Grapefruit Root and Rhizosphere Responses to Varying Planting Densities, Fertilizer Concentrations and Application Methods.

Huanglongbing (HLB) disease has caused a severe decline in citrus production globally over the past decade. There is a need for improved nutrient regimens to better manage the productivity of HLB-affected trees, as current guidelines are based on healthy trees. The aim of this study was to evaluate the effects of different fertilizer application methods and rates with different planting densities on HLB-affected citrus root and soil health. Plant material consisted of 'Ray Ruby' (Citrus × paradisi) grapefruit trees grafted on 'Kuharske' citrange (Citrus × sinensis × Citrus trifoliata). The study consisted of 4 foliar fertilizer treatments, which included 0×, 1.5×, 3× and 6× the University of Florida Institute of Food and Agriculture (UF/IFAS) recommended guidelines for B, Mn and Zn. Additionally, 2 ground-applied fertilizer treatments were used, specifically controlled-release fertilizer (CRF1): 12-3-14 + B, Fe, Mn and Zn micronutrients at 1× UF/IFAS recommendation, and (CRF2): 12-3-14 + 2× Mg + 3× B, Fe, Mn and Zn micronutrients, with micronutrients applied as sulfur-coated products. The planting densities implemented were low (300 trees ha-1), medium (440 trees ha-1) and high (975 trees ha-1). The CRF fertilizer resulted in greater soil nutrient concentrations through all of the time sampling points, with significant differences in soil Zn and Mn. Grapefruit treated with ground-applied CRF2 and 3× foliar fertilizers resulted in the greatest bacterial alpha and beta diversity in the rhizosphere. Significantly greater abundances of Rhizobiales and Vicinamibacterales were found in the grapefruit rhizosphere of trees treated with 0× UF/IFAS foliar fertilizer compared to higher doses of foliar fertilizers.

Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China.

Accurate baseflow estimation is critical for water resources evaluation and management, and non-point source pollution quantification. Nonlinear reservoir algorithm (NRA) has been increasingly applied to baseflow separation because of its good approximation to the real groundwater discharge (commonly dominated by the unconfined aquifer) in most watersheds. However, in the rainy regions, large uncertainties may remain in the traditional NRA-separated baseflow sequences due to its empirical transition function for the rising limb of discharge process, and the evident variations of baseflow recession in the initial period of the falling limb caused by the disturbance from surface flow or rainfall events. To improve the reliability of baseflow separation, a self-adaptive non-linear reservoir algorithm (SA-NRA) was developed in this study based on the NRA, a self-adaptive groundwater discharge modified parameter, and the Particle Swarm Optimization algorithm (PSO). The validation of SA-NRA in a rainy watershed of eastern China showed that SA-NRA could be the approach to provide a goodness-of-fit for baseflow recession behaviors in the rainy regions. The traditional NRA and Eckhardt's two-parameter recursive digital filter (ERDF), calibrated (or validated) only with the pure baseflow recession data, can hardly provide reliable baseflow predictions for the non-pure baseflow recession periods (including the rising limb and the falling limb with surface flow or rainfall disturbance) due to the apparent variations of baseflow recession behavior. Therefore, more attentions should be paid to the uncertainties of baseflow separation for the non-pure baseflow recession periods in the rainy regions.