AI Summary & Key Takeaways
AI InsightThe results suggest that addressing digital infrastructure, advisory-training services, and shared machinery/technology parks together at the provincial scale may help disseminate smart agriculture.
📌 3 Key Takeaways
Abstract
In this study, the aim was to determine the awareness level, perceptions, adoption intentions, and barriers to adoption regarding smart agriculture practices among farmers engaged in maize and wheat cultivation in Kırıkkale province. In the research, frequency data from a 15-question questionnaire were used; univariate descriptive statistics (frequency-percentage), weighted means for Likert items, and multiple-response frequencies for multiple-response questions were reported. The findings show that a significant proportion of participants had heard of the concept of “smart agriculture,” but awareness remained low for component technologies such as IoT. While technologies such as automatic irrigation and drones stand out as the most well-known and widely used applications, it was observed that the proportion of those actually using smart agriculture technology was limited. The main barriers to adoption are high cost and limited information, and the most critical policy topics for wider diffusion are training and awareness programmes and financial incentives. The results suggest that addressing digital infrastructure, advisory-training services, and shared machinery/technology parks together at the provincial scale may help disseminate smart agriculture.
Özet
In this study, the aim was to determine the awareness level, perceptions, adoption intentions, and barriers to adoption regarding smart agriculture practices among farmers engaged in maize and wheat cultivation in Kırıkkale province. In the research, frequency data from a 15-question questionnaire were used; univariate descriptive statistics (frequency-percentage), weighted means for Likert items, and multiple-response frequencies for multiple-response questions were reported. The findings show that a significant proportion of participants had heard of the concept of “smart agriculture,” but awareness remained low for component technologies such as IoT. While technologies such as automatic irrigation and drones stand out as the most well-known and widely used applications, it was observed that the proportion of those actually using smart agriculture technology was limited. The main barriers to adoption are high cost and limited information, and the most critical policy topics for wider diffusion are training and awareness programmes and financial incentives. The results suggest that addressing digital infrastructure, advisory-training services, and shared machinery/technology parks together at the provincial scale may help disseminate smart agriculture.