Examinando por Autor "Lastra Paucar, Sphyros Roomel"

Mostrando 1 - 8 de 8

An evaluation of dryland ulluco cultivation yields in the face of climate change scenarios in the Central Andes of Peru by using the Aquacrop model
(MDPI, 2024-06-26) Flores Marquez, Ricardo; Vera Vilchez, Jesús Emilio; Verástegui Martínez, Patricia; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi
Ullucus tuberosus is an Andean region crop adapted to high-altitude environments and dryland cultivation. It is an essential resource that guarantees food security due to its carbohydrate, protein, and low-fat content. However, current change patterns in precipitation and temperatures warn of complex scenarios where climate change will affect this crop. Therefore, predicting these effects through simulation is a valuable tool for evaluating this crop’s sustainability. This study aims to evaluate ulluco’s crop yield under dryland conditions at 3914 m.a.s.l. considering climate change scenarios from 2024 to 2100 by using the AquaCrop model. Simulations were carried out using current meteorological data, crop agronomic information, and simulations for SSP1-2.6, SSP3-7.0, and SSP5-8.5 of CMIP 6. The results indicate that minimum temperature increases and seasonal precipitation exacerbation will significantly influence yields. Increases in rainfall and environmental CO2 concentrations show an opportunity window for yield increment in the early stages. However, a negative trend is observed for 2050–2100, mainly due to crop temperature stress. These findings highlight the importance of developing more resistant ulluco varieties to heat stress conditions, adapting water management practices, continuing modeling climate change effects on crops, and investing in research on smallholder agriculture to reach Sustainable Development Goals 1, 2, and 13
Compost quality optimization through Plackett-Burman’s design
(OICC Press, 2024-08-28) Ortiz Dongo, Luis Felipe; Mendez Revollar, Yerly; Solórzano Acosta, Richard Andi; Lastra Paucar, Sphyros Roomel; Carrion Carrera, Gladys
Purpose: This research aimed at compost quality optimization through Plackett-Burman’s design application.This statistical method was used to identify and evaluate key factors that impact compost quality and determine its optimal levels. Method: Eight experiments were carried out with variables such as leachate recirculation, Carbon/Nitrogen ratio,manure type, bacterial and fungi incorporation, type of plant material, and compost pile height. Results: Obtained results revealed significant influence of guinea pig manure in compost quality, improving pH and electric conductivity (dS∙m-1) values, as well as its influence on purple corn fresh and dry weight increase. However, the use of guinea pig manure can increase arsenic, mercury, and lead compost levels, but within the range allowed by Peruvian technical standards. Leachate recirculation showed significant effect on compost humidity increase, which decreased its physical quality to not permitted values by Peruvian technical standards. In addition, leachate uses a reduced number of corn leaves, as well as its fresh and dry weight. It was possible to identify optimal conditions to maximize composting process efficiency, through Plackett-Burman’s Design. Conclusion: These findings provide a solid foundation for composting practice's continuous improvement, contributing to high-quality organic fertilizer production more efficiently and sustainably. This study has the potential to guide future research and feasible applications in the agricultural field, favoring more environmentally friendly practices adoption.
Comprehensive spatial mapping of metals and metalloids in the Peruvian Mantaro Valley using advanced geospatial data Integration
(Elsevier, 2024-12-12) Pizarro Carcausto, Samuel Edwin; Pricope , Narcisa G.; Vera Vilchez, Jesús Emilio; Cruz Luis, Juancarlos Alejandro; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi; Verástegui Martínez, Patricia
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.
Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley
(Elsevier, 2024-03-29) Pizarro Carcausto, Samuel; Vera Vilchez, Jesús Emilio; Huamani, Joseph; Cruz, Juancarlos; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi; Verástegui Martínez, Patricia
Quality and safety of the soil are essential to ensure social and economic development and provides the supply of contaminant free food. With agriculture intensification, expansion of urban zones, construction of roads, and mining, some agricultural soils sites become polluted increasing environmental risks to ecosystems functions and human health. Hence the need know the spatial distribution of elements in soils, we mapped 25 elements, namely Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn and V, using various geospatial datasets, such as remote sensing, climate, topography, soil data, and distance, to establish the spatial estimation models of spatial distribution trained trough machine learning model with a supervised dataset of 109 topsoil samples, into Google earth engine platform. Using R2, RMSE and MAE to assess the prediction accuracy. First Random Forest gave satisfactory results in predicting the distribution of analyzed elements in soil, being improved for some elements when adds more trees. Additionally, each element analyzed has a different combination of environmental covariates as predictor, mainly soil, climate, topographic and distance variables especially croplands close to rivers, with less importance for spectral variables. Our results suggest that is possible to identify polluted soils and improved regulations to minimize harm to environmental health and human health, for short-to-medium-term environmental risk control.
Guinea pig manure and mineral fertilizers enhance the yield and nutritional quality of hard yellow maize on the peruvian coast
(MDPI, 2025-04-26) Calero Rios, Emilee Nahomi; Borbor Ponce, Miryam; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi
Sustainable fertilization using local resources such as manure is crucial for soil health. This study evaluated the potential of guinea pig manure to replace mineral fertilizers in hard yellow maize (hybrid INIA 619) under Peruvian coastal conditions. A split-plot design tested four doses of guinea pig manure (0, 2, 5, 10 t⋅ha−1) and four levels of mineral fertilization (0%, 50%, 75%, 100%). The study assessed plant height, ear characteristics, yield, and nutritional quality parameters. The results indicated that 100% mineral fertilization led to the highest plant height (229.67 cm) and grain weight (141.8 g). Yields of 9.19 and 9.08 t⋅ha−1 were achieved with 5 and 10 t⋅ha−1 of manure, while 50% mineral fertilization gave 8.8 t⋅ha−1, similar to the full dose (8.7 t⋅ha−1). The protein content was highest with 10 t⋅ha−1 of manure combined with mineral fertilization. However, no significant differences were found between the 50%, 75%, and 100% mineral fertilizer doses. In conclusion, applying guinea pig manure improved nutrient use efficiency, yield, and grain protein quality in maize, reducing the need for mineral fertilizers by up to 50%. This provides a sustainable fertilization strategy for agricultural systems.
