Abstract
Geographical assessments on the relationship between climate variability and crop production are important for planning adaptation programs to climate change impacts on Asian rice production. This paper analyses the seasonal precipitation consequences to irrigated crop yields, in opposition to the idea that irrigated crop yields are not affected by precipitation changes. Geostatistical methods are applied to assess changes in the patterns of seasonal precipitation and corresponding changes in the Boro crop production in Bangladesh. Surfaces depicting changes in the monsoon, non-monsoon and total precipitation from 2006 to 2007, and changes in three varieties of Boro crop yield and Total Boro yield from 2006-2007 to 2007-2008 crop year are generated through Splines, Inverse Distance Weighting and Ordinary Kriging methods. Performance evaluation of these models is also performed. The relationships between the surfaces of different precipitation seasons and the surfaces of different Boro yield seasons are then assessed. The results show that there is a significant correlation between seasonal precipitation changes and Boro yield changes with notable correlation coefficients and similarity in the patterns. A significant conformity of the high precipitation zones to the high Boro yielding zones is also depicted.
Highlights
Geographical assessments on the relationship between climate variability and crop production are important for planning adaptation programs to climate change impacts on Asian rice production
Geostatistics, which is based on the theory of regionalized variables [1,2,3], is increasingly being preferred to produce interpolated surfaces of spatial attributes because it allows one to capitalize on the spatial correlation between neighboring observations to predict attribute values at unmeasured locations
In the context of climate change impacts on Asian rice production, [14] argue that geo-spatial vulnerability assessments may become crucial for planning targeted adaptation programs
Summary
Geostatistics, which is based on the theory of regionalized variables [1,2,3], is increasingly being preferred to produce interpolated surfaces of spatial attributes because it allows one to capitalize on the spatial correlation between neighboring observations to predict attribute values at unmeasured locations. Spatial interpolation techniques, such as Splines, Inverse Distance Weighting or Kriging methods, are commonly used in climate variability analysis [4,5,6,7,8]; and agriculture management and planning [9,10]. In Bangladesh, lives and livelihoods mainly depend on agriculture and this is one of the most vulnerable countries to climate volatility and change, which potentially increases poverty by affecting agricultural productivity [14,19,20,21,22]
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