Back

Top dressed biochar increases tree seedling growth and decreases sodium leaching

Wagner, B.; Salisbury, A.; Midgley, M. G.

2023-08-05 plant biology
10.1101/2023.08.03.551785 bioRxiv
Show abstract

De-icing salts on roadways are nearly ubiquitous in northern cities during winter months, leading to contamination of soils adjacent to roadways. Sodium chloride salts often have detrimental impacts on water and trees, though some species are more sensitive than others. Biochar has the potential to mitigate sodiums harmful effects due to its large surface area:volume ratio and subsequent ability to sorb ions from solution. We conducted a four-month greenhouse experiment to test if biochar applied as either a top dressing or incorporated into the growing medium reduced sodium leaching and buffered tree responses to sodium stress. We also evaluated the effects of salt addition and biochar on four tree species that vary in salt tolerance: Catalpa speciosa (tolerant), Gleditsia triacanthos (tolerant), Acer saccharum (intolerant), and Quercus rubra (intolerant). We found no interactive effects of sodium addition and biochar on sodium leaching or tree growth and physiology. However, we did find that top dressed biochar broadly decreased sodium leaching, likely via positive effects of top dressed biochar on tree seedling growth, Catalpa speciosa in particular. Incorporated biochar, on the other hand, had positive or neutral effects on sodium leaching and negative effects on the production of new shoots and fine roots. Given that biochar is a relatively expensive amendment, it should be used sparingly to improve urban tree growth and health. Overall, this study shows that biochar application decisions have implications for tree growth and soil management.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.