Using Biological and Chemical

Tests to Predict N Mineralization

Jordon Wade1, Martin Burger1, Steve Culman2, &

William Horwath1

1 Dept. of Land, Air, & Water Resources

University of California, Davis 2 School of Environmental & Natural Resources

Ohio State University

The Problem of Nitrogen Synchrony • Differences in crop N demand and soil N mineralization results in some

“guesswork” in fertilization recommendations

• Soils can supply sufficient N for most crops (up to 300 kg/ha/yr)

• If we can predict N mineralization rates, we can adjust N fertilization

timing/rates accordingly

• Increase fertilizer N-use efficiency

• Estimate N release from organic N sources (cover crops, manures, etc.)

• Reduce N2O emissions

From Cornell Field Crops newsletter

Estimating Potentially Mineralizeable N

• Anaerobic Incubation (Waring and Bremner, 1964): soils are water-

logged for 7 days at 40°C

• Inconsistent correlations between lab and field measures in agricultural

soils (better in forest)

• Aerobic Incubation (Stanford & Smith, 1972): air-dried and then

rewetted soils measured and fit to first-order kinetics

Nt=N0*(1-e-kt)

• Many other tests have been developed,

but none have been successful over a

broad range of sites

Integrating Soil Biology: Respiration

• Biologically-based tests would allow for an estimation of microbial

metabolic processes

• Relationship between N mineralization and burst of respiration

upon the rewetting of soil has been shown

Franzluebbers et al., 2000

Integrating Soil Biology: Respiration

• Many of the data points have respiration rates at the lower end of this relationship

• The relationship varies across climatic regions

Franzluebbers et al., 2000

Dry Wet

Cold Warm

What about unamended CA soils?

• Experimental Design

• Four agricultural regions (Yolo, San

Joaquin, Salinas and Fresno/Kern

counties), representing a climatic

gradient

• Climatic gradient measured as aridity

index (AI)

AI = precipitation/MAT (°C)

• Increasing aridity as we move N to S

• Variety of crops included: corn,

processing tomatoes, sorghum,

almonds, lettuce, & spinach

• Management categorized by

presence of winter cover crop

immediately prior to growing season

• no cover cropped fields found in

Salinas County

Methods

• Chemical Indices

• Net N Mineralization measured in lab (NMINt )

• Change in inorganic N (NO3- + NH4

+) at time(t) =14, 28, 56 & 105 days

• C and N fractions assessed using three methods, which were to be

assessed against one another

• Biological Indices

• Cumulative Respiration (CMIN)

• measured at 6, 24, and 72 Hours after rewetting

• Permanganate-oxidizeable carbon (POXC): assessed as “biologically-

active” carbon

Results: N mineralization • Net N mineralization increased

throughout the incubation; most values fell within the following ranges

• NMIN28: 43.5-75.3 lbs N/ac

• NMIN56: 44.5-72.4 lbs N/ac

• NMIN105: 52.6-88.2 lbs N/ac

• Significant management effects at each date (p<0.05), with cover cropped fields having higher mineralization than non-cover cropped

• Release dynamics are similar

• Stockton field trials (56-days)

• 2013: 66.2 lbs N/ac

• 2014: 47.7 lbs N/ac

0

5

10

15

20

25

0 50 100 150

Net

N M

inera

lizati

on

(m

g N

/k

g A

D s

oil

)

Incubation Date (days)

0

5

10

15

20

25

30

0 20 40 60 80 100 120

Net

N M

inera

lizati

on

(m

g N

/k

g A

D s

oil

)

Incubation Date (days)

cover crops

no cover crops

Results

• Overall, 72-hour CO2 (CMIN0-72) had the best relationships with all N

mineralization values (10-12% of the NMIN values explained)

• 28-day net N mineralization (NMIN28) was best predicted by respiration

(8-14% of NMIN28 explained)

• C and N fractions

• N fractions were better correlated with N mineralization than C values

within each method

• Total N explained the most variation of N mineralization of all of the

measured fractions

• C:N ratio did not significantly effect N mineralization values

• Need to combine biological and chemical factors to better predict N

mineralization

• Factors were created to see the overall effects of climate and

management on 1. Biological Activity

2. Soil Organic Matter

Results: Management and Location Effects

• Overall: mean values of both

Factors are approximately equal

• Management: Cover crops had a

more pronounced effect on

biological activity (Factor1;

p<0.0001) than soil organic matter

(Factor2; p=0.0129), although

both were statistically significant

• Location: climatic factors only

produced significant effects on

biological activity (Factor1)

between the extremes, Yolo and

Fresno/Kern (p<0.0001), but for

soil organic matter (Factor2),

separated the most arid

(Fresno/Kern) from the other

three locations (p<0.05)

