Table 2.
GLMM model results showing the relationship between project effectiveness and a variety of biophysical and socio-economic variables.
| Coefficients | p | |
|---|---|---|
| Fixed effects | ||
| (Intercept) | 1.03 | 0.102 |
| temperature | −0.739 | 0.625 |
| precipitation | −3.427 | 0.006** |
| farmland quality index | 1.996 | 0.269 |
| population density | −0.886 | 0.212 |
| GDP | 0.625 | 0.501 |
| road density | −2.262 | 0.003** |
| distance to city | −1.562 | 0.070* |
| elevation | −1.357 | 0.050** |
| slope | −1.911 | 0.059* |
| shape | 1.132 | 0.343 |
| area | 2.034 | 0.037** |
| factor (land exploitation) | −0.180 | 0.483 |
| factor (and land reclamation) | −0.418 | 0.520 |
| Random effects | Variance | Std.Dev. |
| Province (Intercept) | 0.07 | 0.25 |
The dependent variable is binary (code 1 = projects showing significant increasing NDVI trends, code 0 = all the remaining projects).