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Article

Water-Use Efficiency and Responsiveness of a Popcorn Panel Grown Under Different Water Regimes and Cropping Seasons

by
Monique de Souza Santos
1,
Samuel Henrique Kamphorst
1,*,
Antônio Teixeira do Amaral Junior
1,
Jhean Torres Leite
2,
Valter Jário de Lima
3,
Uéliton Alves de Oliveira
1,
Christiane Mileib Vasconcelos
4,
Flávia Nicácio Viana
1,
Talles de Oliveira Santos
1,
Gabriella Rodrigues Gonçalves
1,
Rogério Figueiredo Daher
1,
Cosme Damião Cruz
1 and
Eliemar Campostrini
1,*
1
Laboratory of Plant Breeding, Center of Agricultural Science and Technology, Darcy Ribeiro State University of Northern Rio de Janeiro, Av. Alberto Lamego 2000, Campos dos Goytacazes 28013-602, RJ, Brazil
2
GDM Seeds, Porto Nacional 77500-000, TO, Brazil
3
Centro de Ciências Agrárias e Biológicas, Universidade Estadual Vale do Acaraú—Campus Ibiapaba, São Benedito 62370-000, CE, Brazil
4
Pharmaceutical Sciences Graduate Program, Vila Velha University, Vila Velha 29102-902, ES, Brazil
*
Authors to whom correspondence should be addressed.
Agronomy 2026, 16(2), 258; https://doi.org/10.3390/agronomy16020258
Submission received: 4 December 2025 / Revised: 7 January 2026 / Accepted: 19 January 2026 / Published: 21 January 2026
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

Climate change has intensified drought events, compromising popcorn production, particularly in tropical regions. This study aimed to identify popcorn inbred lines with superior water-use efficiency and responsiveness, and to examine the relationships among morpho-agronomic traits associated with expanded popcorn volume per hectare (VP). Fifty inbred lines were evaluated under well-watered (WW) and water-stressed (WS) conditions across two cropping seasons (2020 and 2021). Water deficit was imposed at pre-anthesis, with the permanent wilting point occurring during early reproductive stages in 2020 and during grain filling in 2021. Principal component analysis and efficiency/responsiveness classification were used to characterize line performance. Significant genotype × water condition × season interactions affected all traits. Water stress reduced VP by 75% in 2020 and 46% in 2021, reflecting the differing timing of stress. Line L477 showed high efficiency and responsiveness, while genotypes such as L213, L221, and L222 were inefficient and non-responsive in both years. Under WW, VP was mainly associated with hundred-grain weight, ear length, and grain number per row, whereas under WS, ear diameter and number of rows per ear were the strongest contributors, indicating that the available genetic variability is more effectively exploited through selective morpho-agronomic criteria tailored to each water scenario. Contrasting crosses between efficient and non-responsive lines (L325 and L481) and inefficient but responsive lines (L513, L625, and L689) are recommended to support the development of hybrids that combine high yield under irrigation with resilience under water-stress conditions.

1. Introduction

Climate change has intensified the occurrence of extreme events, including as rising temperatures and irregular precipitation, imposing increasingly severe constraints on global agricultural production [1,2,3,4]. Among abiotic factors, drought is considered the most limiting to plant growth and development and can drastically reduce grain yield, especially in tropical and subtropical regions [5,6,7,8]. In Brazil, maize production, including popcorn, is largely concentrated in the second cropping season (February to June), when irregular and scarce rainfall is more frequent, increasing crop exposure to prolonged water deficits [9,10]. Under these conditions, popcorn yield losses exceeding 50% may occur under severe water stress [11].
In Brazil, only one popcorn hybrid cultivar is currently registered for environments with low water availability [12]. To overcome this limitation, the genetic variability preserved in germplasm banks constitutes a strategic resource for developing genotypes adapted to restrictive environments [13]. In this context, the Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF) maintains a diversified germplasm bank containing popcorn inbred lines derived from accessions collected across different climatic zones of Latin America, including tropical, temperate, and Brazilian elite materials [14,15]. This diversity provides opportunities to explore agronomic, physiological, root, and yield-related traits linked to drought tolerance, as well as to select parents for developing superior hybrids and conducting genetic inheritance studies [16,17,18,19].
The selection of parents and the most efficient traits for identifying drought-tolerant genotypes depends strongly on interactions among genotype (G), water condition (WC), and cropping season (CS) [20,21,22]. Previous studies with maize and popcorn indicate that these interactions significantly affect grain-yield variables and related traits and that the timing of the permanent wilting point (PWP) modulates the type and magnitude of yield losses [23]. When water deficit coincides with the pre-anthesis and anthesis stages, grain set is severely compromised [24,25,26,27], whereas stress during grain filling primarily affects grain weight [5,26].
Assessing water-use efficiency and responsiveness represents a promising approach in plant breeding, as it enables the identification of genotypes that not only use available water more efficiently but also respond with increased productivity under favorable water conditions. Efficient and responsive (ER) genotypes are particularly valuable because they combine stability with high yield potential, while efficient and non-responsive (ENR) genotypes may be strategic for cultivation in environments where water deficit is recurrent and supplemental irrigation is limited. Conversely, inefficient and responsive (IR) genotypes may contribute to high yield potential when crossed with more tolerant materials, thereby supporting the development of hybrids that combine productivity and resilience.
Given the above, understanding genotype, water condition, and cropping season interaction, as well as the patterns of association among agronomic traits, is essential for guiding popcorn breeding strategies aimed at selecting drought-tolerant genotypes. This study aimed to evaluate the agronomic performance of a panel of 50 popcorn inbred lines under contrasting water availability conditions (well-irrigated and water-stressed) across two cropping seasons (2020 and 2021), with the goal of identifying superior genotypes for water-use efficiency and responsiveness. Additionally, we sought to understand the relationships among morpho-agronomic traits using principal component analysis, to support the selection of promising genotypes in breeding programs targeting drought tolerance.

2. Materials and Methods

2.1. Germplasm

Fifty popcorn inbred lines from the Germplasm Bank of the Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF) were used in this study. These lines originate from accessions collected across several Latin American countries and represent materials adapted to either temperate or tropical climatic conditions [14] (Table 1).

2.2. Crop Management and Experimental Conditions

During the 2020 and 2021 crop seasons (CSs), field trials were conducted at the Colégio Estadual Agrícola Antônio Sarlo (21°34′31″ S, 41°54′40″ W) in Campos dos Goytacazes, Rio de Janeiro, Brazil. The inbred lines were grown under two contrasting water conditions: a Well-Watered (WW) treatment, in which irrigation followed the crop’s recommended requirements, and a Water-Stressed (WS) treatment, in which irrigation was withheld starting 15 days before male anthesis and remained suspended until physiological grain maturity (harvest). The timing of male anthesis was determined based on previous experiments [11].
The experimental site contains an automatic weather station that records micrometeorological variables. During the experimental period, mean temperatures were 21.77 °C and 21.26 °C; mean relative humidity were 76.85% and 77.27%; vapor pressure deficits were 0.60 kPa and 0.58 kPa; and photosynthetically active radiation averaged 1240.07 µmol m−2 s−1 and 1415.80 µmol m−2 s−1 for the 2020 and 2021 CSs, respectively [11].
The experimental design was a randomized complete block design with three replications. Each experimental unit consisted of a 4.40 m row, with 0.20 m between plants and 0.80 m between rows, totaling 23 plants per plot. A drip irrigation system was installed throughout the experimental area, with one emitter per plant. In the 2020 CS, a total of 157.68 mm (WW) and 87.48 mm (WS) of water were applied via irrigation, representing a 44.5% reduction between water conditions; in the 2021 CS, 132.93 mm (WW) and 78.92 mm (WS) were applied, corresponding to a 50.00% reduction. Rainfall was also monitored, totaling 119.20 mm in 2020 and 170.20 mm in 2021 [11].
Soil water potential was monitored throughout the experiments using Decagon MPS-6 sensors (Decagon, San Francisco, CA, USA), installed between two plants within the row at a depth of 0.20 m. Under WW, plants received full irrigation and the soil remained near field capacity (−0.01 MPa). Under WS, in the 2020 CS, the soil reached the permanent wilting point (−1.5 MPa) at 63 days after sowing (DAS), coinciding with the grain formation stage. In the 2021 CS, the highest soil water tension (−1.5 MPa) occurred at 100 DAS, during grain filling [11].
Regardless of CS or water condition (WC), basal and topdressing fertilization consisted of 30 kg ha−1 N (urea), 60 kg ha−1 P2O5 (triple superphosphate), and 60 kg ha ha−1 K2O (potassium chloride). A second topdressing was applied at 30 DAS, providing an additional 100 kg ha−1 N (urea). Crop management included manual weeding and the control of weeds, pests, and diseases as required.

