Parâmetros genéticos em subacessos de melão da agricultura tradicional

rcaat Revista Caatinga Rev. Caatinga 0100-316X 1983-2125 Universidade Federal Rural do Semi-Árido RESUMO A agricultura familiar possui uma grande diversidade de germoplasma de melão, constituindo-se em uma importante fonte de alelos para uso em programas de melhoramento. Assim, o presente estudo objetivou estimar parâmetros genéticos e selecionar genótipos de uma população de subacessos de melão da agricultura familiar pertencentes a diferentes variedades botânicas, com base em caracteres morfológicos. Foram realizados dois experimentos (2019 e 2020), utilizando 27 subacessos de melão (geração S2) provenientes da agricultura familiar e uma variedade comercial, em Delineamento em Blocos Casualizados (DBC) com três repetições e cinco plantas por parcela. Para avaliação foram utilizados nove descritores quantitativos. Inicialmente testou-se as pressuposições da ANOVA e, em seguida, realizou-se a análise de variância individual e a análise conjunta. Com isso, constatou-se diferença significativa entre os subacessos para todos os descritores avaliados, com herdabilidades superiores a 83% e iteração G x A significativa para 88,8% das características avaliadas. Assim, percebeu-se a existência de variabilidade genética entre os subacessos, com predominância dos efeitos genéticos sobre os ambientais, sendo possível obter ganhos genéticos para o melhoramento de várias características de interesse agronômico. Indica-se os subacessos BGMEL66.0, BGMEL111.0 e BGMEL112.0 para serem inseridos em programas de melhoramento visando obter frutos pequenos, com uma boa prolificidade e elevado teor de sólidos solúveis. Já os subacessos BGMEL (108.3 e 108.5) podem fornecer progênies de elevada prolificidade e os subacessos da variedade momordica podem ser utilizados para extração de progênies que visem diminuir o ciclo da cultura e aumentar o tamanho do fruto. INTRODUCTION Melon (Cucumis melo L. Cucurbitaceae) is a species that presents centers of diversity in Africa and Asia (PITRAT, 2013). However, it has great economic expression in Brazil, mainly those from the inodorus and cantalupensis groups; the Northeast region is responsible for 96.84% of the Brazilian melon production (IBGE, 2020). However, creole varieties have been grown in small rural properties, where farmers use their own seeds for new crops (QUEIRÓZ; BARBIERI; SILVA, 2015), resulting in a high variability. Previous studies using melon germplasm from family farming showed the existence of high variability among melon accessions (DANTAS et al., 2012; ARAGÃO et al., 2013; AMORIM et al., 2016; MACÊDO et al., 2017; ANDRADE et al., 2019). However, some important characters for the improvement of melon were not emphasized in these studies, mainly regarding characteristics of some botanical varieties (PITRAT; HANELT; HAMMER, 2000), which are believed to be important factors and, therefore, should be considered. Information on genetic factors is essential for any breeding program to identify and maintain favorable alleles; thus, obtaining estimates of genetic parameters is essential to identify the action of alleles involved in controlling characters and estimate genetic gains by selection. (CRUZ; CARNEIRO; REGAZI, 2014). Melons from traditional agriculture are an important source of alleles for breeding programs. Although