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Article

Genetic Diversity Based on Nutrient Concentrations in Different Organs of Robusta Coffee

1
Programa de Pós-Graduação em Genética e Melhoramento, Universidade Federal do Espírito Santo, Vitória 29500-000, Espírito Santo, Brazil
2
Programa de Pós-Graduação em Fitotecnia, Universidade Federal de Lavras, Lavras 37200-000, Minas Gerais, Brazil
3
Instituto Federal de Rondônia and Instituição de Ensino Superior de Cacoal-Fanorte, Cacoal 76963-868, Rondônia, Brazil
4
Departamento de Agronomia, Universidade Federal de Rondônia, Rolim de Moura 76940-000, Rondônia, Brazil
5
Centro de Ciências e Tecnologias Agropecuárias, Universidade Estadual Norte Fluminense-Darcy Ribeiro, Rio de Janeiro 28013-602, Rio de Janeiro, Brazil
6
Departamento de Ciências Agrárias e Biológicas, Universidade Federal do Espírito Santo, Vitória 29500-000, Espírito Santo, Brazil
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(3), 640; https://doi.org/10.3390/agronomy12030640
Received: 29 January 2022 / Revised: 21 February 2022 / Accepted: 3 March 2022 / Published: 5 March 2022
(This article belongs to the Special Issue Coffee—from Plant to Cup)

Abstract

:
The objective of this study was to evaluate nutrient concentrations in the flowers, leaves (pre-flowering and grain-filling period), grains, and husks of Robusta coffee genotypes cultivated in the Amazon region, as well as to identify their genetic diversity. This experiment was carried out in Alta Floresta D’Oeste, Rondônia, Brazil, in randomized blocks with three replications; for the leaves, a factorial experimental design of sampling periods was included. The nutrient concentrations of the different evaluated organs were subjected to analysis of variance by the F test (p < 0.01), and the genetic parameters were estimated. To determine the genetic diversity, the genotypes were grouped by the UPGMA hierarchical method, and to predict it the relative importance of traits was analyzed. Genetic divergence among Coffea canephora genotypes was indicated by the leaf nutrient concentrations. At a maximum dissimilarity threshold of 82% for the genotypes, the UPGMA method formed six groups. Concentrations of nitrogen (N) and phosphorus (P) in the leaf sampling periods of pre-flowering and grain filling were not influenced by genotypes. The leaf and flower iron (Fe) concentrations contributed most to genetic divergence. For a nutritional diagnosis of Robusta coffee, it is important to take into account the comparisons of genetic diversity as well as the nutritional requirements during the flowering and grain-filling periods.

1. Introduction

Brazil is the world’s largest coffee producer [1], and the two most important agricultural species of the genus Coffea, Coffea arabica and C. canephora, are produced on a large scale [2]. The cultivation of Robusta or Conilon coffee (C. canephora) is a relevant commercial agricultural activity in a number of Brazilian states, particularly in Espírito Santo, Rondônia, and Bahia [3]. This species accounts for 34% of the coffee output in the country and has other environmental and nutritional demands than C. arabica, in that the former is mainly adapted to hot and low-altitude regions, as found in northern Brazil [4,5,6].
Robusta/Conilon coffees are characterized by a wide genetic diversity and subdivided into several groups and subgroups. Within the species C. canephora, the so-called Congolese group includes the two most commonly cultivated botanical varieties, Conilon (SG1) and Robusta (SG2) [7,8]. Since allogamy and self-incompatibility can both be observed in the species [9,10], each genotype may have a performance pattern of its own in the same cultivation system, mainly with regard to nutrient uptake and response [11,12].
The wider use of management practices on plantations—e.g., pruning systems, soil acidity correction, fertility enhancement, irrigation, and the selection of superior genotypes [13,14]—is key to the success and maintenance of high yields of commercial crops in the country [15]. In view of the high genetic variability within a Robusta coffee plantation and the need for the balanced fertilization management of the different plant materials, the dynamics of accumulation and nutrient concentrations in the plant organs must be well understood to ensure safe decisions are made concerning when, how, and how much fertilizer should be applied to coffee crops [4,12,16].
Nutrients within a plant can be mobile, partially mobile, or immobile, and nutrient transition between plant organs occurs naturally and simultaneously [17,18]. The nutritional demand of flowers, leaves, and fruits is high due to physiological factors that are essential for the plant, such as photosynthesis, stomatal opening, pollination, and fruit formation. The nutrient concentration and accumulation in these organs depend on the plant cycle stage and tend to be particularly high during fruit maturation and vegetative growth [12,19].
Among the coffee trees cultivated in the Rondônia region, new varieties and genotypes pre-selected by producers and greenhouse growers have performed particularly well [20,21]. The Robusta coffee genotypes grown in this state are extremely productive and responsive to fertilization, although their nutrient uptake and concentration dynamics are still unknown. Therefore, the objective of this study was to evaluate the flower and leaf nutrient concentrations in the flowers, grains, husks, and leaves, the latter during both pre-flowering and grain-filling, as well as to identify the genetic diversity in Robusta coffee genotypes grown in the Amazon region.

2. Materials and Methods

2.1. Experimental Installation and Description of Area and Plant Material

The test was carried out on a private property in Alta Floresta D’Oeste–Rondônia, Brazil (latitude 12°08’51.86 S, longitude 62°04’95.03” W, at 440 m asl), where the mean annual temperature is 26 °C. The region has a tropical climate, classified as Aw, according to Köppen, with two distinct seasons: a dry period between June and October (Amazonian summer) and a rainy period between November and May (Amazonian winter) [22]. The chemical and physical properties of the soil, characterized as Latossolo vermelho eutrófico [23], are listed in Table 1.
The crop was planted in April 2018 with a spacing of 3.30 m between rows and 0.8 m between plants—i.e., a plant density of 3700 ha−1. The studied Amazonian Robusta genotypes were arranged in rows, each of which represented a block. The trees were left to grow with two stems (approximately 7000 stems ha−1). The cultural treatments were applied according to the crop needs to optimize the phytosanitary and nutritional crop management, and the water demand during dry periods was met by drip irrigation.
The genotypes used in the study (A106, AS2, GJ25, VP06, AS1, AS7, SN41, AS6, ZD156, AS10, AS4, L140, GJ08, LB080, LB015, and GJ03) were selected by the coffee producers and greenhouse growers of Rondônia based on the yield capacity of the trees. Thus far, they have not been registered by the Brazilian Ministry of Agriculture, Livestock, and Food Supply (MAPA).

2.2. Flower, Fruit, and Leaf Collection

The experiment was performed in randomized blocks with three replications and the leaves were evaluated in a factorial experimental design of sampling periods. The first leaves were collected during the pre-flowering period (July 2019) and the second in the grain-filling period of the coffee trees (February 2020). Twenty pairs of fully developed leaves were taken from the third or fourth rosette on plagiotropic branches in the middle third of the trees on the side with the least wind.
Flowers were collected when fully open and when the number of open flowers on the trees was the highest, which occurred in July 2020 in the morning, right after flowering. They were randomly taken from plagiotropic branches in the middle third on either side of the plant. The samples were stored in properly identified paper bags, placed in ice boxes, and taken to the laboratory.
Fruits were collected in the harvest period, between May and June 2020, when more than 80% of the fruits were ripe. Fruit samples (200 g fresh weight) were collected and dried under direct sun. Subsequently, the grains were processed by separating the beans from the husk. The samples were stored in clearly labelled paper bags, placed in ice boxes, and taken to the laboratory. There, the plant material was dried to a constant weight in a forced air circulation oven at 60 °C.
The flower, fruit, and leaf samples were sent to a laboratory for nutritional chemical analysis to determine the foliar concentrations of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), iron (Fe), zinc (Zn), copper (Cu), manganese (Mn), and boron (B), as described by Silva [24].

2.3. Statistical Analysis

Nutrient concentrations were subjected to an analysis of variance by the F test (p < 0.01) to detect genetic variation between genotypes. The experimental coefficient of variation (CVe), coefficient of genetic variation (CVg), and genotypic determination coefficient (H2) were estimated for the concentration of each nutrient in the flowers, grains, husks, and leaves. The mean nutrient concentrations of the genotypes were grouped by the Scott–Knott test (p < 0.05).
To analyze genetic diversity, the Euclidean distance matrix was established as a dissimilarity measure and the genotypes were grouped by the hierarchical Unweighted Pair Group Method using Arithmetic Means (UPGMA) and the Tocher method. To obtain the distance matrix and the formation of clusters, the concentrations of nutrients in the flower, grain, husk, and leaf were used. The study of the relative importance of nutrient concentrations in predicting genetic diversity was also applied, as proposed by Singh [25]. In addition, Spearman correlation coefficients were calculated for macro- and micronutrient concentrations in the flowers, grains, husks, and leaves. All statistical analyses were performed using the Genes software [26].

3. Results

3.1. Parameters and Genetic Diversity

The genetic parameters were estimated using the F test and no interaction between genotypes and nutrients was detected. The experimental coefficient of variation (CVe) was below 20% for almost all nutrients and organs studied. This low value indicates the low environmental influence and high experimental precision.
On the other hand, an environmental dependence of some nutrients was also observed in some exceptional cases, for example: for nutrient Fe in flowers (45.06%); B in grain (22.41%); N (27.91%), Fe (53.02%), and B (24.15%) in husks; and Fe (20.21%), Cu (25.64%), and B (24.98%) in leaves (Table 2).
The coefficient of genetic variation (CVg) indicates how far the result is influenced by the genetics of a plant—i.e., the higher the coefficient is, the greater the genetic influence is. For all nutrients and evaluated organs, values > 1 were observed; in flowers, the lowest value was found for N (4.26%) and the highest for Fe (45.25%). In coffee beans, the lowest value was found for Fe (3.92%) and the highest for S (19.30%). For husk, the lowest percentage was 3.47% for nutrient S and the highest was 24.48% for Mn. For leaves, the CVg was lowest for P (1.88%) and highest for Fe (29.13%) (Table 2).
The heritability index varied among nutrients and plant organs; values above 80 are considered optimal for selection based on heritability. In coffee flowers, H2 ranged from 41.99% for nutrient P to 82.49% for Mg. For grains, the lowest value was found for Fe (25.97%) and the highest for Ca (89.20%), while the H2 of other nutrients such as K, Mg, S, and Mn also exceeded 80%. In husk, the lowest value was observed for N (21.61%) and the highest for Mn (93.98%), while the values for P, K, and Mg exceeded 80%. In leaves, P had the lowest H2 index (12.93%) and Mn the highest (95.48%). The values of K, Ca, Mg, and Fe exceeded 80% (Table 2).

3.2. Groupings of Genotypes and Genetic Contribution

With the UPGMA hierarchical method, using the Euclidean distance as a measure of dissimilarity and based on the nutrient concentrations in the flowers, grains, husks and leaves, the genotypes were grouped according to the genetic distances among them. At a cut-off level of 82%, six distinct groups were observed (Figure 1). Groups I, II, and VI contained only one genotype (AS6, GJ08, and LB015, respectively), and Group III contained the genotypes AS1, AS7, and VP06. Group IV consisted of AS10, LB080, SN41, and L140 and group V was the most populous, with six genotypes (AS4, GJ03, AS2, GJ25, A106, and ZD156) (Figure 1).
Subtle differences between the clustering methods were observed. Tocher’s method divided the genotypes into seven groups, of which four contained only one genotype. Using the UPGMA method, genotype GJ25 was assigned to group V, and using the Tocher method it was assigned to group II. The UPGMA method grouped genotype ZD156 in V together with five others, and by the Tocher method it was classified alone in group V (Table 3). Although not allocated to an isolated group by the UPGMA method, genotype ZD 156 was also considered to be divergent by this method due to its high degree of dissimilarity; it was not grouped separately only because of the maximum dissimilarity threshold (82%) adopted in the dendrogram.
To determine the relative contribution of macro- and micronutrient concentrations in different plant organs to the genetic diversity in 16 C. canephora genotypes, the method of Singh (1981) and mean Euclidean distance were used. A range of 69.93% to 0.12% was observed. The micronutrient Fe in leaves (69.93%) and flowers (21.47%) contributed most to the genetic diversity (Table 4).

3.3. Nutrient Concentration in Flowers, Grain and Husk

The N, K, and S concentrations in the flowers of Robusta coffee were not influenced by the genotypes, which were all grouped together by the Scott–Knott test (Table 5). For P and Ca, the genotype means were divided into two and for Mg into three dissimilarity groups, among which genotype GJ08 accounted for a group of its own. Genotype GJ25 was allocated to the group with the highest means for all macronutrients (Table 5).
The micronutrients Fe, Zn, Mn, and B were divided into two mean groups. The genotypes AS1, L140, and LB080 were practically always in the group with the highest micronutrient means. Micronutrient Cu was divided into three mean groups, of which genotype GJ08 represented a group of its own (Table 5).
For macronutrient P in the Robusta coffee beans, the genotypes had no influence on the nutrient concentration and were all grouped together by the Scott–Knott test (Table 6). For Mg, the genotypes were divided into two groups, where SN41, GJ08, and LB015 were isolated in the 2nd group, while for nitrogen two groups were formed as well, although with more balanced compositions. The nutrients K, Ca, and S were clustered in three groups of means. For K, genotype ZD156 had the highest mean and was allocated alone in the first group, while for Ca and S, genotype VP06 was grouped together with the genotypes with the highest means (Table 6).
The Fe and B concentrations in Robusta coffee grain did not differ among the genotypes, which were all clustered together. For the Zn and Cu concentrations, two mean groups were formed and three were formed for Mn. Genotype VP06 had the highest mean for Mn and was grouped separately, but was assigned to the groups with the highest means along with A106 for the other micronutrients (Table 6).
The N and S concentrations in the husk of Robusta coffee beans were not influenced by the genotypes, which were all allocated together by the Scott–Knott test (Table 6). The variation in means was greatest for nutrient P, for which the genotypes were separated in four groups, and genotype VP06 had the lowest observed mean (0.90). The macronutrients K and Ca formed two mean groups and for Mg the genotypes were divided into three groups. Genotype AS10 was assigned to the group with the highest means for practically all macronutrients.
The Fe concentration in the husk of Robusta coffee grain was not influenced by the genotypes, resulting in a single group according to the Scott–Knott test (Table 6). Zn, Cu, and B were divided into two mean groups, and for these nutrients the genotypes AS2, GJ25, and LB015 were grouped similarly in the groups with the highest means. The genotype means of micronutrient Mn were the most irregular, forming five groups. The first group consisted of genotype VP06; the second of the genotypes AS6 and AS4; the third of the genotypes AS1, LB015, and GJ03; the fourth of the genotypes AS2, GJ25, and ZD156; and the fifth of the genotypes A106, AS7, SN41, AS10, L140, GJ08, and LB080 (Table 6).

3.4. Nutrient Concentrations in Coffee Leaves in Two Sampling Periods

The nutrient concentrations in the leaves of the genotypes at pre-flowering and grain filling were compared. For N and P, there was no difference between genotypes in the two evaluation periods (Table 7). For nutrient K in pre-flowering, the genotypes VP06, AS1, AS7, SN41, ZD156, AS4, L140, GJ08, and GJ03 were grouped together and A106, AS2, GJ25, AS6, AS10, LB080, and LB015 were grouped together in the second group of means. However, four mean groups were observed for this nutrient in the second evaluation during the grain-filling period—i.e., genotypes AS1 and L140 in the first; VP06 alone in the second; AS2, GJ25, AS7, ZD156, AS10, GJ08, LB080, and GJ03 in the third; and A106, SN41, AS6, AS4, and LB015 in the fourth (Table 7).
For nutrient Ca, an irregular concentration among the genotypes and between the evaluation periods was observed. In the pre-flowering period, the genotypes were grouped into four mean groups: the first with genotypes L140 and LB080; the second with genotypes AS2, GJ25, and LB015; the third with most of the genotypes—namely, A106, AS1, SN41, AS6, ZD156, AS10, AS4, GJ08, and GJ03; and the fourth with the genotypes VP06 and AS7. For the grain-filling period, however, the genotypes were grouped into three mean groups. The first comprised the genotypes SN41, AS6, AS4, LB080, LB015, and GJ03; the second comprised the genotypes GJ25, ZD156, AS10, L140, and GJ08; and the third comprised the genotypes A106, AS2, VP06, AS1, and AS7 (Table 7).
For Mg in the pre-flowering period, the genotypes were grouped into three groups. The first clustered genotypes AS2 and LB080; the second clustered genotypes A106, GJ25, L140, and LB015; and the third clustered the genotypes with the lowest means—i.e., VP06, AS1, AS7, SN41, AS6, ZD156, AS10, AS4, GJ08, and GJ03. In the grain-filling period, the 16 genotypes were also divided into three groups, but differently. The first group consisted of genotypes SN41, AS6, AS4, and LB015, the second of genotypes AS2, GJ25, AS1, ZD156, AS10, GJ08, LB080, and GJ03; and the third of genotypes A106, VP06, AS7, and L140 (Table 7).
For nutrient S in the pre-flowering period, the 16 genotypes were divided into two mean groups. The first group clustered genotypes AS2, AS6, ZD156, AS10, AS4, and GJ08 and the second clustered genotypes A106, GJ25, VP06, AS1, AS7, SN41, L140, LB080, LB015, and GJ03. For the grain-filling period, the genotypes were also grouped into two mean groups; however, genotype GJ03 with the highest mean was grouped alone in the first group and the others without statistical difference in the second (Table 7).
An evaluation of the interaction of genotypes with nutrients showed that the means of genotype GJ08 were equal in all periods and for all nutrients. Genotype LB015 differed only for N, where the mean during the pre-flowering period was higher than that in the grain-filling period. On the other hand, genotype ZD156 differed only for nutrient S; during the pre-flowering period, it had a higher concentration than in the grain-filling period (Table 7). The genotypes A106, AS2, GJ25, AS1, L140, and LB080 had lower means of Ca and Mg in the grain-filling period. For nutrient N, the means of the genotypes AS6, LB015, and GJ03 were irregular, with the highest mean occurring in the grain-filling period.
The performance pattern of the micronutrients differed from that of the macronutrients. For Fe during pre-flowering, the genotypes were grouped into two clusters with similar means within the groups, while for the grain-filling period this pattern was different and the 16 genotypes were divided into four groups. The genotypes LB015 and GJ03 were assigned to the group with the highest means in both physiological plant stages. For the pre-flowering stage, micronutrient Zn was divided into two groups. Similarly, for grain-filling the genotypes were divided into two groups, but differently. Genotype A106 was always in the group with the highest means in both tested phenological stages of the coffee tree (Table 8).
For both phases, the Cu concentrations were separated into three mean groups. Genotypes AS6 and ZD156s were assigned to the group with the highest means during pre-flowering. During grain-filling, the only genotype with a higher mean was LB015. For Mn, the 16 genotypes were divided into four mean groups for the pre-flowering stage and three for the grain-filling stage. The LB015 genotype presented the highest average in both phenological stages and was grouped separately (Table 8).
For B concentrations during pre-flowering and grain-filling, the genotypes were divided into three groups in both stages, although the clusters had different structures. Genotypes GJ25 and VP06 were allocated in the group with the highest mean in both stages; however, in the grain-filling period, another 13 genotypes had similar means (Table 8). The means of genotypes AS1, AS7, and LB080 were not influenced by the evaluation period for any micronutrient. In turn, at least four of the five micronutrient concentrations in genotypes GJ08, SN41, and LB015 were not influenced by the assessment period (Table 8).

3.5. Correlation between Nutrient Concentrations of Flowers, Grain, Husk, and Leaves during Pre-Flowering and during Grain-Filling

Using Spearman’s correlation coefficients for macro- and micronutrient concentrations in the flowers, grains, husks, and leaves of 16 genotypes, a total of 50 significant correlations were detected, 7 of which were negative. Positive and significant correlations were confirmed for the relationships Flower × Grain for the nutrients K, Ca, and Mg and micronutrients Cu, Mn, and B. For the relationship Flower × Husk, significance was only observed for K. For the correlation between Flower × Leaf in the pre-flowering period, positive correlations were observed for macronutrient Mg and for the micronutrients Fe and Mn. The correlation Flower × Leaf in the grain-filling period was only positive for the micronutrients Fe and Zn, and the correlation Flower × Leaf was only positive in both periods for Fe (Table 9).
For the relationships of grain with the other plant parts, only husk had a strong and positive correlation for the macronutrients P, K, and Ca and for the micronutrient Mn. For the relationships of husk with the other organs, only Husk × Leaf had a weak positive correlation for nutrient P during grain-filling, and only Husk × Leaf had a weak positive correlation for micronutrient Cu in both periods (Table 9).
When correlating the two leaf sampling periods (leaf in pre-flowering × leaf in grain-filling), positive correlations were observed for nutrients K and Fe only. However, when correlating pre-flowering and grain-filling with the mean of the two periods, positive correlations were found for all nutrients (Table 9).
Negative correlations were observed between Flower × Husk for the micronutrient Fe and Flower × Leaf in the pre-flowering period for N. Negative correlations with grain were observed for Grain × Leaf in the grain-filling period for Mg and Cu. For Fe, negative values were observed for all correlations of husk with the other organs (Table 9).

4. Discussion

4.1. Parameters and Genetic Diversity

Genetic parameters are great tools in breeding programs, as they determine the genetic and environmental influence on a given trait [20]. The coefficients of genetic variation and heritable index express the genetic variability and how much can be transmitted to descendants [27,28]. Values for CVg above 7% are generally considered high [29,30]. In this study, however, lower values were found, indicating that the nutrient concentration may have been influenced by the cultivation environment of the population.
The nutrient concentrations in plant organs depend on the environmental conditions, as indicated by the much higher CVe than CVg for most nutrients. The environmental variation coefficient is a measure that is to relate the experimental precision and how much a trait is influenced by the environment [28,31,32].
The heritability of Mg and Mn was superior in all evaluated plant organs (flowers, grains, husks, and leaves). High values for this parameter are considered relevant for breeding and for indicating the possibility of selecting genotypes, since heritability also assesses the ability of trait expression in descendants, even under adverse conditions [20,30,33]. However, when studying different coffee genotypes and their interaction with different environments, heritability indices ranging from insignificant to values very close to 1 can be found [6,8,32,34].

