- Email: [email protected]
Differentiated Dairy Grazing Intensity in the Northeast
OUR INDUSTRY TODAY Differentiated Dairy Grazing Intensity in the Northeast J. R. Winsten, R. L. Parsons, and G. D. Hanson Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University, University Park 16802
ABSTRACT As the dairy industry in the Northeast experienced difficult economic conditions in the 1990s, grazing was increasingly viewed as an option for feeding dairy cattle. This analysis used a large sample of dairy farms randomly drawn from three states (Pennsylvania, Vermont, and Virginia) in early 1997 to compare important aspects of the farming operations for four distinct grazing systems: continuous, traditional, moderately intensive, and intensive. Farmers who used intensive grazing tended to be younger, have more cows per acre, and have greater satisfaction with their farming operations. Logit regression results showed that more formal education and a higher debt-to-asset ratio increased the likelihood that a farmer would increase reliance on grazing in the future. (Key words: dairy farming, grazing, technology adoption) Abbreviation key: rBST = recombinant bovine somatotropin. INTRODUCTION The 1990s cost-price squeeze affecting the Northeast dairy industry has forced many producers to exit the industry. Many remaining farms have implemented changes in their production systems to increase profitability. For farms unable or unwilling to pursue capital-intensive, large herd expansions, intensive grazing has become an increasingly popular option (12, 14). Modern pasture management technology can be traced back to the work of Andre Voisin. The principles and practices of what he termed “rational grazing” are explained in his 1959 book, Grass Productivity (13). This technology is also referred to as intensive rotational grazing, management-intensive grazing, or intensive grazing.
Received April 12, 1999. Accepted December 8, 1999. Corresponding author: J. R. Winsten; e-mail: [email protected]. 2000 J Dairy Sci 83:836–842
Historically, animal agriculture in the Northeast was based on the use of pasture. However, in the decades following World War II farmers found that the use of relatively cheap energy, fertilizers, and pesticides, and greatly improved mechanization could improve farm profits. In general, costs for dairy production during this time were low relative to the returns they generated (12). These inputs, which allowed for greatly increased milk production per cow, were substituted for pasture in the production process. This trend gave rise to the predominance of confinement dairy production practices on farms throughout the United States (10). Narrow profit margins in the dairy industry in recent years have made it difficult for many highly leveraged, capital-intensive operations to generate positive returns on investment without adopting efficient but costly production technologies. Intensive grazing may provide certain dairy farmers with a lower investment and lower cost approach to milk production than does standard confinement dairy technology (1, 3, 7, 8). This paper examines many of the farm and farmer characteristics that are associated with different levels of grazing intensity. Information on the profitability of the grazing systems described below is not available in the data set. METHODS The data for this study were obtained from a survey on production practices mailed to dairy farmers in Pennsylvania, Vermont, and Virginia in early 1997. The survey requested information on grazing practices, production practices, farm characteristics, current and future use of technology, satisfaction, and future intentions for herd size, acres farmed, and reliance on grazing. Pennsylvania and Vermont farms were randomly selected for the survey, while all Virginia dairy farms were sampled. The survey was conducted via the Dillman method; survey instruments were mailed to all selected dairy farmers, and nonrespondents were mailed a reminder postcard and then an additional survey instrument (2).
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Grazing intensity was employed as the primary farm classification variable in this study. Grazing classification was based on farmers’ responses to questions asking whether dairy cows were grazed, the typical rotation frequency, the percentage of forage supplied by pasture, and how feed rations were adjusted when the dairy cows were pastured. All farms that had grazed their dairy cows were classified at one of four levels of grazing intensity. Continuous grazing is defined as providing the same pasture for the milking herd for the entire grazing season or providing a fresh pasture no more frequently than every 31 d. Traditional grazing is defined as providing a fresh pasture to the milking herd every 8 to 30 d. Moderately intensive grazing is defined as providing a fresh pasture to the milking herd every 2 to 7 d. Intensive grazing is defined as providing a fresh paddock to the milking herd at least once per day, providing more than 50% of daily forage requirements from pasture, and decreasing the amount of conserved forage fed (i.e., silage, haylage, hay) during periods of pasture availability. The definition of intensive grazing used in this paper was chosen to identify farms that aggressively followed pasture management practices that maximized pasture forage production and reduced handling of stored feeds and manure relative to farms that managed their pastures less intensively. Because the focus of this analysis was dairy farms using grazing practices, farms that did not graze their milking herds during 1996 were classified as confinement systems and were not examined in this study. Key aspects of the farming operation were compared among the four grazing groups defined above. Statistically significant differences between the groups were identified for farm and farmer characteristics, current and future use of technology, and satisfaction with components of the farming operation. In the second part of this analysis, a logit regression model was used to determine the probability that certain farm and farmer characteristics would predict a farmer increasing reliance on grazing within the next 3 yr (6, 9). Although the current literature does not provide much empirical information for developing a priori expectations of factors that would influence plans to increase reliance on grazing, theory and observations of experienced dairy researchers were useful in developing the model. We expected that farms with smaller herds would be more likely to increase reliance on grazing in the future. Concurrent with theories of technology adoption, we assumed that younger farmers with college-level education would be more likely to increase their reliance on grazing in the future (5, 11). Because intensive grazing is presently viewed as an innovative management approach to feeding, we
also assumed that farmers who used newer technologies [e.g., recombinant bovine somatotropin (rBST)] and other management-intensive strategies (e.g., DHIA records, farm computer, and written farm plans) would be more likely to increase their grazing intensity in the future. We theorized that farms with more debt would be more likely to increase their reliance on grazing because it was generally less capital intensive than other dairy production strategies. Farms with higher stocking rates could be less likely to increase their reliance on grazing in the future because of land constraints. We assumed that the current use of grazing would have an influence on future use of grazing. Consistent with the sociological concept of cognitive dissonance, it was also possible that current users of intensive grazing might defend the choice of production system made in the past by responding that they will increase their reliance on grazing in the future. The binary dependent variable for the logistic regression model was equal to 1 if the farmer indicated plans to increase his reliance on grazing in the future and 0 if otherwise. The continuous variables were herd size (COWS), milk production per cow (MILK), farmer age (AGE), and stocking rate of the farm (STCKRATE), which was calculated as number of acres per cow. The polychotomous variables were the farmer’s education level (EDUC), the farm’s debt level (DEBT) as a percentage of total farm assets, and the grazing intensity of the farm (INTENSE). A set of four dichotomous variables related to the use of management aides and technologies included the use of DHIA records for management decisions, the use of recombinant bovine somatotropin (rBST), the use of a computer (COMPUTE), and the use of written farm plans or goals (PLANS). There were also two dummy variables indicating the state that the farm is located in (VERMONT or VIRGINIA) with Pennsylvania farms represented in the constant term. The logistic procedure computes odds ratios that are a measure of association that approximates the relative likelihood that an option is chosen by those with a given characteristic compared with those without that characteristic. For example, an odds ratio greater than 1 implies a positive relationship between the farm characteristic and the dependent variable with higher odds ratios implying a stronger relationship. An odds ratio of less than 1 represents an inverse relationship between the farm characteristic and the dependent variable. For a dummy independent variable, the odds ratio is the exponentiated regression coefficient. RESULTS AND DISCUSSION The mail survey had a 63% return response rate, ranging from 57.9% for Vermont to 67.9% for Virginia, Journal of Dairy Science Vol. 83, No. 4, 2000
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Table 1. Mail survey response rate by state.1
Table 3. Percentage of farmers using grazing by state.
