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...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.展开更多
Maps of erosivity,which are also commonly referred to as isoerodent maps,have played a critical role in soil conservation efforts in the United States and around the world.Currently available erosivity maps for the Un...Maps of erosivity,which are also commonly referred to as isoerodent maps,have played a critical role in soil conservation efforts in the United States and around the world.Currently available erosivity maps for the United States are either outdated,conflict with erosivity benchmarking studies,or utilized less advanced spatial mapping methods.Furthermore,it is possible that the same underlying issues with US maps are impacting global maps as well.In this study,we used more than 340015-min,fixed-interval precipitation gauges to update the isoerodent map of the conterminous United States.Erosivity values were interpolated using universal kriging under several spatial model configurations and resolutions.The optimal spatial model was selected based on which result had the lowest sample variogram error.Rainfall,erosivity,and erosivity density maps were compared to existing products.Some average annual and annual erosivity results were compared to high-quality erosivity benchmarking publications.Erosivity values from both RUSLE2 and Panagos et al.(2017)were generally lower in the eastern United States and mixed in the western United States compared to our results.Topographic effects resulted in much greater erosivity differences in this study as compared to prior maps.Benchmark comparisons revealed that erosivity maps from this study and others were lower than the benchmark by 14%or more(up to 38%).These findings suggest current practices of storm omission and intensity dampening correction need to be revisited,especially in locations with relatively low-to-moderate rainfall erosivity such as the Midwest or Northeast United States,for example.展开更多
文摘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.
文摘Maps of erosivity,which are also commonly referred to as isoerodent maps,have played a critical role in soil conservation efforts in the United States and around the world.Currently available erosivity maps for the United States are either outdated,conflict with erosivity benchmarking studies,or utilized less advanced spatial mapping methods.Furthermore,it is possible that the same underlying issues with US maps are impacting global maps as well.In this study,we used more than 340015-min,fixed-interval precipitation gauges to update the isoerodent map of the conterminous United States.Erosivity values were interpolated using universal kriging under several spatial model configurations and resolutions.The optimal spatial model was selected based on which result had the lowest sample variogram error.Rainfall,erosivity,and erosivity density maps were compared to existing products.Some average annual and annual erosivity results were compared to high-quality erosivity benchmarking publications.Erosivity values from both RUSLE2 and Panagos et al.(2017)were generally lower in the eastern United States and mixed in the western United States compared to our results.Topographic effects resulted in much greater erosivity differences in this study as compared to prior maps.Benchmark comparisons revealed that erosivity maps from this study and others were lower than the benchmark by 14%or more(up to 38%).These findings suggest current practices of storm omission and intensity dampening correction need to be revisited,especially in locations with relatively low-to-moderate rainfall erosivity such as the Midwest or Northeast United States,for example.
