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.
With 74,000 farmers, farming more than 10 million crop acres in Ohio, USA [
In response to water quality concern, there is an increased emphasis on the use of state P indices in the recently revised USDA Natural Resources Conservation Service (USDA-NRCS) Practice Standard Code 590, Nutrient Management [
A sensitivity analysis can provide information as to how sensitive the final P Index score is to changes in inputs [
Typically sensitivity analyses use a deterministic or stochastic approach. A stochastic sensitivity analysis [
Unlike stochastic approaches, deterministic sensitivity analyses based on baseline scenarios have been shown to give results that vary depending on the baseline scenario chosen [
An earlier sensitivity analysis [
Our approach comprised a data generation phase and a data analysis phase. In the first phase, data were generated to create a representative distribution of P Index input parameters across the state of Ohio and five CMSs. These inputs were generated using a combination of stochastic data generation and logical selection of combinations of inputs. Having generated the data, the second phase proceeded by conducting statistical analysis of the simulated data and the final P Index scores derived from those data. The statistical analysis was conducted in three parts corresponding to their explanatory power and possible range of P Index score movement to the combinations based on two types of inputs. The two input types include the P Index parameter and the raw component inputs that contribute to the P Index parameter.
An overview of the Ohio P Index [
Crop Management Scenarios. Crop management scenarios (
Erosion Potential. To compute the soil loss values and field residue cover at each point location, the dll version of the Revised Universal Soil Loss Equation [
Connectivity to Water. The Ohio P index considers the presence or absence of runoff concentrated flow from a field as well as the field’s adjacency to an intermittent or perennial stream (
Runoff Class. Runoff class sub-values at each point location were determined by extracting the representative percent slope steepness and hydrologic soil
Site Characteristic Line Item | Phosphorus Vulnerability Values | ||||||||
---|---|---|---|---|---|---|---|---|---|
1. Soil Erosion | Soil Loss (tons/acre/yr) × 1 (Revised Universal Soil Loss Equation ver. 2, RUSLE2) Includes: Map Unit, Crop Management Zone, Climate, Farmer Management, Slope Length/Steepness | ||||||||
2. Connectivity to Water. Does concentrated flow (via a defined waterway, tile inlet, or surface drain leave the site? | No, and the site is NOT adjacent to an intermittent or perennial stream | No, but the site IS adjacent to an intermittent or perennial stream. | Yes, but the site is Not adjacent to an intermittent or perennial stream | Yes, and the site IS adjacent to and/or the concentrated flow outlets into an intermittent stream or through a tile inlet. | Yes, and the site IS adjacent to and/or the concentrated flow outlets into a perennial stream or through a tile inlet; OR Outlets to a pond or lake within 1 mile. | ||||
Value = 0 | Value = 4.0 | Value = 8.0 | Value = 12 | Value = 16 | |||||
3. Runoff Class | Represents the effect of the Hydrologic Soil Group (A, B, C, D) combined with the effect of slope steepness. This factor represents the site’s surface runoff vulnerability | ||||||||
See Runoff Class Matrix (0 to 15 points) | |||||||||
4. Soil Test Bray-Kurtz P1 PPM | Bray-Kurtz P1 (PPM) × (0.07) | ||||||||
Application Rate 5. Fertilizer (P2O5) 7. Organic (P2O5) | Fertilizer (P2O5) Applied (Lbs/Acre × 0.05) Available Manure/Biosolids (P2O5) (lbs/Acre × 0.06) | ||||||||
Fertilizer OR Manure (P2O5) Application Method | 0 Applied | Immediate Incorporation OR Applied on 80% Cover | Incorporation <1 Week OR Applied on 50% - 80% Cover | Incorporation >1 Week <3 months OR Applied on 30% - 49% Cover | No Incorporation OR Incorporation >3 months OR Applied on <30% Cover | ||||
6. Fert. App. Meth. | Value = 0 | Value = 0.75 | Value = 1.5 | Value = 3.0 | Value = 6.0 | ||||
8. Man. App. Meth. | Value = 0 | Value = 0.5 | Value = 1.0 | Value = 2.0 | Value = 4.0 | ||||
9. Filter Strip | Deduct 2 points if field runoff flows through a designed filter strip-minimum 33 ft. wide | ||||||||
Ohio Phosphorus (P) Risk Index scores and abridged interpretations | |||||||||
P Index Score | P Transport Risk | Abridged Interpretation of P Index and Management | |||||||
<15 | Low | Manure or biosolids can be applied to meet the recommended nitrogen requirements for next grass crop or nitrogen removal of the next legume crop | |||||||
15 to 30 | Medium | Runoff reduction practices should be considered to reduce P loss impacts. Manure/biosolids can be applied to meet the recommended nitrogen requirements for next grass crop or nitrogen removal of the next legume crop. Applications of P at the crop removal rate should be considered | |||||||
31 to 45 | High | Runoff reduction practices should be considered to reduce P loss impacts. Limit application of P to crop removal rates | |||||||
>45 | Very High | Remedial action is required to reduce the risk of P loss. A complete soil and water conservation system is needed. Apply no additional P | |||||||
Source: USDA-NRCS-OH (2001).
