Domesticated crop richness in human subsistence cultivation systems: a test of macroecological and economic determinants

RESEARCHPAPER

Domesticated crop richness in humansubsistence cultivation systems: a test ofmacroecological and economicdeterminantsgeb_687 428..440

Jacob Freeman

School of Human Evolution and Social

Change, SHESC 233, PO Box 872402, Tempe,

AZ 85287-2402, USA

ABSTRACT

Aim To describe patterns of crop richness at a global scale and investigate thepotential influence of energy, water, habitat heterogeneity, area and economic com-mitment to agricultural practices on patterns of crop species richness.

Location World-wide.

Methods Ethnographic literature for 86 subsistence-oriented societies was sur-veyed; 55 of these societies grow crops and 31 do not. These 86 societies occupy therange of latitudes at which subsistence-oriented groups manage domesticated plantspecies. The number of domesticated plants that each society cultivates is reported.Regression analyses were conducted to evaluate predictions for relationshipsbetween crop richness, energy, area, habitat heterogeneity, commitment to cultiva-tion and species taxonomic descriptions.

Results Crop richness is positively and significantly related to energy availabilityand commitment to cultivation. The warmer the environment in which cultivatorslive, the more crop species cultivators grow. The more subsistence farmers committo cultivation, the more crop species subsistence farmers grow. Above a threshold of500 m in elevation range, crop richness is positively related to topographic hetero-geneity. Mean annual rainfall, elevation, area, commitment to domesticatedanimals and exchange are not significantly related to crop richness.

Main conclusions Crop richness in subsistence-oriented farming systems is con-ditioned by energy availability via the physiological tolerances of crop species. Croprichness is secondarily conditioned by topographic heterogeneity, which may serveas an estimate of spatial climatic heterogeneity. Where energy availability is arelatively weak constraint on crop production, commitment to cultivation forsubsistence strongly conditions how much effort producers invest in diversifyingtheir crop base. It is postulated that tolerance to freezing temperatures limits theranges of domesticated plant species. This range limitation might be the result ofniche conservation among domesticated crop species and a legacy of greater ratesof speciation and lower rates of extinction among ‘free living’ species in tropicalsettings.

KeywordsAgricultural change, crop diversity, cultivation intensity, diversity gradient,habitat heterogeneity, latitudinal gradient, species diversity–energy.

*Correspondence: Jacob Freeman, School ofHuman Evolution and Social Change, SHESC233, PO Box 872402, Tempe, AZ 85287-2402,USA.E-mail: [email protected]

INTRODUCTION

Understanding global patterns of species richness is a funda-

mental aim of macroecology (MacArthur, 1972; Brown, 1995;

Rosenzweig, 1995). Although humans manage a wide variety of

domesticated plant species on every continent (except Antarc-

tica), few studies have attempted to describe and explain global

patterns of crop diversity. Crop diversity research is commonly

Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2012) 21, 428–440

DOI: 10.1111/j.1466-8238.2011.00687.x© 2011 Blackwell Publishing Ltd http://wileyonlinelibrary.com/journal/geb428

focused on the distribution and conservation of genetic diver-

sity within a particular crop genus or species (Brush, 2004).

These are important studies that have identified time, variability

in soils, topography, market integration and ethnic diversity as

important correlates of intra-species diversity on farms and

among agricultural settlements (Brush et al., 1992; Bellon &

Brush, 1994; Brush, 1995, 2004; Zimmer, 1998; di Falco & Per-

rings, 2003; Coomes & Ban, 2004; Valdez et al., 2004; Abbott,

2005; Perreault, 2005; Brush & Perales, 2007; Jackson et al., 2007;

Reyes-García et al., 2008; Rerkasem et al., 2009). Research at the

macroscale has great potential for complementing these studies.

This paper documents patterns of crop species richness and

attempts to explain these patterns in human subsistence farming

systems at a global scale. This is a problem of human–plant

biogeography that has not been investigated. Yet such studies

may contribute to understanding global species diversity gradi-

ents, how humans modify biodiversity at a global scale and

patterns of institutional change among human societies. Studies

of ‘free living’ species are used as a starting point to potentially

explain global patterns in crop richness. The relative effects of

energy and water availability, habitat heterogeneity, area and a

populations’ commitment to plant cultivation on levels of crop

richness are examined. The results suggest that similar processes

control biodiversity in both systems heavily managed by

humans and systems that are less affected by human socioeco-

nomic practices.

DEFINITION OF CROP RICHNESS,HYPOTHESES AND PREDICTIONS

Crop richness is one dimension of agrobiodiversity, and agro-

biodiversity is defined as all of the living organisms associated

with human food production practices (Jackson et al., 2007).

Crop richness is defined here as the number of plant species that

are co-dependent on human labour for growth and reproduc-

tion within farming systems. Based on this definition, domesti-

cated crop richness is measured by counting the number of crop

species managed by ethnographically recorded groups (see

Materials and Methods for more detail). These counts are

reported by ethnographers; any plant species identified by an

ethnographer as a domesticated plant species has been counted.

Cultivars, which are typically varieties of individual crop species,

have not been counted. To guide this inquiry into crop richness,

four hypotheses are proposed that may explain global patterns

of crop richness. The first three hypotheses are drawn from

studies aimed at understanding global species diversity gradi-

ents among ‘free living’ organisms.

