Examinando por Materia "Análisis de la regresión"

Mostrando 1 - 2 de 2

Morphological and productive correlations of cutting Pennisetum varieties under conditions of peruvian humid tropics
(Bogor Agricultural University, 2024-08-26) Pinchi Carbajal, Segundo Fidencio; Quispe Ccasa, Hurley Abel; Ampuero Trigoso, Gustavo; Nolasco Lozano, Emily; Saucedo Uriarte, Jose Américo
Livestock farming in the Peruvian tropics is based on the use of grazing forage, but cutting grasses offers greater productivity and seasonality advantages. In this study, the morphological and productive characteristics of King Grass Morado (KGM), Cuba OM-22 (CU), and Maralfalfa (MA) were evaluated and correlated with chlorophyll content under Peruvian humid tropic conditions. Five plots of 1 ha each were installed for the three Pennisetum varieties (2-1-2), with three samples per plot. No significant differences were found in plant height, leaf length, number of nodes, number of leaves/stem, number of stems, stem circumference, length of nodes, leaf, stems, and total weight, chlorophyll index (atLEAF CLOR), performance index (API), and dry matter. KGM stood out in tillering (12.86) (p<0.01), but CU and MA showed greater leaf width (4.16 and 4.42 cm, respectively) (p<0.05). The calculated biomass production was 40.3 t/ha for KGM, 24.5 t/ha for MA, and 76.5 t/ha for CU. MA had higher nitrogen (0.70%) and protein (4.33%) contents (p<0.01). The correlations were significant between stem height with the number of nodes and leaf width, stem circumference with stem, leaf, and total weight (p<0.05), and nitrogen and protein content were estimated with the atLEAF CLOR and API values of the basal leaves with R2 = 0.548 and R2 = 0.563, respectively (p<0.05). In conclusion, KGM, CU, and MA differed in some morphological and productive variables and were correlated with others; furthermore, the protein content could be estimated with the atLEAF CLOR and API values in these Pennisetum varieties.
Yield prediction models for rice varieties using UAV multispectral imagery in the Amazon lowlands of Peru
(MDPI, 2024-08-20) Goigochea Pinchi, Diego; Justino Pinedo, Maikol; Vega Herrera, Sergio Sebastian; Sanchez Ojanasta, Martín; Lobato Galvez, Roiser Honorio; Santillan Gonzales, Manuel Dante; Ganoza Roncal, Jorge Juan; Ore Aquino, Zoila Luz; Agurto Piñarreta, Alex Iván
Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R2 = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R2 = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R2 = 0.62); Conquista Certificada (MCARI, 116 DAS, R2 = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R2 = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture.