Precision applications of nutrients - Dr. Josh McGrath, University of Kentucky, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
This document provides instructions for preparing a soil sample and determining the soil pH using a pH meter. It describes collecting a soil sample from 15 cm below the surface, drying the sample, grinding and sieving it to obtain particles smaller than 2 mm. It recommends mixing 5 grams of the processed soil sample with 50 ml of distilled water to make a solution in a 1:10 proportion for pH testing. The solution is stirred and allowed to settle before filtering and measuring the pH using a pH meter probe placed directly in the filtered soil solution. Maintaining proper procedures in collecting, preparing and testing the soil sample ensures accurate pH results.
Accounting soil moisture assimilation for hydrologic predictionsPawan Jeet
Soil moisture is an important component of land surface hydrology. This document discusses techniques for measuring, modeling, and assimilating soil moisture data. It describes how the Ensemble Kalman Filter can be used to assimilate remote sensing soil moisture observations into hydrologic models to improve soil moisture estimates. Case studies show that data assimilation reduces errors in simulated soil moisture, especially in surface layers, but has more limited impact at depth due to model biases and less influence of surface conditions on deeper layers. Overall, data assimilation is an effective method for integrating soil moisture observations into hydrologic models to enhance predictions.
Removing phosphorus from drainage water the phosphorus removal structureLPE Learning Center
Full proceedings available at: http://www.extension.org/72839
We constructed a phosphorus (P) removal structure on a poultry farm in Eastern OK; this is a BMP that can remove dissolved P loading in the short term until soil legacy P concentrations decrease below levels of environmental concern. A P removal structure contains P sorbing materials (PSMs) and are placed in a location to intercept runoff or subsurface drainage with high dissolved P concentrations. As high P water flows through the PSMs, dissolved P is sorbed onto the materials by several potential mechanisms, allowing low P water to exit the structure. While they vary in form, P removal structures contain three main elements: 1) use of a filter material that has a high affinity for P, 2) containment of the material, and 3) the ability to remove that material and replace it after it becomes saturated with P and is no longer effective.
1) The document discusses the process of soil testing and fertilizer recommendation including collecting soil samples from different soil units, preparing the samples in the lab through drying, grinding, sieving, mixing and weighing.
2) It also discusses determining available nutrients through extraction and analyzing the samples. Fertilizer doses are then recommended based on the soil test values and considering crop type, costs and other factors.
3) A table provides classification of soils into different categories based on organic carbon, nitrogen, phosphorus and potassium levels and how it affects the recommended fertilizer dose.
1. Soil sampling involves taking representative soil cores from throughout a field and mixing them into a composite sample for analysis.
2. In the lab, the soil sample is dried, ground, sieved and analyzed to determine levels of major nutrients like nitrogen, phosphorus, and potassium through extraction methods using different reagents.
3. The nutrient levels are then classified as low, medium, or high to determine fertilizer recommendations needed to provide optimal nutrition for crop yields.
Estimation of phosphorus loss from agricultural land in the southern region o...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72817
The purpose of our work was to determine, within the southern region (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, and TX), the feasibility of using different models to determine potential phosphorus loss from agricultural fields in lieu of phosphorus indices.
Soil Sampling is a very common practice in the Spring and Fall. However in other parts of the country, June and August are very popular months. This document reviews the process of collecting a proper soil for analysis.
Estimation of phosphorus loss from agricultural land in the heartland region ...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72813
Phosphorus (P) indices are a key tool to minimize P loss from agricultural fields but there is insufficient water quality data to fully test them. Our goal is to use the Agricultural Policy/Environmental eXtender Model (APEX), calibrated with existing edge-of-field runoff data, to refine P indices and demonstrate their utility as a field assessment tool capable of protecting water quality. In this phase of the project our goal is to use existing small-watershed data from the Heartland Region (IA, KS, MO and NE) to determine the level of calibration needed for APEX before using the model to generate estimates of P loads appropriate for evaluating a P Index.
