Roundwood Report – AgImpacts

Production and consumption

Roundwood is a raw material critical to the availability of paper products, manufactured products such as furniture, and construction materials like particleboard and strandboard. In 2010, global per capita consumption of forestry products had been increasing by one percent each year for the past three decades. As of 2015, planted forests cover 277.9 million hectares globally, a number that is only increasing. Due to the ubiquity and magnitude of this demand, improving industry sustainability is critical.

In 2012 the FAO (Food and Agriculture Organization of the United Nations) found that the top ten producers were Brazil, the USA, China, India, Chile, New Zealand, Australia, South Africa, Thailand, and Indonesia. More recently in 2016, the FAO found that the top five producers of industrial roundwood were the USA, the Russian Federation, China, Canada and Brazil, together accounting for 55% of global production. Due to availability, our dataset currently represents only Europe, South America, and the USA.

Production Systems

Natural systems: natural regeneration, no site preparation, stand establishment or maintenance.

Extensive systems: artificial regeneration through minimal methods such as seed sowing, no fertilization, minimal initial site clearing and pruning.

Intensive systems: artificial regeneration through seedling cultivation and planting, fertilizer application for site preparation and stand maintenance.

Production Stages

Site preparation: site clearing, initial fertilization and herbicide application, etc

Stand establishment and tending: seedling cultivation, planting, pruning, fertilizer, herbicide, and pesticide application, etc

Logging and Hauling: felling, forwarding, transportation to roadside, etc

Infrastructure establishment and maintenance: creating and maintaining roads, building firebreaks, etc

Relationships between environmental indicators

Figure 5.1C. This graph plots acidification potential in kilograms of SO2 equivalents per cubic meter of underbark roundwood against greenhouse gas emissions in kilograms of CO2 equivalents per cubic meter of underbark roundwood. The points are color coded by production system and overall higher GHG emissions were associated with higher acidification potential.

Figure 5.1B. This graph plots water depletion in liters per cubic meter of under bark roundwood against greenhouse gas emissions in kilograms of CO2 equivalents per cubic meter of underbark roundwood. Points are color coded by production system. Overall, higher GHG emissions were associated with higher water depletion.

Figure 5.1D. This graph plots freshwater eutrophication potential in kilograms of phosphate equivalents per cubic meters of under bark roundwood against greenhouse gases in kilograms of CO2 equivalents per cubic meter of under bark roundwood. Points are color coded by production system. Overall, freshwater eutrophication potential was not correlated with GHG emissions.

Figure 5.1: Land use, water depletion, and acidification potential, and freshwater eutrophication plotted against GHG emissions, color coded by production system

Trade-off

(5.1A) While there is significant spread in the data, higher GHG emissions were associated with lower land use. The trend is only apparent overall and isn’t pronounced in any individual production system, suggesting either that more data would result in no correlation or that the driver of the association transcends production system.

Co-benefit

(5.1B) Data on water depletion was relatively scarce, making conclusions necessarily weak. For the available points, higher GHG emissions were associated with higher water depletion, particularly in intensive systems. Datasets that do not report water depletion likely do so because the plantation is rainfed and not irrigated.

(5.1C) In both intensive and extensive production systems, higher GHG emissions were associated with higher acidification potential. This suggests that GHG emissions and acidification potential may share drivers.

No correlation

(5.1D) Excepting outliers, freshwater eutrophication potential was consistently below .05 kg PO4,3- eq regardless of production system and regardless of increasing GHG emissions. This suggests that the drivers of freshwater eutrophication potential transcend system type and are separate from the drivers of GHG emissions. If this is the case, efforts to reduce GHG emissions do not have a predictable effect on freshwater eutrophication.

Important considerations for production

Logging and hauling stage is highest impact across indicators

Figure 5.2A. This graph plots percent contribution of each production stage to GHG emissions against the total amount of GHG emissions in kilograms of CO2 equivalents per cubic meter of under bark roundwood. Points are color coded by production stage. Overall, the logging and hauling stage has the largest percent contribution, followed by stand establishment and tending.

