- Email: [email protected]
A survey regarding the interest and concern associated with transitioning from conventional to automated (robotic) milking systems for managers of small-to medium-sized dairy farms
The Professional Animal Scientist 30 (2014):418–422; http://dx.doi.org/10.15232/pas.2014-01327 ©2014 American Registry of Professional Animal Scientists
Aand survey regarding the interest concern associated with
transitioning from conventional to automated (robotic) milking systems for managers of smallto medium-sized dairy farms K. M. Moyes,1 L. Ma, T. K. McCoy, and R. R. Peters Department of Animal and Avian Sciences, University of Maryland, College Park 20742
ABSTRACT As herd size increases, new technology that allows small- to medium-sized dairy producers to remain sustainable is greatly desired. Automatic milking systems (AMS) are one way producers may remain competitive and sustainable via improving management and production efficiency, as well as enhancing quality of life and business attractiveness to successors. The aim of this survey was to identify the interest and concerns of managers of small- to medium-sized dairy farms when considering transitioning from conventional milking systems to AMS in the mid-Atlantic region of the United States. In January 2013, 1,355 questionnaires were sent to Maryland and Pennsylvania dairy producers with between 50 to 280 lactating dairy cows. Logistic and linear regression models were used to examine the factors most influencing interest and the major con-
1
Corresponding author: [email protected]
cerns when transitioning to AMS. The response rate was 31%. The number of milking and dry cows averaged 109 and 17, respectively, and this was similar between states. For both states, the majority (74%) of producers had been involved in farming for >20 yr. Most farms were freestall operations. When asked what limits their ability to improve or grow their dairy operation, low profits (41%), land costs (35%), and labor availability (26%) were the primary answers. Of the producers that completed the surveys, 38% expressed interest in transitioning to an AMS. A logistic regression showed that higher education and farms of larger herd-size influenced (P < 0.05) interest in AMS, whereas state and years of age did not. The regression model using the maximum improvement technique showed that improving herd management and improving management of family time were the most influential when considering transitioning to AMS, whereas return on investment or profitability and management changes were of the greatest concern when transitioning to AMS. Results will serve as the basis for education
programs designed to provide farmers with the decision-making tools required to estimate and quantify economic effects, performance outcomes, and lifestyle changes associated with AMS. Key words: automatic milking system, robotic milking system, interest, transition, survey
INTRODUCTION Sustainability of small- to mediumsized dairy herds is an important goal in most regions of the United States due to the high proportion of herds in this category. In fact, small- to medium-sized dairy farms (i.e., those with <200 cows per herd) still account for the vast majority (87%) of the dairy farms (USDA, 2013) in the United States. Although some US producers continue to seek increased efficiencies and reduced cost of production through herd expansion, not all producers are interested or have the ability to increase the size of their operation.
Interest and concern regarding automated milking systems
Automatic milking systems were first introduced commercially in Europe in 1992 and were designed to improve labor efficiency for the typical small- to medium-sized dairy farms found in Europe (Mathijs, 2004). Since the introduction of automated milking systems (AMS), approximately 10,000 farms across the globe are estimated to be milking more than 1.2 million cows with AMS (Rodenburg, 2012; Rodriguez, 2013). Although the major concentration of AMS milking is in Europe, 600 producers in North America have been reported to be robotically milking close to 75,000 cows (Rodriguez, 2013). Whereas AMS is not limited to only small- to medium-sized dairy herds, on average it is currently the most typically sized farm to adopt this technology (Rodriguez, 2013). This technology is fairly new to the United States. In turn, US producers lack information from independent sources regarding return on economic outcomes, production performance, animal health, and lifestyle changes associated with the transition from conventional to AMS for US dairy operations. Thus far, studies of dairy producers in the United States that have that have adopted AMS would be typical of herd sizes found in the mid-Atlantic region (Bentley et al., 2013; Salfer et al., 2013). In January, 2013, a questionnaire survey was sent out to Maryland (MD) and Pennsylvania (PA) dairy farm managers with 50 to 280 milking cows. The aim of our survey was to assess the interest and concerns or challenges for managers of small- to medium-sized dairy herds when considering transitioning to AMS in the mid-Atlantic region.