Parameter Selection

• Management: prediction using respiration works best in cover-cropped fields,

but

• Respiration: 24 hour respiration worked better in most arid, but 72 worked

best in least arid climates

• 72-hour respiration had the most predictive power for each studied N mineralization

timepoint and across all locations

• Models: the best predictive model utilized the water-extractable organic C and

N (WEOC/N) fractions, as well as the 72-hour respiration measurement

• Total C and N (Factor 2) had significant variation between growing regions, but

WEOC/N did not, which allowed it to be applied across all growing regions

• Relationship between WEOC and WEON varied slightly by region, which means

that regional calibrations could improve the accuracy of our predictions (R2 up to

0.82 in Fresno)

Final Predictive Model:

• NMINt= CMIN0-72 + WEOC + WEON where t = 28, 56, or 105 days

Adjusting Predictions by Location • Respiration was an equally strong driver between locations (less important in

non-cover cropped fields)

• Chemical fractions varied in importance, but differences were more

pronounced in cover cropped fields

• Fresno: strongest predictive factor is WEOC

• Had greatest predictive power at 28 days

• Overall, had greater accuracy than Yolo County

• Cover cropped fields had much higher R2 than non-cover cropped

• Yolo: strongest predictor is WEON

• Greatest predictive power at 105 days

• Low overall accuracy

• Cover cropped fields had greater accuracy than non-cover cropped

NMIN28 NMIN105

Cover Crops

No Cover Crops

Cover Crops

No Cover Crops

Fresno/Kern

Counties 0.8116 0.2745 0.7896 0.4029

Yolo County 0.2661 -0.0170 0.3680 -0.0587

Cost Analysis • For use with cover crops only:

• Net Mineralization at 28 days

• Predicted: 48.4-97.5 lbs N/ac mineralized

• $0.82/lb N x 48.4-97.5 lbs N/ac mineralized = $39.69 - $79.95 in potential

savings

• Net N mineralization at 56 days

• Predicted: 49.3-92.2 lbs N/ac mineralized

• $0.82/lb N x 49.3-92.2 lbs N/ac mineralized = $40.43 - $75.60/ac in potential

savings

• Net Mineralization at 105 days

• Predicted: 56.3-111.5 lbs N/ac mineralized

• $0.82/lb N x 56.3-111.5 lbs N/ac mineralized = $46.17-91.43 /ac in potential

savings

• Cost of analysis: $50-75 per sample, but a sample can be used to

represent several acres

• Taking multiple samples from across a field will increase accuracy

• Does not account for potential yield loss due to low N- will vary by crop

Conclusions • Utilizing soil respiration is ineffective when utilized as the sole estimator

of net N mineralization in a wide range of California agricultural soils

• Prediction is only valid on fields with recent cover crop incorporations

• Water-extractable organic carbon and nitrogen (WEOC/WEON) serve

as useful predictors across growing regions

• Regional calibrations of predictions would allow for much greater

certainty

• The expense of the combined respiration and chemical tests can be

offset by savings in N fertilizer

• Potential yield losses due to insufficient N not accounted for

• Utilizing respiration and water-extractable organic fractions has the

potential to help predict N mineralization, although some questions

remain

• Time after cover crop incorporation

• Cover crop quality

• Soil moisture content

Take-Home Messages • Cover cropped fields allow for a better estimation of N release using

these methods than non-cover cropped fields

• Best predictive model incorporates the following factors, although their importance varies

1. 72-hour respiration

2. Water-extractable organic C/N

• Most accurate interval of estimation varies by region: shorter in more arid regions, longer in less arid regions

• Using this suite of tests is cost-effective in terms of savings on fertilizer

• Cost-effectiveness increases as area sampled increases, but comes with accuracy tradeoff

• Non-cover cropped fields are more difficult to predict N mineralization due to heterogeneity of residue composition

• Our dataset suggests that a very rough approximation would be using pre-plant NO3

- levels and dividing by 6, which would give you a rough estimate of 105-day net N mineralization

• Pre-plant NO3- testing can be done in-field without sending samples to a lab

by using nitrate strips

Acknowledgements

• This project was funded (in part) by a grant from the California Department of Food and Agriculture’s Fertilizer Research and Education Program (FREP) and the Fertilizer Inspection Advisory Board.

• Growers that have collaborated on this study

• Professor Horwath & Labmates