Financial Feasibility and Environmental Implications of Adopting Automatic Milking Systems by Dairy Farms in Wisconsin and Minnesota

Pilot Survey – Stage Completed

The aims of this project were developed based on the outcomes of our preliminary study launched in early Spring 2022. This pilot survey was conducted by the Survey Research Center (SRC) at the University of Wisconsin – River Falls (Co-PI Burney is the Director of the SRC). Five hundred Wisconsin dairy farmers were randomly selected and received the survey through mail. The high response (36.4%) obtained in this preliminary study provides evidence of the interest of dairy farmers to learn about robotic milking and the importance of the project. Farmers emphasized in their responses the need for optimizing resources and decrease dependence on labor to keep small farmers in business. Likewise, 37 out of the 182 survey respondents provided their contact information for a subsequent follow-up interview. From this list, we will select twenty-five farmers that will participate in our focus group.  

Advisory Group

An advisory group of five dairy farmers from Minnesota and Wisconsin will provide input throughout the project, meeting twice per year to revise and validate all outcomes of the study. The five dairy farmers have been already identified and their letters of support are attached. Advisory Group meetings will be scheduled for a 5-hour duration and will be used to solicit feedback and guidance from farmers on the research team’s progress to date.

I. Exploratory study

For aims 1 and 2, we will conduct an exploratory study to gauge farmer perceptions of robotic milking and challenges they may face in adopting such technology. A survey will be mailed to 9,100 dairy farmers across Minnesota and Wisconsin, including farmers that have adopted robotic milking and those who have not. The survey will also gather information on methods of production and on the demographic profile of the dairy farmer. The survey will be conducted by the Survey Research Center (SRC), which also conducted the pilot survey. As aforementioned, Dr. Burney (Co-PI) is the Director of the SRC.  

The SRC is a not-for-profit department that provides cost-effective survey services to academics, local governments, chambers of commerce, food co-operatives, and other clients in the public sector and in the agriculture industry. The SRC has conducted several surveys of dairy farmers in Wisconsin over the past 10 years and has a proven record of achieving strong response rates.

The SRC, in collaboration with the research team, will develop the survey that will be distributed to owners/managers in both states through mail. Recipients will have to option to complete the survey online through a URL and QR code provided in the cover letter. To maximize the response rate, the SRC will send one postcard reminder three weeks after the initial mailing of the survey and will send a second copy of the survey two to three weeks after the postcard reminder. Based on historical experience and literature on survey methodology, this method is likely to boost the survey response rate. Data collection will be conducted over an 8-week period. The SRC will clean and compile all responses received and provide the authors with the data in CSV format. The SRC will conduct all tasks associated with survey preparation and mailing.

Farmers will also be invited to voluntarily provide contact information if they would be open to follow-up questions or if they would like to be more directly involved in the project. This information will also be used to recruit farmers to obtain feedback on various other aspects of the research. Farmers who complete the survey will be entered into a random draw to win one of ten-five $50 Visa cash vouchers.

II. Choice-experiment

To accomplish aim 1, we will also employ a hypothetical Choice Experiment (CE) with dairy farmers who are familiar with AMS. Using a CE, we will  quantify the farmers economic value of  different attributes of AMS – highlighted by the exploratory study and literature: such as improvement in farmers quality of life, reduction in time and effort devoted to manage employees, environmental benefits (e.g. efficient water use) (Jacobs & Siegford, 2012; Schulte & Tranel, 2013), yield improvements, decreased reliance on manual labor, labor-cost savings (Tejeda et al., 2020; Tranel, 2017), improved animal welfare and reduction in disease frequency, among other aspects.

CEs have been used widely in different fields, including in agricultural economics. While there are few applications of CEs in the dairy sector to elicit farmers’ preferences and opinions on technology adoption, including on-farm raw milk concentration plants and working conditions (Lips & Gazzarin, 2008; Weissgerber & Hess, 2022), to our knowledge, this is the first time a CE is applied to understand dairy farmers’ preferences related to AMS.

Table 1 describes the potential attributes to be presented to farmers related to AMS that will be evaluated. The number of CE decisions (tasks) and combination levels will be designed based on an efficient design.

Table 1. Attributes and levels of an AMS for CE 

Please select which of the two alternative you prefer:

Exit questions: Following the CE and suggested by previous studies on technology adoption (Carter, 2016), questions investigating farmers’ risk behavior and sense of self-efficacy will be considered. This will allow understanding how these factors affect AMS adoption and farmers’ preferences for AMS attributes. Questions related to basic sociodemographic information will also be included at the end of the CE questionnaire.

Data analysis and target sample: Data from the CE will be collected through a survey and will be measured using advance economic modeling. We expect to mail the survey to 2,000 randomly selected dairy farmers (expecting a 15% response rate) to obtain 300 responses and have a representative sample size. Farmers who will participate in the exploratory study (previous to this experiment) and who are familiar with AMS will be invited to fill out a questionnaire with the CE questions as well as exit questions. The survey will take on average 15 minutes and will be self-administered.

III. Financial assessment

With input from the previous methods, a financial assessment will be used to qualitatively determine the impact of robotic milking on farm profitability, and thus fulfill aim 3. Data will be collected from farm financial statements such as the Farm Financial Management Database (FINBIN), the Agricultural Resource Management Survey (ARMS) and the Farm Business Financial Management application (FARMBENCH). The financial assessment will investigate the financial situation of dairy operations that already adopted the AMS technology compared to their counterparts (farms with non-robotic milking). The results will shed light on the differences in cost structure of both types of dairy farms. This will be particularly valuable for farmers contemplating switching to mechanization of the milking process.

IV. Case Study for Labor

For aim 2, we will also elaborate a case study that explores alternatives for traditional labor-intensive production for farmers who cannot afford robotic technology. We propose to test the hypothesis that domestic labor’s aversion to farm work can be tempered by the different work structure and conditions of dairy farms with AMS. We will employ case study techniques involving three farms identified as non-AMS adopters, employing traditional labor-intensive methods. These case studies will utilize the participating farmers’ production, cost, and other operating decision inputs (especially labor) under an economic-simulation framework. We want to investigate if the adoption of this technology can be enough to lure domestic workers and thus mitigate the hiring issues, especially in the light of a costlier H-2A foreign labor hiring alternative whose existing guidelines are incompatible with the needs of the dairy industry. Results of such analyses will become the basis of this study’s policy recommendations on labor needs for the industry.

VI. Benefit-Cost Analysis for AMS

We will develop case studies that investigates the situation of three selected dairy farms that have already adopted AMS using benefit-cost analysis to quantify the economic impact of adopting robotic milking. We will collect information regarding their experience obtaining loans to finance the AMS investment (i.e., equipment, infrastructure needed to support the milking robot, required changes to barn design), changes in management structure, training to adapt managers into the new technology, and the environmental considerations needed including manure and water management. The results will be calibrated using the data collected throughout the study and will be validated by the farm advisory committee. The outcome of this study will inform farmers of the long-term financial sustainability of AMS adoption compared to a traditional milking system. Likewise, a financial spreadsheet will be developed that can be used by dairy farmers with AMS or by dairy operations contemplating AMS adoption in order to evaluate the financial feasibility of AMS, the optimal use of resources, and the economic and environmental considerations needed in order to implement this technology.

Collaboration with the audience

As aforementioned the Background section, farmers will participate in the design and planning of the study as part of our advisory group. Dairy farmers will also express their opinions and perceptions in the exploratory survey and the choice experiment. Dairy farm managers will expand on their experience regarding labor challenges and their AMS adoption in our case studies. Farmers will also provide feedback in the extension outreach that will be hold in the upcoming webinars and two-day workshop during the fall of the second year (2024). We will also survey participants to evaluate if they have gained knowledge from our outreach activities (explained in the Evaluation Plan section).