Using Crop Management Scenario Simulations to Evaluate the Sensitivity of the Ohio Phosphorus Risk Index

Phosphorus (P) risk indices are commonly used in the USA to estimate the field-scale risk of agricultural P runoff. Because the Ohio P Risk Index is increasingly being used to judge farmer performance, it is important to evaluate weighting/scoring of all P Index parameters to ensure Ohio farmers are credited for practices that reduce P runoff risk and not unduly penalized for things not demonstrably related to runoff risk. A sensitivity analysis provides information as to how sensitive the P Index score is to changes in inputs. The objectives were to determine 1) which inputs are most highly associated with P Index scores and 2) the relative impact of each input variable on resultant P Index scores. The current approach uses simulations across 6134 Ohio point locations and five crop management scenarios (CMSs), representing increasing soil disturbance. The CMSs range from all no-till, which is being promoted in Ohio, rotational tillage, which is a common practice in Ohio to full tillage to represent an extreme practice. Results showed that P Index scores were best explained by soil test P (31.9%) followed by connectivity to water (29.7%), soil erosion (13.4%), fertilizer application amount (11.3%), runoff class (9.5%), fertilizer application method (2.2%), and finally filter strip (2.0%). Ohio P Index simulations across CMSs one through five showed that >40% scored <15 points (low) while <1.5% scored >45 points (very high). Given Ohio water quality problems, the Ohio P Index needs to be stricter. The current approach is useful for Ohio P Index evaluations and revision decisions by spatially illustrating the impact of potential changes regionally and state-wide.

Effectiveness of Dietary Advice to Increase Fish Consumption over a 12-Month Period

Habitual fish consumption is associated with numerous health benefits;however, in Australia fish intake remains low. The aim of this study was to compare the effect of specific or general fish consumption recommendations on fish intake behavior over the duration of a 12-month clinical trial. Participants were randomized into a control group (general dietary advice), and two intervention groups (received dietetic advice to consume 180 g fish/wk), with one intervention group receiving LC omega-3 PUFA supplements. Dietary data was available for n = 117 at baseline, n = 85 at 3 months and n = 63 at 12 months. Total, fatty and lean fish intake (g/day) was calculated, and the change in fish intake between and within groups over the duration of the study was measured. Total fish consumption did not differ significantly between groups or within groups, however fatty fish intake was significantly greater in the intervention groups at three months (p = 0.004). The proportion of study participants complying with fish intake recommendations was also highest at the three month time point for both intervention groups. Overall, compliance to fish intake recommendations was highest at the three month time point and appeared to be influenced by dietetic intervention. Provision of fish may increase compliance in future studies, however if research is to be translated to practice, behavioral approaches are required to increase fish intake in the long term.

Conversion of Australian Food Composition Data from AUSNUT1999 to 2007 in the Clinical Trial Context

An Australian food composition database, AUSNUT1999, does not include long chain omega-3 polyunsaturated fatty acid （LC omega-3 PUFA） data. Measurement of the fatty acid content of diets initially analysed using AUSNUT1999 requires conversion to AUSNUT2007, an updated database inclusive of LC omega-3 PUFA. The aim of this study was to convert clinical trial dietary data from AUSNUT1999 to AUSNUT2007 and measure LC omega-3 PUFA intake. Clinical trial diet history （DH） data was converted from AUSNUTI999 to 2007 using a staged approach. Macronutrient intake from AUSNUTI999 and 2007 were calculated and compared via paired t-tests and Wilcoxon Signed Ranks tests. Mean dietary LC omega-3 PUFA intake and the percentage contribution of food groups to total LC omega-3 PUFA were then calculated. DHs were collected at baseline （n = 118）, three months （n = 86）, and 12 months （n = 64）. The accuracy of the conversion process improved with time, with no significant difference between most macronutrients at 12 months. Mean LC n-3 PUFA intake was 441.87 mg at baseline, 521.07 mg at 3 months, and 442.40 mg at 12 months, and was predominantly provided by fish and seafood, followed by meat products. This study allowed for the measurement of LC omega-3 intake, which was previously impossible using the AUSNUT 1999 database.