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Manejo agronómico del maíz blanco duro INIA 610 NUTRIMAIZ
(Instituto Nacional de Innovación Agraria (INIA), 2026-04) Campos Amasifuen, Héctor Manuel
La variedad INIA 610 – Nutrimaíz es importante para la alimentación humana y animal porque contiene alta calidad de proteína, se puede aprovechar en tres estados de desarrollo de la planta: consumo tierno, choclo y grano seco. El manejo del maíz blanco es similar al maíz amarillo; es decir, las condiciones de producción y los métodos de cultivo son idénticos.
Manejo agronómico del maíz amarillo duro INIA 616-Ucayali
(Instituto Nacional de Innovación Agraria (INIA), 2026-04) Campos Amasifuen, Héctor Manuel
El maíz amarillo duro es el cultivo de mayor importancia en de la canasta alimenticia básica de la población ucayalina, la rentabilidad aumenta cuando se utilizan cultivares mejorados en condiciones favorables y manejo adecuado. La variedad INIA 616 - Ucayali se ha generado en la Estación Experimental Agraria Pucallpa, principalmente para condiciones de suelos de restinga.
Desempeño agronómico de líneas de frijol común (Phaseolus vulgaris L.) bajo abonamiento orgánico en condiciones de casa malla, Chincha, Perú
(Decanato de Agronomía, Universidad Centroccidental Lisandro Alvarado (UCLA), Venezuela, 2026-05-01) Camargo Cobeñas, Marcos Antonio; Almidon Ramirez, Karen Karina; Rojas Meza, María Elena; Aybar Peve, Leandro Joel; Terán Rojas, José Alfonso
El frijol común (Phaseolus vulgaris L.) es un cultivo estratégico por su valor nutricional y su asociación con microorganismos fijadores de nitrógeno, aunque en regiones como Chincha (Perú) los rendimientos permanecen bajos, lo que demanda alternativas de manejo. Se evaluó la respuesta agronómica de las líneas Larán Mejorado y Waf 78/20 a la aplicación de compost y BlackSoil en dosis de 8 y 16 % (p/p), bajo un Diseño en Bloques Completos al Azar (DBCA) con arreglo factorial 2 × 2 × 2 y dos testigos, con cuatro bloques en condiciones de casa malla. Se evaluaron 12 variables agromorfológicas y la densidad aparente del sustrato, analizadas mediante efectos principales e interacciones, pruebas paramétricas y no paramétricas, coeficientes de correlación de Pearson y análisis de componentes principales (ACP). Se obtuvo que el factor genético fue la principal fuente de variación. Larán Mejorado destacó por una respuesta más uniforme, mayor precocidad y mejores componentes de rendimiento, favorecidos por la aplicación de compost; mientras que Waf 78/20 se asoció con mayor crecimiento vegetativo y mayor sensibilidad a cambios en la densidad aparente del sustrato; en esta línea, la aplicación de BlackSoil se relacionó con ciclos fenológicos más prolongados y menor peso de semilla. Los hallazgos evidencian que la genética determina en mayor medida la respuesta al abonamiento orgánico, resaltando la importancia del genotipo en prácticas de manejo; sin embargo, estos resultados deben validarse en condiciones de campo y múltiples ambientes.
Agronomic variables outperform multispectral indices for individual plant yield prediction in Andean quinoa
(Elsevier B.V., 2026-04-18) Pizarro Carcausto, Samuel Edwin; García Seguil, Erika Janina; Gavino, Esthefany; Requena Rojas, Edilson Jimmy; Ortega Quispe, Kevin Abner; Cccopi Trucios, Dennis
Accurate pre-harvest yield estimation is essential for decision-making in high-altitude agriculture. This study evaluated agronomic and multispectral UAV variables for near-harvest prediction of individual quinoa grain weight, with data collected across six phenological stages to identify when predictors achieve reliable performance, under Andean conditions. A total of 374 plants were monitored across six phenological stages at Santa Ana Experimental Station (Huancayo, Peru, 3280 m a.s.l.) during 2024. OLS, Random Forest, Support Vector Machine, and Neural Network models were trained using agronomic-only (AGRO), spectral-only (IND), and combined (COMP) predictor sets, evaluated through 5-fold cross-validation reporting mean ± standard deviation. Agronomic and combined models achieved moderate performance (R² = 0.22–0.25, RPD = 1.10–1.15), suitable for relative plant ranking in breeding programs, while spectral-only models failed across all algorithms (R² ≤ 0.044, CCC ≤ 0.080), constrained by saturation, phenological decoupling, and canopy heterogeneity. Variable importance analysis confirmed that late-season structural traits dominated predictions, while spectral indices contributed marginally despite including red-edge bands. These results challenge spectral-only approaches for individual plant phenotyping in heterogeneous canopies, demonstrating that integrating simple ground measurements with UAV spectral data is essential for reliable quinoa yield estimation.