Energy and water availability

Biogeographic studies consistently document correlations

between species richness and climate variables that serve as esti-

mated measures of energy and water availability (Brown, 1981;

Turner et al., 1987; Currie, 1991; O’Brien, 1998; Hawkins et al.,

2003; Whittaker et al., 2003; Field et al., 2009). The species–

energy hypothesis has been proposed to explain these correla-

tions. Two versions of this hypothesis are extant in the literature

(Hawkins et al., 2003, p. 3106). The ‘productivity hypothesis’

postulates that limits on species richness are set by flows of

energy and water availability that limit the population sizes of

primary producers (Brown, 1981; Wright, 1983; Hawkins et al.,

2003). Thus, ‘plant richness is limited primarily by energy and

water availability (i.e. water–energy dynamics). Herbivore rich-

ness in turn is limited by the net primary production of plants,

predator richness is limited by the secondary production of

herbivores, and so on up the food chain’ (Hawkins et al., 2003, p.

3106).

Increased population sizes for plant species may increase the

number of rare species in assemblages and result in a greater

probability that rare species are counted in samples (Evans et al.,

2005, pp. 4–5). The population size of domesticated plants

co-dependent on humans for reproduction depends on the

amount of area devoted to a particular species. The more area

devoted to a species, the greater the probability that the species

is observed among a sample of fields. For equal areas planted,

energy and water availability should affect population size, but

this is likely to have a small impact relative to area planted on

whether or not an individual species is observed and counted.

Typically, whole gardens are surveyed, thus it is suggested here

that the number and area of gardens are more likely to influence

sampling than productivity per species per unit area.

Increased population size generally decreases extinction risk

among ‘free living’ species, and extinction risk should differen-

tially condition species richness in areas of equal size (Evans

et al., 2005, p. 8). It is probable that the risk of a crop species

going locally extinct within a farming system is a function of its

abundance within the system. Again, abundance is primarily a

function of the area over which farmers cultivate a particular

species and secondarily a function of energy and water availabil-

ity. It is argued here that the productivity mechanism is unlikely

to cause, if they exist, correlations between energy, water and

crop richness. The area devoted to particular species should have

a larger effect on local extinction risk (crop abandonment).

However, this is an empirical question that has not been system-

atically addressed.

The ‘ambient energy hypothesis’ proposes that energy and

water availability limit species richness via the physiological tol-

erances of individual organisms to energy and water availability

(Turner et al., 1987; Currie, 1991; Hawkins et al., 2003, p. 3106).

According to this argument, more species can tolerate warm

rather than cold climates, therefore more species live in warm

settings (Evans et al., 2005, p. 13). The ability of crop plants to

tolerate cold may affect the ability of domesticated species to

expand their ranges into particular farming systems. This may

also apply to water availability. Where water is limited, crop

species that are not drought tolerant might not expand their

range into farming systems that occupy arid environments

(barring infrastructure related to irrigation and genotypic

changes). Given the ambient energy hypothesis, it is predicted

that crop richness is positively correlated with central tendency

measures of energy and water availability (Prediction 1a).

Second, crop richness is positively correlated with the coldest

Macroecological determinants of crop richness

Global Ecology and Biogeography, 21, 428–440, © 2011 Blackwell Publishing Ltd 429

minimum temperature in a region (Prediction 1b). Third, to the

extent that elevation and temperature are positively correlated,

crop richness should correlate negatively with the mean eleva-

tion that a group occupies (Prediction 1c) (Samberg et al.,

2010). Both central tendency measures and minimum tempera-

ture are proxy measures for physiological stresses potentially

induced by energy and water availability.

Habitat heterogeneity

The habitat heterogeneity hypothesis proposes that species rich-

ness increases as the number of habitats in regions of equal area

also increase (Pianka, 1966; Davidowitz & Rosenzweig, 1998).

There is support for this hypothesis at multiple scales (Rosenz-

weig, 1995; Rahbek & Graves, 2001; Jiménez et al., 2009). Brush

& Perales (2007) have documented that the intra-species rich-

ness of maize (Zea mays) is significantly related to topographic

and, by inference, habitat heterogeneity in the highlands of

Mexico.

Topographic heterogeneity may affect habitat diversity as it

relates to crop species in two ways. First, topographic heteroge-

neity is often associated with climatic heterogeneity due to lapse

rates (cooling at higher elevations) and rain shadows. This

might increase the number of climates that farmers can access

and condition crop richness in a similar manner as energy and

water availability. Second, topographic heterogeneity may affect

heterogeneity in vegetation and soils, which farmers often asso-

ciate with particular sets of crops (e.g. Brush, 1977, pp. 70–74;

Wilk, 1997). The more diverse the soil and vegetation of a

region, the more diverse crops farmers might cultivate to accom-

modate the different productive potentials of cropland habitats.

A prediction derived from the habitat heterogeneity hypothesis

(Prediction 2) is that crop species richness, at a global scale, is

positively correlated with topographic heterogeneity, which is a

proxy measure of habitat heterogeneity.

Area

A third factor that may explain species richness is area (e.g.

Preston, 1960; Rosenzweig, 1995; Lomolino, 2000). The greater

the area sampled, the greater the number of species observed. In

addition, if area planted and the risk of local extinction are

negatively correlated, as suggested above, then farming systems

that cover more area should have lower rates of crop species

extinction and greater estimated values of crop richness. Predic-

tion 3 is that the area covered by ethnographers while collecting

data on cultivated plants will correlate positively with the

number of crops that ethnographers report. In the absence of

specific data on the number of gardens, the size of gardens and

the number of informants consulted to collect crop data, the

area occupied by groups serves as a relative measure of the area

over which descriptions of crops were collected and the size of

farming systems (size in terms of area planted).

