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Article

Dickson Quality Index of Cocoa Genotypes Under Water Deficit

by
Rogerio S. Alonso
1,*,
George A. Sodré
2 and
Delmira C. Silva
3
1
Programa de Pós-Graduação em Produção Vegetal, Universidade Estadual de Santa Cruz, Ilhéus 45600-970, Brazil
2
Departamento de Ciências Agrárias e Ambientais, Universidade Estadual de Santa Cruz, Ilhéus 45600-970, Brazil
3
Departamento de Ciências Biológicas, Universidade Estadual de Santa Cruz, Ilhéus 45600-970, Brazil
*
Author to whom correspondence should be addressed.
Forests 2024, 15(12), 2054; https://doi.org/10.3390/f15122054
Submission received: 11 May 2024 / Revised: 14 June 2024 / Accepted: 16 June 2024 / Published: 21 November 2024
(This article belongs to the Section Genetics and Molecular Biology)
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Abstract

:
The aim of this study was to identify patterns of morphological adjustments associated with the Dickson Quality Index (DQI) in Theobroma cacao L. genotypes subjected to water deficit (WD), as a criterion for the pre-selection of drought-tolerant genotypes. Rooted cuttings from seven genotypes were subjected to water deficit (WD). The data from the growth analysis and DQI were subjected to analysis of variance, tests of means, and multivariate analysis. A high correlation was identified between IQD and the variables root dry mass (RDM), leaf dry mass (LDM), stem diameter (SD), and total dry mass (TDM) independently for each genotype; these correlations are more evident in genotypes CP-49, PS-1319, and Cepec-2002. The multivariate analysis divided the genotypes into two major groups: one consisting of the Ipiranga-01, CCN-51, SJ-02, and PH-16 genotypes, and the other comprising the CP-49, Cepec-2002, and PS-1319 genotypes. By correlating the results of the growth analysis with DQI, we were able to identify genotypes CP-49, PS-1319, and Cepec-2002 as tolerant; Ipiranga-01 and CCN-51 as moderately tolerant; and SJ-02 and PH-16 as poorly tolerant to WD. However, it is important that other fields of science are considered to provide greater insights into adaptation to drought.

