Repositorio Institucional INIA (Inst. Nacional de Innovacion Agraria)
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Native strains T. longibrachiatum UCF17-M4 and Trichoderma sp. UCPF2 reduce Cd uptake in cacao CCN51 under controlled conditions
No full textThe cacao trade and export industry has been impacted by cadmium (Cd2+) accumulation in soils, as the metal is absorbed by plants and transferred to the tissues. Consequently, cacao beans and their derivatives can become contaminated, sometimes exceeding permissible limits. In this study, the capacity of native Trichoderma strains to reduce Cd accumulation in cacao was evaluated. Twelve Trichoderma strains were analyzed to assess their cadmium removal capacity through in vitro assays and their ability to reduce Cd concentration in cacao plants under controlled in vivo conditions. The in vitro results showed that several Trichoderma strains could remove cadmium and accumulate it in their biomass. However, this process is complex as it depends on metal concentration and environmental conditions. Notably, T. afroharzianum UCF18-M1 and CP24-6 exhibited high removal efficiencies at 100 ppm (61.79 ± 2.98% and 57.93 ± 4.14%, respectively). In contrast, the in vivo assays revealed that, contrary to expectations, some strains—including those with the highest removal efficiency—stimulated Cd uptake in plants, even at toxic levels, such as T. orientale BLPF1-C1. However, T. longibrachiatum UCF17-M4 and Trichoderma sp. UCPF2-C1 significantly reduced Cd accumulation in the stem. These findings highlight the potential of these strains to mitigate Cd contamination in cacao
Influence of agroclimatic factors on the efficiency of multi-ovulation in cattle in the Peruvian tropics
No full textIntroduction: Agroclimatic conditions are key determinants in the development of animal production and reproduction, with specific breed differences in vulnerability to environmental stress. This research aims to determine the influence of agroclimatic factors on the efficiency of multi-ovulation in cattle in the Peruvian tropics.
Methods: The study was conducted at the “El Porvenir” Agricultural Experimental Station (EEA) of the National Institute of Agricultural Innovation (INIA), located in the district of Juan Guerra, province and department of San Martín, Peru. Throughout a year, four collections of structures were made from 12 Bos indicus donor cows from the genetic nucleus of the PROMEG Tropical project every 2 months under intensive breeding conditions. The cows were classified according to their production: milk (five individuals of the Gyr breed and two of the Guzerat breed) and meat (two individuals of the Nelore breed and three of the Brahman breed), with ages of 3 and 4 years, selected based on specific criteria: regular estrous cycles, no deformities or reproductive problems, and certified pedigree registration. During each collection protocol, the number of viable structures (blastocysts and morulas), non-viable structures (unfertilized oocytes-UFO and degenerated), and agroclimatic factors [temperature (°C), relative humidity (%), precipitation (mm), wind speed (m/s), and the Temperature-Humidity Index (THI)] were evaluated at three times (6 a.m., 1 p.m., and 6 p.m.). A longitudinal experimental design was used for the analysis. Statistical tests were applied, including ANOVA and post-hoc tests (Tukey's Test), to assess the significance of differences between variables, such as the humidity index and temperature in relation to the production of viable structures and non-viable structures. Data visualization was achieved using R Studio libraries, including ggplot2, factoextra, and FactoMineR.
Results: The analyses highlight the influence of the interaction between humidity and temperature, resulting in THI on bovine stress, revealing complex interactions that primarily affect embryo production. Stress peaks, especially under adverse conditions, were observed to significantly impact animal health.