2.3. Phenotyping

The agronomic traits evaluated were: ear length (EL), ear diameter (ED), number of grains per row (NGR), number of rows per ear (NRE), hundred-grain weight (100 GW), grain yield (GY), popping expansion (PE), and expanded popcorn volume per hectare (VP). NGR and NRE were estimated by counting. These traits were measured in a sample of 10 plants, and the corresponding ears were harvested at random. The 100 GW trait was quantified using two subsamples of 100 grains per plot after shelling the ears. GY was obtained by shelling all ears from each plot and correcting grain weight to 13% moisture (kg ha−1). PE was estimated by popping 30 g of grains in a microwave oven (1000 W) for 2 min in a kraft paper bag, and the popped volume was measured in a 2000 mL graduated cylinder. The ratio between the popped volume and the 30 g grain mass determined PE, expressed in mL g−1. VP was calculated by multiplying GY by PE (m3 ha−1).

2.4. ANOVA

The combined ANOVA was based on the following statistical model: Yijkp = µ + (B/WC)/CS)jkp + Gi + (WC)k+ (CS)p + (GWC)ik + (GCS)ip + (WCCS)kp + (GWCCS)ikp + εijkp, where: Yijkp = observed value for the trait under evaluation in the j-th replication of the combination between the i-th level of the Genotype factor, the k-th level of the Water Condition factor, and the p-th level of the Crop Season factor; µ = overall mean; (B/WS)/CS)jkp = block effect; Gi = fixed effect of the i-th level of the Genotype factor; (WC)k = fixed effect of the k-th level of the Water Condition factor; (CS)p = fixed effect of the p-th level of the Crop Season factor; (GWC)ik = fixed effect of the interaction between the i-th level of Genotype and the k-th level of Water Condition; (GCS)ip = fixed effect of the interaction between the i-th level of Genotype and the p-th level of Crop Season; (WCCS)kp = effect of the interaction between the k-th level of Water Condition and the p-th level of Crop Season; (GWCCS)ikp = fixed effect of the interaction among Genotype, Water Condition, and Crop Season; and εijkp = andom error associated with observation Yijkp, assuming NID (0, σ2).
The individual ANOVA was based on the statistical model: Y i j k =   µ + B j + G i + ε i j k , where: Y i j k = observed value for the trait in the j-th replication of the i-th level of Genotype; µ = overall mean; B j = fixed effect of the j-th replication; G i = effect of the i-th level of the Genotype factor; ε i j = random experimental error associated with observation Y i j , assuming NID (0, σ2).
Means of treatments and genotypes were compared using the Scott–Knott test at the 5% significance level, performed with the Genes software 1990.2024.30 [28].

2.5. Classification of Inbred Lines for Water-Use Efficiency and Responsiveness

The inbred lines were evaluated for water-use efficiency and responsiveness based on the deviation of each line’s mean PE and GY values relative to the overall mean of each trait within each water condition. These deviations were plotted in scatter diagrams, where the x-axis represented deviations under the favorable water condition (water-use responsiveness—WW), and the y-axis represented deviations under the water-stressed condition (water-use efficiency—WS).
Water-use efficiency (E) was calculated using the expression: E = Yi WS − Y ¯ WS, where Yi WS is the mean value of line i under the water-stressed (WS) condition, and Y ¯ WS s the overall mean of all lines under WS. Water-use responsiveness (Ruse) was calculated as: Ruse = Yi WW − Y ¯ WW, where Yi WW is the mean value of line i under the well-watered condition (WW), and Y ¯ WW is the overall mean of all lines under WW.
In summary, the lines were distributed into four quadrants in the scatter plot according to their performance under the contrasting WCs, and classified as efficient and responsive (ER), efficient and non-responsive (ENR), inefficient and responsive (IR), and inefficient and non-responsive (INR).

2.6. Principal Component Analysis

Principal component analysis (PCA) was performed to identify the interrelationships among the evaluated variables across cropping seasons (CSs) and water conditions (WCs). Initially, the mean of each variable was standardized using the expression: Z i k = ( X i k − X _ i ) / σ k ^ , where Xik corresponds to the mean of the i-th treatment for the k-th variable, and Ŝ_k corresponds to the standard deviation of the k-th variable [29].
The classification of inbred lines in the PCA biplots was based on the VP variable, selected because it results from the multiplication of PE and GY. Moreover, VP accounted for a greater proportion of the total variation among genotypes compared with GY.

3. Results

3.1. Combined Analysis of Variance for Agronomic Traits

In the joint analysis of variance, all sources of variation—crop season (CS), water condition (WC), and genotype (G)—showed significant effects (p ≤ 0.01) for all agronomic traits evaluated (Table 2). The two-way interactions CS × WC, CS × G, and WC × G were also statistically significant (p ≤ 0.01). In addition, the three-way interaction CS × WC × G exhibited significant effects for all analyzed variables (p ≤ 0.01).

3.2. Analysis of Variance and Overall Means of Agronomic Traits in the 2020 and 2021 Crop Seasons

In the individual analyses for each crop season (2020 and 2021), all agronomic traits showed significant differences (p ≤ 0.01) for the effect of genotypes (G) and for the G × water condition (WC) interaction (Table 3). The experimental coefficients of variation (CVe) were below 20% for all variables (Table 3).
In the 2020 crop season, water restriction resulted in decreases of −75.88% for VP, −69.66% for GY, and −21.44% for PE compared with WW. Additional reductions greater than 15% were observed for hundred-grain weight (100 GW; −15.49%) and number of rows per ear (NRE; −18.30%) (Table 3). In the 2021 crop season, the largest reductions under water stress were recorded for VP (−45.69%), GY (−36.82%), and PE (−15.38%) (Table 3).

3.3. Mean Estimates for Agronomic Traits of Popcorn Inbred Lines Grown Under Well-Watered (WW) and Water-Stressed (WS) Conditions Across Cropping Seasons