1. Introduction

Theobroma cacao L. is grown between latitudes 20°N and 20°S around the world; it is adapted to temperatures between 18 °C and 28 °C and requires at least 1400 mm of rainfall distributed throughout the year [1,2]. Traditionally, T. cacao L. is cultivated in humid environments under the forest canopy—a practice known in southern Bahia as “cabruca” [3]. In dry and arid climate conditions, T. cacao L. undergoes changes in dry biomass as a way of adapting to water stress, mainly in the roots and leaves, generating variables that can be used to select genotypes capable of withstanding environments with reduced water availability [4].
Water deficit is one of the factors that most impacts T. cacao L. [5,6], and irregular rains also affect the crop’s performance in the field [7], which can alter its morphology when the water retention ability in the soil is reduced [8], as a form of adaptation to stress, such as reduction in leaf area, stem diameter, and overall growth [7,9]. In regions that receive less than 1200 mm of rain annually, the economic viability of growing this crop is only viable with the implementation of an irrigation system [10,11].
These changes in cocoa tree growth due to morphological adaptations to avoid dehydration affect not only the growth of the area and leaf area [2,6,12,13], but also physiological processes [14].
Some research with cocoa trees under water deficit used these morphological changes to select drought-tolerant cocoa genotypes [15]. In another approach, a relationship was established between growth variables and physiological parameters. For example, Santos et al. [12] attributed drought tolerance to genotypes that did not show changes in their growth when subjected to water deficit. Because of this, some genotypes were found to be more efficient at maintaining physiological processes, such as net photosynthetic rate, stomatal conductance, transpiration, and efficiency in water use.
In cocoa trees, the main consequences of water deficit in growth are an increase in the biomass and diameter of the stem and root biomass, and a reduction in the leaf area and dry biomass of the aerial part; these changes are currently used to identify the genotypes that are drought tolerant [16,17]. These morphological changes in T. cacao L. genotypes under WD mainly occur in organs with a greater water loss capacity (leaves), while tissues with a greater water absorption capacity (roots) develop under water stress, maintaining physiological processes [12,15,17,18].
These growth characteristics better reflect the ability of plants to adapt to drought, as demonstrated in research studies [15,17,19]. Among the cocoa genotypes, the allocation of biomass as a way to avoid dehydration occurs at different intensities due to the genetic diversity between them [20], and these changes can be used to select drought-tolerant cacao genotypes [15,16,17,18,19,20,21].
In this context, the Dickson DQI Quality Index is used as a plant selection criterion. It is considered appropriate for evaluating their quality [22] as it reflects the balance between the distribution of the phytomass in plants, which can define your quality and performance on the field with greater precision [23]. From the growth analysis, it is possible to estimate the quality of plants using IQD [22] as well as to estimate the optimization of water use in T. cacao L. [24], making it possible to identify plants of a higher quality in relation to water use through plant selection [25]. Because the cocoa tree is widely produced in nurseries [26], knowledge of the morphological changes can be used to create economically viable commercial nurseries in relation to plant quality and adequate water use [27].
The quality of T. cacao L. used in the establishment of plantations is fundamental for its adaptation, mainly because it is a perennial crop [28]. Some earlier studies have been carried out to select, based on morphological changes, the genotypes for cultivation in irrigated systems in non-traditional regions, such as the coastal plateaus and semi-arid regions of Northeast Brazil [29,30]. Some previous studies have also focused on genotypes with acceptable yield levels in dry environments for cultivation in areas marginal to those where it is traditionally grown [15,31,32,33,34].
Although previous studies have reported morphological changes and indicate genotypes with different responses to WD, data on the quality and which morphological characteristics are involved are still scarce, especially among the commercial genotypes studied here. However, the integration of anatomical and physiological knowledge, associated with the quality of cocoa seedlings, is essential for the production of high-quality seedlings. This approach deserves to be applied to these genotypes, because the more we know about the changes caused by water deficit, the more guarantees we have of selecting high-quality genotypes that are prepared for establishment in the field.
Therefore, we hypothesize that adaptive morphological patterns of T. cacao L. genotypes grown under WD may serve as criteria for the pre-selection of drought-tolerant genotypes. We aim to identify morphological adjustments associated with DQI. The objective of this study was to identify patterns of morphological adjustments associated with DQI in T. cacao L. genotypes subjected to WD as a criterion for the pre-selection of drought-tolerant genotypes.

2. Materials and Methods

2.1. Growing Conditions

Rooted cuttings taken from the plagiotropic branches of T. cacao L. genotypes Ipiranga-01, CCN-51, SJ-02, PH-16, PS-1319, Cepec-2002, and CP-49, selected based on the method recommended by Sodré and Marrocos [26] and Sodré and Gomes [35], were obtained from commercial nurseries at the Instituto Biofábrica da Bahia, Brazil. These were chosen for their characteristics, such as self-compatibility, productivity, resistance to the disease Moniliophtora perniciosa, and adaptation to the Af climate according to the Köppen-Geiger classification [36]. Cuttings that were five months old after rooting were transplanted from 288 cm3 tubes into 5 L pots filled with 4.3 kg of soil. The soil had a clay texture with 126 g kg−1 of coarse sand, 138 g kg−1 of fine sand, 236 g kg−1 of silt, and 500 g kg−1 of clay, with a chemical composition of pH = 4.7, Al = 0.8 cmolc dm−3, H + Al = 5.8 cmolc dm−3, Ca = 0.5 cmolc dm−3, Mg = 0.2 cmolc dm−3, K = 22 mg dm-3, P = 2 mg dm−3, Fe = 114 mg kg−1, Zn = 0.2 mg dm−3, Cu = 1.4 mg kg−1, Mn = 30 mg dm−3, C = 8.7 g dm−3, N = 1.32 g dm−3, SB = 0.8, CTC = 6.6, and V = 59%, m = 5%. The soil was fertilized for the experiment as recommended by Souza Júnior, Sodré, and Neves [37].
The genotypes were kept in a greenhouse at the Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil (39°13′59″ W; 14°45′15″ S), initially for a period of 10 days for acclimatization before the treatments were applied. During the experiment period, these genotypes were maintained under the same conditions of relative humidity (RH%), temperature (t °C), and photosynthetic photon flux density (μmol photons m−2 s−1). The experiment, which lasted 30 days (from 15 October to 15 November 2021), was monitored using a data logger (Onset Computer—humidity and temperature meter—U-12, Bourne, MA, USA and Akrom—Kr822 v2.1 Lux meter, São Leopoldo, RS, Brazil). The experimental was completely randomized design (CRD), involving seven cocoa genotypes subjected to two treatments: TR1: well-watered (WW), 90% of pot capacity (PC) and TR2: with water deficit (WD), 40% of PC, with four replications (n = 4).

