Published

2026-05-14

Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis

Estabilidad del rendimiento en genotipos de Physalis peruviana L. a través de análisis multiambientales

DOI:

https://doi.org/10.15446/rfnam.v79.122607

Keywords:

Adaptability, AMMI, Cape gooseberry, Genotype-environment interaction, MTSI (en)
Adaptabilidad, AMMI, Uchuva, Interacción genotipo-ambiente, MTSI (es)

Downloads

Authors

Cape gooseberry (Physalis peruviana L.) is a high-value Andean fruit crop with increasing economic importance due to its nutritional properties and export potential, whose productivity is strongly influenced by environmental heterogeneity and pronounced genotype by environment interaction (GEI). These challenges hinder the identification of genotypes that combine high yield with stability, a key requirement for advancing breeding programs and ensuring consistent production in diverse climatic conditions. Therefore, this study aimed to evaluate the yield performance, stability, and adaptability of 40 genotypes grown across four contrasting high-Andean environments in Nariño, Colombia, using a randomized complete block design with three replications during a single production cycle conducted between 2019 and 2020. Multi-environment trial data were analyzed using AMMI, GGE biplot, and the Multi-Trait Stability Index (MTSI), focusing on fruit weight without calyx (FWWC), fruit number per plant, and total yield. Environmental effects contributed most to GEI, with AMMI1 efficiently capturing major interaction patterns. Gualmatán was identified as the most favorable site. Genotypes UN45, 09U089, and Puracé showed broad adaptability to both Gualmatán and Ipiales, with UN45 also expressing the highest overall yield performance. Genotypes 12U347 and UN34 displayed specific adaptation to Gualmatán, while UN52 and 13U407 exhibited high stability across environments. The MTSI index identified six top-performing genotypes (09U099, 12U352, UN49, 12U360, 12U350, and 12U374), all achieving positive selection differentials (<1.20) and classified as stable and high-performing genotypes. These findings provide robust evidence to support targeted selection of superior Physalis peruviana genotypes and strengthen breeding strategies aimed at achieving stable and high-yielding cultivars across diverse Andean environments.

La uchuva (Physalis peruviana L.) es un cultivo andino de alto valor, con creciente importancia económica debido a sus propiedades nutricionales y su potencial de exportación, cuyo rendimiento se ve fuertemente influenciado por la heterogeneidad ambiental y por una marcada interacción genotipo × ambiente (IGA). Estas condiciones dificultan la identificación de genotipos que combinen alto rendimiento con estabilidad, un requisito esencial para fortalecer los programas de mejoramiento y garantizar una producción consistente en diversas condiciones climáticas. Por ello, este estudio tuvo como objetivo evaluar el rendimiento, la estabilidad y la adaptabilidad de 40 genotipos cultivados en tres ambientes contrastantes del departamento de Nariño, Colombia, usando el diseño de bloques completos al azar con tres repeticiones durante un ciclo productivo del cultivo entre 2019 y 2020. Los datos de ensayos multiambiente se analizaron mediante los modelos AMMI, GGE biplot y el Índice de Estabilidad Multicaracterística (IEM), considerando variables clave como peso de fruto sin cáliz (PFSC), número de frutos por planta y rendimiento total. Los efectos ambientales fueron los principales contribuyentes a la IGA, y el modelo AMMI1 capturó de manera eficiente los patrones de interacción más relevantes. Gualmatán se identificó como el sitio más favorable. Los genotipos UN45, 09U089 y Puracé mostraron amplia adaptabilidad a Gualmatán e Ipiales, y UN45 presentó el mayor rendimiento promedio. Los genotipos 12U347 y UN34 evidenciaron adaptación específica a Gualmatán, mientras que UN52 y 13U407 destacaron por su alta estabilidad. El índice IEM seleccionó seis genotipos sobresalientes (09U099, 12U352, UN49, 12U360, 12U350 y 12U374), los cuales alcanzaron diferenciales de selección positivos (<1,20) y clasificados como genotipos estables y de alto rendimiento. Estos resultados aportan evidencia robusta para orientar la selección dirigida de genotipos superiores de uchuva y fortalecen las estrategias de mejoramiento orientadas a obtener cultivares estables y de alto rendimiento en diversos ambientes altoandinos.

