LandGriffon in action: an example case study.

LandGriffon helps companies strategize the sustainable transformation of their supply chains by using technology, data and scientific-based analysis to manage environmental impacts. This blog is part of our methodology series. You can find more blogs on our methodology here, or head to our website and download the complete paper.

As LandGriffon requires company procurement data that is typically closely guarded, we provide an example analysis of the impact of hypothetical sourcing of 1000 tonnes of palm oil in Aceh, Indonesia, with different levels of spatial sourcing precision and exploration of scenarios.

Ingestion of company data

Supply chain data information regarding purchasing commodities for a given company needs to be ingested into the LandGriffon platform as an initial step. This information is ingested using a template spreadsheet directly through the platform.

Ingestion of company data into LandGriffon.

During this preparation, as a minimum requirement, we need to provide the yearly purchased supply chain volumes for any given commodity. Additionally, the spreadsheet can also incorporate information regarding business units, suppliers, and location types.

We understand that the level of information regarding the supply chain commodities can vary significantly across companies, tiers, or business units, so the data ingestion in LandGriffon v0.1 is purposely designed to fit these discrepancies using the different location types. While populating this information, we can also help identify any third-party data sources that can enrich the company’s supply chain profile.

Example of the spreadsheet template for supplier data ingestion. The basic information that needs to be provided is the material and volume purchased. The user can also provide information regarding the sourcing location.

Data is validated during ingestion (see Annex 2 in the full methodology), and locations are geolocated. The outputs of the ingestion are visible at all times in the LandGriffon platform under the admin tab. This allows quick exploration and editing through the user interface of the company data ingested.

Example of ingested data in the LandGriffon platform.

Impact calculation

The impact associated with a particular indicator and the company supply chain data is calculated during the ingestion process.

To calculate indicators we follow different approaches depending on the location type, as explained in previous blogs (see Modeling spatial sourcing and Impact indicator calculation). In this section, we aim to represent how impact estimates may vary depending on the location type by selecting a use case in Aceh, Indonesia.

For this use case, we are considering that a company is buying 1000 tonnes of palm oil in Aceh, Indonesia in a) a geolocated point of production; b) an aggregation point using the same coordinates (using a 50km buffer); and, c) an administrative area (Aceh, Indonesia).

We compute land use (farm level indicator) and deforestation risk (landscape-level indicator) as indicators and use data of the 2021 forest loss in Indonesia (Satelligence) and 2010 palm oil production and harvest area from MapsSPAM.

Palm oil production (t) and palm oil harvest area (ha) in Aceh, Indonesia. Data based on MapSPAM.

Deforestation (ha) that is taking place in Aceh, Indonesia in 2021 based on Satelligence dataset.

The land use indicator indicates the total land area required to produce 1000 tonnes of palm oil in each location type. The impact factor is the average impact in each pixel within the sourcing geometry, weighted by the production in each pixel.

Deforestation risk is classified as a landscape-level indicator, therefore, we follow the methodology previously presented in the Landscape-level impacts section to account for land expansion. For landscape-level impacts, the production map is buffered using a radius kernel before using it as the weighted layer to capture areas near production regions. Using the buffered production map, the impact factor is calculated as the production-weighted average within the sourcing geometry.

50 km2 buffer kernel applied to the deforestation raster.

The table below summarizes the results for the land use and deforestation risk calculations for each location type:

LandGriffon v0.1 assumes that the impact is distributed across the areas of commodity production. So, a higher probability of impact is associated with areas of high production and vice versa. The image below shows the result of distributing the impact calculated previously:

Distribution of deforestation risk (ha) impact across the location types based on palm oil production. Higher impact is associated with higher palm oil production.

The impact indicators in table 1 show that depending on the precision of the sourcing information, the estimate of farm-level land area used to produce the 1000 tonnes of palm oil sourced varies from 63 ha for a point of production location to 87 ha if the sourcing was only known to come from the Aceh region. For deforestation, the example results show that there is a risk that this sourcing contributed to 0.082 ha of deforestation, when the point of production was known, 0.063 ha for the example supplier aggregation point or 0.084 ha when sourcing was only resolved to the Aceh region.