In the 2020 crop season under well-watered (WW) conditions, the group with the highest mean EL, identified by the Scott–Knott test at the 5% probability level, comprised lines L292, L358, L391, L472, L480, L502, and L688. For ED, the lines L292, L381, L477, L503, L652, and L693 stood out. With respect to NGR, lines L472, L480, L502, L503, and L513 showed the highest values, whereas lines L381 and L652 were superior for NRE. For 100 GW, the highest means were recorded in lines L294, L321, L358, L391, and L688. For GY, lines L292, L358, L391, L480, and L510 were the best performers. The highest PE values were observed in lines L204, L220, L294, and L688. Finally, the greatest VP estimates were recorded in lines L292, L324, L358, L391, and L510 (Table S1).
In the 2020 crop season under water-stressed (WS) conditions, the highest EL means were observed in lines L294 and L358. For ED, lines L381, L652, and L693 stood out. Regarding NGR, the highest values were recorded in lines L204, L219, L291, L294, L325, L477, L502, L594, and L688, whereas lines L381 and L652 showed the highest means for NRE. Lines L264 and L381 had the greatest 100 GW means. For GY, the best performances were observed in lines L291, L325, L477, and L481. The highest PE values were recorded in lines L221 and L384. Finally, the greatest VP values were obtained for lines L291, L325, L477, and L481 (Table S2).
In the 2021 crop season under well-watered (WW) conditions, lines L507 and L691 stood out with the highest EL means. For ED, line L480 showed the highest value. With respect to NGR, lines L203, L220, L222, L273, L291, L324, L381, L382, L503, L507, and L691 exhibited the greatest values, and these same lines, L203, L217, L222, L273, L291, L324, L381, L382, L503, L507, and L691, also showed superior performance for NRE. For 100 GW, the highest mean was observed in line L691. For GY, lines L203, L292, and L480 were the most outstanding. Regarding PE, the highest means were recorded in lines L219, L220, L222, L291, L513, and L625. Finally, for VP, lines L203 and L219 showed the highest values (Table S3).
In the 2021 crop season under water-stressed (WS) conditions, the highest means for both EL and ED were observed in line L217. For NGR, lines L273, L381, L386, L391, L625, and L693 exhibited the greatest values. For NRE, lines L204, L217, L273, L291, L294, L381, L503, L509, and L693 stood out. The highest 100 GW means were recorded in lines L213, L217, L273, L321, L655, L625, and L693. With respect to GY, lines L204, L217, L220, and L509 showed the best performance. For both PE and VP, the highest mean was obtained by line L503 (Table S4).

3.4. Water-Use Efficiency and Responsiveness of the Inbred Lines Across Crop Seasons

In the 2020 CS, the scatter plot of the inbred lines for water-use efficiency and responsiveness based on VP allowed their classification into four distinct groups. Lines L263, L291, L292, L321, L358, L477, L480, L502, and L507 were classified as efficient and responsive (ER—red), as they performed above the overall mean under both well-watered (WW) and water-stressed (WS) conditions. Conversely, lines L203, L213, L219, L220, L221, L222, L322, L326, L366, L382, L386, L472, L476, L501, L594, L652, L655, L684, and L693 were positioned in the inefficient and non-responsive quadrant (INR—purple), exhibiting low VP values in both water conditions. Lines L204, L217, L273, L324, L332, L391, L503, L510, L513, L625, and L689 were classified as responsive and inefficient (IR—green), showing high productive performance only under WW, with a marked decline under WS. Finally, lines L294, L325, L328, L330, L381, L383, L384, L481, L509, L688, and L691 were classified as efficient and non-responsive (ENR—blue), standing out for their stable performance under water stress, even with mean values close to or below the overall mean under WW (Figure 1).
In the 2021 CS, lines L220, L332, L381, L472, L476, L477, L501, L503, L509, L688, and L691 were classified as efficient and responsive (ER—red), showing VP values above the overall mean under both water conditions. Conversely, lines L213, L221, L222, L263, L294, L324, L326, L328, L330, L358, L366, L382, L384, L391, and L510 were positioned in the inefficient and non-responsive quadrant (INR—purple), with low productive performance under both irrigation and stress. Lines L203, L219, L291, L292, L383, L840, L507, L513, L594, L625, L655, and L689 were classified as responsive and inefficient (IR—green), as they showed high VP means only under WW, with a marked decline under WS. Finally, lines L204, L217, L273, L321, L322, L325, L386, L481, L502, L652, L684, and L693 were classified as efficient and non-responsive (ENR—blue), with performance above the mean under water stress, even if not always exceeding the mean under irrigation (Figure 2).

3.5. Principal Component Analysis of Agronomic Traits in the 2020 (WW and WS) and 2021 (WW and WS) Crop Seasons

The first two principal components explained 62.39% (PC1 = 34.24% + PC2 = 28.15%) of the total variation for 2020 under WW; 58.98% (PC1 = 33.45% + PC2 = 25.53%) for 2020 under WS; 62.12% (PC1 = 41.37% + PC2 = 20.74%) for 2021 under WW; and 60.66% (PC1 = 43.06% + PC2 = 17.60%) for 2021 under WS (Figure 3).
In the 2020 CS under WW, VP was associated with 100 GW, EL, and NGR, with lines L217, L292, L358, L391, L480, and L688 standing out. The variables ED and NRE were associated independently of VP (Figure 3A). Under WS in the same 2020 CS, VP was positioned centrally between the association groups formed by 100 GW, NRE, and ED, and by EL and NGR. Lines L291, L292, L294, and L688 were most strongly represented within these associations (Figure 3B).
In the 2021 CS under WW, VP was associated with 100 GW, NRE, and ED, with lines L203, L383, L480, L655, and L688 standing out. The variables NGR and EL showed associations independent of VP (Figure 3C). Under WS in the same 2021 CS, VP was closely associated with NRE and ED, with lines L273, L381, and L265 showing the closest relationships. In this WC, 100 GW, as well as NGR and EL, clustered together in the plot and remained independent of VP (Figure 3D).

4. Discussion

4.1. Genotype × Water Condition × Crop Season Interaction and Its Implications for Plant Breeding

The joint analysis of variance showed that all evaluated traits exhibited significant differences for the sources of variation genotype (G), water condition (WC), and crop season (CS), as well as for their interactions. These results demonstrate the complexity of the phenotypic response and highlight how genotypic performance is influenced by water availability and seasonal conditions.
The water condition (WC), simulated by withholding irrigation at pre-anthesis, was effective in inducing stress and differentiating genotypes. This timing for the imposition of water stress is well established in studies with both maize and popcorn under drought conditions [6,30,31,32,33,34,35], as it allows for a gradual acclimation of plants to stress [36], while enabling the assessment of agronomic performance under water limitation [37]. This has direct implications for selecting genotypes adapted to environments with low soil water availability.
The presence of significant interactions among genotype, water condition, and crop season indicates that line performance was environment-dependent and that the genotypic response varied according to the climatic context of each season. Although such phenotypic instability poses a challenge to direct selection, it can also be strategically exploited to identify genotypes with broad or specific adaptation [38,39]. The significant three-way interaction (G × WC × CS) for all variables reinforces the need to evaluate materials across multiple years and cultivation conditions (particularly different timings of permanent wilting point occurrence) to support more reliable recommendations of genotypes suited to specific environments.

4.2. Impact of Water Limitation on Agronomic Traits Across Crop Seasons

In the 2020 and 2021 CSs, water limitation markedly affected several evaluated variables—here defined as reductions greater than 15%—as a result of reduced water availability through irrigation (45% reduction in the first season and 50% in the second). In addition to the difference in irrigation reduction between seasons, the timing at which the permanent wilting point (PWP) occurred also differed: in 2020, the PWP was reached during the early reproductive stages (pre-anthesis, anthesis, and early grain filling), whereas in 2021 it occurred later, during grain filling. The interaction between the timing of PWP occurrence and the percentage reduction in water availability affected the agronomic traits in distinct ways.
Among the yield components, NGR (in 2020) and 100 GW (in 2021) were markedly affected by water limitation (>15%). This difference between CSs is associated with the timing at which the PWP was established. In 2020, soil water stress occurred earlier, during pre-anthesis and anthesis, directly affecting pollen formation and release, fertilization, and the initial stages of grain development. As a consequence, there was a strong reduction in the number of grains per ear (NGR), reflecting the high sensitivity of early reproductive stages to water limitation. In contrast, in 2021, water stress intensified later, during grain filling. At that stage, fertilization had already been completed and the number of grains per ear had already been determined. However, the restriction of soil moisture impaired reserve accumulation in the grains, resulting in lower hundred-grain weight (100 GW). This distinction between the effects of early versus late stress is well documented in maize studies, which show that water deficit during anthesis tends to reduce grain number, whereas stress during grain filling primarily affects grain size and weight [5,6,32].
The combined influence of water stress on yield components directly affected key variables for the phenotypic expression of popcorn, such as PE, GY, and VP. PE, a trait related to grain quality, was negatively impacted, with reductions of −21.44% in 2020 and −15.38% in 2021. GY showed even greater reductions, −69.66% in 2020 and −36.82% in 2021. As a result of the combined effect on GY and PE, VP was the most severely affected variable under water limitation, decreasing by more than 75% in 2020 and more than 45% in 2021. These results reinforce that reductions in yield components, whether in grain number or grain weight, not only decrease production volume but also impact final grain quality in terms of popping expansion, the principal commercial attribute of popcorn.