References

Abreu Hadassa KA, Ceccon G, Correa AM et al (2019) Adaptability and stability of cowpea genotypes via REML/BLUP and GGE biplot. Bioscience Journal 35(4): 1071–1082. https://doi.org/10.14393/BJ-v35n4a2019-42125

Achenef G (2022) Advancement of analytical models quantifying G×E interactions and stability analysis in multi-environment trial. International Journal of Research in Agricultural Sciences 9(4): 103– 120. https://www.researchgate.net/publication/363404851

Azevedo C, Ribeiro T, Silva D, Carbonell S and Chiorato A (2015) Adaptabilidade, estabilidade e resistência a patógenos em genótipos de feijoeiro. Pesquisa Agropecuária Brasileira 50(10): 912–922. https://doi.org/10.1590/S0100-204X2015001000007

Burgess AJ, Masclaux-Daubresse C, Strittmatter G, Weber APM, Taylor SH et al (2022) Improving crop yield potential: underlying biological processes and future prospects. Food and Energy Security 12(1): e435. https://doi.org/10.1002/fes3.435

Criollo H, Lagos-Burbano TC, Fischer G, Mora L and Zamudio L (2014) Comportamiento de tres genotipos de uchuva (Physalis peruviana L.) bajo diferentes sistemas de poda. Revista Colombiana de Ciencias Hortícolas 8(1): 34–43.

Esan V, Oke G, Ogunbode T and Obisesan I (2023) AMMI and GGE biplot analyses of Bambara groundnut [Vigna subterranea (L.) Verdc.] for agronomic performances under three environmental conditions. Frontiers in Plant Science 13: 997429. https://doi.org/10.3389/fpls.2022.997429

Halimatus S and Alfian FH (2016) AMMI model for yield estimation in multi-environment trials: a comparison to BLUP. Agriculture and Agricultural Science Procedia 9: 163–169. https://doi.org/10.1016/j.aaspro.2016.02.113

Herrera A, Fischer G and Chacón M (2012) Agronomical evaluation of cape gooseberries (Physalis peruviana L.) from central and north-eastern Colombia. Agronomía Colombiana 30(1): 15–24. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-99652012000100003

Huanuqueño H, Zolla G and Jimenez J (2022) Selección de líneas estables y de alto rendimiento de maíz morado (Zea mays L.) var. reventón usando el índice de estabilidad de múltiples caracteres (MTSI). Scientia Agropecuaria 13(2): 125–133. https://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S2077-99172022000200002

Kang MS (2020) Genotype–environment interaction and stability analyses: an update. In: Kang MS (ed.). Quantitative genetics, genomics and plant breeding. Second edition. CABI, Wallingford, UK. 140–161 p.

Khan MMH, Rafii MY, Ramlee SI, Jusoh M and Mamun MA (2021) AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under multi-environment trials (METs). Scientific Reports 11: 22791. https://doi.org/10.1038/s41598-021-01411-2

Lagos-Burbano TC, Mejía-España DF, Arango-Bedoya O, Villaquirán-Samboni ZY et al (2020) Physicochemical properties of Colombian cape gooseberry hybrids in the selection of high-quality materials. Pesquisa Agropecuária Brasileira 55: e01905. https://www.researchgate.net/publication/348407257

MADR – Ministerio de Agricultura y Desarrollo Rural (2019) Red de información y comunicación del sector agropecuario Colombia. https://www.agronet.gov.co/estadistica/paginas/home.aspx?cod=1

Maharana J, Panda C and Jakhar P (2017) Genotype × environment interaction and stability analysis of kharif potato in Koraput region of Odisha, India. International Journal of Current Microbiology and Applied Sciences 6(5): 1159–1166. https://doi.org/10.20546/ijcmas.2017.605.126

Mayorga-Cubillos F, Argüelles-Cárdenas J, Rodríguez-Velásquez E, González-Almario C et al (2019) Yield and physicochemical quality of Physalis peruviana L. fruit related to resistance response against Fusarium oxysporum f.sp. physali. Agronomía Colombiana 37(2): 120–128. https://doi.org/10.15446/agron.colomb.v37n2.77550