Discussion: This response to stress can affect both overall well-being and productive performance. Additionally, it should be noted that this impact varies according to the adaptability and resilience of the breed. Therefore, it is suggested to continue this study, as the literature on this topic is limited, and to conduct further research to optimize the well-being and productivity of livestock
Effect of silvopastoral systems with integrated forest species from the Peruvian tropics on the soil chemical properties
No full textVegetation and trees in Amazonian ecosystems influence soil chemistry. Understanding these effects is essential for selecting the right tree species in silvopastoral systems to promote soil conservation. The objective of the study was to evaluate the effect of different silvopastoral systems (SPS) on the soil chemical properties within a livestock system. The research was developed at the Estación Experimental Agraria El Porvenir in San Martín Department, Peru, which is characterized by a humid tropical climate, with an annual temperature of 33°C, humidity levels between 70 and 80%, and precipitation of 1,225 mm. Six SPS [Bolaina (Guazuma crinita Mart.), Teak (Tectona grandis L.), an arboretum, Pucaquiro (Sickingia tinctoria Schult.), Quinilla (Manilkara bidentata A. DC.), and a natural forest - NF] and two sampling depths were compared, with two replicas for each. The main effect showed that the Quinilla SPS was higher in pH (p < 0.05), while the Quinilla SPS, Pucaquiro SPS, and NF stood out in K⁺ and Ca²+ (p < 0.05). Organic matter (OM) and nitrogen content were higher at the 0–10 cm depth; however, there was an interactive effect on EC, OM, and nitrogen in the Quinilla SPS (p < 0.05). A total of 65.31% of the variance is explained by exchangeable cations (47.98%) and OM and nitrogen (17.33%). The planting of M. bidentata A. DC. and S. tinctoria Schult. trees in SPS could enhance soil nutrient availability similarly to natural forests, although the age of systems may influence these outcomes
Agromorphological characterization and phenotypical diversity of common bean from Peru
No full textAntecedentes. El frijol común (Phaseolus vulgaris L.) es una especie de gran importancia en Perú debido a su alto valor nutricional, por su amplia diversidad y por constituir una fuente importante de los ingresos económicos de miles de familias dedicadas a la producción de este cultivo. Objetivo. Caracterizar la diversidad agromorfológica de 50 accesiones de frijol común de Perú. Metodología. Se evaluaron 21 caracteres cualitativos y 17 cuantitativos mediante análisis descriptivo, correspondencia múltiple, análisis de varianza, prueba de agrupamiento Scott-Knott, componentes principales, coeficiente de correlación de Pearson y análisis de agrupamiento jerárquico con distancia euclidiana y método de Ward. Resultados. El análisis de correspondencia múltiple reveló mayor diversidad en los caracteres cualitativos de color predominante de las alas, limbo del estandarte, de vaina en verde, así como la forma predominante de la semilla y color primario de semilla; y en caracteres cuantitativos fueron longitud de hipocótilo, longitud de epicótilo, ancho de vaina, longitud de ápice de vaina, días a la floración, peso de 100 semillas y rendimiento. El coeficiente de correlación de Pearson mostró correlaciones positivas para longitud y ancho de vaina, longitud y ancho de grano y peso de 100 semillas. El análisis de componentes principales mostró que las dos primeras dimensiones explicaron 47.6 % de la varianza total y se establecieron cinco grupos jerárquicos. Implicaciones. Describir la diversidad agromorfológica de frijol común coadyuva en el conocimiento de esta especie en Perú para establecer mecanismos para su preservación. Conclusiones. La diversidad de frijol de Perú es notable y se explica a través por la variabilidad de caracteres cualitativos y cuantitativos e influida principalmente por su origen geográfico, destacando la accesión PER013004 por su productividad y precocidad en la floración.La Estación Experimental Agraria Chincha del INIA financió este trabajo
Sustainability of production of hard yellow corn (Zea mays L.) with certified seed in Bajo Piura, Peru
No full textEl maíz amarillo duro es el principal cultivo de los agricultores del Valle del Bajo Piura, por ello el objetivo fue determinar la mejora en sostenibilidad a través del cálculo de las variaciones en los beneficios económicos empleando el Modelo de Presupuesto Parcial), sociales con el Modelo de Cambio de Excedentes y ambientales aplicando el Modelo de Cam bio en el Cociente de Impacto Ambiental, derivados de la siembra de semilla certificada de maíz amarillo duro (Zea mays L.) en este valle. Se encuestó a 70 productores de maíz amarillo duro para obtener datos de producción y costos. Se obtuvo un Índice de Beneficio Costo Marginal de 1.17, con 80.2% de escenarios positivos. Los consumidores incrementan excedentes en 45.8 millones de soles, los productores en 89.5 millones y el excedente social crece en 135 millones. Para el Perú, la inversión en generación y transferencia de una semilla certificada de maíz amarillo duro de alto rendimiento es rentable, pues el VAN asciende a 128 millones de soles, en un 95% de escenarios positivos, con una TIR de 103%. El Cociente de Impacto Ambiental (EIQ=Environmental lmpact Quotient) se reduciría en 73.6% al usar semilla certificada en la producción de maíz amarillo duro en el Valle del Bajo Piura, por el menor uso de pesticidas
Modeling growth curve parameters in Peruvian llamas using a Bayesian approach