4.3. Water-Use Efficiency and Responsiveness of the Inbred Lines Across Crop Seasons

The evaluation of water-use efficiency and responsiveness in the 2020 and 2021 CSs showed that only a few lines maintained the same classification across years, highlighting the strong influence of the environment and of the timing and intensity of soil water stress on performance for VP. Within the group of efficient and responsive lines—ER (red; Figure 1 and Figure 2)—only L477 remained in this class in both CSs, suggesting physiological robustness and adaptive stability. This consistency may indicate a combination of mechanisms—possibly related to the maintenance of photosynthetic activity, greater root exploration, and efficient stomatal regulation—that supports superior performance under contrasting water conditions [40,41,42,43].
At the opposite extreme, seven lines—L213, L221, L222, L322, L326, L366, and L382—remained in the inefficient and non-responsive group—INR (purple; Figure 1 and Figure 2)—in both CSs, indicating limitations in agronomic potential under adequate water availability as well as low capacity to sustain performance under drought. The presence of these genotypes in the INR quadrant indicates reduced adaptive plasticity and low water-use efficiency, making them poorly suited for cultivation in environments with irregular water supply.
Among the efficient and non-responsive lines—ENR (blue; Figure 1 and Figure 2)—only L325 and L481 remained in this group across both CSs. This pattern suggests that these lines can sustain performance under water stress but do not exhibit substantial gains when water availability becomes favorable. Such productive stability, even without strong responsiveness, may be desirable in regions where drought is recurrent and supplemental irrigation is limited, serving as a genetic base for crosses aimed at maintaining yield in low-input environments [44,45,46].
In the group of inefficient and responsive lines—IR (green; Figure 1 and Figure 2)—L513, L625, and L689 were consistent across both CSs, showing high productive performance only under full irrigation, but with sharp reductions under drought. This pattern indicates that although these lines have high yield potential, they lack adaptive physiological mechanisms to tolerate water stress. For breeding programs, IR genotypes (green; L513, L625, and L689) may be used as parents in specific crosses with ER lines (red; L477) or ENR lines (blue; L325 and L481), aiming to combine high yield potential with greater stability under fluctuating water availability.
Finally, the contrast between the positive stability of L477 and the negative stability of the INR lines (purple) reinforces that ER and INR classifications are not inherently static, but when consistent across years, they provide valuable information about adaptability and yield resilience. Genotypes such as L477 represent strategic genetic resources for breeding programs targeting drought tolerance with yield stability, whereas INR lines may serve as parents in studies focused on the genetic basis of maladaptation under water-deficit conditions.

4.4. Principal Component Analysis

The principal component analysis revealed distinct patterns of association between VP and the yield components—EL, ED, NRE, NGR, and 100 GW—modulated by both water condition (WC) and crop season (CS).
In the 2020 CS under well-watered (WW) conditions, VP was associated with 100 GW, EL, and NGR, indicating that, under adequate soil water availability, increases in grain mass and the formation of longer ears with higher grain density were key determinants of high VP values. Still under WW, ED and NRE clustered independently of VP, suggesting that although agronomically relevant, they were not decisive for VP in this scenario. Under water-stressed conditions (WS) in 2020, VP assumed a central position between two clusters: one formed by 100 GW, NRE, and ED, and the other by EL and NGR. This configuration suggests that, under water deficit, both the components related to ear weight and architecture and those related to grain number and ear length contributed to VP performance [47,48].
In the 2021 CS under WW conditions, VP was primarily associated with 100 GW, NRE, and ED, indicating that, in this season, grain mass and ear structure were the main determinants of VP in irrigated environments. Unlike 2020, NGR and EL positioned independently, suggesting a lower direct contribution of these traits to yield. Under WS in 2021, VP was associated with NRE and ED, highlighting the importance of these two components in determining yield under late-season drought and indicating that maintaining the number of rows and ear diameter was crucial for mitigating losses. In contrast, 100 GW and the NGR–EL cluster positioned independently of VP, suggesting that grain weight and ear length exerted less influence on yield under stress in this season.
The results reinforce that the relative importance of yield components for VP was dynamic, modulated by the interaction among genotype, water condition, and crop season. Overall, under irrigated conditions (WW), hundred-grain weight (100 GW) and structural attributes of the ear—diameter (ED) and number of rows (NRE)—were the main determinants of VP, whereas under water stress (WS), the components related to ear architecture—number of rows (NRE), diameter (ED), and, in some cases, ear length (EL)—played a greater role in sustaining yield.

4.5. Implications for Plant Breeding Programs

The genetic variability observed among lines and the significant genotype × water condition × crop season interactions indicate that the response to water stress was modulated by the environment and by the timing of stress occurrence. This scenario reinforces the importance of selection strategies that consider not only mean performance but also productive stability and the ability to maintain yield under adverse conditions, aiming at the development of cultivars more resilient to soil drought.
The characterization of water-use efficiency and responsiveness, combined with multivariate analysis, provided a solid foundation for shaping these strategies. Genotypes classified as efficient and responsive across multiple environments, such as L477, should be prioritized as base parents for transmitting traits related to stability and broad adaptation. In contrast, inefficient and non-responsive genotypes, such as L213, L221, L222, L322, L326, L366, and L382, may be directed to exploratory crosses depending on the absence of agronomic traits of interest.
Efficient and non-responsive genotypes (L325 and L481) represent useful sources for developing cultivars targeted to stability in environments with recurrent drought, whereas inefficient and responsive genotypes (L513, L625, and L689) may contribute high yield potential when combined with more tolerant lines, enabling the development of hybrids that reconcile productivity and resilience.
The patterns identified by PCA indicate that under irrigated conditions, attributes such as grain weight (100 GW) and ear conformation (EL, NGR) should be prioritized, whereas in water-deficit environments, emphasis should be placed on maintaining the number of rows (NRE) and ear diameter (ED). Thus, selection criteria tailored to each water scenario allow for more effective use of available genetic variability.
In practice, the combined use of efficiency/responsiveness analyses, principal component analysis, and multi-environment trials enables the design of more effective crossing schemes, integrating genotypes with high yield potential, stability, and drought tolerance. This approach can accelerate genetic gains and increase the likelihood of recommending cultivars adapted to water-limited conditions.

5. Conclusions

The genetic variability and the significant genotype × water condition × cropping season interactions demonstrated the strong influence of the environment on the agronomic performance of popcorn inbred lines, reinforcing the need to evaluate materials across multiple years and cultivation conditions, particularly under different timings of permanent wilting point occurrence, to support more reliable genotype recommendations for specific environments.
The available genetic variability is more effectively exploited through selective morpho-agronomic criteria tailored to each water scenario.
Contrasting crosses between efficient and non-responsive lines (L325 and L481) and inefficient but responsive lines (L513, L625, and L689) are recommended to support the development of hybrids that combine high yield under irrigation with resilience under water-stress conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy16020258/s1, Table S1: Mean estimates for agronomic traits of 50 popcorn lines grown under irrigated conditions (WW) in the 2020 season; Table S2: Mean estimates for agronomic traits of 50 popcorn lines grown under water-stress conditions (WS) in the 2020 season; Table S3: Mean estimates for agronomic traits of 50 popcorn lines grown under irrigated conditions (WW) in the 2021 season; Table S4: Mean estimates for agronomic traits of 50 popcorn lines grown under water-stress conditions (WS) in the 2021 season, including grain yield (GY), popping expansion (PE), and popcorn volume per hectare (VP).