4.2. Genotype Clustering and Genetic Contribution

Based on the dissimilarity of nutrient concentrations in different plant organs of the 16 genotypes, these were clustered into six groups. Of the six, three groups contained only one genotype. If a group consists of only a single genotype, this means that it has distinct characteristics in relation to the others [35,36,37]. For Robusta/Conilon coffee trees, it is expected that the larger the population is, the greater the heterogeneity of plants will be and the higher the diversity between groups will be [33,38]. Based on Singh’s method, the micronutrient Fe in leaves (69.93%) and flowers (21.47%) contributed most to the genetic diversity. The Fe concentrations in the leaves and flowers were also the variables with the highest CVg, which reinforces the result obtained for the genetic contribution even more.
Group V was the most populous of the six groups, with six genotypes found (AS4, GJ03, AS2, GJ25, A106, and ZD156). For nutritional factors, genotypes with affinities in nutrient uptake and concentration and fruit maturation cycle facilitate crop management, mainly in terms of fertilization and fertigation [11,16]. In seven Conilon coffee genotypes, Gomes et al. [11] evaluated leaf nutrient concentrations and observed two distinct groups formed by Mahalanobis’ distance method, where the first group comprised four genotypes and the second three.
However, the more heterogeneous the genetic diversity patterns were and thus the greater the number of divergent genotypes there were in the plantations, the better the guarantees were, mainly with regard to pollination and yield stability, since Robusta/Conilon coffee trees are allogamous and self-incompatible, which reduces the availability of viable pollen at flowering [5,9,35].
Using the Tocher method, seven groups were formed. Four genotypes were grouped independently of the others, showing the heterogeneity of the crop. In a study on genetic divergences in the root system of 43 Conilon coffee genotypes, Silva et al. [38] observed small differences between the methods. The UPGMA and Tocher optimization methods determine the distance between the genotypes based on different criteria [39]. Consequently, the similarity between the groups formed by these two methods is a guarantee of the heterogeneity of the population and the reliability of the genetic distances observed between the genotypes [28,30,38].

4.3. Nutrient Concentration in Flowers, Grain and Husk

The pattern of concentrations of a nutrient in the plant organs flowers, grains, and husk was similar for the analyzed genotypes, differing only among the evaluated nutrients [4,16]. Although the maturation of all analyzed genotypes was intermediate, the flowering and maturation phases differed slightly between them; nevertheless, all were harvested at the same time of the year in the cherry stage [40]. According to Marré et al. [41], the micronutrient accumulation rates of genotypes with early, intermediate, or late cycles followed the same pattern throughout the fruit maturation cycle, differing only from super late genotypes. For these genotypes, some factors interfered directly with the genetic expression, such as irrigation management, fertigation, intensity of the dry period, environmental conditions, and fertilization management [16,41,42]. With the exception of Ca, the nutritional concentrations in the flower were higher in relation to the other organs sampled, evidencing the high demand for nutrients in the fruit formation phase. Grains and husk contained higher concentrations of N, P, K, and Ca than the other macronutrients. These nutrients are essential for plant growth and required at high levels during fruit formation [4,16].

4.4. Nutrient Concentrations in Coffee Leaves in Two Sampling Periods

The influence of genotypes on the leaf nutrient concentrations of the coffee trees was observed in both sampling periods, but the foliar concentrations of N and P did not differ between the genotypes in the two periods, showing the importance of these macronutrients for plant physiological functions [18]. Under controlled conditions, Starling et al. [43] found no change in the N concentration in leaf tissues of different genotypes under water stress.
Nutritional assessments of leaves are important for crop monitoring and decision-making for fertilization management, above all to determine the optimal application time of the split rates of essential elements such as N, K, and P [11,41]. According to Oliosi et al. [12], the N and P concentrations vary in the different periods and genotypes during the fruiting period of the coffee trees, showing that early and intermediate genotypes have similar accumulation and concentration rates [41]. The highest means during the grain-filling period may have been related to the applied fertilization management, since N and K are essential nutrients for vegetative growth and fruit formation, which occur concomitantly [4,44].
Effects of the evaluated periods and genotypes on Ca and K concentrations were observed, indicating a high demand in the grain-filling period. Calcium is the second most accumulated nutrient in Conilon coffee leaves, being important for meeting the high demands during the flowering, early stage of fruit growth, and rapid fruit expansion [45]. Potassium plays a fundamental role in coffee fruit formation, especially in the grain-filling phase, when starch synthesis occurs intensely [45]. This high requirement was indicated by the variation in concentration between the sampling periods, where large differences between the concentrations of the studied genotypes were observed, as also reported by Oliosi et al. [12].
The macronutrients Mg and S varied most in terms of absolute values between the evaluated periods. These nutrients are essential for the leaf structure and play a fundamental role in photosynthesis and the Krebs cycle to generate energy and reserves for the plant [18,35].
In general, pre-flowering and flowering occur in the period of the most severe drought intensity in the Amazon region, causing a reduction in dry matter accumulation and, consequently, a lower nutrient accumulation [4,6]. After flowering, there is a phase of high demand of photoassimilates for fruit development that coincides with the vegetative growth stage, requiring a higher nutritional supply in January/February [11,12,16].
Differences between genotypes for nutrient concentrations in the same evaluation period were also reported by Silva et al. [46]. The C. canephora genotypes differ in relation to the root system [38], with a direct influence on uptake rate, compartmentalization, and nutrient translocation. Consequently, vegetative growth must also be considered distinct, which in turn influences biomass accumulation [47]. Thus, nutrient dilution occurs in genotypes with higher rates of biomass accumulation and nutrient concentration in genotypes with lower biomass accumulation rates. Thus, shoot tissues such as the leaves of coffee genotypes can have different concentrations for the same nutrient, be it by the uptake effect or biomass accumulation during vegetative growth. The means reported in this study differ from those reported by Wadt and Dias [48] for the establishment of DRIS standards for coffee trees in Rondônia and also from the means calculated by DRIS standards for two evaluation periods (pre-flowering and grain-filling) for Conilon genotypes in Espírito Santo by Partelli et al. [44].
Micronutrients are characterized by a certain instability in plant demand and supply. They are required at very low quantities, which makes the range between lack and excess extremely narrow. This aspect affects decision-making in fertilizer splitting, in particular with regard to the timing for an optimized efficiency of the application [42].
During the plant cycle, Fe is the most accumulated micronutrient. It plays an essential role in chlorophyll biosynthesis and is fundamental for the photosynthetic machinery of the plant [18,35]. This nutrient contributed most to genetic diversity within the genotypes studied. The influence and changes in genotypes and concentrations were greatest during the grain-filling period.
The micronutrients Zn and B are relevant for plants, as they participate in cell division and contribute to the release of growth hormones [18,35]. Boron, together with Ca, plays a fundamental role in the leaf structure and the plant demand during the pre-flowering and flowering periods is high [12]. Genotype A106 had the lowest B concentration in the grain-filling period, indicating a high demand during the evaluated periods.
Copper had a higher mean in the pre-flowering than the grain-filling stage, unlike Mn, which had a higher mean in the grain-filling period than in the pre-flowering period. Both observed nutrient means were within the sufficiency ranges established by Partelli et al. [44], although the means were lower.

