tems and management practices, as well as the social and economic outcomes for farming families and communities.
     There  are critical  synergies  between livelihood out­comes and the stock of "capitals" on which livelihoods are based. Uncertain and declining livelihoods often result in depreciation of the capital stock, especially natural capital, further increasing vulnerability. By comparison, secure and improving livelihoods can support investment and enhance­ment of capital stocks, such as improved land management skills and practices.
     AKST is a critical component of the stock of capital in the livelihoods framework. It is recommended that the sustainable livelihood framework, adapted to accommodate local conditions, is used to inform future development of AKST to meet the social and economic needs of farming households and communities, especially targeting the needs of the most vulnerable groups.

6.2.10.4 Understanding farmer attitudes and behavior
The development and successful application of AKST de­pends on the attitudes, motivation and behavior of the potential user community, especially land managers. An un­derstanding of the processes by which land managers learn about, evaluate and adopt or reject new technologies is es­sential for the management of technology change and the design of appropriate AKST.
     Innovation-decision models have long been used to ex­plain technology adoption behavior among rural communi-

ties (Ryan and Goss, 1943; Rogers, 2003). Prior conditions, such as policy drivers or perceived needs, shape the disposi­tion of potential adopters towards a new product or prac­tice. This process is influenced by characteristics of decision makers (such as personal and contextual social, economic and  cultural  factors)   and characteristics  of innovations (such as relative advantage, compatibility with values and preferences, simplicity and ability to trial and observe ben­efits). These models also confirm the importance of com­munication channels, agents of change and contextual and cultural factors, including the relative balance of individual and collective decision making. These models have however been criticized as too rigid, seeing adoption as an externally driven, linear process. Alternative models emphasize differ­ent elements of the decision process, namely systems models, information models, models of reasoned action and learning and knowledge transfer models (Garforth and Usher, 1997; Beedell and Rehman, 1999; Morris et al., 2000; Phillipson and Liddon, 2007).
     In this context, there is an urgent call for improvement in the understanding of technology change and adoption be­havior, in particular to:
•     Improve the understanding of variation in farmer mo­tivation and behavior with respect to new technologies and how this is shaped by policy and market drivers, personal circumstances, common practices, local and distant institutions, issues of gender and ethnicity and perceptions of risk;