Author Contributions

Conceptualization, S.H.K., A.T.d.A.J. and E.C.; methodology, M.d.S.S., S.H.K., A.T.d.A.J., J.T.L., V.J.d.L., U.A.d.O., C.M.V., F.N.V., T.d.O.S., G.R.G., R.F.D., C.D.C. and E.C.; software, S.H.K. and V.J.d.L.; validation, S.H.K. and V.J.d.L.; formal analysis, S.H.K.; investigation, M.d.S.S., S.H.K., A.T.d.A.J., J.T.L., V.J.d.L., U.A.d.O., C.M.V., F.N.V., T.d.O.S., G.R.G., R.F.D., C.D.C. and E.C.; resources, S.H.K. and A.T.d.A.J.; data curation, S.H.K. and A.T.d.A.J.; writing—original draft preparation, M.d.S.S., S.H.K., A.T.d.A.J., J.T.L., V.J.d.L., U.A.d.O., C.M.V., F.N.V., T.d.O.S., G.R.G., R.F.D., C.D.C. and E.C.; writing—review and editing, M.d.S.S., S.H.K., A.T.d.A.J., J.T.L., V.J.d.L., U.A.d.O., C.M.V., F.N.V., T.d.O.S., G.R.G., R.F.D., C.D.C. and E.C.; visualization, S.H.K.; supervision, S.H.K. and A.T.d.A.J.; project administration, S.H.K. and A.T.d.A.J.; funding acquisition, S.H.K. and A.T.d.A.J. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding authors upon reasonable request.

Acknowledgments

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (304470/2023-6); Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (E-26/200.957/2022) conceded to E.C.

Conflicts of Interest

Author Jhean Torres Leite was employed by the company GDM Seeds. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ANOVAAnalysis of variance
CSCropping season
EDEar diameter
ELEar length
ENREfficient and non-responsive
EREfficient and responsive
GGenotype
NGRGrain number per row
GYGrain yield
INRInefficient and non-responsive
IRInefficient and responsive
MPaMegapascal
PCAPrincipal component analysis
PEPopping expansion
PWPPermanent wilting point
NRERow number per ear
VPExpanded popcorn volume per hectare
WCWater condition
WSWater-stressed
WWWell-watered

