Factors affecting farmer adoption of remotely sensed imagery for precision management in cotton production

Larson, James A.; Roberts, Roland K.; English, Burton C.; Larkin, Sherry L.; Marra, Michele C.; Martin, Steven W.; Paxton, Kenneth W.; Reeves, Jeanne M.

doi:10.1007/s11119-008-9065-1

Cited by 75 publications

(66 citation statements)

References 14 publications

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“…This was considered to be a consequence of older farmers having shorter planning horizons, diminished incentives to change and less exposure to PATs, mainly due to their hesitation to use the computer (Roberts et al [9]). In this context, younger farmers were postulate as having longer career horizon and being more technologically orientated (Larson et al [13]). Other studies, on the other hand found age as a positive determinant (e.g.…”

Section: Resultsmentioning

confidence: 99%

Willingness to Adopt Technologies of Precision Agriculture: A Case Study of the Czech Republic

Kušová¹,

Těšitel²,

Boukalová³

2017

WIT Transactions on Ecology and the Environment

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The idea of precision agriculture is to optimize production inputs (fertilizers, irrigation water, etc.) when trying to achieve desired harvest. More targeted use of inputs is expected to benefit for the environment, including quality of underground water. It can be said to contribute to reconciling a situation frequently occurring in watershed, where the need to sustain high quality of water resources has to be balanced with other interests related to land-use. Technologies are available. Nevertheless, availability of technologies is a precondition necessary, but not sufficient. As in other cases, their practical implementation is influenced by many socio-economic and cultural factors, making any application site-specific. This paper builds on results of a sociological questionnaire survey conducted in selected rural areas of the Czech Republic. It tries to respond to the question to which extent factors such as tradition, overall economic situation, economic power of a farm and personality of a farmer can be considered predictors for willingness of a farmer to adopt precision agriculture technologies.

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Section: Resultsmentioning

confidence: 99%

Willingness to Adopt Technologies of Precision Agriculture: A Case Study of the Czech Republic

Kušová¹,

Těšitel²,

Boukalová³

2017

WIT Transactions on Ecology and the Environment

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“…Previous studies have used the random utility model framework to study adoption decisions (Rahm & Huffman, 1984;Roberts et al, 2004;Larson et al, 2008;Walton et al, 2008;Jara-Rojas, Bravo-Ureta, Engler, & Diaz, 2013;Lambert et al, 2014), where a producer adopts a technology when the expected utility of profits is higher for the adoption scenario compared to the non-adoption scenario. Let E U π AG (E U π NAG ) be the expected utility of profits of adopting (non-adopting) AG systems for producer i.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

“…Variables previously identified as factors influencing the decision to adopt PA technologies include age, education, farm size, sources used to obtain PA information, and the use of best management practices (BMPs). Banerjee et al (2008), Larson et al (2008), Walton et al (2008), and D'Antoni, Mishra, and Joo (2012) included age in the adoption equations and found that younger farmers with longer planning horizons were more likely to adopt PA technologies than older producers. Thus, farmer age (AGE) was hypothesized to have a negative effect on the adoption of ASC technologies and AG systems.…”

Section: Empirical Modelmentioning

confidence: 99%

Automatic Section Control Technologies and GPS Auto-guidance Systems Adoption in Cotton Production

Edge¹,

Velandia²,

Boyer³

et al. 2018

JAS

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Using data from a survey of cotton producers in 14 US states, and a bivariate probit regression, this study examined the effects of the following measured parameters on the adoption of Automatic Section Control (ASC) technologies and GPS Auto-Guidance (AG) systems: age, education, farm size, field geometry, information sources, as well as the use of specific production practices and other Precision Agriculture (PA) technologies. Results suggest that younger, more educated producers, consulting farm dealers for information about PA technologies, using other PA technologies, and managing larger farming operations located in counties with more irregularly shaped fields are more likely to adopt ASC technologies and AG systems. Predicted adoption probabilities estimated using regression results suggest the use of other PA technologies and farm dealers as a source of precision farming information have the largest impact on the probability of adopting ASC by cotton farmers. Additionally, these results suggest farmers with operations in eastern Arkansas, western Tennessee, and a couple of counties in middle Tennessee are more likely to adopt ASC technologies. Producers in these regions had the highest percentages of users of other PA technologies and farm dealers to obtain PA information.

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“…As there is no clearly dominant approach in the literature, we opted to use the more traditional ordered probit procedure with thresholds not varying with respondent characteristics. 7 Readers interested in more detail about the sampling procedures, the survey methods and the rest of the survey instrument are referred toRoberts et al (2002) 8 The 2001 survey data used in this study are collected the same way as the data inLarson et al (2008), which were published in a recent issue of the Precision Agriculture Journal. Hence, the survey also asked questions about adoption of various precision technologies (i.e.…”

mentioning

confidence: 99%

Estimating the demand and willingness-to-pay for cotton yield monitors

et al. 2009

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Survey data from cotton farmers in six southeastern states of the USA were used to estimate the demand and willingness-to-pay (WTP) for either retrofitting yield monitors onto cotton pickers or to purchase a yield monitor as an option with a new cotton picker. 'Don't know' responses were either omitted, combined with 'no' responses or included as a separate category for comparing WTP and estimates of the price elasticity of demand. Our results suggest that treating the 'don't know' response as a separate category provides WTP estimates that are more consistent with expectations than the other approaches. The estimated price elasticities and demand curves indicate that previous users of precision technology are more responsive to changes in price of cotton yield monitors and would be more likely to adopt them when the price decreases. These demand and WTP estimates provide important information that can be used by those who sell cotton yield monitors, as well as policy-makers who may wish to subsidize this technology. Referendum contingent valuation was useful for evaluating the demand for any new technology.

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Factors affecting farmer adoption of remotely sensed imagery for precision management in cotton production

Cited by 75 publications

References 14 publications

Willingness to Adopt Technologies of Precision Agriculture: A Case Study of the Czech Republic

Willingness to Adopt Technologies of Precision Agriculture: A Case Study of the Czech Republic

Automatic Section Control Technologies and GPS Auto-guidance Systems Adoption in Cotton Production

Estimating the demand and willingness-to-pay for cotton yield monitors

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