Guinea Pig Manure and Mineral Fertilizers Enhance the Yield and Nutritional Quality of the INIA 619 Maize Variety on the Peruvian Coast
(Preprints.org, 2025-02-28) Calero Rios, Emilee Nahomi; Borbor Ponce, Miryam; Lastra Paucar, Sphyros Roomel; Solórzano Acosta, Richard Andi
Sustainable fertilization using local resources like manure is crucial for soil health. This study evaluated the potential of guinea pig manure to replace mineral fertilizers in hard yellow maize (hybrid INIA 619) under Peruvian coastal conditions. A split-plot design tested four doses of guinea pig manure (0, 2, 5, 10 t ha-1) and four levels of mineral fertilization (0%, 50%, 75%, 100%). The study assessed plant height, ear characteristics, yield, and nutritional quality parameters. The results indicated that 100% mineral fertilization led to the highest plant height (229.67 cm) and grain weight (141.8 g). Yields of 9.19 and 9.08 t ha-1 were achieved with 5 and 10 t ha-1 of manure, while 50% mineral fertilization gave 8.8 t ha-1, similar to the full dose (8.7 t ha-1). Protein content was highest with 10 t ha-1 of manure combined with mineral fertilization. However, no significant differences were found between the 50%, 75%, and 100% mineral fertilizer doses. In conclusion, applying guinea pig manure improved nutrient use efficiency, yield, and grain protein quality in maize, reducing the need for mineral fertilizers by up to 50%. This provides a sustainable fertilization strategy for agricultural systems.
Nutritional quality of the “Algarrobo” neltuma pallida fruit and its relationship with soil properties and vegetation indices in the dry forests of Northern Peru
(MPDI, 2025-09-16) Salazar Coronel, Wilian; Cruz Grimaldo, Camila Leandra; Lastra Paucar, Sphyros Roomel; Rengifo Sanchez, Raihil Rabindranath; Vargas de la Cruz, Celia; Godoy Padilla, David; Sessarego Davila, Emmanuel Alexander; Cruz Luis, Juancarlos Alejandro; Solórzano Acosta, Richard Andi
The dry forests of northern Peru are home to extensive populations of algarrobo (Neltuma spp.). Its fruit serves as feed for goats and is used in various agro-industrial products. However, the nutritional quality can be influenced by the physicochemical properties of the soil and vegetation coverage. The objective of this study was to understand and predict the concentration of protein and ether extracts of carob and evaluate its relationship with soil properties and vegetation indices. Principal component analysis (PCA) and correlation analyses were conducted. The prediction of protein and ether extract was carried out using the Eureqa-Formulize software 1.24.0. In the PCA, protein showed a positive relationship with ash and ether extract but a negative relationship with moisture. Likewise, moderate correlations were observed between protein and ash content (0.51). Protein also showed positive correlations with pH (r = 0.19), BI (r = 0.22), and NDSI (r = 0.22). Additionally, the ether extract exhibited correlations with sand content (r = 0.22), Ca2+ (r = −0.26), Cu (r = −0.20), S5 (r = 0.26), and Si (r = 0.24). Protein predictions showed moderate performance (CC = 0.73 and R2 = 0.53), as did ether extracts (CC = 0.68 and R2 = 0.46). These findings contribute to a better understanding of the factors that influence the nutritional quality of carob and can be used for the development of sustainable management strategies in the dry forests of northern Peru.
Synergy Between Microbial Inoculants and Mineral Fertilization to Enhance the Yield and Nutritional Quality of Maize on the Peruvian Coast
(MDPI, 2024-12-21) López Montañez, Ruth; Calero Rios, Emilee Nahomi; Quispe Matos , Kenyi Rolando; Huasasquiche Sarmiento, Lucero; Lastra Paucar, Sphyros Roomel; La Torre , Braulio; Solórzano Acosta, Richard Andi
Hard yellow maize is a crucial crop in Peruvian agriculture that plays a significant role in food security and livestock production. However, intensive fertilization practices in agronomic management have negatively impacted soil health. To explore more sustainable agricultural technologies, researchers investigated solutions using microorganisms to enhance plant growth. This study assessed the synergistic effects of microbial inoculants and mineral fertilization on INIA 619 and Dekal B-7088 maize varieties' yield and nutritional quality. A split-plot design was employed, incorporating four inoculation treatments—no inoculant, Bacillus subtilis, Trichoderma viride, and Pseudomonas putida—combined with fertilization levels of 0%, 50%, 75%, and 100%. The findings revealed that Bacillus subtilis boosted yields by 13.1% in INIA 619 and 55.5% in Dekal B-7088. Additionally, combined with 100% fertilization, microbial inoculation increased protein content by 47% and carbohydrates by 6% in INIA 619 while maintaining nutritional quality with 75% fertilization. Similarly, in Dekal B-7088, inoculation with total fertilization enhanced protein content by 54% and fiber by 27%. These results demonstrated that microbial inoculation could reduce mineral fertilization by up to 25% while sustaining high yields and improving the nutritional quality of maize.