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作 者:Ramon Amaro de Sales Alexandre Cândido Xavier Evandro Chaves de Oliveira Fábio Luiz de Oliveira Diego Mathias Natal da Silva Sávio da Silva Berilli
机构地区:[1]Departament of Plant Science,Federal University of Viçosa,Minas Gerais,Brazil [2]Center of Agrarian Sciences and Engineering,Federal University of Espirito Santo,Espírito Santo,Brazil [3]Federal Institute of Education,Science and Technology of Espírito Santo-Campus Itapina,Espírito Santo,Brazil [4]Federal Institute of Education,Science and Technology of Southeast Minas Gerais-Campus Manhuaçu,Minas Gerais,Brazil
出 处:《International Journal of Agricultural and Biological Engineering》2020年第3期123-128,共6页国际农业与生物工程学报(英文)
摘 要:Due to the current water scarcity in the world,it is extremely important to improve the use of this natural and exhaustible resource in agriculture,by contributing to increase agricultural production and sustainability.Several models of crop growth simulation were developed to predict the edaphoclimatic effects on crop yield.These models are calibrated and validated for a given region using the data generated from field experiments.Therefore,the objective of this study was to calibrate and validate the FAO AquaCrop model for yacon(Smallanthus sonchifolius)crop in a tropical climate.The experiment was conducted in an experimental area located in the municipality of Ibatiba,state of Espírito Santo(Brazil)during the years of 2013 and 2014.The calibration was done using the Autumn planting and validation with the Winter and Spring plantings.For the statistical analysis,the coefficient of determination,Willmott concordance index,bias for the systematic error,root mean square error and the mean absolute error to test the model performance were used.In general,the FAO AquaCrop model predicted the root yield,total biomass and harvest index with acceptable accuracy,and with deviations of less than 6%for total and root biomass.Late planting of yacon showed a reduction in yield as well as total biomass.
关 键 词:Smallanthus sonchifolius root yield simulation modelling agrometeorology
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