State
Total dairy farms
Surveys mailed
Possible sample2
Returned surveys
Response rate (%)
Pennsylvania Vermont Virginia
10,500 1952 1068
1496 400 1068
1438 380 1044
874 220 709
60.8 57.9 67.9
1
Source: Mail survey conducted in Pennsylvania, Vermont, and Virginia; January to March, 1997. 2 Possible sample is the number of surveys mailed less those returned for incorrect address or are no longer dairy farming.
with significant differences in farm size and structure found among the three states (Tables 1 and 2). Virginia farms tended to be larger in herd size and land area and have higher milk production per cow (Table 2). Pennsylvania farms tended to be the smallest of the three states in all categories. Pennsylvania may have had smaller but more numerous farms because of the large number of Old Order Amish and Mennonites that operate smaller fluid quality dairy farms throughout the state. The exact number of members of Old Order Amish and Mennonite groups that operate dairy farms in Pennsylvania is not precisely known but knowledgeable estimates generally range from 25 to 30%. As might be expected from climatic and topographical differences, Vermont dairy farms tended to grow less corn and other crops, but have more grassland acres. Vermont farms also had a higher stocking density than did dairy farms in Pennsylvania or Virginia. Table 3 shows the extent of pasture use and the percentage of dairy farms at each grazing intensity level in each of the three states. Vermont had the largest percentages of dairy farmers who utilized graz-
Table 2. Farm characteristics by state. Means Farm Characteristics Number of cows (milking and dry) Milk production per cow (lb/lactation) Crop and pasture acreage Corn acreage Grassland acreage Other crop acreage Stocking density (acres/cow)
Pennsylvania (n = 874)
Vermont (n = 220)
Virginia (n = 709)
65a,b
99a,c
115b,c
17,476b 222a,b 81a,b 114a,b 28a,b
17,650c 288a,c 64a,c 224a 4a,c
18,192b,c 383b,c 130b,c 212b 44b,c
3.52a
3.17a
3.35
a Statistically significant difference (P < 0.05) between Pennsylvania and Vermont. b Statistically significant difference (P < 0.05) between Pennsylvania and Virginia. c Statistically significant difference (P < 0.05) between Vermont and Virginia.
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Grazed milkers during 1996 (%) Continuous grazing (%)1 Traditional grazing (%)2 Moderately intensive grazing (%)3 Intensive grazing (%)4
Pennsylvania
Vermont
Virginia
57.6a,b 30.3a 11.1
69.4a,c 17.6a,c 13.4
50.4b,c 26.9c 9.6
12.4a 3.8a
26.8a,c 11.6a,c
10.0c 3.8c
a Statistically significant difference (P < 0.05) between Pennsylvania and Vermont. b Statistically significant difference (P < 0.05) between Pennsylvania and Virginia. c Statistically significant difference (P < 0.05) between Vermont and Virginia. 1 Farms that keep their milking cows in the same pasture for 30 d or longer. 2 Farms that move their milking cows to a fresh pasture every 8 to 30 d. 3 Farms that move their milking cows to a fresh pasture every 2 to 7 d. 4 Farms that move their milking cows to a fresh pasture at least once a day, rely on pasture for more than 50% of dairy forage requirements, and decrease amount of stored forages fed when milking cows are on pasture.
