Q1: Do inherent and farmer-modified attributes of coffee agroecosystems appear to have an effect on properties associated with soil health?

Method of Analysis

To explore the covariance of within the groups of variables (independent and dependent variables), below we use: /n - Correlation matrices of continuous variables

Factors of Soil Health (Independent Variables)

Correlation matrices show relationships at p <0.01 Key: Green = positive relationship, Red = negative relationship

Factors of Soil Health (Independent Variables)

Probability of observing erosion based on the other factors

Here we use a multinomial logistic regression to understand the influence of the other factors on the probability of observing erosion within a study block.

Since not all dependent variables were collected at each site, below there are correlation matrices that relate to differently-sized datasets depending on the completeness of the data set. The n for these datasets is described in the table below.

Core Soil Dependent Variables

Correlation matrices show relationships at p <0.01

Core Soil Dependent Variables Plus Bulk Density and Aggregate Stability

Correlation matrices show relationships at p <0.01

Core Soil Dependent Variables Plus Bulk Density, Aggregate Stability and Macrofauna

Correlation matrices show relationships at p <0.05

Simple correlations between continuous IVs and DVs

Showing results significant at p-value <0.01

Q1: To what extent do attributes of soil health correspond to yields within coffee plots when controling for other factors?

Descriptives

Yield by Community