Farmers' views of the prospects for agriculture in a metropolitan area

Agricultural Systems 23 (1987) 43-61

Farmers' Views of the Prospects for Agriculture in a Metropolitan Area William Lockeretz, Julia F r e e d g o o d & K a t h e r i n e C o o n School of Nutrition, Tufts University,Medford, Massachusetts 02155, USA (Received 2 June 1986; accepted 9 July 1986)

SUMMARY To determine how farmers view their future in agriculture under metropolitan development pressures, we interviewed 52 dairy and fruit/ vegetable farmers in the suburbs of Worcester, Massachusetts, a metropolitan area in the heavily urbanized northeast region of the US. Three measures were used for whether the farmers are building up their farms or are anticipating having to leave agriculture: actions taken in the past five years, actions planned for the next five years and expectations of the future status of the land. The farmers were more positive and optimistic than would be expected from the 'impermanence syndrome' thought to cause the decline of agriculture near cities. Their answers varied more with personal and household characteristics than characteristics of the farm or of the surrounding area. This contradicts the belief that farmers' response to metropolitan pressures mainly is a consequence of land-use competition and the presence of a non-farming population.

I N T R O D U C T I O N : F A R M - C I T Y INTERACTIONS The difficulties of sustaining farming in metropolitan areas have received considerable attention in the United States in recent years, a reaction to an earlier disregard for how farmers are affected by unrestrained suburban expansion. Current concern for farms near cities has several sources: appreciation of the aesthetic and environmental value of farms as open 43 Agricultural Systems 0308-521X/87/$03'50 @ Elsevier Applied Science Publishers Ltd, England, 1987. Printed in Great Britain

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William Lockeretz, Julia Freedgood, Katherine Coon

space and wildlife habitat, consumers' interest in fresh produce and a desire to preserve the nation's agricultural resources to meet future food demands. In response, many states and communities have taken steps to resolve the conflicts between residential development and commercial agriculture. The most widespread measure is tax assessment of land at its agricultural value, rather than its development value, as long as it is kept in agriculture. Others include purchase of development rights, zoning, agricultural districts and 'right to farm' laws to protect farmers against being charged with nuisance violations by non-farming neighbors (Bills et al., 1980; Lapping, 1980; Lapping et al., 1983). Although many such measures are in force around the country, a frequently expressed view is that they are inadequate to permit agriculture to survive near cities (Conklin & Lesher, 1977; Hexem et al., 1980; Lapping, 1980; Peterson, 1983) and that they, at most, can delay farming's inevitable decline in the face of development pressures (e.g. Berry, 1978; Coughlin, 1979). Only rarely is the farmer thought to benefit from being close to a city; for example, because an off-farm job permits part-time farming, whereas the only alternative would be to leave farming completely if it is unprofitable (Hart, 1968). Similarly, Lee (1978) noted that part-time farming in an urban area is a positive choice that enables the farmer to enjoy the farm life-style. It is not, as is sometimes thought, simply the lot of people who cannot do any better economically, nor is it a transient stage on the way to either fulltime farming or abandoning farming altogether. But with few exceptions, most authors on this subject emphasize the harmful consequences of the city. For example, Conklin & Lesher (1977) assert that availability of off-farm employment makes farming on the metropolitan fringe unattractive. This interpretation is completely contradicted by the very common pattern of continuing with part-time (but still commercial) farming even after taking an off-farm job. The most common measures to help agriculture survive in an urbanizing area, such as those, mentioned earlier, are concerned mainly with land use, since competition for land for non-agricultural uses is regarded as the main threat to farming. This reasoning, in turn, leads to reducing the problem of preserving farming to that of preserving farmland. The pessimistic expectation mentioned earlier derives first from the high value of land for development, which can be more than an order of magnitude higher than its value in farming. Even if the farmer does not find it 'irresistibly profitable' to sell to a developer (Lapping, 1980), it is very expensive--very likely prohibitively so--to buy the land needed to use large-scale, modern production technologies. Secondly, relations between farmers and their neighbors can be strained. Non-farmers are thought to be intolerant of