MATERIALS AND METHODS Our survey was approved and followed all guidelines set forth by the Institutional Review Board at the University of Maryland (IRBNet ID 378730–2). Two states, MD and PA, were selected to represent dairy farmers in the mid-Atlantic region of the United States. A total of 1,355 surveys were sent to MD (n = 257) and
PA (n = 1,098) dairy farmers with 50 to 280 milking cows. A producer list in PA of farms with a herd size between 50 and 280 cows was accomplished with a data set supplied from the Animal Diagnostic Laboratory at Pennsylvania State University (University Park; courtesy of E. P. Hovingh). In Maryland, producers were identified with a list of all licensed dairy farms supplied by the Center for Milk Control, Office of Food Protection and Consumer Health Services, Maryland Department of Health and Mental Hygiene (Baltimore; courtesy of L. Bucher). This list was shared with the extension county agents and they were able to determine which herds in their respective county had herd sizes between 50 and 280 cows. All producers selected were mailed a presurvey introductory letter indicating the purpose of the survey. The survey packet was mailed 1 wk after the introductory letter including a preaddressed, stamped return envelope and a chance for a cash incentive upon return (i.e., 3 of the returned surveys were randomly picked to receive a $50 gift card). Two reminder letters were sent to all producers in the next 2 consecutive weeks. The survey consisted of 25 multiple choice and fill-in-the blank questions regarding herd size (i.e., lactating and dry cows) and zip code. When appropriate, the last selection was “other,” allowing producers the opportunity to identify an answer if it was not previously listed. Questions were segregated as shown in Figure 1. Responses within each survey were reviewed for logic and completeness. For those interested in AMS, currently owned AMS, or had financing in place, producers were then asked a series of questions about their interest in AMS (Figure 1). To examine the factors most influencing interest in AMS (i.e., yes or no), we used logistic regression of binary interest in AMS for state (i.e., MD or PA), education level (i.e., college and no college), age (i.e., > 50 and <50 yr of age), farm type (i.e., freestall or other), and number of lactating cows using SAS version 9.3
419
(SAS, 2010). For validation purposes, we calculated the probabilities of being interested in AMS for the 13 farms that had already purchased AMS based on the logistic regression model. To study the factors influencing the level of interest in AMS, we coded the interest and answers to questions numerically. The interest level was coded as 0, 1, 2, 3, and 4 respectively for “no,” “not sure,” “slight,” “moderate,” and “very.” The answers to questions were coded as 0, 1, 2, 3, and 4, respectively, for “no,” “little,” “somewhat,” “concern” and “very.” Pearson correlations (PROC CORR) were used to generate correlations between potential answers within a question. Linear regression analyses (PROC REG) using the maximum improvement technique (MAXR) were used to identify factors that were the main interest and primary concerns when considering transitioning to AMS.
RESULTS AND DISCUSSION Table 1 shows the general survey results. The overall response rate was 31%, with 42% for MD farmers and 28% for PA farmers. Of the surveys returned, several herds were too small (<50) or large (>280) and were omitted from the final analysis. The final data set included 96 MD and 252 PA farms. The number of milking and dry cows averaged 109 and 17, respectively, and this was similar between states. For both states, the majority (74%) of farmers had been involved in farming for >20 yr. Most farms (n = 202; 59%) were freestall operations. When asked what limits their ability to improve or grow their dairy operation, low profits (41%), land costs (35%), and labor availability (26%) were the primary answers. Answer choices also included neighbor complaints, encroachment of development, lack of interest from the next generation family member, nutrient management laws, enough land to spread manure, manure storage, estate planning, personal health, cost of health insurance, government regulations, and lack of time. This supports the
420
Moyes et al.
Figure 1. Overview of survey questions. Demographics of the dairy operation included zip code, years engaged in dairy farm production, type of dairy operation, limitations to improve or grow the dairy operation, and herd size. Demographics of the farmer included age, sex, and formal education.
results reported in a survey assessing dairy producer education needs from 2006, where the 3 most limiting factors to growing their dairy business were land costs, low profitability, and labor availability (Peters et al., 2007). Land cost is one factor that cannot be controlled at the management level. One of the major interests when transitioning to AMS is improving labor efficiency (Bijl et al., 2007; Bentley et al., 2013). Little information is available regarding reductions in labor costs for farms with AMS in the United States. A recent survey conducted via Iowa State University Extension and Outreach showed a 70% decrease in milking labor for 8 producers that transitioned to AMS (Bentley et al., 2013). In the Netherlands, Bijl et al. (2007) reported 29% less labor for farms whereas Steeneveld et al. (2012)
Table 1. General survey results Item Herds sent survey, no. Response rate, no. returned (% of total) Number of surveys used in final analysis1 (i.e., 50 to 280 milking cows) Average total milk cows (range) Average herd size, milking, and dry cows (range) Average number of years farming, no. of herds (% of total) <1 yr 1–5 yr 6–10 yr 11–20 yr >20 yr Type of operation,2 no. of herds (% of total) Freestall Tiestall Grazing Other3 Automated milking system (AMS), no. of herds (% of total) Own Do not own Financing in place Interest in AMS, no. of herds (% of total) Not interested Slight Moderate Very Not sure
Maryland 257 109 (42%) 96 110 (50–270) 127 (56–300) 0 (0%) 5 (5%) 3 (3%) 14 (14%) 75 (78%) 57 (60%) 15 (16%) 17 (18%) 6 (6%) 0 (0%) 90 (97%) 3 (3%) 58 (62%) 19 (20%) 10 (11%) 3 (3%) 4 (4%)
Pennsylvania 1,098 305 (28%) 252 109 (50–280) 127 (55–350) 0 (0%) 12 (5%) 14 (5%) 43 (17%) 183 (73%) 145 (58%) 66 (26%) 31 (12%) 8 (3%) 5 (2%) 238 (96%) 4 (2%) 139 (57%) 55 (23%) 25 (10%) 16 (6%) 11 (4%)
Results in this table are based on the number of surveys used in the final analysis. More than 1 answer may be given per farm. 3 Includes, for example, milking herd only, organic, agro-tourism, value-added products, or other. 1 2
Total 1,355 414 (31%) 348 109 (50–280) 126 (55–350) 0 (0%) 17 (5%) 17 (5%) 57 (16%) 258 (74%) 202 (59%) 81 (23%) 48 (14%) 14 (4%) 5 (1%) 328 (96%) 8 (2%) 197 (58%) 74 (22%) 35 (10%) 19 (6%) 15 (4%)
421
Interest and concern regarding automated milking systems
Table 2. Parameter estimates and regression analysis information using the maximum R2 improvement for the regression model selected to predict the major factors that influenced interest and concerns with regard to automatic milking systems Mean square
Regression step
Variable
Factors of influence 1 2
Intercept Improving herd management Intercept Improving herd management Improving management of family time Intercept Improving herd management Improving management of family time Appeal to successors Intercept Return on investment/profits Intercept Return on investment/profits Management changes Intercept Return on investment/profits Management changes Barn design
3 Factors of concern 1 2 3
reported no improvement in labor cost between AMS and conventional milking systems. Hence, more information is needed regarding changes in labor efficiency when transitioning to AMS in the United States. Of the producers that did not currently own an AMS, 38% stated they were slightly (n = 74), moderately (n = 35), or very interested (n = 19) in transitioning; whereas 58% (n = 197) were not interested and 4% (n = 15) were not sure about their interest in AMS (Table 1). When evaluating the producers that were or were not interested in AMS, results were grouped by (1) education level, as either high school and less than high school (n = 256) or college and some college (n = 85); (2) age, as <50 (n = 123) and >50 (n = 66) yr of age; and (3) type of farm operation, as freestall (n = 202) and other (n = 81). A simple linear regression (R2 = 0.14) showed that higher education and farms of larger herd size were more interested (P < 0.05) in AMS, whereas state
25.0 15.5
Estimate
1.28 0.39 0.97 0.30 0.20
SE
0.20 0.08 0.23 0.08 0.08
P-value
<0.001 <0.001 <0.001 0.01
R2
0.18 0.22
10.8
0.92 0.27 0.19
0.24 0.08 0.08
<0.001 0.002 0.02
0.23
15.1 10.3 7.0
0.10 1.15 0.32 1.23 0.40 −0.17 1.29 0.41 −0.15 −0.05
0.08 0.29 0.08 0.29 0.09 0.07 0.30 0.09 0.07 0.08
0.19 <0.001 <0.001 <0.001 0.02 <0.001 <0.001 0.04 0.52
0.09 0.13 0.14
and years of age were not factors that influenced interest in AMS. The low R2 indicates that other factors (e.g., improving herd management and improvement of family time) may partly explain interest in AMS. When identifying what factors most influenced the level of interest, improving animal health or production and herd management were highly correlated (r = 0.66; P < 0.001); therefore, improving animal health and production was removed from the regression model. The regression model indicated that improving herd management and management of family time were the most influential when considering transitioning to AMS (Table 2). Appeal to successors did not improve the model but was considered the third most important influence when transitioning to AMS. When identifying the major concerns when transitioning to AMS, financing or cash flow and return on investment or profitability were highly correlated (r = 0.75; P < 0.001); therefore,
financing or cash flow was removed from the regression model. The regression model (Table 2) showed that return on investment or profitability and management changes were of the greatest concern when transitioning to AMS. Barn design did not improve the model but was considered the third most important concern when transitioning to AMS. Only 3% (n = 13) of producers stated they either owned an AMS or had financing in place. These producers were used to test our logistic regression model with education and herd size as predictors (Table 3). Using our estimated logistic regression model, we calculated the probability of being interested in AMS for these 13 farms. We found that 8 out of 13 farms were predicted to be interested in AMS (probability of being interested >0.5). This supports that producer interest and concerns regarding AMS were similar to those of producers that have transitioned to AMS.
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Moyes et al.
Table 3. Validation using farms (n = 13) that have automatic milking systems or financing in place with the logistic model using education and number of lactating cows State1
Education level2
PA PA Blank PA MD PA PA MD PA MD PA PA PA
High School High School Some College High School Blank
No. of lactating cows
Probability of interest
240 170 120 240 110 65 130 65 220 65 70 210 250
0.60 0.46 0.53 0.60 0.81 0.18 0.39 0.28 0.56 0.59 0.43 0.54 0.61
PA = Pennsylvania; MD = Maryland. 2 For statistical analysis, education level was grouped as no college (high school/
IMPLICATIONS
ACKNOWLEDGMENTS
Our survey identified the interests and concerns that are important when considering transitioning from conventional milking systems to AMS in the mid-Atlantic region. Of the 38% of producers that were interested in AMS, improving herd management and improving management of family time were the most influential, whereas return on investment or profitability and management changes were of the greatest concern when transitioning to AMS. Our results will serve as the basis for future studies and education programs that estimate and quantify economic effects, performance outcomes, and lifestyle changes associated with AMS in the mid-Atlantic region.
The authors thank the Maryland and Pennsylvania dairy producers for their participation and the Maryland Agricultural Experiment Station (College Park) for funding this survey. Gratitude is extended to Audrey Ervin and Allison Roe (University of Maryland, College Park) for data collection and data summary, respectively.
LITERATURE CITED Bentley, J. A., L. F. Tranel, L. L. Timms, and K. Schulte. 2013. Automatic milking systems (AMS)—Producer surveys. Accessed Oct. 29, 2013. http://lib.dr.iastate.edu/cgi/ viewcontent.cgi?article=1826&context=ans_ air.
Bijl, R., S. R. Kooistra, and H. Hogeveen. 2007. The profitability of automatic milking on Dutch dairy farms. J. Dairy Sci. 90:239–248. Mathijs, E. 2004. Socio-economic aspects of automatic milking. Pages 45–55 in Automatic Milking: A Better Understanding. A. Meijering, H. Hogeveen, and C. J. A. M. de Koning, ed. Wageningen Academic Publishers, Wageningen, the Netherlands. Peters, R. R., K. M. Wilson, M. R. Bell, R. A. Erdman, S. W. Fultz, J. E. Hall, R. A. Kohn, W. D. Lantz, J. W. Semler, and M. A. Varner. 2007. Trends in Maryland dairying and future prospects. J. Dairy Sci. 90(Suppl. 1):26. Rodenburg, J. 2012. The impact of robotic milking on milk quality, cow comfort and labor issues. Pages 125–137 in Natl. Mastitis Counc. Ann. Meet. Proc. St. Pete Beach, FL, Natl. Mastitis Counc. Madison, WI. Rodriguez, F. 2013. The realities of robotic milking technology today. Progressive Dairyman. Accessed Oct. 29, 2013. http://www. progressivedairy.com/index.php?option=com_ content&id=9244:the-realities-of-robotic-milking-technology-today&Itemid=75. Salfer, J. A., M. I. Endres, and D. W. Kammel. 2013. Housing and management characteristics of 53 farms using automatic milking systems. J. Dairy Sci. 96(Suppl. 1):600. SAS. 2010. SAS User’s Guide. Statistics, ver. 9.3. SAS Institute Inc., Cary, NC. Steeneveld, W., L. W. Tauer, H. Hogeveen, and A. G. Oude Lansink. 2012. Comparing technical efficiency of farms with an automatic milking system and a conventional milking system. J. Dairy Sci. 95:7391–7398. USDA. 2013. Farms, land in farms, and livestock operations 2012 summary. National Agricultural Statistics Service, USDA, Washington, DC. Accessed Oct 29, 2013. http:// usda01.library.cornell.edu/usda/current/ FarmLandIn/FarmLandIn-02-19-2013.pdf.
Aand survey regarding the interest concern associated with
transitioning from conventional to automated (robotic) milking systems for managers of smallto medium-sized dairy farms K. M. Moyes,1 L. Ma, T. K. McCoy, and R. R. Peters Department of Animal and Avian Sciences, University of Maryland, College Park 20742
ABSTRACT As herd size increases, new technology that allows small- to medium-sized dairy producers to remain sustainable is greatly desired. Automatic milking systems (AMS) are one way producers may remain competitive and sustainable via improving management and production efficiency, as well as enhancing quality of life and business attractiveness to successors. The aim of this survey was to identify the interest and concerns of managers of small- to medium-sized dairy farms when considering transitioning from conventional milking systems to AMS in the mid-Atlantic region of the United States. In January 2013, 1,355 questionnaires were sent to Maryland and Pennsylvania dairy producers with between 50 to 280 lactating dairy cows. Logistic and linear regression models were used to examine the factors most influencing interest and the major con-
1
Corresponding author: [email protected]
cerns when transitioning to AMS. The response rate was 31%. The number of milking and dry cows averaged 109 and 17, respectively, and this was similar between states. For both states, the majority (74%) of producers had been involved in farming for >20 yr. Most farms were freestall operations. When asked what limits their ability to improve or grow their dairy operation, low profits (41%), land costs (35%), and labor availability (26%) were the primary answers. Of the producers that completed the surveys, 38% expressed interest in transitioning to an AMS. A logistic regression showed that higher education and farms of larger herd-size influenced (P < 0.05) interest in AMS, whereas state and years of age did not. The regression model using the maximum improvement technique showed that improving herd management and improving management of family time were the most influential when considering transitioning to AMS, whereas return on investment or profitability and management changes were of the greatest concern when transitioning to AMS. Results will serve as the basis for education
programs designed to provide farmers with the decision-making tools required to estimate and quantify economic effects, performance outcomes, and lifestyle changes associated with AMS. Key words: automatic milking system, robotic milking system, interest, transition, survey
INTRODUCTION Sustainability of small- to mediumsized dairy herds is an important goal in most regions of the United States due to the high proportion of herds in this category. In fact, small- to medium-sized dairy farms (i.e., those with <200 cows per herd) still account for the vast majority (87%) of the dairy farms (USDA, 2013) in the United States. Although some US producers continue to seek increased efficiencies and reduced cost of production through herd expansion, not all producers are interested or have the ability to increase the size of their operation.