ing and who practiced moderate and intensive grazing management techniques. Pennsylvania had the largest percentage of dairy farmers following continuous grazing practices. Characteristics by Grazing System As indicated above, the analysis in this paper focused on the farms that reported grazing their milking cows. This criterion was met by 1008 farms, which represent 55.9% of the 1803 respondents in the sample. Some of the differences among grazing production systems regarding general farm characteristics can be seen in Table 4. Although not statistically significant, the intensive grazing farms had slightly less average milk production per cow. Decreased production would be consistent with difficulties in ration balancing when pasture provided the majority of forage intake, as well as to greater energy expenditure by the cows while grazing (4). The intensive grazing farms tended to have a smaller land area but had a larger percentage of grassland and a higher stocking density (fewer acres per cow). The continuous and traditional grazing farms had >70% more acres of corn than the farms using intensive grazing, despite having similar herd sizes. Much of this additional acreage was likely being used by these farms to produce and store feed for the dairy cows, while the farms using intensive grazing relied more on pasture for forage needs. The results show that grazing intensity was strongly correlated with farmer age (negatively) and with level
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OUR INDUSTRY TODAY Table 4. Farm characteristics by grazing system.1
Farm Characteristic
Continuous (n = 492)
Traditional (n = 194)
Moderatively intensive (n = 237)
Intensive (n = 85)
Number of cows Milk prod/cow (lb/lactation) Crop and pasture acreage Corn acreage Grassland acreage Other crop acreage Stocking density (acres/cow) Years using grazing system Farmer age College education (%) Sole proprietorship (%) Debt/asset ratio greater than 40% Off-farm income >$12,000
70 17,213 259c 73b,c 163 23c 3.81b,c 21a,b,c 49a,b,c 15c 71 15b 23b
74 17,104 260e 75d,e 165 21e 3.75e 13a,d,e 47a,e 19 71 20 29
70 16,725 240 58b,d 154 25f 3.43b,f 10b,d,f 46b,f 19 74 22b 31b
69 16,518 204c,e 42c,e 156 9c,e,f 2.96c,e,f 7c,e,f 42c,e,f 27c 78 18 27
Statistically significant difference (P < 0.05) between continuous and traditional. Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. d Statistically significant difference (P < 0.05) between traditional and moderate. e Statistically significant difference (P < 0.05) between traditional and intensive. f Statistically significant difference (P < 0.05) between moderate and intensive. 1 Includes only those farms that grazed their milking cows. a b
of formal education (positively). The intensive graziers tended to be 4 to 7 yr younger and more likely to have a college education (Table 4). Approximately 27% of the intensive graziers had completed a 4-yr college education compared with 15, 19, and 19% for the continuous, traditional, and moderately intensive graziers, respectively. Statistically significant differences were not observed between the groups for the type of farm ownership. The farms using moderately intensive grazing were more likely to have a debt to asset ratio greater than 40% and have the farm operator or spouse earn more than $12,000 in off-farm income than were farms practicing continuous grazing. Current and Future Use of Technology Table 5 shows the percentage of farms in each grazing intensity group that used different technology fea-
tures. Significant differences between the grazing groups existed only on the use of personal computers and written farm plans and goals. The data indicate no significant differences among the groups regarding the current use of milking parlors, TMR (use of a TMR includes the feeding of a complete TMR during nongrazing months and likely but not necessarily feeding mixed supplemental forage and grain during grazing periods), DHIA production monitoring, or rBST. The future use of TMR, rBST, computers, and written farm plans (Table 6) was expected to increase in the next 3 yr, above the level indicated by all grazing groups at the time of this survey (Table 5). Although there was no distinct pattern, the traditional and moderately intensive grazing groups generally showed a greater increase in use of these technologies. Only the intensive grazing farms expected to increase future use of milking parlors.
Table 5. Percentage of farms currently using technology by grazing system.1
Technology
Continuous (%)
Traditional (%)
Moderately intensive (%)
Intensive (%)
Milking parlor Total mixed ration (TMR) DHIA Bovine somatotropin (rBST) Computer Written farm plan/goals
45 28 49 12 23a,b,c 18a,b,c
42 32 56 13 33a 31a
39 34 53 15 33b 33b
40 27 54 10 40c 36c
Statistically significant difference (P < 0.05) between continuous and traditional. Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. 1 Includes only those farms that grazed their milking cows. a b
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WINSTEN ET AL. Table 6. Percentage of farms planning to use technology within 3 yr by grazing system.
Technology
Continuous (%)
Traditional (%)
Moderately intensive (%)
Intensive (%)
Milking parlor TMR DHIA Bovine somatotropin (rBST) Computer Written farm plan/goals
45 37 46b 15 36b,c 29a,b,c
42 48 53 19 47 48a
39 42 57b 21 52b 45b
47 36 53 11 53c 53c
Statistically significant difference (P < 0.05) between continuous and traditional. Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. a b
Farmer Satisfaction The survey results included an ordinal ranking of each farmer’s satisfaction with certain aspects of their farming operation (1 = very dissatisfied to 5 = very satisfied). Table 7 shows the mean farmer satisfaction with certain components of the farming operation for each group and indicates statistically significant differences between the groups. For every component considered, with the exception of milk production per cow, the intensive graziers experienced the greatest satisfaction. The next most satisfied group was farmers who practiced continuous grazing; they ranked second in satisfaction in each category, except for milk production per cow for which they were most satisfied. The traditional grazing farms appeared to be least satisfied, ranking lowest for all categories except for milk per cow, machinery repair expense, time away from the farm, and anxiety and stress level.
Intensive graziers registered significantly more satisfaction than the other groups regarding purchased feed costs and machinery repair expenses. Apparently, farmers who employ intensive grazing are satisfied that their production system has reduced several major cost components. The survey data indicated that farmers using intensive grazing had significantly greater satisfaction regarding reduced anxiety and stress and financial progress made since 1990. This finding on financial progress happened to coincide with the 7-yr average time the intensive graziers had been using their production system. The results of this analysis indicated that intensive graziers as a group were differentiated from other farmers with less intensive grazing systems. To the extent that higher satisfaction with the aforementioned farm expense components represented lower costs, intensive grazing systems appear to be a viable option for some Northeastern dairy
Table 7. Average farmer satisfaction levels by grazing system. Mean satisfaction scores for grazing systems Farmer satisfaction with
Continuous
Traditional
Moderately intensive
Intensive
Milk production per cow Herd health Purchased feed costs Hired labor costs Operator labor requirements Capital replacement costs Machinery repair expense Time away from farm Anxiety/stress level Profit level (1996) Financial progress (1990 to 1996)
3.17 3.52 2.06c 3.40a 3.08a 2.71a 2.64c 2.47 2.57c 2.50a,b 2.70c
3.09 3.50 1.93e 3.15a,e 2.85a,e 2.51a,e 2.56e 2.43 2.45e 2.28a,e 2.59e
2.94 3.52 1.97f 3.30 2.99 2.57f 2.48f 2.31f 2.38f 2.36b,f 2.57f
3.07 3.77 2.31c,e,f 3.61e 3.15e 2.90e,f 2.96c,e,f 2.68f 2.87c,e,f 2.67e,f 3.02c,e,f
*Satisfaction scored on a scale of 1 = very dissatisfied to 5 = very satisfied. a Statistically significant difference (P < 0.05) between continuous and traditional. b Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. d Statistically significant difference (P < 0.05) between traditional and moderate. e Statistically significant difference (P < 0.05) between traditional and intensive. f Statistically significant difference (P < 0.05) between moderate and intensive. Journal of Dairy Science Vol. 83, No. 4, 2000
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OUR INDUSTRY TODAY Table 8. Odds ratios for the logit regression. Characteristic
Plan to increase reliance on grazing
Intercept Herd size (COWS) Avg. milk production/cow (MILK) Farmer age (AGE) Farm in Vermont (VERMONT) Farm in Virginia (VIRGINIA) Level of education (EDUC) Level of debt (DEBT) Stocking rate (STCKRATE) Use of DHIA (DHIA) Use of rBST (rBST) Use of computer (COMPUTE) Use of written plans (PLANS) Grazing intensity (INTENSE)
0.010 0.964* 0.973 1.030 0.851 2.087*** 1.465*** 1.439*** 1.017 0.797 1.602* 1.000 1.001 1.778***
*P < 0.10, **P < 0.05, ***P < 0.01.
producers. However, the next most satisfied group was farmers who practiced the least intensively managed grazing systems. Therefore, satisfaction is not necessarily contingent on grazing intensity but more likely is related to the results of management applied to the particular production system. Predicting Future Reliance on Grazing Logit regression was used to predict the probability that a given farmer planned to increase reliance on grazing in the future. This question is of practical interest to extension professionals and agribusinesses involved with grazing activities. Odds ratios produced by the logit regression, presented in Table 8, are a measure of association that approximates the relative likelihood that an option is chosen by those with a given characteristic. For example, the odds ratio associated with the location of a farm in Virginia was 2.09, which suggested that Virginia dairy farmers were two times more likely to increase their reliance on grazing compared with dairy farms located in Pennsylvania. For a continuous independent variable, a meaningful unit of measurement over which to consider its impact on the dependent variable must be incorporated into the odds ratio. For the variable MILK, a logical unit over which to consider a change was 1000 lb of milk per cow per year. To calculate the appropriate odds ratio, the regression coefficient or log-odds was multiplied by 1000 before exponentiating (9). The variables in the model that were significant at the 90% confidence level were COWS, EDUC, DEBT, RBST, VIRGINIA, and INTENSE. The influence of herd size was inversely related to the choice of future reliance on grazing. For every 10-cow increase in herd size, the probability that a farmer would increase his future reliance on grazing decreased by 3%. More for-
mal education increased the probability that a farmer planned to increase his reliance on grazing. This pattern was found to be consistent with theories on technology adoption (5, 11). Higher farm debt also increased the probability that a farmer planned to increase reliance on grazing. As discussed earlier, this was most likely due to the capital constraints related to investment in more conventional production practices. Current use of rBST was a significant factor in plans to increase reliance on grazing in the future. This was likely a result of a management style that was intensive. Compared with Pennsylvania graziers, Virginia graziers were twice as likely to increase their reliance on grazing. The more intensive the farmer’s use of pasture, the more likely that future reliance on grazing would increase. While this finding might be a result of cognitive dissonance, it may also indicate an underlying satisfaction with the current performance of grazing systems. CONCLUSIONS The analysis, based on results from a large sample of dairy farms randomly drawn from Pennsylvania, Vermont, and Virginia provides insights into issues relating to use of grazing systems in the Northeast. After presenting average farm and farmer characteristics for each state, this paper examined differences in technology use, satisfaction, and characteristics among farms in four differentiated categories of pasture use intensity. Intensive grazing farms constituted less than 4% of total dairy farms in Pennsylvania and Virginia and 11.5% in Vermont. These farms tended to be slightly smaller than average, with slightly lower milk production per cow. Farmers using intensive grazing technology tended to be younger and have more years of formal education. A key finding was that intensive grazing farmers tended to be significantly more satisfied with many aspects of their farm business. Logit regression analysis based on farm and farmer characteristics was used to identify significant factors in farmers’ plans to increase future reliance on grazing intensity. The results of the logit model indicated those farms with larger than average herd size are less likely to increase future reliance on grazing intensity. Farmers with a higher debt and formal education and who use rBST, and a more intensive grazing system, and are located in Virginia were found to be more likely to increase reliance on grazing in the future. By identifying factors associated with key trends among alternative grazing systems, this analysis can also help policy makers, extension specialists, and agribusinesses to more accurately tailor education and busiJournal of Dairy Science Vol. 83, No. 4, 2000
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ness development activities toward differentiated segments of dairy graziers. REFERENCES 1 Barnham, B., J. P. Chavas, and R. Klemme. 1994. Low capital dairy strategies in Wisconsin: Lessons from a new approach to measuring profitability. Department of Agricultural Economics, University of Wisconsin-Madison, Madison, WI. 2 Dillman, D. A. 1978. Mail and Telephone Surveys: The Total Design Method. Wiley, NY. 3 Elbehri, A., and S. A. Ford. 1995. Economic analysis of major dairy forage systems in Pennsylvania: the role of intensive grazing. J. Prod. Agric. 8:501–507. 4 Fales, S. L., L. D. Muller, S. A. Ford, M. O’Sullivan, R. J. Hoover, L. A. Holden, L. E. Lanyon, and D. R. Buckmaster. 1995. Stocking rate affects production and profitability in a rotationally grazed pasture system. J. Prod. Agric. 8:88–96. 5 Feder, G., R. E. Just, and D. Zilberman. 1985. Adoption of agricultural innovations in developing countries: a survey. Econ. Dev. Cult. Change 34:255–298.
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6 Greene, W. H. 1993. Econometric Analysis. 2nd ed. PrenticeHall, Inc., Englewood Cliffs, NJ. 7 Hanson, G. D., L. Cunningham, S. A. Ford, L. D. Muller, and R. L. Parsons. 1998. Increasing intensity of pasture use with dairy cattle: an economic analysis. J. Prod. Agric. 11:175–179. 8 Hanson, G. D., L. Cunningham, M. Morehart, and R. L. Parsons. 1998. Profitability of moderate intensive grazing of dairy cows in the northeast. J. Dairy Sci. 81:821–829. 9 Hosmer, D. W., and S. Lemeshow. 1989. Applied Logistic Regression. John Wiley & Sons, NY. 10 Murphy, B. 1994. Greener pastures on your side of the fence. Third ed. Arriba Publishing, Colchester, VT. 11 Nelson, R. R., and E. S. Phelps. 1966 May. Investment in humans, technological diffusion, and economic growth. Am. Econ. Rev. 56:69–75. 12 Parker, W. J., L. D. Muller, S. L. Fales, and W. T. McSweeny. 1993. A survey of dairy farms in Pennsylvania using minimal or intensive pasture grazing systems. Prof. Anim. Sci.. 9:77–85. 13 Voisin, A. 1959. Grass Productivity. Philosophical Library, Inc. NY. 14 Winsten, J. R., and B. T. Petrucci. 1996. The Vermont dairy profitability project. Center for Agriculture in the Environment. DeKalb, IL. Electronic publication. http://farm.fic.niu.edu/cae/ caepubs/dairy/vt.dairy.html. Accessed 2/6/2000.
