Biodegradable mulch films (BDMs) technology is an environmentally-friendly substitute to traditional plastic mulch films in agricultural production. Given the high price and it is new to the market, it is not easy for farmers to accept and adopt it. This paper aims to explore the key factors affecting farmers’ adoption of and willingness to pay for BDMs to understand the complex process of farmers’ decision-making. This paper employs a double hurdle model to explore the multi-stage decision-making process in the adoption of BDMs using the sample of 1247 observations from Yunnan province China, where two mechanisms of decision-making (i.e., direct rejection of technology and lack of resources) were used to capture zero willingness to pay (WTP) for BDMs. The results indicate the two-stage decision-making process, where the role of technology-specific characteristics is more important than adopter-specific characteristics in the adoption of BDMs in China – training for understanding and using the technology has a positive effect on both the adoption and willingness to pay. The paper is the first attempt that empirically analyses the determinants of farmers’ WTP for BDMs. It contributes to the literature on adoption analysis by 1) considering farmers’ adoption choices as a two-step process by using a hurdle model and 2) addressing the importance of technology-specific characteristics on farmers’ WTP for BDMs. Understanding the role of factors on different stages of farmers’ decision-making could assist policymakers in designing programs, specifically tackling difficulties confronting farmers at different stages of decision-making.
Research Article
Factors affecting Farmers' Adoption of and Willingness to Pay for Biodegradable Mulch Films in China
https://doi.org/10.21203/rs.3.rs-1432510/v1
This work is licensed under a CC BY 4.0 License
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double hurdle model
biodegradable mulch film
farmer
China
WTP
Table.1 descriptions and descriptive statistics of the variables
|
Variables |
Description |
Mean |
S.D. |
|
Outcome variable |
|
|
|
|
Adoption |
Farmers' choices of adopting degradable mulch films, =1 to adopt, = 0 not to adopt. |
0.83 |
0.38 |
|
WTP |
Farmers' willingness to pay for degradable mulch films in China yuan. |
13.7 |
8.3 |
|
Independent variablea |
|
|
|
|
Age |
Age of the farmer (owner/ manager) in yearsb. |
47.6 |
10.03 |
|
Male |
Dummy variable representing gender =1 Male, = 0 Female. |
0.75 |
0.43 |
|
Ethnicity |
Dummy variable =1 Han, = 0 Others. |
0.77 |
0.42 |
|
Education |
The education level defined as schooling years. |
6.84 |
3.54 |
|
Labour |
The number of labor employed by the farm. |
2.61 |
1.05 |
|
Farm type |
Dummy variable, =1 Small farm, =0 Others. |
0.95 |
0.22 |
|
Farm size |
Total planting area measured in Mu. |
18.29 |
32.27 |
|
Household income |
Total household income from all source measures in Chinese yuan (CNY). |
63252.7 |
245753 |
|
% Agricultural Income |
Percentage of household income from agriculture. |
79 |
39 |
|
Main crop |
Categorical variables representing the main crops planted. |
|
|
|
Food crop |
=1 if mainly planting food crops, such as rice and wheat, =0 otherwise (set as the base). |
0.21 |
0.42 |
|
Tobacco |
=1 if mainly planting tobacco, =0 otherwise. |
0.38 |
0.41 |
|
Fruit & vegetable |
=1 if mainly planting fruits and vegetables, =0 otherwise. |
0.37 |
0.28 |
|
Other crops |
=1 if mainly planting other crops, such as flowers and trees, =0 otherwise. |
0.04 |
0.11 |
|
Mulch usage |
Categorical variables representing changes in PE film usage in the past five years, |
|
|
|
Mulch usage – decreased |
=1, if the usage of PE films has decreased in the past five years, =0 otherwise (set as the base). |
0.11 |
0.32 |
|
Mulch usage – same
|
=1, if the usage of PE films has had no change in the past five years, =0 otherwise. |
0.61 |
0.60 |
|
Mulch usage – increased |
=1, if the usage of PE films has increased in the past five years, =0 otherwise. |
0.28 |
0.42 |
|
Recycle subsidy |
The farmer receives subsidies for recycling plastic mulch films, =1 Yes, = 0 No. |
0.34 |
0.47 |
|
Training |
Farmer received training for film mulching technology, =1 Yes, = 0 No. |
0.39 |
0.49 |
|
BDMs attributes |
Categorical variables representing the most important attribute that affect farmers’ choice of different plastic mulch films. |
|
|
|
BDMs – Price |
=1 price is regarded as the most important attribute for purchasing plastic mulch films, = 0 otherwise (set as the base). |
0.23 |
0.42 |
|
BDMs – Efficiency |
=1 efficiency is regarded as the most important attribute for purchasing plastic mulch films, = 0 otherwise. |
0.58 |
0.49 |
|
BDMs – Brand |
=1 brand is regarded as the most important attribute for purchasing plastic mulch films, = 0 otherwise. |
0.12 |
0.40 |
|
BDMs – others |
=1 other attributes, such as quality, is regarded as the most important attribute for purchasing plastic mulch films, = 0 otherwise. |
0.07 |
0.24 |
|
BDMs Promotion |
The farmer is aware of promotions for adopting BDMs from the government and/ or industry, 1=Yes, 0=No. |
0.21 |
0.41 |
|
Damage to environment |
Perceived negative impact of PE mulch films to the environment, =1 Yes and = 0 No. |
0.79 |
0.41 |
|
Usefulness to agriculture |
Categorical variables representing farmers' perception of the usefulness of plastic mulch film to agricultural production. |
|
|
|
Usefulness – not important |
=1, plastic mulch film is regarded to be not important to agricultural production, =0 otherwise (set as the base) |
0.04 |
0.23 |
|
Usefulness – important |
=1, plastic mulch film is regarded to be important to agricultural production, =0 otherwise. |
0.32 |
0.46 |
|
Usefulness – very important |
=1, plastic mulch film is regarded to be very important to agricultural production, =0 otherwise. |
0.64 |
0.56 |
Note: a. county effects are controlled and included as categorical variables (10 countries in total); b. the variables of farmer characteristics are about the owner (for small scale farms) or manager (for commercial farms) who make the primary decision-making of agricultural production of the farm.
Table 2: Double-hurdle and Tobit model estimation results of factors influencing WTP for BMDs
|
Factorsb |
Double-hundle modela |
Tobit model |
|
|
WTA (Probit estimator n=1247) |
WTP (Truncated Normal estimator n=1052) |
WTP (n=1247) |
|
|
Intercept |
3.07***(0.73) |
0.94***(0.10) |
2.61*** (0.47) |
|
Age |
0.0002(0.005) |
0.0001(0.001) |
0.001(0.004) |
|
Gender |
0.04(0.11) |
-0.01(0.03) |
-0.07(0.08) |
|
Ethnicity |
-0.14(0.11) |
-0.06(0.07) |
-0.12(0.11) |
|
Education |
0.03**(0.014) |
0.01***(0.003) |
0.03*** (0.01) |
|
Labour |
0.04(0.04) |
0.01(0.01) |
0.03(0.03) |
|
Farm type |
-0.158(0.25) |
-0.10(0.06) |
-0.14(0.18) |
|
Farm size |
0.003(0.002) |
0.001(0.0004) |
0.01(0.001) |
|
Log (Household income) |
0.13***(0.05) |
0.0001(0.0005) |
0.04(0.04) |
|
% Agricultural Income |
0.23*(0.09) |
0.01(0.03) |
0.12(0.08) |
|
Main crop - Tobacco |
-0.47***(0.12) |
-0.04(0.03) |
-0.41***(0.09) |
|
Main crop - Fruit & vegetable |
0.21**(0.08) |
0.02(0.13) |
0.25**(0.09) |
|
Main crop - others |
-0.26*(0.11) |
0.01(0.24) |
-0.34*(0.15) |
|
Mulch usage – same |
0.25(0.16) |
0.03(0.04) |
-0.16(0.12) |
|
Mulch usage – increase |
0.49**(0.18) |
0.09**(0.04) |
-0.25*(0.13) |
|
Recycle subsidy |
-0.05(0.12) |
-0.04(0.03) |
0.03(0.09) |
|
Training |
0.55***(0.11) |
0.05***(0.01) |
0.37***(0.08) |
|
Damage to environment |
0.27*(0.12) |
-0.02(0.03) |
0.15(0.09) |
|
Usefulness – important |
1.25***(0.47) |
0.16***(0.06) |
0.32*(0.19) |
|
Usefulness – very important |
1.34***(0.47) |
0.13**(0.06) |
0.37*(0.19) |
|
BDMs Promotion |
-0.02(0.11) |
-0.02(0.03) |
-0.08(0.08) |
|
BMDs attributes - Efficiency |
- |
-0.08***(0.02) |
-0.05***(0.01) |
|
BMDs attributes - Brand |
- |
-0.001(0.02) |
-0.02(0.08) |
|
BMDs attributes - others |
- |
-0.13***(0.03) |
-0.03(0.09) |
|
|
0.84***(0.03) |
- |
|
|
Loglike |
-734.43 |
-1860.9 |
|
|
AIC |
-754.91 |
7644.48 |
|
|
BIC |
-395.91 |
7767.56 |
|
Note: a this is the double-hurdle model assuming the errors of two equations are correlated, i.e., 
; b.Standard error in parathethese and *p<0.1; **p<0.05; ***p<0.01; c. the variables of BDMs attributes are only included in the second hurdle of the double hurdle model.
Table 3. Predicted WTA and WTP by types of main crops
|
|
WTA (probability) |
WTP (per kg BDMs) |
||
|
|
||||
|
Food crop |
0.87 |
11.83 |
||
|
Tobacco |
0.78 |
10.26 |
||
|
Fruit & vegetable |
0.89 |
14.42 |
||
|
Other crops |
0.76 |
11.33 |
||
No competing interests reported.