Appendix 1 Ranking data

We asked farmers to rank all their crops in order of importance using photo flashcards.

Farmers were asked to interpret the meaning of the word “important” as they best

understood it.

Figure 1. Rank index data collection using photo flashcards

Farmers gave reasons for assigning rankings to the crop they designated as most

important, as well as the reasons for assigning the designated rank to buckwheat.

Of the 201 farms completing the exercise that currently plant buckwheat, only 8 farms

ranked buckwheat as the most important crop, and 15 farms ranked buckwheat as least

important. This contrasts with 96 of these same farms reporting potato as the most

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important crop, 21 farms ranking white beans as the most important, 18 for maize, and 12

for rice. The strong representation of potatoes is due to the fact that the potato and

buckwheat climatic optimum overlaps almost completely, and it is potato that is the

agricultural workhorse in most of these systems. It is eaten by humans and livestock and

also sold commercially. The agricultural bureau has also promoted potato growing by

providing improved “seed” potatoes and subsidizing fertilizer and plastic mulch to improve

potato production. Government buyers also purchase potatoes from village cooperatives in

our study area, which guarantees a market. In contrast, maize and rice, though regionally

important in Yunnan, tend not to produce consistently in the relatively cool, high elevation

locations of the study area.

Because the total number of crops varied by farm and agroecosystem, we devised a ranking

index to allow comparison of farms. The index was calculated as follows:

rank.index = 2*((1 - Rank of tartary buckwheat/total number of crops)-0.376)

This produces in an index, roughly centered on zero, with the following properties:

1. Between farms with the same number of crops, a higher rank of buckwheat

results in a higher index value.

2. Between farms with the same rank given to buckwheat, a higher number of crops

planted results in a higher index value.

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3. For farms where buckwheat is ranked above the median total number of crops,

the index value is positive.

4. For farms where buckwheat is ranked below the median total number of crops,

the index value is negative.

5. For farms where buckwheat rank is equal to the median total crop value, the

index value is roughly equal to zero. (In practice this number varies from 0.0129

bw rank = 5/9 to -0.209; bw rank = 2/3, with a mean of -0.0647)

The main drawback of this metric is that it requires adjustment around zero, and some

farms where buckwheat is ranked at the median its index value is negative, an in some

cases is positive. For example, for a farm with only 3 crops buckwheat ranked at the

median has an index value of -0.209, and for farms with 9 crops, buckwheat ranked at the

median is 0.0129.

The mean rank index value for all farmers was -0.08131, significantly different from

zero with a p val = 0.0174. This suggests that buckwheat is valued toward the middle of

the cropping system for the farmers in the survey. When farmers were split into two

groups by ethnicity, Yi farmers had a significantly higher rank index than non-Yi farmers.

Non-Yi farmers index was -0.1748 while Yi farmers had an index of 0.01311.

Because of the limitations with the rank index mentioned above, an alternative

metric for ranking data was developed based directly on the rank of buckwheat above the

median value, or “ram”. This metric is defined as follows:

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ram = - (Rank of buckwheat -( Total number of crops+1)/2)

This metric ranged from 4 to -7, with a mean of -0.1288 and a median and mode of 0.5.

This metric has the following properties:

1. Between farms with the same number of crops, a higher rank of buckwheat

results in a higher ram value.

2. Between farms with the same rank given to buckwheat, a higher number of

crops planted results in a higher ram value.

3. For farms where buckwheat is ranked above the median total number of

crops, the index value is positive.

4. For farms where buckwheat is ranked below the median total number of

crops, the index value is negative.

5. For farms where buckwheat rank is equal to the median total crop value, the

ram value is equal to zero.

The main limitation of this metric is that the distribution is skewed because farms with

more crops typically rank buckwheat lower. When farmers were split into two groups by

ethnicity, Yi farmers had a significantly higher ram value than non-Yi farmers. Non-Yi

farmers index was -0.520 while Yi farmers had an index of 0.783. Graphical representation

of the distribution of the metrics is shown below.

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Figure 2 Studywide distribution of ranking index

Figure 3 Studywide distribution of ram variable

Single-variable models were fitted using variables of interest.

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Table 1. Variable coefficients for single variable models to tartary buckwheat ranking data

using two different metrics. Single variable models were fitted to explain variance in the

response buckwheat ranking index and the rank over the median value.

Variable

Rank indexRank over

median

Estimat

e

P

value

Estimat

e

P

value

Log of buckwheat land 0.084 0.001*

*0.406 0.000 ***

Yi 0.188 0.005*

*0.783 0.006 **

Square root of household income -0.005 0.003*

*-0.021 0.006 **

Log of farmland 0.146 0.001*

*0.743 0.000 ***

Farmer age -0.001 0.766 0.001 0.927

Residence time -0.002 0.387 -0.007 0.382

Total crops planted -0.027 0.018 * -0.239 0.000 ***

Elevation -0.018 0.031 * 0.001 0.015 *

Number of adults in farm household -0.039 0.100 -0.043 0.670

Proportion female 0.043 0.879 0.400 0.734

Selling tartary buckwheat 0.143 0.104 0.387 0.284

Number of eating uses for buckwheat 0.013 0.403 0.002 0.970

Number of cultural uses for buckwheat 0.067 0.001*

*0.177 0.036 *

Proportion of farmland planted to buckwheat 0.575 0.002*

*1.979 0.013 *

Promotion of crops other than buckwheat-0.215 0.001

*

*-1.219 0.000 ***

Promotion of TBW 0.198 0.045 * 1.000 0.021 *

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In single-variable models the two metrics identified identical significant variables with the

same directionality: total buckwheat land, Yi ethnicity, household income, total farmland,

total crops planted, elevation, cultural uses of buckwheat, proportion of farmland planted

to buckwheat, promotion of TBW and promotion of other crops.

The two indices estimated opposite directional effects for farmer age and elevation.

The selling variable was not significant in either model but positive, suggesting a potential

positive association between selling and ranking buckwheat that would be supported by

theory and the qualitative data. High rank is correlated with more cultural uses, but the

eating uses predictor was found to be non-significant. We interpret this result to indicate

perceived importance is more sensitive to cultural use values than eating values. This is

surprising because eating values correspond more closely to planting trends and planted

buckwheat land.

Distribution of the metrics with respect to variables of interest in the study are

shown below.

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Figure 4 Ranking index and Yi ethnicity

Figure 5 Ranking index and square root of household monthly income

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Figure 6 Ranking index and total crops planted

Figure 7 Ranking index and village elevation

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Figure 8 Ranking index and proportion of land planted to buckwheat

Figure 9 Ranking index and number of cultural uses of buckwheat

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Figure 10 Ranking index and level of access to markets. Ordinal variable 1-4, where

1 is most accessible and 4 is least accessible

Figure 11 Ranking index and promotion of other crops. Binary variable, where 1 is

government promotion of crops other than buckwheat, 0 otherwise.

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Figure 12 Ranking index and promotion of tartary buckwheat (TBW). Binary

variable, where 1 is government promotion of TBW, 0 otherwise.

Figure 13 Ranking index and number of buckwheat varieties planted.

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Figure 14 Ranking index and log of total farmed land (in mu).

Figure 15 Rank over median (RAM) and log of total farmed land (in mu).

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Figure 16 Rank over median (RAM) and number of buckwheat varieties planted.

Figure 17 Rank over median (RAM) and promotion of tartary buckwheat (TBW).

Binary variable, where 1 is government promotion of TBW, 0 otherwise.

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Figure 18 Rank over median (RAM) and promotion of other crops. Binary variable,

where 1 is government promotion of crops other than buckwheat, 0 otherwise.

Figure 19 Rank over median (RAM) and level of access to markets. Ordinal variable

1-4, where 1 is most accessible and 4 is least accessible

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Figure 20 Rank over median (RAM) and village elevation in meters.

Figure 21 Rank over median (RAM) and number of cultural uses

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Figure 22 Rank over median (RAM) and number of crops planted.

Figure 23 Rank over median (RAM) and square root of household monthly income

in Chinese Yuan (RMB).

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Figure 24 Rank over median (RAM) and Yi ethnicity. Binary variable where 1 means

farmer is Yi, 0 otherwise.

We also fit a model using predictors in the trend regression with the ranking

metrics as the response. Both metrics were modeled as continuous variables with a

Gaussian distribution using glm() in R. A model was fit to explain relationships

between model variables and the ranking metrics:

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Table 2. Full model parameters for general linear model of

ranking index and rank over the median.

