Citas bibligráficas
Carrasco, V., (2016). Relación entre las temperaturas y el rendimiento del olivo (Olea auropaea) en la zona de Lanchas - Pisco [Tesis, Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/2802
Carrasco, V., Relación entre las temperaturas y el rendimiento del olivo (Olea auropaea) en la zona de Lanchas - Pisco [Tesis]. : Universidad Nacional Agraria La Molina; 2016. https://hdl.handle.net/20.500.12996/2802
@mastersthesis{renati/1116025,
title = "Relación entre las temperaturas y el rendimiento del olivo (Olea auropaea) en la zona de Lanchas - Pisco",
author = "Carrasco Delgado, Víctor Reynaldo",
publisher = "Universidad Nacional Agraria La Molina",
year = "2016"
}
In February 2004 a study was conducted at "El Alamein" farm, located in Lanchas, district of Paracas, province of Pisco, Ica region, to predict olive yield, cv. Coratina using temperature data. This general objective was achieved through the following sequential steps which constitute the following specific objectives: (i) Determine the months before harvest where temperatures are critical to olive yield, (ii) identify critical phenological phases corresponding to the months with the highest association between yield and temperature (iii) olive yield forecast with temperature data and (iv) effect of thermal anomalies in crop yield. Temperature daily data (maximum, minimum and average) from January 1986 to January 2002 and yield data from 1987 to 2003 were analyzed. It was determined that the minimum temperatures in the first half of July, a period in which occurs the breaking of dormancy, and maximum temperatures for October, November, December and January, a period in which the fruit set, growth and endocarp hardening takes place, had high coefficients of simple linear correlation with future yields of olives with these variables multiple linear correlations being in all cases high correlation coefficients were performed. With these temperatures multiple linear correlations and their respective regression equations for predicting yields, which showed quite accurately when subjected to Chi Square tests were performed. It was found that the slightly negative anomalies of minimum temperatures in the first half of July and slightly positive anomalies of maximum temperatures from October to December determined good yields.
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