4.5. Correlation between the Nutritional Concentrations of Flowers, Grain and Husk, and Leaves during Pre-Flowering and Grain-Filling

Positive correlations were observed between the two leaf sampling periods for all nutrients and positive correlations between the other plant organs. Positive and negative correlations between nutrients for foliar analyses in different periods were reported by Lana et al. [49]; these authors emphasized that attention should be paid to antagonistic nutrients for fertilizations and especially to the need for a balanced nutrient supply preceding phases of increased nutritional demand, such as the stages of flowering, fruiting, and grain-filling.

5. Conclusions

Genetic divergence among C. canephora genotypes for leaf nutrient concentration was observed in the phenological stages of pre-flowering and grain-filling. However, the concentration patterns of flowers, grains, and husks were similar among the genotypes. The 16 genotypes were clustered into six distinct groups. Genotype LB015 was clustered alone. According to the genetic distance calculated by the UPGMA method, the nutrient concentration pattern of the genotypes AS4, GJ03, AS2, GJ25, A106, and ZD156 was the same.
The foliar nutrient concentrations of N and P were not influenced by the genotypes in the pre-flowering and grain-filling sampling periods. Iron in leaves and flowers was the nutrient concentration that contributed the most to genetic divergence. For a nutritional diagnosis of Robusta coffee, it is important to take into account the comparisons of genetic diversity as well as the nutritional requirements during the flowering and grain-filling periods.
The results of this study can be useful as a guide for the use of genetic resources of interest in breeding programs to obtain Robusta coffee cultivars.

