In the Mediterranean, durum wheat is one of the major crops, but a high variability of grain yield and protein concentration (GPC) prevents an adequate agronomic planning at the farm or consortium level. Although there are many studies on monitoring of crop production and early prediction of yields, little has been done at the local scale. The aim of this study was to assess simplified integration algorithms (SIAs) for integrating remote sensing information with a crop model, to forecast the GPC and grain yield at the field scale. To this end, the CERES-Wheat model was run to simulate the seasonal average of grain yield (AVE) and GPC in Val d’ Orcia (Tuscany Region, Italy) during the 2009-2010 and 2010-2011 growing seasons. The performances of different vegetation indices from MODIS imagery in harvest forecasting were assessed and compared. The SIA formulation was based on the simulated AVE and GPC, and on their spatialization in relation to the intraannual variability between the fields described by vegetation indices. The simulated AVE traced the observed trend. The fraction of absorbed photosynthetically active radiation (fPAR) was the best index in describing grain yield, and the related SIA showed at validation good performance at the field scale (r2 = 0.74). Conversely, the SIA was unable to predict GPC due to the low performance of CERES-Wheat in capturing the interannual variability and to the failure of the fPAR in describing the GPC interfields variability at intermediate canopy reflectance values.

Integration of remote sensing and crop modeling for the early assessment of durum wheat harvest at the field scale / F. Orlando, A. Dalla Marta, M. Mancini, R. Motha, J.J. Qu. - In: CROP SCIENCE. - ISSN 0011-183X. - 55:3(2015), pp. 1280-1289. [10.2135/cropsci2014.07.0479]

Integration of remote sensing and crop modeling for the early assessment of durum wheat harvest at the field scale

F. Orlando;
2015

Abstract

In the Mediterranean, durum wheat is one of the major crops, but a high variability of grain yield and protein concentration (GPC) prevents an adequate agronomic planning at the farm or consortium level. Although there are many studies on monitoring of crop production and early prediction of yields, little has been done at the local scale. The aim of this study was to assess simplified integration algorithms (SIAs) for integrating remote sensing information with a crop model, to forecast the GPC and grain yield at the field scale. To this end, the CERES-Wheat model was run to simulate the seasonal average of grain yield (AVE) and GPC in Val d’ Orcia (Tuscany Region, Italy) during the 2009-2010 and 2010-2011 growing seasons. The performances of different vegetation indices from MODIS imagery in harvest forecasting were assessed and compared. The SIA formulation was based on the simulated AVE and GPC, and on their spatialization in relation to the intraannual variability between the fields described by vegetation indices. The simulated AVE traced the observed trend. The fraction of absorbed photosynthetically active radiation (fPAR) was the best index in describing grain yield, and the related SIA showed at validation good performance at the field scale (r2 = 0.74). Conversely, the SIA was unable to predict GPC due to the low performance of CERES-Wheat in capturing the interannual variability and to the failure of the fPAR in describing the GPC interfields variability at intermediate canopy reflectance values.
Vegetation indexes; Yield estimation; use efficiency; winter-wheat; grain-Yield; agriculture; information; performance; simulation; radiation
Settore AGR/02 - Agronomia e Coltivazioni Erbacee
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/595520
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