Analysis of the Minnesota Agricultural Water Quality Certification Program s assessment tool

Transcription

1 Analysis of the Minnesota Agricultural Water Quality Certification Program s assessment tool Presenter: Peter Gillitzer Minnesota Department of Agriculture Dennis Fuchs, Stearns SWCD Ben Jordan, Sense AI Jim Klang, Kieser and Associates MN Water Resources Conference Oct 13 th, 2015

2 Outline Overview: why we completed the analysis Methodology Framework for analyzing and adjusting the assessment tool

3 Overview Minnesota Agriculture Water Quality Certification Program (MAWQCP): Whole-farm, risk management planning Promotes best management practices Recognizes producers Provides regulatory certainty Provides dedicated financial/technical assistance

4 Overview One step of certification involves measuring farm via MAWQCP assessment tool Based on NRCS s Water Quality for Agricultural Runoff Index (Lal and McKinney, 2012) Unitless index score from 1-10; risk to water quality based on a set of practices and conditions Adopted for Minnesota agriculture and piloted (Lal, Harbans and McKinney, Shaun WQIag. NRCS National Water Quality and Quantity Team. Portland, OR)

5 Overview Research questions: Does the assessment tool capture all the interrelated and complex factors related to agriculture and water quality risk management? (i.e., does it work?) What is the process under which the assessment tool should be changed and improved?

6 Overview Team included: conservation professionals, engineers, environmental scientists, and mathematical modelers Methodology: Literature review Local sensitivity analysis Data visualization and descriptive statistics Compare against edge of field monitoring data

7 Sensitivity analysis Sensitivity analysis is a statistical method used to determine how the output of a given model varies with each of the model input variables (Saltelli, 2000; 2006) Results make contributions to both the choice and configuration of model input variables (Saltelli, A Sensitivity analysis practices: strategies for model-based inference. Reliability Engineering and System Safety (2006): ) (Saltelli, A Sensitivity analysis as an ingredient of modeling. Statistical Science (2000): )

8 Sensitivity analysis 1. Choose a variable for adjustment 2. Sample a large number of possible values for that variable from the entire data set 3. Calculate the score for the field x cropping scenario, substituting the sampled for the variable under consideration 4. Summarize the resulting scores.

9 . Sensitivity analysis

10 Sensitivity analysis

11 Data visualization Why data visualization?: 1. Recognize intended/unintended patterns 2. Actual results vs. mathematical extremes

12 Data visualization

13 Data visualization

14 Compare to monitoring data Why compare to edge of field monitoring data? 1. The index is based on empirical research and expert s best professional judgment 2. Over time and across scenarios, actual edge of field monitoring data should compare with a measure of risk (i.e., assessment tool) Utilized Minnesota s extensive network of edge of field monitoring stations through Discovery Farms

15 Comparison to water quality monitoring Sum of select Assessment Tool nutrient management, tillage management and physical factors vs. total nitrogen load (lbs acre-1), surface runoff TN Load (lbs/acre) Sum of Raw WQI-fs, WQI-nm & WQI-tm

16 Comparison to water quality monitoring Sum of select Assessment Tool nutrient management, tillage management and physical factors vs. Total Suspended Solids (lbs acre-1), surface runoff 2, , TSS Load (lbs/acre) 1, , Sum of Raw WQI-fs, WQI-nm & WQI-tm Scores

17 Framework for analysis/adjustment Beginning premise: Start somewhere and collect data! Gather a technical team Principle 1: A value system must be understood and predefined for each core components before the ranking and weighting factors are adjusted. Principle 2: Inputs should be tested based on the conclusions developed in the literature review, sensitivity analysis, data visualization, monitoring data, etc. Principle 3: Track and record all decisions/rationale regarding adjustments.

18 Framework for analysis/adjustment Example: Tillage management Principle 1: No till practices provide the high water quality benefits by increasing infiltration and stable aggregate formation (Flanzluebbers, 2002) increasing soil organic matter (Dolan, 2006) and therefore should accordingly receive a higher score then tillage practices with greater soil mixing. Franzluebbers, A.J Water infiltration and soil structure related to organic matter and its stratification with depth. Soil & Tillage Research 66 (2002) Dolan M.S., C.E. Clapp, R.R. Allmaras, J.M Baker, J.A.E. Molina Soil organic carbon and nitrogen in a Minnesota soil as related to tillage, residue and nitrogen management. Soil & Tillage Research 89 (2006)

19 Framework for analysis/adjustment Example: Tillage management Principle 1: Value statement STIR>10 STIR STIR STIR >80 No-till= good

20 Framework for analysis/adjustment Example: Tillage management Principle 2: Complete literature review Data visualization with before-after scenarios Sensitivity analysis Compare against edge of field monitoring data Consult subject matter experts Principle 3: Record rationale and supporting information; make changes

21 Questions?

22 Cost-Effective Agricultural BMP Planning Using Precision Conservation Principles and Advanced GIS Tools: A Case Study in the Squaw Creek Watershed, Iowa Jason Ulrich & Pat Conrad, Emmons and Olivier Resources (EOR), Oakdale, MN

23 OUTLINE Background Methodology Results Lessons: What worked, what didn t Other methods available Potential shortcomings of all current methods w a t e r I e c o l o g y I c o m m u n i t y

24 SQUAW CREEK WATERSHED, IOWA 7 HUC-12 s 150,000 acres 80% corn/soybean 70% drain tiled Avg NO3-N: 6.5 mg/l Avg Ortho P: 0.3 mg/l GOALS: 1. 29% P reduction 2. 41% N reduction 3. Flood mitigation 4. Increase soil health w a t e r I e c o l o g y I c o m m u n i t y Ames

25 GIS-based BMP Planning Tools: Tomer Framework Agricultural Conservation Planning Framework (ACPF): Field Prioritization and BMP suitability GIS Toolset w a t e r I e c o l o g y I c o m m u n i t y

26 BMP Planning Framework PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Find the N, P, sediment hotspots Tomer Framework: How much reduction is expected? Find terrain-suitable BMP How sites much will it cost? Hydro/WQ Models Restored Wetlands What EQIP cost-share funding is available? SWAT Ag practices, Drain WASCOBs tile R-SWMM Riparian Buffers Values from literature and agency sources HSPF Grassed Waterways Iowa Nutrient Reduction Strategy GIS LiDAR based Contour Buffer Strips MN Nutrient Reduction Strategy Tomer Framework (ACPF) Controlled Drainage MN AG BMP Handbook PTMapp Bioreactors Saturated Buffers Overlapping capabilities are good = Weight-of-evidence approach Results Scenarios Reductions Costs Cost-effectiveness Optimal Plan(s)

27 PRIORITIZATION TARGETING SWAT MODELED HOTSPOTS Nitrate PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN SQUAW CREEK WATERSHED, IOWA Phosphorus Sediment water I ecology I community

28 PRIORITIZATION TARGETING SWAT MODELED HOTSPOTS Primary Factors: Tile/No-Tile Drained/Un-drained depressions Manured/Non-manured Nitrate PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN SWAT MODELED HOTSPOTS Important Conclusions: 85% of total watershed N from drain-tiled row crops 33-50% of total field P & sediment from 20% of the watershed SQUAW CREEK WATERSHED, IOWA Phosphorus Sediment water I ecology I community

29 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Manure Hotspots Bank Erosion Hotspots

30 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN TARGETING: BMP opportunities identified with LiDAR Analysis (Tomer Framework) Potential Grassed Waterways & Field Hotspots

31 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN TARGETING: BMP opportunities identified with LiDAR Analysis (Tomer Framework) Nutrient Removal Wetlands Riparian Buffers Iowa CREP wetland: pool area/drainage area ratio optimized for max denitrification w a t e r I e c o l o g y I c o m m u n i t y Buffers sited in surface and/or sub-surface flowpaths for max sediment & P trapping, denitrification

32 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Category BMP Unit Reductions, Costs, Cost-Effectiveness Practice % Reduction per acre Est. Cost $/ac/yr N+P Cost- Effectiveness N P Efficient N & P app. Moving fall anhydrous N fertilizer application to spring preplant Efficient N & P app. P rate reduction in fields that have high to very high soil test P Efficient N & P app. Reducing N app. rate to MRTN 133 lb N/ac on Corn/Soy, Reducing N app. rate to MRTN 190 lb N/ac on Cont. Corn Efficient N & P app. Sidedress all spring applied N Efficient N & P app. Using a nitrification inhibitor with all fall applied N fertilizer Cover crops Fall planted cover crops Reduced Tillage Intensive tillage to conservation tillage Cover Crops/ Reduced Tillage Increasing soil organic matter by 100% (3% to 6%) 10 0 NA NA Land Use Change Corn/Soybean to Pasture and/or Land Retirement Land Use Change Corn/Soybean to Perennials/Energy Crops Land Use Change Corn/Soybean with extended alfalfa rotations Edge-of-Field Controlled Drainage Edge-of-Field Denitrification bioreactors Edge-of-Field Grassed Waterways Edge-of-Field Nutrient Removal Wetlands Edge-of-Field Riparian Buffers Edge-of-Field Saturated Buffers Edge-of-Field Sediment Basins w a t e r I e c o l o g y I c o m m u n i t y