several studies have shown variability among melon accessions (AMORIM et al., 2016; MACÊDO et al., 2017), there is no study on this germplasm, focused on generating information for selection of genotypes. It denotes the need for studies using melon germplasm from family farmers for morphological characterization of different botanical varieties based on genetic parameters, focused on selecting superior genotypes. Thus, the objective of this study was to estimate genetic parameters and select genotypes from a population of melon sub-accessions from different botanical varieties grown by family farmers, based on morphological parameters. MATERIAL AND METHODS Two experiments were conducted, one in 2019 and another in 2020, at the Experimental Field of the Department of Technology and Social Sciences of the Bahia State University (DTCS/UNEB), in Juazeiro, Bahia, Brazil (09°25'04.92271"S and 40°29'04.73710"W, and altitude of approximately 352 meters). Twenty-seven melon sub-accessions (AMORIM et al., 2016) (S2 generation) from the botanical varieties momordica, cantalupensis, and makuwa and some accessions not identified were evaluated. These accessions were from the traditional agriculture of the state of Maranhão, Brazil (Table 1) that were stored in the Active Germplasm Bank of Cucurbitaceae from the Northeast Region at the Brazilian Agricultural Research Corporation (Embrapa Semiarid), in Petrolina, Pernambuco. A commercial variety (Melao Amarelo) was used as control. Table 1 Passport data of sub-accessions of Cucumis melo from the Active Germplasm Bank of Cucurbitaceae from the Northeast Region at the Brazilian Agricultural Research Corporation (Embrapa Semiarid), evaluated in 2019 and 2020. Sub-accession Variety* Municipality of collection Municipality coordinates BGMEL 10.0 momordica São João of Patos 6°29'43"S, 43°42'10"W BGMEL 66.0 makuwa Colinas 7°6'59"S, 46°15'26"W BGMEL 67.0 makuwa Colinas 7°6'59"S, 46°15'26"W BGMEL 68.1 momordica Colinas 7°6'59"S, 46°15'26"W BGMEL 68.2 ND Colinas 7°6'59"S, 46°15'26"W BGMEL 68.3 ND Colinas 7°6'59"S, 46°15'26"W BGMEL 77.1 momordica Coroatá 4°7'31"S, 44°7'49"W BGMEL 77.3 ND Coroatá 4°7'31"S, 44°7'49"W BGMEL 78.0 cantalupensis Codó 4°27'18"S, 43°52'44"W BGMEL 82.2 cantalupensis Itapecuru Mirim 3°23'42"S, 44°21'36"W BGMEL 83.1 ND Itapecuru Mirim 3°23'42"S, 44°21'36"W BGMEL 83.2 ND Itapecuru Mirim 3°23'42"S, 44°21'36"W BGMEL 86.1 cantalupensis Codó 4°27'18"S, 43°52'44"W BGMEL 86.2 ND Codó 4°27'18"S, 43°52'44"W BGMEL 86.3 ND Codó 4°27'18"S, 43°52'44"W BGMEL 87.1 momordica São Luís Gonzaga 4°22'51"S, 44°40'14"W BGMEL 87.2 cantalupensis São Luís Gonzaga 4°22'51"S, 44°40'14"W BGMEL 87.3 ND São Luís Gonzaga 4°22'51"S, 44°40'14"W BGMEL 97.1 cantalupensis Caxias 4°52'29"S, 43°20'49"W BGMEL 98.0 ND Caxias 4°52'29"S, 43°20'49"W BGMEL 108.3 ND Caxias 4°52'29"S, 43°20'49"W BGMEL 108.4 ND Caxias 4°52'29"S, 43°20'49"W BGMEL 108.5 ND Caxias 4°52'29"S, 43°20'49"W BGMEL 109.2 ND Caxias 4°52'29"S, 43°20'49"W BGMEL 111.0 makuwa Colinas 7°6'59"S, 46°15'26"W BGMEL 112.0 makuwa Colinas 7°6'59"S, 46°15'26"W BGMEL 115.0 makuwa São Vicente Ferrer 2°53'44"S, 44°52'53"W *Botanical classification according to Amorim et al. (2016); ND = botanical variety not defined. Thirty seeds of each sub-accession were sown in plastic trays filled with a commercial substrate, in a greenhouse covered with a 50% shade screen, and irrigated