Interest and concern regarding automated milking systems
Automatic milking systems were first introduced commercially in Europe in 1992 and were designed to improve labor efficiency for the typical small- to medium-sized dairy farms found in Europe (Mathijs, 2004). Since the introduction of automated milking systems (AMS), approximately 10,000 farms across the globe are estimated to be milking more than 1.2 million cows with AMS (Rodenburg, 2012; Rodriguez, 2013). Although the major concentration of AMS milking is in Europe, 600 producers in North America have been reported to be robotically milking close to 75,000 cows (Rodriguez, 2013). Whereas AMS is not limited to only small- to medium-sized dairy herds, on average it is currently the most typically sized farm to adopt this technology (Rodriguez, 2013). This technology is fairly new to the United States. In turn, US producers lack information from independent sources regarding return on economic outcomes, production performance, animal health, and lifestyle changes associated with the transition from conventional to AMS for US dairy operations. Thus far, studies of dairy producers in the United States that have that have adopted AMS would be typical of herd sizes found in the mid-Atlantic region (Bentley et al., 2013; Salfer et al., 2013). In January, 2013, a questionnaire survey was sent out to Maryland (MD) and Pennsylvania (PA) dairy farm managers with 50 to 280 milking cows. The aim of our survey was to assess the interest and concerns or challenges for managers of small- to medium-sized dairy herds when considering transitioning to AMS in the mid-Atlantic region.
MATERIALS AND METHODS Our survey was approved and followed all guidelines set forth by the Institutional Review Board at the University of Maryland (IRBNet ID 378730–2). Two states, MD and PA, were selected to represent dairy farmers in the mid-Atlantic region of the United States. A total of 1,355 surveys were sent to MD (n = 257) and
PA (n = 1,098) dairy farmers with 50 to 280 milking cows. A producer list in PA of farms with a herd size between 50 and 280 cows was accomplished with a data set supplied from the Animal Diagnostic Laboratory at Pennsylvania State University (University Park; courtesy of E. P. Hovingh). In Maryland, producers were identified with a list of all licensed dairy farms supplied by the Center for Milk Control, Office of Food Protection and Consumer Health Services, Maryland Department of Health and Mental Hygiene (Baltimore; courtesy of L. Bucher). This list was shared with the extension county agents and they were able to determine which herds in their respective county had herd sizes between 50 and 280 cows. All producers selected were mailed a presurvey introductory letter indicating the purpose of the survey. The survey packet was mailed 1 wk after the introductory letter including a preaddressed, stamped return envelope and a chance for a cash incentive upon return (i.e., 3 of the returned surveys were randomly picked to receive a $50 gift card). Two reminder letters were sent to all producers in the next 2 consecutive weeks. The survey consisted of 25 multiple choice and fill-in-the blank questions regarding herd size (i.e., lactating and dry cows) and zip code. When appropriate, the last selection was “other,” allowing producers the opportunity to identify an answer if it was not previously listed. Questions were segregated as shown in Figure 1. Responses within each survey were reviewed for logic and completeness. For those interested in AMS, currently owned AMS, or had financing in place, producers were then asked a series of questions about their interest in AMS (Figure 1). To examine the factors most influencing interest in AMS (i.e., yes or no), we used logistic regression of binary interest in AMS for state (i.e., MD or PA), education level (i.e., college and no college), age (i.e., > 50 and <50 yr of age), farm type (i.e., freestall or other), and number of lactating cows using SAS version 9.3
419
(SAS, 2010). For validation purposes, we calculated the probabilities of being interested in AMS for the 13 farms that had already purchased AMS based on the logistic regression model. To study the factors influencing the level of interest in AMS, we coded the interest and answers to questions numerically. The interest level was coded as 0, 1, 2, 3, and 4 respectively for “no,” “not sure,” “slight,” “moderate,” and “very.” The answers to questions were coded as 0, 1, 2, 3, and 4, respectively, for “no,” “little,” “somewhat,” “concern” and “very.” Pearson correlations (PROC CORR) were used to generate correlations between potential answers within a question. Linear regression analyses (PROC REG) using the maximum improvement technique (MAXR) were used to identify factors that were the main interest and primary concerns when considering transitioning to AMS.