2.2. Irrigation

Irrigation was defined based on a previous study of the soil’s moisture content, which was obtained using the gravimetric method [38,39] to obtain PC. In this determination, five 25 cm high plastic pots with a volume of 5.0 L were filled with 4.3 kg of the soil used in the experiment. These pots were then subjected to saturation through capillary action from the holes located at the bottom, which had a drainage area of approximately 4 cm2. The pots were submerged in a container filled with 1000 mL of water for 12 h. After this period, the pots were removed from the container and sealed with plastic wrap at the top to prevent water loss through evaporation. They were then left to drain freely for a period of 8 h, after which the draining ceased. The difference between the initial and final mass of the substrate was used to determine the amount of water retained and the PC value. This value was 0.25 g of water per gram of soil; thus, 4300 g of soil absorbed 1085 g of water, with this being the 100% PC value. A sufficient amount of this same soil was used in the experiment, with TR1: WW, with 90% of PC, with a total value of soil plus water in each pot of 5.275 g, and TR2: under water deficit, with 40% of PC, with a value total soil plus water in each pot of 4.735 g. The pots were monitored by weighing three times a day using a platform scale (Balmak, BK-100, São Paulo, SP, Brazil); in this way, it was possible to maintain the PC with a total weight in each pot of 5.275 g in TR1 and 4.735 g in TR2, adding the amount of water appropriate when necessary.

2.3. Growth Analysis

Data for growth analysis were collected at the beginning of the experiment (T0) and 30 days after the start of the experiment (T1), according to Hunt [40]. At the end of the experiment, the plants were separated into roots, stems, and leaves; dried in a ventilated oven at 70 °C until they reached a constant weight; and their dry masses were quantified on a precision scale. The values for the root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), total dry mass (TDM), and shoot dry mass (SDM) were obtained the value of the aerial part SDM + LDM. The height (H) and stem diameter (SD) were measured using a ruler and calipers, respectively, and the root to shoot ratio (R:S) was obtained from the ratio between the dry mass of the aerial part and the dry mass of the root. Considering that T0-T1 represents the time difference between the start and end of the experiment, DQI [41] was calculated based on the values of H, DP, LDM, SDM, and RDM obtained at the end of the experiment (T1), both for the control treatments and for those subjected to WD, using the following equation:
D Q I = T D M H S D + L D M + S D M R D M

2.4. Statistics

The data were subjected to analysis of variance (ANOVA) and the F-test, and the means were discriminated by the Scott–Knott grouping at a 5% probability level. The behavior of the data was interpreted using multivariate cluster analysis and similarity dendrograms. A perceptual map was created using principal component analysis (PCA), and a similarity dendrogram was generated through multivariate cluster analysis with a hierarchical model. This involved employing the clustering and distance method, using the Bray–Curtis similarity index and the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) algorithm for the growth data. The dendrogram was independently constructed, and the statistical analyses were conducted using P.A.S.T.© software (version 2.17c) [42], and R statistical software (version 4.4.1) [43].