daily. The seedlings were transplanted to soils previously prepared with plowing and harrowing 15 days after sowing. The experiments were conducted in a complete randomized block design, with three replications, five plants per plot, and spacings of 2.5 m between rows and 0.8 m between plants, under localized drip irrigation. The experiments were conducted approximately in the same period (January to April) in 2019 and 2020. Weeding and plant health status monitoring were carried out; the natural soil fertility was adopted, since this system is commonly used under traditional agriculture. The evaluations were carried out using the following quantitative descriptors (IPGRI, 2003; PITRAT; HANELT; HAMMER, 2000): fruit weight (kg); fruit diameter and length (cm); fruit cavity diameter and length (cm); pulp thickness (cm); soluble solid contents (°Brix) in pulp composite samples homogenized in a kitchen food processor; earliness (number of days from transplanting to harvest), and prolificacy (number of fruits per plant, counted at the end of the crop cycle). Regarding the statistical analyses, firstly, the assumptions of ANOVA were tested, transforming the variables when necessary. Individual analysis of variance was then performed for each growing year to assess whether the sub-accessions differed from each other. Subsequently, test of homogeneity of variances was applied (Fmax: ratio between the highest and lowest residual mean square for each descriptor). Joint analysis (AxJ3 simple factorial) was carried out using the model: Yijk=μ+Gi+Aj+GAij+B/Ajk+εijk, where μ = overall mean; Gi = effect of the i-th genotype; Aj = effect of the j-th environment GAij = effect of the interaction of the i-th genotype with the j-th environment; B/Ajk = effect of the k-th block inside the j-th environment; and Eijk = random error and effects: G (random) and A (fixed) (CRUZ; REGAZI; CARNEIRO, 2012). All genetic and statistical analyses were processed using the program Genes (CRUZ, 2013). RESULTS AND DISCUSSION The data of analysis of variance showed significant differences among melon sub-accessions for all characteristics evaluated (Table 2), denoting the existence of genetic variability among sub-accessions. Similar results were found in previous studies on melon accessions from traditional agriculture (AMORIM et al., 2016; MACÊDO et al., 2017; ANDRADE et al., 2019), supporting those found in the present study and, therefore, denoting the possibility of selecting agronomically superior accessions for the characteristics analyzed (CRUZ; REGAZI; CARNEIRO, 2012). Table 2 Test of means for nine characters of melon sub-accessions from family farmers of the state of Maranhão, Brazil, evaluated in 2019 and 2020. SUB EARL PROL FW FD FL 19 20 19 20 19 20 19 20 19 20 10.0 mo 49.9dA 53.5bA 3.2cA 3.8cA 1.1aA 1.3cA 11.3cA 11.5cA 24.1bA 26.0bA 68.1 mo 51.7dA 53.6bA 2.7cB 6.6bA 1.1aA 1.3cA 11.2cA 12.2cA 24.9bA 24.5cA 77.1 mo 50.3dA 52.1bA 4.3cA 3.6cA 1.3aA 1.4cA 11.6cA 11.7cA 27.9aA 28.5aA 87.1 mo 58.8cA 56.0bA 1.8cA 2.2cA 0.9bB 2.1bA 11.0cA 12.6bA 19.6dB 29.9aA Meanmo 52.73 53.83 3.03 4.09 1.17 1.56 11.31 12.05 24.16 27.24 66.0 mk 54.6dA 55.0bA 3.0cB 6.7bA 0.3cA 0.41A 8.0eA 8.6dA 11.6eA 12.61A 67.0mk 55.4cA 53.3bA 2.3cA 4.1cA 0.5cA 0.41A 9.3dA 8.6dA 13.2eA 12.1fA 111.0 mk 54.0dA 54.0bA 1.8cB 7.7bA 0.3cA 0.31A 8.1eA 7.6eA 11.5eA 10.31A 112.0 mk 55.7cA 54.2bA 3.4cA 