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Abstract
In countries like Argentina, whose economy depends heavily on crop production, the estimation of harvests is an elementary requirement. Besides providing objectivity, the use of remote sensing allows estimating yield in advance. Since the time of maximum leaf area in wheat corresponds with the critical period of the crop, a good relationship is expected between the Normalized Difference Vegetation Index (NDVI) and yield. The present study was carried out [ver mas...]
dc.contributor.authorLopresti, Mariano Francisco
dc.contributor.authorDi Bella, Carlos Marcelo
dc.contributor.authorDegioanni, Américo José
dc.coverage.spatialBuenos Aires (province)eng
dc.date.accessioned2017-09-18T13:43:23Z
dc.date.available2017-09-18T13:43:23Z
dc.date.issued2015-07-18
dc.identifier.issn2214-3173
dc.identifier.otherhttps://doi.org/10.1016/j.inpa.2015.06.001
dc.identifier.urihttp://hdl.handle.net/20.500.12123/1236
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S221431731500027X
dc.description.abstractIn countries like Argentina, whose economy depends heavily on crop production, the estimation of harvests is an elementary requirement. Besides providing objectivity, the use of remote sensing allows estimating yield in advance. Since the time of maximum leaf area in wheat corresponds with the critical period of the crop, a good relationship is expected between the Normalized Difference Vegetation Index (NDVI) and yield. The present study was carried out in the North of Buenos Aires province, Argentina. Based on the type of soil, the study area can be divided into two homogeneous subzones: a subzone with lower clay content in the southwest and a subzone with higher clay content in the northeast. Nine growing seasons (2003–2011) were studied. In the first five years, an empirical model was calibrated and validated with field-observed wheat yields and MOD13q1 product-NDVI data, whereas in the other four years, the calibrated model was applied by means of yield maps and by comparing with official yields. The MOD13q1 image corresponding to Julian day 289 showed the best fit between NDVI and yield to estimate wheat yield early. Through yield maps, better weather conditions showed higher yields and higher soil productivity presented a greater proportion of the area occupied by higher yields. At department level, an R2 value of 0.75 was found after relating the estimation of the calibrated empirical model with official yields. The method used allows predicting wheat yield 30 days before harvest. Through yield maps, the NDVI perceived the temporal and spatial variability in the study area.eng
dc.formatapplication/pdfeng
dc.language.isoeng
dc.rightsinfo:eu-repo/semantics/restrictedAccesseng
dc.subjectTrigoes_AR
dc.subjectCubierta Vegetal
dc.subjectPlant Covereng
dc.subjectSensores
dc.subjectSensors
dc.subjectModelos de Simulación
dc.subjectSimulation Modelseng
dc.subjectRendimiento
dc.subjectYieldseng
dc.subjectTeledetección
dc.subjectRemote Sensingeng
dc.subjectTécnicas de Predicción
dc.subjectForecastingeng
dc.subjectClimatic Factorseng
dc.subject.otherBuenos Aires
dc.subject.otherSensores Remotos
dc.subject.otherMODIS-NDVI
dc.titleRelationship between MODIS-NDVI data and wheat yield : a case study in northern Buenos Aires province, Argentinaeng
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.description.filFil: Lopresti, Mariano Francisco. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino; Argentina
dc.description.filFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.filFil: Degioanni, Américo José. Universidad Nacional Río Cuarto, Facultad de Agronomía y Veterinaria, Departamento de Ecología Agraria; Argentina
dc.subtypecientifico


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