.org/10.3390/agriculture13102005

Chicago/Turabian Style

Nowosad, Kamila, Jan Bocianowski, Farzad Kianersi, and Alireza Pour-Aboughadareh. 2023. "Analysis of Linkage on Interaction of Main Aspects (Genotype by Environment Interaction, Stability and Genetic Parameters) of 1000 Kernels in Maize (Zea mays L.)" Agriculture 13, no. 10: 2005. https://doi.org/10.3390/agriculture13102005

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop