Shriar,, Agricultural Intensity and Its Measurement

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Agricultural intensity and its measurement in frontierregions

A. J. SHRIAR Department of Geography, University of Florida, Box 117315, Gainesville, FL 32611-7315 USA;

E-mail: [email protected]

Key words: agricultural intensification, agrotechnologies, cropping frequency, farming systems,

intensity index

Abstract. Agricultural intensification is a process of raising land productivity over time throughincreases in inputs of one form or another on a per unit area basis. Its study is important for

several reasons, many of which relate to agroforestry objectives. However, the concept of

intensification often is poorly defined in the literature and existing methods and approaches to

measuring agricultural intensity are problematic, making it difficult to precisely compare par-

ticular farming systems. This paper examines the concept of agricultural intensity, its relevance

to efficient and sustainable land use, and the ways it can be measured. A review of existing

approaches and methods for measuring intensity, such as those based on output, cropping fre-

quency, and agrotechnologies, revealed that these feature a number of problems. Some of these

problems relate simply to imprecision and inaccuracy. But others are due to the unsuitability of

these approaches in the context of farming environments, such as frontiers, which are charac-

terized by limited production data; unconventional cropping cycles; myriad production strate-

gies, cropping patterns, and crop-fallow cycles on a single farm; and a high level of system

dynamism and production variability over time. A modified approach to measuring agricultural

intensity, based on fieldwork in Petén, Guatemala, is presented. This approach, which is better

suited to frontier regions, employs an agricultural intensity index to help evaluate the intensityof particular farm units based on the technologies and practices used by the farmer, and the

degree to which they are used.

Introduction and rationale

Since the 1960s, when Boserup (1965) wrote her thesis of agricultural change

and intensification as a response to population growth, numerous studies have

dealt with issues of agricultural intensification. Unfortunately, in many of

these studies the meaning of intensification is described only vaguely or is

not explained at all. In other reports we find considerable variation in the

expressed meaning of the term, and as a consequence, substantial variation

in the way it is measured. In some cases the terms ‘intensity’ and ‘intensifi-cation’ are applied to land per se, as in its productivity or output, whereas in

others they are applied to inputs or other factors of production such as labor,

capital, and management practices. This paper examines the concept of agri-

cultural intensity and the ways it can be measured, and presents a modified

approach to measuring agricultural intensity that is better suited to the con-

ditions of frontier regions, based on fieldwork in Petén, Guatemala (Shriar,

1999).

Agroforestry Systems 49: 301–318, 2000.© 2000 Kluwer Academic Publishers. Printed in the Netherlands.



Frontier regions feature some distinct characteristics that have a profoundinfluence on the strategies of farmers therein. These characteristics include an

abundance of land but limited labor, land tenure insecurity, poor market

conditions and infrastructure, and limited presence of research, extension or

other agricultural services. Frontier farming systems are therefore difficult to

study and evaluate in terms of agricultural intensity because of: limited pro-

duction data and high production and price variability over time and space;

unconventional cropping cycles associated with perennial crops and improved

fallowing techniques; and the simultaneous use of various fallowing prac-

tices and crop-fallow cycles on a single farm.

Intensification is ‘a process of increasing the utilization or productivity of

land currently under production, and it contrasts with expansion, that is, the

extension of land under cultivation’ (Netting, 1993: 262). Brookfield (1993:28) describes it as, ‘in relation to constant land, the substitution of labour,

capital or technology for land, in any combination, so as to obtain higher

long-term production from the same area.’ Thus we see that definitions of

intensification relate to increasing yield and productivity over time through

increases in inputs of one form or another on a per unit area basis.

Intensification is worthy of study for several reasons. One reason is to test

theories, such as those of Boserup (1965) and the neo-Malthusians (e.g.

Ehrlich and Ehrlich, 1990), that address the effects of population growth. In

addition, agricultural intensity can usefully form the basis for a classification

or typology of different farming systems, based on the degree to which the

managers of these systems have taken steps to intensify production. This is

covered further below. A more important reason relates to the growing chal-

lenge of meeting human needs and wants in a sustainable manner. Two

dominant features of the modern world are very rapid rates of population

growth, about 1.6% per year at present (Brown et al., 1996), and increasing

levels of market penetration, trade and consumerism (Peet, 1989; Thrift, 1989;

Chisholm, 1990). These trends are placing huge demands on land and other

natural resources. In the past such demands most commonly were met by

extending the area subject to cultivation, wood cutting, or other resource

extraction, rather than by intensifying, i.e. increasing output per unit area per

unit time. However, in most regions and countries this no longer is possible

because agricultural frontiers have closed in many parts of the world.

Moreover, there is growing scientific and public concern for maintaining

global biodiversity (Wilson, 1988; Gradwohl and Greenberg, 1988; NRC,

1993). The concern for biodiversity manifests itself in terms of variousobjectives, of which three salient ones pertain to: 1) conserving remaining

areas of mature, natural forest, particularly in the tropics where these areas

are most threatened and contain the highest levels of biodiversity (Terborgh,

1992; Wilson, 1988); 2) ensuring that human patterns of biological resource

use conserve the resource base on which they rely (Blaikie and Brookfield,

1987; York, 1988; Johnson and Lewis, 1995); and 3) developing land use

systems that also provide for the survival and restoration of other species

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that depend on these areas (Smith, 1990; NRC, 1993; Srivastava, Smith andForno, 1996).

In the context of agriculture, these objectives have led to growing recog-

nition of the need for more land intensive farming systems as a means to

help reduce pressure to clear remaining forest. Studies by Palerm (1955) and

Bandy et al. (1993) reveal significant differences in the amount of land and

hence, forest clearing, needed to support families under various tropical land

use systems. Netting (1986: 70) writes on the basis of Palerm’s (1955) work

that ‘a given amount of land can support 32 times as many families under a

Chinampa regime on the same area exploited by shifting techniques.’ The

concern for reducing deforestation is particularly important in many frontier

zones (Pichon, 1996; Moran, 1993; Collins, 1986), including Petén (Schwartz,

1995), because they commonly contain the last remaining large tracts of relatively undisturbed closed forest within a given country or region.

