Human Capital and Adoption of Innovations: Policy Implications

188

103

Adoption of agricultural innovations has been an important factor affecting the welfare of farmers, the productivity of agriculture and the economics of the food sector. This paper reviews the literature on technology adoption in agriculture with a focus on the role of uncertainty and learning. It examines the factors affecting adoption benefits for farmers and their linkages with the innovation process. It also discusses the welfare implications of innovation and adoption for farmers and consumers.

Section: Uncertainty and Learning In Technology Adoptionmentioning

confidence: 99%

Uncertainty, Learning, and Technology Adoption in Agriculture

Chavas¹,

Nauges²

2020

188

103

“…They note that traditional social networks have increasingly been complemented by virtual networks, enabled by modern communication technologies. Huffman (2020) argues that higher levels of formal education increase farmers' abilities to adapt to changing conditions and improve technology choices. 4.…”

Section: Innovative Aspects Of the Adoption Literaturementioning

confidence: 99%

“…We invited leading researchers to prepare papers on important and pertinent topics. Some of the papers provide syntheses of well-researched topics (e.g., Chavas and Nauges 2020;Huffman 2020). Others address novel or underresearched issues (e.g., Heiman, Ferguson, and Zilberman 2020;Llewellyn and Brown 2020).…”

Section: Introductionmentioning

confidence: 99%

Understanding Adoption of Innovations and Behavior Change to Improve Agricultural Policy

Pannell¹,

Zilberman²

2020

Research on the adoption of new practices and new behaviors in agriculture continues to grow and evolve, and its relevance to policy remains high. This special issue presents 10 papers that provide overviews of important aspects of the recent adoption literature, or identify gaps and opportunities in the literature. Adoption research has been innovative in a number of ways, including its recognition of adoption decision making as a process, not an event; its emphases on heterogeneity and learning; and its strong multidisciplinary flavor. There is scope for further innovation through efforts to enhance predictions of adoption, better understanding the balance between profit and non‐profit motivations, and better recognizing the influence on adoption of characteristics of the innovations, not just characteristics of the potential adopters and their contexts. Opportunities for policy include effort to support women farmers' adoption of beneficial innovations in developing countries, and use of tools and approaches from marketing in public extension programs

“…Tey and Brindal (2012) repeated the approach of Knowler and Bradshaw (2007) but focused specifically on the factors affecting the adoption of precision agriculture technologies. The major drivers were those that affect the relative returns from using the new technology including human capital (Huffman 2020), farm characteristics (i.e. farm size, profitability, tenure arrangements), agro-ecological factors (i.e.…”

Section: Review Of Factors Affecting Adoptionmentioning

confidence: 99%

Limits to Profit Maximization as a Guide to Behavior Change

Weersink¹,

Fulton²

2020

Economists have long been interested in why farmers decide to adopt new technologies. Modeling decision making requires modeling behavior; the key behavioral assumption is profit maximization. In addition, most empirical studies assume a binary decision—adopt or not. This paper argues that adoption should be understood as a process with multiple stages in which the final decision to use the new technology only occurs if the previous stages are completed. While profit considerations are clearly important, particularly in the later stages of the process, they need to be supplemented with other social and cognitive considerations, particularly in the early stages. Understood this way, profit maximization assumptions can provide predictions for the upper or lower bounds of adoption. The adoption rate suggested by profit maximization will be an upper bound if noneconomic factors are expected to either slow down or deter adoption, while that rate will be a lower bound if noneconomic factors are expected to encourage adoption. To capture these elements, econometric models need to pay attention to the timing of decisions and use techniques that condition later‐stage decisions on previous stage outcomes. In addition, the paper suggests the use of expert systems that have economic, learning and social aspects explicitly built into them would be valuable.