Commitment to cultivation

Tradeoffs between different resource acquisition strategies are

expected to condition patterns of crop richness. Actors within

socioeconomic systems face tradeoffs in how they proportion

their time and energy to procure resources for consumption.

Human actors can procure resources via four different strate-

gies: foraging, cultivating, herding and exchange. All four

domains encompass a diverse array of tactics and options.

However, the four domains are differentiated because they

require partially different knowledge sets, technological capaci-

ties, labour investments and land-use strategies. Investments

made in economic activities other than plant cultivation

(beyond some minimum threshold) necessarily decrease time,

energy and knowledge otherwise invested in crop production.

Prediction 4 is that the more calories farmers obtain from cul-

tivating domesticated plants, the greater the number of domes-

ticated plant species farmers will grow.

MATERIALS AND METHODS

The data used in this analysis were gathered from 55 ethno-

graphically recorded societies that grow crops and 31 societies

that do not grow crops (Appendices S1 & S2 in Supporting

Information). The 55 crop-growing societies represent a global

sample drawn from the Ethnographic Atlas (Murdock, 1967).

The term subsistence-oriented refers to groups of people who

produce most of their own food. Market participation, however,

is present in 31 of the societies surveyed. Crop-growing societies

were selected by stratifying the Ethnographic Atlas to represent

the range of latitudes where people are recorded as cultivating

domesticated plants and a range in the degree to which subsis-

tence is dominated by domesticated plants. This list included

some 350 recorded societies. Literature searches were then con-

ducted on 170 societies.

The primary limit on the sample size of crop-growing soci-

eties is the availability of ethnographic sources that report data

on domesticated plant use. Ethnographies are not standardized

descriptions of cultural groups. In many cases, ethnographic

descriptions contain no information on crops. The sample of 55

crop-growing societies is biased to groups in North America,

South America, Africa and Southeast Asian islands. The 31 soci-

eties that do not grow domesticated plants were compiled from

Binford’s (2001) compendium of 339 hunter–gatherer societies

and a study conducted by Johnson (1997) on pastoral societies.

The societies that do not grow crops were selected to represent

the range of rainfall and temperature environments from which

‘cropless’ societies are recorded. Data were collected on societies

that do not grow domesticated crops to help clarify the relative

importance of commitment to cultivation relative to environ-

mental constraints on crop richness.

Table 1 lists the ethnographic and environmental variables

estimated for each society. Crop data for 52/55 societies were

collected from one ethnographic source. Two sources were used

for three groups (Sebei, Dogon and Iban). Crop counts refer to

groups and the area they occupy rather than particular farms.

All four subsistence variables listed in Table 1 are estimates

recorded as percentages. Diet variables are interpreted to

measure the proportion of calories obtained through foraging,

farming, herding or exchange activities (cf. Murdock & Morrow,

J. Freeman

Global Ecology and Biogeography, 21, 428–440, © 2011 Blackwell Publishing Ltd430

1980). These values represent an average individual and smooth

out individual and seasonal variation.

The area over which ethnographic descriptions were recorded

was collected from ethnographic sources as an estimate for the

area over which observations of crops were made. The elevation

for the location assigned to each group by ethnographers was

collected from a 30 ¥ 30 arcsec digital elevation model (USGS,

2006). Elevation range data were collected from this digital

elevation model. Using ArcGis 9.3, buffer polygons were

created that approximate the area occupied by each group as a

circle. The highest and lowest elevation raster cells within each

buffer polygon were recorded, and the maximum elevation

minus the minimum elevation was calculated to measure topo-

graphic heterogeneity.

Mean monthly rainfall and temperature data were collected

from weather stations and used to calculate mean annual tem-

perature, mean annual rainfall and the month of the year with

the coldest mean minimum temperature. Mean annual tem-

perature and annual rainfall are measures of central tendencies

in energy and water availability. The month with the coldest

minimum temperature is an estimate of the lower limit of

energy availability in a group’s territory. The point locations of

weather stations were obtained from Wernstedt (1972), Rudloff

(1981) and digital publications from the National Oceano-

graphic and Atmospheric Administration (US Department of

Commerce, 1998, 2001) (Fig. 1).

The weather station closest to each ethnographic group’s

location was used to record temperature and rainfall observa-

tions. Where more than one weather station was at equal dis-

tance, the values for the weather stations were averaged.

Elevation and the density of weather stations can bias this strat-

egy. All central tendency data for cases were calculated from

mean monthly averages obtained over at least 30-year intervals.

In 10 cases this was not possible while obtaining mean

minimum monthly temperatures. Weather station data used in

these 10 cases were obtained from stations with at least 10 years

of recorded data. Thirty-two ethnographic sources for crop-

growing societies reported information on climate. Weather

station data were compared to climate information reported for

these 32 cases, and mean annual temperature and rainfall values

recorded for weather stations were consistent with the climate

data reported in these sources.

The primary methodological obstacle to this study was defin-

ing crop richness in a manner that is comparable across cases.

The approach taken here was to count the number of domesti-

cated species reported for each ethnographic case. This strategy

measures inter-species diversity; landrace varieties of crops,

when reported, were not counted. The primary weakness of this

strategy is that 18/55 ethnographic sources reported crops using

Linnaean taxonomy, while 37/55 sources reported crops using

English folk taxonomic distinctions or a combination of folk

and Linnaean taxonomy.

Linnaean plant identifications are based on a formal classi-

fication system while English folk identifications have not been

standardized. This raises two issues: is the English folk classi-

fication of plants comparable to the Linnaean classification,

and are ethnographies that report folk names for plants com-

parable to each other? The first question can be investigated

empirically through a binary classification (CROPID) that tests

differences between cases where folk versus Linnaean taxo-

nomic identifications have been used. The second question

raises more difficult issues. When counting crops, every crop

identified by an ethnographer as a domesticated species was

counted. If an ethnographer listed potatoes, sweet potatoes,

maize, beans, squash, and gourds, then six crops were recorded

for that group.

There is potential bias introduced by ethnographers not con-

sistently using the same folk crop names for the same crop

species, or by one ethnographer subsuming a greater number of

crop species or genera under one folk name compared to other

ethnographers. Unfortunately, it is very difficult to investigate

these possibilities. One indirect source of information on the

consistency and breadth of folk classification names is the eth-

nographic sources that report botanical species names. Of these

sources, 18/18 also report the English folk names of botanical

specimens. Folk names were compared with scientific names

across these 18 sources. In these sources, folk names are consis-

tently paired with scientifically identified species and genera. For

instance, in 10/10 sources that reported ‘yam’ this crop is always

paired with the genus Dioscorea. In 8/8 sources that contain

reference to sweet potatoes this crop is always paired with the

species Ipomoea batatas. This consistency does not guarantee

that folk names are consistently applied across all sources, but it

is the best indication available that folk names are consistently

applied. This suggests that sources reporting only folk names are

comparable. Finally, some crop folk names consistently under-

represent the taxonomic richness of associated species (e.g. yam

refers to a genus), and, for other crops, folk names are synony-

mous with a single species (e.g. sweet potatoes). This is a bias

that cannot be eliminated. It is imperative to interpret the results

of this study cognizant of this bias.

Table 1 Definitions of variables collected from ethnographicsources, weather stations and elevation maps.

Variables Definitions

NUMCRP Number of domesticated species grown by a society

CROPID Records the taxonomic description of plants. 0 =English folk taxonomy or a combination of folk and

Linnaean taxonomy. 1 = Linnaean taxonomy

FORAGING The percentage of diet obtained from foraging

CULTIVATE The percentage of diet obtained from cultivation

DOMA The percentage of diet obtained from domesticated

animals

EXCH The percentage of diet obtained from exchange

MAT Mean annual temperature

CMMT Coldest minimum monthly temperature

CRR Mean annual rainfall

ELEVATION Mean elevation

ELVRANGE Elevation range

AREA Area occupied by an ethnographic group



Relationships between crop richness and predictor variables

were observed using Pearson product moment correlations for

all 86 societies. Two backward stepwise multiple least squares

regressions were used to test the significance of predictor vari-

ables for modelling crop richness. The first stepwise regression

tests the significance of predictor variables for all 86 societies

that grow and do not grow crops. The second stepwise regres-

sion tests the significance of predictor variables among only

those societies that grow crops. Ordinary multiple regression

was used to test the possibility that different taxonomic descrip-

tions of crops (folk versus Linnaean) represent significantly dif-

ferent groups in the data set. To test the presence of two groups,

crop richness was regressed on each of the best regression

models identified in the first two stepwise multiple regressions

with the taxonomic indicator variable (CROPID) added to each

of these models. Partial F tests were conducted to evaluate if

adding the variable CROPID to the best models identified by

stepwise multiple regression significantly increased the variance

in crop richness explained by the regression equations

(Dielman, 2001, 192). All statistical analyses were conducted

using the PASW statistics package version 18 (2009) and these

results were checked using the statistical computing language R

(Ihaka & Gentleman, 1996).

The least squares technique assumes that regression model

errors are independent. Spatial autocorrelation is one pattern

that can introduce dependence into model error terms (Griffith,

1987; Cressie, 1991). Moran’s I was calculated to test for global

spatial autocorrelation among regression model residuals.

Moran’s I is a measure of univariate correlation that measures

how similar or different points clustered in space are from each

other (Griffith, 1987, 41). ArcGis 9.3 was used to create buffer

polygons of 1000 km diameter around each group. These poly-

gons were used to create the weighted distance matrix necessary

to calculate Moran’s I. ArcGis 9.3 was used to calculate Moran’s

I and significance level. Any Moran’s I with a P-value less than

0.1 was considered sufficient evidence for autocorrelation.

RESULTS

Bivariate correlations

Crop richness (NUMCRP) is significantly related to 6/10 pre-

dictor variables (Table 2). Crop richness is significantly and

positively related to measures of energy availability (CMMT and

MAT), water availability (CRR) and commitment to cultivation

(CULTIVATE). It is important to note that temperature and

rainfall variables are significantly and moderately correlated in

this data set. The hotter the environment a group occupies, the

more rain that falls in that environment. However, crop-growing

societies in this data set occupy both hot and dry and hot and

Figure 1 Global distribution of societies analysed and a global, non-random sample of weather stations consulted in this study. Blackpentagons = societies; small grey triangles = weather stations. Aitoff equal area projection.