RESULTS AND DISCUSSION Table 1 shows the general survey results. The overall response rate was 31%, with 42% for MD farmers and 28% for PA farmers. Of the surveys returned, several herds were too small (<50) or large (>280) and were omitted from the final analysis. The final data set included 96 MD and 252 PA farms. The number of milking and dry cows averaged 109 and 17, respectively, and this was similar between states. For both states, the majority (74%) of farmers had been involved in farming for >20 yr. Most farms (n = 202; 59%) were freestall operations. When asked what limits their ability to improve or grow their dairy operation, low profits (41%), land costs (35%), and labor availability (26%) were the primary answers. Answer choices also included neighbor complaints, encroachment of development, lack of interest from the next generation family member, nutrient management laws, enough land to spread manure, manure storage, estate planning, personal health, cost of health insurance, government regulations, and lack of time. This supports the
420
Moyes et al.
Figure 1. Overview of survey questions. Demographics of the dairy operation included zip code, years engaged in dairy farm production, type of dairy operation, limitations to improve or grow the dairy operation, and herd size. Demographics of the farmer included age, sex, and formal education.
results reported in a survey assessing dairy producer education needs from 2006, where the 3 most limiting factors to growing their dairy business were land costs, low profitability, and labor availability (Peters et al., 2007). Land cost is one factor that cannot be controlled at the management level. One of the major interests when transitioning to AMS is improving labor efficiency (Bijl et al., 2007; Bentley et al., 2013). Little information is available regarding reductions in labor costs for farms with AMS in the United States. A recent survey conducted via Iowa State University Extension and Outreach showed a 70% decrease in milking labor for 8 producers that transitioned to AMS (Bentley et al., 2013). In the Netherlands, Bijl et al. (2007) reported 29% less labor for farms whereas Steeneveld et al. (2012)
Table 1. General survey results Item Herds sent survey, no. Response rate, no. returned (% of total) Number of surveys used in final analysis1 (i.e., 50 to 280 milking cows) Average total milk cows (range) Average herd size, milking, and dry cows (range) Average number of years farming, no. of herds (% of total) <1 yr 1–5 yr 6–10 yr 11–20 yr >20 yr Type of operation,2 no. of herds (% of total) Freestall Tiestall Grazing Other3 Automated milking system (AMS), no. of herds (% of total) Own Do not own Financing in place Interest in AMS, no. of herds (% of total) Not interested Slight Moderate Very Not sure
Maryland 257 109 (42%) 96 110 (50–270) 127 (56–300) 0 (0%) 5 (5%) 3 (3%) 14 (14%) 75 (78%) 57 (60%) 15 (16%) 17 (18%) 6 (6%) 0 (0%) 90 (97%) 3 (3%) 58 (62%) 19 (20%) 10 (11%) 3 (3%) 4 (4%)
Pennsylvania 1,098 305 (28%) 252 109 (50–280) 127 (55–350) 0 (0%) 12 (5%) 14 (5%) 43 (17%) 183 (73%) 145 (58%) 66 (26%) 31 (12%) 8 (3%) 5 (2%) 238 (96%) 4 (2%) 139 (57%) 55 (23%) 25 (10%) 16 (6%) 11 (4%)
Results in this table are based on the number of surveys used in the final analysis. More than 1 answer may be given per farm. 3 Includes, for example, milking herd only, organic, agro-tourism, value-added products, or other. 1 2
Total 1,355 414 (31%) 348 109 (50–280) 126 (55–350) 0 (0%) 17 (5%) 17 (5%) 57 (16%) 258 (74%) 202 (59%) 81 (23%) 48 (14%) 14 (4%) 5 (1%) 328 (96%) 8 (2%) 197 (58%) 74 (22%) 35 (10%) 19 (6%) 15 (4%)
421
Interest and concern regarding automated milking systems
Table 2. Parameter estimates and regression analysis information using the maximum R2 improvement for the regression model selected to predict the major factors that influenced interest and concerns with regard to automatic milking systems Mean square
Regression step
Variable
Factors of influence 1 2
Intercept Improving herd management Intercept Improving herd management Improving management of family time Intercept Improving herd management Improving management of family time Appeal to successors Intercept Return on investment/profits Intercept Return on investment/profits Management changes Intercept Return on investment/profits Management changes Barn design
3 Factors of concern 1 2 3
