Climate data from these series alone, however, are insufficient to tell us how variability in agricultural productivity will change, and we must empirically determine the crop productivity response to temperature and precipitation. A widely used statistical approach is the Ricardian technique pioneered by Mendelsohn et al (1994). This approach has been applied to examine the impact of climate change on African agriculture – albeit not for Tanzania – as reviewed in Dinar et al (2008), and in various other studies (see Kurukulasuriya et al 2006; Kurukulasuriya and Mendelsohn, 2007). The Ricardian approach takes advantage of climate variation across space to estimate the impact of decadal‐scale climate outcomes on land rents, crop value or production in a given year. It presumes that the “Ricardian” returns to land have adjusted to reflect differences in climate across locations.

However, in order to quantify poverty vulnerability to volatility, our study focuses on changes in temperature and precipitation that occur on interannual time‐scales – a period over which farmers do not have the ability to anticipate or significantly adapt to climate variations. As such, if we used Ricardian estimates of crop responses to analyze interannual volatility, we would be inappropriately applying estimates of farm responses to gradual climate changes in our analysis of short run variation in temperature and precipitation. Hence, a time series estimation of crop yields with annual temperature and precipitation among the explanatory variables (e.g. Lobell et al, 2006, 2008) is deemed more appropriate.

To that end, monthly climate data from the CRU TS 3.0 dataset (CRU, 2008) were used in linear regression models to analyze the relationship between mean temperature (°C) and precipitation (mm/month), and crop yields for several grains. This analysis was done at the sub‐ national level, more specifically at the administrative region level from 1992 to 2005. The climate data were also adapted to the growing season calendar as provided by the Famine Early Warning Systems NETwork (FEWS NET, 2008). Based on this calendar, we used a single growing season for maize, sorghum, and rice<sup>4</sup> , extending from January to June. Thus, for each year, the 0.5° gridded climate data were averaged temporally over this 6‐month period and spatially for each administrative region.

For each crop, data on harvested area and production from the Tanzanian Ministry of Agriculture as well as from Agro‐MAPS (Monfreda et al, 2009) were compiled for each of the 17 regions and converted to yields (tonnes/ha). These data were available from 1992 to 2005. Forward stepwise multiple linear regression models were developed for each of the three crops linking yields to mean temperature and precipitation while accounting for temporal trends. Inclusion of higher order terms (e.g., temperature squared) would be appealing but is not supported by the limited availability of the time series data. A few observations were removed from analysis as they presented highly unlikely yield values. Moreover, harvested areas were used as weights in the fitting process.

The analysis finds that when considering a yields as functions of climate and time trends (where significant), the temperature coefficients are negative, while the coefficients for precipitation are positive (Table 3). Coefficients on both climate variables are highly significant in all models. That is, rising temperatures will put downward pressure on grains yields, while rising precipitation will enhance yields. An increase in average growing season precipitation by one mm/month is enough to increase maize and rice yields by 0.005 tonnes per hectare, and

> 4 Selected due to reliable production data availability, representing 93 percent of cereals production (FAO, 2009)

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