33 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Category BMP Unit Reductions, Costs, Cost-Effectiveness Practice % Reduction per acre Est. Cost $/ac/yr N+P Cost- Effectiveness N P Efficient N & P app. Moving fall anhydrous N fertilizer application to spring preplant Efficient N & P app. P rate reduction in fields that have high to very high soil test P Efficient N & P app. Reducing N app. rate to MRTN 133 lb N/ac on Corn/Soy, Reducing N app. rate to MRTN 190 lb N/ac on Cont. Corn Efficient N & P app. Sidedress all spring applied N Efficient N & P app. Using a nitrification inhibitor with all fall applied N fertilizer Cover crops Fall planted cover crops Reduced Tillage Intensive tillage to conservation tillage Cover Crops/ Reduced Tillage Increasing soil organic matter by 100% (3% to 6%) 10 0 NA NA Land Use Change Corn/Soybean to Pasture and/or Land Retirement Land Use Change Corn/Soybean to Perennials/Energy Crops Land Use Change Corn/Soybean with extended alfalfa rotations Edge-of-Field Controlled Drainage Edge-of-Field Denitrification bioreactors Edge-of-Field Grassed Waterways Edge-of-Field Nutrient Removal Wetlands Edge-of-Field Riparian Buffers Edge-of-Field Saturated Buffers Edge-of-Field Sediment Basins w a t e r I e c o l o g y I c o m m u n i t y These are free or profitable

34 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Category BMP Unit Reductions, Costs, Cost-Effectiveness Practice % Reduction per acre Est. Cost $/ac/yr N+P Cost- Effectiveness N P Efficient N & P app. Moving fall anhydrous N fertilizer application to spring preplant Efficient N & P app. P rate reduction in fields that have high to very high soil test P Efficient N & P app. Reducing N app. rate to MRTN 133 lb N/ac on Corn/Soy, Reducing N app. rate to MRTN 190 lb N/ac on Cont. Corn Efficient N & P app. Sidedress all spring applied N Efficient N & P app. Using a nitrification inhibitor with all fall applied N fertilizer Cover crops Fall planted cover crops Reduced Tillage Intensive tillage to conservation tillage Cover Crops/ Reduced Tillage Increasing soil organic matter by 100% (3% to 6%) 10 0 NA NA Land Use Change Corn/Soybean to Pasture and/or Land Retirement Land Use Change Corn/Soybean to Perennials/Energy Crops Land Use Change Corn/Soybean with extended alfalfa rotations Edge-of-Field Controlled Drainage Edge-of-Field Denitrification bioreactors Edge-of-Field Grassed Waterways Edge-of-Field Nutrient Removal Wetlands Edge-of-Field Riparian Buffers Edge-of-Field Saturated Buffers Edge-of-Field Sediment Basins w a t e r I e c o l o g y I c o m m u n i t y Increase soil organic matter

35 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Category BMP Unit Reductions, Costs, Cost-Effectiveness Practice % Reduction per acre Est. Cost $/ac/yr N+P Cost- Effectiveness N P Efficient N & P app. Moving fall anhydrous N fertilizer application to spring preplant Efficient N & P app. P rate reduction in fields that have high to very high soil test P Efficient N & P app. Reducing N app. rate to MRTN 133 lb N/ac on Corn/Soy, Reducing N app. rate to MRTN 190 lb N/ac on Cont. Corn Efficient N & P app. Sidedress all spring applied N Efficient N & P app. Using a nitrification inhibitor with all fall applied N fertilizer Cover crops Fall planted cover crops Reduced Tillage Intensive tillage to conservation tillage Cover Crops/ Reduced Tillage Increasing soil organic matter by 100% (3% to 6%) 10 0 NA NA Land Use Change Corn/Soybean to Pasture and/or Land Retirement Land Use Change Corn/Soybean to Perennials/Energy Crops Land Use Change Corn/Soybean with extended alfalfa rotations Edge-of-Field Controlled Drainage Edge-of-Field Denitrification bioreactors Edge-of-Field Grassed Waterways Edge-of-Field Nutrient Removal Wetlands Edge-of-Field Riparian Buffers Edge-of-Field Saturated Buffers Edge-of-Field Sediment Basins w a t e r I e c o l o g y I c o m m u n i t y Very effective but expensive

