Development of preliminary multielement norms for the Diagnosis and Recommendation Integrated System (DRIS) in sweet potato, Ipomoea batatas (L.) Lam., in Cañete Province, Peru

Autores/as

  • Ronald Alexis Cortez-Lázaro Universidad Nacional de Cañete, San Luis, Cañete, Lima, Perú.
  • Juan Waldir Mendoza-Cortez Universidad Nacional Agraria La Molina, La Molina, Lima, Perú.
  • Sady Javier García-Bendezú Universidad Nacional Agraria La Molina, La Molina, Lima, Perú.
  • Andrés Virgilio Casas-Díaz Universidad Nacional Agraria La Molina, La Molina, Lima, Perú.
  • Anthony Apolinario Cortez-Lázaro Universidad Nacional José Faustino Sánchez Carrión, Huacho, Huaura, Lima, Perú.
  • Roberto Coaquira-Incacari Universidad Nacional de Cañete, San Luis, Cañete, Lima, Perú.

DOI:

https://doi.org/10.17268/sci.agropecu.2026.52

Palabras clave:

Foliar analysis, Mineral nutrition, Nutrient balance, Nutritional assessment, Nutrient interactions

Resumen

Nutritional balance is a key factor influencing sweet potato productivity, particularly under intensive production systems where multiple nutrient interactions may affect crop performance. Reliable foliar diagnostic approaches are therefore needed to assess these interactions and identify nutritional imbalances associated with yield. This study aimed to develop Diagnosis and Recommendation Integrated System (DRIS) norms and characterize multielement nutritional balance in sweet potato using a yield-linked foliar database from Cañete, Lima, Perú. The database comprised 160 foliar samples collected from commercial fields and experimental plots, each associated with a corresponding yield record and analyzed for 13 elements (N, P, K, Ca, Mg, S, Na, Cl, Cu, Zn, Mn, Fe, and B). Following complementary univariate and multivariate screening, 120 observations were retained and stratified by yield, with the high-yield population used as the reference population for norm development. Of the 156 direct and inverse elemental relationships evaluated, 72 were retained as DRIS norms after normality screening and subsequent selection based on the variance-ratio criterion. The mean DRIS profile of the low-yield population showed pronounced differences in the direction and magnitude of the elemental indices, with Zn showing the largest negative mean index (−69.76) and Ca the largest positive mean index (+55.49). Associations between individual DRIS indices and yield varied in both direction and magnitude. The resulting norms provide a multielement reference framework for characterizing nutritional balance under the production conditions represented by the Cañete dataset. However, the DRIS indices should be interpreted as indicators of relative nutritional imbalance rather than as experimentally validated nutrient requirements. Therefore, the diagnostic accuracy and transferability of these norms should be evaluated using independent datasets and nutrient-response experiments before they are applied to support nutrient-management recommendations.

Referencias

Agbangba, E. C., Yalinkpon, F., Sossa, E. L., Ehnon Gongnet, E., & Glèlè Kakaï, R. (2024). A simulation study on the comparison of Diagnosis and Recommendation Integrated System (DRIS), Modified-DRIS (M-DRIS), and Compositional Nutrient Diagnosis (CND) for pineapple nutrient diagnosis. Agronomy Research, 22(Special Issue 3), 1380–1404. https://doi.org/10.15159/AR.24.101

Bataglia, O. C., Quaggio, J. A., Santos, W. R. dos, & Abreu, M. F. de. (2004). Diagnose nutricional do cafeeiro pelo dris variando-se a constante de sensibilidade dos nutrientes de acordo com a intensidade e freqüência de resposta na produção. Bragantia, 63(2), 253–263. https://doi.org/10.1590/S0006-87052004000200010

Beaufils, E. R., & Sumner, M. E. (1977). Effect of time of sampling on the diagnosis of the N, P, K, Ca, and Mg requirements of sugarcane by the DRIS approach. Proceedings of the South African Sugar Technologists’ Association, 62–67.

Cavalcanti, A. C., Partelli, F. L., Gontijo, I., Dias, J. R. M., Freitas, M. S. M., & de Carvalho, A. J. C. (2021). Establishment of leaf nutrient patterns for the nutritional diagnosis of Urochloa brizantha pastures in two seasons. Acta Scientiarum - Agronomy, 43, e50359. https://doi.org/10.4025/actasciagron.v43i1.50359

Conceição, M. P. da, Rozane, D. E., Pereira, E. F., Oliveira, C. T. de, Lima, J. D., & Lima Neto, A. J. de. (2024). Nutritional reference values using the DRIS method and sample size for peach palm production. Revista Brasileira de Ciencia do Solo, 48, e0230076. https://doi.org/10.36783/18069657rbcs20230076

de Souza, H. A., Rozane, D. E., Vieira, P. F. de M. J., Sagrilo, E., Leite, L. F. C., de Brito, L. C. R., Conceição, M. P., & Ferreira, A. C. M. (2023). Accuracy of DRIS and CND methods and nutrient sufficiency ranges for soybean crops in the Northeast of Brazil. Acta Scientiarum - Agronomy, 45, e59006. https://doi.org/10.4025/actasciagron.v45i1.59006

Escobedo, P. M. T., & Salas, P. M. J. A. (2008). P. Ch. Mahalanobis y las aplicaciones de su distancia estadística. Culcyt, 5(27), 13–20.

