A comparative analysis of the performances of some drought indices in monitoring and predicting sunflower and sorghum crop yield in Central Italy was carried out. Considered drought indices include: Palmer drought indices (PDSI, Z, CMI), Standardized Precipitation Index (SPI) and a severity index (RS) derived from a Run theory applied to the soil water content time series. The indices were computed weekly using climatic data recorded from 1978 to 2003 in four sites for which also pedo-hydrological and crop data are available. An intra-seasonal correlation analysis enabled to pick out the week during which each index shows the best correlation with the seasonal yield. Weekly indices values, cumulated for each of the four growth stages, were used in a stepwise regression technique to identify the yield-drought index models. Model’s performances were evaluated using different goodness-of-fit measures. RS index proved to be the most suitable to predict agricultural drought conditions. Its strength point is the ability to account for the specific crop characteristics also if more input data are required. SPI, despite of the limited data requirement and the simple algorithm led to appreciable results similar to those obtained by using Z and CMI that derive from more complex algorithms. PDSI models were sometimes not significantly related with crop yield and in general they exhibited a lower reliability for its prediction.

An evaluation of some drought indices in the monitoring and prediction of agricultural drought impact in central Italy

TODISCO, Francesca;VERGNI, LORENZO;MANNOCCHI, Francesco
2008

Abstract

A comparative analysis of the performances of some drought indices in monitoring and predicting sunflower and sorghum crop yield in Central Italy was carried out. Considered drought indices include: Palmer drought indices (PDSI, Z, CMI), Standardized Precipitation Index (SPI) and a severity index (RS) derived from a Run theory applied to the soil water content time series. The indices were computed weekly using climatic data recorded from 1978 to 2003 in four sites for which also pedo-hydrological and crop data are available. An intra-seasonal correlation analysis enabled to pick out the week during which each index shows the best correlation with the seasonal yield. Weekly indices values, cumulated for each of the four growth stages, were used in a stepwise regression technique to identify the yield-drought index models. Model’s performances were evaluated using different goodness-of-fit measures. RS index proved to be the most suitable to predict agricultural drought conditions. Its strength point is the ability to account for the specific crop characteristics also if more input data are required. SPI, despite of the limited data requirement and the simple algorithm led to appreciable results similar to those obtained by using Z and CMI that derive from more complex algorithms. PDSI models were sometimes not significantly related with crop yield and in general they exhibited a lower reliability for its prediction.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11391/37445
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