bioRxiv preprint doi: https://doi.org/10.1101/2021.03.18.436039; this version posted March 20, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Original article 1 Analysis of correlation and trail coefficients for componentperformance s in nine experimental 2 tomato lines 3 Gonzalo Quispe Choque* 4 National Institute of Agricultural and Forestry Innovation. National Directorate of Innovation 5 National Vegetable Project 6 7 Villa Montenegro 23 1 / 2Km, Cochabamba-Oruro highway 8 Email for correspondence: *[email protected] 9 10 11 Abstract 12 The objective of the research was to analyze the main variables related to tomato yield, and 13 guide the selection of materials for the INIAF vegetable improvement program. The 14 experiment was carried out in the open field using nine tomato lines on the grounds of the 15 National Vegetable Seed Production Center, during the 2017-2018 agricultural campaign. A 16 randomized complete block experimental design was used, with three repetitions and 10 plants 17 per experimental unit. For the analysis of the data, the variable yield was considered as 18 dependent and the variables number of flowers per inflorescence, number of clusters per plant, 19 number of fruits per plant, weight of fruit, equatorial and polar diameter as independent 20 variables. Analysis of variance, phenotypic correlations and path coefficients were performed. 21 The performance of the L015 line was 80. 79 t ha-1 higher than the L014, L019 and Rio 22 Grande lines. The fruit yield had a significant correlation with the weight of fruit per plant 23 followed by the polar diameter, equatorial diameter, number of fruits per plant and weight of 24 fruit. The analysis of path coefficients showed that the number of fruits per plant had the 25 highest direct positive effect on the fruit yield, fruit weight and equatorial diameter that have a 26 significant correlation and a direct effect on the fruit yield, emerged as the components with 27 coefficients of 0.96 and 0.52 respectively. These characters may be relevant within the 28 selection criteria in the development of new varieties. number of fruits per plant and weight of 29 fruit. The analysis of path coefficients showed that the number of fruits per plant had the 30 highest direct positive effect on the fruit yield, fruit weight and equatorial diameter that have a 31 significant correlation and a direct effect on the fruit yield, emerged as the components with 32 coefficients of 0.96 and 0.52 respectively. These characters may be relevant within the 33 selection criteria in the development of new varieties. number of fruits per plant and weight of 34 fruit. The analysis of path coefficients showed that the number of fruits per plant had the 35 highest direct positive effect on the fruit yield, fruit weight and equatorial diameter that have a 36 significant correlation and a direct effect on the fruit yield, emerged as the components with 37 coefficients of 0.96 and 0.52 respectively. These characters may be relevant within the 38 selection criteria in the development of new varieties. 39 40 Keywords: path coefficients, performance components, lines 41 42 43 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.03.18.436039; this version posted March 20, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Original article 44 Introduction 45 Tomato (2n = 24), belonging to the Solanaceae family, is a important vegetable in the world with a 46 potential of Performance 33.98 t ha-1 and a kind well studied in terms of genetics (Foolad, 2007 and 47 FAOSTAT, 2018). This fact derives from the different types of fruits that the species presents and the 48 varied forms of consumption that it offers., particularly like a rich source vegetable of 49 carotenoidsvitamins, carbohydrates, as well as other essential minerals (Bergougnoux, 2014; Schwarz 50 et al., 2014; Giovannucci et al., 2002 and Patiño et al., 2015). In Bolivia, its cultivated areaa in the 51 main producing regions It is 4691 hectares with a production from 61,360 to 63,454 t year-1 and a yield 52 per unit area of 12 to 13 t ha-1 that is less than the half its potential performance. These data 53 demonstrate the greatimportance socioeconomic of this crop (OAP, 2019). 