5.9bA 0.3cA 0.21A 8.0eA 7.5eA 11.6eA 10.1fA 115.0 mk 56.1cA 55.4bA 2.0cB 5.5bA 0.4cA 0.5eA 8.3eA 9.2dA 12.3eA 13.7eA Mean mk 55.19 54.40 2.51 6.02 0.42 0.41 8.38 8.35 12.09 11.79 78.0 c 65.8bA 59.6aB 1.6cA 2.5cA 1.1aA 1.5cA 10.6cA 10.8cA 20.6cB 24.1cA 82.2 c 63.1bA 55.1bB 1.1cA 3.3cA 0.2cB 0.7eA 7.6eB 9.8dA 7.4fA 14.4eA 86.1 c 63.0bA 55.0bB 1.0cA 2.3cA 0.5cA 0.8eA 9.2dA 10.6cA 14.0eA 15.9eA 87.2 c 67.6bA 61.0aB 0.5cA 1.0cA 1.4aB 1.9bA 11.7cB 13.7bA 18.8dB 21.9dA 97.1 c 71.3aA 59.3aB 0.4cA l.lcA 0.8bA 0.8eA 11.8cA 10.7cA 14.4eA 15.9eA Mean c 66.22 60.17 0.96 2.06 0.86 1.16 10.22 11.13 15.10 18.47 68.2 nd 54.1dA 55.1bA 2.6cA 4.2cA 0.8bA 0.8eA 10.8cA 11.1cA 17.8dA 16.5eA 68.3 nd 62.4bA 55.0bB 1.5cA 1.9cA 0.5cB 1.0dA 8.1eA 9.0dA 18.4dB 25.6bA 77.3 nd 66.8bA 58.6aB 1.9cA 0.9cA 1.4aA 1.0dB 13.2bA 10.4cB 21.8cA 20.2dA 83.1 nd 57.6cA 55.0bA 1.5cA 3.6cA 1.3aA 1.0dA 14.8aA 13.3bA 14.4eA 14.6eA 83.2 nd 71.6aA 55.3bB 0.5cA 1.2cA 0.3cB 0.7eA 8.7dB 11.3cA 10.4fA 11.5fA 86.2 nd 56.3cA 57.0bA 1.2cB 5.1bA 0.4cB 0.8eA 9.4dB 11.3cA 8.9fA 11.5fA 86.3 nd 59.6cA 56.0bA 0.5cA 1.6cA 0.5cA 0.6eA 9.2dA 9.9dA 12.3eA 13.3fA 87.3 nd 58.5cA 58.5aA 1.5cA 2.1cA 1.5aB 3.1aA 12.7bB 15.6aA 22.3cB 27.1bA 98.0 nd 72.0aA 61.0aB 0.7cA 2.0cA 0.3cB 0.7eA 7.8eB 10.4cA 10.8fB 14.3eA 108.3 nd 54.3dA 51.0bA 11.5aA 10.9aA 0.3cA 0.3fA 7.7eA 7.6eA 11.4eA 12.5fA 108.4 nd 61.1cA 56.5bA 5.2cA 6.9bA 0.5cA 0.6eA 9.1dA 9.5dA 14.0eA 14.4eA 108.5 nd 52.5dA 51.3bA 8.0bA 10.8aA 0.2cA 0.3fA 7.2eA 7.7eA 8.2fA 9.8fA 109.2 nd 59.0cA 52.8bB 3.2cA 3.8cA 0.2cA 0.4fA 7.0eA 7.9eA 10.7fA 12.8fA Mean nd 60.48 55.64 3.10 4.26 0.68 0.92 9.72 10.43 14.01 15.75 Ama 72.3aA 61.0aB 0.6cA 2.1cA 0.5cB 0.9dA 8.9dB 10.6cA 13.8eA 16.6eA Min 45.0 49.0 0.2 0.4 0.44 0.25 6.73 7.27 7.1 9.0 Max 76.0 61.0 15.8 16.0 1.52 3.34 15.15 16.7 29.98 32.8 Mean 59.88 55.77 2.51 4.08 0.81 0.96 9.77 10.42 15.30 17.20 Fc ** ** ** ** ** ** ** ** ** ** CV% 6.60 3.53 75.87 45.99 12.81 22.50 11.16 8.04 11.04 11.29 SUB FCD FCL PT SS 19 20 19 20 19 20 19 20 10.0 mo 5.9bA 6.1aA 20.0bA 21.3bA 2.3bA 2.4bA 4.4dA 3.7dA 68.1 mo 5.7bA 6.4aA 20.5bA 20.0bA 2.4bA 2.6bA 4.3dA 3.7dA 77.1 mo 6.4bA 6.2aA 23.7aA 24.1aA 2.5aA 2.6bA 4.4dA 4.2dA 87.1 mo 5.0cB 6.4aA 16.8cB 24.8aA 2.1bB 3.0bA 4.8dA 3.8dA Mean mo 5.81 6.31 20.28 22.60 2.38 2.71 4.48 3.85 66.0 mk 4.4cA 5.0bA 9.1eA 10.1eA 1.4cA 1.8cA 8.6aA 8.1aA 67.0 mk 5.0cA 4.8bA 9.9eA 9.7eA 1.7cA 1.7dA 7.5bA 6.7aA 111.0 mk 4.6cA 4.4bA 9.0eA 8.2eA 1.4cA 1.4dA 9.5aA 7.7aB 112.0 mk 4.4cA 4.7bA 9.0eA 8.2eA 1.5cA 1.3dA 9.2aA 7.7aB 115.0 mk 4.6cA 5.0bA 9.6eA 11.5dA 1.5cB 2.0cA 7.5bA 5.8bB Mean mk 4.63 4.84 9.36 9.56 1.53 1.67 8.46 7.20 78.0 c 5.2cA 4.9bA 15.6dB 19.8bA 2.6aA 2.7bA 6.0cA 4.3dB 82.2 c 4.3cA 4.6bA 5.2fB 11.0dA 1.5cB 2.5bA 7.4bA 5.1cB 86.1 c 4.8cA 5.6aA 10.0eA 10.9dA 2.1bA 2.3cA 6.4cA 5.8dA 87.2 c 4.6cA 5.2bA 13.7dB 16.9cA 3.1aB 4.1aA 6.0cA 5.7bA 97.1 c 7.3aA 5.9aB 11.0eA 12.1dA 1.9cA 2.0cA 4.7dA 4.2dA Mean c 5.29 5.29 11.14 14.18 2.27 2.76 6.10 5.02 68.2 nd 6.0bA 6.1aA 14.4dA 12.9dA 2.1bB 3.0bA 6.1cA 4.6dB 68.3 nd 3.8cA 4.2bA 15.5dB 21.6bA 1.7cA 2.2cA 4.1dA 5.0cA 77.3 nd 8.1aA 6.3aB 17.2cA 15.8cA 2.3bA 1.9cA 5.8cA 4.4dB 83.1 nd 8.4aA 7.1aB 9.9eA 9.9eA 2.8aA 2.7bA 5.8cA 5.3cA 83.2 nd 4.4cB 5.8aA 6.4fA 7.7eA 1.9cB 2.5bA 6.0cA 6.9aA 86.2 nd 5.2cA 5.6aA 6.2fA 8.6eA 1.7cB 2.6bA 4.2dA 5.3cA 86.3 nd 5.4cA 6.0aA 8.9eA 10.1eA 1.7cA 1.8cA 5.4cA 5.9bA 87.3 nd 6.1bA 7.0aA 17.4cB 21.1bA 2.7aB 4.2aA 4.7dA 