References

  1. Schmitt, K.F.M.; do Amaral Junior, A.T.; Kamphorst, S.H.; Pinto, V.B.; de Lima, V.J.; de Oliveira, U.A.; Viana, F.N.; Leite, J.T.; Gomes, L.P.; Silva, J.G.d.S.; et al. Decoding the Effects of Drought Stress on Popcorn (Zea mays Var. Everta) Flowering Combining Proteomics and Physiological Analysis. Plant Physiol. Biochem. 2024, 208, 108444. [Google Scholar] [CrossRef]
  2. Salami, M.; Tan, H.; Alizadeh, B.; Heidari, B. Photosynthetic Performance, Pigments and Biochemicals Influence Seed Yield in Rapeseed under Water Deficit Conditions: MGIDI Index Helps Screening Drought-Tolerant Genotypes. Field Crops Res. 2025, 322, 109733. [Google Scholar] [CrossRef]
  3. Araus, J.L.; Kefauver, S.C. Breeding to Adapt Agriculture to Climate Change: Affordable Phenotyping Solutions. Curr. Opin. Plant Biol. 2018, 45, 237–247. [Google Scholar] [CrossRef]
  4. Abberton, M.; Batley, J.; Bentley, A.; Bryant, J.; Cai, H.; Cockram, J.; Costa de Oliveira, A.; Cseke, L.J.; Dempewolf, H.; De Pace, C.; et al. Global Agricultural Intensification during Climate Change: A Role for Genomics. Plant Biotechnol. J. 2016, 14, 1095–1098. [Google Scholar] [CrossRef]
  5. Araus, J.L.; Serret, M.D.; Edmeades, G.O. Phenotyping Maize for Adaptation to Drought. Front. Physiol. 2012, 3, 305. [Google Scholar] [CrossRef]
  6. Cairns, J.E.; Sanchez, C.; Vargas, M.; Ordoñez, R.; Araus, J.L. Dissecting Maize Productivity: Ideotypes Associated with Grain Yield under Drought Stress and Well-Watered Conditions. J. Integr. Plant Biol. 2012, 54, 1007–1020. [Google Scholar] [CrossRef]
  7. Adebayo, M.A.; Menkir, A. Combining Ability of Adapted and Exotic Drought-Tolerant Maize Inbred Lines Under Full Irrigation and Rainfed Conditions in Nigeria. J. Crop Improv. 2015, 29, 117–130. [Google Scholar] [CrossRef]
  8. Adebayo, M.A.; Menkir, A.; Blay, E.; Gracen, V.; Danquah, E.; Hearne, S. Genetic Analysis of Drought Tolerance in Adapted × Exotic Crosses of Maize Inbred Lines under Managed Stress Conditions. Euphytica 2014, 196, 261–270. [Google Scholar] [CrossRef]
  9. Silva, M.B.; Amaral, A.M.; Pereira, S.B.; Santos, M.Â.C.M.d.; Brito, A.F.C.d.; Anorato, L.R. Desenvolvimento Inicial de Diferentes Híbridos de Milho Sob Variabilidade Hídrica No Solo. Rev. Delos 2025, 18, e4035. [Google Scholar] [CrossRef]
  10. Vidotto, M.L.; Santos, R.F.; Nogueira, C.E.C.; Da Silva, E.V.C.; Frenhe, R.M.; Bissani, F.M.; Dos Santos, N.B.; Zitha, A.R. Influência das chuvas no rendimento do milho safrinha em cascavel, pr. DESAFIOS—Rev. Interdiscip. Univ. Fed. Tocantins 2025, 12, 5. [Google Scholar] [CrossRef]
  11. Leite, J.T.; Amaral Junior, A.T.d.; Kamphorst, S.H.; Lima, V.J.d.; Santos Junior, D.R.d.; Alves, U.O.; Azeredo, V.C.; Pereira, J.L.; Bispo, R.B.; Schmidt, K.F.M.; et al. All Are in a Drought, but Some Stand Out: Multivariate Analysis in the Selection of Agronomic Efficient Popcorn Genotypes. Plants 2022, 11, 2275. [Google Scholar] [CrossRef] [PubMed]
  12. Lima, V.J.d.; Amaral Júnior, A.T.d.; Kamphorst, S.H.; Santos, A.d.; Schmidt, K.F.M.; Azeredo, V.C.; Leite, J.T.; Santos Junior, D.R.d.; Santos, T.d.O.; Bispo, R.B.; et al. UENF WS01: Popcorn Hybrid with Water Use Efficiency for the State of Rio de Janeiro. Crop Breed. Appl. Biotechnol. 2021, 21, e375821411. [Google Scholar] [CrossRef]
  13. Anglin, N.L.; Wenzl, P.; Azevedo, V.; Lusty, C.; Ellis, D.; Gao, D. Genotyping Genebank Collections: Strategic Approaches and Considerations for Optimal Collection Management. Plants 2025, 14, 252. [Google Scholar] [CrossRef] [PubMed]
  14. Vittorazzi, C.; Júnior, A.T.A.; Guimarães, A.G.; Silva, F.H.L.; Pena, G.F.; Daher, R.F.; Gerhardt, I.F.S.; Oliveira, G.H.F.; Santos, P.H.A.D.; Souza, Y.P.; et al. Evaluation of Genetic Variability to Form Heterotic Groups in Popcorn. Genet. Mol. Res. 2018, 17, 18083. [Google Scholar] [CrossRef]
  15. Santos, T.d.O.; Amaral Junior, A.T.d.; Bispo, R.B.; Lima, V.J.d.; Kamphorst, S.H.; Leite, J.T.; Santos Júnior, D.R.d.; Santos, P.H.A.D.; Oliveira, U.A.d.; Schmitt, K.F.M.; et al. Phenotyping Latin American Open-Pollinated Varieties of Popcorn for Environments with Low Water Availability. Plants 2021, 10, 1211. [Google Scholar] [CrossRef]
  16. Teixeira, F.F.; Gomide, R.L.; Albuquerque, P.E.P.d.; Andrade, C.L.T.d.; Leite, C.E.P.; Parentoni, S.N.; Guimarães, P.E.d.O.; Guimarães, L.J.M.; Silva, A.R.; Bastos, E.A.; et al. Evaluation of Maize Core Collection for Drought Tolerance. Crop Breed. Appl. Biotechnol. 2010, 10, 312–320. [Google Scholar] [CrossRef]
  17. Dias, K.O.D.G.; Gezan, S.A.; Guimarães, C.T.; Parentoni, S.N.; Guimarães, P.E.d.O.; Carneiro, N.P.; Portugal, A.F.; Bastos, E.A.; Cardoso, M.J.; Anoni, C.d.O.; et al. Estimating Genotype × Environment Interaction for and Genetic Correlations among Drought Tolerance Traits in Maize via Factor Analytic Multiplicative Mixed Models. Crop Sci. 2018, 58, 72. [Google Scholar] [CrossRef]
  18. Adebayo, M.A.; Menkir, A.; Hearne, S.; Kolawole, A.O. Gene Action Controlling Normalized Difference Vegetation Index in Crosses of Elite Maize (Zea mays L.) Inbred Lines. Cereal Res. Commun. 2017, 45, 675–686. [Google Scholar] [CrossRef]
  19. Gao, Y.; Lynch, J.P. Reduced Crown Root Number Improves Water Acquisition under Water Deficit Stress in Maize (Zea mays L.). J. Exp. Bot. 2016, 67, 4545–4557. [Google Scholar] [CrossRef]
  20. Bernini, C.S.; Guimarães, P.d.S.; Gallo, P.B.; Paterniani, M.E.A.G.Z. Parâmetros genéticos de progênies interpopulacionais de milho para caracteres relacionados com tolerância à seca. Sci. Electron. Arch. 2021, 14, 9. [Google Scholar] [CrossRef]
  21. Wang, L.; Yu, X.; Gao, J.; Ma, D.; Guo, H.; Hu, S. Patterns of Influence of Meteorological Elements on Maize Grain Weight and Nutritional Quality. Agronomy 2023, 13, 424. [Google Scholar] [CrossRef]
  22. Minuzzi, R.B.; Medeiros, S.R.R. Influência de Secas Agrícolas e Ondas de Calor Na Produtividade de Milho e Da Soja Em Chapecó, Estado de Santa Catarina. Rev. Delos 2025, 18, e4528. [Google Scholar] [CrossRef]
  23. Kamphorst, S.H.; do Amaral Junior, A.T.; de Lima, V.J.; Carena, M.J.; Azeredo, V.C.; Mafra, G.S.; Santos, P.H.A.D.; Leite, J.T.; Schmitt, K.F.M.; dos Santos Junior, D.R.; et al. Driving Sustainable Popcorn Breeding for Drought Tolerance in Brazil. Front. Plant Sci. 2021, 12, 732285. [Google Scholar] [CrossRef] [PubMed]
  24. Zinselmeier, S.A.; Lauer, M.J.; Boyer, J.S. Reversing Drought-Induced Losses in Grain Yield: Sucrose Maintains Embryo Growth in Maize. Crop Sci. 1995, 35, 1390. [Google Scholar] [CrossRef]
  25. Lu, Y.; Hao, Z.; Xie, C.; Crossa, J.; Araus, J.; Gao, S.; Vivek, B.S.; Magorokosho, C.; Mugo, S.; Makumbi, D.; et al. Field Crops Research Large-Scale Screening for Maize Drought Resistance Using Multiple Selection Criteria Evaluated under Water-Stressed and Well-Watered Environments. Field Crops Res. 2011, 124, 37–45. [Google Scholar] [CrossRef]
  26. Zia, S.; Romano, G.; Spreer, W.; Sanchez, C.; Cairns, J.; Araus, J.L.; Müller, J. Infrared Thermal Imaging as a Rapid Tool for Identifying Water-Stress Tolerant Maize Genotypes of Different Phenology. J. Agron. Crop Sci. 2013, 199, 75–84. [Google Scholar] [CrossRef]
  27. Zhuang, Y.; Ren, G.; Yue, G.; Li, Z.; Qu, X.; Hou, G.; Zhu, Y.; Zhang, J. Effects of Water-Deficit Stress on the Transcriptomes of Developing Immature Ear and Tassel in Maize. Plant Cell Rep. 2007, 26, 2137–2147. [Google Scholar] [CrossRef]
  28. Cruz, C.D. GENES—A Software Package for Analysis in Experimental Statistics and Quantitative Genetics. Acta Sci. Agron. 2013, 35, 271–276. [Google Scholar] [CrossRef]
  29. Cruz, C.D.; Regazzi, A.J.; Carneiro, P.C.S. Modelos Biométricos Aplicados Ao Melhoramento Genético; UFV: Viçosa, Brazil, 2012; Volume 1, ISBN 9788572694339. [Google Scholar]
  30. Araus, J.L.; Sánchez, C.; Cabrera-Bosquet, L. Is Heterosis in Maize Mediated through Better Water Use? New Phytol. 2010, 187, 392–406. [Google Scholar] [CrossRef]
  31. Kamphorst, S.H.; Gonçalves, G.M.B.; Amaral Júnior, A.T.d.; Lima, V.J.d.; Schmitt, K.F.M.; Leite, J.T.; Azeredo, V.C.; Gomes, L.P.; Silva, J.G.d.S.; Carvalho, C.M.; et al. Supporting Physiological Trait for Indirect Selection for Grain Yield in Drought-Stressed Popcorn. Plants 2021, 10, 1510. [Google Scholar] [CrossRef]
  32. Liu, S.; Qin, F. Genetic Dissection of Maize Drought Tolerance for Trait Improvement. Mol. Breed. 2021, 41, 8. [Google Scholar] [CrossRef] [PubMed]
  33. Lindsey, A.J.; Barker, D.J.; Metzger, J.D.; Mullen, R.W.; Thomison, P.R. Physiological and Morphological Response of a Drought-Tolerant Maize Hybrid to Agronomic Management. Agron. J. 2018, 110, 1354. [Google Scholar] [CrossRef]
  34. Nemali, K.S.; Bonin, C.; Dohleman, F.G.; Stephens, M.; Reeves, W.R.; Nelson, D.E.; Castiglioni, P.; Whitsel, J.E.; Sammons, B.; Silady, R.A.; et al. Physiological Responses Related to Increased Grain Yield under Drought in the First Biotechnology-Derived Drought-Tolerant Maize. Plant Cell Environ. 2015, 38, 1866–1880. [Google Scholar] [CrossRef]
  35. Lunduka, R.W.; Mateva, K.I.; Magorokosho, C.; Manjeru, P. Impact of Adoption of Drought-Tolerant Maize Varieties on Total Maize Production in South Eastern Zimbabwe. Clim. Dev. 2019, 11, 35–46. [Google Scholar] [CrossRef] [PubMed]
  36. Kamphorst, S.H.; Amaral Júnior, A.T.d.; Vergara-Diaz, O.; Gracia-Romero, A.; Fernandez-Gallego, J.A.; Chang-Espino, M.C.; Buchaillot, M.L.; Rezzouk, F.Z.; Lima, V.J.d.; Serret, M.D.; et al. Heterosis and Reciprocal Effects for Physiological and Morphological Traits of Popcorn Plants under Different Water Conditions. Agric. Water Manag. 2022, 261, 107371. [Google Scholar] [CrossRef]
  37. Kim, K.-H.; Lee, B.-M. Effects of Climate Change and Drought Tolerance on Maize Growth. Plants 2023, 12, 3548. [Google Scholar] [CrossRef]
  38. Hallauer, A.R.; Carena, M.J.; Miranda Filho, J.B. Quantitative Genetics in Maize Breeding; Springer: New York, NY, USA, 2010; ISBN 978-1-4419-0765-3. [Google Scholar]
  39. Cruz, C.D.; Carneiro, P.C.S.; Regazzi, A.J. Modelos Biométricos Aplicados Ao Melhoramento Genético—Vol. II; UFV: Viçosa, Brazil, 2014; ISBN 8572691510. [Google Scholar]
  40. Oguz, M.C.; Aycan, M.; Oguz, E.; Poyraz, I.; Yildiz, M. Drought Stress Tolerance in Plants: Interplay of Molecular, Biochemical and Physiological Responses in Important Development Stages. Physiologia 2022, 2, 180–197. [Google Scholar] [CrossRef]
  41. Chairi, F.; Elazab, A.; Sanchez-Bragado, R.; Araus, J.L.; Serret, M.D. Heterosis for Water Status in Maize Seedlings. Agric. Water Manag. 2016, 164, 100–109. [Google Scholar] [CrossRef]
  42. Buckley, T.N. How Do Stomata Respond to Water Status? New Phytol. 2019, 224, 21–36. [Google Scholar] [CrossRef]
  43. Hetherington, A.M.; Woodward, F.I. The Role of Stomata in Sensing and Driving Environmental Change. Nature 2003, 424, 901–908. [Google Scholar] [CrossRef]
