SPAM2000v3.0.6 Release

There has been a new release of the Spatial Production Allocation Model with updates in the countries listed below.

In the notes below, you will notice one of the main corrections that was applied to each country was “fixing of 0 and -999 inconsistencies”.  Wondering what that means?  When we process  subnational agricultural production statistics for each country we make a distinction between when we have data for that administrative district, and that value is equal to zero, and when we know that district exists, yet we have no production data for that district.

In other words, “no data” (-999) is not the same as “zero”.  The more precise we can be with this distinction in the input agricultural production statistics, the better out outputs.

So how do we fix these inconsistencies?  When we revisit a country for re-run we re-visit input statistics.  When we see that larger administrative units (Regions, or Level 1) report zero, the smaller administrative units with that unit (districts, or level2) within that unit should also be equal to zero, not -999 as in this example.  The user changes these -999 values to zero and re-runs the model for that country.  This would be the fix in “wheat” example below.  However, the -999 value under rice is fine since this indicates we don’t have statistics for that district, not that the value is zero.

 Excerpt from ReadMe.txt associated with this release:

Bangladesh … complete new dataset from [1], calibrated to 2006-2007, and National data for totals

India … corrected groundnut data (use information from ICRISAT [2], but only at state level),
some corrections for wheat and rice using [3] and fixing of 0 and -999 inconsistencies in all crops
Burundi … fixing of 0 and -999 inconsistencies
Congo DR … fixing of 0 and -999 inconsistencies
Rwanda … fixing of 0 and -999 inconsistencies
Tanzania … fixing of 0 and -999 inconsistencies
Uganda … fixing of 0 and -999 inconsistencies
Zimbabe … fixing of 0 and -999 inconsistencies
Kenya … complete new dataset from Kenya [4] calibrated to avg 2004-2006,FAO calibration
South Africa … fixing of 0 and -999, use only level1 data in the runs, ALSO include WHEAT (which had previously been missed)
DUPLICATE pixels in South Africa and adjoining countries were fixed (eliminated)
Nigeria … new data set at national and level1 [5] for cassava, millet, groundnut, sorghum, other pulses (bean), maize, yam, rice and cotton.
Aggregate 37 states into 31 (to match previous Nigeria data)
rest of the crops from previous Nigeria data, with totals from FAO avg(2005-2007).
Assume sugar-cane area and production = 0 since data is not consistent with reports.

New SPAM runs since V3.0.5:
Centr.African Rep … fixing of 0 and -999 inconsistencies, run all crops
Gabon … fixing of 0 and -999 inconsistencies, run all crops
Liberia … fixing of 0 and -999 inconsistencies, run all crops
Mauritania … fixing of 0 and -999 inconsistencies, run all crops
Togo … fixing of 0 and -999 inconsistencies, run all crops