3.4dB 98.0 nd 5.0cA 6.1aA 8.0eB 11.3dA 1.4cB 2.1cA 6.1cA 4.3dB 108.3 nd 4.3cA 3.7bA 8.7eA 8.7eA 1.6cA 1.9cA 3.8dA 3.8dA 108.4 nd 4.5cA 5.1bA 10.5eA 11.3dA 2.0bA 2.1cA 5.9cA 5.3cA 108.5 nd 3.9cA 4.3bA 6.1fA 7.7eA 1.3cA 1.6dA 6.4cA 4.2dB 109.2 nd 3.7cA 4.0bA 7.8eA 9.5eA 1.4cA 1.8cA 7.2bA 6.8aA Mean nd 5.33 5.51 10.58 12.05 1.94 2.36 5.50 5.02 Ama 4.50cA 4.81bA 9.75eA 12.00dA 2.11bB 2.78bA 5.6cA 4.4dA Min 3.37 3.47 5.0 6.7 1.2 1.2 2.93 2.87 Max 10.15 8.0 24.85 27.2 4.06 4.45 10.4 9.2 Mean 5.23 5.43 11.82 13.49 1.9 2.37 6.03 5.24 Fc ** ** ** ** ** ** ** ** CV% 8.77 9.23 3.41 6.97 7.28 14.17 13.82 14.40 Means followed by same lowercase letter in the columns, or uppercase letter in the rows, are not statistically different from each other by the Scott Knott test at 5% significance. SUB = sub-accession; 19 and 20 = evaluation years of 2019 and 2020; FW = fruit weight (Kg); EARL: earliness (number of days from transplanting to harvest); PROL: prolificacy (number of fruits per plants, counted at the end of the crop cycle); FD and FL= fruit diameter and length, respectively (cm); FCD and FCL = fruit cavity diameter and length, respectively (cm); PL = pulp thickness (cm); SS: soluble solid contents (°Brix); Min and Max refer to the individual values and show the variation within the sub-accession; Fc = Snedecor's F distribution. ** = significant at 1%; CV(%) = coefficient of variation; Ama = commercial variety Melao Amarelo; and mo, mk, c, and nd = varieties momordica, makuwa, cantalupensis, and not defined, respectively. Regarding the homogeneity of sub-accessions in the different growing years, the test of means (Table 2) showed that 22.2% of the sub-accessions (BGMEL10.0, BGMEL77.1, BGMEL67.0, BGMEL86.3, BGMEL108.3, and BGMEL108.4) presented no difference to each other for the characters evaluated. However, 25.9% of the sub-accessions (BGMEL77.3, BGMEL82.2, BGMEL83.2, BGMEL87.1, BGMEL87.2, BGMEL87.3, and BGMEL98.0) presented variations higher than 55.5%; the highest variations were found for earliness (37%), soluble solid contents (37%), fruit weight (33.3%), and pulp thickness (33.3%). The earliness in the different growing years presented variations in for 37% of the sub-accessions: five from the variety cantalupensis (BGMEL78.0, BGMEL82.2, BGMEL 86.1, BGMEL 87.2, and BGMEL97.1), five from botanical varieties not identified (BGMEL68.3, BGMEL77.3, BGMEL83.2, BGMEL98.0, and BGMEL109.2), and the control. According to the means of the melon varieties, the highest means were found in the growing year I (2019), except for momordica. The lowest means were found for momordica and the highest for the commercial variety (Melao Amarelo) (Table 2), denoting the potential of the variety momordica for selection focused on increasing earliness. Prolificacy and fruit cavity diameter presented only 18.5% variation, highlighting the variety makuwa and botanical varieties not defined (ND), respectively. Regarding the other characteristics, the highest variations were found for sub-accessions of ND varieties (Table 2); these variations are probably because of the high variability among plants within the sub-accessions, which is due to the introgression of alleles among different botanical varieties (MACÊDO et al., 2017; AMORIM et al., 2016). All characteristics were, in general, favored in the growing year II (2020), except soluble solid contents. Sub-accessions