  44. Blankenagel, S.; Yang, Z.; Avramova, V.; Schön, C.-C.; Grill, E. Generating Plants with Improved Water Use Efficiency. Agronomy 2018, 8, 194. [Google Scholar] [CrossRef]
  45. Blum, A. Effective Use of Water (EUW) and Not Water-Use Efficiency (WUE) Is the Target of Crop Yield Improvement under Drought Stress. Field Crops Res. 2009, 112, 119–123. [Google Scholar] [CrossRef]
  46. Zhang, C.; Zhang, J.; Zhang, H.; Zhao, J.; Wu, Q.; Zhao, Z.; Cai, T. Mechanisms for the Relationships between Water-Use Efficiency and Carbon Isotope Composition and Specific Leaf Area of Maize (Zea mays L.) under Water Stress. Plant Growth Regul. 2015, 77, 233–243. [Google Scholar] [CrossRef]
  47. Sousa, R.S.; Bastos, E.A.; Cardoso, M.J.; Ribeiro, V.Q.; Brito, R.R. Desempenho Produtivo de Genótipos de Milho Sob Déficit Hídrico. Rev. Bras. De Milho E Sorgo 2015, 14, 49–60. [Google Scholar] [CrossRef]
  48. Reddy, S.G.M.; Lal, G.M.; Krishna, T.V.; Reddy, Y.V.S.; Sandeep, N. Correlation and Path Coefficient Analysis for Grain Yield Components in Maize (Zea mays L.). Int. J. Plant Soil Sci. 2022, 34, 24–36. [Google Scholar] [CrossRef]
Agronomy 16 00258 g001
Figure 1. Scatter plot of 50 popcorn inbred lines for water-use efficiency and responsiveness based on VP in the 2020 crop season. Efficient and responsive (red); efficient and non-responsive (blue); inefficient and responsive (green); and inefficient and non-responsive (purple). The blue dashed line represents the overall mean of VP under the well-watered condition (WW), and the red dashed line represents the overall mean of VP under the water-stressed condition (WS).
Figure 1. Scatter plot of 50 popcorn inbred lines for water-use efficiency and responsiveness based on VP in the 2020 crop season. Efficient and responsive (red); efficient and non-responsive (blue); inefficient and responsive (green); and inefficient and non-responsive (purple). The blue dashed line represents the overall mean of VP under the well-watered condition (WW), and the red dashed line represents the overall mean of VP under the water-stressed condition (WS).
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Agronomy 16 00258 g002
Figure 2. Scatter plot of 50 popcorn inbred lines for water-use efficiency and responsiveness based on VP in the 2021 crop season. Efficient and responsive (red); efficient and non-responsive (blue); inefficient and responsive (green); and inefficient and non-responsive (purple). The blue dashed line represents the overall mean of VP under the well-watered condition (WW), and the red dashed line represents the overall mean of VP under the water-stressed condition (WS).
Figure 2. Scatter plot of 50 popcorn inbred lines for water-use efficiency and responsiveness based on VP in the 2021 crop season. Efficient and responsive (red); efficient and non-responsive (blue); inefficient and responsive (green); and inefficient and non-responsive (purple). The blue dashed line represents the overall mean of VP under the well-watered condition (WW), and the red dashed line represents the overall mean of VP under the water-stressed condition (WS).
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Figure 3. Principal component analysis (PCA) of agronomic traits (EL—ear length, ED—ear diameter, NGR—grain number per row, NRE—number of rows per ear, 100GW—hundred-grain weight, and VP—expanded popcorn volume per hectare) in popcorn inbred lines evaluated under contrasting water conditions across two crop seasons. (A) 2020 crop season under well-watered conditions (WW); (B) 2020 crop season under water-stressed conditions (WS); (C) 2021 crop season under well-watered conditions (WW); and (D) 2021 crop season under water-stressed conditions (WS).
Figure 3. Principal component analysis (PCA) of agronomic traits (EL—ear length, ED—ear diameter, NGR—grain number per row, NRE—number of rows per ear, 100GW—hundred-grain weight, and VP—expanded popcorn volume per hectare) in popcorn inbred lines evaluated under contrasting water conditions across two crop seasons. (A) 2020 crop season under well-watered conditions (WW); (B) 2020 crop season under water-stressed conditions (WS); (C) 2021 crop season under well-watered conditions (WW); and (D) 2021 crop season under water-stressed conditions (WS).
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Table 1. Description of the 50 popcorn inbred lines and information regarding generation, country of origin, source, and climatic adaptation.
Table 1. Description of the 50 popcorn inbred lines and information regarding generation, country of origin, source, and climatic adaptation.
LinePedigreeCountry of OriginDonor InstitutionClimatic Adaptation
L203IAC 125BrazilIACTropical
L204IAC 125BrazilIACTropical
L213IAC 125BrazilIACTropical
L217IAC 125BrazilIACTropical
L219IAC 125BrazilIACTropical
L220IAC 125BrazilIACTropical
L221IAC 125BrazilIACTropical
L222IAC 125BrazilIACTropical
L263PARA 172ParaguaiCIMMYTTemperada
L273PARA 172ParaguaiCIMMYTTemperada
L291URUG 298UruguaiCIMMYTTemperada
L292URUG 298UruguaiCIMMYTTemperada
L294URUG 298UruguaiCIMMYTTemperada
L321UFV M-2 Barão de ViçosaBrazilUFVTropical
L322UFV M-2 Barão de ViçosaBrazilUFVTropical
L324UFV M-2 Barão de ViçosaBrazilUFVTropical
L325UFV M-2 Barão de ViçosaBrazilUFVTropical
L326UFV M-2 Barão de ViçosaBrazilUFVTropical
L328UFV M-2 Barão de ViçosaBrazilUFVTropical
L330UFV M-2 Barão de ViçosaBrazilUFVTropical
L332UFV M-2 Barão de ViçosaBrazilUFVTropical
L358PR 023BrazilUEMTropical
L366PR 023BrazilUEMTropical
L381SAMUSAUSATemperada
L382SAMUSAUSATemperada
L383SAMUSAUSATemperada
L384SAMUSAUSATemperada
L386SAMUSAUSATemperada
L391SAMUSAUSATemperada
L472SE 013BrazilUEMTropical
L476SE 013BrazilUEMTropical
L477SE 013BrazilUEMTropical
L480SE 013BrazilUEMTropical
L481SE 013BrazilUEMTropical
L501PA 170 RoxoParaguayCIMMYTTemperada
L502PA 170 RoxoParaguayCIMMYTTemperada
L503PA 170 RoxoParaguayCIMMYTTemperada
L507PA 170 RoxoParaguayCIMMYTTemperada
L509PA 170 RoxoParaguayCIMMYTTemperada
L510PA 170 RoxoParaguayCIMMYTTemperada
L513PA 170 RoxoParaguayCIMMYTTemperada
L594RS 20BrazilIPAGRO/AGROESTETemperada
L625PA 091BrazilUEMTropical
L652ARZM 13 050ArgentinaCIMMYTTemperada
L655ARZM 13 050ArgentinaCIMMYTTemperada
L684UENF 14BrazilUENFTropical
L688UENF 14BrazilUENFTropical
L689UENF 14BrazilUENFTropical
L691UENF 14BrazilUENFTropical
L693UENF 14BrazilUENFTropical
USA—United States of America; UFV—Universidade Federal de Viçosa; IAC—Instituto Agronômico de Campinas; CIMMYT—International Maize and Wheat Improvement Center; UEM—Universidade Estadual de Maringá; IPAGRO—Instituto de pesquisas Agronômicas; e UENF—Universidade Estadual do Norte Fluminense Darcy Ribeiro.
Table 2. Summary of the combined analysis of variance for agronomic traits evaluated in popcorn inbred lines grown under contrasting water conditions (WC—WS and WW) and crop seasons (CS—2020 and 2021).
Table 2. Summary of the combined analysis of variance for agronomic traits evaluated in popcorn inbred lines grown under contrasting water conditions (WC—WS and WW) and crop seasons (CS—2020 and 2021).
TraitsCombined ANOVA
CSWCGCS × WCCS × GWC × GCS × WC × G
ED33.02***719.20***32.44***25.68***14.80***20.88***25.76***
EL613.30***120.30***11.23***18.04***12.32***7.89***5.49***
NRE 6.46***186.90***11.78***12.97***8.19***5.55***5.15***
NGR502.00***720.30***69.50***560.90***68.84***34.02***43.61***
100 GW33.17***246.91***13.24***28.28***9.88***8.36***7.93***
PE73.99***2578.20***71.78***83.39***76.27***44.72***50.82***
GY506,485.01***87,937,436.50***721,163.11***12,850,978.60***727,817.32***611,937.75***494,822.69***
PV169.39***55,167.20***363.09***5890.98***406.61***319.17***305.18***
CS—Crop season; WC—Water condition; WS—Water stressed; WW—Well watered; G—Genotype. ED—Ear diameter; EL—Ear length; NRE—Row number per ear; NGR—Grain number per row; 100 GW—100-grain weight; PE—Popping expansion; GY—Grain yield; VP—Expanded popcorn volume per hectare. *** indicate statistical significance at 0.01% probability levels, respectively, according to the F test; ns: not significant at 5% probability level.
Table 3. Summary of the individual analysis of variance, mean estimates, mean squares, coefficients of variation, and standard error associated with agronomic traits of popcorn lines under two water conditions (well-watered—WW and water-stressed—WS) in the 2020 and 2021 crop seasons.
Table 3. Summary of the individual analysis of variance, mean estimates, mean squares, coefficients of variation, and standard error associated with agronomic traits of popcorn lines under two water conditions (well-watered—WW and water-stressed—WS) in the 2020 and 2021 crop seasons.
TraitsWCMS (DF = 49)Means and SEProportional Reduction (%)CVe (%)
GG × WC
2020
Ear diameter (mm)WW15.43 **6.59 **27.79 ± 1.799.395.57%
WS23.16 **25.18 ± 2.07
Ear length (cm)WW7.40 **5.08 **10.96 ± 1.3411.316.93%
WS10.78 **9.72 ± 1.51
Row number per ear (unit)WW6.25 **3.56 **22.90 ± 3.2118.35.90%
WS9.37 **18.71 ± 2.86
Grain number per row (unit)WW48.01 **24.66 **13.11 ± 1.0711.218.19%
WS34.83 **11.64 ± 1.34
100-grain weight (g)WW11.54 **6.05 **10.91 ± 1.6015.496.30%
WS8.51 **9.22 ± 1.39
Popping expansion (g ml−1)WW69.53 **42.93 **22.20 ± 3.8721.446.70%
WS66.83 **17.44 ± 3.90
Grain yield (kg ha−1)WW1,118,030.00 **556,780.08 **1519.64 ± 504.0469.6614.46%
WS195,448.00 **461.02 ± 200.62
Expanded popcorn volume per hectare (m3 ha−1)WW647.68 **307.20 **33.46 ± 11.6375.8818.10%
WS70.56 **8.07 ± 3.91
2021
Ear diameter (mm)WW29.43 **40.06 **27.85 ± 2.376.406.85%
WS25.88 **26.07 ± 2.13
Ear length (cm)WW8.18 **8.30 **12.63 ± 1.334.365.52%
WS10.57 **12.08 ± 1.39
Row number per ear (unit)WW6.35 **7.14 **22.79 ± 3.011.108.67%
WS8.72 **22.54 ± 4.23
Grain number per row (unit)WW43.48 **52.97 **12.61 ± 1.226.5010.92%
WS89.64 **11.79 ± 1.30
100-grain weight (g)WW11.38 **10.25 **10.00 ± 1.568.5013.02%
WS7.99 **9.15 ± 1.25
Popping expansion (g ml−1)WW58.62 **52.61 **22.16 ± 3.4715.386.98%
WS48.61 **18.76 ± 3.18
Grain yield (kg ha−1)WW803,262.00 **549,979.57 **1285.05 ± 419.5936.8214.98%
WS439,000.00 **812.08 ± 303.25
Expanded popcorn volume (m3 ha−1)WW457.25 **317.14 **28.25 ± 9.6245.6918.69%
WS218.59 **15.34 ± 6.47
DF—degrees of freedom; SE—Standard Error; G—Genotype; WC—Water condition; WS—Water stressed; WW—Well watered. ED—Ear diameter; EL—Ear length; NRE—Row number per ear; NGR—Grain number per row; 100 GW—100-grain weight; PE—Popping expansion; GY—Grain yield; VP—Expanded popcorn volume per hectare. ** indicate a significant difference at the level of 1% by the F test.
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Santos, M.d.S.; Kamphorst, S.H.; Amaral Junior, A.T.d.; Leite, J.T.; Lima, V.J.d.; Oliveira, U.A.d.; Vasconcelos, C.M.; Viana, F.N.; Santos, T.d.O.; Gonçalves, G.R.; et al. Water-Use Efficiency and Responsiveness of a Popcorn Panel Grown Under Different Water Regimes and Cropping Seasons. Agronomy 2026, 16, 258. https://doi.org/10.3390/agronomy16020258

