using biological and chemical tests to predict n ... · using biological and chemical tests to...
TRANSCRIPT
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