36 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Category BMP Unit Reductions, Costs, Cost-Effectiveness Practice % Reduction per acre Est. Cost $/ac/yr N+P Cost- Effectiveness N P Efficient N & P app. Moving fall anhydrous N fertilizer application to spring preplant Efficient N & P app. P rate reduction in fields that have high to very high soil test P Efficient N & P app. Reducing N app. rate to MRTN 133 lb N/ac on Corn/Soy, Reducing N app. rate to MRTN 190 lb N/ac on Cont. Corn Efficient N & P app. Sidedress all spring applied N Efficient N & P app. Using a nitrification inhibitor with all fall applied N fertilizer Cover crops Fall planted cover crops Reduced Tillage Intensive tillage to conservation tillage Cover Crops/ Reduced Tillage Increasing soil organic matter by 100% (3% to 6%) 10 0 NA NA Land Use Change Corn/Soybean to Pasture and/or Land Retirement Land Use Change Corn/Soybean to Perennials/Energy Crops Land Use Change Corn/Soybean with extended alfalfa rotations Edge-of-Field Controlled Drainage Edge-of-Field Denitrification bioreactors Edge-of-Field Grassed Waterways Edge-of-Field Nutrient Removal Wetlands Edge-of-Field Riparian Buffers Edge-of-Field Saturated Buffers Edge-of-Field Sediment Basins w a t e r I e c o l o g y I c o m m u n i t y Treat both N and P cost-effectively

37 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Scenarios Development Approach: Focused on upper 25-50% of ranked field hotspots Nitrate: Tiled, Continuous Corn, manured Phosphorus: Higher slopes, closer to streams, manured Intersected hotspots with Tomer BMP contrib. drainage areas Tested both parallel and serial ( treatment train ) BMP scenarios Omitted controlled drainage or other in-field storage BMPs Included Land-Use Change and soil health BMPs despite cost

38 PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Ag BMP Category/Practice Treatment Area Squaw Creek Watershedwide Reductons Resulting BMP planning example scenario % of watershed Acres % of Ag % N reduc. % P reduc. Efficient N and P applications 32 47, Cover crops: Fall planted rye 16 23, Reduced tillage: No-till 16 23, Edge-of-field: Nutrient removal wetlands Riparian buffers WASCOBs Grassed waterways Land Use Change: Pasture/land retirement 32 47, , TOTAL REDUCTIONS ANNUAL COST (20 years) $1,200,000 ANNUAL COST PER TREATED ACRE $23 RESULTS AREA TREATED 20-40% of Ag acres REDUCTIONS vs GOALS N reduction goal 41% BMP reduction 43% P reduction goal 29% BMP reduction 41% COST/yr ~ $23 per treated acre Estimated Corn/Soy profit per acre (2014) ~ $300

39 BMP Planning Framework -- Shortcomings PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN

40 BMP Planning Framework -- Shortcomings PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN SWAT Tomer Framework Literature Sources Other GIS Excel R code PTMapp?

41 BMP Planning Framework -- Shortcomings PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN GIS/models not substitutes for local knowledge & research Field validation of proposed BMPs needed Local trends/buy-in with certain BMPs? Known field-scale pollutant sources Understanding of watershed scale pollutant sources & sinks o Ephemeral Gully Erosion (likely locations but how much?) o Streambank/Bluff Erosion (fingerprinting? BANCS/BEHI?) o Dissolved P pathways (weak consensus; need local monitoring)

42 BMP Planning Framework -- Shortcomings PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN TSS Source Budget Phosphorus Source Budget??????? How does this uncertainty affect BMP planning outcomes? Acceptance by farming community?

