ABSTRACT
This article draws on observations by local farmers to analyse changes in climate and their effects on mountain farming in the Karakoram of northern Pakistan, where little scientific knowledge about the local manifestations of climate change exists. It shows that farmers clearly perceive a warming trend in the valleys that has led to a significant reduction of snowfall and snow cover over the last 30–40 years, while observations of rainfall changes are rather mixed. These perceptions are in line with trends detected at the nearest weather station with long-term observations. In contrast to common assumptions about climate change impacts in this high mountain region, the local effects of these changes are rather diverse and strongly depend on microclimatic and other factors: while many farmers acknowledge improved cropping conditions due to an extended agricultural season, a slight majority of respondents evaluate the observed changes in rather negative terms, among others due to an aggravated water scarcity in spring. The findings highlight the importance of local contexts and show that especially in mountain regions, general assumptions about climate change impacts should be avoided.
Acknowledgements
The author expresses his gratitude to Ishaq, Hadi, Saulat, Mastan Ali, and many other Nagarkuts for their invaluable support of this research. Thanks go to the Pakistan Meteorological Department for providing meteorological data, and to Stefan Schütte and two anonymous reviewers for their valuable comments on earlier versions of the manuscript. Research for this article was generously funded by the German Research Foundation as part of the project ‘Climate change and multiple stressors in high mountain areas: Vulnerability, adaptive capacity and human security in Nagar (Karakoram), Pakistan’ (KR 1467/19-1) at the Centre for Development Studies (ZELF), Freie Universität Berlin, Germany.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1 P-value of less than 0.01 according to the Mann-Kendall test for monotonous trends. Here, trends detected with a p-value of less than 0.05 are considered significant, and trends with a p-value of less than 0.01 highly significant.