AMA Style

Santos MdS, Kamphorst SH, Amaral Junior ATd, Leite JT, Lima VJd, Oliveira UAd, Vasconcelos CM, Viana FN, Santos TdO, Gonçalves GR, et al. Water-Use Efficiency and Responsiveness of a Popcorn Panel Grown Under Different Water Regimes and Cropping Seasons. Agronomy. 2026; 16(2):258. https://doi.org/10.3390/agronomy16020258

Chicago/Turabian Style

Santos, Monique de Souza, Samuel Henrique Kamphorst, Antônio Teixeira do Amaral Junior, Jhean Torres Leite, Valter Jário de Lima, Uéliton Alves de Oliveira, Christiane Mileib Vasconcelos, Flávia Nicácio Viana, Talles de Oliveira Santos, Gabriella Rodrigues Gonçalves, and et al. 2026. "Water-Use Efficiency and Responsiveness of a Popcorn Panel Grown Under Different Water Regimes and Cropping Seasons" Agronomy 16, no. 2: 258. https://doi.org/10.3390/agronomy16020258

APA Style

Santos, M. d. S., Kamphorst, S. H., Amaral Junior, A. T. d., Leite, J. T., Lima, V. J. d., Oliveira, U. A. d., Vasconcelos, C. M., Viana, F. N., Santos, T. d. O., Gonçalves, G. R., Daher, R. F., Cruz, C. D., & Campostrini, E. (2026). Water-Use Efficiency and Responsiveness of a Popcorn Panel Grown Under Different Water Regimes and Cropping Seasons. Agronomy, 16(2), 258. https://doi.org/10.3390/agronomy16020258

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