from momordica stood out for earliness and characteristics related to fruit size (fruit weight, fruit diameter and length fruit cavity diameter and length, and pulp thickness). In addition, they presented a good prolificacy (Table 2), denoting that this botanical variety is important for selection processes focused on improving these characteristics. Sub-accessions from ND varieties presented the highest prolificacy (Table 2), mainly the sub-accessions BGMEL108.3 and BGMEL 108.5, however they presented small fruits with low soluble solid contents. Sub-accessions from makuwa also presented prolificacy and progenies with high soluble solid contents, mainly BGMEL66.0, BGMEL111.0, and BGMEL112.0, with small fruits and good prolificacy, which can be a novelty in the market. The control (commercial variety) presented low prolificacy and medium-sized fruits with low solid soluble contents, under the same crop conditions. The commercial variety presented low performance was probably due to the use of the natural soil fertility management, which is common for traditional agricultural crops, since high chemical fertilizer rates are applied to soils for commercial crops. The joint analysis of variance (Table 3) showed coefficients of variation varying from 5.40 (earliness) to 57.38 (prolificacy). Joint analysis is recommended only for environments with homogeneous residual variances. According to Cruz, Regazi and Carneiro (2012), several tests can be used to evaluate the homogeneity of residual variances, however, they have limitations or restrictions of use; thus, a practical criterion that can be adopted for grouping experiments to proceed joint analysis is to combine trials whose residual mean squares do not exceed the approximate ratio of 7:1 in the same group. In the present study, the ratios between the highest and lowest variances were between 1.03 and 4.01 for all characteristics evaluated (Table 3), which allowed to proceed the joint analysis and assess the genotype-environment interaction. The high variability found among sub-accessions is partially due to existing differences among plants within each sub-accession. Studies on melon germplasm showed a high variation among accessions (DANTAS et al., 2012; ARAGÃO et al., 2013; TRIMECH et al., 2013; YILDIZ ; AKGUL; SENSOY, 2014; ANDRADE et al., 2019) and among plants within accessions (AMORIM et al., 2016; MACÊDO et al., 2017). Table 3 Joint analysis of variance among melon sub-accessions from family farmers of the state of Maranhão, Brazil, evaluated in 2019 and 2020. SV DF Mean square EARL FW FD FL FCD FCL PT SS PROL G 27 12.99** 32.90** 22.70** 57.90** 12.00** 57.12** 19.64** 17.51** 10.18** E 1 7.85ns 3.99ns 2.38ns 4.84ns 2.02ns 5.56ns 9.38ns 14.68ns 6.69* G×E 27 3.71** 5.33** 2.72** 3.52** 1.80* 2.97** 3.06** 2.04** 1.04ns RES 9.77 0.04 0.94 3.31 0.48 2.64 0.09 0.63 3.58 CV 5.40 25.41 9.63 11.20 13.08 12.83 13.91 14.11 57.38 Mean 57.82 0.84 10.09 16.25 5.33 12.65 2.18 5.64 3.29 Fmax 4.01 1.03 1.68 1.31 2.87 1.97 1.57 1.21 1.03 SV = source of variation; DF = degrees of freedom; EARL = earliness; FW = fruit weight; FD = fruit diameter; FL = fruit length; FCD = fruit cavity diameter; FCL = fruit cavity length;; PT = pulp thickness; SS = soluble solid contents (°Brix); PROL = prolificacy; G = genotype; E = environment; G×E = genotype-environment interaction; RES = residue; CV = coefficient of variation; Fmax = ratio between the highest and lowest residual mean square; **, * = significant at 1% and 5% significance, respectively; ns = not significant. The joint analysis of variance (Table 3) showed that the genotype effect was highly significant (p≤0.01) for all variables, whereas the environmental effect was not significant, except for prolificacy. The genotype-environment interaction was significant for all variables, except prolificacy. The predominance of estimates of genetic effects over environmental effects indicates that the genetic factors had a greater effect on the observed phenotype. However, the significant interaction for 88.8% of the variables denotes that the relative performance of the sub-accessions (BORÉM; MIRANDA, FRITSCHE-NETO, 2017) varied in the two growing years for all variables evaluated, except prolificacy. However, the temperature data were similar in the two growing years: mean temperatures varied from 27.46 to 28.03 °C (2019) and from 26.56 to 27.28 °C (2020). Rainfall data showed a small difference between growing years: 0.26 to 5.27 mm (2019) and 1.19 to 9.15 mm (2020) (AGRITEMPO, 2021). High heritability was found for the genetic parameters, with estimates higher than 83% for all characters evaluated (Table 4), mainly for some characteristics related to fruit size (fruit length, cavity length, and fruit weight). These results denote a great potential for successful selection focused on these characters, as the observed phenotype was mostly affected by the genetic factor. The sub-accessions BGMEL 77.1 and BGMEL 87.1 (variety momordica) and BGMEL 87.3 (variety not defined) stood out for these characteristics (Table 2), showing to be promising for selection processes focused on increasing fruit size. Table 4 Genetic parameters for characters of melon sub-accessions from family farmers of the state of Maranhão, Brazil, evaluated in 2019 and 2020. Genetic parameters Character Year σ 2 F σ 2 E σ 2 G h2(%) CVg(%) CVe(%) ( CV g / CV e ) EARL 19 46.45 5.22 41.23 88.76 10.72 6.61 1.62 20 0.03 0.005 0.02 83.31 2.24 1.73 1.29 PROL 19 0.26 0.03 0.22 85.54 26.88 19.2 1.40 20 7.50 1.17 6.33 84.34 61.64 46.00 1.34 FW 19 0.02 0.001 0.02 93.07 11.75 5.56 2.11 20 0.04 0.001 0.04 95.97 14.73 5.22 2.82 FD 19 4.01 0.39 3.61 90.11 19.45 11.17 1.74 20 4.01 0.23 3.77 94.15 18.63 8.06 2.31 FL 19 29.56 0.95 28.61 96.77 34.93 11.05 3.16 20 38.32 1.25 37.06 96.71 35.39 11.29 3.13 FCD 19 0.05 0.008 0.04 83.86 8.38 6.39 1.31 20 0.81 0.08 0.72 89.63 15.67 9.27 1.69 FCL 19 23.57 0.59 22.98 97.48 40.55 11.29 3.59 20 0.46 0.01 0.44 95.79 17.75 6.45 2.75 PT 19 0.23 0.02 0.21 89.93 23.15 13.45 1.72 20 0.03 0.002 0.02 92.45 9.35 4.62 2.02 SS 19 2.35 0.23 2.12 90.12 24.12 13.86 1.74 20 1.78 0.19 1.59 89.29 24.03 14.47 1.66 σ2F = phenotypic variation; σ2E = environmental variation; σ2G = genetic variation; h2(%) = heritability; CVg(%) = coefficient of genetic variation; CVe(%) = coefficient of environmental variation; (CVg/CVe) = CVg to CVe ratio; FW = fruit weight (kg); EARL = earliness; PROL = prolificacy; FD = fruit diameter (cm); FL = fruit length (cm); FCD = fruit cavity diameter (cm); FCL = fruit cavity length (cm); PT= pulp thickness; and SS = soluble solid contents (°Brix). Lower results were found by Valadares et al. (2017) for fruit weight (86.00%) and length (93.00%) when evaluating heritability of 