3. Results

In the analysis of variance conducted using the F test, significant differences (p < 0.05) were identified both when comparing the genotypes within the control treatment and within the treatment under WD, as well as between the two treatments. The growth variables of LDM, SDM, TDM, and SD showed significant differences among the genotypes in the control treatment, while LDM, SDM, TDM, and H differed significantly among the genotypes under WD (Table 1).
There was a significant difference in DQI values (p < 0.05) between the genotypes, with CCN-51 and SJ-02 contributing the most to this result. These genotypes exhibited lower biomass values in the WD treatment compared with WW (Table 1). There was significant variation (p < 0.05) between the treatments in terms of growth variables, including DQI. It was observed that significant differences existed in all of the variables and parameters studied, both within and between the treatments. This indicates not only varied responses of the genotypes to WD, but also the expression of inter-genotypic differences within each treatment (Table 1). The DQI values for the CCN-51 and SJ-02 genotypes decreased under drought conditions, while those of the other genotypes remained similar to the control treatment, even with reduced water supply, indicating that the latter maintained their performance despite the reduced water availability. However, there was no significant difference in DQI among the different treatments for all of the genotypes analyzed.
In the genotypes CCN-51, CP-49, PS-1319, and Ipiranga-02, RDM was higher in the treatment under WD compared with the WW treatment. However, the genotypes Cepec-2002 and SJ-02 showed higher RDM values in the control treatment. These genotypes did not perform as well as the others under the WD treatment; Cepec-2002 showed no significant difference in RDM between the treatments, while SJ-02 exhibited a reduced RDM. The other genotypes showed an increase in RDM under WD conditions (Table 1).
The behavior of genotypes CCN-51, CP-49, PS-1319, and Ipiranga-01 under WD indicates that there was greater growth in RDM compared with LDM. This growth was lower in genotypes CCN-51 and SJ-2 across treatments. For genotypes Cepec-2002 and PH-16, the growth in RDM was similar across treatments. The increase in RDM led to higher R:S ratio values in the CCN-51, CP-49, PS-1319, and Ipiranga-02 genotypes. Meanwhile, the CP-49 and Cepec-2002 genotypes exhibited the highest LDM values under WD treatment. In the control treatment, the genotypes with the highest LDM values were CP-49, Cepec-2002, CCN-51, and PH-16 (Table 2).
The CCN-51 genotype was the only one that showed a difference in all of the growth variables. The others exhibited unchanged values in some variables. For example, in the CP-49 genotype, the LDM and TDM variables remained the same. The same was true for the SDM, LDM, and TDM in the PS-1319 genotype; the SDM, LDM, RDM, and R:S in Cepec-2002, the SDM and LDM in Ipiranga-01, and H and R:S in SJ-02. It should be noted that the PH-16 genotype exhibited the smallest significant differences across the most variables between the treatments, specifically in SD, SDM, RDM, TDM, and RDM:SDM. There was also no significant difference in DQI values between treatments for the CP-49, PS-1319, Cepec-2002, Ipiranga-01, SJ-02, and PH-16 genotypes (Table 1).
The results of the growth analysis were correlated with DQI (Table 2). RDM was correlated with TDM, DQI, and R:S. LDM was correlated with TDM and DQI, while TDM was only correlated with DQI (Table 2). These correlations are demonstrated by the acute angles between the corresponding vectors in the principal component analysis (PCA) (Figure 1), which can be seen more clearly in the heat map (Figure 2), which shows that the CP-49, PS-1319, and Cepec-2002 genotypes exhibited a stronger correlation among the growth variables TDM, SDM, SD, LDM, and the parametric index DQI compared with the other genotypes. In the CCN-51 and Ipiranga-01 genotypes, the variables most closely correlated were RDM, H, and R:S. Although the PH-16 and SJ-02 genotypes exhibited differences in growth (Table 1), their growth variables did not show any correlation.
The multivariate two-way clustering and heatmap analysis divided the evaluated genotypes into two main groups (Figure 2) based on their morpho-anatomical characteristics: one consisting of the Ipiranga-01, CCN-51, SJ-02, and PH-16 genotypes, and the other comprising the CP-49, Cepec-2002, and PS-1319 genotypes. Within the first group, two subclusters emerged. The first subcluster was characterized by a higher ratio of RDM and RDM:SDM variables (Table 1, Figure 2), including the Ipiranga-01 and CCN-51 genotypes. The second subcluster consisted of the SJ-02 and PH-16 genotypes.
Despite a significant difference in the means of the LDM and H growth values under WD treatment between them (Table 1), these genotypes were grouped together because the growth variables exhibited a lower correlation with DQI compared with the other genotypes (Table 1 and Figure 1 and Figure 2).
The second largest group comprised genotypes CP-49, Cepec-2002, and PS-1319. These genotypes shared growth characteristics closely associated with DQI (Figure 1 and Figure 2), including increased RDM, higher R:S ratios, reduced dry mass of the aerial parts, and consistent DQI values across both treatments, indicating adaptation to drought conditions. However, it should be noted that the PS-1319 genotype, which was segregated within this group, maintained the same growth and quality characteristics under WD, albeit with reduced values.