Gimenez, M., Nieves, M., Gimeno, H., Martínez, J., & Martínez-Nicolás, J. J. (2021). Nutritional diagnosis norms for three olive tree cultivars in superhigh-density orchards. International Journal of Agriculture and Natural Resources, 48(1), 34–44. https://doi.org/10.7764/ijanr.v48i1.2227

He, W., Li, J., Lu, Y., Chen, S., Deng, L., Xu, X., Zhu, Y., Jin, M., Liu, Y., Lu, G., & Lv, Z. (2023). Development of critical K dilution curves for diagnosing sweetpotato K status. Frontiers in Plant Science, 14, 1124328. https://doi.org/10.3389/fpls.2023.1124328

Liu, B. K., Xv, B. J., Si, C. C., Shi, W. Q., Ding, G. Z., Tang, L. X., Xv, M., Shi, C. Y., & Liu, H. J. (2023). Effect of potassium fertilization on storage root number, yield, and appearance quality of sweet potato (Ipomoea batatas L.). Frontiers in Plant Science, 14, 1298739. https://doi.org/10.3389/fpls.2023.1298739

MIDAGRI. (2024). Análisis de la tendencia comercial del camote. Ministerio de Desarrollo Agrario y Riego.

Parent, L. E., & Dafir, M. (1992). A theoretical concept of compositional nutrient diagnosis. Journal of the American Society for Horticultural Science, 117(2), 239-242. https://doi.org/10.21273/JASHS.117.2.239

Pereira, A., Marchetti, M. E., Bungenstab, D. J., Gonçalves da Silva, M. A., Pereira Serra, R., Nunes Guimarães, F. C., Do Amaral Conrad, V., & de Morais, H. S. (2013). Diagnosis and Recommendation Integrated System (DRIS) to assess the nutritional state of plants. In M. D. Matovic (Ed.), Biomass now - Sustainable growth and use (pp. 129-146). IntechOpen. https://doi.org/10.5772/54576

Ramakrishna, A., Bailey, J. S., & Kirchhof, G. (2009). A preliminary diagnosis and recommendation integrated system (DRIS) model for diagnosing the nutrient status of sweet potato (Ipomoea batatas). Plant and Soil, 316(1–2), 107–116. https://doi.org/10.1007/s11104-008-9763-5

Reis Junior, R. dos A., & Monnerat, P. H. (2003). Norms establishment of the Diagnosis and Recommendation Integrated System (DRIS) for nutritional diagnosis of sugarcane. Pesquisa Agropecuária Brasileira, 38(2), 277–282. https://doi.org/10.1590/S0100-204X2003000200015

Shehu, B. M., Garba, I. I., Jibrin, J. M., Kamara, A. Y., Adam, A. M., Craufurd, P., Aliyu, K. T., Rurinda, J., & Merckx, R. (2023). Compositional nutrient diagnosis and associated yield predictions in maize: A case study in the northern Guinea savanna of Nigeria. Soil Science Society of America Journal, 87(1), 63–81. https://doi.org/10.1002/saj2.20472

Shu, X., Jin, M., Wang, S., Xu, X., Deng, L., Zhang, Z., Zhao, X., Yu, J., Zhu, Y., Lu, G., & Lv, Z. (2024). The effect of nitrogen and potassium interaction on the leaf physiological characteristics, yield, and quality of sweet potato. Agronomy, 14(10), 2319. https://doi.org/10.3390/agronomy14102319

Tanaka, M., Ishiguro, K., Oki, T., & Okuno, S. (2017). Functional components in sweetpotato and their genetic improvement. Breeding Science, 67(1), 52–61. https://doi.org/10.1270/jsbbs.16125

Teixeira, M. B., Donato, S. L. R., Da Silva, J. A., & Rodrigues Onato, P. E. (2019). Establishment of DRIS norms for cactus pear grown under organic fertilization in semiarid conditions. Revista Caatinga, 32(4), 952–959. https://doi.org/10.1590/1983-21252019v32n411rc

Thornthwaite, C. W., & Mather, J. R. (1955). The water balance. Publications in Climatology, 8(1), 1–104. Drexel Institute of Technology, Laboratory of Climatology.

Villaseñor-Ortiz, D., de Mello Prado, R., Pereira da Silva, G., & Lata-Tenesaca, L. F. (2022). Applicability of DRIS in bananas based on the accuracy of nutritional diagnoses for nitrogen and potassium. Scientific Reports, 12(1), 18718. https://doi.org/10.1038/s41598-022-22554-w

Walworth, J. L., & Sumner, M. E. (1987). The Diagnosis and Recommendation Integrated System DRIS. In B. A. Stewart (Ed.), Advances in Soil Science (Vol. 6, pp. 149-188). Springer. https://doi.org/10.1007/978-1-4612-4682-4_4

Zhao, X., Wang, S., Qiu, X., Liu, J., Bai, J., Zhang, Z., Xu, X., Zhu, Y., Lu, G., & Lv, Z. (2026). The development of a N/K ratio model for diagnosing the nitrogen–potassium balance of sweet potato. Agriculture, 16(8), 836. https://doi.org/10.3390/agriculture16080836

Zhao, Z., Bai, B., Qie, Z., He, Z., Zhang, S., Zhang, X., Li, Y., & Hou, W. (2025). Effects of nitrogen, phosphorus, and potassium fertilizers on storage root yield, nutrient use efficiency, and soil nutrient balance of sweetpotato. BMC Plant Biology, 25(1). https://doi.org/10.1186/s12870-025-07503-9

Descargas

Publicado

2026-09-24

Número

Sección

Artículos originales

Cómo citar

Cortez-Lázaro, R. A., Mendoza-Cortez, J. W., García-Bendezú, S. J., Casas-Díaz, A. V., Cortez-Lázaro, A. A., & Coaquira-Incacari, R. (2026). Development of preliminary multielement norms for the Diagnosis and Recommendation Integrated System (DRIS) in sweet potato, Ipomoea batatas (L.) Lam., in Cañete Province, Peru . Scientia Agropecuaria, 17(3), 777-787. https://doi.org/10.17268/sci.agropecu.2026.52