Monroy Cárdenas DM (2020) Diferenciación tentativa por metabolómica no dirigida y caracterización fisicoquímica de tres genotipos de uchuva (Physalis peruviana L.) en tres localidades (Tesis de maestría). Universidad Nacional de Colombia, Bogotá, Colombia. 125 p. https://bffrepositorio.unal.edu.co/server/api/core/bitstreams/8cee0d81-352d-44b1-a271-01b46939084f/content

Neisse AC, Kirch JL and Hongyu K (2018) AMMI and GGE biplot for genotype × environment interaction: a medoid-based hierarchical cluster analysis approach for high-dimensional data. Biometrical Letters 55(2): 97–121. https://doi.org/10.2478/bile-2018-0008

Olivoto T, Lúcio ADC, Silva JAG, Marchioro VS, Souza VQ and Jost E (2019) Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal 111(6): 2949–2960. https://doi.org/10.2134/agronj2019.03.0220

Olivoto T, Nardino M, Meira D, Meier C et al (2021) Multi-trait selection for mean performance and stability in maize. Agronomy Journal 113(5): 3968–3974. https://doi.org/10.1002/agj2.20741

Oliveira TRA, Carvalho HWL, Oliveira GHF, Costa EFN et al (2019) Hybrid maize selection through GGE biplot analysis. Bragantia 78(2): 166–174. https://www.scielo.br/j/brag/a/3s6Dr5dxkQX8mqFddpPHhjm/?lang=en

Pour-Aboughadareh A, Khalili M, Poczai P and Olivoto T (2022) Stability indices to deciphering the genotype-by-environment interaction (GEI) Effect: An applicable review for use in plant breeding programs. Plants 11(3): 414. https://doi.org/10.3390/plants11030414

Quevedo García E, Sánchez García O and Veloza Sandoval CE (2015) Efecto del tutorado y distancias de siembra sobre el rendimiento de Physalis peruviana L. Revista U.D.C.A Actualidad & Divulgación Científica 18(1): 91–99. https://doi.org/10.31910/rudca.v18.n1.2015.457

Ramírez Gómez MM, Serralde Ordóñez DP, Núñez Zarantes VM et al (2023) Avances de investigación en nutrición, manejo y control de enfermedades en el cultivo de uchuva. Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA). https://doi.org/10.21930/agrosavia.analisis.7406979

Rathod S, Paul NC, Ponnaganti N et al (2025) Design of Experiments and Biometrical Analysis. Training manual of the twenty-one-day online training programme on “Advanced statistical and machine learning techniques for data analysis using open-source software for abiotic stress management in agriculture” (Vol. 2). ICAR-National Institute of Abiotic Stress Management. ISBN 978-81-985897-3-6.

Rodríguez Puertas D, Luna Mancilla LT, Campo Quesada JM, Guerrero Díaz GF et al (2021) Tipología de productores de uchuva en el departamento de Nariño, Colombia. Revista Mexicana de Ciencias Agrícolas 12(7): 1313–1318. https://doi.org/10.29312/remexca.v12i7.2766

Ruiz Gaitán LM, Castellanos González DC and Villamizar J (2018) El cultivo de la uchuva (Physalis peruviana L.). Revista Científica Agroecosistemas 6(1): 46–53. https://www.researchgate.net/publication/325108112

Shirzad A, Asghari A, Moharramnejad S, Shiri M and Ebadi A (2025) Integrated analysis of genotype by yield trait and genotype by environment interactions for selecting superior maize genotypes. Scientific Reports 15: 41372. https://doi.org/10.1038/s41598-025- 25254-3

Wardofa GA, Asnake D and Mohammed H (2019) GGE biplot analysis of genotype by environment interaction and grain yield stability of bread wheat genotypes in central Ethiopia. Journal of Plant Breeding and Genetics 7(2): 75–85. https://doi.org/10.33687/pbg.007.02.2846

Yan W (2014) Crop variety trials: data management and analysis. Wiley-Blackwell, Chichester. https://doi.org/10.1002/9781118688571