Farmers' views o f prospects in a metropolitan area

45

odors, dust and noise from farms, while farmers complain of vandalism, theft and trespassing (Toner, 1979). Thirdly, farmers need suppliers of services and inputs. When farming declines, suppliers also pull out, adding to production costs because farmers must travel further. This pessimistic scenario feeds on itself. As neighboring farmers pull out, an individual farmer will find it harder to remain in farming; for example, because of loss of supporting services. When farmers become a minority, local government may turn unsympathetic and impose burdensome restrictions (Berry & Plaut, 1978). Moreover, a farmer facing an uncertain future may not make the capital improvements necessary to keep the farm profitable, since the farm may not remain in business long enough for investment to be recaptured. Similarly, farmers may prematurely idle cropland while waiting until the land can be sold (Conklin & Dymsza, 1972; Berry & Plaut, 1978). Of course, not providing for the future will, by itself, help guarantee that the farm has no future. This process has come to be known as the 'impermanence syndrome'. Thus agricultural decline is insidiously self-perpetuating, and, once begun, is supposed to accelerate, perhaps until farming has disappeared almost entirely. But this pessimistic scenario, although plausible and widely accepted, in fact is founded on surprisingly little systematic data (Fischel, 1982), and only rarely on data obtained from farmers. One study that did deal with individual farmers (Molnar, 1985) found that they felt a generalized concern over loss of farmland to development, but did not strongly support governmental measures that would impose any restrictions on their own actions. The study did not investigate how farmers' plans or activities were related to their perceptions of the effects of metropolitan development in their area. Many authors, having described the conditions prevailing in a metropolitan area, simply set forth what they expect farmers to do under such circumstances, which, in turn, may get transmuted into what farmers actually do. Much of the literature has a secondary and derived flavor to it. Where actual studies were done, they often report only unsystematic, anecdotal data that are no more than suggestive. Also, there is sometimes an implicit assumption that farmers are passive in this process, exercising no choice regarding how they respond to a given set of external demographic and land-use conditions. The analysis presented here concerns not only the external conditions facing farmers near a city, but also their reactions to those conditions. Also, we are interested both in their attitudes and their actions, past and anticipated. Agriculture in the study area seems to be showing considerable vitality after a long decline, although hard data are unavailable. We hoped

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William Lockeretz, Julia Freedgood, Katherine Coon

that by going to those who embody this apparent vitality, we could better understand whether the disappearance of farming in a metropolitan area indeed is inevitable and irreversible. SAMPLING AND DATA COLLECTION

The study area The 52 farms in this study are in the inner portion of the Worcester Metropolitan Statistical Area in central Massachusetts. Massachusetts is among the most urbanized states in the country and is the most densely populated of the six states in the northeast corner that comprise New England. Although New England was an important agricultural region early i.n the history of the United States, its position declined steadily as long-distance transportation opened up the major agricultural regions further west, beginning in the 19th century. The study area's high population density and primarily non-agricultural character make it a suitable site to examine the farm/city conflicts discussed above. The city of Worcester itself, with 162000 people in 1980, is the second largest city in New England. The population of the entire metropolitan area was 373 000 in 1980. To concentrate on the portion of the metropolitan area most strongly under the influence of the central city, the study was limited to the nine towns immediately adjacent to the city of Worcester and nine in the next ring of towns outward. (In Massachusetts, all land, no matter how sparsely settled, belongs to some town or city; 'town' does not necessarily denote an entirely built-up area, as in other parts of the country.) The study area's population density was 166 per km 2 in 1980, compared to only 9 per km 2 in the non-metropolitan portions of the country (which is where about four-fifths of all United States farmland is found). From 1970 to 1980 its population increased by 9%. Only 1"0% of the workforce in the study area is employed in agriculture, and only about 8% of its land is active farmland.

Sampling To have a good representation of farms very close to the city, we included equal numbers from the inner and outer rings of towns, although there actually are about twice as many farms in the outer zone. The farms are all within 20 km of the city line, with the closest almost abutting the city. The sample was further stratified so that each zone included 13 dairy farms and 13 fruit/vegetable farms. These are the most prevalent types by

Farmers' views o f prospects in a metropofitan area

47

far. Since they happen to be about equally common, this stratification was of little consequence for the sampling. Produce farmers had to have at least 2 ha of harvested cropland or 4 ha of orchards. No minimum size was imposed on the ,dairy farms; the smallest had 28 cows. Within each type/zone category, the sample was randomly selected from a total of 89 names provided by the local office of the Extension Service or by the initial group of respondents. For dairy farmers, this list was 88% complete, as shown by comparison to state government statistics on dairy farms by town (no comparable statistics are available for vegetable farms). We contacted 57 farmers to obtain the 52 eventually interviewed (91% participation).

Interviews We interviewed farmers between December, 1984, and May, 1985. Typically, the interviews took from one to two hours. We considered as 'operators' everyone described by the respondent as having a major share in management decisions. When there were several operators, we asked to interview the person considered to have the main managerial responsibility. Factual data on personal characteristics are reported for all operators, not just respondents. DATA ANALYSIS

Purpose and structure of the analysis The analysis is concerned with whether a farm is being built up and strengthened for the future, or whether the farmer seems to be anticipating its end as a commercial enterprise. We used multiple regressions to attempt to relate this to personal factors and to characteristics of the farm and the area it is in. The direction in which a farm is moving involves specific actions the farmer has already taken or plans to take as well as subjective expectations. If one believes in the 'impermanence syndrome', the two are strongly related, in that farmers who see their prospects as bleak will not take actions to keep their farms strong. In this study, however, we have not assumed that actions and expectations necessarily are consistent, and therefore have studied both. We chose to ask the farmers to look only five years ahead because we believed that this reflects how they actually think about the future, and that asking much beyond that would be unrealistic, even though the full effects of changes in a metropolitan area would take longer to be revealed. Three kinds of independent variables were used, relating, respectively, to

William Loekeretz, Julia Freedgood, Katherine Coon

48

the farm itself, the area it is in, and the people who operate it. The first two groups were expected to be relevant because of the studies cited above. In contrast, personal variables are mentioned much less frequently by authors whose orientation is towards land prices, land-use competition and demographic changes. We included them here because this omission seemed serious, although we generally did not know in which direction they could be expected to influence the dependent variables. The analysis emphasizes classes of independent variables, not individual ones. We tried to cover each category of explanatory factors comprehensively enough to be sure to see its contribution to the regression. Therefore we permitted redundancies; that is, within each category some of the independent variables are correlated. The number of independent variables (17) is therefore large in relation to the number of cases (52).

Dependent variables Each of the three dependent variables was constructed by summing five components that all refer to actions or expectations that are independent of each other but that bear on the same general concept. Because there was no clear reason to regard certain components as especially important, they were given equal weight. B U I L D U P . The index of past build-up of the farms was the sum of the following items, each scored as + 1 for a positive change in the past five years, - 1 for the opposite change, or 0 if no change: 1. 2. 3. 4.

5.

An increase in fraction of household income from farming. Net purchase of at least 2 ha of land. At least 1.2 ha of idle land cleared for cropping ( - 1 if cropland was idled). Construction or rehabilitation of at least one facility used in production, processing, crop storage, or sales ( - 1 if any taken down or no longer used). Total production higher than five years ago.

PLANS. An analogous index of plans for the next five years was also constructed, with a score of + 1 for each positive change that the farmer regarded .as at least 'fairly likely', - 1 for the opposite change, or 0 if no change was expected or the change was less than 'fairly likely'. The first item was an expected increase (decrease) in total amount of labor devoted to the farm by the operator, family members and hired help. The remaining four corresponded to items 2 to 5 above. No farmers reported that they expect to idle cropland. Even those who will do so might report 'no change planned'. We compensated for this by

Farmers' views o f prospects in a metropolitan area

49

scoring each negative response as - 1.25 instead of - 1. The most negative set of responses that we could actually expect to get (cutting back in all items except No. 3) would score - 5 rather than - 4 , just as a completely positive set of responses scored + 5. PROSPECTS. The third index was intended to measure farmers' expectations concerning the future of their farms, particularly regarding development pressures and transfer to the next generation. The following five components were scored either 1 or 0 (giving PROSPECTS a range of 0 to 5, whereas B U I L D U P and PLANS vary from - 5 to + 5): 1.

2.

3.

4.

5.

Whether the farm has been placed under a restriction on sale for non-agricultural purposes in return for being taxed only at its value in agriculture ( + 1 if yes; 0 if no). Expected fate of the farm when the oldest operator is no longer farming it (asked only if there was at least one operator older than 55). ( + 1 if it is expected to be operated by a member of the next generation or sold or rented to another working farmer; 0 if it is expected to be sold for development or left idle). Chances that in five years the farm will still be owned (or rented, for farmers who do not own any land) and operated by the family. ( + 1 if considered 'probable'; 0 if 'about 50-50' or 'unlikely' or 'don't know'). How any uncertainty regarding the future of the farm affects the farmer's plans for the future. (+ 1 if the farmer does not feel uncertain or makes plans irrespective of uncertainties; 0 if the farmer is reluctant to make long-range improvements because of uncertainties). Response to an offer to buy the farm for development (offers actually were received by 83% of the respondents), or likely response to such an offer. ( + 1 if'not interested' or if development rights have already been transferred to the government; 0 if 'would consider', or land is already on the market, or 'have been thinking about it', or if previous offer was turned down only because it was too low).

Independent variables Farm-related 1. TYPE.. Type was (arbitrarily) coded + 1 and 0 for produce and dairy farms, respectively. 2. RENTED. Rented land as a fraction of total land farmed. 3. SIZE. Size was either the number of cows in the dairy herd, or the total amount of land (rented and owned) for fruit and vegetable farms. To place these two measures on a common scale, they were

William Lockeretz, Julia Freedgood, Katherine Coon

50

4.

logarithmically transformed and standardized (expressed as standard deviations from the respective group mean). ONFARMSALES. A semiquantitative measure of the importance of on-farm sales (pick-your-own and the farmer's own roadside stand). The index ranges from 0 to 1, the latter indicating that all produce was sold on the farm where it was produced. This variable was not computed for dairy farms. Therefore, only its simple correlation coefficients are given but it is not included in the regressions.

TYPE should matter because dairy farms require more land than produce farms, which puts them at a disadvantage where there is strong non-agricultural demand for land. Also, being close to a city can be advantageous to produce farmers but not dairy farmers because it permits direct retail sales. (Massachusetts does not permit the sale of raw milk.) Consequently, an interaction term TYPE x DISTANCE (see below) was included, but it did not improve the regression and was dropped. The same was true for an interaction TYPE x SIZE, which was tried because economies of scale could be different for the two kinds of farm. Renting land is considered a source of uncertainty and insecurity, since renters do not control the fate of the land they operate. Also, owners who do not farm themselves may be less committed than owner-operators to keeping their land in farming. Where non-agricultural competition for land is strong, farmers may be unable to expand enough to capture economies of scale and remain competitive (Bailey et al., 1982). We therefore would expect operators of smaller than average farms to be less optimistic about the future. Finally, on-farm marketing should be a positive factor since it draws an advantage from being near a population center. Area-related I. DISTANCE. Distance from downtown Worcester, in kilometres. (Distance to the city line typically is 3 to 5 km less.) 2. DENSITY. Population density of the respondent's town in 1980, per square kilometre. 3. P O P C H A N G E . Change in population (%) of the respondent's town from 1970 to 1980. 4. F A R M W O R K . Employment in agriculture (self-employed or hired) as a per cent of total work force in the town. 5. FARMTREND. Recent trend in the amount of farming in the farm's immediate area, as described by the respondent. Coded from - 2 to + 2 for declining significantly, declining somewhat, holding steady, incFeasing somewhat, or increasing significantly.

Farmers' views o f prospects in a metropolitan area

6.

7.

51

PROBLEMS. We asked farmers how seriously each of seven problems currently affects them, and whether it is becoming more or less severe: vandalism/theft/trespassing; restrictions on production methods; complaints from neighbors; taxes; availability of supplies and services; availability of labor; availability of credit. Each problem was scored from 0 (negligible) to 9 (serious and getting worse); 4 means 'moderate and staying about the same'. The seven problems were averaged with equal weight in an index with a range from 0 to 9. FURTHER. This index was constructed from several questions on the relative desirability of the current location compared to being further from Worcester. It ranges from - 5 to + 5, where a positive value means the current location is preferable. (A similar index, CLOSER, rated the current location compared to being closer. This index was calculated for 38 farmers only, since the others already were so close to Worcester that the question was meaningless. Consequently, it was omitted in the multiple regression analysis presented later, and only its simple correlation coefficients are given.)

These variables were all expected to be correlated with the three dependent variables with a predictable sign, except for DISTANCE. Different distances could be either advantageous or disadvantageous, but in any case the dependent variables should be positively correlated with FURTHER and CLOSER, since these indexes are positive if the farmer thinks the farm's current location is preferable. For reasons discussed earlier, farmers should be optimistic where farming is stable or increasing, and where the area is more strongly rural and farm-oriented. Consequently, the dependent variables should be positively correlated with FARMTREND and FARMWORK, and negatively with DENSITY and POPCHANGE. Farmers' self-reports of the seriousness of the problems they face obviously should be negatively correlated with the dependent variables.

Personal~family variables 1. 2. 3. 4.

AGE. The respondent's age, or, in farms with more than one operator, the average age of the oldest and youngest operators. AGESPREAD. The difference between ages of the oldest and youngest operators on the farm (0 if only one operator). EDUCATION. Respondent's education, coded from 0 (did not complete high school) to 4 (graduate or professional degree). OFF-FARM. Fraction of the respondent's total household income

52

William Lockeretz, Julia Freedgood, Katherine Coon

• from off-farm sources, whether earned by the respondent or by other family members. 5. BORNHERE. Was at least one current operator born on this farm? (+ 1 if yes; 0 if no). 6. SINCECHILD. Has at least one current operator worked in farming since childhood? (+ 1 if yes; 0 if no). 7. POLITICS. The respondents rated the federal, state and local government, the environmental movement and the generaI public on how they affected the respondent's operation. The evaluation covered the current situation (rated good, fair, or poor) and whether the particular entity was improving, getting worse, or remaining the same. The ratings of each of these entities were given equal weight in an index ranging from - 5 to + 5. The age variables were included because older farmers may be looking towards retirement. It was not clear whether the age of the older or the younger farmer in a two-generation farm would be more important, so both were included (through AGE and AGESPREAD). POLITICS, which should be positively correlated with the dependent variables, was considered a personal variable, whereas PROBLEMS was included with the area-related variables. This is because PROBLEMS concerns phenomena that vary from place to place, whereas all respondents were rating the same political entities, except for local government. Therefore, POLITICS is more a matter of an individual's opinion, whereas PROBLEMS, at least in part, relates to external conditions. Moreover, the scores for the individual components of POLITICS were more highly correlated among themselves than were those of PROBLEMS, suggesting that a given respondent tended to look either favorably or unfavorably on various political entities in general, but evaluated the various problems separately. In any case, transferring PROBLEMS to the personal category did not change the regressions substantially. We also asked respondents why they farm. However, this is a complex, multi-dimensional phenomenon that is not readily combined with the variables just described. Consequently, we discuss this question separately as a self-contained issue that sheds light on the conclusions obtained from the multiple regression analysis. RESULTS Characteristics of the farms, operators and the area

Tables 1 and 2, respectively, describe the sample of farm operators and their farms. Table 3 also presents descriptive statistics on the sample, but

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Farmers' views qf'prospects in a metropolitan area

TABLE 1 Characteristics of Farm Operators (n = 103)

Age Less than 35 35 to 54 55 or older Education High school graduate or less More than high school but less than college degree College degree or more

24% 40% 36%

49% 31% 20%

Sex Male Female Farming background Born on current farm Previously lived on another farm Farming since childhood Household income from farming Less than 50% 50 to 99% 100%

75% 25% 33% 25% 71% 39% 16% 45%

in the form of the independent variables to be used in the regression analysis. Most farmers in the sample have been farming all their lives, and often the farm has been in the family for several generations. Typically, the farms are family owned and operated, with only moderate hired labor. Off-farm income is important in these households, but the farms are commercial enterprises that generate family income. Respondents generally reported that farming is decreasing in their vicinity. Typically, they did not report serious problems (mean of PROBLEMS was 2.3 on a scale from 0 to 9). Trespassing/theft and taxes, the two considered most serious, were rated no worse than moderate (3-9 and 3.5 respectively). On average, the respondents considered they were TABLE 2 Farm Characteristics (n = 52)

Tenure Owned only Rented and owned Rented only Year acquired by current operator's family Before 1940 1940 to 1959 Since 1960 Family workers per farm Working on farm Working off farm Farms with one or more fulltime year-round employees

31% 59% 10%

39% 25% 36% 2.9 1-0 24%

Average size Total land owned (ha) Dairy farms Fruit/vegetable farms Land in tilled crops and orchards (owned plus rented, ha) Dairy farms Fruit/vegetable farms Milking herd

82 35

16 24 55

54

William Lockeretz, Julia Freedgood, Katherine Coon TABLE 3 Mean and Standard Deviation of Independent Variables

Personal and Family Related AGE 47.8 ( 1 2 " 5 ) AGESPREAD 10-0 ( 1 4 " 2 ) EDUCATION 2.9 ( 1 " 0 6 ) OFF-FARM 0.36 (ff38) BORNHERE 0.43 (0.50) SINCECHILD 0-80 (0.40) POLITICS 0-35 ( 1 " 4 ) Farm-Related 0"0 (1.0) (by definition) TYPE 0"5 (0"5) (by definition) RENTED 0.27 (0.31) ONFARMSALES ~ 0.59 (0.24) (produce farms only, n = 26)

Area- Related DISTANCE 14.7 (4.8) DENSITY 165 (84) POPCHANGE 9.8 (12"2) FARMX~,ORK 1.4 (1.4) F A R M T R E N D - 0 . 6 (1.1) PROBLEMS 2.3 (1-4) FURTHER 0.9 (2-0) CLOSER ° 1-4 (2.7) (n = 38)

SIZE

Not included in multiple regressions.

neither helped nor harmed by government, the public and the environmental movement (mean of POLITICS was 0.4 on a scale from - 5 to + 5). The respondents usually preferred their current location to being either closer or further away. Some mentioned that being further away was disadvantageous for family living (less access to urban amenities), even if advantageous for production.

Indexes of the farms' past and future Table 4 shows the three indexes that describe recent and anticipated changes in the farms, along with the components going into each. 1. BUILDUP. A value of 0 for B U I L D U P is meaningful in that, for each component, values of + 1 and - 1 represent opposite changes. Consequently, the sample can be described as having positively built up their farms: the mean of 1.47 for B U I L D U P is significantly greater than 0 (P < 0.001, by the Wilcoxon test). Even if different weights were given to certain components, the mean of the resulting index would necessarily remain positive, since the five separate components all have positive means. 2. PLANS. Here, too, all components of this index are positive on average, with the mean of 1-71 for the overall index significantly greater than 0 (P < 0-001, Wilcoxon test). This index was intended to have a true 0, but it may be positively biased

Farmers' views of prospects in a metropofitan area

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TABLE 4 Components of B U I L D U P , P L A N S , a n d P R O S P E C T S Indexes* Per ceHl respondents coded as: -1

0

+1

7 4 2 4 18

71 80 61 48 21

21 15 36 48 62

8 10 0 2 7

57 66 60 52 29

35 24 40 46 64

---

32 31

68 69

----

29 23 27

71 77 73

B U I L D U P ( M e a n = 1-47, S D = 1.61)

Change in % income from farming Bought/sold land Cleared/idled cropland A d d e d / t o o k down buildings Output P L A N S ( M e a n = 1.71, S D - - 1.93)

Total work in 5 years Buy/sell land Clearing land/expect to idle land A d d / t a k e down buildings Expected future output P R O S P E C T S ( M e a n = 3.55, S D = 1-13)

Under use-value assessment? What if no longer farming? Probably still owned/rented in 5 years Uncertainty Response to development offer

* See text for discussion of the meaning of the values - 1, 0 and + 1, and for explanation of how each index was computed from components.

in that farmers may not like to think about cutting back, or to report it if they are thinking about it. The adjustment mentioned earlier was made to offset this bias by giving more weight to plans to cut back. Still, PLANS may be more valid for comparing different farmers than as an absolute measure, but this still leaves it suitable for the regression analysis reported later. The reasonableness of PLANS is suggested by its high positive correlation (r=0.71, P<0-001) with what farmers have actually done (BUILDUP). 3. PROSPECTS. Here 0 simply means the lowest value on an arbitrary scale from 0 to 5, and there is no point in computing the statistical significance of a difference from 0. Qualitatively, the mean of 3.55 indicates that the farmers generally are optimistic regarding the prospects for their farms to remain active. They are not overwhelmed by the inevitability of having to leave farming, at least not in the short run. PROSPECTS was intended to be related to, but somewhat distinct from, B U I L D U P and PLANS, and, as expected, it is positively and significantly

56

William Lockeretz, Julia Freedgood, Katherine Coon

TABLE 5 Simple Correlations between Dependent and Independent Variables~

Personal/Family AGE AGESPREAD EDUCATION OFF-FARM BORNHERE SINCECHILD POLITICS Related to Farm TYPE SIZE RENTED ONFARMSALES ~ (produce farms only; n = 26) Related to Area the Farm is in DISTANCE DENSITY POPCHANGE FARMWORK FARMTREND PROBLEMS FURTHER CLOSER b (n = 38)

Expected sign

BUILDUP

PLANS

PROSPECTS

(-)

-0"59 0"15 0"42 0"19 0.15 -0-38 0"35

-0.53 0"05 0.38 0-36 0"15 -0"31 0"30

-0.18 0.22 0-21 - 0.10 0.17 0.01 0-46

(+)

0-04 0"03 - 0"08 0-24

- 0"02 0"08 - 0.03 0.10

0-18 0"17 - 0"12 - 0.14

(-) (-) (+) (+ ) (-) (+) (+)

~ 14 -0"32 0"11 0.18 0"03 0"03 0"01 0.05

0"11 -0.18 0-15 0"23 0"14 0.04 0"06 0-00

- 0'06 -0"01 0"02 0"06 0'01 -0.24 0.21 0-21

(+) (+) (+)

(-)

" Significance levels are not shown because of the large number of coefficients computed. The conventionally calculated significance levels would be P < 0.05 for Irl > 0.27; P < 0.02 for Irl > 0"32; P < if01 for Irl > 0'35; P < 0.002 for Irl > 0.44. Not used in regressions. c o r r e l a t e d w i t h b o t h (r = 0.35, P < 0.02 w i t h B U I L D U P ; r = 0"40, P < 0-01 w i t h P L A N S ) . At the s a m e time, the c o r r e l a t i o n s are w e a k e r t h a n the c o r r e l a t i o n b e t w e e n B U I L D U P a n d P L A N S , for w h i c h r = 0.71. T h i s is also to be e x p e c t e d , since B U I L D U P a n d P L A N S b o t h refer to t a n g i b l e changes rather than subjective outlook.

Relation of the three indexes to the independent variables T a b l e 5 s h o w s t h a t several p e r s o n a l / f a m i l y f a c t o r s a r e well c o r r e l a t e d with the d e p e n d e n t v a r i a b l e s . In c o n t r a s t , v a r i a b l e s r e l a t i n g to the f a r m a n d its n e i g h b o r h o o d c o n s i s t e n t l y s h o w e x t r e m e l y low c o r r e l a t i o n s . O f the 36 c o r r e l a t i o n s in the last t w o g r o u p s , o n l y one, if t a k e n b y itself, w o u l d be

57

Farmers' views o f prospects in a metropolitan area

TABLE 6 Multiple Regressions and Canonical Correlations of All Indexes on Groups of Independent Variables and on All Independent Variables Variables included Seven Personal and Family R2

Adjusted R 2 E1 - (1 - R2)*(N - 1)/DF'[ Ten Farm and Area-related R2

Adjusted R 2 ALL R2

Adjusted R 2 Significance of adding personal/family to farm and area vars. Significance of adding farm and area to personal/family ears.

BUILDUP

PLANS

PROSPECTS

ALL

0-55 (P < 0.001) 0.50

0-49 (P < 0.001) 0.44

0-29 (P < 0.025) 0-21

0-39* (P < 0.001)

0-12 NS (P > 0.50) 0

0.11 NS (P > 0-50) 0

0.22 NS (P > 0.30) 0.07

0-15 ° NS (P > 0-50)

0-55a (P < 0.001)

0-68

0.65

0.48

(P < 0.001) 0.55

(P < 0.001) 0.50

(P < 0-05) 0.26

P < 0.001

P < 0.001

P < 0.05

P < 0.001

NS (P > 0-20)

NS (P > 0.10)

NS (P > 0.20)

NS (P > 0.20)

° Redundancy of three dependent variables on indicated independent variables (Cooley & Lohnes, 1971, Chap. 6). Significance is by Wiiks' Lambda for the hypothesis that all coefficients are 0 in all three regressions. NS, Not significant.

considered statistically significant at the 0.05 level. This finding was completely unexpected, since these variables were chosen for their presumed relevance, whereas the personal variables (other than age) were included just in case they turned out to be relevant. The three indexes were regressed on groups of independent variables as well as on all variables simultaneously. The seven personal variables account for about one-fifth to one-half of the variance in the various indexes, as shown by the adjusted R 2, with all three regressions highly significant (Table 6). In contrast, the 10 farm- and area-related variables (shown together in the interests of simplicity) account for almost none of the variance in these same indexes, which is no longer surprising in view of their low simple correlations (Table 5). The overall regressions are not appreciably improved by including these variables along with the personal

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William Lockeretz, Julia Freedgood, Katherine Coon

TABLE 7 Responses to Question 'Why do you Farm?'

Because I love it Economics Born into it/it's in my blood/ I always have done it/ it's been in family long time/ to keep it for my children I like the lifestyle It's what I know best

Leading reason (%)

Second or third reason (%)

Not a reason (%)

63 10

27 25

10 65

12 8 6

23 19 17

65 73 77

variables. The increase in the r a w R 2 occurs primarily because more 'explanatory' variables are available, as seen from how little the adjusted R 2 values increase and from the negligible statistical significance of the 'improvement' in the regressions. Table 6 also shows the canonical correlation of all three indexes regressed simultaneously on the same independent variables. About twofifths of the combined variance is accounted for by the family variables. Although this appears to increase somewhat (0.39 to 0.54) when the remaining variables are included, once again it is because more variables are available. The increase is not statistically significant (P > 0-20). Reasons for farming The respondents' reason for farming was asked in open-ended form. The answers fell into the five categories (set up after the fact) shown in Table 7. (The final reason might have been included i n the third category, which relates to family hrritage and tradition. However, it was kept separate because of a negative implication--'I couldn't do anything else'--whereas 'born into it' was stated in a way that clearly implied pride regarding the respondent's role in life.) Overwhelmingly, the respondents farm because they love it. Economics is a very small factor, mentioned at all by only a third of the respondents, and even then most often as a secondary reason. Besides love of farming, other non-economic reasons, like tradition and lifestyle, are comparable in importance to economics. (Some respondents, after immediately responding 'Because I love it,' would add 'I certainly am not in it for the money.' Often, this would be followed by 'I could make a lot more if I sold the farm and lived off the interest.'

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CONCLUSIONS AND DISCUSSION This study has dealt with moderate-sized commercial dairy and produce farms in densely populated and primarily non-agricultural suburbs of a large city in a highly urbanized region. These farms could be expected to feel the farm/non-farm conflicts considered to make the decline of farming inevitable in the face of metropolitan expansion. But despite a decline in agriculture compared to a few decades ago, the remaining farmers mostly view their near-term future in agriculture with optimism. In general, they expect to continue to operate their farms, to pass them on to the next generation, and to maintain and build them up regardless of possible uncertainties. Most have placed their farms under a restriction which, in exchange for tax benefits, imposes a penalty if the land is sold for non-agricultural uses, and most have turned down offers to buy their land for development. Moreover, their actions are consistent with their stated views. Most have increased their commitment to farming compared to five years ago. On average, they are producing more and getting a greater share of their total family income from farming. They are more likely to have bought land than to have sold it, and they have cleared overgrown land for cropping and added buildings and facilities. They plan to continue this build-up. These findings conflict sharply with a prevailing belief that when residential development encroaches on an agricultural area, it eventually overpowers farmers' ability to remain in farming. In this view, the high price obtainable by selling for development is irresistibly tempting for many farmers, while those who choose to try to continue are overwhelmed by problems. Regardless of individual resolve, the loss of other farms in the vicinity makes survival even more difficult. Moreover, even before a farmer decides to call it quits, the expectation of quitting supposedly leads to premature idling of land and an end to investments and improvements, which, in turn, makes the farm even more non-competitive and hastens its eventual end. Our second unexpected finding concerns why some farmers are more optimistic and resolute than others. Ten factors involving the farm itself or the area it is in were uncorrelated either with how the farm has been built up in the recent past, or with plans to build it up, or with the farmer's expectations regarding its future status. In contrast, seven personal and family factors proved, to be far more important, even though only one of these (age) is routinely mentioned in discussions of the fate of farms near metropolitan areas. Most authors writing about the future of metropolitan agriculture in densely populated areas of the United States are much more pessimistic

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than are the farmers we interviewed. Typically, their predictions are based mainly on economic considerations, such as returns to land in farming compared to higher value uses like residential development. Such an analysis assumes that farmers' decisions are primarily, or even entirely, based on economics, which is not true for our sample. Some authors assume that even when farmers choose to remain in farming, it is necessarily because of economics. An example is the following explanation (given with no supporting data) of why farmers agree not to sell for development in exchange for lower taxes: [M]ost farmers within the I-urban] fringe appear to realize that not all of them can sell at urban values possibly for decades .... With no assurance of an immediate non-farm sale ... farmers have opted for farm-value assessments ... [so that] they can continue to produce efficiently until urban pressure becomes great enough to bring them an offer' (Conklin & Lesher, 1977). The possibility that farmers might not w a n t to sell was apparently never considered. Obviously, whatever their motives, commercial farmers must achieve some minimum economic performance level. But to acknowledge economic realities is not equivalent to assuming that farmers' motivations are exclusively economic. Such an assumption neglects their strong determination to stay in farming because of love, pride, tradition, and lifestyle. (The particularly strong non-economic attraction of dairy farming may explain why we found no difference between dairy and produce farmers' actions or intentions regarding the future, whereas conventional wisdom predicts a greater decline in dairying in metropolitan areas.) Traditional agrarian ideals appear to be thriving among the farmers in our sample, even though they are a very small minority in an overwhelmingly industrial state, and even though they no longer live in rural isolation but instead are close to a metropolitan center. Of course, these farmers' optimistic expectations may not be fulfilled, since external economic and demographic forces could overcome any level of determination and commitment. At the very least, however, when one looks at the future of farming, one should consider not only external forces, but also how these forces are interpreted and responded to by the people who---despite being a very small minority--still have a good deal to do with whether farming survives.

ACKNOWLEDGEMENT This research was supported by a grant from the Jessie B. Cox Charitable Trust, Boston, Massachusetts.

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