ABSTRACT As the dairy industry in the Northeast experienced difficult economic conditions in the 1990s, grazing was increasingly viewed as an option for feeding dairy cattle. This analysis used a large sample of dairy farms randomly drawn from three states (Pennsylvania, Vermont, and Virginia) in early 1997 to compare important aspects of the farming operations for four distinct grazing systems: continuous, traditional, moderately intensive, and intensive. Farmers who used intensive grazing tended to be younger, have more cows per acre, and have greater satisfaction with their farming operations. Logit regression results showed that more formal education and a higher debt-to-asset ratio increased the likelihood that a farmer would increase reliance on grazing in the future. (Key words: dairy farming, grazing, technology adoption) Abbreviation key: rBST = recombinant bovine somatotropin. INTRODUCTION The 1990s cost-price squeeze affecting the Northeast dairy industry has forced many producers to exit the industry. Many remaining farms have implemented changes in their production systems to increase profitability. For farms unable or unwilling to pursue capital-intensive, large herd expansions, intensive grazing has become an increasingly popular option (12, 14). Modern pasture management technology can be traced back to the work of Andre Voisin. The principles and practices of what he termed “rational grazing” are explained in his 1959 book, Grass Productivity (13). This technology is also referred to as intensive rotational grazing, management-intensive grazing, or intensive grazing.
Received April 12, 1999. Accepted December 8, 1999. Corresponding author: J. R. Winsten; e-mail: [email protected]. 2000 J Dairy Sci 83:836–842
Historically, animal agriculture in the Northeast was based on the use of pasture. However, in the decades following World War II farmers found that the use of relatively cheap energy, fertilizers, and pesticides, and greatly improved mechanization could improve farm profits. In general, costs for dairy production during this time were low relative to the returns they generated (12). These inputs, which allowed for greatly increased milk production per cow, were substituted for pasture in the production process. This trend gave rise to the predominance of confinement dairy production practices on farms throughout the United States (10). Narrow profit margins in the dairy industry in recent years have made it difficult for many highly leveraged, capital-intensive operations to generate positive returns on investment without adopting efficient but costly production technologies. Intensive grazing may provide certain dairy farmers with a lower investment and lower cost approach to milk production than does standard confinement dairy technology (1, 3, 7, 8). This paper examines many of the farm and farmer characteristics that are associated with different levels of grazing intensity. Information on the profitability of the grazing systems described below is not available in the data set. METHODS The data for this study were obtained from a survey on production practices mailed to dairy farmers in Pennsylvania, Vermont, and Virginia in early 1997. The survey requested information on grazing practices, production practices, farm characteristics, current and future use of technology, satisfaction, and future intentions for herd size, acres farmed, and reliance on grazing. Pennsylvania and Vermont farms were randomly selected for the survey, while all Virginia dairy farms were sampled. The survey was conducted via the Dillman method; survey instruments were mailed to all selected dairy farmers, and nonrespondents were mailed a reminder postcard and then an additional survey instrument (2).
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Grazing intensity was employed as the primary farm classification variable in this study. Grazing classification was based on farmers’ responses to questions asking whether dairy cows were grazed, the typical rotation frequency, the percentage of forage supplied by pasture, and how feed rations were adjusted when the dairy cows were pastured. All farms that had grazed their dairy cows were classified at one of four levels of grazing intensity. Continuous grazing is defined as providing the same pasture for the milking herd for the entire grazing season or providing a fresh pasture no more frequently than every 31 d. Traditional grazing is defined as providing a fresh pasture to the milking herd every 8 to 30 d. Moderately intensive grazing is defined as providing a fresh pasture to the milking herd every 2 to 7 d. Intensive grazing is defined as providing a fresh paddock to the milking herd at least once per day, providing more than 50% of daily forage requirements from pasture, and decreasing the amount of conserved forage fed (i.e., silage, haylage, hay) during periods of pasture availability. The definition of intensive grazing used in this paper was chosen to identify farms that aggressively followed pasture management practices that maximized pasture forage production and reduced handling of stored feeds and manure relative to farms that managed their pastures less intensively. Because the focus of this analysis was dairy farms using grazing practices, farms that did not graze their milking herds during 1996 were classified as confinement systems and were not examined in this study. Key aspects of the farming operation were compared among the four grazing groups defined above. Statistically significant differences between the groups were identified for farm and farmer characteristics, current and future use of technology, and satisfaction with components of the farming operation. In the second part of this analysis, a logit regression model was used to determine the probability that certain farm and farmer characteristics would predict a farmer increasing reliance on grazing within the next 3 yr (6, 9). Although the current literature does not provide much empirical information for developing a priori expectations of factors that would influence plans to increase reliance on grazing, theory and observations of experienced dairy researchers were useful in developing the model. We expected that farms with smaller herds would be more likely to increase reliance on grazing in the future. Concurrent with theories of technology adoption, we assumed that younger farmers with college-level education would be more likely to increase their reliance on grazing in the future (5, 11). Because intensive grazing is presently viewed as an innovative management approach to feeding, we
also assumed that farmers who used newer technologies [e.g., recombinant bovine somatotropin (rBST)] and other management-intensive strategies (e.g., DHIA records, farm computer, and written farm plans) would be more likely to increase their grazing intensity in the future. We theorized that farms with more debt would be more likely to increase their reliance on grazing because it was generally less capital intensive than other dairy production strategies. Farms with higher stocking rates could be less likely to increase their reliance on grazing in the future because of land constraints. We assumed that the current use of grazing would have an influence on future use of grazing. Consistent with the sociological concept of cognitive dissonance, it was also possible that current users of intensive grazing might defend the choice of production system made in the past by responding that they will increase their reliance on grazing in the future. The binary dependent variable for the logistic regression model was equal to 1 if the farmer indicated plans to increase his reliance on grazing in the future and 0 if otherwise. The continuous variables were herd size (COWS), milk production per cow (MILK), farmer age (AGE), and stocking rate of the farm (STCKRATE), which was calculated as number of acres per cow. The polychotomous variables were the farmer’s education level (EDUC), the farm’s debt level (DEBT) as a percentage of total farm assets, and the grazing intensity of the farm (INTENSE). A set of four dichotomous variables related to the use of management aides and technologies included the use of DHIA records for management decisions, the use of recombinant bovine somatotropin (rBST), the use of a computer (COMPUTE), and the use of written farm plans or goals (PLANS). There were also two dummy variables indicating the state that the farm is located in (VERMONT or VIRGINIA) with Pennsylvania farms represented in the constant term. The logistic procedure computes odds ratios that are a measure of association that approximates the relative likelihood that an option is chosen by those with a given characteristic compared with those without that characteristic. For example, an odds ratio greater than 1 implies a positive relationship between the farm characteristic and the dependent variable with higher odds ratios implying a stronger relationship. An odds ratio of less than 1 represents an inverse relationship between the farm characteristic and the dependent variable. For a dummy independent variable, the odds ratio is the exponentiated regression coefficient. RESULTS AND DISCUSSION The mail survey had a 63% return response rate, ranging from 57.9% for Vermont to 67.9% for Virginia, Journal of Dairy Science Vol. 83, No. 4, 2000
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Table 1. Mail survey response rate by state.1
Table 3. Percentage of farmers using grazing by state.