Author Contributions

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

Funding

This research was funded by the Fundação de Amparo à Pesquisa e Inovação do Espírito Santo (FAPES, grant number 84320893), Conselho Nacional de Desenvolvimento Científico e Tecnológico, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Finance Code 001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the first breeders (i.e., the farmers who performed the initial selection of most superior genotypes currently available) and the farmer Ademar Schmidt.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Dendrogram representing the genetic dissimilarity among 16 Coffea canephora genotypes based on the UPGMA grouping method and using the Euclidean distance to analyze the nutrient concentrations in the flower, grain, husk, and leaf tissues. Cophenetic correlation: 0.68.
Figure 1. Dendrogram representing the genetic dissimilarity among 16 Coffea canephora genotypes based on the UPGMA grouping method and using the Euclidean distance to analyze the nutrient concentrations in the flower, grain, husk, and leaf tissues. Cophenetic correlation: 0.68.
Agronomy 12 00640 g001
Table 1. Particle size and chemical properties of six soil layers in an irrigated area planted with Robusta coffee (Coffea canephora) in Alta Floresta D’Oeste, Rondônia, Brazil.
Table 1. Particle size and chemical properties of six soil layers in an irrigated area planted with Robusta coffee (Coffea canephora) in Alta Floresta D’Oeste, Rondônia, Brazil.
Particle Size FractionsSoil Layers (cm)
0–1010–2020–3030–4040–5050–60
Total sand (g kg−1)172180180174174198
Silt (g kg−1)428400440406386342
Clay (g kg−1)400420380420440460
Chemical PropertiesSoil Layers (cm)
0–1010–2020–3030–4040–5050–60
P (mg kg−1)31153214
K (mg kg−1)448772604813
S (mg kg−1)5107748
Ca (cmol kg−1)4.44.74.84.44.54.8
Mg (cmol kg−1)0.70.80.70.70.70.9
Al (cmol kg−1)0.00.30.10.00.00.5
H + Al (cmol dm−3)3.14.23.63.33.35.0
pH-H2O5.85.35.65.85.95.5
SOM (dag kg−1)2.12.52.52.12.43.1
Fe (mg kg−1)1118099969278
Zn (mg kg−1)1.29.91.71.81.58.8
Cu (mg kg−1)2.42.82.82.82.44.7
Mn (mg kg−1)184208196207168287
B (mg kg−1)0.250.580.620.830.510.71
Na (mg kg−1)6.07.05.06.05.09.0
CEC (cmol kg−1)8.349.959.318.588.6411.07
H + Al: potential soil acidity; SOM: soil organic matter; CEC: cation exchange capacity.
Table 2. Estimates of the experimental coefficient of variation (CVe), coefficient of genetic variation (CVg), and genotypic determination coefficient (H2) for nutrient concentrations in the flowers, grains, husks, and leaves of 16 Coffea canephora genotypes. Alta Floresta D’Oeste, RO, Brazil.
Table 2. Estimates of the experimental coefficient of variation (CVe), coefficient of genetic variation (CVg), and genotypic determination coefficient (H2) for nutrient concentrations in the flowers, grains, husks, and leaves of 16 Coffea canephora genotypes. Alta Floresta D’Oeste, RO, Brazil.
NutrientsFlowerGrainHuskLeaf
CVeCVgH2CVeCVgH2CVeCVgH2CVeCVgH2
%
N7.024.2652.506.774.9561.5727.918.4621.619.504.6058.44
P10.255.0441.9912.647.0948.529.3918.8592.3611.941.8812.93
K13.038.6857.0711.0713.9282.5712.7715.7381.9814.3915.8887.96
Ca13.1212.4873.097.4912.4289.2015.1713.3469.8511.8115.9491.62
Mg8.9311.1982.499.3311.3781.6711.7418.4588.1219.2117.0882.58
S8.995.5253.0614.8319.3083.577.173.4741.2114.513.1121.65
Fe45.0645.2575.1611.463.9225.9753.0222.0434.1520.2129.1392.58
Zn13.038.9058.3215.6415.6474.9919.3220.1576.5411.799.5179.63
Cu12.9613.4376.3214.3914.4775.2120.0413.4357.4125.6417.7274.14
Mn13.3410.0663.068.2511.7785.9410.7324.4893.9812.9224.2595.48
B15.2613.4569.9722.418.9332.2724.1516.1257.1924.9817.3974.41
Table 3. Clustering of 16 Coffea canephora genotypes by the Tocher method based on the Euclidean distance, considering the nutrient concentrations of flowers, grains, husks, and leaves. Alta Floresta D’Oeste, Rondônia, Brazil.
Table 3. Clustering of 16 Coffea canephora genotypes by the Tocher method based on the Euclidean distance, considering the nutrient concentrations of flowers, grains, husks, and leaves. Alta Floresta D’Oeste, Rondônia, Brazil.
Groups (Tocher)GenotypesGroups (UPGMA)
IAS4, GJ03, AS2, A106V
IIAS1, AS7, VP06, GJ25 *III
IIIAS10; LB080, SN41, L140IV
IVGJ08II
VZD156 *V
VIAS6I
VIILB015VI
* Genotypes grouped together by UPGMA.
Table 4. Relative contribution of the macro- and micronutrient concentrations in flowers, grains, husks, and leaves to genetic diversity in 16 genotypes of Coffea canephora, according to the Singh method [25] and based on the Euclidean distance. Alta Floresta D’Oeste, Rondônia, Brazil.
Table 4. Relative contribution of the macro- and micronutrient concentrations in flowers, grains, husks, and leaves to genetic diversity in 16 genotypes of Coffea canephora, according to the Singh method [25] and based on the Euclidean distance. Alta Floresta D’Oeste, Rondônia, Brazil.
VariablesS.jValue (%)Cumulative Value (%)
Leaf Fe1140153.5469.9369.93
Flower Fe350017.2821.4791.40
Leaf Mn93046.055.7197.10
Leaf B13688.460.8497.94
Flower B10051.630.6298.56
Husk B5224.380.3298.88
Husk K3145.260.1999.07
Flower Cu2110.620.1399.20
Husk Mn2060.740.1399.33
Leaf Ca1951.910.1299.45
Flower Mn1637.250.1099.55
Other variables7371.720.45100
S.j: value estimated by the statistic of Singh [25].
Table 5. Macro- and micronutrient concentrations of flowers of 16 Coffea canephora genotypes. Alta Floresta D’Oeste, Rondônia, Brazil.
Table 5. Macro- and micronutrient concentrations of flowers of 16 Coffea canephora genotypes. Alta Floresta D’Oeste, Rondônia, Brazil.
GenotypesMacronutrientsMicronutrients
NPKCaMgSFeZnCuMnB
(g kg−1)(mg kg−1)
A10630.40 a3.03 b26.27 a3.80 b2.83 a3.10 a63.10 b14.07 b21.63 a23.43 a32.12 b
AS227.37 a3.50 a23.77 a3.57 b2.73 a2.90 a31.87 b15.43 b20.17 a19.80 b41.77 b
GJ2527.83 a3.53 a28.23 a4.67 a2.87 a3.40 a44.67 b17.00 a22.60 a24.80 a39.08 b
VP0629.70 a3.10 b25.33 a4.23 a2.50 b3.23 a28.50 b13.87 b19.03 b21.77 a35.49 b
AS127.57 a3.23 b31.10 a4.57 a3.17 a2.93 a32.53 b17.03 a22.80 a22.10 a49.47 a
AS729.23 a3.50 a25.67 a3.80 b2.57 b3.13 a33.33 b14.93 b17.90 b21.23 a42.29 b
