Crossing the Chasm of Technology Adoption in Agriculture

As population increase and wealth grow, demand for resource-intensive food rise. Pressure for land, water, energy and other input are building-up. Coupled with challenges from Climate change, biodiversity and economic viability (Garnett et al., 2013), farmers must take smarts decision in such complex dynamic system. Producers have the power to reduce pressure on the production system and fulfill challenges as a decision-maker in their own operation sector, where facts and figures shown by research can become daily practices and routines (Lindblom, Lundström, Ljung, & Jonsson, 2017). However, they must be supported to take the best decision toward their operation and common challenges. One of the support they can rely on is information technology (IT) based innovation such as Precision Agriculture (PA), where IT is used to monitor field variability, parameters and the current state of crop condition to optimize treatment of a specific location (Aubert, Schroeder, & Grimaudo, 2012). After getting the data through IT, the information can is represented in a Decision Support System (DSS), which allows the farmers and agricultural advisors to take the more effective decision based on collected data.  Although these new tools have positive impact on the production, social and environmental aspect of agriculture, many problems have been observed concerning their adoptions, which has been called the “problem of implementation” (Rossi, Salinari, Poni, Caffi, & Bettati, 2014). Despite these problems, improvement in both customer and Ag-tech company can be made to access the full potential of available tools and technologies.

On the customer side, a variety of factors are limiting the adoption rate. Other than the cost of acquisition, Kitchen (2002) have shown that low customer adoption rate is partially due to inadequate or ineffective educational effort. Proper implementation requires accessibility to well-trained professional, knowledgeable peoples and the availability to obtain a quality education. To unlock to full potential of Precision Agriculture, a “natural learning process” have been developed and tested (Kitchen, Snyder, Franzen, & Wiebold, 2002);

  1. Learn and understand the concept of spatial data management and their value
  2. Learn the proper use of sensor to obtain intensive sample at relatively low cost
  3. Learn to use computer and software to map data properly (Fig 1)
  4. Identification and analysis of relevant and manageable yield influencing factors
  5. Develop site-specific management plan (SSM)
  6. Strategic sampling and on-farm trials to test and optimize SSM plan

Fig 1: Farmer explaining differences in Biomass variation between two different images, an older image on the left and the present image on the right (Lundström & Lindblom, 2018)

It is important to specify that Precision Agriculture isn’t a “return guaranteed” type of management, but once producers understand how to use PA at its full potential in their farm, the implementation problem partially dissolve and the adoption rate can grow.

On the Ag-tech company side, the tools are not always well-design for the customer needs and so, reduce the rate of adoption. Taking the Decision Support System (DSS) as an example, most of the time they fail to capture the tacit knowledge and practical needs of farmers (Lindblom et al., 2017). Few other characteristics customer reproach to DSS are; poor user interfaces design, the time requirement for DSS usage, profitability, tedious data input requirement and level of knowledge of user (Rossi et al., 2014). These issues are mostly related to the interaction between the user and the system. A company must do more extensive testing and validation of their product to see if they effectively help farmers to take a better decision in practice (Rossi et al., 2014). By doing so, it helps to solve the problem of implementation which could result in higher adoption rate.

In conclusion, there are different ways to solve the “problem of implementation” and drive the technological adoption rate up. The goal of this article is not to sell PA or DSS, but to further understand what limits the use and adoption of these tools and technologies to unlock their full potential, allowing farmers to take a thoughtful decision in assessing current and future issue in agriculture.

Author: Maxime Leclerc



Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision Support Systems, 54(1), 510-520. doi:10.1016/j.dss.2012.07.002

Garnett, T., Appleby, M. C., Balmford, A., Bateman, I. J., Benton, T. G., Bloomer, P., . . . Godfray, H. C. J. (2013). Sustainable intensification in agriculture: Premises and policies. Science, 341(6141), 33-34. doi:10.1126/science.1234485

Kitchen, N. R., Snyder, C. J., Franzen, D. W., & Wiebold, W. J. (2002). Educational needs of precision agriculture. Precision Agriculture, 3(4), 341-351. doi:10.1023/A:1021588721188

Lindblom, J., Lundström, C., Ljung, M., & Jonsson, A. (2017). Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precision Agriculture, 18(3), 309-331. doi:10.1007/s11119-016-9491-4

Lundström, C., & Lindblom, J. (2018). Considering farmers’ situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT. Agricultural Systems, 159, 9-20. doi:

Rossi, V., Salinari, F., Poni, S., Caffi, T., & Bettati, T. (2014). Addressing the implementation problem in agricultural decision support systems: The example of®. Computers and Electronics in Agriculture, 100, 88-99. doi:10.1016/j.compag.2013.10.011

One response to “Crossing the Chasm of Technology Adoption in Agriculture”

  1. cesareemoriergxoyiya says:

    The article is interesting as it covers a topic, information technology, very relevant in agriculture today. It is also informative as it explains the basic principles of precision agriculture, decision support system and the many challenges of incorporating information technology on the farm. It clearly make the case for farmers to be more trained to use such technology on their farm. The article emphasizes the fact that tools and technologies like precision agriculture could benefit producers a lot, but that they are not tailored for the clientele. It seems to me that producers are faced with their job becoming increasingly complicated and the post highlights the need for technology that is easy to use by farmers. In order to improve the article, I suggest reviewing the grammar and structure, it would help the reader understand better the point the author is trying to make. I think the article could be even more convincing it if discussed more in depth the advantages linked with the adoption of information technology on the farm. For example, I would like to know about concrete instances or testimonies where producers achieved higher yields, reduced inputs, manage their time more efficiently, etc. using information technology. Moreover, I would like to know in which agricultural sectors this type of technology can be implanted with the highest potential. Given the cost associated with such technology, would it benefit cash crop producers the same way that it would help small-scale horticultural farmers?

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