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Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform
by
García, Ligia
, Veneros, Jaris
, Chavez, Segundo G.
, Olivares, Neiro
, Rojas-Briceño, Nilton B.
, Oliva-Cruz, Manuel
in
Agricultural production
/ Agricultural research
/ Altitude
/ Biodiversity
/ Climate
/ Climate change
/ Climate effects
/ Climate Engine
/ Climate models
/ Coffee
/ Crop diseases
/ Crop management
/ Crops
/ Drought
/ Environmental aspects
/ Flowers & plants
/ Humidity
/ Linear models (Statistics)
/ linear regression model
/ Linear regression models
/ Minimum temperatures
/ NDVI
/ Normalized difference vegetative index
/ Precipitation
/ Productivity
/ Radiation
/ Rainfall
/ Regression analysis
/ Relative humidity
/ Remote sensing
/ Rodriguez de Mendoza
/ Temperature
/ Time series
/ Trends
/ Vegetation
/ Vegetation index
2024
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Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform
by
García, Ligia
, Veneros, Jaris
, Chavez, Segundo G.
, Olivares, Neiro
, Rojas-Briceño, Nilton B.
, Oliva-Cruz, Manuel
in
Agricultural production
/ Agricultural research
/ Altitude
/ Biodiversity
/ Climate
/ Climate change
/ Climate effects
/ Climate Engine
/ Climate models
/ Coffee
/ Crop diseases
/ Crop management
/ Crops
/ Drought
/ Environmental aspects
/ Flowers & plants
/ Humidity
/ Linear models (Statistics)
/ linear regression model
/ Linear regression models
/ Minimum temperatures
/ NDVI
/ Normalized difference vegetative index
/ Precipitation
/ Productivity
/ Radiation
/ Rainfall
/ Regression analysis
/ Relative humidity
/ Remote sensing
/ Rodriguez de Mendoza
/ Temperature
/ Time series
/ Trends
/ Vegetation
/ Vegetation index
2024
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Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform
by
García, Ligia
, Veneros, Jaris
, Chavez, Segundo G.
, Olivares, Neiro
, Rojas-Briceño, Nilton B.
, Oliva-Cruz, Manuel
in
Agricultural production
/ Agricultural research
/ Altitude
/ Biodiversity
/ Climate
/ Climate change
/ Climate effects
/ Climate Engine
/ Climate models
/ Coffee
/ Crop diseases
/ Crop management
/ Crops
/ Drought
/ Environmental aspects
/ Flowers & plants
/ Humidity
/ Linear models (Statistics)
/ linear regression model
/ Linear regression models
/ Minimum temperatures
/ NDVI
/ Normalized difference vegetative index
/ Precipitation
/ Productivity
/ Radiation
/ Rainfall
/ Regression analysis
/ Relative humidity
/ Remote sensing
/ Rodriguez de Mendoza
/ Temperature
/ Time series
/ Trends
/ Vegetation
/ Vegetation index
2024
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Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform
Journal Article
Construction of Linear Models for the Normalized Vegetation Index (NDVI) for Coffee Crops in Peru Based on Historical Atmospheric Variables from the Climate Engine Platform
2024
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Overview
The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices.
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