4. Discussion

Dry biomass accumulation is an important growth variable for identifying a plant’s response to WD [44]. The changes in growth observed in the CCN-51, CP-49, PS-1319, and Ipiranga-01 genotypes occurred mainly in RDM. The increase in root growth in plants under WD is an adaptation to reduced water supply, a characteristic reported by Kull et al. [45] in tropical plants. Plants under WD tend to mitigate water scarcity by balancing water absorption and loss [46]. The genotypes CCN-51, CP-49, PS-1319, and Ipiranga-01, when subjected to WD, exhibited this characteristic by developing a larger root system. This morphological adaptation aims to avoid WD through access to water in a larger volume of soil [14,16,18,47].
Changes in RDM are associated with an increase in the volume of fine and thick roots to allow the plant to maintain an adequate structure and guarantee its metabolic functions [15,16,17,18], as root expansion in dry environments contributes to improving the absorption of water and nutrients [12,48], becoming a reliable trait for the selection of drought-tolerant genotypes [21].
The R:S ratio is considered the balance between the efficiency of field survival strategies and water use, indicating an ideal expression of agronomic performance [6,13,49]. The highest R:S ratios for genotypes CCN-51, CP-49, PS-1319, and Ipiranga-01, attributed to the greater accumulation of dry mass in the roots, facilitate greater water absorption to meet transpiration demands [12,18]. The decrease in LA and LN significantly reduces the leaf area for transpiration, which can be considered as an adaptation strategy to water deficit [50]. The reduction in transpiration affects other physiological parameters [51], such as Aa, gs, Aa/gs, and Aa/E. This is a crucial trait during this growth phase, which is useful for selecting plants that are tolerant to WD [52,53]. Plants with this trait are considered drought tolerant by Moradi [45], as they avoid dehydration through this adaptive strategy, thus increasing their RDM [6]. Santos et al. [15] and Santos [16] used this characteristic to select drought-tolerant T. cacao L. genotypes.
TDM is used as an indicator of plant performance in the field. Plants with a higher TDM value tend to have greater growth potential and quality [9], as the net gain in dry mass is considered one of the best parameters to indicate plant quality [21,44]. In addition to changes in TDM, the tendency towards reduced growth was evidenced by changes in the stem diameter and height of genotypes CCN-51, CP-49, PS-1319, Cepec-2002, and Ipiranga-01, as a form of adaptation to WD. Plants with these characteristics tend to have greater drought tolerance [7,9,13].
According to Mathias et al. [54], TDM values, together with stem diameter, are the variables most related to IQD. According to Posse [24], in addition to the dry biomass of the cocoa tree’s organs, the diameter of the stem is an important variable that can be used to accurately determine the best water depth to be used in irrigation. In this aspect, only the PH-16 genotype did not show a reduction in DS under WD. Regarding TDM, only genotypes CP-49, PS-1319, and PH-16 maintained their values without reduction; the others showed a reduction under WD. The correlation of variables used in IQD, which describe plant growth, increases the effectiveness of decision making to select plants with high-quality attributes [55].
Despite the differences observed in the growth variables of the studied genotypes, the fact that some variables remained unchanged suggests that the reduction in water supply did not compromise the quality of these genotypes. They exhibited the same performance with a lower water demand, as observed by Ilyas et al. [56], as a typical feature of WD-avoiding plants. This adaptation was demonstrated by similar IQD values in both treatments for genotypes CP-49, PS-1319, Cepec-2002, Ipiranga-01, and PH-16. The characteristics that define this uniform behavior between genotypes with this behavior can be used to optimize water use during the initial growth phase [57]. It is interesting to note that Cepec-2002 is the most used rootstock in Bahia, and the results indicate that this genotype can be used in areas with and without WD [26].
Thus, the average values indicate that the best quality genotypes were those that showed the greatest variation in dry mass between treatments, indicating that these genotypes maintain their quality under WD. This is because adaptive characteristics, including an increase in RDM, a decrease in SD, and reductions in H and TDM, helped maintain the IQD of the WD treatment close to that of the WW. The genotypes developed survival mechanisms in conditions of reduced water supply. These growth characteristics need to be evaluated in more detail to identify drought-tolerant genotypes that also maintain an adequate productive performance, as cocoa genotypes often show changes in gene expression due to water stress [12,15,16,17,20].
These results suggest that DQI is an effective model for the pre-selection of WD-tolerant T. cacao L. genotypes. The CP-49, PS-1319, Cepec-2002, Ipiranga-01, and PH-16 genotypes, under WD, maintained satisfactory values due to the adaptive strategies expressed through the morphological changes. They performed qualitatively as well as the WW treatment despite a reduced water supply, showing no significant difference in DQI. This indicates that these genotypes are capable of withstanding drought conditions. Only the CCN-51 and SJ-02 genotypes, known for being large clones, were negatively affected by WD, indicating a loss of quality due to reduced water supply and a lower DQI than the others. These findings enhance our understanding of the adaptive morphological mechanisms T. cacao L. employs in response to water stress. The growth analysis values can be used to differentiate cacao genotypes based on their quality under WD. Additionally, the varying degrees of morfological plasticity among genotypes can serve as a criterion for pre-selecting drought-tolerant genotypes for both breeding programs and commercial plantations.

5. Conclusions

Our investigations revealed that morphological responses to WD are associated with plant quality. Based on the growth variables of RDM, LDM, and R:S correlated with DQI, it was possible to distinguish the genotypes of Theobroma cacao L. CP-49, PS-1319, and Cepec-2002 as tolerant to WD. The genotypes Ipiranga-01 and CCN-51 were identified as moderately tolerant, while genotypes SJ-02 and PH-16 were classified as poorly tolerant. However, an anatomical and physiological approach to these genotypes associated with seedling quality has a lot to offer in relation to the selection of cocoa genotypes.

Author Contributions

Conceptualization, R.S.A.; methodology, R.S.A. and D.C.S.; software, R.S.A.; validation, R.S.A., G.A.S. and D.C.S.; formal analysis, R.S.A. and D.C.S.; investigation, R.S.A. and D.C.S.; resources, R.S.A. and D.C.S.; data curation, R.S.A. and D.C.S.; writing—original draft preparation, R.S.A.; writing—review and editing, R.S.A., G.A.S., and D.C.S.; visualization, R.S.A., G.A.S., and D.C.S.; supervision, G.A.S. and D.C.S.; project administration, R.S.A.; funding acquisition, R.S.A. and D.C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fundação de Amparo à pesquisa do Estado da Bahia (FapesB), grant number 327/2021.

Data Availability Statement

Data is contained within the article.

Acknowledgments

Thanks to Biofábrica da Bahia, Brazil, for donating seedlings and to the plant anatomy laboratory and technicians for their assistance, the Universidade Estadual de Santa Cruz—UESC and to Coordination for the Improvement of Higher Education Personnel (CAPES).

