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Low seed viability and germination in Polylepis flavipila hinder forest restoration: The role of seed mass and maternal effects
(Elsevier B.V., 2024-12-05)
Seed physiological quality is usually determined by the interaction between genetics and the mother tree’s environment, as well as by intrinsic characteristics, such as seed mass. In the Andean highlands, massive sapling production for forest restoration projects is constrained by seed availability and quality. There, species of the genus Polylepis dominate the remnant forests. The associations of seed viability with seed mass and maternal effects were evaluated in P. flavipila, a threatened tree species endemic to the Peruvian Andes. The characteristics and environments of 18 mother trees from two sites were measured, and seed quality was assessed in three tests. Seed mass was 3.49 ± 1.76 mg (range: 0.5–16 mg) and showed the greatest variability within mother trees, followed by variability among trees and among sites. Viability rates, standard germination and greenhouse germination (2.06 ± 1.35 %, 0.59 ± 0.89 % and 0.64 ± 1.11 %; respectively) were low at both sites. These results were attributed to the presence of seeds with non-viable or absent embryos. Seed viability increased with seed mass and mother tree height, and was positively associated with number of seedlings (r = 0.56). The deficient seed viability and germination found in P. flavipila are the lowest reported for the genus. The low seed physiological quality detected is a risk factor that exacerbates the species’ degree of threat, posing a challenge for sapling production. The reported associations of P. flavipila seed viability with seed mass and maternal char acteristics and environment may guide the selection of better seed quality and serve as a basis for future studies on the challenges and limitations of the reproductive biology of this species
A Comparison of Classification Algorithms for Predicting Distinctive Characteristics in Fine Aroma Cocoa Flowers Using WEKA Modeler
(2024-09-24)
The expression of crop functional traits is influenced by environmental and management conditions, which in turn is reflected in genetic diversity. This study employed a data mining approach to determine the functional traits of flowers that influence cocoa diversity. A total of 1,140 flowers from 228 trees were utilized in this study, with 177 representing fine aroma cocoa trees and 51 trees belonging to other commercial cultivars. Three attribute evaluators (InfoGainAttributeEval, CorrelationAttributeEval and GainRatioAttributeEval), and six algorithms (Naive Bayes, Multinomial Logistic Regression, J48, Random Forest, LTM and Simple Logistic) were employed in this study. The findings indicated that the GainRatioAttributeEval attribute generator was the most efficacious in discerning the functional trait in cocoa diversity flowers. The algorithms Simple Logistic and LMT were the most accurate and specific, while Naive Bayes was the most efficient in terms of computational complexity for model building. This research provides a comprehensive overview of the use of machine learning to analyze functional traits of flowers that most influence cocoa genetic diversity. It also highlights the need to further improve these models by integrating additional techniques to increase their efficiency and extend the data mining approach to other agricultural sectors.
Aqueous-Medium Arsenic(V) Removal Using Iron Oxide-Coated Ignimbrite
(MDPI, 2024-12-28)
Arsenate As(V) is a toxic contaminant commonly found in aquifers and groundwater that poses significant risks to human health. The effective treatment of arseniccontaminated water is therefore crucial for safeguarding public health. This study investigates removing As(V) using iron oxide-coated ignimbrite in batch experiments by varying the adsorbent dosage, initial As(V) concentration, contact time, and system temperature. The adsorption experiments revealed that the Langmuir isotherm model better fit the data (R2 = 0.99) than the Freundlich model (R2 = 0.73). According to the Langmuir model, the maximum adsorption capacity of As(V) on the iron oxide-coated ignimbrite was 4.84 mg·g −1 ± 0.12 mg·g −1 of As(V), with a standard deviation of ±0.05 mg·g −1 after 2 h of exposure with 0.15 g/50 mL iron oxide-coated ignimbrite adsorbent concentration. In the kinetic analysis, the pseudo-first-order model best described the adsorption process at 283 K, 293 K, and 303 K, although the pseudo-second-order model also showed an adequate fit, particularly at 293 K. This indicates that, while the pseudo-first-order model is generally more suitable under these conditions, the pseudo-second-order model may also apply under certain circumstances. The results of the batch experiments demonstrate that iron oxide-coated ignimbrite is a promising adsorbent for effectively reducing high concentrations of As(V) in contaminated water
Characterization of Goat Production Systems in the Northern Dry Forest of Peru Using a Multivariate Analysis
(MDPI, 2025-02-16)
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.
