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		<doi>10.1007/s00704-023-04736-2</doi>
		<issn>0177-798X</issn>
		<citationkey>CasaroliSQEAFMBRC:2024:UsCrMo</citationkey>
		<title>Using crop models, a decline factor, and a “multi-model” approach to estimate sugarcane yield compared to on-farm data</title>
		<year>2024</year>
		<month>Mar.</month>
		<typeofwork>journal article</typeofwork>
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		<author>Casaroli, Derblai,</author>
		<author>Sanches, Ieda Del'Arco,</author>
		<author>Quirino, Dayanna Teodoro,</author>
		<author>Evangelista, Adão Wagner Pêgo,</author>
		<author>Alves Júnior, José,</author>
		<author>Flores, Rilner Alves,</author>
		<author>Mesquita, Márcio,</author>
		<author>Battisti, Rafael,</author>
		<author>Rodigheri, Grazieli,</author>
		<author>Capuchinho, Frank Freire,</author>
		<orcid>0000-0001-8041-0066</orcid>
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		<group>SER-SRE-DIPGR-INPE-MCTI-GOV-BR</group>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation></affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Universidade Federal de Goiás (UFG)</affiliation>
		<electronicmailaddress>derblai@ufg.br</electronicmailaddress>
		<electronicmailaddress>ieda.sanches@inpe.br</electronicmailaddress>
		<electronicmailaddress>dayanna_teodoro@hotmail.com</electronicmailaddress>
		<electronicmailaddress>awpego@gmail.com</electronicmailaddress>
		<electronicmailaddress>josealvesufg@yahoo.com.br</electronicmailaddress>
		<electronicmailaddress>rilner@ufg.br</electronicmailaddress>
		<electronicmailaddress>marcio.mesquita@ufg.br</electronicmailaddress>
		<electronicmailaddress>battisti@ufg.br</electronicmailaddress>
		<electronicmailaddress>grazielirodigheri@gmail.com</electronicmailaddress>
		<electronicmailaddress>frankfreirec@gmail.com</electronicmailaddress>
		<journal>Theoretical and Applied Climatology</journal>
		<volume>155</volume>
		<number>3</number>
		<pages>2177-2193</pages>
		<secondarymark>A1_GEOGRAFIA A1_ENGENHARIAS_I A2_INTERDISCIPLINAR A2_GEOCIÊNCIAS A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B1_ARQUITETURA_E_URBANISMO B2_CIÊNCIAS_BIOLÓGICAS_I B3_ASTRONOMIA_/_FÍSICA</secondarymark>
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		<abstract>Sugarcane is an important crop in Brazilian agribusiness due to its diversified use. Crop forecast models are important tools for planning and making decisions regarding crop management. These models can be simple or complex, and choosing them will depend on the knowledge level of those using them. Thus, this study aimed to compare different methods for estimating sugarcane yield in three crop cycles. Data collection occurred in a sugarcane field in the municipality of Santo Antônio de Goiás, Brazil. The sugarcane variety evaluated was CTC-04. This variety was cultivated under dryland conditions, in cane plant, ratoon 1, and ratoon 2 cycles. Agrometeorological, biometric, and crop yield data were analyzed. Five crop models were used to estimate sugarcane yield: (i) FAO-Agroecological Zone (AEZ), (ii) agrometeorological-spectral (AEZs), (iii) Monteith (M), (iv) Scarpari (S), and (v) Martins and Landell (ML). Models AEZ, AEZs, M, and S showed average yield differences of about 15%, with the largest difference recorded by the ML model (39%). All models detected yield decline as a function of the number of harvests (kdec = &#8722; 0.70). The multi-model approach reduced the differences between estimated and actual values, especially for the combinations AEZ + AEZs and AEZ + AEZs + M. The present findings contribute to the investigation of different models with the potential to estimate sugarcane productivity.</abstract>
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		<language>en</language>
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