Development of a tool to monitor crop growth and grain yield in Córdoba, Argentina Antonio de la Casa and Gustavo Ovando Facultad de Ciencias Agropecuarias.

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Presentation transcript:

Development of a tool to monitor crop growth and grain yield in Córdoba, Argentina Antonio de la Casa and Gustavo Ovando Facultad de Ciencias Agropecuarias – UNC CREAN CREAN Córdoba, Argentina

Relevance of the topic selected

FAO model (Doorembos and Kassam, 1979) Where Ya/Ymax is the relative yield; (1-Ya/Ymax) the relative yield decrease; ETa/ETmax the relative evapotranspiration; (1-ETa/ETmax) the water stress or relative evapotranspiration deficit; Ky is the response of yield to water stress for a given environment. ∑NDVIa: actual NDVI sum for crop cycle ∑NDVImax: maximum NDVI sum for crop cycle Relative NDVI values substitutes those of Relative evapotranspiration (Funk and Buddle, 2009) Conceptual framework Operative framework

Objective of application  Develop a procedure to estimate corn and soybean yield in Cordoba province, Argentina.  Use different ILWIS routines to calculate FAO Model crop productivity from NDVI MODIS and local (in situ) data.

Flow diagram

❶ MODIS 250 m NDVI images

❸ ❸ Image Classification Late soybean: iff(band_1>0.6,iff(band_7 0.7,1,0),0),0) Early soybean: iff(band_1 0.7,iff(band_15<0.3,1,0),0),0) Corn iff(band_1 0.6,iff(band_15<0.3,1,0),0),0) iff(band_1 0.7,iff(band_15<0.3,1,0),0),0) soybean:=iff((soybean_1=1 )OR (soybean_2=1),1,0) corn:=iff((corn_1=1 )OR(corn_2=1),1,0) Late soybean Early soybean Corn

❼ YMax=∑(fCov*PAR)*ε*HI Ymax_soybean:=iff(soybean=1,Sum_fCov_Par_01*1.8*0.47*10,?) Ymax_corn:=iff(corn=1,Sum_fCov_Par_01*3.8*0.47*10,?)

❽ Ya=Ymax-Ymax*Ky*(1-∑NDVI/∑NDVImax) ❽ Ya=Ymax-Ymax*Ky*(1-∑NDVI/∑NDVImax) Ya_soybean:=iff(soybean=1,Ymax_soybean-Ymax_soybean*1.3*(1-ndvi_suma/7.6),?) Ya_corn:=iff(corn=1,Ymax_corn-Ymax_corn*1.5*(1-ndvi_suma/7.6),?)

❾ Image masking

❿ Departmental Yield soybeancorn

Conclusions It was estimated corn and soybean yields in Cordoba, Argentina, using only NDVI and solar radiation data as background information. This first prototype, despite its simplified design, produce objective results to contrast with the more qualitative method currently used by the state agency. The FAO method for estimating crop productivity structured in GIS is flexible to incorporate modular routines to calculate Ymax, ETmax and ETa using methodologies of more complexity and accuracy derived from remote sensing. Improving the procedure at a later stage should take into account satellite imagery to evaluated the different sensitivity to water stress of crops at different phenological stages.