Suitability of the Amazonas region for beekeeping and its future distribution under climate change scenarios
(Elsevier, 2025-02-17)
Beekeeping plays an important role in global food production and the conservation of wild species. However, determining territorial suitability and future distribution under climate change scenarios is a relatively under-studied area in Peru. This study assessed the beekeeping suitability of the Amazonas region and its variation under climate change scenarios in two projected periods (2041-2060 and 2081-2100), according to Shared Socioeconomic Pathways (SSP). The methodological framework integrated the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), and the Hadley Centre Global Earth Model - Global Coupled configuration 3.1 (HadGEM3-GC31-LL) was used for future climate analysis. The beekeeping suitability of the region was determined based on eleven criteria: four climatic, three topographic, and four environmental. The results indicate that beekeeping suitability is distributed as follows: 3.4% (1417.90 km²) with 'High' suitability, 79.2% (33,318.61 km²) with 'Moderate' suitability, 17.2% (7230.26 km²) with 'Marginal' suitability, and 0.2% (83.64 km²) as 'Not suitable'. Moreover, the average temperature across the region is projected to increase by approximately 3 °C under the SSP2-4.5 scenario and between 6 °C and 8 °C under the SSP5-8.5 scenario during the projected periods. Precipitation will decrease in the northern part of the region, while the southwestern part will experience an increase. In the highly suitable beekeeping area, a temperature increases up to 10.8 °C is expected, with frequent variations around 3 °C to 8 °C, affecting more than 500 km². Additionally, a reduction in precipitation up to 311 mm/year is projected, with predominant variations ranging from -49.5 to 32.8 mm/year over approximately 600 km². Therefore, it is suggested to implement strategies to mitigate these upcoming challenges, particularly if the modeled economic development under the SSPs continues. This study modeled and mapped areas with present conditions suitable for beekeeping and future climate behavior. The modeling aims to guide beekeepers and local authorities in developing sustainable practices and implementing preventive measures to address future climatic challenges.
Diprosopus y otras malformaciones en bovino: Reporte de Caso
(División de Investigación, Facultad de Ciencias Veterinarias, Universidad del Zulia, 2025-01-31)
Diprosopus is a congenital malformation result, also known as craniofacial duplication, which is widely recognized in humans and there are also reports in many animal species. The objective of this research is to learn about the clinical manifestations and post mortem findings in cattle diprosopus. Female cattle with incomplete cephalic facial cephalic duplication (diprosopus), product of the eighth gestation of a Creole cow with, from the village center of Quidén, district of Paccha, province of Chota, region of Cajamarca, Peru, with extensive production breeding system. The cattle presented two lower jaws with double oral and nasal cavity, two tongues and supernumerary teeth, the vault was double and single neck. It is confirmed that the case described is diprosopus in cattle.
Sustainability Potential of Kikuyu Grass (Pennisetum clandestinum) in Livestock Farming of Peru's Highland Regions
(MDPI, 2024-12-16)
The productive sustainability of Pennisetum clandestinum in the Peruvian highlands was evaluated through productivity, growth and chemical composition. The effect of the nitrogen (N) rate, organic matter application, and cutting frequency on Kikuyu grass's yield, chemical composition, plant height, and growth rate was investigated. Experimentation followed a randomized block design with split plots. Multivariate analysis of variance (MANOVA) assessed differences across study factors. Applying 120 kg of N ha-1 yr-1 raised the protein yield to 3454.53 kg ha¯¹, with a crude protein (CP) content of 23.54%. Moreover, cypress (Cupressus lusitanica) trees influenced the Kikuyu biomass, producing 19,176.23 kg of dry matter (DM) ha-1 yr-1 at 8.5-11.5 m from the tree base. Organic matter enhanced Kikuyu aboveground biomass. While dry matter production showed no significant difference between 30- and 60-day cutting intervals, CP content was higher at 30 (p < 0.05). Peak daily dry matter (DM) production occurred at 45 days, achieving a biomass accumulation of 21,186.9 kg DM ha-1 yr-1. Given its high yield and favorable chemical composition, Kikuyu is a viable option for dairy cattle feed, especially in highland areas. Implementing a plant improvement program for Kikuyu could further enhance its nutritional value for high-production dairy cows.
Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
(Elsevier, 2024-12-12)
The quality and safety of soil are crucial for ensuring social and economic development and providing contaminant-free food. The availability and quality of soil data, particularly for multiple metals and metalloids, are often insufficient for comprehensive analysis. Soil formation and the distribution of metals are shaped by various factors such as geology, climate, topography, and human activities, making accurate modeling highly challenging. Additionally, agricultural intensification, urban expansion, road construction, and mining activities frequently result in soil pollution, posing serious risks to ecosystems and human health. This study aims to integrate diverse geospatial datasets with machine learning for high resolution soil contamination mapping (10 m spatial resolution) in a major agricultural region of Peruvian highlands. This study mapped 25 elements (Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn, V) in the Peruvian Mantaro Valley using a training dataset of 109 topsoil samples combined with various geospatial datasets (remote sensing, climate, topography, soil data, and distance). The model provided satisfactory results in predicting the spatial distribution of the selected elements, with R² values ranging from 0.6 to 0.9 for most elements. Edaphic, climate, and topographic covariates were the most significant predictors, particularly for croplands near rivers, whereas spectral variables were less important. The results reveal As, Pb, and Cd concentrations significantly above permissible limits, highlighting urgent health risks. These findings suggest that it is feasible to identify polluted soils and improve regulations based on widely available geospatial datasets with minimal training data. The study contributes to the development of models to assess the impact of pollutants on environmental and human health in the short-to-medium term, emphasizing the need for further research on the translocation of toxic metals into food crops and the implications for public health.
Impact of Green Manuring and Nitrogen Fertilization on Rice Cultivation: A Peruvian Amazon Forest Study in San Martín Province
(Scielo Preprint, 2024-03-12)
Green manuring is an environmentally friendly technology aimed at providing nutrients to plants, enhancing soil fertility, mitigating soil degradation, controlling weeds and pests, and decreasing reliance on inorganic fertilizers. However, it requires dissemination and support to be adopted, especially in the poorest agricultural communities in Latin America. The study was conducted at the El Porvenir INIA in San Martín, Perú; it assessed two treatment sets: (1) green manure Crotalaria juncea (CroJ), Canavalia ensiforme (CanE), no green manure; and (2) nitrogen fertilizer dose (FN75, FN100). It was arranged in a split-plot design with four replications. During the experiment we detected an important fluctuation in soil parameters, however, it is the diminished levels of soil carbon and nitrogen, which were presumably the outcomes of microorganism processes. Otherwise, we observed that CanE significantly reduced the diseased tillers by "White Leaf Virus" (RHBV) by 2.82% compared to the control. The superior outcomes were achieved through CanE, and the highest yield was 8.36 t.ha¯¹ with the CanE - FN100 treatment. Additionally, the nutritional quality of rice was not altered by green manures or chemical nitrogen fertilization doses tested.
Reporte de Repositorio Institucional del 01 al 31 de Enero 2025
(Instituto Nacional de Innovación Agraria, 2025-01-31)
Durante el mes de enero se incorporaron 06 publicaciones técnico científicas, en el Repositorio Institucional del INIA, contando a la fecha con un total de 2495 publicaciones, divididas en comunidades y colecciones. El objetivo de este reporte es mantener actualizados los datos sobre las publicaciones técnico-científicas que vienen siendo incorporadas por el área a cargo de la administración del Repositorio Institucional del INIA.