J. Freeman


humid environments. In this data set, crop-growing societies are

absent from temperate habitats with over 1200 mm of rainfall

per year and arctic habitats (Fig. 2). This pattern may suggest

important environmental constraints on crop production in

subsistence-level systems. The percentage of the diet obtained

from foraging (FORAGING) and cultivation (CULTIVATE) are

reciprocal measures, and the percentage of foraging is elimi-

nated from regression analysis.

Multiple regression

In regression 1 all predictor variables are regressed on crop

richness for all 86 societies (Table 3). Three variables are posi-

tively and significantly related to crop richness: mean annual

temperature (MAT), elevation range (ELVRANGE) and cultiva-

tion intensity (CULTIVATE) (R2 = 0.60; Moran’s I = 0.02, P >0.1). This analysis also indicates that the form of the relationship

between predictor variables and crop richness is curvilinear

(Fig. 3). Table 4 presents the results of all predictor variables

regressed on the natural log of crop richness (LNNUMCRP) for

crop-growing societies (regression 2). The coldest minimum

monthly temperature (CMMT), cultivation intensity (CULTI-

VATE) and elevation range (ELVRANGE) are significant predic-

tors of crop richness (LNNUMCRP) among crop-growing

societies (R2 = 0.67; Moran’s I = -0.03, P > 0.1).

Regression models 1 and 2 are consistent with the ambient

energy hypothesis. Crop richness is significantly and positively

related to energy availability, as measured by temperature vari-

ables. However, the mean elevation that groups reside at is

neither significantly related to crop richness nor to temperature

variables (Table 2). Regression models 1 and 2 also support the

cultivation intensity hypothesis. Neither the correlation matrix

(Table 2) nor regressions 1 and 2 provide support for the crop

richness–area hypothesis. Elevation range (ELVRANGE) is a

Table 2 Bivariate Pearson correlation matrix for 11 continuous variables analysed in this study.

NUMCRP CMMT MAT CRR AREA ELEVATION ELVRANGE FORAGING CULTIVATE DOMA EXCH

NUMCRP 1

CMMT 0.34 1

MAT 0.37 0.85 1

CRR 0.38 0.45 0.38 1

AREA -0.04 0.01 0.05 0.14 1

ELEVATION 0.11 -0.13 -0.05 -0.24 -0.15 1

ELVRANGE 0.29 -0.12 -0.084 0.14 0.18 0.34 1

FORAGING -0.55 0.19 -0.29 -0.10 -0.05 -0.41 -0.29 1

CULTIVATE 0.71 0.21 0.24 0.29 -0.12 0.20 0.16 -0.74 1

DOMA -0.12 -0.08 0.02 -0.27 0.27 0.21 0.15 -0.42 -0.16 1

EXCH -0.03 0.11 0.18 0.09 0.11 0.37 0.21 -0.46 0.12 0.38 1

Crop richness (NUMCRP) is displayed in the first column (n = 86).Bold values are significant at a = 0.05.CMMT, coldest mean minimum monthly temperature; MAT, mean annual temperature; CRR, annual rainfall; AREA, area occupied by ethnographicgroups; ELEVATION, mean elevation in a group’s range; ELVRANGE, difference between the highest and lowest elevations in a group’s range;FORAGING, percentage of diet obtained from foraging; CULTIVATE, percentage of diet obtained from cultivation; DOMA, percentage of diet obtainedfrom domesticated animals; EXCH, percentage of diet obtained from exchange.

500040003000200010000

500040003000200010000

40

30

20

10

0

- 10

- 20

40

30

20

10

0

- 10

- 20

mean annual rainfall

mea

n an

nual

tem

pera

ture

Figure 2 Sample of crop-growing and ‘cropless’ societiesoverlaying a sample of approximately 9000 weather stations. Blackcircles = crop-growing societies; black squares = ‘cropless’societies; grey triangles = weather stations. Grey enclosed areashighlight environments where subsistence agricultural societiesare not located in this data set.



significant predictor of crop richness in regressions 1 and 2.

Figure 4 illustrates that the relationship between elevation range

(ELVRANGE) and crop richness (LNNUMCRP) is not linear.

For crop-growing societies that live in areas where elevation

range is less than 500 m (n = 35), topographic heterogeneity is

unrelated to crop richness. Above an elevation range of 500 m (n

= 20), crop richness is related in a positive linear fashion to crop

richness. The effect of elevation range is important among cases

where elevation range is greater than 500 m.

Table 5 presents the results of two ordinary multiple regres-

sions that include the taxonomic indicator variable (CROPID)

added to each of the significant factors identified in Tables 3 & 4.

The taxonomic indicator variable significantly improves the

model fit in regressions 3 and 4 compared with regressions 1 and

2 (partial F = 38.78 and 20.0, P < 0.05 in both cases). The

prediction wherein descriptions using folk versus Linnaean tax-

onomies form two distinct groups in this data set is supported.

Cases described using Linnaean taxonomic distinctions consis-

tently have greater estimated values of crop richness than cases

described using English folk names. Spatial autocorrelation was

tested for the residuals of regressions 3 and 4. Significant and

minor autocorrelation was observed for regression 3 error terms

(Moran’s I = 0.09, P < 0.1) and significant autocorrelation was

not observed for regression 4 (Moran’s I = 0.05, P > 0.1).

A critical question is whether domesticated plant species rich-

ness is primarily limited by energy availability or by commit-

ment to domesticated plant cultivation. Figure 2 indicates that

crop-growing societies occupy environments that are broadly

similar to the environments that non-crop-growing societies

occupy, except arctic and very wet temperate settings. This sug-

gests that subsistence crop production is partly limited by

climate-imposed costs on domesticated plant production in

arctic and wet temperate settings. A further clue to the relative

importance of energy availability versus commitment to culti-

vation on crop richness comes from observing five cultivating

societies that engage in very casual cultivation: the Yavapai,

Lipan, Cheyenne, Siriono and Nuer. All five groups cultivate

fewer crops than groups that live in similar environments

(Fig. 5). While the other 50 crop-growing groups in the data set

are tied to their fields for subsistence, cultivation among the

Yavapai, Lipan, Cheyenne, Siriono and Nuer is an ancillary eco-

nomic activity.

For instance, the Siriono planted small gardens of manioc and

maize at locations where they anticipated residing during the

rainy season when their lowland habitat flooded and restricted

their ability to move between foraging locations (Holmberg,

1969, pp. 47–50). The Nuer also planted small millet gardens on

high ridges during the wet season in southern Sudan when

Table 3 Significant and excluded variables for all 10 predictor variables regressed on crop richness (NUMCRP) (n = 86).

R2 d.f. F-statistic Variables included Variables excluded Variable coefficient t-statistic P-value

0.60 83 39.8* Constant (b) -12.781 -3.4 < 0.05

MAT 0.557 3.1 < 0.05

ELVRANGE 0.008 2.9 < 0.05

CULTIVATE 0.343 8.0 < 0.05

CMMT 0.1 0.8

CRR 0.1 0.8

ELEVATION -0.1 0.9

AREA 0.1 0.9

DOMA -0.1 0.9

EXCH -0.1 0.9

*Indicates that the F-statistic is less than P = 0.05.CMMT, coldest minimum monthly temperature; MAT, mean annual temperature; CRR, annual rainfall; AREA, area occupied by ethnographic groups;ELEVATION, mean elevation in a group’s range; ELVRANGE, difference between the highest and lowest elevations in a group’s range; CULTIVATE,percentage of diet obtained from cultivating domesticated plants; DOMA, percentage of diet obtained from domesticated animals; EXCH, percentageof diet obtained through exchange and purchase.

3210- 1- 2

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80

60

40

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0

100

80

60

40

20

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crop

ric

hnes

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standardized predicted values

Figure 3 Relationship between crop richness and standardizedpredicted values of crop richness for regression 1.

J. Freeman


flooding eliminated their ability to herd cattle and move

between fishing camps (Evans-Pritchard, 1947, pp. 79–80). The

Lipan Apache would plant small maize fields along river

bottoms and ‘depart for the hunt with the intention of harvest-

ing whatever survived after their return’ (Opler, 2001, p. 948).

This plant and leave cultivation strategy was also practised by

many Yavapai and Cheyenne foragers (Gifford, 1936, p. 262;

Grinnell, 1962, p. 253).

When these groups are removed from analysis, energy avail-

ability is a stronger predictor of crop richness than cultivation

intensity (Table 6). Table 6 compares a multiple regression of the

coldest mean minimum temperature (CMMT) and taxonomic

identification (CROPID) run on crop richness (LNNUMCRP)

with a regression of commitment to cultivation (CULTIVATE)

and taxonomic identification (CROPID) run on crop richness

(LNNUMCRP). Temperature clearly explains more of the varia-

tion among committed cultivation cases. Tests for spatial

autocorrelation run on regressions 5 and 6 indicate that auto-

correlation is not significant for regression 5 (Moran’s I = 0.07,

P > 0.1), but the residuals of regression 6 are significantly and

positively autocorrelated (Moran’s I = 0.13, P < 0.1). The auto-

correlation observed for the residuals of regression 6 is intuitive.

Groups living close together also live in habitats with similar

temperatures. Thus, when temperature is not controlled for, a

small but significant spatial autocorrelation is observed.

DISCUSSION AND CONCLUSION

Levels of crop species richness in subsistence agricultural

systems are primarily conditioned by energy availability and

secondarily, by habitat heterogeneity. Commitment to cultiva-

tion also plays a strong role in constraining the effort producers

invest in crop diversification. The taxonomic description of

Table 4 Variables and significancetests for predictors regressed on thenatural log of crop richness(LNNUMCRP) for crop-growingsocieties (n = 55).

R2 d.f. F-statistic Variables included Variables excluded

Variable

coefficient t-statistic P-value

0.67 52 35.5* Constant (b) 0.798 3.6 < 0.05

CMMT 0.037 4.7 < 0.05

CULTIVATE 0.027 7.1 < 0.05

ELVRANGE† 0.00026 2.0 < 0.05

MAT 1.1 0.2

CRR 0.4 0.6

ELEVATION 0.2 0.8

AREA -0.4 0.6

DOMA 1.1 0.2

EXCH 1.0 0.2

*Indicates that the F-statistic is less than P = 0.05.†ELVRANGE, elevation range; note that the coefficient for elevation range was estimated using theprogram R. t-test results are consistent between PSAW and R.CMMT, coldest minimum monthly temperature; MAT, mean annual temperature; CRR, annualrainfall; AREA, area occupied by ethnographic groups; ELEVATION, mean elevation in a group’srange; ELVRANGE, difference between the highest and lowest elevations in a group’s range; CULTI-VATE, percentage of diet obtained from cultivating domesticated plants; DOMA, percentage of dietobtained from domesticated animals; EXCH, percentage of diet obtained through exchange andpurchase.

25002000150010005000

25002000150010005000

5

4

3

2

1

0

5

4

3

2

1

0

elevation range

ln c

rop

richn

ess

Yavapai

Cheyenne

Figure 4 Nonlinear relationship between the natural log of croprichness and elevation range. Above an elevation range of 500 m,crop richness increases as a positive linear function of elevationrange. Circles = crop richness estimated from English folk names.Triangles = crop richness estimated from Linnaean taxonomicidentifications. The Yavapai and Cheyenne are casual cultivatingsocieties discussed in the text.



crops by ethnographers (folk versus scientific) also significantly

affects estimates of crop richness. Groups described using a

scientific taxonomy display greater estimated values of crop

richness than groups described using English folk names. These

results raise an important point. Crop species richness in

subsistence-level farming systems is consistent with the species

richness–latitude gradient observed among ‘free living’ organ-

isms (Fig. 6). The crop richness–latitude gradient seems to result

from the correlation of energy availability and crop richness.

This raises the possibility that similar processes cause diversity

gradients in ecological systems both heavily managed and

unmanaged by human economic practices.

The availability of water is not significantly related to crop

richness. Groups living in very dry habitats grow many crops.

For instance, the Tuareg live in a habitat that receives about

100 mm of rainfall per year. Through irrigation, certain classes

of Tuareg are able to cultivate many crop species (Nicolaisen &

Nicolaisen, 1997, 269). Irrigation infrastructure may mitigate

the effects of water deficit on individual crop species. If this idea

has merit, then groups practising irrigation should grow more

crops relative to those not practising irrigation among habitats

that have similar levels of energy and water availability. Area

occupied is also not significantly related to crop richness in this

data set. This finding does not support the area hypothesis. It is

probable that the area variable used here is too general to

capture meaningful variation with respect to the area over which

observations of crops were made. More relevant variables would

be the total hectares of fields on which ethnographers made crop

observations and the number of farms visited by ethnographers.

These data are not reported in the ethnographic sources con-

sulted here and should be a focus of data collection efforts in the

future. The mean elevation that groups occupy is also not sig-

nificantly related to crop richness. Holding latitude constant, if

the sample size is enlarged, future studies may find a significant

relationship between elevation and crop richness.

The foregoing results suggest that crop species richness may

display three different equilibrium states among subsistence-

level societies. The first state is defined by zero commitment to

domesticated plant cultivation, in which case domesticated crop

species richness is zero. The costs of adopting crops among these

Table 5 Regression equations thatinclude the taxonomic indicator variable(CROPID).

d.f. R2 F-statistic Model variables t-statistic P-values

Regression 3 equation

NUMCRP = -13.677 +0.005ELVRANGE +0.627MAT +0.259CULTIVATE +18.325CROPID

82 0.73 56.2 MAT 4.5 < 0.05

CULTIVATE 7.2 < 0.05

ELVRANGE 2.5 < 0.05

CROPID 6.5 < 0.05

Regression 4 equation

lnNUMCRP = 0.721 +0.041CMMT +0.025CULTIVATE +0.0002ELVRANGE +0.664CROPID

51 0.79 60.0* CMMT 6.5 < 0.05

CULTIVATE 8.5 < 0.05

ELVRANGE 1.4 0.1

CROPID 5.1 < 0.05

*Indicates that the F-statistic is less than P = 0.05.MAT, mean annual temperature; CULTIVATE, percentage of diet obtained from cultivation; CROPID,taxonomic indicator variable; ELVRANGE, difference between the highest and lowest elevations in agroup’s range; CMMT, coldest minimum monthly temperature. Note that the coefficient for elevationrange was not displayed in PASW but was calculated in R; t-tests were consistent between the twoprograms.

3020100- 10- 20

3020100- 10- 20

5

4

3

2

1

0

5

4

3

2

1

0

coldest mean minimum temperature

ln c

rop

richn

ess

Figure 5 Relationship between the natural log of crop richnessand the coldest minimum monthly temperature. Casualcultivating societies are shaded in grey and grow fewer crops thangroups living in similar environments. Circles = crop richnessestimated from English folk names. Triangles = crop richnessestimated from Linnaean taxonomic identifications.

J. Freeman


cases may be strongly conditioned by energy availability as

mediated through the physiology of particular crop species

(Fig. 2). The second state is characterized by a very low commit-

ment to plant cultivation for subsistence (0–25%). In this state,

crop richness values are intermediate between zero and the

values observed among more committed cultivators. The third

state occurs among committed cultivators. Energy availability

and habitat heterogeneity primarily condition levels of crop

richness among these societies. Habitat heterogeneity has an

effect among cases where elevation range is greater than 500 m

of elevation. These results are consistent with the conclusion

that both climate and topography control global patterns of

species richness (Rahbek & Graves, 2001, 4538). However, the

mechanism linking topographic variability and crop richness

requires further investigation. The relationship may result from

microclimatic variability in temperature where farmers can

access both cold and warm cropland habitats.

A major issue is how energy availability potentially causes

patterns of crop species richness through the biological pro-

cesses of dispersal, speciation and extinction (Hawkins et al.,

2003; Currie et al., 2004; Evans et al., 2005; Jocqué et al., 2010).