No full textThe objective of this study was to fit four nonlinear models (Brody, von Bertalanffy, Gompertz and Logistic) to realizations of llama weight, using frequentist and Bayesian approaches. Animals from both sexes and types (K'ara and Ch'accu) were observed. Data consisted of 43,332 monthly body weight records, taken from birth to 12 months of age from 3611 llamas, collected from 1998 to 2017 in the Quimsachata Experimental Station of the Instituto Nacional de Innovación Agraria (INIA) in Peru. Parameters for Non-linear models for growth curves were estimated by frequentist and Bayesian procedures. The MCMC method using the Metropolis-Hastings algorithm with noninformative prior distributions was applied in the Bayesian approach. All non-linear functions closely fitted actual body weight measurements, while the Brody function provided the best fit in both frequentist and Bayesian approaches in describing the growth data of llamas. The analysis revealed that female llamas reached higher asymptotic weights than males, and K'ara-type llamas exhibited higher asymptotic weights compared to Ch'accu-type animals. The asymptotic body weight, estimated for all data using the Brody model, was 42 kg at 12 months of age in llamas from Peru. The results of this research highlight the potential of applying nonlinear functions to model the weight-age relationship in llamas using a Bayesian approach. However, limitations include the use of historical data, which may not fully represent current growth patterns, and the reliance on non-informative priors, which could be improved with prior knowledge. Future studies should refine these aspects.The authors thank FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais - process number 5.02/2022), the Federal University of Lavras, Brazil, for their funding support. We also thank the 067_PI project of the National Agricultural Innovation Program (PNIA) of INIA for the financial support and data, Dr. Teodosio Huanca, and the technical staff of the Quimsachata Experimental Center, INIA, Puno, Peru, for their assistance in carrying out this research
Libro de resúmenes: II Convención internacional de suelos y XIX Congreso de la ciencia del suelo
No full textEl presente Libro de Resúmenes de la II Convención Internacional de Suelos y XIX Congreso Peruano de la Ciencia del Suelo recopila 128 trabajos de investigación, distribuidos en ocho ejes temáticos. Los temas abordados comprenden tecnologías para el manejo y estudios de suelos; su restauración y conservación; así como sus propiedades, clasificación, biodiversidad, génesis y actividades de transferencia tecnológica relacionadas con este recurso
INIA 333 – CHUGAYNA new Potato Variety Resilient to Climate Change for the Family Farming System with Tolerance to Frost, Resistant to Late Blight and high Quality for Fresh Consumption
Full text linkThe new potato variety INIA 333–CHUGAYNA, is the result of the joint work of the NGO Asociación Pataz, INIA and the International Potato Center, it was generated through traditional breeding and the use of the participatory varietal selection methodology, as a variety resilient to climate change with frost tolerance, resistance to late blight, compared to the improved variety INIA 302-Amarilis and the native varieties, Huevo de Indio. This new variety is also resilient to climate change, tolerant to frost, with field resistance to late blight, high tuber yield, low glycoalkaloid content and high quality for fresh consumption, adapted up to 4000 m above sea level. It was released in 2023 and officially registered in the national registry of commercial varieties of Peru. The new variety INIA 333-CHUGAYNA requires minimal use of fungicides and has a high economic profitability that will improve the living standards of small and medium-sized farmers in Peru. It can also be used as a parent in breeding programs in other countries in development, to confront climate change, especially frost.We thank the entire technical and administrative team of the NGO Asociación Pataz and the Mining Company Poderosa S.A., for its financial support for carrying out this study, to the National Institute of Agrarian Innovation (INIA) Baños del Inca Agrarian Experiment Station, to the International Potato Center (CIP), to the District Municipality of Chugay. To the farmers, who participated in this research work. Funding support for this work was provided by Mining Company Poderosa S.A
Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru
No full textRemote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (-0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices.The Ministry of Agrarian Development and Irrigation (MIDAGRI) of the Peruvian Government provided funding for this study through the project “Creación del servicio de agricultura de precisión en los Departamentos de Lambayeque, Huancavelica, Ucayali y San Martín 4 Departamentos" (grant number CUI 2449640). It also received support from the Vice-Rectorate for Research of the Universidad Nacional del Amazonas Toribio Rodríguez de Mendoza—UNTRM. Special thanks are extended to the collaborators involved in field data collection and assistants of the Precision Agriculture Project (CUI 2449640) as well as other research programs of the “Estación Experimental Agraria Santa Ana”, INIA
Characterization of Goat Production Systems in the Northern Dry Forest of Peru Using a Multivariate Analysis
Full text linkThe document states that the article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Goat production in the dry forest of northern Peru is essential for rural livelihoods but remains poorly characterized regarding its productivity and sustainability. This study used multivariate techniques—a multiple correspondence analysis (MCA), principal component analysis (PCA), factor analysis of mixed data (FAMD), and hierarchical cluster analysis (HCA)—to analyze data from 284 producers in Tumbes, Piura, and Lambayeque. Surveys captured 48 variables (41 qualitative, seven quantitative) on productivity, socioeconomics, and management. The MCA explained 22.07% of the variability in two dimensions, while the PCA accounted for 63.9%, focusing on productivity and diversification. The FAMD integrated these variables, explaining 51.12% of variability across five dimensions, emphasizing socioeconomic and management differences. The HCA identified three clusters: cluster 1 featured intensive systems with advanced management and commercial focus, cluster 2 included extensive systems limited by water scarcity, and cluster 3 reflected semi-intensive systems with irrigation and diversified production. These findings provide a detailed understanding of goat systems in northern Peru, identifying opportunities to improve resource use and tailor strategies to enhance sustainability. The multivariate analysis proved effective in capturing the complexity of these systems, supporting productivity and improving livelihoods in rural areas.This study was financially supported by the Instituto Nacional de Innovación Agraria (INIA), through the research goat project (CUI Nº 2506684)