Fatalism, Climate Resiliency Training and Farmers’ Adaptation Responses: Implications for Sustainable Rainfed-Wheat Production in Pakistan - Mahmood, Nasir; Arshad, Muhammad Umair; Kaechele, Harald; Shahzad, Muhammad Faisal; Ullah, Ayat; Mueller, Klaus (Sustainability, 2020)
Climate change is a severe threat to the agricultural sector in general and to rainfed farming in particular. The aim of this study was to investigate the factors that can potentially affect the adaptation process against climate change. This study focused on wheat farmers and farming systems in the rainfed agroecological zone of Pakistan. Farmers’ data related to climate change fatalism, the availability of climate-specific extension services, socioeconomic and institutional variables, and farm characteristics were collected. A logit model to assess farmers’ decisions to adopt an adaptation measure and a multinomial logit model to assess their choice of various adaptation measures were used. The results showed that fatalistic farmers were unlikely to implement climate change adaptation measures. The variables related to the climate-specific extension services, including farmers’ participation in training on climate-resilient crop farming and the availability of mobile communication-based advisory services, had highly significant and positive impacts on farmers’ decisions and their choice of adaptation measures. Input market access and tractor ownership also had positive and significant impacts on farmers’ decisions to adapt and their choice of adaptation measures. This study highlights the need to improve rainfed-wheat farmers’ education levels to change their fatalistic attitudes towards climate change. Furthermore, government action is needed to provide climate-specific extension services to ensure sustainable production levels that will ultimately lead to food and livelihood security under a changing climate.
Keywords: C:Pakistan; _z:class:countries; _z:class:themes; oa:keyword:climate resiliency training; oa:topic:T10367 Agricultural Innovation and Livelihood Diversification; oa:topic:T10439 Adaptation to Climate Change in Agriculture; oa:topic:T11886 Risk Management and Vulnerability in Agriculture; openalex:import; openalex:yes

Application of Machine Learning Techniques to Delineate Homogeneous Climate Zones in River Basins of Pakistan for Hydro-Climatic Change Impact Studies - Nusrat, Ammara; Gabriel, Hamza Farooq; Haider, Sajjad; Ahmad, Shakil; Shahid, Muhammad; Jamal, Saad Ahmed (Applied sciences, 2020)
Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.
Keywords: C:Pakistan; _z:class:countries; _z:class:themes; oa:sdg:13 Climate action; oa:topic:T10029 Climate Change and Variability Research; oa:topic:T10330 Hydrological Modeling and Water Resource Management; oa:topic:T10466 Numerical Weather Prediction Models; openalex:import; openalex:yes