Farm and Operator Characteristics Affecting the Awareness and Adoption of Precision Agriculture Technologies in the US

By S. Daberkow, W. McBride. Precision Agriculture, 2003

435 citations recorded by the source corpus.

Subfields: General Agricultural and Biological Sciences

View Paper: https://www.semanticscholar.org/paper/03d884cab892e0e0af0e8c85d25cda05ab9d3957

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In General Agricultural and Biological Sciences

Citations in the corpus

Why farmers adopt best management practice in the United States: a meta-analysis of the adoption literature. (2012, methodology): “…been considered important influences on a farmer’s decision to adopt a BMP. Acres is a measure of farm size and has been used as a measure of capital (Norris and Batie, 1987), or scale of economy (see e.g., Belknap and Saupe, 1988; Caswel et al., 2001; Daberkow and McBride, 2003: Khanna, 2001).”
Farm Profits and Adoption of Precision Agriculture (2016, background): “Daberkow and McBride (2003) found that full-time farming was significant in both the first and second stages of their PA awareness and adoption empirical model.”
Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany (2018, methodology): “Studies on the adoption of precision technologies often employ logit model with the objective of explaining adoption behaviour of individuals (Daberkow and McBride 2003).”
IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology (2012, methodology): “Daberkow and McBride [17] found mixed results when looking at the relationship between credit availability and the adoption decision.”
Precision Agriculture Technologies positively contributing to GHG emissions mitigation, farm productivity and economics (2017, neutral): “Those characteristics were: (i) the large farm sizes; (ii) the organised extension system mainly by the government and universities; (iii) the farmers/entrepreneurs willingness for progress and technology adoption; (iv) the high income; (v) the possibility of financing investment; and (vi) the limit”
Determinants of agricultural best management practice adoption: Evidence from the literature (2008, background): “Renters, on the other hand, have less to personally gain from installing practices (see, e.g., Daberkow and McBride 2003).”
Factors influencing the adoption of precision agricultural technologies: a review for policy implications (2012, background): “Therefore, computer use has commonly been found as a predictor for the adoption of PATs (e.g. Isgin et al. 2008; Daberkow and McBride 1998, 2003).”