Electrophoretic Profiles of Alpaca Seminal Plasma Proteins and Their Association With Sperm Quality Parameters During Cryopreservation Processes
(John Wiley & Sons Ltd., 2026-03-24) Guillen Palomino, Crissthel Yverlin; Mujica Lengua, Fidel Rodolfo; Contreras Huamaní, Mijail; Carretero , María Ignacia; Rueda Alfonso, Fabian Leonardo; Orellana Berrocal, Harumi
The aim of the study was to characterize the electrophoretic profiles of alpaca seminal plasma (SP) proteins and establish their association with sperm quality parameters at different cryopreservation stages. Sperm quality was assessed in raw, cooled, and thawed semen from 128 ejaculates collected from 16 Huacaya alpacas, and SP proteins were analysed by SDS-PAGE in raw samples. Statistical associations were determined using Spearman's rank correlation (p ≤ 0.05). Twenty-three protein bands were identified: 21 bands ranging from 9.23 to 138.38 kDa, and 2 below 6.5 kDa. Notably, the 21.03 kDa protein was absent in six males, five of whom also lacked the 18.88 kDa band. These individuals exhibited superior post-thaw sperm quality, particularly higher motility. The 21.03 kDa protein showed a negative correlation (p ≤ 0.05) with sperm motility and membrane function in raw, cooled, and thawed semen, and a positive correlation with acrosome integrity in thawed semen. Similarly, the 18.88 kDa protein showed a negative correlation with sperm motility and membrane function, but a positive correlation with acrosome integrity in thawed semen (p ≤ 0.05). In conclusion, these findings suggest that specific SP proteins may serve as potential biomarkers for sperm quality and cryotolerance in alpacas, reflecting individual variability in response to cryopreservation.
Parámetros alométricos y contenido de betalaínas en accesiones de ayrampo (Airampoa soehrensii) del banco de germoplasma del INIA, Perú
(Universidad Centroccidental Lisandro Alvarado (UCLA), 2026-05-01) Dadther Huaman, Hans Adams; Hilari Hilari, Mari Carmen; Gonzales Hancco, Haydee; Jimenez Paye, Abraham; Calla Cornejo, Nancy Vanessa; Pacheco Lizarraga, Gonzalo Antonio
El ayrampo es una especie nativa altoandina con alto valor cultural, nutricional y potencial agroindustrial por su contenido de compuestos funcionales, como las betalaínas; no obstante, a pesar de su relevancia, existe escasa información científica sobre la caracterización físico-química de sus frutos. Se evaluaron parámetros alométricos, colorimétricos y contenido de betalaínas en 13 accesiones de ayrampo conservadas en el Banco de Germoplasma del INIA, en el Centro Experimental Santa Rita (Arequipa), durante la campaña agrícola 2024-2025. Se analizaron el peso de fruto, el peso fresco de pulpa y semilla, el peso seco de pulpa y semilla, la materia seca, coordenadas de color (L*, a*, b*) y la concentración de betacianinas y betaxantinas mediante espectrofotometría UV-VIS. Los datos se sometieron a análisis de varianza, y comparaciones múltiples mediante la prueba de Tukey, análisis de correlación de Pearson, componentes principales y agrupamiento jerárquico. Se evidenció una alta variabilidad fenotípica entre accesiones. El PER018775 presentó el mayor peso seco de pulpa y semilla (2,99 g); el PER018759 destacó en materia seca (38,94%); el PER018762 mostró los valores más altos de los componentes a* y b*; y el PER018767 tuvo la mayor concentración de betalaínas (158,78 mg·100 g⁻¹ de materia seca). El análisis de agrupamiento jerárquico clasificó a las accesiones en tres clústeres diferenciados por rendimiento, colorimetría y contenido de betalaínas. El ayrampo mostró un alto potencial como fuente de pigmentos naturales, especialmente betalaínas, con potencial aplicación en la industria alimentaria y la farmacéutica.
Vis-NIR spectroscopy and machine learning for prediction of soil fertility indicators and fertilizer recommendation in Andean highland and rainforest agroecosystems
(MDPI, 2026-04-26) Pizarro Carcausto, Samuel Edwin; Ccopi Trucios, Dennis; Ortega Quispe, Kevin Abner; Contreras Pino, Duglas Lenin; Ñaupari, Javier; Cano, Deyvis; Patricio Rosales , Solanch Rosy; Loayza, Hildo; Apolo Apolo, Orly Enrique
This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R2 > 0.70), while pH and texture fractions showed moderate performance (R2 = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K2O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients.