23 melon accessions from the momordica group. However, this difference can be attributed to the use of another set of genotypes in the experiment conducted under greenhouse conditions, which allows for more control of environmental effects. Aragão, Nunes and Queiróz (2015) evaluated melon families and found lower heritabilities than those found in the present study, for all characters evaluated. The estimated genetic, phenotypic, and environmental variations (Table 4) indicated that the genetic variation (σ2G) was higher than the environmental variation (σ2E) for all characters evaluated. Thus, it can be said that genetic effects predominate in the expression of the phenotype, indicating greater reliability and greater genetic gains in phenotypic selection. However, more significant variations between growing years were found for earliness, prolificacy, fruit length, and fruit cavity length. The coefficients of genetic variation (CVg) found varied from 2.24% to 61.64% (Table 4) and were higher than the coefficients of environmental variation (CVe) for all characters evaluated. The lowest CVg were found for earliness and the highest for prolificacy, fruit cavity length fruit length and soluble solids. However, the highest CVe were also found for prolificacy and soluble solid contents, indicating that these characteristics were highly affected by environmental factors. Valadares et al. (2017) evaluated melon accessions from the variety momordica and found the highest CVg for pistil scar size (72.04) and soluble solid contents (55.34). Ferreira et al. (2016) evaluated pumpkin accessions and found the highest CVg for fruit weight and prolificacy, which denotes a greater effect of genetic factors on the expression of the phenotype and reinforces the existence of high variability in the germplasm. The CVg to CVe ratio (CVg/CVe) presented values >1 for all characters (Table 4). CVg/CVe equal to or higher than 1 and heritability higher than 80% are favorable conditions for selection (CRUZ; REGAZI; CARNEIRO, 2012), which were found for all characteristics evaluated, denoting great potential for a successful selection. CONCLUSIONS The melon sub-accessions evaluated present genetic variability, with predominance of genetic effects over environmental effects, denoting the possibility of obtaining genetic gains by improving several characteristics of agronomic interest. The sub-accessions BGMEL 66.0, BGMEL111.0, and BGMEL112.0 from the variety makuwa are an important source of germplasm for the obtaining of good prolificacy and small fruits with high soluble solid contents. However, the sub-accessions BGMEL 108.3 and BGMEL 108.5, from botanical varieties not defined, can generate progenies with a high prolificacy; whereas sub-accessions from the variety momordica can be used for generation of progenies focused on shortening the crop cycle and increasing fruit size. ACKNOWLEDGEMENTS The authors thank the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) for granting scholarships and financial support for the development of the present study; and the Bahia State University (UNEB), State University of Feira de Santana (UEFS), and Brazilian Agricultural Research Corporation (Embrapa Semiarid) for financial support, infrastructure, and access to the genotypes evaluated. REFERENCES AGRITEMPO. 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