Examinando por Materia "multivariate analysis"

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Characterization of Goat Production Systems in the Northern Dry Forest of Peru Using a Multivariate Analysis
(MDPI, 2025-02-16) Temoche Socola , Victor Alexander; Acosta Granados , Irene Carol; Gonzales, Pablo; Godoy Padilla, David; Jibaja, Omar; Cruz Luis, Juancarlos Alejandro; Corredor Arizapana, Flor Anita
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.
Yield predictions of ‘Del Cerro’ cotton (Gossypium hirsutum L.) germplasm by multispectral monitoring in the north coast of Peru
(Instituto de Investigaciones Agropecuarias, INIA, 2025-02-01) Cruz Grimaldo, Camila Leandra; Nieves Rivera, Marite Yulisa; Vera Díaz, Elvis; Durán Gómez, Moisés Rodrigo; Morales Pizarro, Davies Arturo; Salazar Coronel, Willian; Arbizu Berrocal, Carlos Irvin
Peruvian cotton (Gossypium hirsutum L.) has great acceptance and demand in the national and international textile market due to the excellent quality of its extra-long fiber, durability and resistance. To evaluate cotton cultivar performance, we need to use tools such as drones + sensors. However, these tools have not been widely used in the Peruvian agricultural area. Here we evaluated seven agro-morphological characters of 21 accessions of Del Cerro cotton cultivar from the National Institute of Agrarian Innovation of Peru with highthroughput phenotyping methods. We employed a Matrice 300 RTK unmanned aerial vehicle (UAV) with the MicaSense Dual Red Edge Blue multispectral sensor to assess plant height, yield, and spectral signature during physiological maturity stage; other morphological characters were manually scored. Multispectral monitoring revealed the phytosanitary status of the crop, which begins to enter senescence after 130 d after sowing (DAS) due to the decrease of the vegetation indices (VI). Pearson correlations between yield and VI showed favorable values, exceeding 0.60 at 94 DAS for normalized difference vegetation index (NDVI), relative vigor index (RVI), and normalized difference red edge index (NDRE). Principal component analysis (PCA) was conducted on the same date, a significant correlation was found between NDVI and yield. Additionally, yield prediction equations were generated with the normalized difference water index (NDWI) showing an R value of 0.74 at 130 DAS. The findings of this study suggest that remote sensing evaluation is suitable for estimating ‘Del Cerro’ cotton yield in infrared (IR) bands, providing a tool for germplasm evaluation that can influence decision-making and better conservation strategies.