Conflicts of Interest

The all authors declare that they have no conflicts of interest in the submission of this manuscript.

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Forests 15 02054 g001
Figure 1. Perceptual biplot map of the Principal Component Analysis (PCA) of growth variables: root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), stem dry mass (SDM), stem diameter (SD), height (H), and root-to-shoot ratio (R:S); and the Dickson quality index (DQI) for seven genotypes of Theobroma cacao L. under water deficit (WD), grouped according to cluster analysis.
Figure 1. Perceptual biplot map of the Principal Component Analysis (PCA) of growth variables: root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), stem dry mass (SDM), stem diameter (SD), height (H), and root-to-shoot ratio (R:S); and the Dickson quality index (DQI) for seven genotypes of Theobroma cacao L. under water deficit (WD), grouped according to cluster analysis.
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Figure 2. Two-way cluster with a heatmap of morphological characteristics, root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), height (H), stem diameter (SD), total dry mass (TDM), Dickson Quality Index (DQI), and root-to-shoot ratio (R:S) of genotypes of Theobroma cacao L. under water deficit (WD).
Figure 2. Two-way cluster with a heatmap of morphological characteristics, root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), height (H), stem diameter (SD), total dry mass (TDM), Dickson Quality Index (DQI), and root-to-shoot ratio (R:S) of genotypes of Theobroma cacao L. under water deficit (WD).
Forests 15 02054 g002
Table 1. Average values and standard deviation for stem diameter (SD), height (H), stem dry mass (SDM), leaf dry mass (LDM), root dry mass (RDM), root-to-shoot ratio (R:S), and Dickson Quality Index (DQI) of Theobroma cacao L. genotypes under Well-watered (WW) condition and water deficit (WD).
Table 1. Average values and standard deviation for stem diameter (SD), height (H), stem dry mass (SDM), leaf dry mass (LDM), root dry mass (RDM), root-to-shoot ratio (R:S), and Dickson Quality Index (DQI) of Theobroma cacao L. genotypes under Well-watered (WW) condition and water deficit (WD).
VariableTreatmentCCN-51CP-49PS-1319Cepec-2002Ipiranga-01SJ-02PH-16
SDWW0.53 ± 0.06a0.58 ± 0.1a0.60 ± 0.0a0.60 ± 0.0a0.53 ± 0.0a0.55 ± 0.1a0.45 ± 0.1b
WD0.40 ± 0.05b0.45 ± 0.1b0.48 ± 0.1b0.50 ± 0.0b0.45 ± 0.0b0.40 ± 0.0b0.48 ± 0.1b
HWW29.17 ± 4.3b26.60 ± 2.1b32.67 ± 3.8a32.68 ± 2.8a33.68 ± 2.43a29.12 ± 1.8b28.82 ± 1.7b
WD20.70 ± 2.1b23.45 ± 1.4b26.90 ± 3.0b25.17 ± 2.3b27.27 ± 3.2b28.07 ± 2.0b22.75 ± 2.7b
STMWW2.22 ± 0.3a2.11 ± 0.4a1.85 ± 0.2a2.76 ± 0.4a1.57 ± 0.4b1.92 ± 0.0a1.37 ± 0.2b
WD1.89 ± 0.5b1.54 ± 0.3b2.16 ± 0.5a2.35 ± 0.4a1.24 ± 0.3b1.39 ± 0.2b1.16 ± 0.2b