However, farming systems also must be sustainable in that they must

maintain future production potential and not inflict unacceptable long-term

damage to other ecosystem components (NRC, 1993). In other words, as is

clear to agroforestry researchers and proponents, we need to develop and foster

agricultural (and silvicultural) systems aimed at sustainable intensification

that will increase production of desirable products or services (e.g. food,

money, nutrition, building materials) per unit area without causing unaccept-

able harm to the resource base or other components of the ecosystem.

This need is recognized in relation to a variety of farming systems, from

contemporary unbalanced forms of shifting cultivation (Sanchez, 1994; Brady,

1996; Harwood, 1996), as practiced by ‘shifted cultivators’ in the words of

Myers (1993: 10), to highly intensive, ‘industrial’ systems that rely heavily

on inputs of agrochemicals and have great potential for off-site impacts and

long-term soil and water degradation (Hatfield and Karlen, 1994; Johnson and

Lewis, 1995). Of course, in the case of the ‘industrial’ agricultural systems

the emphasis for their improvement lies not in further intensification through

prevailing strategies but rather, in finding alternative means of sustaining

production without the heavy reliance on monoculture and agrochemical and

fossil fuel use. The identification of alternative agricultural systems is a key

objective of the growing field of agroecology (Altieri, 1987; Altieri, 1989;

Tivy, 1990) and of agroforestry (MacDicken and Vergara, 1990; Swaminathan,

1987). The main emphasis in both fields is on the application of knowledge

and skills to manage the biological cycles and interactions that influence crop

productivity and agroecosystem properties. By understanding the specificecological relations and processes of agroecosystems, the latter can be manip-

ulated to produce better and more sustainably with fewer industrial inputs and

negative side effects (Hecht, 1987).

This paper deals with only one aspect of the agricultural challenge outlined

above – intensification. More specifically, it examines approaches and methods

aimed at measuring the intensity of agriculture. Such measures are needed to

compare land use systems based on their potential for higher yield over time.

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Such a comparison can then be used to classify land use systems in terms of this fundamental criterion, as has been done by Boserup (1965), Duckham and

Masefield (1969), and others. More importantly, comparison of farms or

farming regions on the basis of agricultural intensity is essential for any

analysis that seeks to identify the conditions under which land is used more

intensively (e.g. Shriar, 1999). If these conditions or ‘ingredients’ to intensi-

fication can be identified, policies and programs can be implemented that

foster these conditions and thus stimulate intensification.

However, two points must be borne in mind in relation to the importance

of intensification. First, the degree to which intensification is a priority concern

relative to other social and environmental objectives will vary from region to

region. As noted above, where industrial agriculture prevails, other objectives,

such as reducing agrochemical pollution, may predominate, although this isunlikely to be a priority concern in frontier regions.

Second, where intensification is seen as a priority, it likely will be a nec-

essary but insufficient factor in achieving the broader objectives to which it

relates. For example, if poverty alleviation is a key objective in a region, an

increase in yield (i.e. intensification) achieved, say, through technological

change, may not in and of itself have the desired effect. Increases in yield

may generate higher land values which in turn could lead to greater land

concentration. The final result might then be more landlessness and poverty,

despite any benefits of higher yield accrued to a smaller group of remaining

land owners. Thus other initiatives or mechanisms pertaining to land tenure

may also be essential. And where protection of forest is an objective, inten-

sification can have the effect of generating more rather than less forest

clearing. In the Ecuadorian Amazon higher yields obtained on richer soils

led farmers to invest the profits this generated in land uses that involve more

extensive deforestation, namely cattle ranching (Pichon, 1996). Thus, farmers

on poor soils had an average of 70% of their farms under forest whereas those

on the higher yielding volcanic soils had an average of 50% forest coverage

because more land had been cleared for pasture (Pichon, 1996). Under such

conditions, efforts aimed at limiting the relative appeal to farmers of cattle

ranching (e.g. by developing markets for alternative agricultural products)

may also be needed for agricultural intensification to have its desired effect.

Relative intensity thus is only one important aspect to be analyzed in

considering different agricultural systems in relation to the broader develop-

ment and conservation objectives discussed above. The research carried out

over recent decades increasingly suggests that a collection of biophysical,technological, economic, social and political factors at various scales can have

a profound influence on environmental conditions, development levels, and

the sustainability of human activities (Blaikie and Brookfield, 1987; Turner

et al., 1990).

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Approaches to measuring agricultural intensity

There are many different approaches to measuring the intensity of agriculture.

This is reflected in a statement of Leaf (1987: 248) regarding changes that

have occurred in Punjab (India) agriculture. He writes that ‘between 1965 and

1978 farming in this area became more intensive by all the usual measures:

area under irrigation, number of harvests per year, input and output per hectare

and per person, capitalization per hectare and per person, population densi-

ties per hectare, energy consumption per hectare, and total tonnage and calories

produced per hectare and per person.’

Most of the measures he mentions fit conveniently into particular categories

or types that can be identified. These include measures based on: 1) produc-

tion intensity, i.e. productivity per unit area per unit time of some desirableproduct or service (in ‘tonnage’ or ‘calories’ etc.); 2) cropping frequency (as

in his mention of ‘number of harvests per year’) and/or the relative propor-

tion of cultivated land to fallow or forest; and 3) inputs or application of

materials, labor, skill, technologies, or other resources (as reflected in the

author’s mention of ‘irrigation,’ ‘capitalization per hectare,’ ‘energy con-

sumption per hectare’). However, ‘population densities per hectare’ is more

appropriately considered a factor that generates intensification, as per Boserup

(1965), or a product of intensification (if human carrying capacity is affected),

rather than a measure of intensity, as implied by Leaf (1987: 248).

In any case, we can see that the first approach referred to above uses output

as the measure of a system’s intensity. In this sense it is related to yield. The

other two approaches are essentially surrogate measures that can be used

where output data are unavailable or weak, for example because of a lack of

written records, climatic variability, mixed cropping, and different harvest

schedules. They are valid as surrogates to the extent that cropping frequency

and the application of technological, labor or other inputs do in fact corre-

spond with land productivity over time.

Output as a measure of agricultural intensity

Output per unit area is likely to be the ideal measure of intensity because it

makes no presumptions about the effect of inputs on productivity (Netting,

1993: 262) and because, after all, production is the principal objective of

agricultural activity. Depending on the purposes of the farming system, or of

the study being undertaken, output can be considered in numerous ways,including food tonnage, caloric value, protein value, production of building

materials or other products, or value of production in monetary or pecuniary

terms. Turner and Doolittle (1978) suggest using a 20 year time frame for

such analyses, for example, caloric value of food produced per ha over a

twenty year period. This makes it possible to accommodate swidden or other

long fallow systems in which a complete agricultural cycle may be up to

twenty years long. It also is useful in that the comparative stabilities of various

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production systems in the face of climatic or other environmental perturba-tions can be assessed.

However, even where data are available and reliable, problems with this

approach may arise. Crops vary from food to fiber to condiments. Energy

measures such as caloric value thus may not be appropriate for fibre crops.

Root crops are much heavier than cereals but the latter may have higher caloric

value. In addition, the monetary value of production will not necessarily be

reflected in measures of weight or volume, except perhaps in systems pro-

ducing the same crop. Thus to the extent possible it will be worthwhile to

identify some common denominator to obtain a clearer overall picture of

output (Kates et al., 1993). This may involve standardizing the harvest on

the basis of some staple food equivalent, such as maize in the case of Central

America, or by assigning a monetary value to products of different types. Interms of monetary value, a complicating factor is that farm gate prices for

different products vary greatly both temporally and spatially, particularly in

frontier regions characterized by poor marketing infrastructure and systems.

These issues reflect the fact that production or output based measures of

intensification should be explicit with respect to the system attribute that is

under consideration, namely monetary value, protein value, caloric value, and

so on, and to how it is calculated.

Cropping frequency and farm unit cropping area as surrogate measures

The use of frequency of cultivation or land use as a measure of agricultural

intensity has been relatively common in the literature, reportedly because of

its general correspondence with total land productivity. As noted by Netting

(1993), it is most appropriate for comparing farming situations where similar

technologies are being used. However, many descriptions of the use of this

measure are fraught with severe problems of vagueness and confusion.

Boserup (1965, 1980) used frequency of cropping (staple food crops) as a

measure of agricultural intensity, although she seems to have defined this

concept in two rather different ways and the relationship between them is

unclear but is not explained in her works. In her 1965 book she defines five

categories or types of land use, based principally on a somewhat vague

measure of the length of time a parcel is cropped as compared to the time it

subsequently is left fallow, as follows (Boserup, 1965: 15–16):

1) Forest Fallow Cultivation: The plot is sown or planted for a year or two,

and then left fallow for ‘a number of years sufficient for the forest to regain

the land . . . at least some twenty to twenty-five years.’

2) Bush Fallow Cultivation: The fallow period under this system is ‘much

shorter, usually somewhere between six and ten years.’ However, the

periods of uninterrupted cultivation vary considerably, from just one to

two years, to as long as the fallow period, i.e. six to ten years.

3) Short Fallow Cultivation: The fallow period lasts only one or two years,

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such that ‘nothing but wild grasses can invade the fallow before the cul-tivator returns to the same plot or field.’

4) Annual Cropping: The land is left fallow only for a few months, between

the harvesting of one crop and the planting of the next. This category

includes systems of annual rotation, ‘in which one or more of the suc-

cessive crops are sown grass or other produced fodder.’

5) Multi-Cropping: Under this most intensive system ‘the same plot bears

two or more successive crops every year.’ The fallow period between

harvesting and planting ‘is short or even negligible.’

In Boserup’s (1981) later work, the same category titles (forest fallow, bush

fallow, etc.) are presented in a table, along with a ‘frequency of cropping’

column in which a percentage range appears next to each category. However,

in this case the frequency of cropping is defined as the ‘average cultivatedarea as percentage [sic] of cultivated plus fallow area’ and the frequency of

cropping percentages listed for categories 1–5 are, respectively: 0% to 10%;

10% to 40%; 40% to 80%; 80% to 100%; and 200% to 300% (Boserup, 1981:

19). Clearly these are two distinct measures of ‘frequency,’ as one is temporal,

while the other is spatial and relates to a particular time. Moreover, it is

questionable whether the measure on which the percentages are based can

accurately be referred to as cropping frequency, since frequency is a temporal,

rather than spatial concept, regardless of any spatial implications it might have.

In any event, the correspondence between the two measures is far from

obvious. For example, since Boserup’s (1981) later definition of ‘frequency

of cropping’ is based on areal proportions it would be impossible to arrive at

a figure of 200% to 300% for multicropping, as one cannot crop more landin spatial terms than is available at a given time. No explanation is offered

regarding how or where the listed percentages were determined or identified.

Boserup’s (1965) cropping frequency categories are quite general and may

not be suitable for precisely comparing farming systems within a single agri-

cultural type or category in which similar technologies/practices are employed,

for example, comparing two bush fallow farmers in terms of who is more

intensive. However, more precise analyses of frequency have been used

(Turner and Doolittle, 1978). These also are based on Boserup’s (1965)

temporal concept of cropping frequency. For example, a 1/2:8 crop fallow

cycle refers to one crop in each of two years, followed by eight years of fallow.

A multi-cropping cycle of 2/1:0 implies two crops per year with no fallowing.

However, Turner and Doolittle (1978) recognize that for more detailed analysis

the data must be converted into a form that enables statistical manipulation.

They achieve this through the following approach: First, a 1/2:8 crop fallow

cycle is proportionally equivalent to 1/1:4 as the basic cultivation cycle occurs

once for each four year fallow cycle. This in turn can be converted to a value

of 1/5 or 0.2 as one crop is cultivated over a total five year period. The latter

represents a slightly higher frequency or cropping intensity than in a system

where one crop is obtained in each of three years, followed by 15 years of

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fallow, i.e. 1/3:15, which translates to three crops per 18 year total period or1/6 = 0.16 (Turner and Doolittle, 1978).

Another cropping frequency measure is described by the FAO (1984: 37),

based on the Land Use Factor, L, which is simply the inverse of the ratio

used by the other analysts, i.e. L = years of cultivation plus years of

fallow/years of cultivation. The advantages, if there are any, of using the

ratio in this manner are unclear.

As noted by Netting (1993) intensity measures based on (temporal)

cropping frequency are complicated by various factors. One of these is the

practice of multiple cropping, that is, obtaining several harvests per year (or

other relevant time span) on a single plot. However, this can be dealt with and

compared through the approach of Turner and Doolittle (1972). Thus if two

crops per year are obtained in each of two years and the land is then left fallowfor seven years, we have a 2/2:7 sequence, which is equivalent to 4/9 or a

cropping frequency of 0.44.

A more complicated factor is the simultaneous use of various fallowing

practices within the same farming system. This is handled in most studies by

focusing only on the crop fallow cycle that prevails for the ‘major crop’ (e.g.

Turner et al., 1977). Another approach that can be used, if more detailed farm

analyses can be undertaken, is to consider all cropping frequencies or crop-

fallow cycles that exist for the various land use sub-systems on the farm and

then weight them on the basis of their areal extent. Thus if 30% of the culti-

vated area of the farm is characterized by a 0.2 frequency and 70% of the area

has a frequency of 0.5, the overall weighted frequency would be 0.41 or 41%.

However, in many environments, such as frontiers, farming systems are

changing rapidly and thus the area devoted to each farm ‘sub-system’ (with

different fallow periods) varies over time. Furthermore, it is very common to

find farmers using different crop fallow cycles on land used for the same crop,

for example, because of fertility differences that affect fallow regeneration

rates.

Another source of confusion relates to cultivars, such as cassava and the

tree crops used in agroforestry systems, that are not necessarily planted and

harvested within one ‘crop cycle.’ Perhaps in this case a frequency value could

be assigned depending on the relative amount of harvest that typically is

obtained on a per year basis. For instance, if the amount of produce obtained

over a given year (per hectare) is more or less equivalent in caloric, monetary,

or other terms (depending on the nature and objectives of the analysis) to what

might be expected through a single harvest of a ‘conventional’ annual crop,the frequency can be considered as one harvest per year. If it yields twice as

much of the desired output, it can be calculated as two harvests per year.

And if the latter system involves no fallow period, that is, a cycle of 2/1:0,

the frequency value would then be 2 or 200%. However, cropping frequency

measures, and all measures for that matter, also are challenged by the fact that

tree crops, and some other crop, can take several years before yielding

produce. Adjustments thus must be made to account for this lag time.

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Similar to Boserup’s (1981) spatial measure of ‘frequency of cropping’ isRuthenberg’s R Value (1976: 15–17). The R value is calculated as the pro-

portion of the farm unit area under cultivation relative to the total available

for arable farming, multiplied by 100 (1976: 15–17). A value of R < 33 is

classified as shifting cultivation, R = 33 to 66 consists of semi-permanent

fallowing systems, and R > 66 represents permanent systems with mostly

annual or perennial crops, as well as features like manuring, possibly irriga-

tion or tree crops, and fields that are clearly demarcated.

This R value is similar to Pryor’s CI or cropping index (1985: 737), defined

as the ‘ratio of the land area used annually to the total land area used pri-

marily for plant agriculture.’ If the CI is greater than 1, it implies the land is

multicropped. But as noted by Pryor (1985) only vague farming system

categories emerge through the use of this measure because only major cropsare considered.

In relation to these spatial measures of cropping intensity it is not speci-

fied how ‘improved fallow’ systems, based on either herbaceous or woody

species (Nair, 1993: 55–83), would be factored into the analysis. Would they

fit under the area ‘used annually’ or as fallow land?

It is clear that there are numerous potential complications associated with

using cropping frequency and cropping area as a surrogate measure of agri-

cultural intensity. This is particularly true for analyses that seek to compare

farming systems characterized by a range of production strategies and crop

fallow cycles, such as those in frontier regions. However, if a single dominant

cropping pattern exists and is relatively stable over time, these measures,

particularly the true (i.e. temporal) cropping frequency measure, should help

reveal some distinct differences between particular farming units or regions.

The use of agrotechnologies and other inputs as a surrogate measure

Where crop-fallow cycles are variable within a given farming system because

of differences in land quality and diverse production strategies, and particu-

larly where these strategies are in flux, as in frontier regions, it may be prefer-

able to compare farms on the basis of inputs applied more or less at a particular

time. Labor or fertilizer inputs per hectare in an average year can be compared,

for example. Clearly the need for such inputs will vary depending on the nature

of the farming system and the soil on which it is based, but this nevertheless

can reveal some distinct differences between two systems in terms of ‘input

intensity’ and probably other dimensions. Another possibility is to considerthe agrotechnologies or practices used in the farming systems.

There are many examples of this in the literature on agricultural intensity

or intensification, but the degree of detail used in analyzing the practices or

technologies employed varies considerably. Perhaps the earliest explicit

analyses of agrotechnologies in relation to agricultural intensity were those

of Brookfield (1962) and Brookfield and Hart (1971) in their geographical

studies of islands in the South Pacific. Based on data obtained by many

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different investigators at 44 locations on the island of New Guinea and severalnearby islands, Brookfield and Hart (1971) rank the farming systems at each

location in terms of their ‘intensity.’ This ranking is based primarily on cul-

tivation methods (a form of agrotechnological input) but also on qualitative

categories of cultivation frequency (three categories) and crop segregation

(five categories). The precise meaning of the term ‘crop segregation’ as well

as its use in the analysis is unclear. However, rather than place value on the

segregation of crops into monocultural fields, it appears to enable a distinc-

tion between situations where there is a ‘complete mixture of all crops in a

single undifferentiated swidden, and the complete separation of crops into

plots that are managed in quite different ways.’ The authors report that with

greater differentiation there will be greater intensity of labor input per unit

area over time (Brookfield and Hart, 1971: 89). This may be true, but it isnow recognized that monoculture is not necessarily more intensive (in terms

of outputs) than polyculture (Nair, 1993). Furthermore, it may offer lower

returns to labor than polyculture, particularly in areas such as Petén,

Guatemala, where intercropping helps suppress weeds.

In any case, Brookfield and Hart (1971) assign points to each

location/farming system on the following basis. In terms of cultivation

methods, a total of 14 possible methods are listed and described, including

such activities as ‘clearing without fire,’ mounding, compost use, simple or

terrace irrigation, and control of fallow cover. Each farming system is assigned

one or two points if a given method is used depending on whether it is merely

present (one point) or present ‘with high significance’ (two points). Zero points

are received if the method in question is not used at all. Thus a total score in

relation to cultivation methods is obtained for each system, but no effort is

made to assign different weights to different methods. Added to this score

are points for cultivation frequency: three points in the case of ‘continuous

cultivation,’ two points for no long fallow (where the fallow period ‘is shorter

in time than the period under cultivation’), and one point for repeated culti-

vation (of two or three crops ‘after a single clearing from fallow’). Finally,

points are assigned for crop segregation: zero points if ‘none, or insignifi-

cant,’ one point if ‘some but minor,’ two points if ‘partial, or for minor crops

only,’ three points if there are ‘distinct open fields,’ and four points if crop

segregation is ‘very complete, general.’ All three criteria are considered

interrelated, however, because ‘skills applied to land preparation facilitate crop

segregation, and the two together facilitate more frequent cultivation of the

same land’ (Brookfield and Hart, 1971: 105). Indeed, the data presented bythe authors in tabular form reveal an association between the three sets of

scores.

Each location/farming system thus was assigned a total overall ‘intensity

ranking’ based on the total number of points assigned. For the 44 locations

these ranged from zero, where reliance on wild foods is very high, to a ranking

of 26 in Central New Caledonia where agricultural intensity is highest. A

break in the array of scores suggest that two general classes (‘low intensity’

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and ‘high intensity’) exist because 34 of the 44 places have scores betweenzero and 12 while the remainder have scores ranging from 17 to 26. The

authors assert that ‘the method is no more than a rough-and-ready first approx-

imation of an “intensity ranking”, but the internal consistency of the table

suggests some rough validity’ (Brookfield and Hart, 1971: 105).

Turner and Doolittle (1978) also present an intensity ranking based on

agrotechnology inputs and cropping frequency (but not crop segregation), an

improvement over a similar ranking presented previously in a study of the

New Guinea Highlands by Brown and Podelofsky (1976). The cropping fre-

quency measure of Turner and Doolittle (1978) has already been described

above. It can lead to values ranging from about 0.05 to 2.9. Added to these

values are scores based on the agricultural technologies used, with total values

for a farming system ranging from 0 to 2, for a total agricultural intensityscore of 0 to 5. These authors provide five different possible scores or ‘weight’

values for each technology or practice (0.1 to 0.5), based on the authors’

‘interpretation of its signifance to increasing crop production’ (Turner and

Doolittle, 1978: 300). Values are assigned for one or more types of: 1) Crop

Preparation, e.g. partial clearance (with a weight value of 0.1), total clear-

ance (0.2), deep tillage (0.4), etc.; 2) Crop Protection; 3) Erosion Control;

4) Hydraulic Controls; 5) Soil Fertility Maintenance; and 6) Plant Preparations.

The authors note that ‘[they] recognize the subjective nature of the values

given to each variable and the need for refinement in the agrotechnological

index, particularly as production data per technology are improved. The values

assigned to each variable, however, probably reflect the broader patterns

applicable to most instances of subsistence cultivation, although they will vary

with crop types and habitat’ (Turner and Doolittle, 1978: 300).

Of course, one can reduce the ‘subjective nature’ of the values assigned to

each variable, and in general ‘refine’ the index, first, by consulting local

farmers to arrive at typical production values and hence, appropriate weights,

associated with each technology; and second, by considering the proportion

of the farmer’s property or cropping area on which each technology is used.

These points are returned to later.

In another study, Pingali et al. (1987) also compare the technologies and

operations used in 52 farming systems (in nine countries of sub-Saharan

Africa) at varying levels of population density and cultivation frequency. The

systems observed at increasing levels of population density are forest fallow,

bush fallow, short fallow, emerging annual cultivation, annual cultivation, and

multiple cropping. Particular technologies or types of technologies are foundto be associated with each system and thus they consider these to be valid as

general indicators of the prevailing intensity of a farming system, with

implications for the population densities it can support.

However, on the general issue of the link between technologies/practices

and output levels, it is crucial to bear in mind that some ‘low tech’ systems

are more land-intensive than high tech ones. Tractors, for example, make it

possible to cultivate a large area with less labor (per unit area), but the effect

311



on yield per unit area may be negative. Not surprisingly, perhaps, 1997 wheatyields in the highly mechanized US and Canada averaged 2655 kg/ha and

2128 kg/ha, respectively, while the corresponding figures for Mexico and

Central America amounted to 4735 kg/ha and 4689 kg/ha, respectivley (FAO,

1999). In most studies that consider agricultural intensity in terms of inputs,

there appears to be an implicit assumption that with increases of labor,

management, technologies or other inputs per unit area, the effect will be

higher yield (per unit area). Although this is often or usually valid as an

assumption, the relationship between technological or other inputs and yield

per unit area ideally should be demonstrated rather than assumed (Netting,

1993: 262–263).

A modified approach for measuring agricultural intensity in frontier

regions

It is clear from the previous section that no approach or method for measuring

agricultural intensity is perfect, particularly in the dynamic and land abundant

conditions found in frontier regions, but an effort can be made to tailor a

measure in a way that is appropriate for the characteristics of the region under

study. As already noted, production-based measures are the preferred option,

but in frontier regions such as Petén, Amazonia, and Sumatra, production data

generally are not available, output is highly variable, both temporally and

spatially, and long fallow systems are common. If a given study can gather

such data over a relatively long time frame, such as ten to twenty years, useful

comparisons of intensity could be made, but only after ten or twenty years.

Moreover, if economic output is the desired measure of output, the extreme

price volatility that commonly exists in frontier regions will complicate

matters.

Surrogate measures based on cropping frequency are problematic because

they are of limited precision and accuracy, particularly in the conditions that

prevail on the frontier. Cropping frequency often varies considerably among

different sections of a farmer’s property, and it changes over time. Let us

consider, for example, a farmer who reports that he is cultivating a field that

was first cleared of primary forest fifteen years earlier. He initially cropped

it for six years, twice per year, and then left it fallow for six years before

slashing and burning the secondary forest on the site to bring the field back

into cultivation. He is now in his third year of this second cultivation phase,but weed and fertility problems have resulted in much lower yields than he

had in the first phase, and he reports that he will now leave the site fallow.

He is uncertain of how long he will leave the site fallow, and of how pro-

ductive the field will be thereafter. His intention for the next few years is to

cultivate elsewhere on his property or, if he has the cash, to rent a patch of

forested land from a neighbor, and practice swidden there, so as to limit

pressure on the primary and secondary forest that remains on his own property.

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In such a situation, which is far from atypical, at least in Petén, what can weconclude about the ‘cropping frequency’ in use by the farmer? In many cases

no set pattern of cropping frequency has been established and in fact, this is

what we would expect, almost by definition, in a dynamic, frontier environ-

ment where people are adapting to the potentials and constraints of their new

home.

We therefore are left with evaluating agricultural intensity through the other

surrogate approach, based on the agrotechnologies and other strategies

employed by the farmer to increase output per unit area per unit time, that is,

to intensify production. As discussed above, this approach to measuring inten-

sity has been used by Brookfield (1962), Brookfield and Hart (1971), Brown

and Podelofsky (1976), and Turner and Doolittle (1978), but we can improve

the precision and accuracy of such measures by doing two things: first, byconsidering the proportion of the cropped area, or some other indication of

the scale, on which the farmer employs each intensification strategy; and

second, by seeking local knowledge and, if available and valid, experimental

data, on the degree to which each strategy contributes to higher yield per

unit area per unit time.

This information formed the basis of the agricultural intensity index I

developed to measure and compare the agricultural intensity of farming

systems in Petén, Guatemala, a lowland tropical frontier region in which

population growth (mostly through in-migration) and deforestation have been

rapid (Shriar, 1999). Through a survey of 118 farmers I gathered information

on the activities and strategies used by each in the survey year (1997–98). I

also collected data on the area on which each land use strategy was carried

out, so that it could be calculated as a proportion of the farmer’s total cropped

area. This made it possible to rank each household based on the extent or scale

at which each activity was carried out. For example, each household was

ranked as a low, medium, or high scale intercropper (or a non-intercropper),

based on the average proportion of its total cropped area that was intercropped

in the survey year (the average proportion over the first and second growing

seasons of the year). The same was done in relation to the use of green manure

( Mucuna spp.), plowing, and other land use strategies. The use of chemical

inputs such as fertilizer and herbicide were scaled based on the average per

ha expenditures on these items in each season. Ranching intensity was based

on stocking rates. Therefore, in contrast to the earlier work done with inten-

sity indices, this approach requires additional data to evaluate with some pre-

cision the extent or scale at which particular strategies are utilized by eachfarmer (Table 1).

The next step is to assign a weight to each intensification activity based

on the degree to which its use contributes to higher production per unit area

of land, per unit time. But this begs the question: production of what? Well,

given that the vast majority of the households surveyed in Petén live at a

subsistence or near subsistence level, it seemed logical to weight each strategy

based on the degree to which it produces basic ‘food security.’ The latter can

313



be defined, for the purposes of this analysis, if not more broadly, as food and

cash with which to buy food and other necessities. Of course, it is difficult

to arrive at objective and accurate assessments of each strategy’s per ha

contribution to household food security, but this task can be made easier

through discussions with farmers and extension staff in the region. As listed

on Table 1, the weights I arrived at range from one to 3.5 points. For example,

established mucuna-based green manure plots received the highest weighting

of 3.5 points, higher than plowing (2.5 points). Although both reportedly yield

similar quantities of maize ( Zea mays), production values are likely to be more

sustainable over time with mucuna, a nitrogen-fixing legume. Intercropping

received an even lower weight of two points, because planting densities in

intercropped plots tend to be quite low in the areas surveyed. The same holds

true in relation to homegardens and to permanent crops grown on the fieldplots; both generally are planted quite sparsely, and thus received a weighting

of 1.5 points.

For each household these weight values were then multiplied by a number

linked to the scale at which the activity was being used by the household.

For example, a household plowing at a medium (or two point) level would

receive ‘intensity points’ amounting to 2 × 2.5 (the weight associated with

plowing), or a total of five points for their plowing activity. A household

314

Table 1. Scale ranges and weights associated with the agricultural intensity index.

Intensification activity Scale range Weight Max. points

Scale of established Mucuna plotsa 0–3 3.5 10.5

Scale of high value crop production 0–3 2.5 07.5

Scale of plowing 0–3 2.5 07.5

Scale of ranching intensityb 0–3 2.5 07.5

Scale of intercropping 0–3 2 06

Scale of fertilizer use per ha 0–2 3 06

Scale of pesticide use per ha 0–3 2 06

Scale of permanent crop cultivation 0–3 1.5 04.5

Homegarden quality scale 0–3 1.5 04.5

Vegetable plot in Season 1 0–1 2 02

Vegetable plot in Season 2 0–1 2 02

Mucuna planted at some pointc 0–1 1 01Use of other organic pest control 0–1 1 01

Use of other organic fertilization 0–1 1 01

Total 67

a This refers to established Mucuna plots that already are usable, or in use, for maize produc-

tion.b Ranching intensity is based on stocking rate: number of cows (both farmer’s own and those

of others) per ha of pasture land on all household plots combined.c This merely indicates whether or not the farmer claimed to have planted Mucuna green manure

at some point in the past. This does not imply that a fully established Mucuna system was

developed. The farmer may have neglected the crop, it may not have thrived, it may have burned,

etc.



with a high rank (or three point) homegarden, based on density and diversity,would receive 3 × 1.5 points, or 4.5 points for their homegarden management.

The points associated with each activity were then summed up to arrive at an

overall intensity score for the household.

The scales, weights, and maximum points associated with each activity

are described in Table 1. A scale range of zero to one implies a yes/no, dummy

variable situation. Either the household is engaged in the activity, and gets

one point, or they are not, and receives no points. In contrast, a scale range

of zero to three indicates whether the household undertakes the activity, and

if so, at a low (one point), medium (two point) or high (three point) scale. As

is evident from the table, not all farming activities could be assessed in

sufficient detail to justify using a zero to three scaling.

The farm intensity scores for each household can serve as a dependentvariable in statistical analyses aimed at identifying household scale factors,

such as property size, wealth, and labor supply, that condition agricultural

intensification. In addition, the scores for households within a given com-

munity or region can be averaged to enable us to compare it with other

communities or regions. Any substantial or significant differences among com-

munities or regions in this regard likely point to community or regional scale

factors that influence the level of agricultural intensity, such as market access,

population density, land quality, and off-farm income opportunities.

As with any approach for measuring intensity, the modified approach

described above also faces some challenges and limitations. First, consider-

able work is required to obtain data on the proportion of a farmer’s cropped

area or total property on which each strategy is used, especially when a large

sample size is used. With advances in remote sensing technology, this soon

may become easier, as long as property boundaries and ownership can be

clearly distinguished.

Second, it invariably is difficult to arrive at objective weights that accu-

rately reflect the contribution of each strategy to overall production of food

security or any other output of interest. As noted, experimental data, if avail-

able, and local knowledge can facilitate this process. In regards to the input

of local knowledge, a number of participatory techniques can be helpful in

arriving at some consensus among local farmers and/or extension staff on

the contribution of each strategy to overall output. Of course, adjustments will

be needed to account for the time lags that exist between planting and pro-

duction of desired outputs, such as in the case of many agroforestry products.

One should therefore compare systems based on a long enough time framethat includes both the growth and production phases.

Finally, the modified index described above relates only to on-farm culti-

vation and livestock management practices. Other resource use strategies, such

as harvesting of forest products, which are important household activities in

some frontier areas, are not covered. Conceivably, a broader ‘resource use

intensity index’ could be developed to account for the full array of strategies

used by colonists in frontier areas, but this has not been my objective to date.

315



Conclusions

Agricultural intensification through sustainable approaches will become

increasingly important in the 21st century, to meet both production and con-

servation objectives. Reliable methods for measuring agricultural intensity are

essential for studies that seek to compare farms or regions, monitor agricul-

tural change, or identify the factors that drive agricultural change and inten-

sification.

In frontier regions, where conservation and development objectives are

particularly important, intensity measurements are complicated by the paucity

of reliable production data and by the difficulties of determining cropping

frequency with a sufficient degree of accuracy. An approach based on the

agrotechnologies and strategies used by farmers seems to provide the bestoption, although it can be time consuming and require a large amount of data

collection.

The agrotechnologies and strategies used by farmers can be assessed and

incorporated into an agricultural intensity index that is appropriate for a par-

ticular region. Such an index makes it possible to assign each household an

overall intensity score based on the degree to which their farming activities

contribute to higher production (of some desired output) per unit area, per unit

time. There are, of course, many different ways in which a household can

intensify. Use of an index enables a relatively objective and explicit com-

parison of households or study areas, not in terms of just one strategy, such

as green manure or homegarden usage, but rather, in terms of the overall

complex of activities in use to intensify agricultural production. The preci-

sion and accuracy of the index as an indicator of farming systems intensitywill be substantially improved through two steps: first, by relying on local

knowledge and, if available, experimental data, to help assess the contribu-

tion of each technology/strategy to output; and second, by considering the

scale at which each activity is in use by farmers, for instance by determining

the proportion of cropped area on which it is carried out.

This paper is the first in a series that will be submitted on this general topic.

A second paper will describe an application of the intensity index scores in

statistical analyses to determine the factors influencing the intensity of farming

systems in Petén, Guatemala. A subsequent paper will demonstrate reliance

on the same approach to develop a modified index to compare the intensities

of particular agroforestry systems, using data from another region.

Acknowledgments

The author is deeply indebted to the many farmers in Petén, Guatemala who

shared their time, knowledge, opinions, and patience with me. I also thank

the members of my dissertation committee at the University of Florida for

their excellent guidance, support, and advice throughout my course work and

research: Abraham C. Goldman, Peter E. Hildebrand, P. K. R. Nair, Marianne

316



Schmink, Nigel J. H. Smith, and Marilyn (Mickie) Swisher. Finally, I grate-fully acknowledge the financial support I received from the US National

Science Foundation.

References

Altieri MA (ed) (1987) Agroecology: The Scientific Basis of Alternative Agriculture. Westview

Press, Boulder, Colorado, 227 pp

Altieri MA (1989) Agroecology: a new research and development paradigm for world agricul-

ture. Agriculture, Ecosystems and Environment 27: 37–46

Blaikie P and Brookfield HC (1987) Land Degradation and Society. Methuen, London, 296 pp

Brady NC (1996) Alternatives to slash-and-burn: a global imperative. Agriculture, Ecosystems

and Environment 58: 3–11

Brookfield HC (1962) Local study and comparative method: an example from central NewGuinea. Annals of the Association of American Geographers 52: 242–254

Brookfield HC and Hart D (1971) Melanesia: A Geographical Interpretation of an Island World.

Methuen, London, 464 pp

Brookfield HC (1993) Notes on the theory of land management. PLEC News and Views 1: 28–32

Brown L (1996) State of the World 1996: A Worldwatch Institute Report on Progress Toward

a Sustainable Society. WW Norton and Company, New York, 249 pp

Brown P and Podelofsky A (1976) Population density, agricultural intensity, land tenure and

group size in the New Guinea highlands. Ethnology 15: 211–238

Chisholm M (1990) The increasing separation of production and consumption. In Turner BL II ,

Clark WC, Kates RW, Richards JF, Mathews JT and Meyer WB (eds) The Earth as

Transformed by Human Action: Global and Regional Changes in the Biosphere over the Past

300 Years, pp 87–101 (Chapter 6). Cambridge University Press, Cambridge, 713 pp

Collins JL (1986) Smallholder settlement of tropical South America: the social causes of eco-

logical destruction. Human Organization 45(1): 1–10

Duckham AN and Masefield GB (1969) Farming Systems of the World. Praeger Publishers,New York and Washington, 542 pp

FAO (1984) Improved production systems as an alternative to shifting cultivation. FAO Soils

Bulletin 53. FAO, Rome

FAO (1999) FAOSTAT Agriculture Data. Available at: htttp://apps.fao.org/

Gradwohl J and Greenberg R (eds) (1988) Saving the Tropical Forests. Earthscan Publications,

London, 214 pp

Harwood RR (1996) Development pathways toward sustainable systems following slash-and-

burn. Agriculture, Ecosystems and Environment 58: 75–86

Hatfield JL and Karlen DL (eds) (1994) Sustainable Agricultural Systems. Lewis Publishers,

Boca Raton, FL, 316 pp

Hecht SB (1987) The evolution of agroecological thought. In: Altieri MA (ed) Agroecology:

The Scientific Basis of Alternative Agriculture, pp 1–20 Westview Press, Boulder, 227 pp

Johnson DL and Lewis LA (1995) Land Degradation: Creation and Destruction. Blackwell,

Oxford, UK and Cambridge, USA, 335 pp

Kates RW, Hyden G and Turner BL II (1993) Theory, evidence, study design. In: Turner BLII, Hyden G and Kates RW (eds) Population Growth and Agricultural Change in Africa, pp

1–40 (Chapter 1). University Press of Florida, Gainesville FL, 461 pp

Leaf MJ (1987) Intensification in peasant farming: Punjab in the green revolution. In: Turner

BL II and Brush SB (eds) Comparative Farming Systems, pp 248–275 (Chapter 9). The

Guilford Press, New York and London, 428 pp

MacDicken KG and Vergara NT (1990) Agroforestry: Classification and Management. John

Wiley and Sons, New York, 382 pp

Moran E (1993) Deforestation and land use in the Brazilian Amazon. Human Ecology 21(1):

1–21

317



Myers N (1993) Tropical forests: the main deforestation fronts. Environmental Conservation20(1): 9–16

NRC (National Research Council) (1993) Sustainable Agriculture and the Environment in the

Humid Tropics. National Academy Press, Washington DC, 702 pp

Nair PKR (1993) Introduction to Agroforestry. Kluwer Academic Publishing, Dordrecht, The

Netherlands, 499 pp

Paulino LA (1987) The evolving food situation. In: Mellor JW, Delgado CL and Blackie MJ

(eds) Accelerating Food Production in Sub-Saharan Africa. Johns Hopkins University Press,

Baltimore, 417 pp

Peet R (1989) World capitalism and the destruction of regional cultures. In: Johnston RJ and

Taylor PJ (eds) A World in Crisis? Geographical Perspectives, pp 175–199 (Chapter 7).

2nd Edition. Basil Blackwell Ltd., Oxford, UK and Cambridge, USA, 308 pp

Pichon FJ (1996) The forest conversion process: a discussion of the sustainability of predomi-

nant land uses associated with frontier expansion in the amazon. Agriculture and Human

Values 13(1): 32–51.

Pingali P, Bigot Y and Binswanger HP (1987) Agricultural Mechanization and the Evolutionof Farming Systems in Sub-Saharan Africa. Johns Hopkins University Press for the World

Bank, Baltimore, 216 pp

Pryor FL (1985) The invention of the plow. Comparative Studies in Society and History 27:

727–743.

Ruthenberg H (1976) Farming systems in the tropics. Clarendon Press, Oxford, 366 pp

Sanchez PA (1994) Alternatives to slash and burn: a pragmatic approach for mitigating tropical

deforestation. In: Anderson JR (ed) Agricultural Technology: Policy Issues for the

International Community, pp 451–479 (Chapter 25). CAB International, London, 680 pp

Schwartz NB (1995) Colonization, development, and deforestation in Petén, northern Guatemala.

In: Painter M and Durham WH (eds) The Social Causes of Environmental Destruction in Latin

America, pp 101–130 (Chapter 3). The University of Michigan Press, Ann Arbor, 274 pp

Shriar AJ (1999) Agricultural Intersification and Resource Conservation in the Buffer Zone of

the Maya Biosphere Reserve, Petén, Guatemala. PhD Thesis, University of Florida,

Gainesville, 344 pp

Smith NJH (1990) Strategies for sustainable agriculture in the tropics. Ecological Economics

2: 311–323

Srivastava JP, Smith NJH and Forno DA (1996) Biodiversity and Agricultural Intensification:

Partners for Development and Conservation. The World Bank, Washington, 128 pp

Swaminathan MS (1987) The promise of agroforestry for ecological and nutritional security.

In: Steppler HA and Nair PKR (eds) Agroforestry: A Decade of Development, pp 25–41

(Chapter 3). ICRAF, Nairobi, 335 pp

Terborgh J (1992) Diversity and the Tropical Rain Forest. Scientific American Library, New

York, 242 pp

Thrift N (1989) The geography of international economic disorder. In: Johnston RJ and Taylor

PJ A World in Crisis? Geographical Perspectives, pp 16–78 (Chapter 2). 2nd Edition. Basil

Blackwell Ltd, Oxford, UK and Cambridge, USA, 308 pp

Tivy J (1990) Agricultural Ecology. Longman Scientific, New York, 288 pp

Turner BL II, Clark WC, Kates RW, Richards JF, Mathews JT and Meyer WB (eds) (1990)

The Earth as Transformed by Human Action: Global and Regional Changes in the Biosphere

over the Past 300 Years. Camridge University Press, Cambridge, UK, 713 pp

Turner BL II and Doolittle WE (1978) The concept and measure of agricultural intensity. The

Professional Geographer 30: 297–301

Turner BL II, Hanham RQ and Portararo AV (1977) Population pressure and agricultural

intensity. Annals of the Association of American Geographers 67(3): 384–396

Wilson EO (1988) The current state of biological diversity. In: Wilson EO (ed) Biodiversity.,

pp 3–18 (Chapter 1). National Academy Press, Washington, DC, 521 pp

York ET (1988) Improving sustainability with agricultural research. Environment 30(9): 18–20,

36–40

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