Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa
Abstract
1. Introduction
2. Research Methodology
2.1. Description of the Study Areas
2.2. Research Design
2.2.1. Sampling Method (s) and Sample Size
- Ga-Makanye (n) =
- Gabaza (n) =
- Giyani (n) =
2.2.2. Data Collection
2.2.3. Model Specification
- is considered the variable that explains the decision to adopt CSA by smallholder maize farmers;
- is the variable that is observed adoption decision and takes the value of 1 if the smallholder farmer is willing to adopt at least three CSA practices; it is 0 if otherwise;
- is a dormant variable used to describe the decision on factors affecting the adoption of CSA practices;
- is observable variable of adoption measured as the number of CSA practices to adopt;
- C and X gives the direction for independent variables for the decision to adopt;
- and are the parameters to be estimated.
2.2.4. Analytical Techniques
Descriptive Statistics
Double-Hurdle Regression Model
Contingent Valuation Method
3. Results
3.1. Descriptive Results
3.1.1. Smallholder Maize Farmers’ Willingness to Adopt CSA in Ga-Makanye, Gabaza, and Giyani
3.1.2. Measures of Dispersion of the Sampled Smallholder Maize Farmers in Ga-Makanye, Gabaza, and Giyani
3.2. Econometric Results
3.2.1. Test for Multicollinearity
3.2.2. First Hurdle: Probit Regression Model of Results of Sampled Smallholder Maize Farmers in Ga-Makanye, Gabaza, and Giyani (n = 209)
3.2.3. Second Hurdle: Probit Regression Model of Results of Sampled Smallholder Maize Farmers in Ga-Makanye, Gabaza, and Giyani (n = 209)
4. Discussion
5. Conclusions and Recommendations
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dependent Variable | Description and Unit of Measurement | ||
---|---|---|---|
Willingness to adopt CSA | WTA*i | Binary: 1 = farmer is willing to adopt climate-smart agriculture 0 = otherwise | |
Variable label | Variable type | Description | Expected sign |
Farm size (FS) | Continuous | Size of the farm in hectares | +/- |
Educational level (EL) | Continuous | Number of years spend in school | + |
Gender (GND) | Dummy | 1 = if the farmer is a female, 0 = otherwise | + |
Age (AG) | Continuous | Age of the farmers in years | +/- |
Agricultural experience (AE) | Continuous | Number of years practicing agriculture | +/- |
Household size (HS) | Continuous | Number of household members | +/- |
Income diversification (ID) | Dummy | 1 = farmer diversify their level of income, 0 = otherwise | + |
Crop diversification (CD) | Dummy | 1 = farmer diversify their crop production, 0 = otherwise | + |
Access to extension services (AES) | Dummy | 1 = farmer has access to extension services, 0 = otherwise | + |
Information about climate-smart agriculture (ICSA) | Dummy | 1 = farmer has access to information, 0 = otherwise | + |
Exposure of the farm to climate risks (E) | Dummy | 1 = farmer is exposed to climate risks, 0 = otherwise | + |
Sensitivity to climate risks (S) | Dummy | 1 = farmer is sensitive to climate risks, 0 = otherwise | +/- |
Insurance (IS) | Dummy | 1 = farmer has insurance, 0 = otherwise | - |
Cooperative membership (CM) | Dummy | 1 = farmer is cooperative member, 0 = otherwise | +/- |
Socioeconomic Variable | Ga-Makanye (%) | Gabaza (%) | Giyani (%) |
---|---|---|---|
Gender | |||
Female | 50 | 77 | 70.8 |
Male | 50 | 23 | 29.2 |
Educational level | |||
No education | 15.4 | 33.3 | 42.7 |
Primary | 26.9 | 24.1 | 35.4 |
Secondary | 42.3 | 27.6 | 11.5 |
Tertiary | 15.4 | 14.9 | 10.4 |
Access to extension services | 59.3 | 66.7 | 49 |
Access to Information about CSA | 50 | 44.8 | 45.8 |
Exposure to climate risks | 85 | 86 | 85 |
Sensitivity to climate risks | 73 | 63 | 67 |
Socioeconomic Variable | Mean | St. Deviation | Min | Max | t-Test (Sig. 2-Tailed) |
---|---|---|---|---|---|
Age (years) | 60 | 18.57 | 21 | 83 | 51.7 ** |
Experience (years) | 24 | 20.59 | 3 | 70 | 78.9 ** |
Household size (per head) | 5 | 2.21 | 2 | 11 | 93.2 ** |
Farm size (hectares) | 4 | 4.63 | 0, 50 | 19 | 60.7 ** |
Socioeconomic Variable | Mean | St. Deviation | Min | Max | t-Test (Sig. 2-Tailed) |
---|---|---|---|---|---|
Age (years) | 67 | 14.75 | 23 | 94 | 37.9 ** |