reported no improvement in labor cost between AMS and conventional milking systems. Hence, more information is needed regarding changes in labor efficiency when transitioning to AMS in the United States. Of the producers that did not currently own an AMS, 38% stated they were slightly (n = 74), moderately (n = 35), or very interested (n = 19) in transitioning; whereas 58% (n = 197) were not interested and 4% (n = 15) were not sure about their interest in AMS (Table 1). When evaluating the producers that were or were not interested in AMS, results were grouped by (1) education level, as either high school and less than high school (n = 256) or college and some college (n = 85); (2) age, as <50 (n = 123) and >50 (n = 66) yr of age; and (3) type of farm operation, as freestall (n = 202) and other (n = 81). A simple linear regression (R2 = 0.14) showed that higher education and farms of larger herd size were more interested (P < 0.05) in AMS, whereas state
25.0 15.5
Estimate
1.28 0.39 0.97 0.30 0.20
SE
0.20 0.08 0.23 0.08 0.08
P-value
<0.001 <0.001 <0.001 0.01
R2
0.18 0.22
10.8
0.92 0.27 0.19
0.24 0.08 0.08
<0.001 0.002 0.02
0.23
15.1 10.3 7.0
0.10 1.15 0.32 1.23 0.40 −0.17 1.29 0.41 −0.15 −0.05
0.08 0.29 0.08 0.29 0.09 0.07 0.30 0.09 0.07 0.08
0.19 <0.001 <0.001 <0.001 0.02 <0.001 <0.001 0.04 0.52
0.09 0.13 0.14
and years of age were not factors that influenced interest in AMS. The low R2 indicates that other factors (e.g., improving herd management and improvement of family time) may partly explain interest in AMS. When identifying what factors most influenced the level of interest, improving animal health or production and herd management were highly correlated (r = 0.66; P < 0.001); therefore, improving animal health and production was removed from the regression model. The regression model indicated that improving herd management and management of family time were the most influential when considering transitioning to AMS (Table 2). Appeal to successors did not improve the model but was considered the third most important influence when transitioning to AMS. When identifying the major concerns when transitioning to AMS, financing or cash flow and return on investment or profitability were highly correlated (r = 0.75; P < 0.001); therefore,
financing or cash flow was removed from the regression model. The regression model (Table 2) showed that return on investment or profitability and management changes were of the greatest concern when transitioning to AMS. Barn design did not improve the model but was considered the third most important concern when transitioning to AMS. Only 3% (n = 13) of producers stated they either owned an AMS or had financing in place. These producers were used to test our logistic regression model with education and herd size as predictors (Table 3). Using our estimated logistic regression model, we calculated the probability of being interested in AMS for these 13 farms. We found that 8 out of 13 farms were predicted to be interested in AMS (probability of being interested >0.5). This supports that producer interest and concerns regarding AMS were similar to those of producers that have transitioned to AMS.
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Table 3. Validation using farms (n = 13) that have automatic milking systems or financing in place with the logistic model using education and number of lactating cows State1
Education level2
PA PA Blank PA MD PA PA MD PA MD PA PA PA
High School High School Some College High School Blank
No. of lactating cows
Probability of interest
240 170 120 240 110 65 130 65 220 65 70 210 250
0.60 0.46 0.53 0.60 0.81 0.18 0.39 0.28 0.56 0.59 0.43 0.54 0.61
PA = Pennsylvania; MD = Maryland. 2 For statistical analysis, education level was grouped as no college (high school/
IMPLICATIONS
ACKNOWLEDGMENTS
Our survey identified the interests and concerns that are important when considering transitioning from conventional milking systems to AMS in the mid-Atlantic region. Of the 38% of producers that were interested in AMS, improving herd management and improving management of family time were the most influential, whereas return on investment or profitability and management changes were of the greatest concern when transitioning to AMS. Our results will serve as the basis for future studies and education programs that estimate and quantify economic effects, performance outcomes, and lifestyle changes associated with AMS in the mid-Atlantic region.
The authors thank the Maryland and Pennsylvania dairy producers for their participation and the Maryland Agricultural Experiment Station (College Park) for funding this survey. Gratitude is extended to Audrey Ervin and Allison Roe (University of Maryland, College Park) for data collection and data summary, respectively.
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