43 BMP Planning Framework -- Shortcomings PRIORITIZATION TARGETING PREDICTED OUTCOMES COSTS AND FUNDING BMP IMPLEMENTATION PLAN Acceptance/Adoption Challenges Communication/Collaboration with farmers Local Citizen (farmer & non-farmer) advocates Engaged and respected Fed/State/local NR & AG professionals Identifying potential influential innovators & early adopters Use of social science principles/models more research needed ACCEPTANCE ADOPTION

44 Thank you! Questions? Jason Ulrich / w w w. e o r i n c. c o m

45 Watershed nutrient reduction planning using N-BMP and P-BMP spreadsheet tools David Wall (MPCA) William Lazarus (UMN) David Mulla (UMN) Jacob Galzki (UMN)

46 Minnesota s Nutrient Reduction Strategy

47 Phosphorus reduction needs for Minnesota s rivers & lakes Phosphorus Impaired Waters Phosphorus load reduction needed (avg) Impaired rivers* 41%** Lakes (520) 45% * River eutrophication standards ** reductions needed from loads

48 Phosphorus & nitrogen reductions needed downstream of Minnesota 10-50% 45% Baseline

49 Phosphorus load - Mississippi River

50 Nitrogen load Mississippi River

51 Watershed planning tools for reducing cropland nutrient losses to waters N-BMP Nitrogen BMP spreadsheet P-BMP Phosphorus BMP spreadsheet o University of Minnesota William Lazarus, David Mulla, Jacob Galzki, others o Spreadsheets & documentation on-line

52 N-BMP & P-BMP helps with two important questions 1. Which cropland BMPs to focus on? 2. How much new BMP adoption to reach goals? Highest N loads shaded dark Per 114D.26, Subd. 1, (8) WRAPS shall include actions capable of cumulatively achieving needed pollution load reductions

53 Presentation Outline 1. Selecting BMPs using the tools and other information BMPs with most potential to reduce N & P in the Mississippi Most cost effective BMPs for N & P Multiple benefits and other considerations 2. Using the tools to estimate needed BMP adoption levels 3. Using the tools in series with other planning tools

54 Selecting BMPs Potential for reducing nutrients Cost effectiveness of BMPs ($/lb reduced) Multiple benefit potential N-BMP P-BMP o Water quality: Nitrogen, phosphorus, sediment, pesticides o Soil: O.M., biology, water, resilience o Air: Carbon sequestration, Nitrous oxide o Wildlife & pollinator habitats Acceptance by agricultural community Availability of government cost share & assist.

55 Nitrogen reduction potential* (%) Mississippi Basin - Minnesota Cover crops drilled corn/soy 19 Fertilizer rate MRTN $5 corn 13 Marginal land to perennials 7 Cover Crops aerial seed to c/s Wetland construction 4 4 Saturated buffer 4 Controlled drainage Cover crops - early harvest crops Riparian buffers Fall fertilizer to spring 1 Tile line bioreactors % nitrogen reduced to waters in Mississippi Basin *BMPs on 80% of suitable acres (N-BMP tool)

56 Nitrogen reduction potential* (%) Mississippi Basin - Minnesota Cover crops drilled corn/soy 19 Fertilizer rate MRTN $5 corn 13 Marginal land to perennials 7 Cover Crops aerial seed to c/s Wetland construction 4 4 Saturated buffer 4 Controlled drainage Cover crops - early harvest crops Riparian buffers Fall fertilizer to spring 1 Tile line bioreactors % nitrogen reduced to waters in Mississippi Basin *BMPs on 80% of suitable acres (N-BMP tool)

57 Phosphorus reduction potential* (%) Cover crops drilled corn/soy 19 Riparian buffers 9 Reduced tillage on slopes >2% 7 Fertilizer rate optimization 5 Controlled drainage 4 Marginal land to perennials 3 Cover crops - early harvest crops 3 Manure immediately incorporated Tile intake riser pipes % Phosphorus reduced to waters in Mississippi Basin *BMPs on 80% of suitable acres (P-BMP tool)

58 Combined benefits N & P manure incorporated Alternative intakes tillage reduced Riparian buffer Nitrogen reduction % Phosphorus reduction % bioreactor Saturated buffer controlled drainage Cover crops - short season Cover crops in corn/soy (drilled) Marginal land to perennials Wetland construction Fertilizer rate (UMN) % nutrients reduced into waters