SN4128.07 a3.53 a27.70 a3.27 b2.27 b3.13 a106.47 a17.87 a22.27 a16.37 b41.27 b
AS628.30 a3.53 a22.87 a2.87 b2.33 b3.23 a52.43 b12.40 b16.80 b17.70 b37.05 b
ZD15627.80 a2.93 b23.77 a3.73 b2.40 b3.43 a77.23 b15.07 b18.97 b18.07 b33.03 b
AS1026.63 a3.60 a20.00 a3.17 b2.30 b3.00 a62.73 b16.87 a17.73 b19.00 b52.58 a
AS427.80 a3.10 b26.37 a4.10 a2.67 a3.50 a131.37 a15.23 b20.67 a19.77 b31.46 b
L14031.57 a2.90 b23.73 a3.57 b2.30 b3.77 a113.80 a17.30 a19.63 a20.40 b51.05 a
GJ0829.43 a2.97 b20.00 a3.10 b1.83 c2.90 a38.00 b12.60 b11.80 c18.67 b43.17 b
LB08032.97 a3.53 a23.17 a4.03 a2.43 b3.07 a119.63 a18.63 a22.90 a23.03 a41.10 b
LB01529.47 a3.13 b24.23 a4.70 a2.50 b3.23 a118.03 a16.60 a17.23 b25.23 a36.47 b
GJ0330.63 a3.37 a24.57 a4.00 a2.33 b3.43 a116.90 a15.00 b16.57 b18.40 b36.53 b
F Test Probability
NPKCaMgSFeZnCuMnB
Genotypes1.99 ns3.98 *1.96 ns3.72 **5.71 **1.84 ns4.03 **2.39 *4.22 **2.71 **3.33 **
Mean29.053.2824.803.822.503.2173.1615.6219.2920.6140.25
Means followed by a same letter in a column do not differ according to the Scott–Knot test at 5% probability. ns, **, and * mean not significant, significant at 1% probability, and significant at 5% probability, respectively, according to the F test.
Table 6. Macro- and micronutrient concentrations in the grain and husk of 16 Coffea canephora genotypes. Alta Floresta D’Oeste, Rondônia, Brazil.
Table 6. Macro- and micronutrient concentrations in the grain and husk of 16 Coffea canephora genotypes. Alta Floresta D’Oeste, Rondônia, Brazil.
GenotypesMacronutrientsMicronutrients
NPKCaMgSFeZnCuMnB
(g kg−1)(mg kg−1)
Grain
A10620.70 b1.63 a13.17 b2.10 c1.40 a1.60 c20.93 a6.37 a14.40 a12.70 b12.18 a
AS220.70 b1.83 a13.97 b2.33 b1.47 a1.83 b16.97 a4.77 b12.73 a13.10 b14.77 a
GJ2519.63 b2.17 a15.40 b2.43 b1.40 a1.70 b18.50 a5.77 b13.60 a13.90 b16.30 a
VP0621.97 a1.90 a13.77 b2.77 a1.73 a2.27 a18.80 a7.30 a12.13 a15.67 a12.68 a
AS123.27 a2.00 a14.57 b2.33 b1.53 a1.70 b22.27 a5.63 b12.40 a14.03 b13.98 a
AS722.40 a1.83 a13.33 b2.07 c1.43 a2.23 a18.13 a5.27 b11.23 a12.03 c17.88 a
SN4119.87 b1.83 a12.77 b1.83 c1.00 b1.87 b19.90 a3.63 b10.07 b9.60 c12.80 a
AS623.90 a2.00 a12.93 b2.23 b1.33 a1.67 b20.97 a4.77 b10.43 b11.97 c13.97 a
ZD15623.03 a2.20 a19.03 a2.10 c1.43 a1.27 c20.40 a4.77 b12.50 a11.30 c15.04 a
AS1022.00 a2.40 a11.23 c1.93 c1.40 a1.13 c21.23 a5.27 b10.33 b11.93 c18.01 a
AS422.83 a1.97 a13.63 b2.57 a1.40 a1.87 b19.67 a4.77 b11.60 a13.50 b12.38 a
L14020.47 b1.67 a10.70 c2.00 c1.30 a1.50 c18.17 a6.50 a9.27 b10.47 c16.13 a
GJ0822.80 a1.83 a11.73 c1.70 c1.10 b1.13 c22.20 a4.30 b7.00 b10.53 c18.13 a
LB08023.67 a1.80 a10.90 c2.03 c1.37 a1.47 c19.43 a5.23 b9.93 b11.10 c12.70 a
LB01520.50 b1.87 a11.80 c2.37 b1.20 b1.27 c19.50 a6.97 a9.27 b13.17 b12.21 a
GJ0321.57 b1.97 a14.23 b2.57 a1.53 a1.47 c18.67 a5.53 b11.43 a13.20 b11.52 a
F test probability
NPKCaMgSFeZnCuMnB
Genotypes2.60 *1.94 ns5.74 **9.26 **5.46 **6.08 **1.35 ns3.99 **4.03 **7.11 **1.48 ns
Mean21.831.9313.322.211.381.6219.735.4311.1512.3914.42
Husk
A1068.73 a1.70 b19.90 b4.17 b1.2 b3.13 a36.57 a6.20 a15.77 a9.37 e24.03 a
AS26.63 a1.60 b21.77 a5.13 a1.33 b3.37 a44.20 a6.80 a11.83 a11.17 d24.20 a
GJ258.50 a1.53 b26.33 a4.13 b1.03 c3.07 a36.70 a6.10 a13.03 a11.67 d23.32 a
VP0613.93 a0.90 d27.57 a4.77 a1.33 b3.50 a26.20 a4.50 b9.53 b17.70 a27.78 a
AS19.67 a1.27 c23.43 a3.80 b1.30 b2.97 a52.80 a4.23 b8.17 b12.87 c24.51 a
AS78.50 a1.17 c18.87 b3.70 b1.17 b3.00 a28.30 a5.03 b9.17 b9.53 e20.65 a
SN419.20 a1.40 c22.67 a3.37 b0.90 c3.03 a29.23 a6.00 a10.70 b7.73 e23.19 a
AS68.97 a1.63 b24.13 a5.50 a1.63 a3.33 a42.87 a4.03 b13.07 a15.63 b15.39 b
ZD1568.73 a2.00 a22.50 a3.70 b1.20 b3.37 a20.53 a8.30 a12.47 a12.00 d17.50 b
AS109.90 a2.13 a22.47 a4.47 a1.17 b3.20 a19.43 a6.13 a12.80 a8.87 e16.43 b
AS410.63 a1.53 b17.93 b5.03 a1.47 a2.97 a19.77 a4.87 b10.10 b15.00 b31.19 a
L1407.33 a1.37 c20.03 b3.83 b1.17 b3.13 a22.33 a3.77 b11.43 a9.17 e24.38 a
GJ087.57 a1.33 c16.90 b3.57 b0.93 c2.97 a22.20 a5.23 b9.03 b9.03 e13.87 b
LB0808.30 a1.47 c16.63 b3.90 b0.97 c3.17 a18.30 a4.43 b11.40 a8.63 e17.60 b
LB0158.77 a1.63 b14.90 b5.33 a1.77 a3.13 a17.97 a7.20 a12.73 a13.30 c23.32 a
GJ038.50 a1.53 b17.43 b4.93 a1.07 c3.37 a21.00 a4.87 b9.40 b14.03 c22.92 a
F test probability
NPKCaMgSFeZnCuMnB
Genotypes1.27 ns13.10 **5.55 **3.32 **8.41 **1.70 ns1.52 ns4.26 **2.35 *16.60 **2.34 *
Mean8.991.5120.844.331.233.1728.655.4811.2911.6121.89
Means followed by the same letter in a column do not differ according to the Scott–Knot test at 5% probability. ns, **, and * mean not significant, significant at 1% probability, and significant at 5% probability, respectively, according to the F test.
Table 7. Leaf macronutrient concentrations in 16 Coffea canephora genotypes during pre-flowering and grain-filling. Alta Floresta D’Oeste, Rondônia, Brazil.
Table 7. Leaf macronutrient concentrations in 16 Coffea canephora genotypes during pre-flowering and grain-filling. Alta Floresta D’Oeste, Rondônia, Brazil.
GenotypesMacronutrients (g kg−1)
NPKCaMgS
Pre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-Filling
A10617.07 Aa17.07 Aa1.20 Aa1.33 Aa8.47 Ba8.87 Da18.00 Ca12.37 Cb4.20 Ba2.50 Cb2.10 Ba1.73 Ba
AS219.67 Aa21.83 Aa1.03 Aa1.23 Aa8.53 Bb11.93 Ca21.67 Ba12.83 Cb5.50 Aa2.73 Bb2.27 Aa1.87 Ba
GJ2515.73 Aa17.90 Aa1.10 Aa1.13 Aa8.07 Bb11.10 Ca20.50 Ba13.80 Bb4.70 Ba2.80 Bb1.93 Ba1.63 Ba
VP0617.07 Aa16.83 Aa1.13 Aa0.90 Ab9.80 Ab13.70 Ba14.03 Da11.17 Ca2.77 Ca2.10 Ca1.80 Ba1.87 Ba
AS117.27 Aa18.13 Aa1.07 Aa1.10 Aa11.53 Ab16.93 Aa16.07 Ca12.13 Cb3.40 Ca2.67 Ba2.00 Ba1.47 Bb
AS718.33 Aa17.70 Aa0.97 Ab1.27 Aa10.20 Aa11.97 Ca12.60 Da10.57 Ca2.87 Ca1.87 Ca2.03 Ba1.57 Bb
SN4117.50 Aa18.13 Aa1.27 Aa1.00 Ab10.57 Aa8.13 Db17.50 Ca20.57 Aa2.83 Cb5.03 Aa1.93 Ba1.70 Ba
AS615.53 Ab18.80 Aa1.20 Aa1.07 Aa8.17 Ba7.20 Da16.83 Ca19.70 Aa3.17 Cb4.77 Aa2.43 Aa1.77 Bb
ZD15616.40 Aa17.23 Aa1.10 Aa0.97 Aa10.90 Aa11.00 Ca16.90 Ca15.50 Ba3.07 Ca3.27 Ba2.47 Aa1.50 Bb
AS1015.30 Aa17.03 Aa1.23 Aa1.07 Aa8.60 Bb12.07 Ca17.67 Ca16.27 Ba3.77 Ca3.43 Ba2.33 Aa1.50 Bb
AS418.13 Aa18.13 Aa1.13 Aa1.13 Aa9.67 Aa9.03 Da18.00 Cb21.60 Aa3.63 Ca4.40 Aa2.20 Aa1.37 Bb
L14016.60 Aa16.40 Aa1.23 Aa1.13 Aa9.33 Ab15.77 Aa23.37 Aa13.77 Bb4.53 Ba2.00 Cb2.07 Ba1.57 Bb
