2021 ESA Annual Meeting (August 2 - 6)

Spatial variability in soil legacy phosphorus in subtropical grasslands under different management intensities

On Demand
Jiangxiao Qiu, University of Florida;
Background/Question/Methods

Excess phosphorus (P) fertilizer and manure application in agriculture-dominated landscapes could result in elevated P levels in soils, wetlands, and streams and lake sediments. Such P accumulation over time can serve as a long-term regional non-point source of P to surface waters downstream, even decades after discontinuing P inputs (i.e., ‘soil legacy P’). Long-term P buildup in soils can be further exacerbated by extreme precipitation, thus compromising efforts to improve water quality. However, few studies have examined fine-scale spatial variability of soil legacy P and what factors drive their spatial variations. In this research, we performed an extensive gridded (150-m interval) field sampling of surface soils (0-15 cm) in 2020 in an exemplar subtropical grassland in central Florida managed for cattle production. There are two dominant management intensities typifying of the region: (1) high-intensity where a historical P fertilization lasting 20-30 years was ceased in 1986; (2) low-intensity where no P has been applied. Soil samples were analyzed for total and available P, along with other soil covariates. Empirical Bayesian Kriging was performed to identify hotspots of soil P; linear-mixed effects models were used to analyze factors explaining their spatial variability.

Results/Conclusions

Our preliminary results revealed substantial spatial variability in total soil P and available P (i.e., Mehlich-1, Mehlich-3) across the landscape (based on a total of ~1,400 soil samples). Land-use intensity contributed to variations in total soil P and Mehlich-3 P, where high-intensity grasslands, even after 25 years of ceased P fertilization, still showed overall significantly higher total and Mehlich-3 P levels than low-intensity grasslands (both P<0.001), indicative of persistent legacy effects. However, no significant differences were found in Mehlich-1 P across management intensities. Empirical Bayesian kriging also showed hotspots of soil P levels across the landscape that are most prone to P losses and also where interventions are needed to recover and curb soil P so as to reduce P runoffs. Further analyses will be conducted to determine additional factors (e.g., soil texture, pH, soil hydrologic condition, livestock management, vegetation) in explaining P spatial variations. Our research can help identify factors contributing to soil legacy P, and reveal where and what management practices can be implemented to mitigate soil P levels. Our fine-resolution data can also help inform and parameterize biophysical models to evaluate impacts of best management practices on P loading and predict future losses in soil P under changing climatic conditions.