Login / Signup

The status of phosphorus levels in Iranian agricultural soils - a systematic review and meta-analysis.

Mohsen JalaliWolfram BussFatemeh ParvizniaMahdi Jalali
Published in: Environmental monitoring and assessment (2023)
Phosphorus (P) inputs are essential for maximizing agronomic potential, yet high P inputs and subsequent P losses can cause eutrophication of water bodies. There is a need to evaluate P contents in agricultural soils globally both from an agronomic and environmental perspective. This systematic review and meta-analysis estimated the pooled mean levels of P contents of Iran. In this study, data on available and total P contents of Iran's calcareous soils was compiled (main focus on Olsen P) and compared to (i) estimated Iranian background and global agricultural soil P contents, and (ii) agronomic and (iii) environmentally critical Olsen P values. The pooled mean estimate from the meta-analysis indicates that the levels of Olsen P across 425 soil samples (27 studies) were 21.3 mg kg -1 and total P across 190 soil samples (12 studies) 805.5 mg kg -1 . Using 26 mg kg -1 as the agronomic critical Olsen P value above which no increase in crop yield occurs, crops grown on 61% of the soil samples in the investigated region would respond to P fertilizer and 20% of soils are currently in the optimum category (26-45 mg kg -1 Olsen P). The environmentally critical Olsen P value (~ 63 mg kg -1 ), defined as the amount above which P leaches from soil rapidly, was exceeded by 11% of soils with a further 4% of soils with elevated eutrophication risk. To maximize crop yields while maintaining a minimal risk of P leaching in Iran's calcareous soils, we suggest an ideal Olsen P of 26 mg kg -1 . The outcomes from this study inform about the P status of Iranian soils and could help update recommendations for P fertilizer applications in calcareous soils globally. The framework presented here could further be adopted to evaluate the P status in other soil types.
Keyphrases
  • heavy metals
  • human health
  • sewage sludge
  • risk assessment
  • climate change
  • systematic review
  • mass spectrometry
  • electronic health record
  • machine learning
  • deep learning
  • big data
  • case control