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2023-01-04T13:22:28Z2023-01-04T13:22:28Z2020241172172710.1590/1807-1929/agriambi.v24n11p721-7271807-1929http://hdl.handle.net/1843/48630https://orcid.org/0000-0001-5196-0851The understanding of plant behavior and its reflexes on yield is essential for rural planning; thus, the biomathematical models are promising in the yield prediction of cactus pear cv. Gigante. This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. Gigante. The study, using homogeneous treatments, was developed at the Instituto Federal Baiano, Campus of Guanambi, Bahia, Brazil. Data were collected in an area consisting of 384 basic units (plants), in which the yield, defined as a dependent variable, and the predictor variables: plant height (PH), cladode length (CL), cladode width (CW), and cladode thickness (CT), number of cladodes (NC), cladode area (CA), and total cladode area (TCA) were evaluated. Simple linear regression models, multiple regression models only with simple effects for the explanatory variables, and the multiple regression models considering the simple and quadratic effects, and all its possible interactions were adjusted. From this last model, a reduced model was obtained by discarding the less relevant effects, using the Stepwise methodology. The use of the vegetative traits, TCA, NC, CA, CL, CT, and CW, through the adoption of multiple linear regression, quadratic interaction or just the variable TCA by the use of simple linear regression, allows the yield prediction of cactus pear, with adjusted R² of 0.82, 0.76, and 0.74, respectively.O entendimento sobre o comportamento vegetal e seus reflexos sobre a produtividade é essencial ao planejamento rural, com isso, os modelos biomatemáticos são promissores na predição da produtividade da palma forrageira cv. Gigante. Objetivou-se com este estudo ajustar por meio de análises de regressão simples e múltipla modelos para predição da produtividade da palma forrageira cv. Gigante. O estudo, em formato de homogeneidade de tratamentos, foi desenvolvido no Instituto Federal Baiano, Campus Guanambi, Bahia, Brasil Os dados foram coletados em área constituída de 384 unidades básicas, nas quais se mensuraram a produtividade, definida como variável dependente, e as variáveis preditoras: altura da planta (PH), comprimento (CL), largura (CW) e espessura do cladódio (CT), número de cladódio (NC), área do cladódio (CA), e área total do cladódio (TCA). Foram ajustados modelos de regressão linear simples, modelos de regressão múltipla com efeitos simples apenas para as variáveis explicativas e modelos de regressão múltipla, considerando tanto os efeitos simples, quadráticos e todas as suas interações possíveis. A partir deste último modelo citado, foi obtido um modelo reduzido pelo descarte dos efeitos menos relevantes, por meio da metodologia Stepwise. O uso das características vegetativas TCA, NC, CA, CL, CT e CW, por meio de adoção da regressão linear múltipla, interação quadrática ou somente a variável TCA pelo emprego da regressão linear simples, permite a predição da produtividade da palma forrageira, com R² ajustado de 0,82, 0,76 e 0,74, respectivamente.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorOutra AgênciaengUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASRevista Brasileira de Engenharia Agrícola e AmbientalPalma forrageiraProdutividade agrícolaAgricultura -- Estimativa de rendimentoRegression models for productivity prediction in cactus pear cv. GiganteModelos de regressão para predição de produtividade em palma forrageira cv. Giganteinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1590/1807-1929/agriambi.v24n11p721-727Bruno Vinícius Castro GuimarãesSergio Luiz Rodrigues DonatoIgnacio AspiazúAlcinei Místico AzevedoAbner José de Carvalhoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/48630/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALRegression models for productivity prediction in cactus pear cv. gigante.pdfRegression models for productivity prediction in cactus pear cv. gigante.pdfapplication/pdf2174525https://repositorio.ufmg.br/bitstream/1843/48630/2/Regression%20models%20for%20productivity%20prediction%20in%20cactus%20pear%20cv.%20gigante.pdf6d445bce02efca89b279c91d4c0796cdMD521843/486302023-01-04 10:22:28.664oai:repositorio.ufmg.br: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Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-01-04T13:22:28Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
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