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The adoption of agricultural machinery in countries with a developing economy can have a significant impact on improving well-being and pro-poor growth. However, this requires farmers to buy into mechanized farming, which is more likely to happen if the machinery meets their needs. The objective of this paper is to identify deciding factors for traditional farmers to adopt machinery and identify design requirements. Payback models were developed based on these design requirements, willingness to pay, and expected returns. Thirty-six farmers in Sudan were interviewed throughout 2019–2021. Six of these farmers were provided tractors during 2020 and 2021. Differences in net-profits between the 30 control and 6 treatment farms during the mechanized farming seasons were used in the models for expected profits. There were no significant differences in tractor design preferences between the treatment and control groups. Two cost models were estimated using a 95% confidence interval: entire Δ profit (entire additional profit from mechanized farming above nonmechanized) and percentage of total profit (percentage of total net-profits willing to spend). For the average farm size in this study (44.39 acres) and a market available tractor that satisfied all farmer needs, payback was 3.92 years [2.34, 8.54] and 4.57 years [3.39, 6.38] for the models, respectively.

The adoption of agricultural technologies in developing economy countries has the potential to reduce poverty through sustainable intensification. Mechanized farming can also improve perceptions of farming and mitigate rural out-migration. However, many traditional farmers do not have access to machinery and/or machinery is cost prohibitive. The objective of this paper is to quantify how the use of machinery affects costs, revenue, net-profits, and returns on investment for a case study of farmers in Sudan, Africa. A treatment control study (N = 36) was performed across the 2019 (baseline), 2020, and 2021 farming seasons, where the treatment group was provided tractors. ANOVAs and t-tests were used to compare financial values between these groups across the farming seasons, to quantify economic differences associated with farming machinery. We show that all farmers had similar net-profits when farming without machinery, while mechanized farming yielded significantly higher net-profits (USD 16.61/acre more in 2020, USD 27.10/acre more in 2021). Our study also finds that the volatility of the black-market exchange rate and labor shortages have a significant impact on farming net-profits. These results provide a quantified difference between farming with and without machinery, which can provide a financial basis for purchasing and borrowing models, machinery design requirements, and educational value to farmers.

Objective: A driving simulator study was conducted to evaluate the longitudinal effects of an intervention and withdrawal of a lane keeping system on driving performance and cognitive workload. Background: Autonomous vehicle systems are being implemented into the vehicle fleet. However, limited research exists in understanding the carryover effects of long-term exposure. Methods: Forty-eight participants (30 treatment, 18 control) completed eight drives across three separate days in a driving simulator. The treatment group had an intervention and withdrawal of a lane keeping system. Changes in driving performance (standard deviation of lateral position [SDLP] and mean time to collision [TTC]) and cognitive workload (response time and miss rate to a detection response task) were modeled using mixed effects linear and negative binomial regression. Results: Drivers exposed to the lane keeping system had an increase in SDLP after the system was withdrawn relative to their baseline. Drivers with lane keeping had decreased mean TTC during and after system withdrawal compared with manual drivers. There was an increase in cognitive workload when the lane keeping system was withdrawn relative to when the system was engaged. Conclusion: Behavioral adaptations in driving performance and cognitive workload were present during automation and persisted after the automation was withdrawn. Application: The findings of this research emphasize the importance to consider the effects of skill atrophy and misplaced trust due to semi-autonomous vehicle systems. Designers and policymakers can utilize this for system alerts and training.