Variable

Ranking index Rank over

median

Estimate pvalEstimat

epval

(Intercept) 0.251 0.545 2.5640.13

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elev 0.000 0.129 -0.0010.05

4.

totalcrops -0.006 0.646 -0.1850.00

1**

numvar 0.098 0.048 * 0.3190.12

0

log(farmland

)0.092 0.052 . 0.532

0.00

7**

yi1 0.228 0.002 ** 0.8240.00

7**

eating -0.030 0.059 . -0.0870.18

4

culture 0.048 0.040 * 0.1790.06

4.

sqrt(income) -0.005 0.010 * -0.0190.01

1*

access2 -0.114 0.266 -0.9130.03

3*

access3 -0.009 0.931 -0.3000.51

1

access4 -0.303 0.003 ** -1.5520.00

0

**

*

selling1 0.044 0.597 0.2480.46

9

trade1 0.048 0.638 0.031 0.94

19

1

age 0.002 0.393 0.0100.40

7

adulted 0.004 0.755 0.0170.73

1

numadult 0.009 0.696 0.0350.71

0

propfem 0.109 0.658 0.5460.59

4

res.time -0.003 0.212 -0.0070.44

4

SLCP 0.001 0.435 0.0020.53

5

prom.other1 -0.222 0.005 ** -0.8240.01

1*

prom.tbw1 0.227 0.030 * 0.6630.12

4

In general there was good agreement between the models. The significant

predictors in both models were Yi ethnicity, income, prom.other and the 4th level of

access. Significant predictors specific to the ranking index model were numvar,

culture, and prom.tbw. Predictors specific to the rank above the median metric

were totalcrops, total farmland, and the second level of access.

Using the stepwise procedure for model fitting implemented in the step()

function in R the model terms were: prom.other, prom.tbw, yi, culture,

log(farmed.land), sqrt(income)+access+num.var.

Table 3. Reduced model parameters and confidence intervals for general linear model of ranking index.

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Estimate SE 2.50% 97.50% pval

(Intercept) -0.167 0.145 -0.451 0.118 0.253

prom.other1 -0.103 0.073 -0.246 0.041 0.162

prom.tbw1 0.177 0.101 -0.022 0.375 0.083 .

yi1 0.168 0.072 0.028 0.309 0.020 *

culture 0.047 0.021 0.006 0.088 0.025 *

sqrt(hh.monthly.ncome) -0.005 0.002 -0.008 -0.002 0.003 **

log(farmed.land) 0.083 0.044 -0.004 0.170 0.062 .

access2 -0.278 0.102 -0.479 -0.077 0.007 **

access3 -0.207 0.106 -0.415 0.001 0.052 .

access4 -0.483 0.100 -0.678 -0.287 0.000 ***

num.var 0.152 0.050 0.055 0.249 0.002 **

I was also interested in farmers reasons for ranking some crops high and some

crops low. The farmers were asked about the highest ranked crop: what is the

reason it is most highly ranked? (Table 4)

Table 4. Reasons for ranking the highest ranked crops, n = 174

Reason Frequency Proportion of respondents

Income source 108 0.61

Need it for eating 100 0.57

Feed animals 71 0.40

High yield 21 0.12

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Cultural reasons 18 0.10

This gives us insight into the reasons behind farmers decisions. Crops that are

ranked highest are ranked this way because of their economic value for sale, then

for human food, and then for livestock production. (Table 4). Crops ranked lowest

are considered unnecessary (Table 5).

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Table 5. Reasons for ranking the lowest ranked crops, n = 125

Reason Frequency Proportion of respondents

Don't need it very much 70 0.56

Cant' sell/low price 29 0.23

Low yield 27 0.22

Don't need it at all 10 0.08

Table 6. Reasons for ranking buckwheat, n = 116Reason Frequency Proportion of respondents

Feed animals 69 0.59

Need it for eating 46 0.40

Income source 17 0.15

Cultural reasons 16 0.14

Don't need it very much 11 0.09

What qualities of buckwheat affect its perceived importance to growers?

Buckwheat’s reason for being ranked highly most frequently was because of its use as animal feed, than for eating, selling, and then for cultural reasons (Table 6). Over half of the respondents that ranked buckwheat relative high said it was important because of its usefulness as livestock feed. It is perhaps instructive that there are several different reasons that buckwheat is considered important but high yield is not one of them. This is because buckwheat does not have a high yield. The main reasons farmer gave for ranking buckwheat highly were for farmers’ own household demand. The most frequent demand was livestock feed and the next most common demand was for human consumption. A small but important percentage used buckwheat for selling or cultural reasons.

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