State
Total dairy farms
Surveys mailed
Possible sample2
Returned surveys
Response rate (%)
Pennsylvania Vermont Virginia
10,500 1952 1068
1496 400 1068
1438 380 1044
874 220 709
60.8 57.9 67.9
1
Source: Mail survey conducted in Pennsylvania, Vermont, and Virginia; January to March, 1997. 2 Possible sample is the number of surveys mailed less those returned for incorrect address or are no longer dairy farming.
with significant differences in farm size and structure found among the three states (Tables 1 and 2). Virginia farms tended to be larger in herd size and land area and have higher milk production per cow (Table 2). Pennsylvania farms tended to be the smallest of the three states in all categories. Pennsylvania may have had smaller but more numerous farms because of the large number of Old Order Amish and Mennonites that operate smaller fluid quality dairy farms throughout the state. The exact number of members of Old Order Amish and Mennonite groups that operate dairy farms in Pennsylvania is not precisely known but knowledgeable estimates generally range from 25 to 30%. As might be expected from climatic and topographical differences, Vermont dairy farms tended to grow less corn and other crops, but have more grassland acres. Vermont farms also had a higher stocking density than did dairy farms in Pennsylvania or Virginia. Table 3 shows the extent of pasture use and the percentage of dairy farms at each grazing intensity level in each of the three states. Vermont had the largest percentages of dairy farmers who utilized graz-
Table 2. Farm characteristics by state. Means Farm Characteristics Number of cows (milking and dry) Milk production per cow (lb/lactation) Crop and pasture acreage Corn acreage Grassland acreage Other crop acreage Stocking density (acres/cow)
Pennsylvania (n = 874)
Vermont (n = 220)
Virginia (n = 709)
65a,b
99a,c
115b,c
17,476b 222a,b 81a,b 114a,b 28a,b
17,650c 288a,c 64a,c 224a 4a,c
18,192b,c 383b,c 130b,c 212b 44b,c
3.52a
3.17a
3.35
a Statistically significant difference (P < 0.05) between Pennsylvania and Vermont. b Statistically significant difference (P < 0.05) between Pennsylvania and Virginia. c Statistically significant difference (P < 0.05) between Vermont and Virginia.
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Grazed milkers during 1996 (%) Continuous grazing (%)1 Traditional grazing (%)2 Moderately intensive grazing (%)3 Intensive grazing (%)4
Pennsylvania
Vermont
Virginia
57.6a,b 30.3a 11.1
69.4a,c 17.6a,c 13.4
50.4b,c 26.9c 9.6
12.4a 3.8a
26.8a,c 11.6a,c
10.0c 3.8c
a Statistically significant difference (P < 0.05) between Pennsylvania and Vermont. b Statistically significant difference (P < 0.05) between Pennsylvania and Virginia. c Statistically significant difference (P < 0.05) between Vermont and Virginia. 1 Farms that keep their milking cows in the same pasture for 30 d or longer. 2 Farms that move their milking cows to a fresh pasture every 8 to 30 d. 3 Farms that move their milking cows to a fresh pasture every 2 to 7 d. 4 Farms that move their milking cows to a fresh pasture at least once a day, rely on pasture for more than 50% of dairy forage requirements, and decrease amount of stored forages fed when milking cows are on pasture.
ing and who practiced moderate and intensive grazing management techniques. Pennsylvania had the largest percentage of dairy farmers following continuous grazing practices. Characteristics by Grazing System As indicated above, the analysis in this paper focused on the farms that reported grazing their milking cows. This criterion was met by 1008 farms, which represent 55.9% of the 1803 respondents in the sample. Some of the differences among grazing production systems regarding general farm characteristics can be seen in Table 4. Although not statistically significant, the intensive grazing farms had slightly less average milk production per cow. Decreased production would be consistent with difficulties in ration balancing when pasture provided the majority of forage intake, as well as to greater energy expenditure by the cows while grazing (4). The intensive grazing farms tended to have a smaller land area but had a larger percentage of grassland and a higher stocking density (fewer acres per cow). The continuous and traditional grazing farms had >70% more acres of corn than the farms using intensive grazing, despite having similar herd sizes. Much of this additional acreage was likely being used by these farms to produce and store feed for the dairy cows, while the farms using intensive grazing relied more on pasture for forage needs. The results show that grazing intensity was strongly correlated with farmer age (negatively) and with level
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OUR INDUSTRY TODAY Table 4. Farm characteristics by grazing system.1
Farm Characteristic
Continuous (n = 492)
Traditional (n = 194)
Moderatively intensive (n = 237)
Intensive (n = 85)
Number of cows Milk prod/cow (lb/lactation) Crop and pasture acreage Corn acreage Grassland acreage Other crop acreage Stocking density (acres/cow) Years using grazing system Farmer age College education (%) Sole proprietorship (%) Debt/asset ratio greater than 40% Off-farm income >$12,000
70 17,213 259c 73b,c 163 23c 3.81b,c 21a,b,c 49a,b,c 15c 71 15b 23b
74 17,104 260e 75d,e 165 21e 3.75e 13a,d,e 47a,e 19 71 20 29
70 16,725 240 58b,d 154 25f 3.43b,f 10b,d,f 46b,f 19 74 22b 31b
69 16,518 204c,e 42c,e 156 9c,e,f 2.96c,e,f 7c,e,f 42c,e,f 27c 78 18 27
Statistically significant difference (P < 0.05) between continuous and traditional. Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. d Statistically significant difference (P < 0.05) between traditional and moderate. e Statistically significant difference (P < 0.05) between traditional and intensive. f Statistically significant difference (P < 0.05) between moderate and intensive. 1 Includes only those farms that grazed their milking cows. a b
of formal education (positively). The intensive graziers tended to be 4 to 7 yr younger and more likely to have a college education (Table 4). Approximately 27% of the intensive graziers had completed a 4-yr college education compared with 15, 19, and 19% for the continuous, traditional, and moderately intensive graziers, respectively. Statistically significant differences were not observed between the groups for the type of farm ownership. The farms using moderately intensive grazing were more likely to have a debt to asset ratio greater than 40% and have the farm operator or spouse earn more than $12,000 in off-farm income than were farms practicing continuous grazing. Current and Future Use of Technology Table 5 shows the percentage of farms in each grazing intensity group that used different technology fea-
tures. Significant differences between the grazing groups existed only on the use of personal computers and written farm plans and goals. The data indicate no significant differences among the groups regarding the current use of milking parlors, TMR (use of a TMR includes the feeding of a complete TMR during nongrazing months and likely but not necessarily feeding mixed supplemental forage and grain during grazing periods), DHIA production monitoring, or rBST. The future use of TMR, rBST, computers, and written farm plans (Table 6) was expected to increase in the next 3 yr, above the level indicated by all grazing groups at the time of this survey (Table 5). Although there was no distinct pattern, the traditional and moderately intensive grazing groups generally showed a greater increase in use of these technologies. Only the intensive grazing farms expected to increase future use of milking parlors.