Experience (years) | 25 | 19.57 | 1 | 75 | 16.2 ** |
Household size (per head) | 5 | 3.04 | 1 | 14 | 28.5 ** |
Farm size (hectares) | 2 | 1.20 | 0.25 | 8 | 60.3 ** |
Socioeconomic Variable | Mean | St. Deviation | Min | Max | t-Test (Sig. 2-Tailed) |
---|---|---|---|---|---|
Age (years) | 64 | 13.75 | 30 | 85 | 17.0 ** |
Experience (years) | 27 | 16.04 | 12 | 50 | 95.9 ** |
Household size (per head) | 6 | 2.37 | 0 | 12 | 3.2 ** |
Farm size (hectares) | 2 | 1.99 | 0.25 | 12 | 78.7 ** |
Explanatory Variables | Collinearity Statistics | |
---|---|---|
VIF | 1/VIF | |
Farm size (in hectares) | 1.097 | 0.911 |
Educational level | 1.805 | 0.554 |
Gender of a maize farmer | 1.069 | 0.935 |
Agricultural experience | 1.900 | 0.526 |
Household size | 1.058 | 0.945 |
Income diversification | 1.332 | 0.750 |
Crop diversification | 1.200 | 0.833 |
Access to extension services | 1.169 | 0.855 |
Information about CSA | 1.201 | 0.833 |
Exposure to climate risks | 1.263 | 0.792 |
Sensitivity to climate risks | 1.335 | 0.749 |
Farmers’ cooperative membership | 1.033 | 0.968 |
Mean VIF | 1.2885 |
Coef. | Std. Err. | Z | p ≤ z | |
---|---|---|---|---|
Farmers’ characteristics | ||||
Constant | 0.3029 | 0.7824 | 0.39 | 0.700 |
Farm size (FS) | 0.0038 | 0.0504 | 0.07 | 0.940 |
Education (EL) | 0.2961 ** | 0.1365 | 2.17 | 0.030 |
Gender (GND) | 0.0518 | 0.2358 | 0.22 | 0.826 |
Age (AGE) | −0.0009 | 0.0099 | −0.09 | 0.928 |
Agricultural Experience (AE) | −0.1621 ** | 0.0072 | 2.26 | 0.024 |
Household size (HS) | −0.0726 ** | 0.0378 | −1.92 | 0.055 |
Vulnerability indicators | ||||
Exposure to climate risks (E) | 0.4800 | 0.3087 | 1.55 | 0.120 |
Sensitivity to climate risks (S) | −0.1833 | 0.2387 | −0.77 | 0.442 |
Factors influencing Willingness to adopt Climate-Smart Agriculture | ||||
Income diversification (ID) | 0.2923 | 0.2363 | 1.24 | 0.216 |
Crop diversification (CD) | 0.4276 ** | 0.2231 | 1.92 | 0.055 |
Access to extension services (AES) | −0.2294 | 0.2167 | −1.06 | 0.290 |
Information about CSA (ICSA) | 0.5034 ** | 0.2199 | 2.29 | 0.022 |
Cooperative membership (CM) | −0.1346 | 0.2602 | −0.52 | 0.605 |
Number of observations = 209 | ||||
Log Likelihood −105.66451 Likelihood Ratio Chi2 (13) = 55.71 Chi square (p) = <0.001 *** |
Parameter. | Coef. | Std. Err. | T | p > |t| | |
---|---|---|---|---|---|
Farmers’ characteristics | |||||
Constant | 1.0396 | 0.6622 | 1.57 | 0.118 | |
Farm size (FS) | 0.0022 | 0.0428 | 0.05 | 0.959 | |
Educational Level (EL) | 0.2816 ** | 0.1191 | 2.36 | 0.019 | |
Gender (GND) | 0.0421 | 0.1956 | 0.21 | 0.830 | |
Age (AGE) | 0.0004 | 0.0085 | 0.06 | 0.956 | |
Agricultural Experience (AE) | −0.0134 ** | 0.0061 | −2.21 | 0.029 | |
Household size (HS) | −0.0061 ** | 0.0309 | −1.95 | 0.052 | |
Vulnerability indicators | |||||
Exposure to climate risks (E) | 0.4047 | 0.2611 | 1.55 | 0.123 | |
Sensitivity to climate risks (S) | −0.1463 | 0.2051 | −0.76 | 0.476 | |
Factors influencing Willingness to adopt Climate-Smart Agriculture | |||||
Income diversification (ID) | 0.2630 | 0.2003 | 1.31 | 0.191 | |
Crop diversification (CD) | 0.3881 ** | 0.1866 | 2.08 | 0.039 | |
Access to extension services (AES) | −0.1846 | 0.1806 | −1.02 | 0.308 | |
Information about CSA (ICSA) | 0.4355 ** | 0.1888 | 2.31 | 0.022 | |
Number of observations = 209 | |||||
Pearson Goodness-of-Fit Test Likelihood Ratio Chi-Square (12) | Chi- Square | Log Likelihood | Sig. | ||
57.28 | −161.172 | <0.001 *** |
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Machete, K.C.; Senyolo, M.P.; Gidi, L.S. Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa. Climate 2024, 12, 74. https://doi.org/10.3390/cli12050074
Machete KC, Senyolo MP, Gidi LS. Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa. Climate. 2024; 12(5):74. https://doi.org/10.3390/cli12050074
Chicago/Turabian StyleMachete, Koketso Cathrine, Mmapatla Precious Senyolo, and Lungile Sivuyile Gidi. 2024. "Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa" Climate 12, no. 5: 74. https://doi.org/10.3390/cli12050074
APA StyleMachete, K. C., Senyolo, M. P., & Gidi, L. S. (2024). Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa. Climate, 12(5), 74. https://doi.org/10.3390/cli12050074