Title:
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Modelling productivity of rain-fed agriculture under scenarios of
climate change in Sulaymaniyah, Iraq
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This project applied an Agri-meteorological model (FAD AquaCrop v4) to predict the
likely changes in the yield of rain-fed grain crops in Iraqi Kurdistan resulting from
projected climate change. The research was carried out in three main stages. Firstly,
Landsat Thematic Mapper imagery was used to classify the study area into different land
cover types. The effect of rainfall variability on vegetation productivity in areas classified
as 'rain-fed agriculture' was determined using a monthly composite time series of
Normalised Difference Vegetation Index (NDVI) measurements from July 1981 to
December 2006 derived from the National Oceanic and Atmospheric Administration's
(NOAA) Advanced Very High Resolution Radiometer (AVHRR). These data were
compared to monthly precipitation records from Sulaymaniyah Meteorological Station to
characterise the nature of vegetation response to rainfall. A strong positive correlation was
found between vegetation productivity and precipitation patterns with a 2-month lag
period. The second stage involves applying the AquaCrop model to predict winter wheat
crop performance for the growing seasons 1986-2006. It was found that the simulated
grain yield (GY) and above ground biomass (AGB) were consistent with the measured GY
and AGB, with corresponding coefficients of determination (r) of 0.85 GY and 0.81 AGB.
These results indicate that the AquaCrop model can be used for predicting winter wheat
grain production. The last stage involved studying the impact of projected climate change
for the 2020, 2050 and 2080 derived from the HadCM3 General Circulation Model. The
AquaCrop model was re-run using the predicted changes in the climate values for
temperature/precipitation for the selected decades, to simulate the impact on winter wheat
yields. The findings indicate that the average yield of rain-fed crops will be reduced, with
scenario A2a experiencing more reduction relative to that predicted by scenario B2a.
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