Table 5. Percentage of farms currently using technology by grazing system.1
Technology
Continuous (%)
Traditional (%)
Moderately intensive (%)
Intensive (%)
Milking parlor Total mixed ration (TMR) DHIA Bovine somatotropin (rBST) Computer Written farm plan/goals
45 28 49 12 23a,b,c 18a,b,c
42 32 56 13 33a 31a
39 34 53 15 33b 33b
40 27 54 10 40c 36c
Statistically significant difference (P < 0.05) between continuous and traditional. Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. 1 Includes only those farms that grazed their milking cows. a b
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WINSTEN ET AL. Table 6. Percentage of farms planning to use technology within 3 yr by grazing system.
Technology
Continuous (%)
Traditional (%)
Moderately intensive (%)
Intensive (%)
Milking parlor TMR DHIA Bovine somatotropin (rBST) Computer Written farm plan/goals
45 37 46b 15 36b,c 29a,b,c
42 48 53 19 47 48a
39 42 57b 21 52b 45b
47 36 53 11 53c 53c
Statistically significant difference (P < 0.05) between continuous and traditional. Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. a b
Farmer Satisfaction The survey results included an ordinal ranking of each farmer’s satisfaction with certain aspects of their farming operation (1 = very dissatisfied to 5 = very satisfied). Table 7 shows the mean farmer satisfaction with certain components of the farming operation for each group and indicates statistically significant differences between the groups. For every component considered, with the exception of milk production per cow, the intensive graziers experienced the greatest satisfaction. The next most satisfied group was farmers who practiced continuous grazing; they ranked second in satisfaction in each category, except for milk production per cow for which they were most satisfied. The traditional grazing farms appeared to be least satisfied, ranking lowest for all categories except for milk per cow, machinery repair expense, time away from the farm, and anxiety and stress level.
Intensive graziers registered significantly more satisfaction than the other groups regarding purchased feed costs and machinery repair expenses. Apparently, farmers who employ intensive grazing are satisfied that their production system has reduced several major cost components. The survey data indicated that farmers using intensive grazing had significantly greater satisfaction regarding reduced anxiety and stress and financial progress made since 1990. This finding on financial progress happened to coincide with the 7-yr average time the intensive graziers had been using their production system. The results of this analysis indicated that intensive graziers as a group were differentiated from other farmers with less intensive grazing systems. To the extent that higher satisfaction with the aforementioned farm expense components represented lower costs, intensive grazing systems appear to be a viable option for some Northeastern dairy
Table 7. Average farmer satisfaction levels by grazing system. Mean satisfaction scores for grazing systems Farmer satisfaction with
Continuous
Traditional
Moderately intensive
Intensive
Milk production per cow Herd health Purchased feed costs Hired labor costs Operator labor requirements Capital replacement costs Machinery repair expense Time away from farm Anxiety/stress level Profit level (1996) Financial progress (1990 to 1996)
3.17 3.52 2.06c 3.40a 3.08a 2.71a 2.64c 2.47 2.57c 2.50a,b 2.70c
3.09 3.50 1.93e 3.15a,e 2.85a,e 2.51a,e 2.56e 2.43 2.45e 2.28a,e 2.59e
2.94 3.52 1.97f 3.30 2.99 2.57f 2.48f 2.31f 2.38f 2.36b,f 2.57f
3.07 3.77 2.31c,e,f 3.61e 3.15e 2.90e,f 2.96c,e,f 2.68f 2.87c,e,f 2.67e,f 3.02c,e,f
*Satisfaction scored on a scale of 1 = very dissatisfied to 5 = very satisfied. a Statistically significant difference (P < 0.05) between continuous and traditional. b Statistically significant difference (P < 0.05) between continuous and moderate. c Statistically significant difference (P < 0.05) between continuous and intensive. d Statistically significant difference (P < 0.05) between traditional and moderate. e Statistically significant difference (P < 0.05) between traditional and intensive. f Statistically significant difference (P < 0.05) between moderate and intensive. Journal of Dairy Science Vol. 83, No. 4, 2000
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Plan to increase reliance on grazing
Intercept Herd size (COWS) Avg. milk production/cow (MILK) Farmer age (AGE) Farm in Vermont (VERMONT) Farm in Virginia (VIRGINIA) Level of education (EDUC) Level of debt (DEBT) Stocking rate (STCKRATE) Use of DHIA (DHIA) Use of rBST (rBST) Use of computer (COMPUTE) Use of written plans (PLANS) Grazing intensity (INTENSE)
0.010 0.964* 0.973 1.030 0.851 2.087*** 1.465*** 1.439*** 1.017 0.797 1.602* 1.000 1.001 1.778***
*P < 0.10, **P < 0.05, ***P < 0.01.