CMS | Crop Management Scenario Operations |
---|---|
1 | Soybeans: no-till, drill or air seeder with single disk opener planter, harvest 30% standing stubble Corn: no-till, double disk opener with fluted coulter planter, harvest 60% standing stubble |
2 | Soybeans: no-till, double disk opener w/fluted coulter, harvest 30% standing stubble Corn: no-till, double disk opener w/ fluted coulter planter, harvest 60% standing stubble |
3 | Soybeans: no-till, drill or air seeder with single disk opener planter, harvest 30% standing stubble Corn: no-till, double disk opener with fluted coulter planter, harvesting crop 60% standing stubble, coulter caddy w/ smooth coulters & rolling basket incorporator |
4 | Soybeans: no-till drill or air seeder with single disk opener planter, harvest 30% standing stubble fall chisel, straight point Corn: spring field cultivator, double disk opener planter, harvest 60% standing stubble |
5 | Soybeans: chisel, straight point, field cultivator, drill or air seeder with single disk opener planter, harvest 30% standing stubble, fall moldboard plow Corn: spring disk, field cultivator, double disk opener planter, harvest 60% standing stubble |
group from the gSSURGO data [
Soil Test Phosphorus. Soil test P (STP) values were randomly selected from a distribution of possible STP values derived from data provided by the three largest soil test laboratories servicing Ohio (A & L Great Lakes Laboratories, Brookside Laboratories Inc, and Spectrum Analytic) at a zip code resolution. All STP (>500,000) values from 2009 to 2012 were candidates for use in the sensitivity analysis. Possible values ranged from 1 to 4172 mg・kg−1 Bray-P1. However across 88 Ohio counties the 50 percentile ranged from 6.3 to 131 mg・kg−1 Bray-P1. A STP value was randomly drawn from all values within the same sub-basin (USGS Hydrologic Unit Code-8) as the point location.
Fertilizer Application Amount. Fertilizer application amount was determined for the rotation based on the Tri-State Fertility Guidelines [
Fertilizer Application Method. The fertilizer application method sub-value is based on whether or not fertilizer is applied, time until applied fertilizer is incorporated and/or the amount of field cover at the time of application (
The sensitivity analyses were performed using a Monte Carlo approach in which P Index values were simulated for a variety of field conditions in Ohio agriculture. All data simulation was performed using the R language and environment and was coded from scratch [
Analysis I, Sub-Value Contribution. The first stochastic sensitivity analysis examined the relationship between the P Index parameter sub-values and the final score. A linear regression was constructed using the final P Index score as the dependent variable and each sub-value as independent variables. Type III Sums of Squares was used to quantify each input’s explanatory power. Across all inputs, the Type III Sums of Squares were normalized to sum to 1 to provide a simple measure of relative explanatory power for each input.
Analysis II, Raw Component Contribution. The second stochastic sensitivity analysis examined the relationship between raw component inputs and crop management practices and the final P Index score. This analysis differs from the previous analysis in that raw component inputs and crop management practices are used as the independent variables rather than parameter sub-values. As with the parameter sub-value analysis a regression model was fitted to the data and the Type III Sums of Squares were used to estimate explanatory power for each of the characteristics.