59 Cost per pound of N reduced 25 Annual cost ($) per pound of N reduced in water

60 Cost per pound of P reduced 800 Annual cost $ per pound of P reduced in waters

61 Ratio of cost to nutrient reduction (N+P) 50.0 Ratio of cost (million $/yr) to benefit (% nutrient reduction in water)

62 Selecting BMPs Potential for reducing nutrients Cost effectiveness of BMPs ($/lb reduced) Multiple benefit potential o o o o o Water quality: Nitrogen, phosphorus, sediment, pesticides Soil: O.M., biology, water, resilience Air: Carbon sequestration, Nitrous oxide Wildlife & pollinator habitats Well water Acceptance by agricultural community Availability of government cost share & assist. Other social, regulatory, cultural, & technical factors

63 Multiple benefits N + P + Sed + Other manure incorporated Alternative intakes tillage reduced Riparian buffer Air Soil Health, Air Wildlife Nitrogen reduction % Phosphorus reduction % Sediment reduction % bioreactor Saturated buffer controlled drainage Cover crops - short season Soil Health, Air, Wildlife, Wells Cover crops in corn/soy (drilled) Soil Health, Air, Wildlife, Wells Marginal land to perennials Wetland construction Fertilizer rate (UMN) Wildlife Soil Health, Wildlife, Wells Air, Wells % nutrients and sediment reduced into waters

64 Selecting BMPs Potential for reducing pollutants Cost effectiveness of BMPs ($/lb reduced) Multiple benefit potential o o o o o Water quality: Nitrogen, phosphorus, sediment, pesticides Soil: O.M., biology, water, resilience Air: Carbon sequestration, Nitrous oxide Wildlife & pollinator habitats Wells Acceptance by agricultural community Availability of government cost share & assistance Other social, regulatory, cultural, & technical factors

65 Presentation Outline 1. Selecting BMPs using the tools and other information BMPs with most potential to reduce N & P in the Mississippi Most cost effective BMPs for N & P Multiple benefits and other considerations 2. Using the tools to estimate needed BMP adoption levels 3. Using the tools in series with other planning tools

66 Select any HUC8 or Basin

67 Select subwatersheds, if desired

68 Cannon River nitrogen reductions (starting with BMPs needed for multiple benefits)

69 Cannon River nitrogen reduction (adding cost-effective BMPs)

70 Cannon River nitrogen reduction (adding tile drainage treatment)

71 Presentation Outline 1. Selecting BMPs using the tools and other information BMPs with most potential to reduce N & P in the Mississippi Most cost effective BMPs for N & P Multiple benefits and other considerations 2. Using the tools to estimate needed BMP adoption levels 3. Using the tools in series with other planning tools

72 Using tools together Priority areas Which BMPs N & P Initial planning Which BMPs N, P, Sed, ++ BMP suites & adoption levels to reach goals? BMP placement in watershed? HSPF-SAM HSPF-SAM HSPF-SAM N-BMP/ P-BMP N-BMP/ P-BMP PTM-app PTM-app PTM-app PTM-app Zonation ACPF (Tomer) ACPF (Tomer) PZM

73 Conclusions Watershed planners can use existing tools like N-BMP and P-BMP to help evaluate which BMPs to use and BMP adoption levels needed to achieve goals. These tools provide quick estimates and should be used along with other tools. All tools show only part of the picture. Need to also consider multiple benefit opportunities and social/human factors.

74 Conclusions (continued) Nitrogen reduction potential in Mississippi Basin largest with: successful cover crops (21%) and fertilizer efficiency gains (10-20%). The most cost-effective BMPs for N include N fertilizer efficiency and wetlands, saturated buffers and controlled drainage. Phosphorus reduction potential in Mississippi Basin largest with cover crops (22%), riparian buffers (9%), reduced/conservation tillage (7%). Most cost-effective BMPs for P include P fertilizer efficiency, reduced tillage, tile intake riser pipes, & manure incorporated. When multiple benefits are desired, cost-effective BMPs can also include cover crops, vegetated buffers, and perennials on marginal lands.

75

76 Cost to benefit ratio Nitrogen only 50.0 Ratio of cost (million $/yr) to benefit (% nutrient reduction in water)

77 High corn prices increase BMP fertilizer rate (thus less reduction of N to waters) 25 % N reduction to waters Low $ fert Med $ fert High $ fert 0 $3 corn $5 corn $7 corn