LDMWW3.74 ± 0.5a3.96 ± 1.4a2.14 ± 0.4b4.15 ± 0.3a2.79 ± 0.6b2.74 ± 0.4b3.42 ± 0.3a
WD1.61 ± 0.5b3.59 ± 0.6a2.24 ± 0.5b3.39 ± 0.9a1.96 ± 0.7b1.98 ± 0.1b2.26 ± 0.6b
RDMWW0.96 ± 0.0b0.85 ± 0.0b0.84 ± 0.0b1.88 ± 1.0a1.05 ± 0.3b1.43 ± 0.5a1.10 ± 0.4b
WD1.49 ± 0.5a1.63 ± 0.2a1.63 ± 0.6a1.54 ± 1.2a1.44 ± 0.1a0.75 ± 0.1b0.92 ± 0.5b
TDMWW6.86 ± 0.5b6.92 ± 1.7b4.93 ± 0.2b8.79 ± 1.1a5.38 ± 0.1b6.09 ± 0.9b5.89 ± 0.3b
WD4.99 ± 0.7b6.76 ± 0.6b5.86 ± 0.2b7.37 ± 0.2b4.64 ± 0.3b4.11 ± 0.6b4.35 ± 1.4b
R:SWW0.16 ± 0.0b0.15 ± 0.0b0.21 ± 0.0b0.27 ± 0.1b0.26 ± 0.0b0.31 ± 0.1b0.24 ± 0.1b
WD0.44 ± 0.1a0.32 ± 0.1a0.37 ± 0.2a0.28 ± 0.2b0.50 ± 0.1a0.23 ± 0.0b0.26 ± 0.1b
DQIWW0.11 ± 0.0a0.13 ± 0.0a0.08 ± 0.0b0.15 ± 0.0a0.08 ± 0.0b0.11 ± 0.0a0.09 ± 0.0b
WD0.09 ± 0.0b0.12 ± 0.0a0.10 ± 0.0b0.13 ± 0.0a0.08 ± 0.0b0.05 ± 0.0b0.09 ± 0.0b
Values followed by the same lowercase letter within the same column indicate that there is no significant difference between WW and WD according to the Scott–Knott test at a 5% significance level.
Table 2. The Pearson correlation coefficient matrix for the Dickson Quality Index (DQI) and growth variables: root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), height (H), stem diameter (SD), total dry mass (TDM), and root-to-shoot ratio (R:S) of Theobroma cacao L. genotypes under well-watered (WW) condition and water deficit (WD).
Table 2. The Pearson correlation coefficient matrix for the Dickson Quality Index (DQI) and growth variables: root dry mass (RDM), stem dry mass (SDM), leaf dry mass (LDM), height (H), stem diameter (SD), total dry mass (TDM), and root-to-shoot ratio (R:S) of Theobroma cacao L. genotypes under well-watered (WW) condition and water deficit (WD).
RDMSDM LDMHSDTDMDQIR:S
RDM1
SDM0.6 ns1
LDM0.4 ns0.4 ns1
H−0.2 ns0.1 ns0.0 ns1
SD0.3 ns0.5 ns0.6 ns0.1 ns1
TDM0.7 *0.8 ns0.8 **−0.10.6 ns1
DQI0.7 *0.6 ns0.8 **−0.4 ns0.7 ns0.9 *1
R:S0.5 *0.0 ns−0.4 ns−0.08 ns−0.17 ns−0.06 ns0.0 ns1
Pearson correlation significance levels: * = p < 0.05; ** = p < 0.01; ns = not significant.
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Alonso, R.S.; Sodré, G.A.; Silva, D.C. Dickson Quality Index of Cocoa Genotypes Under Water Deficit. Forests 2024, 15, 2054. https://doi.org/10.3390/f15122054

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Alonso RS, Sodré GA, Silva DC. Dickson Quality Index of Cocoa Genotypes Under Water Deficit. Forests. 2024; 15(12):2054. https://doi.org/10.3390/f15122054

Chicago/Turabian Style

Alonso, Rogerio S., George A. Sodré, and Delmira C. Silva. 2024. "Dickson Quality Index of Cocoa Genotypes Under Water Deficit" Forests 15, no. 12: 2054. https://doi.org/10.3390/f15122054

APA Style

Alonso, R. S., Sodré, G. A., & Silva, D. C. (2024). Dickson Quality Index of Cocoa Genotypes Under Water Deficit. Forests, 15(12), 2054. https://doi.org/10.3390/f15122054

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