Abstract
Abstract Passion fruit crop yield depends on the behavior of climatic variables, and modeling the dependence relationship of these variables regarding crop yield offers information aimed at facilitating agribusiness decision making. As main aim, passion fruit crop yield was estimated using mathematical models. A multivariate and univariate statistical analysis of meteorological variables was carried out during the observation period between 2007 and 2014 of selected weather stations, identified and located in the Colombian middle tropics (County of Huila). The relationship between yield with the following agroclimatic variables were analyzed: temperature, sunlight, relative humidity, rainfall and ENSO at monthly resolution with empirical and mechanistic models, recommended in scientific literature. Results showed that the multiple regression model requires the highest yield peaks; the adjustment of the multiple regression model is low, while univariate models such as the ARIMA model showed better adjustment in the time series analyzed. The Stewart’s water-yield model has better performance to estimate yield as a function of evapotranspiration in the different phenological phases.
Highlights
Climate plays an important role in the development of crops, and the three most important climate components are light, temperature and rainfall (CAMPOS ARANDA., 2005)
Relative humidity and the El Niño-Niña Southern Oscillation (ENSO) climatic pattern measured from the ONI index are considered, because these extreme phenomena have an impact on crop yields, causing threats to food security (IDEAM, 2013)
Where Si is the relative value obtained from the yield mRoedgeal radnidnMg iAisRthIMe oAb,seRrvoebd(uc1sr1to)pR yeigerldesvsailoune. and multilayer perceptron models, it is important to note that for the selection of the best model to predict the series under study, the Akaike information criteria (AIC) (CRYER; CHAN, 2008) was used, for which the lower the values of these measures, the better the model is in terms of relative quality in relation to the loss of information of the statistical model estimated for the crop yield series
Summary
Climate plays an important role in the development of crops, and the three most important climate components are light, temperature and rainfall (CAMPOS ARANDA., 2005). The scientific-technical background of production forecasts is based on the knowledge of the relationship between ecophysiological requirements of the species and the environmental supply, referring in this particular case to the supply and intensity of meteorological factors, with fundamental support of statistics (KANTANANTHA; STEWART, 2007), so that they accurately fulfill the prediction and are useful for other specialists (DELGADILLO-RUIZ et al, 2016; RUÍZ-RAMÍREZ; HERNÁNDEZ-RODRÍGUEZ; ZULETA RODRÍGUEZ, 2011; MARTÍNEZ VENTURA, 2006). In this regard, forecasting models that estimate production volumes in semi-permanent fruit species such as passion fruit contribute to its understanding. Forecasts aim to capture more closely the dynamics of each of cultural practices (transitory and permanent), taking into account the production cycle (MARTÍNEZ VENTURA, 2006)
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