Across the Aceh landscape, each points impact ranges from 31 ha/1000 tonnes to 1000 ha/1000 tonnes. Comparing these point of production estimates for land impact to those for the administrative region (87 ha/1000 tonnes) demonstrates the degree of over or underestimation that could result from lower accuracy supply chain data. Using estimates for the administrative region could overestimate the farm-level land use by 280% if the palm oil was actually produced from the most productive locations in Aceh. Meanwhile if the palm oil was produced in the least productive location, the regional estimate would represent only 8.7% of the point of production land area.

Scenario analysis

After ingesting the data and calculating the impacts, the user can also explore mitigation of impacts through scenario planning directly through LandGriffon. This aligns with the prepare to respond element of the LEAP approach proposed by the Taskforce for Nature Related Financial Disclosure’s beta framework (TNFD 2022).

To create a scenario the user needs to set the company’s forecasted growth rates and add the impact mitigation actions that could be implemented. Mitigation actions are added through the creation of interventions.\

LandGriffon platform showing a set of scenarios created with the associated interventions.

In this example we create a scenario with a single intervention to explore how changing the volume of palm oil purchased in a supplier’s location may reduce impacts given a default growth rate of 1.5% per year.

Creation of a scenario with the default linear growth rate of 1.5% for the entire company and no interventions.

We apply the intervention to 50% of the total volume purchased by the company in an aggregation point location in Aceh, Indonesia. See the picture below for the information selected to apply the intervention.

Initial filtering in the creation of an intervention. The image shows the selection of the 50% of total volume purchased of palm oil and its fractions for the entire company, as a business unit. The region selected is Aceh, Indonesia and the supplier selected is Bahari Dwikencana Lestari. The year of implementation is 2020.

To this first selection, the user is able to apply different types of interventions. We describe the results of different types of interventions in the following sections.

Switch to a new material

We apply an intervention to change to a new material to evaluate the effect of a change of the recipe, which will result in the change of commodity composition in the company’s supply chain.

In this intervention, the user has to specify the material they want to change the initial selection to and the new location where they will source this new material. The location can also be the same as the initial selection. Additionally, the user can select a new supplier, if needed, or provide custom impact factors for the new material to compute the calculations.

Change product formula by replacing the 50% of palm oil with rape seed oil, sourced from the United Kingdom.

Source from a new supplier or location

An alternative intervention is related to sourcing the same commodity from another producer with a lower environmental footprint. In this particular case we add an intervention for sourcing 50% of the palm oil purchased from a supplier in Aceh, Indonesia, from a different supplier located in the same region. To this end we need to add the location of the new supplier. The user can again provide custom impact factors instead of the LandGriffon default estimates to compute the impacts.

Source from a new supplier or location by moving the 50% of the palm oil purchased volume to a different supplier in Aceh, Indonesia.

Change production efficiency

As another alternative, LandGriffon also evaluates an intervention option for examining how impacts may be reduced and yield can increase by working with farmers. The user can test this by changing production efficiency. In this intervention, we need to set the impact factor for each indicator that we want to recompute.

Change production efficiency for the 50% volume of palm oil purchased from a location in Aceh, Indonesia.

Scenario outputs and data comparison

Once we have compiled a scenario to be evaluated, consisting of a set of intervention actions, we can save the scenario and analyze the results. Similar to the analysis performed with the company’s data during the ingestion process, the various interventions are analyzed and the output is presented in a table, chart and map view, showing the impacts estimated in each scenario.

The interventions can be compared against the original data ingested by the company. This in turn can be compared against company targets to check how measures can mitigate environmental impacts and help construct a pathway of actions to achieve sustainability goals.

Table view of the various impacts of scenarios each composed of a set of interventions.

Table view of impact comparison between actual data and a test scenario. Numbers in red show an increase in impact while numbers in green show reduction in the impact produced.

Chart view of the comparison produced between the actual data (area chart in green), a test scenario (line in dark green) and a company’s target (dotted line) for each selected indicator.

To learn more about the science and technology of LandGriffon, you can download the full methodology from our website.

Interested in what LandGriffon could do for your company?

Contact us now at hello@landgriffon.com

LandGriffon is developed by Satelligence and Vizzuality, and advised by the Stockholm Environment Institute and their Trase Initiative.

Thank you Mike Harfoot, Elena Palao, Francis Gassert and Rens Masselink for preparing the methodology.