Examinando por Materia "Correlation"

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Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
(Walter de Gruyter GmbH, 2024-06-18) Yalta Vela, Juan; Silva Valqui, Gelver; Ampuero Trigoso, Gustavo; Quispe Ccasa, Hurley Abel; Saucedo Uriarte, José Américo
The objective of the study was to evaluate the effect of pruning height (PH) and organic fertilization dose (FD) on the morphology and productive characteristics of Moringa oleifera Lam. We germinated seeds collected from 10-year-old shrubs, and 2-month-old seedlings were transplanted in the final field. We used a two-factor design of PH (PH1:0.4, PH2:0.8, and PH3:1.2m from the ground) and FD (FD0:0, FD1:500, FD2:750, and FD3:1,000 g of decomposing goat manure). We carried out an initial pruning 4 months after transplanting and the harvests every 45 days. After three consecutive harvests, PH3 improved N° branches (12.53 ± 3.09) and dry matter (21.98 ± 1.30%), but PH1 showed greater stem lengths (1.65 ± 0.24 m) (p < 0.01). There was no difference in the stems and leaf weights between PH2 and PH3, and no trait varied according to FD (p > 0.05). The PH × FD interaction can improve the plant diameter (p < 0.01) and dry matter (p < 0.05) with PH2 (56.79 ± 3.71 mm) and PH3 (23.20 ± 1.04%) from FD1. We found an increasing trend in N° branches, plant diameter (p < 0.01), and the leaf–stem ratio. However, in the third harvest, the biomass production trend was downward for a short period for an adequate replacement of nutrients from the incorporated organic fertilizer. It is recommended to prune M. oleifera at 1.2m from the ground to stimulate greater biomass and maintain the leaf–stem ratio throughout the evaluated harvests and apply more than 500 g of goat manure after each harvest to restore the nutrients extracted from the soil.
Evaluación de siete variedades de alfalfa para el mejoramiento alimenticio de la ganadería en Ceja de Selva, Amazonas
(Dirección de Desarrollo Tecnológico Agrario. Instituto Nacional de Innovación Agraria, 2020-11-13) Vásquez Pérez, Héctor Vladimir; Carrasco Chilón, William Leoncio
El objetivo fue evaluar siete variedades de alfalfa: Beacon, WL-625 HQ, WL-440, WL-330, WL-450, WL-350 y Rebound para el mejoramiento alimenticio de la ganadería en el distrito la Florida, Amazonas. Se instalaron parcelas de 6,5 m2 por cada tratamiento bajo un Diseño en Bloques Completamente Randomizado con submuestreo; la evaluación fue durante diez cortes. Se evaluaron variables como altura de planta (AP), Materia Seca (MS), Diámetro Basal (DB), Forraje Verde (FV) y relación hoja /tallo. Se realizó un análisis de varianza y prueba de comparaciones múltiples de Duncan para ver las diferencias significativas entre tratamientos; además, se calculó el coeficiente de correlación que permitió medir el grado de asociación de las variables AP, FV con DB y FV con MS, haciendo uso del software Statistical Analysis System Versión 8. Los resultados encontrados muestran que la mayor altura fue para la variedad WL-625 HQ (53,43 ± 6 cm), mayor producción de FV para la variedad Beacon con 220 t /ha/año y la variedad con mayor rendimiento de MS fue WL- 440 con 56 t de MS /ha/año. El diámetro basal fue diferente entre variedades. La variedad WL-450 mostró la mayor relación hoja/tallo (1,54 cm) con 58% de hojas y 42% de tallos. La mayor correlación para altura de planta se presentó en las variedades WL-625 HQ y Beacon para todos los periodos fenológicos. En conclusión, las variedades que representan una alternativa para el mejoramiento alimenticio de la ganadería en Ceja de Selva son Beacon y WL-625 HQ.
Predictive modeling of honey yield in rural apiaries: insight from Chachapoyas, Amazonas, Peru
(MDPI, 2025-11-18) Briceño Mendoza, Yander Mavila; Saucedo Uriarte, José Américo; Quiñones Huatangari, Lenin; Gaslac Gomez, Jhoyd B.; Quispe Ccasa, Hurley Abel; Cayo Colca, I.S.
Honey production is influenced by multiple factors, including climatic conditions, hive management practices, and harvest scheduling. This study evaluated the predictive capacity of statistical modeling techniques using data mining algorithms (MARS, CHAID, CART, and Exhaustive) and artificial neural network algorithms (Multilayer Perceptron, MLP) to estimate honey yields in apiaries located in northeastern Peru. A structured survey was conducted with sixty-nine beekeepers across nineteen districts in the Chachapoyas province. Variables included beekeeper experience, instruction, hive count, visit frequency, harvest frequency, additional income-generating activities, and geographic location. Descriptive statistics, non-parametric tests, Spearman correlations, and exploratory factor analysis were applied to identify latent structures. A linear mixed-effects model was used to assess the combined influence of predictors on honey production, with district included as a random effect. Results indicated that hive number, beekeeping experience, harvest frequency, and exclusive engagement in apiculture were statistically associated with increased honey yields. The model explained a substantial proportion of variance, supporting the integration of technical and socio-demographic variables in production forecasting. These findings demonstrate the utility of predictive modeling for informing hive management strategies and improving the operational efficiency of small-scale beekeeping systems in Andean regions.