producers. However, the next most satisfied group was farmers who practiced the least intensively managed grazing systems. Therefore, satisfaction is not necessarily contingent on grazing intensity but more likely is related to the results of management applied to the particular production system. Predicting Future Reliance on Grazing Logit regression was used to predict the probability that a given farmer planned to increase reliance on grazing in the future. This question is of practical interest to extension professionals and agribusinesses involved with grazing activities. Odds ratios produced by the logit regression, presented in Table 8, are a measure of association that approximates the relative likelihood that an option is chosen by those with a given characteristic. For example, the odds ratio associated with the location of a farm in Virginia was 2.09, which suggested that Virginia dairy farmers were two times more likely to increase their reliance on grazing compared with dairy farms located in Pennsylvania. For a continuous independent variable, a meaningful unit of measurement over which to consider its impact on the dependent variable must be incorporated into the odds ratio. For the variable MILK, a logical unit over which to consider a change was 1000 lb of milk per cow per year. To calculate the appropriate odds ratio, the regression coefficient or log-odds was multiplied by 1000 before exponentiating (9). The variables in the model that were significant at the 90% confidence level were COWS, EDUC, DEBT, RBST, VIRGINIA, and INTENSE. The influence of herd size was inversely related to the choice of future reliance on grazing. For every 10-cow increase in herd size, the probability that a farmer would increase his future reliance on grazing decreased by 3%. More for-
mal education increased the probability that a farmer planned to increase his reliance on grazing. This pattern was found to be consistent with theories on technology adoption (5, 11). Higher farm debt also increased the probability that a farmer planned to increase reliance on grazing. As discussed earlier, this was most likely due to the capital constraints related to investment in more conventional production practices. Current use of rBST was a significant factor in plans to increase reliance on grazing in the future. This was likely a result of a management style that was intensive. Compared with Pennsylvania graziers, Virginia graziers were twice as likely to increase their reliance on grazing. The more intensive the farmer’s use of pasture, the more likely that future reliance on grazing would increase. While this finding might be a result of cognitive dissonance, it may also indicate an underlying satisfaction with the current performance of grazing systems. CONCLUSIONS The analysis, based on results from a large sample of dairy farms randomly drawn from Pennsylvania, Vermont, and Virginia provides insights into issues relating to use of grazing systems in the Northeast. After presenting average farm and farmer characteristics for each state, this paper examined differences in technology use, satisfaction, and characteristics among farms in four differentiated categories of pasture use intensity. Intensive grazing farms constituted less than 4% of total dairy farms in Pennsylvania and Virginia and 11.5% in Vermont. These farms tended to be slightly smaller than average, with slightly lower milk production per cow. Farmers using intensive grazing technology tended to be younger and have more years of formal education. A key finding was that intensive grazing farmers tended to be significantly more satisfied with many aspects of their farm business. Logit regression analysis based on farm and farmer characteristics was used to identify significant factors in farmers’ plans to increase future reliance on grazing intensity. The results of the logit model indicated those farms with larger than average herd size are less likely to increase future reliance on grazing intensity. Farmers with a higher debt and formal education and who use rBST, and a more intensive grazing system, and are located in Virginia were found to be more likely to increase reliance on grazing in the future. By identifying factors associated with key trends among alternative grazing systems, this analysis can also help policy makers, extension specialists, and agribusinesses to more accurately tailor education and busiJournal of Dairy Science Vol. 83, No. 4, 2000
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ness development activities toward differentiated segments of dairy graziers. REFERENCES 1 Barnham, B., J. P. Chavas, and R. Klemme. 1994. Low capital dairy strategies in Wisconsin: Lessons from a new approach to measuring profitability. Department of Agricultural Economics, University of Wisconsin-Madison, Madison, WI. 2 Dillman, D. A. 1978. Mail and Telephone Surveys: The Total Design Method. Wiley, NY. 3 Elbehri, A., and S. A. Ford. 1995. Economic analysis of major dairy forage systems in Pennsylvania: the role of intensive grazing. J. Prod. Agric. 8:501–507. 4 Fales, S. L., L. D. Muller, S. A. Ford, M. O’Sullivan, R. J. Hoover, L. A. Holden, L. E. Lanyon, and D. R. Buckmaster. 1995. Stocking rate affects production and profitability in a rotationally grazed pasture system. J. Prod. Agric. 8:88–96. 5 Feder, G., R. E. Just, and D. Zilberman. 1985. Adoption of agricultural innovations in developing countries: a survey. Econ. Dev. Cult. Change 34:255–298.
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6 Greene, W. H. 1993. Econometric Analysis. 2nd ed. PrenticeHall, Inc., Englewood Cliffs, NJ. 7 Hanson, G. D., L. Cunningham, S. A. Ford, L. D. Muller, and R. L. Parsons. 1998. Increasing intensity of pasture use with dairy cattle: an economic analysis. J. Prod. Agric. 11:175–179. 8 Hanson, G. D., L. Cunningham, M. Morehart, and R. L. Parsons. 1998. Profitability of moderate intensive grazing of dairy cows in the northeast. J. Dairy Sci. 81:821–829. 9 Hosmer, D. W., and S. Lemeshow. 1989. Applied Logistic Regression. John Wiley & Sons, NY. 10 Murphy, B. 1994. Greener pastures on your side of the fence. Third ed. Arriba Publishing, Colchester, VT. 11 Nelson, R. R., and E. S. Phelps. 1966 May. Investment in humans, technological diffusion, and economic growth. Am. Econ. Rev. 56:69–75. 12 Parker, W. J., L. D. Muller, S. L. Fales, and W. T. McSweeny. 1993. A survey of dairy farms in Pennsylvania using minimal or intensive pasture grazing systems. Prof. Anim. Sci.. 9:77–85. 13 Voisin, A. 1959. Grass Productivity. Philosophical Library, Inc. NY. 14 Winsten, J. R., and B. T. Petrucci. 1996. The Vermont dairy profitability project. Center for Agriculture in the Environment. DeKalb, IL. Electronic publication. http://farm.fic.niu.edu/cae/ caepubs/dairy/vt.dairy.html. Accessed 2/6/2000.