Indirect monitoring of heterogeneous tropical agroforestry systems using active and passive remote sensing
(Elsevier B.V., 2026-03-11) Sánchez Fuentes, Teiser; Gómez Fernández, Darwin; Fernandez Jibaja, Jorge Antonio; Oblitas Troyes, Jhon Franklin; Chuquibala Checan, Beimer; Tafur Culqui, Josué; Quichua Baldeon, Rosalia; Taboada Mitma, Víctor Hugo; Tineo Flores, Daniel; Goñas Goñas, Malluri; Atalaya Marin, Nilton
Monitoring agroforestry systems remains challenging due to canopy heterogeneity and the coexistence of species with contrasting dynamics. While field-based methods offer high accuracy, they are inefficient for rapid and multitemporal structural assessments. This study integrated LiDAR and multispectral data collected using a Matrice 350 RTK equipped with a Zenmuse L2 sensor and a RedEdge-P camera. Raw LiDAR data were processed in DJI Terra v4.1 and subsequently pre-processed and corrected in TerraSolid v23.011, whereas multispectral products were generated in Agisoft Metashape Professional v2.2.1. The derived metrics indicated greater growth in System A, driven by fast-growing species, whereas System B showed an overall reduction with slight increases in the upper percentiles. In addition, MSAVI and MTVI2 were sensitive to canopy structure, while GNDVI and NDRE responded to foliage content. The agreement analysis revealed a slight bias (0.09 m) toward height overestimation by LiDAR compared to the hypsometer, with no apparent proportional error. This approach provides a replicable framework for multitemporal monitoring of structural and physiological changes in tropical vegetation, with potential for regional scaling and application in sustainable forest system management.
Análisis de la cadena de valor del Camu Camu en Ucayali, Perú
(CGIAR / Centro Internacional de Agricultura Tropical (CIAT), 2026-03) Ramírez, Shallinny; Charry, Andrés; Grandez, Sergio; Córdova, Carla; Córdova, Witle; Velazco, Ena; Antonio, Carmen; Amasifuen, Rafael; Saboya, Pablo; Ahuanari, Gloria; Barriga, César; Ehrstrom, Dag; Perez, Diana; Mori, Miriam; Zegarra, Fernando; Arévalo, Rister; Salazar, Javier; Piñas, Arturo; Dávila, Lorena; De Souza, Rodman; Vásquez Macedo, Miguel; Iman Correa, Sixto Alfredo; Pérez Arirama, Jorge Enrique; Pinedo, Mario; Bravo, John; Panduro, Nadia; Leandro, Caleb; Asencios, Vitelio; Sanchez, José; Truyenque, Ronald; Solano, Claudia; Soto, Aldo; Torres, Nelson; Ramos, Carmen; Guerra, Bladimir; Villachica, Hugo; Jones, Andrew
El presente documento desarrolla un análisis integral de la cadena de valor del camu camu en el departamento de Ucayali, Perú, en el marco del programa de ciencias de Paisajes Multifuncionales del CGIAR. El estudio se concibe como una línea base técnica y estratégica orientada a comprender el estado actual de la cadena, sus principales dinámicas productivas, comerciales e institucionales, así como los factores que condicionan su competitividad y sostenibilidad en mercados nacionales e internacionales. Al incorporar la participación de múltiples actores de la cadena, tanto como fuentes de información y coautores, como especialmente en calidad de protagonistas del proceso de validación de resultados y de la definición y priorización de acciones, este informe se constituye en un referente para alinear el conocimiento existente y fortalecer la orientación estratégica y la toma de decisiones de la cadena en los próximos años.
Soil spatial variability in high-yield Peruvian Amazon coffee: a geostatistical approach for precision fertilization
(Frontiers Media SA, 2025-12-18) Mejía Maita, Sharon Yahaira; Quispe Matos, Kenyi Rolando; Díaz Chuquizuta, Henry; Rengifo Sánchez, Raihil Rabindranath; Mercado Chinchay, Ruth Lizbeth; Cuevas Gimenez, Juan Pablo; Solórzano Acosta, Richard Andi
Fertilization practices in coffee plantations often overlook the spatial variability of soils, particularly in mountainous regions with acidic conditions. Although geostatistics has been used to map nutrient distributions, its integration with multivariate analysis to identify differentiated fertilization zones in coffee systems remains limited. This study evaluated the influence of soil properties, altitude, and crop age on coffee yield by combining principal component analysis (PCA) and ordinary kriging to design site-specific fertilization strategies. A total of 70 soil samples were collected from three districts of the Peruvian high jungle (San Martín and Amazonas), measuring physical and chemical properties, altitude, and crop age. The following analyses were applied: (1) Spearman correlations to assess associations with yield, (2) PCA to identify fertility gradients, and (3) geostatistical models with cross-validation. The PCA identified two main gradients: PC1 (32.41% of variance) associated with cation exchange capacity (CEC) and organic matter, and PC2 (17.88%) associated with the availability of K and P and crop age. Cross-validation confirmed high accuracy in the spatial prediction of available P and K across the three study areas. Kriging maps revealed zones with high available K (>150 mg kg⁻¹) and P (>20 mg kg⁻¹) associated with yields >1.5 t ha⁻¹. The integration of PCA and geostatistics enabled the delineation of management zones with differentiated nutrient requirements, reducing fertilization needs by up to 30% in areas with high fertility potential (e.g., Alto Saposoa). Overall, the results provide a solid methodological basis for implementing precision fertilization strategies in tropical coffee systems, promoting more efficient nutrient use and greater production sustainability.