SAM and the SDGs: Navigating the Tradeoff/Synergy Analysis Application

Kevin Jackson

For those interested in using our SAM-SDG Tradeoff/Synergy Analysis application, I’m including this brief write up on how to navigate the tool and interpret the products. Please read below for additional information on our Tradeoff/Synergy Analysis Application which can be accessed here.

SAM-SDG Tradeoff/Synergy Analysis

Major objectives of this application are to:

  1. Explore the full range of tradeoffs and synergies among SAM indicators and between SAM indicators and SDGs
  2. Understand the socioeconomic and biophysical drivers of these tradeoffs and synergies, &
  3. Inform actions by stakeholders

Some useful definitions include:

Sustainable Agriculture Matrix (SAM) Indicators – metrics developed by Zhang et al., 2021 to monitor Agricultural Sustainability

Sustainable Development Goals (SDGs) – 17 sustainable goals ratified in 2015 by UN member nations


Tradeoff and Synergy Classifications

When classifying relationships between SAM and SDG data we test for monotonicity (spearman’s rank coefficient) and linearity (linear regression analysis). This allows us to determine tradeoffs and synergies and whether conditions are improving or worsening with time  for a given country (Figure 1). For more information on these classifications, scroll to the bottom of the page for more details on methodology.

Figure 1: 6 type of sub classifications for tradeoffs and synergies.


Visualizing Tradeoffs & Synergies by Country: World Maps

One way to visualize these relationships is with our ‘World Map’ feature which allows users to:

  • choose any unique SAM (n = 18) and SDG (n = 113) index pair,
  • display significant occurrences of tradeoff and synergies on the world map, as well as
  • view SAM SDG scores (each standardized on a 0-100 scale) with increasing values corresponding to improving sustainability performance.

Figure 2: the world map feature of this application allows users to see the variety of classifications projected on a world map for user defined unique SAM & SDG index pairs. Users can also select specific countries to see the historical trends of standardized SAM and SDG scores. World Map figure displays the SDG1 (‘Zero Poverty’) overall goal index scores in relation to ‘Agricultural Labor Productivity’, a SAM indicator. Multiline plot displays this data for Pakistan in this example.

Visualizing Correlations by Country Groupings: Stacked Bar Plots & Classification Matrices

In combination, Stacked Bar Plots & Classification Matrices allow us to move beyond individual country’s and consider patterns across country groupings while also maintaining our ability to assess granular information (Figure 3). In short, stacked Bar Plots allow us to see the proportions of observed tradeoffs and synergies across a suite of index pairs and for a given country grouping; and each cell of our classification matrix identifies the dominant relationship for a given index pair and country grouping.

Figure 3: The stacked bar plot (shown on the left) shows proportions of tradeoffs & synergies for SDG `Goal Index Scores` & N Surplus amongst our total country dataset (n = 167). We can see in this case that SDG 13 has the highest proportion of observed synergies with respect to N Surplus reduction and that the sub classification ‘Synergy (worsening)” has a plurality. this plurality is reflected as one cell in the classification matrix (shown on the right) which reflects the dominant relationship type for a given unique SAM-SDG index pair and across a country grouping (groupings are by income and region).








Data Sources & Preparation

SDG Index Scores

  • Collected from the Sustainable Development Report 2022 (
  • The SDG Index Score as well as goal & indicator scores (113 metrics)
  • Data was constrained to the years 2000-2015
  • Index scores, were first established at the indicator level; after rescaling (normalization) of data on 0-100 scale (1- worst, 100 – best), scores for goal index scores & the overall SDG index are calculated as the arithmetic mean of available indicator scores (Sachs et al., 2022).

SAM Index Scores

Counting Tradeoffs & Synergies

Step 1: Testing for Monotonicity (Distinguishing Tradeoffs and Synergies)

Following data preparation/cleaning, we:

  • Tested Spearman’s rank correlation coefficient for each unique index pair (i.e., each SAM Indicator against each SDG Index Score, n =2034) for each country (n = 162).
  • synergies (p < 0.05 and rho > 0)
  • trade-offs (p < 0.05 and rho < 0)
  • insignificant relationships (p > 0.05)
  • no data (less than 3 years of overlapping data)

Step 2: Testing Linearity (Further Sub-Classifying Tradeoffs and Synergies)

To further classify relationships, we tested using linear regression whether both SDG and SAM index scores were improving or worsening over time

  • improving (p < 0.05 and rho > 0)
  • worsening (p < 0.05 and rho < 0)


Disclaimer:  The statements made in the blog posts are the opinions and thoughts of the writers and do not reflect the collective opinion of the Consortium.