GJ0817.03 Aa19.23 Aa1.17 Aa1.20 Aa11.67 Aa9.93 Ca17.43 Ca15.33 Ba3.00 Ca2.93 Ba2.30 Aa1.87 Ba
LB08015.97 Aa18.37 Aa1.17 Aa1.20 Aa7.43 Bb10.50 Ca25.57 Aa19.50 Ab5.93 Aa3.37 Bb2.03 Ba1.47 Bb
LB01516.60 Ab19.90 Aa1.17 Aa1.07 Aa6.73 Ba8.40 Da20.37 Ba21.00 Aa4.63 Ba4.33 Aa1.83 Ba1.73 Ba
GJ0315.50 Ab19.33 Aa1.13 Aa1.13 Aa9.83 Aa10.93 Ca16.60 Ca18.83 Aa3.10 Ca3.43 Ba1.47 Bb2.57 Aa
Mean16.86 b18.25 a1.14 a1.12 a9.34 b11.09 a18.32 a15.93 b3.82 a3.23 b2.07 a1.70 b
F test probability
NPKCaMgS
Genotype (G)2.441 *1.17 ns8.57 **11.97 **5.78 **1.30 ns
Seasons (S)17.07 **0.71 ns34.99 **33.52 **18.49 **46.42 **
G × S1.57 *1.99 *4.61 **6.49 **7.02 **4.64 **
Means followed by equal letters, lowercase in rows and uppercase in columns, do not differ from each other according to the F and Scott–Knott test, respectively, at 5% probability. ns, **, and * mean not significant, significant at 1% probability, and significant at 5% probability, respectively, according to the F test.
Table 8. Leaf micronutrient concentrations in 16 Coffea canephora genotypes during the pre-flowering and grain-filling periods. Alta Floresta D’Oeste, Rondônia, Brazil.
Table 8. Leaf micronutrient concentrations in 16 Coffea canephora genotypes during the pre-flowering and grain-filling periods. Alta Floresta D’Oeste, Rondônia, Brazil.
GenotypesMicronutrients (mg kg−1)
FeZnCuMnB
Pre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-FillingPre-FloweringGrain-Filling
A106173.93 Ba178.03 Da7.93 Ab10.93 Aa11.10 Ba5.30 Cb92.20 Ba72.70 Cb36.26 Ba13.03 Cb
AS2172.10 Ba124.60 Da8.33 Aa8.77 Ba7.97 Ca3.13 Cb83.30 Ba63.03 Cb34.93 Ba31.42 Ba
GJ25181.30 Ba144.57 Da8.03 Aa7.90 Ba10.80 Ba5.90 Cb76.43 Ca61.10 Ca56.99 Aa40.36 Ab
VP06148.87 Ba179.30 Da7.00 Ba6.73 Ba7.87 Ca3.30 Cb61.10 Da66.03 Ca56.30 Aa38.94 Ab
AS1124.67 Ba166.00 Da7.17 Ba8.13 Ba8.13 Ca5.33 Ca68.90 Ca71.87 Ca43.70 Ba43.67 Aa
AS7170.30 Ba181.60 Da5.87 Ba7.00 Ba6.80 Ca5.83 Ca53.93 Da54.47 Ca34.46 Ba45.62 Aa
SN41177.10 Ba251.57 Ca6.57 Ba7.57 Ba8.07 Ca6.13 Ca55.43 Db82.70 Ba20.45 Cb42.03 Aa
AS6172.03 Bb265.03 Ca6.20 Ba6.97 Ba14.53 Aa5.17 Cb57.27 Db76.37 Ca14.46 Cb45.69 Aa
ZD156210.27 Ba221.93 Ca6.43 Bb8.30 Ba14.43 Aa5.50 Cb76.93 Ca85.33 Ba40.82 Ba37.70 Aa
AS10207.47 Bb327.17 Ba6.00 Bb7.97 Ba6.70 Ca6.63 Ca57.43 Db93.40 Ba31.29 Ca42.51 Aa
AS4269.07 Ab359.27 Ba6.93 Bb8.93 Ba10.70 Ba4.33 Cb68.30 Cb90.43 Ba26.44 Cb50.85 Aa
L140213.13 Ba213.37 Ca6.93 Bb10.37 Aa6.43 Ca8.07 Ba90.07 Ba71.47 Cb22.51 Cb45.24 Aa
GJ08238.57 Aa261.10 Ca6.87 Ba7.90 Ba8.33 Ca7.47 Ba80.47 Ca95.60 Ba30.37 Cb46.84 Aa
LB080235.50 Aa290.63 Ba6.77 Ba8.00 Ba6.13 Ca5.13 Ca94.23 Ba97.77 Ba37.65 Ba48.58 Aa
LB015286.43 Ab426.47 Aa7.30 Ba8.73 Ba8.43 Ca11.10 Aa133.43 Ab152.60 Aa45.38 Ba46.48 Aa
GJ03297.37 Ab415.83 Aa8.03 Aa8.63 Ba5.37 Ca6.23 Ca61.87 Db92.87 Ba15.97 Cb31.94 Ba
Mean204.88 b250.40 a7.02 b8.30 a8.86 a5.91 b75.71 b82.98 a34.25 b40.68 a
F Test Probability
FeZnCuMnB
Genotypes (G)13.8776 **4.79 **3.91 **22.83 **3.78 **
Seasons (S)24.2149 **47.0371 **59.05 **12.47 **10.98 **
G × S2.2987 *3.16 **5.51 **4.99 **4.34 **
Means followed by equal letters, lowercase in rows and uppercase in columns, do not differ from each other according to the F and Scott–Knott test, respectively, at 5% probability. ** and * mean significant at 1 and 5% probability, respectively, according to the F test.
Table 9. Spearman correlation coefficients for macro- and micronutrient concentrations in the flowers, grains, husks, and leaves of 16 genotypes of Coffea canephora. Alta Floresta D’Oeste, Rondônia–Brazil.
Table 9. Spearman correlation coefficients for macro- and micronutrient concentrations in the flowers, grains, husks, and leaves of 16 genotypes of Coffea canephora. Alta Floresta D’Oeste, Rondônia–Brazil.
VariablesNutrients
NPKCaMgSFeCuMnZnB
Flower × Grain−0.090.110.31 *0.50 **0.49 **0.120.020.36 *0.42 **0.080.29 *
Flower × Husk−0.130.150.32 *0.210.280.03−0.52**0.090.070.13−0.04
Flower × Leaf in pre-flowering−0.32 *−0.020.100.080.29 *−0.040.58 **0.120.39 **−0.040.10
Flower × Leaf in grain-filling0.060.090.10−0.06−0.24−0.120.57 **−0.10−0.050.32 *0.25
Flower × Leaf in both periods−0.110.010.080.020.07−0.080.65 **−0.020.130.180.21
Grain × Husk0.080.32 *0.29 *0.53 **0.16−0.020.000.020.60 **−0.09−0.22
Grain × Leaf in pre-flowering0.03−0.050.11−0.100.02−0.070.080.25−0.060.250.09
Grain × Leaf in grain-filling0.15−0.200.12−0.09−0.33 *0.170.15−0.28 *−0.180.060.05
Grain × Leaf in both periods0.25−0.140.13−0.09−0.210.040.110.01−0.250.180.19
Husk × Leaf in pre-flowering0.090.050.050.020.110.01−0.57 **0.25−0.160.140.22
Husk × Leaf in grain-filling−0.27−0.030.220.28 *0.070.19−0.69 **0.14−0.050.03−0.04
Husk × Leaf in both periods−0.150.030.170.260.100.28−0.74 **0.31 *−0.210.090.09
Leaf in pre-flowering × Leaf in grain-filling0.02−0.010.32 *0.22−0.04−0.160.59 **−0.170.240.260.11
Leaf in pre-flowering × Leaf both periods0.60 **0.64 **0.65 **0.65 **0.66 **0.54 **0.78 **0.71 **0.78 **0.70 **0.84 **
Leaf in grain-filling × Leaf in both periods0.76 **0.71 **0.90 **0.83 **0.65 **0.67 **0.95 **0.52 **0.75 **0.85 **0.55 **
Values in bold and asterisks indicate significant correlations (* p < 0.05 and ** p < 0.01).
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Schmidt, R.; Silva, C.A.d.; Dubberstein, D.; Dias, J.R.M.; Vieira, H.D.; Partelli, F.L. Genetic Diversity Based on Nutrient Concentrations in Different Organs of Robusta Coffee. Agronomy 2022, 12, 640. https://doi.org/10.3390/agronomy12030640

AMA Style

Schmidt R, Silva CAd, Dubberstein D, Dias JRM, Vieira HD, Partelli FL. Genetic Diversity Based on Nutrient Concentrations in Different Organs of Robusta Coffee. Agronomy. 2022; 12(3):640. https://doi.org/10.3390/agronomy12030640

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Schmidt, Raquel, Cleidson Alves da Silva, Danielly Dubberstein, Jairo Rafael Machado Dias, Henrique Duarte Vieira, and Fábio Luiz Partelli. 2022. "Genetic Diversity Based on Nutrient Concentrations in Different Organs of Robusta Coffee" Agronomy 12, no. 3: 640. https://doi.org/10.3390/agronomy12030640

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