Yoseph T, Mekbib F, Amsalu B and Tadele Z (2022) Genotype by environment interaction and yield stability of drought tolerant mung bean (Vigna radiata L. Wilczek) genotypes in Ethiopia. Journal of Agriculture and Environmental Sciences 7(1): 43–62. https://boris-portal.unibe.ch/server/api/core/bitstreams/ac361021-fc6c-4ae7-9d93-5b3ebd34bd61/content

How to Cite

APA

Duarte-Alvarado, D. E., Lagos-Santander, L. K., Lagos-Burbano, T. C. & Benavides-Cardona, C. A. (2026). Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis. Revista Facultad Nacional de Agronomía Medellín, 79, e122607. https://doi.org/10.15446/rfnam.v79.122607

ACM

[1]
Duarte-Alvarado, D.E., Lagos-Santander, L.K., Lagos-Burbano, T.C. and Benavides-Cardona, C.A. 2026. Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis. Revista Facultad Nacional de Agronomía Medellín. 79, (Jan. 2026), e122607. DOI:https://doi.org/10.15446/rfnam.v79.122607.

ACS

(1)
Duarte-Alvarado, D. E.; Lagos-Santander, L. K.; Lagos-Burbano, T. C.; Benavides-Cardona, C. A. Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis. Rev. Fac. Nac. Agron. Medellín 2026, 79, e122607.

ABNT

DUARTE-ALVARADO, D. E.; LAGOS-SANTANDER, L. K.; LAGOS-BURBANO, T. C.; BENAVIDES-CARDONA, C. A. Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis. Revista Facultad Nacional de Agronomía Medellín, [S. l.], v. 79, p. e122607, 2026. DOI: 10.15446/rfnam.v79.122607. Disponível em: https://revistas.unal.edu.co/index.php/refame/article/view/122607. Acesso em: 15 jul. 2026.

Chicago

Duarte-Alvarado, David Esteban, Liz Katherine Lagos-Santander, Tulio Cesar Lagos-Burbano, and Carlos Andres Benavides-Cardona. 2026. “Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis”. Revista Facultad Nacional De Agronomía Medellín 79 (January):e122607. https://doi.org/10.15446/rfnam.v79.122607.

Harvard

Duarte-Alvarado, D. E., Lagos-Santander, L. K., Lagos-Burbano, T. C. and Benavides-Cardona, C. A. (2026) “Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis”, Revista Facultad Nacional de Agronomía Medellín, 79, p. e122607. doi: 10.15446/rfnam.v79.122607.

IEEE

[1]
D. E. Duarte-Alvarado, L. K. Lagos-Santander, T. C. Lagos-Burbano, and C. A. Benavides-Cardona, “Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis”, Rev. Fac. Nac. Agron. Medellín, vol. 79, p. e122607, Jan. 2026.

MLA

Duarte-Alvarado, D. E., L. K. Lagos-Santander, T. C. Lagos-Burbano, and C. A. Benavides-Cardona. “Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis”. Revista Facultad Nacional de Agronomía Medellín, vol. 79, Jan. 2026, p. e122607, doi:10.15446/rfnam.v79.122607.

Turabian

Duarte-Alvarado, David Esteban, Liz Katherine Lagos-Santander, Tulio Cesar Lagos-Burbano, and Carlos Andres Benavides-Cardona. “Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis”. Revista Facultad Nacional de Agronomía Medellín 79 (January 15, 2026): e122607. Accessed July 15, 2026. https://revistas.unal.edu.co/index.php/refame/article/view/122607.

Vancouver

1.
Duarte-Alvarado DE, Lagos-Santander LK, Lagos-Burbano TC, Benavides-Cardona CA. Yield stability of Physalis peruviana L. genotypes through multi-environment trial analysis. Rev. Fac. Nac. Agron. Medellín [Internet]. 2026 Jan. 15 [cited 2026 Jul. 15];79:e122607. Available from: https://revistas.unal.edu.co/index.php/refame/article/view/122607

Download Citation

CrossRef Cited-by

CrossRef citations0

Dimensions

PlumX

Article abstract page views

68

Downloads

Download data is not yet available.