Factors Influencing the Selection of Precision Farming Information Sources by Cotton Producers

Abstract

Precision farming information demanded by cotton producers is provided by various suppliers, including consultants, farm input dealerships, University Extension systems, and media sources. Factors associated with the decisions to select among information sources to search for precision farming information are analyzed using a multivariate probit regression accounting for correlation among the different selection decisions. Factors influencing these decisions are age, education, and income. These findings should be valuable to precision farming information providers who may be able to better meet their target clientele needs.

A Study of Household Food security and Adoption of Biofortified Crop Varieties in Tanzania: The Case of Orange- Fleshed Sweetpotato

“…The multivariate probit regression technique has been used in several past studies [17][18][19]. It is an extension of bivariate probit model [20] and uses Monte Carlo simulation techniques to jointly estimate the multivariate probit regression equation system [21].…”

“…To examine whether these variables jointly affect the decision to plant the four sweetpotato variables analyzed in this study, we conducted appropriate hypothesis tests for each. Following [18,19], we also examined if the varietal traits affect the likelihood that a farmer will plant these improved varieties. Specifically, a Wald joint-exclusion test of the agroecology variables (proxied aezP5N10, aezP8) and access to the valley bottom, all of which are associated with moisture availability, yielded a Chi-square and p-value of 60.88 and 0.0000, respectively.…”

Smartphone use and willingness to adopt digital pest and disease management: Evidence from litchi growers in rural China

“…The finding that education has a positive impact on willingness to adopt DPDM is in line with that of previous studies. Evidence suggests that highly educated cotton (Jenkins et al, 2011), corn (Gardezi & Bronson, 2019), and grain producers (Pivoto et al, 2019) are more likely to adopt digital agriculture innovations. Moreover, healthier farmers may be more capable of field data collection and human−smartphone interaction.…”

Variable-Rate Application on Fertilizer Use in Cotton Production

“…Producers who used university Extension (EX) as a PA information source were 38% less likely to decrease fertilizer and 23% more likely have no change in fertilizer use with VRT management (Table 3). University Extension generates information for a wide range of farmers in a particular region as opposed to other sources of PA information that may provide a farmer with detailed information customized for their particular operation (Velandia et al, 2010;Jenkins et al, 2011). Thus, university Extension is often regarded as providing general, objective information (Larson et al, 2008;Jenkins et al, 2011).…”

“…University Extension generates information for a wide range of farmers in a particular region as opposed to other sources of PA information that may provide a farmer with detailed information customized for their particular operation (Velandia et al, 2010;Jenkins et al, 2011). Thus, university Extension is often regarded as providing general, objective information (Larson et al, 2008;Jenkins et al, 2011). The perceived impartiality of university Extension may influence farmers to be more conservative in adjusting their fertilizer use with VRT.…”