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Performance of advanced potato genotypes in organic and conventional production systems

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DataCite Commons2020-08-25 更新2024-07-28 收录
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https://scielo.figshare.com/articles/Performance_of_advanced_potato_genotypes_in_organic_and_conventional_production_systems/12056763/1
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ABSTRACT Potato is responsive to intensive agricultural input use; however, it can be produced in less intensive production systems (such as the organic system) by using appropriate production techniques and genotypes adapted to this system. This study aimed to evaluate the performance of advanced potato genotypes for tuber yield under conventional and organic production systems, in order to select potential genotypes to become new cultivars adapted to these systems. Fifteen advanced potato clones and two controls were evaluated under organic and conventional production systems, in 2016 and 2017, in Brasília-DF, Brazil. The experimental design was randomized blocks with three replicates and plots composed of two rows with 10 plants each, spaced 0.35 m between plants and 0.80 m between rows. Total (mass) and marketable (mass and number of tubers) productivities were evaluated. Variance analysis showed significant differences among genotypes for all traits. Despite the lower average tuber yield in the organic system, selecting genotypes with high potential productivity was possible in this system, such as F158-08-01 and F158-08-02, showing high marketable tuber yield, with values equivalent to the conventional system. Clones F102-08-04, F13-09-07, F-18-09-03, F-183-08-01, F-21-09-07, F31-08-05, F63-10-07 and F97-07-03 also outperformed the control cultivars in organic system. For conventional system, F158-08-01, F158-08-02 and F183-08-01 were superior, and F18-09-03, F21-09-07, F63-10-07, F97-07-03, PCDINV10 and PCDSE090 showed performance similar or superior to the most productive control (cultivar Asterix). Genotypes F158-08-01 and F158-08-02 were superior in both conventional and organic systems, with potential to become new cultivars recommended for both production systems.
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SciELO journals
创建时间:
2020-04-01
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