Factors Influencing the Adoption of Automatic Section Control Technologies and GPS Auto-Guidance Systems in Cotton Production

Precision agriculture (PA) technologies allow producers to obtain information about their fields and use this knowledge to apply inputs and manage time more efficiently. PA technologies such as Automatic-Section Control (ASC) reduce inefficiencies such as overlapping application of inputs (e.g., seed, chemicals). Additionally, technologies such as Auto-Guidance (AG) systems complement ASC technologies and allow producers to work longer hours by reducing fatigue. Both ASC and AG technologies appear to be quickly adopted by producers because of their relatively low cost compared to other precision farming technologies. The objective of this study is to determine the factors influencing the adoption of Automatic Section Control (ASC) technologies and GPS Auto-guidance (AG) systems among cotton producers. Using data from a survey of cotton producers in 14 states, this study evaluates the effect of age, education, farm size, use of information sources, and the use of specific production practices on the adoption decisions. Additionally, various field shape measures created using data from the NASS Crop Data Layer are included in the ASC equation to evaluate the influence of field shape on ASC adoption. Results suggest that younger, more educated producers, managing larger farming operations, and consulting farm dealers for information about PA technologies are more likely to adopt ASC and AG technologies. The influence of field shape on the adoption of ASC technologies is inconclusive

Changes in the Use of Precision Farming Information Sources Among Cotton Farmers and Implications for Extension

Using information from precision farmer surveys conducted in the southern United States in 2005 and 2013, we evaluated changes in the use of precision farming information sources among cotton producers. Although Extension remains an important source for producers interested in precision farming information, the percentage of cotton producers using Extension to obtain precision farming information has decreased over time. Results from our study should motivate Extension professionals to re-evaluate the precision farming information they provide to producers and the practices they use to provide such information in efforts to maintain Extension\u27s importance as an information source

Evaluating the use of electrical conductivity for defining variable-rate management of nitrogen and seed for corn production

This paper uses data from thirty-three on-farm experiments to explore the use of electrical conductivity (EC) for defining seeding and nitrogen rates for corn production. We estimate the yield response to nitrogen and seeding rates, including an interaction term with EC for each of the trial years. We then determine the optimal uniform and variables rates and compare the profits. If EC can be used on different fields and years, then the correlation between EC and the optimal rates should be consistent across fields and years. We find that the optimal variables rates do not produce profits above $5 an acre for the majority of the fields. Additionally, in different years on the same field, the high EC areas may require more or less of the inputs. The inconsistency of the relationship between EC and the optimal rates does not enable EC to be accurately used for variable rate applications across different growing years. While EC will continue to be important in detecting salt affected soils and can be calibrated for detection of specific soil elements, the use of EC for variable-rate input management is not recommended

Processing of On-Farm Precision Experiment Data in the DIFM Project

This work summarizes the challenges brought to data processing in the context of on-farm precision experiments (OFPE). We review the errors and proposed data cleaning from previous literature on cleaning agricultural data and discuss how these previous methods can or cannot be applied to agricultural data from an OFPE. Finally, we present the details of the DIFM data processing protocol and where it can be improved based on the literature

Economically Optimal Nitrogen Side-dressing Based on Vegetation Indices from Satellite Images Through On-farm Experiments

A methodology is introduced that combines data from on-farm precision experimentation (OFPE) with remotely sensed vegetative index (VI) data to derive site-specific economically optimal side-dressing N rates (EONRs). An OFPE was conducted on a central Illinois field in the 2019 corn growing season; the trial design targeted six side-dressing N rates ranging from 0 and 177 kg ha-1 on field plots, and yields were recorded at harvest using a standard GPS-linked yield monitor. NDRE values were calculated from Sentinel-2 satellite imagery during the V10 to V12 corn growth stages of the experiment’s crop. After partitioning the field by NDRE quartile, economically N side-dressing rates were calculated after estimating each quartile’s yield response function. Consistent with agronomic expectations, results showed that the parts of the field with lower NDRE values had higher yield; but the impact of increasing NDRE levels on the side-dressing rate’s marginal product and EONR was not monotonic. Simulations predicted that compared to the side-dressing strategy the farmer would have implemented if not participating in the OFPE, net revenues could have been increased by $54 ha-1 by using the methodology presented, suggesting high potential value of combining OFPE and VI data. A key advantage of the proposed methodology is that the data’s inference space is the field to be managed. Further study is needed to improve the featured methodology

Factors Influencing the Adoption of Automatic Section Control Technologies and GPS Auto-Guidance Systems in Cotton Production

This study examines the factors influencing the adoption of ASC technologies and AG systems among cotton producers in the Southern United Sates. The data used is from a survey of cotton producers in 14 states. Using a random intercept bivariate probit regression, we evaluated the influence of crop acres harvested, education level, age of producer, field shape, use of farm dealers to obtain PA information, and the use of cover crops on the adoption decisions. Data from the NASS Crop Data Layer was used to estimate field shape measures included in this analysis. Results suggest that producers who are younger have more years of education, harvest more crop acres, and use farm dealers for information about precision agriculture are more likely to adopt ASC technologies and AG systems. Additionally, farms located in counties likely to have more irregularly shaped fields have a higher probability of adopting ASC technologies