Analysis III, Sub-Value Potential Impact. While the normalized Sums of Squares provide information about the unique explanatory power of each variable, they do not provide information about how much influence each sub-value can have on the final P Index score. Following Brandt and Elliot [
Erosion Potential. Increased levels of soil disturbance, resulting from increasing tillage across the CMSs, resulted in increased erosion. The percent frequency of erosion results of <2.24 Mg・ha−1・y−1 (<1 t・ac−1・y−1), 2.24 to 4.47 Mg・ha−1・y−1 (1 to 2 t・ac−1・y−1), 4.48 to 6.71 Mg・ha−1・y−1 (2 to 3 t・ac−1・y−1) and >6.72 Mg・ha−1・y−1 (>3 t・ac−1・y−1) are shown in
(CMS1) or double (CMS2) disk opener on the soybean planter, had similar low erosion levels with 97.1% and 93.2% of point locations having <2.24 Mg・ha−1・y−1 respectively. For other CMSs, erosion levels were distributed across the erosion classes <2.24 to >6.72 Mg・ha−1・y−1 (
Running RUSLE2.dll simulations across Ohio point location using appropriate gridded SSURGO [
RUSLE2 outputs for field cover, which are used in the determination of fertilizer placement method are presented in
Connectivity to Water. Based on the decision making criteria, established with Ohio USDA-NRCS personnel, approximately 28% of point locations are presumed to have concentrated surface flow leaving the field. Defined waterways account for approximately 13% and surface drains 15% of the concentrated flow. Additionally, approximately 12.4% of point locations were considered adjacent (≤250 m) to an intermittent or perennial stream. Approximately 60% of P Index connectivity to water sub-values were 0 while 8.5%, 28%, 3% and 1% of sub- values were 4, 8, 12 and 16 points.
Runoff Class. Approximately 66% of point locations are in the C hydrologic soil group with other hydrologic soil groups having a percent occurrence of 2 (A), 17 (B) and 16 (D). Runoff class sub-values for the point locations ranged from 0 to 15 with a sub-value of 4 having the highest frequency of occurrence at approximately 31%. Approximately 32% of runoff class sub-values are <4, while 37% are >4.
Soil Test Phosphorus. Following the Tri-State Fertility Guidelines [
Fertilizer Application Amount. Fertilizer application amount was determined based on Tri-State Fertility Guidelines [
Fertilizer Application Method. A frequency distribution of fertilizer application method sub-values across CMSs is presented in
Ohio Phosphorus Risk Index Scores. A frequency distribution of Ohio P Index scores across crop management scenarios is presented in
The increase in P Index score due to crop management scenario is apparent and is a result of increased soil disturbance and decreased field cover. Increased soil disturbance increases erosion potential while decreased field cover increases both erosion potential and possible fertilizer placement method sub-values. Across the crop management scenarios, one through five, respectively, the current
study found that, 64.0%, 63.0%, 57.6%, 51.1%, and 40.7% scored <15 points (low); 35.1%, 36.0%, 40.6%, 45.7%, and 52.3% scored 15 to 30 points (medium); 0.65%, 0.67%, 1.42%, 2.66%, and 5.73% scored >30 to 45 points (high); while only 0.29%, 0.29%, 0.39%, 0.57%, and 1.24% scored very high or >45 points (
The current approach of evaluating Ohio P Index scores using simulations across a wide range of CMSs, a spatial distribution of point locations and running the RUSLE2.dll, provides improved soil erosion estimates as well as calculated field cover, resulting in a refinement of earlier work. The P Index scores in the current study are considerably lower than those reported in Williams et al. [
Statistical Analyses. The first (sub-value) and second (raw component) assessment (
Factor | Parameter | Explanatory Power (%) |
---|---|---|
Ohio P Index Sub-value Inputs | ||
Soil erosion | 1 | 13.4 |
Connectivity to water | 2 | 29.7 |
Runoff class | 3 | 9.5 |
Soil test phosphorus | 4 | 31.9 |
Fertilizer application amount | 5 | 11.3 |
Fertilizer application method | 6 | 2.2 |
Filter strip | 9 | 2.0 |
Raw Component Inputs | ||
Slope length | 1 | 0.1 |
Slope steepness | 1, 2, 3 | 19.1 |
Soil erodibility | 1 | 0.0 |
Rainfall erosivity | 1 | 0.2 |
Crop management | 1, 5, 6 | 0.4 |
Soil texture | 1 | 0.0 |
Concentrated flow leaving field | 2 | 24.4 |
Stream adjacent | 2 | 4.6 |
Hydrologic soil group | 3 | 3.6 |
Soil test phosphorus | 4, 5 | 36.5 |
Fertilizer application amount | 5 | 6.0 |
Field cover | 6, 1 | 0.1 |
Fertilizer incorporation/timing | 6 | 2.4 |
Filter strip | 9 | 2.7 |
power in the raw component analysis were two components of connectivity to water, slope steepness (19.1%) and concentrated flow (24.4%). However, adjacency to an intermittent/perennial stream, also a component of connectivity to water, contributed only 4.6% explanatory power. This suggests that a presumption of concentrated flow and slope steepness rather than stream adjacency strongly influenced the connectivity to water sub-value. Slope steepness is also a component of erosion potential and runoff class, which may also contribute to its high explanatory power. In the sub-value analysis erosion had the 3rd highest explanatory power at 13.4%. Of the components of soil erosion in the raw component analysis, slope steepness had the greatest explanatory power (19.1%), while slope length (0.1%), soil erodibility (0.0%), rainfall erosivity (0.2%), crop management (0.4%), soil texture (0.0%), and residue cover (0.1%) contributed little. In the sub-value analysis, fertilizer application amount provided the 4th highest explanatory power at 11.3%, and was similarly moderate at 6% in the
