Water and Nitrate Movement in Poultry Litter Amended Soils Jaime F. Sanchez

Overall Objectives 1)Describe nitrate movement in an Ultisol under long term poultry litter application 2)Determine the physical characteristics and organic carbon content of the soil to describe the water movement in selected profiles 3)Determine the nitrification rates of poultry litter used in the study area 4)Evaluate the scenarios technique as a tool for assessment of nitrate pollution risk

Suwannee River Basin North Florida 11,440 km 2 12 counties 290,000 people Live Oak Poultry Farm CR RD N Suwannee County, Fl SRWMD. 2000

Monitoring Area Poultry Houses Monitoring Area Poultry Houses Transect points Random points Wells

Site Characteristics Poultry litter spread area: 11 ha Loamy, siliceous, thermic, Arenic Paleudult Bahia grass, Paspalum notatum Litter production Mg y years of poultry litter application Grazed 40% of the time by 55 heads

1. Nitrate movement in soils Describe nitrate movement in an Ultisol under long term poultry litter application –Determine soil nitrate levels in a grazed bahia pasture under a long term poultry litter application –Determine the influence of rainfall the poultry litter applications on soil and groundwater nitrate levels

Location and methods Nitrate:10:1 solution in 0.01N KCl Determined with a Alpkem FlowSolution IV Autoanalyzer Weather station: established at the farm in 2001 ET: Hargreaves, 1975 and Hargreaves & Samani, 1982 Wells Soil sampling points

Characteristic Total solids % Total ash % Total N mg kg -1 31,23830,625 NH 3 -N mg kg -1 5,2064,988 Total P mg kg -1 13,87516,500 Total K mg kg -1 19,70725,590 Moisture % pH Fresh poultry litter composition per year

Feb – 2.7 Mg17Apr – 2.1 Mg22Feb – 4.5 Mg 11Jul – 0.8 Mg 31Aug – 2.1 Mg Nitrate changes on soil - Point 2

Conclusions The levels of nitrate-N in the groundwater never exceeded the 10 mg L -1 during the study period Although soil nitrate levels sometimes exceeded 10 mg kg -1 when amended with poultry litter at prescribed rates over the 18-month period; these peaks however, did not increase the nitrate levels over 10 mg L -1 in the groundwater Prior management factors may be responsible for higher initial nitrate levels observed in the soils

2. Soil Characterization Determine the soil physical characteristics and soil organic carbon to describe the water movement in selected profiles –Physical parameters determined Bulk Density Saturated Hydraulic Conductivity Moisture Retention Curves Particle Size Analysis

Specific Objectives –To describe water movement through a pedo-transfer function –Verify and compare the pedo-transfer function obtained with other models of water movement Soil Physical Characterization

Location and methods Bulk Density: Cylinder method Saturated Hydraulic Conductivity: Constant head Organic Carbon: Walkley-Black Particle Size Analysis: Pipet method Water Retention Curves: Tempe Cells Soil profiles 1 2 3

Bulk density 1 2 3

Saturated Hydraulic Conductivity 1 2 3

Organic Carbon 1 2 3

Clay 1 2 3

Water Retention Curves Undisturbed samples Tempe cells Ceramic plates, ½ bar Water column pressures –0.3, 2.,0, 2.9, 4.4, 5.9, 7.8, 9.8, 14.7, 19.6, 33.8 kPa Pressure Plate Extractor –490.4 and kPa

Pedo Transfer Functions kPa Factor Cumulative R 2 Clay (C)0.57 Bulk Density (BD)0.69 Organic Carbon (OC)0.75 Medium Sand (M)0.78 Coarse (C)0.80 Θ 33.8kPa = – *C *M *CLAY *OC *BD

Pedo Transfer Functions kPa Factor Cumulative R 2 Clay0.54 Bulk Density (BD)0.66 Medium Sand (M)0.71 Coarse (C)0.79 Organic Carbon (OC)0.80 Very Fine Sand (VF)0.81 Θ 490.4kPa = – *C *M *VF *CLAY *OC *BD

Θ kPa = – *C *M *CLAY *BD Pedo Transfer Functions -1,471.3 kPa Factor Cumulative R 2 Clay (C)0.54 Bulk Density (BD)0.63 Medium Sand (M)0.67 Coarse (C)0.77

Campbell Water Retention Equation & Modifications h = a ( θ / θ s ) -b Where θ s is water content at saturation and a and b are constants a (1 - θ / θ s ) 1/2 (θ c / θ s ) -b (1- θ c / θ s ) 1/2 h = h c = a [2b / (1+2b)] -b θ c = 2bθ s / (1+2b) Where h c θ c is the point of intersection of the exponential and parabolic curves and, The composite water retention curve is sigmoidal, continuous and has a differential water capacity of zero at saturation Campbell Hutson and Cass

StatisticCampbellPTF R2R Mean Absolute Error Root Mean Square error Model Predictions – kPa suction

StatisticCampbellPTF R2R Mean Absolute Error Root Mean Square error Model Predictions – kPa suction

StatisticCampbellPTF R2R Mean Absolute Error Root Mean Square error Model Predictions – -1,471.3 kPa suction

Conclusions The soil water retention increased as the clay content increased with depth With some exception, water retention and bulk density were well correlated The Campbell model better predicted the water content at different suctions compared to the PTF

3. Nitrification of Poultry Litter Determine the nitrification rates of poultry litter used in the study area –Procedure 450 gr of soil 7 rates of poultry litter application (0,3,6,9,12,15,18 and 21 Mg ha -1 ) Incubated at 40 ºC Sampling at 0,1,2,3,5,7,10,15,20,30,45 and 60 days after application N pool and first order rate constant –Nm = No [1 – e (-kt)]

N Mineralized Poultry Litter (gr) Total mineralized N (mg kg -1 ) Incubation Time (Days)

% N Mineralized of N applied Poultry Litter (gr) % mineralizaed of N applied Incubation Time (Days)

Treatment poultry litter weight gr N o % Mineralizable N pool k d -1 rate constant of mineralization a a ab0.14 a ab0.12 a ab0.17 a ab0.13 a ab0.13 a b0.13 a Model parameters

Conclusions Higher applications of poultry litter yielded higher levels of mineralized N After 60 days of incubation, the net N mineralized was 61 mg kg -1 when the litter application rate was 3 Mg ha -1 ; the net mineralized N increased to 167 mg kg -1 when the rate of litter application was increased to 21 Mg ha -1 38% of the nitrogen was mineralized when 21 Mg ha -1 of poultry litter was applied vs 68% mineralized when 3 Mg ha -1 litter was applied

4. Scenarios Technique Each scenario or coherent series of assumptions expressed in figures (numbers, tables, illustrations) - Godet, 1987 Suggested name - Exploratory Prospective Analysis - defined as “a panorama of possible futures, or scenarios, which are not improbable in the light of past causalities, and the interaction between the intentions of interested parties”

Objectives Evaluate the scenarios technique as a tool for assessment of nitrate pollution risk –Scenarios based on: Water retention characteristics Weather time series

Leachn - Hutson and Wagenet, 1992 Richard’s equation to predict soil water dynamics Simulate the N cycle according to Johnson et.al., 1987 Humification process is simulated, allowing for reorganization of mineral nitrogen and CO 2 losses Ammonia pool is subjected to nitrification, volatilization and leaching and the nitrate pool is subjected to denitrification and leaching

Climatic variables Mayo station –30º 03’ N –83º 11’ W A time series of 50 years –Rainfall –Maximum and minimum temperature

Best Management Practices Application of manure between end of March and beginning of September Application of fresh poultry manure to cover nitrogen Bahia grass, Paspalum notatum requirements No extra inorganic fertilizers Grazing, 40% of the time

Leachn parameters – soil physical characteristics Water retention curves Determined Campbell parameters Derived Saturated hydraulic conductivity Determined Particle size analysis Determined

Weather – Mayo Fl, H Rainfall, mm Day of the Year Temperature C

Water Content - Validation

Soil Nitrate - Validation

Scenarios SCENARIO 1IFAS BMP: 50 kg ha -1 N 100 kg ha -1 N 160 kg ha -1 N SCENARIO 2IFAS BMP + 50 kg ha -1 Ammonium Nitrate SCENARIO 3IFAS BMP on SAND PROFILE SCENARIO 4IFAS BMP on Wet-Dry Sequence of years

Scenario 1 – IFAS BMPs Statistic Nitrate in the soil, mg kg -1 Total <=10>10, <=20>20 50 kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent

Scenario 1 – IFAS BMPs

Nitrate mg kg kg ha -1 year -1 N Depth, cm Year Scenario 1 – IFAS BMPs

Scenario 2 – IFAS BMPs + 50 kg ha -1 Ammonium nitrate 50 kg ha -1 year -1 form poultry litter + 50 kg ha -1 year -1 N from fertilizer Frequency Expected Cell chi square Percent kg ha -1 year -1 form poultry litter + 50 kg ha -1 year -1 N from fertilizer Frequency Expected Cell chi square Percent kg ha -1 year -1 form poultry litter + 50 kg ha -1 year -1 N from fertilizer Frequency Expected Cell chi square Percent

Scenario 2 – IFAS BMPs + 50 kg ha -1 Ammonium nitrate

Scenario 3 – IFAS BMPs - Sand Profile Statistic Nitrate in the soil, mg kg -1 Total <=10>10, <=20>20 50 kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent

Scenario 3 – IFAS BMPs - Sand Profile Nitrate in drainage, kg ha Year of simulation Scenario 50N 100N160N 50S100S160S

Scenario 3 – IFAS BMPs - Sand Profile

Scenario 4 – IFAS BMPs - Wet-Dry years Statistic Nitrate in the soil, mg kg -1 Total <=10>10, <=20>20 50 kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent kg ha -1 year -1 from poultry litter Frequency Expected Cell chi square Percent

Scenario 4 – IFAS BMPs - Wet-Dry years

LEACHM is a potential tool for the analysis of BMP scenarios in Ultisols of North Florida. The scenarios technique is able to differentiate between the different levels of nutrient BMPs for bahia grass fertilization. Conclusions

The application of additional nitrogen from ammonium nitrate with the application of poultry litter as the main source of nitrogen is a potential source of nitrate leaching in Ultisols of North Florida. The scenarios established with a sequence of wet dry years had a strong effect on the nitrate leaching. The changes in the soil profile have less effect in nitrate accumulation and nitrate leaching than the weather conditions.

Work ahead - 1 Due to new legislation on the pipeline, more research should be done in mineralization rates of manures to determine closer laboratory indexes to field conditions Pedotansfer functions should be developed for selected soils as a first step to recognize soil water movement in Florida soils

Work ahead - 2 LEACHM is a potential tool to analyze new BMPs and evaluate the nitrate pollution risk in Florida soils using the information from the IFAS soil survey database LEACHM can be used with a crop simulation model to evaluate the role of crops in nitrate uptake

Work ahead - 3 Special emphasis should be given to pastures/grazing combination as a new component of BMP development

FDEP - USEPA Sec 319 Grant Funding Committee Chair: Dr. Rao Mylavarapu Members of the Committee: Drs. Kenneth Portier, Donald Graetz, George Hochmuth and Art Hornsby Steve Robinson, Dawn Lucas, Joseph Nguyen, Martin Anderson and the field crew Mr. Harold Barnes Daniel Herrera and Fernando Muñoz Acknowledgements

ThanksMuchas gracias Enjoy the Colombian Coffee