<mark>other thing to note is that crop models that have been used so far only take into account the impact of drought and if there were other perils such as those of pests, crop disease, etc., these need to be recorded in the crop yield data if these perils had a significant impact on the actual yield. Yet another issue is intercropping that is quite prevalent in Africa which makes it difficult to establish crop area for the purpose of calculating productivity (yield per unit area). A possible solution for this is to make available a near‐real time crop mask.</mark> There is a pressing need to improve the confidence in the methods and tools in order to minimize the basis risk, both the spatial aspect of the hazard and the crop yield aspect. WRSI is a widely used drought index (though less usable for excessive rainfall) but assessments of other drought indices and their associated crop growth models, (e.g. water stress index, WSPD, and the DSSAT crop models) need to be considered to test if gridded crop yield data using other indices and their associated crop models leads to a reduction in basis risk. The key use of the DSSAT model (which shows the highest complexity but also the highest relationship between modeled and actual yields, given all input parameters are available) is to first study which parameters drive and determine yields. The type of evaluation of gridded crop models should of course be done for the developing countries for which food security insurance schemes are being devised taking into account the completeness and quality of data available in these countries. The global proliferation of smart phones makes it possible for crowdsourcing to radically improve the collection of crop data but creating the proper platform for data cleansing, data verification and data mining techniques for processing crop data has yet to happen in the manner that crowdsourcing has now become acceptable in disaster response (Butler 2013).

<mark>An operational issue that is related to the use of grids in a world that is typically sub‐divided into administrative units is the consistency with which forward and backward mapping between grids used in the crop model and the sub‐national administrative boundaries at which populations and</mark> crop information are available.

<mark>On the food security vulnerability assessment side, it is critical to expand our understanding of drought impacts. A comprehensive way of doing this is through the collection of household data on income, expenditure, assets, food security status, coping strategies. Collecting this type of data is resource‐intensive and time‐consuming but it is only through such surveys would it be possible to get a better understanding of the impact on drought at the household level and with this better</mark> understanding it may just be possible to build simple but robust models for assessing food security.

<mark>Data on food aid needs following droughts from field surveys and post‐disaster donor cash/food aid should be systematically collected and made available for lookup by type of disaster. Currently, the WFP maintains the DACOTA database</mark><sup>36</sup> <mark>that lists the number of beneficiaries of food aid and the UNOCHA has a Financial Tracking Service</mark><sup>37</sup> <mark>for tracking global humanitarian aid flows However, unless the information is given by type of disaster, it may be difficult to separate the number of beneficiaries into those that are food insecure because of the drought from those that are chronically food insecure. Getting the number of beneficiaries whose food insecurity is attributable</mark>

> 36 The DACOTA database is available at http://appsrv01.wfp.org/Dacota/.

> 37 UNOCHA’s Financial Tracking Service for tracking global humanitarian aid flows is available at <u>http://fts.unocha.org/pageloader.aspx?page=search‐ reporting_display&showDetails=&CQ=cq090312150914gaMlaOfsXX.</u>

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