Perceived and measured climate variability and change in semi-arid environments in Tanzania: experiences from Iramba and Meatu Districts

Abstract

This paper combines farmers' perceptions of climate variability and change and meteorological data trends to generate empirical evidence to broaden an understanding of the phenomena. The results show an agreement on changing rainfall patterns. Bad years described by drought frequencies, temperature, and dry spell have increased since the 1970s. Crop growing period has decreased by one month in Meatu and by more than a month in Iramba. As hypothesised, the Mann-Whitney U test shows similar men and women's perceptions at 5% level of significance (P value = 0.701). Similarly, the Kruskal-Wallis H test indicates that the poor, not so poor and the rich have the same perceptions (P value = 0.281). These results have implications on crop and livestock production systems and on livelihoods more generally. We conclude that climate variability and change manifestations overlap, making it a complex phenomena perceived equally by men, women, the poor and non-poor. This is understood holistically by combining farmers' perceptions and meteorological data trends to inform adaptation strategies related decision making.

On farmers’ perceptions of climate change and its nexus with climate data and adaptive capacity. A comprehensive review

“…However, for authors such as Baul and McDonald (2015), there is no permanent trend perceived in rainfall patterns because they are erratic or unpredictable. About half of the sample agrees with this statement by consistently referring to changes in the onset and offset (cessation) of rainy seasons (Shameem et al 2015, Kabote et al 2017, Mulenga et al 2017. Likewise, rainfall outside of rainy seasons is perceived to be more frequent (Le Dang et al 2014), while decreasing rainfall is registered during the wet season (Simelton et al 2013, Sutcliffe et al 2016, affecting the expected crop cultivation period (Chaulagain and Rimal 2019).…”

“…However, most studies recognize limitations in the station-based data, including missing values and measurement errors (Esayas et al 2019, Budhathoki andZander 2020). Likewise, some studies inform about the lack of meteorological stations near the case study and how this affects accuracy (Zampaligre et al 2014, Kabote et al 2017, so farmers should essentially pick and choose helpful information to compare with their own experience (Muita et al 2016). This point is crucial because farmers' perceptions are more likely to be similar to climate data from the nearest meteorological stations, which requires being more cautious while interpreting farmers' perceptions at a larger geographical level (Panda 2016).…”