Figure 5.2C. This graph plots percent contribution of production stages to freshwater eutrophication potential against the total eutrophication potential in kilograms of phosphate equivalents per cubic meter of roundwood. Points are color coded by production stage. The logging and hauling stage has a very high contribution alongside site preparation.

Figure 5.2B. This graph plots the percent contribution of production stages to acidification potential against the total acidification potential in kilograms of SO2 equivalents per cubic meter of under bark roundwood. Points are color coded by production system. Overall, the logging and hauling stage has the highest percent contribution followed by the stand establishment and tending stage.

Figure 5.2: Percent contributions of production stages to impact of each indicator plotted against total impact, color coded by production stage

The logging and hauling stage has the highest impact on GHG emissions (5.2A) and acidification potential (5.2B), and is one of the highest impacts on freshwater eutrophication (5.2C), making it a significant target for impact reduction. While being the stage with the highest impact, it also displayed extreme variability, indicating significant room for industry improvement.

Rotation length is a weak predictor of impact

Figure 5.3A. This graph plots GHG emissions in kilograms of CO2 equivalents per cubic meter of under bark roundwood against the rotation length of production in years. Points are color coded by production system. There is significant spread, with lower GHG emissions being weakly associated with longer rotation length.

Figure 5.3C. This graph plots freshwater eutrophication potential in kilograms of phosphate equivalents per cubic meter of under bark roundwood against rotation length in years, color coded by production system. Rotation length was not associated with freshwater eutrophication potential.

Figure 5.3B. This graph plots acidification potential in kilograms of SO2 equivalents per cubic meter of under bark roundwood against the rotation length of production in years, color coded by production system. Longer rotation length was weakly associated with lower acidification potential.

Figure 5.3: Rotation length plotted against GHG emissions, acidification potential, and freshwater eutrophication potential, color coded by production system

While shorter rotation length was weakly associated with lower impact on GHG emissions (5.3A) and acidification potential (5.3B), many short rotation plantations had very low impact in these areas, indicating that improving sustainability may not require focus on rotation length. Rotation length had no association with freshwater eutrophication potential (5.3C).

Tree species is not an effective predictor of impact

Figure 5.4A. This graph plots GHG emissions in kilograms of CO2 equivalents per cubic meter of under bark roundwood, color coded by tree species (pine, poplar, maritime pine, and eucalyptus). Most tree species have less than five points of data, and those with more have large spread. GHG emissions were not associated with tree species.

Figure 5.4C. This graph plots freshwater eutrophication potential in kilograms of phosphate equivalents per cubic meter of under bark roundwood, color coded by tree species (pine, poplar, maritime pine, eucalyptus). Freshwater eutrophication potential was not associated with tree species.

Figure 5.4B. This graph plots acidification potential in kilograms of SO2 equivalents per cubic meter of under bark roundwood, color coded by tree species (pine, poplar, maritime pine, eucalyptus). Eucalyptus had comparatively high impact but also very high spread. Other tree species have few data points and little clustering. Acidification potential was not highly associated with tree species.

Figure 5.4D. This graph plots land use in hectare years per cubic meter of under bark roundwood, color coded by tree species (pine, poplar, maritime pine, eucalyptus). Individual tree species had relatively few data points. Overall, land use was not associated with tree species.

Figure 5.4: Rotation length plotted against GHG emissions, acidification potential, freshwater eutrophication potential, and land use, color coded by production system.

Impact was highly variable within and between tree species in GHG emissions, freshwater eutrophication, and land use (5.4A, 5.4C, 5.4D). Eucalyptus appears to be associated with higher acidification potential (5.4B) than the other analyzed tree species, but lack of data and outliers make it difficult to draw conclusions. Overall this indicates that tree species may not be a critical consideration to sustainability.

Future Steps

Next steps could involve investigating the relationship between acidification potential and GHG emissions using an analysis of machinery usage and fuel type. Alternatively, future inquiries could focus on deepening our analysis of land use since land conversion is one of the main drivers of environmental degradation caused by the forestry industry in Southeast Asia, where much of the world’s roundwood is sourced. Data on land conversion, soil carbon, and abiotic depletion could be procured for this reason. Finally, future work could involve building on our dataset to make it more representative of the major producers of roundwood, particularly China, India, Thailand and Indonesia.