raw component analysis. The 5th highest explanatory power in the sub-value analysis was runoff class (9.5%). Components of runoff class, slope steepness and hydrologic soil group, had explanatory power of 19.1% and 3.6% respectively, in the raw component analysis. Fertilizer application method provided the 6th highest explanatory power (2.2%) in the sub-value analysis. Similarly, in the raw component analysis the components of fertilizer application method field cover and fertilizer incorporation/timing had explanatory powers of 0.1% and 2.4%. Considering the abundance of work [
The third investigation evaluates the potential impact of each parameter on the range of P Index score are illustrated as a tornado plot (
While results from the two types of sensitivity analysis may appear to be inconsistent, they are actually providing different pieces of information. The first two analyses, focusing on explanatory power, give insight into which inputs are actually strongly associated with final P Index scores across the range of field conditions and CMSs. In contrast, the third analysis, focusing on potential influence of each input on the range of final P Index score, gives the hypothetical influence of each input across its observed range. In cases where the actual distribution of an input was highly skewed, it often had explanatory power lower than would be suggested by its potential influence. As an example, the parameter for fertilizer application amount has approximately one third the explanatory power of soil test phosphorus, but its range of impact is comparable to that of soil test phosphorus (
two variables is multiplied by the weights (to obtain potential influence), the values are approximately the same. However, the highly skewed distribution of scores for fertilizer application amount means that it is not highly correlated with final P Index score. As a result, its explanatory power is low despite its large potential for influencing the final P Index score. For example fields that have applied fertilizer can have their P Index score impacted highly. However, 32.2% of fields receive a score of zero for both fertilizer application amount and method due to STP > 40 mg・kg−1 and therefore their P Index score is highly impacted. Taking into account these differences should assist with appropriate re-weight- ing of Ohio P Index parameters.
Soil test P accounted for a high degree of explanatory power on the final P Index score (31.9%), however, following the Tri-State Fertility Guidelines fertilizer application amount only accounted for 11.3% of explanatory power. Based on the agronomic approach used 32.2% percent of point locations allowed for no P applications, however, in the current P Index, there is no actual prohibition against additional P application until a very high (>45 points) score is reached. This illustrates that, currently, for Ohio, the P Index approach could be perceived as less restrictive with regards to P application than an agronomic approach [
Slope steepness, an integral part of connectivity to water, soil erosion and runoff class which ranked 2nd, 3rd and 5th in the sub-value sensitivity analysis, had the 3rd highest explanatory power (19.1%) in the raw components analysis and was responsible for the 2nd highest potential range of P Index score movement. Even though soil erosion had the 3rd highest explanatory power in the sub-value analysis, crop management, which is an integral part of soil erosion, had very little explanatory power in the raw component analysis. In fact, slope steepness seems to have had a greater contribution to soil erosion. For example (
The current analysis cannot provide insight into whether the P Index, as currently defined, is a useful measure of phosphorus runoff risk. Field scale studies are currently underway to assess the level of association between the P Index and measured runoff values. However, the sensitivity analysis does provide insight into the ways in which farmers are currently credited or penalized.
Given Ohio water quality issues, perhaps the P Index should be stricter. Even across a broad range of CMSs and STP levels very few P Index scores were in the high or very high categories. Additionally, the current interpretation of P Index score is heavily focused on manure/biosolids applications. A revised P Index needs to be more broadly useful by providing information regarding field-scale P runoff risk to all Ohio farmers not only those applying manure/biosolids. As P indices are increasingly being used to judge farmer performance [
This work was funded by USDA-NRCS Conservation Innovation Grant (69- 3A75-12-231), The Ohio Soybean Council and Ohio Corn & Wheat. Thanks to Steve Baker, State Soil Scientist, Mike Monnin, State Conservation Engineer and Thomas J. Oliver, Soil Conservationist at USDA-NRCS-Ohio for assistance with decision making criteria used in this work.
Dayton, E.A., Holloman, C.H., Subburayalu, S. and Risser, M.D. (2017) Using Crop Management Scenario Simulations to Evaluate the Sensitivity of the Ohio Phosphorus Risk Index. Journal of Environmental Protection, 8, 141-158. https://doi.org/10.4236/jep.2017.82012