54 Systematic study and evaluation of germplasm tomato is of great importance for agronomic and genetic 55 improvement current and future cultivation (Reddy et al., 2013). In general, when working with tomato 56 cultivation, a large number of variables are measured to obtain a data set that allows the most varied 57 types of statistical evaluations and analyzes. When numerous variables are studied at the same time, 58 correlations between them can be calculated, which are important for the selection of characteristics of 59 interest for plant breeding (Moreira et al., 2013). Without embargo, the acquaintancethe relationship 60 between andl performance and other characters of the plant and its relative contribution to performance 61 it is very useful when formulating the selection scheme. As performance is a complete characterjor, it is 62 difficult to explore multiple characters that contribute to the same to through of the correlation, so so 63 much, it is important to carry out other analyzes that include the coefficients de path that provide a 64 clear indication for the selection criteria. In this way, the path analysis is a statistical analysis capable 65 of recognizing cause and effect relationships (Wright, 1921), displaying the correlation coefficients in 66 the direct and indirect effects of the independent variables in a dependent variable. 2 bioRxiv preprint doi: https://doi.org/10.1101/2021.03.18.436039; this version posted March 20, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Original article 67 National Vegetable Project of the National Institute of Agricultural and Forestry Innovation, comes 68 identifying high-yielding genotypesin fruit, quality and tolerance to adverse abiotic and biotic factors, 69 evaluating a large number of variables that allow to explain performance components by medium of a 70 simple model, analyzing its numerical components, such as the number of fruits per plant that is 71 determined by the number of flowers that are fertilized and the final weight of the same. This work 72 aimed to analyze the main variables related to tomato yield, and guide the selection of materials for the 73 INIAF vegetable improvement program. 74 75 Materials and methods 76 Essay It has been made in the National Center for Vegetable Seed Production of the INIAF, locatedat 77 municipality of Sipe Sipe, Quillacollo province, of the Cochabamba department. Geographically it is 78 located 17°26'24.4" South latitude; 66 ° 20'38.9" west longitude and at a height of 2505 m.s.n.m, 79 during the 2018-2019 agricultural season. 80 Table 1. Origin and agronomic characteristics of lines experimental tomato tested during the 2018-2019 agricultural season 81 at the INIAF National Vegetable Seed Production Center, Cochabamba, Bolivia. No. Experimental Line Origin Fruit Cycle 1 L014 PNH (INIAF) Oblong Early 2 L015 PNH (INIAF) Ovoid Semi early 3 L027 PNH (INIAF) Round Semi early 4 L031 PNH (INIAF) Round Semi early 5 L019 PNH (INIAF) Oblong Semi early 6 AVTO1003 AVRDC (Taiwan) Oblong Semi early 7 AVTO1007 AVRDC (Taiwan) Square Semi early Cultivars 8 Rio Grande CNPSH (INIAF) Piriform Early 9 Lia (tester) Sakata Piriform Early 82 PNH: National Vegetable Project. INIAF, Cochabamba, Bolivia 83 AVRDG: Asian Vegetable Research and Development Center. Shanhua, Taiwan. 84 CNPSH: National Center for Vegetable Seed Production. INIAF, Cochabamba, Bolivia 85 For the development from work sand they used seven experimental tomato lines, to this material was 86 addedor two varietyit is As a Witness (Lia and Rio Large), in order to compare the superiority or 87 inferiority of the materials in terms of productivity (Table 1). The sowing of the genetic material was 88 carried out in multicell trays of 128 alveothe under glass, with rice husk, lama and topsoil as substrate. 3 bioRxiv preprint doi: https://doi.org/10.1101/2021.03.18.436039; this version posted March 20, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Original article 89 LThe seedlings were transplanted 36 days after sowing in open field conditions when they presented 90 cinco true leaves, using a stocking density of 20,000 pl ha-1.Lthe plants were tutored when they reached 91 15 cm Tall, the leaf removal of the lower leaves was carried out once the fruits of the first cluster were 92 formed. I knowused drip irrigation 20 cm apart, with two daily irrigations of 20 min each, applying 93 approximately 1.13 l per plant day 1. The fertilizers were applied by fertigation with direct suction 94 through a Venturi, the daily doses were according to the phenological stage of the crop, the total 95 applied was: 260N-330P-330K. The fruit harvest began 75 days after transplantation, manually, once a 96 week. 97 I know usedA statistical design of complete random blocks, with nine treatments (experimental lines) 98 and three repetitions, the experimental unit consisted of 10 plants distributed in 2 rows, 80 cm apart and 99 2 m long each. For harvest purposes, 5 plants were taken per experimental unit.