It is assumed here that dispersal of crops occurs primarily

through society to society transmission of seeds or other repro-

ductive parts. Making the simplifying assumption that the

ability of crops species to move between societies is equal, then

post-dispersal establishment is a critical factor that constrains a

crop species’ range. From the perspective of the cultivator, the

critical question is: why add another species to the mix? Adding

a species will have both benefits and costs. The tolerance of

individual crops to freezing temperatures is an important factor

that should affect the costs of adopting crop species in particular

environments. The critical variables are the duration of time

from planting a crop to production of fruits, seeds or tubers for

consumption (maturation period) and length of the growing

season. If tolerance to cold partly determines the costs of culti-

vating a crop species, it is expected that crop species do not

expand their ranges into environments where the length of the

growing season is less than a crop species’ maturation period.

If environmental limitation primarily restricts the range of

domesticated crop species, then the question becomes: why do

Table 6 Comparison of the fit ofregressions 5 and 6. In regressions 5 and6 the Yavapai, Lipan, Cheyenne, Nuerand Siriono are excluded.

Regression number R2 Adjusted R2 Standard error n Predictor variables

5 0.64 0.62 0.44 50 CMMT, CROPID

6 0.40 0.38 0.56 50 CULTIVATE, CROPID

CMMT, coldest minimum monthly temperature; MAT, mean annual temperature; CULTIVATE,percentage of diet obtained from cultivating domesticated plants; CROPID, taxonomic indicatorvariable.

3020100- 10- 20

3020100- 10- 20

50

25

0

- 25

- 50

50

25

0

- 25

- 50

543210

543210

50

25

0

- 25

- 50

50

25

0

- 25

- 50

latit

ude

latit

ude

natural log of crop richness coldest mean minimum temperature

(a) (b)

Figure 6 (a) Relationship between latitude and the natural log of crop richness. (b) Relationship between latitude and the coldest meanminimum monthly temperature. In both graphs: circles = committed cultivating societies; triangles = casual cultivating societies. Croprichness and energy availability both peak just north of the equator.



so many species tolerate warm environments (Evans et al., 2005,

p. 14)? One possibility is that tropical environments have

covered more of the earth’s surface over the last 65 million years.

This time–area affect is argued to cause higher speciation rates

and lower extinction rates among free-standing species (Fine &

Ree, 2006, 801–802), which conceivably has resulted in a greater

inventory of plants that might be domesticated in the tropics. If

crop species conserve the fundamental niche of their ‘wild’

ancestors (Peterson et al., 1999), then only a few plants domes-

ticated in the tropics may have the requisite life-history charac-

teristics to thrive in temperate settings. In order for crops to

adapt to cold climates they must first be exposed to those cli-

mates. Crop species that do not produce well in cold climates are

likely to be abandoned by farmers before genotypic changes that

increase tolerance to cold occur. This ‘niche conservation

hypothesis’ would also predict that few plants originally domes-

ticated in temperate settings will spread into tropical settings

(Wiens & Donoghue, 2004). As Wiens & Donoghue (2004, p.

643) point out, another prediction is that crop species confined

to the tropics should also be members of older clades compared

with temperate zone species and members of clades confined to

tropical settings.

Alternatively, energy availability may drive higher net diver-

sification rates among ‘free living’ species and may also lead to

more species that are adapted to high-energy environments and

a greater inventory of potential domesticates (Evans & Gaston,

2005; Mittelbach et al., 2007). Plants living in high-energy envi-

ronments may experience higher mutation rates (Evans &

Gaston, 2005). If this is the case, mutations among ‘wild’ plants

advantageous to intensive cultivator–plant interactions may be

more likely to occur in high-energy environments. This could

lead to higher levels of human-directed selection and greater

rates of crop domestication via artificial selection of subpopu-

lations of ‘wild’ species across the tropics compared with tem-

perate environments.

Biogeographic studies have typically not investigated biologi-

cal species closely managed by humans (Guernier et al., 2004;

Terrell, 2006). However, the study of global diversity patterns

among these organisms has great potential. As an initial attempt

to compare global patterns of crop diversity, this study has

examined four hypotheses. The results indicate that patterns of

crop species richness are conditioned by the interaction of

human economic practices, energy availability and topography.

Future investigations of this kind have great potential to

improve our understanding of both diversity gradients and how

humans affect and manage biodiversity through socioeconomic

practices.

ACKNOWLEDGEMENTS

I would like to thank Amber Johnson, John M. Anderies,

Micheal Barton and Margaret C. Nelson for encouraging this

manuscript. The manuscript was significantly improved by the

thoughtful comments of two anonymous referees, and I am very

appreciative of their efforts. I am also grateful for financial

support partially provided by the National Science Foundation

grants BCS-0527744 and SES-0645789.

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BIOSKETCH

Jacob Freeman is a PhD student at the School of

Human Evolution and Social Change, Arizona State

University. His interests include the biogeography of

human societies, the evolution of domesticated plant

use and robustness–vulnerability tradeoffs in

socio-natural systems.

Editor: Karl Evans

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online

version of this article:

Appendix S1 Data used for analysis.

Appendix S2 Ethnographic references consulted to compile

crop diversity, area and diet data.

As a service to our authors and readers, this journal provides

supporting information supplied by the authors. Such materials

are peer-reviewed and may be re-organized for online delivery,

but are not copy-edited or typeset. Technical support issues

arising from supporting information (other than missing files)

should be addressed to the authors.

J. Freeman


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Domesticated crop richness in human subsistence cultivation systems: a test of macroecological and economic determinants