This document provides instructions for preparing a soil sample and determining the soil pH using a pH meter. It describes collecting a soil sample from 15 cm below the surface, drying the sample, grinding and sieving it to obtain particles smaller than 2 mm. It recommends mixing 5 grams of the processed soil sample with 50 ml of distilled water to make a solution in a 1:10 proportion for pH testing. The solution is stirred and allowed to settle before filtering and measuring the pH using a pH meter probe placed directly in the filtered soil solution. Maintaining proper procedures in collecting, preparing and testing the soil sample ensures accurate pH results.
Accounting soil moisture assimilation for hydrologic predictionsPawan Jeet
Soil moisture is an important component of land surface hydrology. This document discusses techniques for measuring, modeling, and assimilating soil moisture data. It describes how the Ensemble Kalman Filter can be used to assimilate remote sensing soil moisture observations into hydrologic models to improve soil moisture estimates. Case studies show that data assimilation reduces errors in simulated soil moisture, especially in surface layers, but has more limited impact at depth due to model biases and less influence of surface conditions on deeper layers. Overall, data assimilation is an effective method for integrating soil moisture observations into hydrologic models to enhance predictions.
Removing phosphorus from drainage water the phosphorus removal structureLPE Learning Center
Full proceedings available at: http://www.extension.org/72839
We constructed a phosphorus (P) removal structure on a poultry farm in Eastern OK; this is a BMP that can remove dissolved P loading in the short term until soil legacy P concentrations decrease below levels of environmental concern. A P removal structure contains P sorbing materials (PSMs) and are placed in a location to intercept runoff or subsurface drainage with high dissolved P concentrations. As high P water flows through the PSMs, dissolved P is sorbed onto the materials by several potential mechanisms, allowing low P water to exit the structure. While they vary in form, P removal structures contain three main elements: 1) use of a filter material that has a high affinity for P, 2) containment of the material, and 3) the ability to remove that material and replace it after it becomes saturated with P and is no longer effective.
1) The document discusses the process of soil testing and fertilizer recommendation including collecting soil samples from different soil units, preparing the samples in the lab through drying, grinding, sieving, mixing and weighing.
2) It also discusses determining available nutrients through extraction and analyzing the samples. Fertilizer doses are then recommended based on the soil test values and considering crop type, costs and other factors.
3) A table provides classification of soils into different categories based on organic carbon, nitrogen, phosphorus and potassium levels and how it affects the recommended fertilizer dose.
1. Soil sampling involves taking representative soil cores from throughout a field and mixing them into a composite sample for analysis.
2. In the lab, the soil sample is dried, ground, sieved and analyzed to determine levels of major nutrients like nitrogen, phosphorus, and potassium through extraction methods using different reagents.
3. The nutrient levels are then classified as low, medium, or high to determine fertilizer recommendations needed to provide optimal nutrition for crop yields.
Estimation of phosphorus loss from agricultural land in the southern region o...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72817
The purpose of our work was to determine, within the southern region (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, and TX), the feasibility of using different models to determine potential phosphorus loss from agricultural fields in lieu of phosphorus indices.
Soil Sampling is a very common practice in the Spring and Fall. However in other parts of the country, June and August are very popular months. This document reviews the process of collecting a proper soil for analysis.
Estimation of phosphorus loss from agricultural land in the heartland region ...LPE Learning Center
Full Proceedings is available at: http://www.extension.org/72813
Phosphorus (P) indices are a key tool to minimize P loss from agricultural fields but there is insufficient water quality data to fully test them. Our goal is to use the Agricultural Policy/Environmental eXtender Model (APEX), calibrated with existing edge-of-field runoff data, to refine P indices and demonstrate their utility as a field assessment tool capable of protecting water quality. In this phase of the project our goal is to use existing small-watershed data from the Heartland Region (IA, KS, MO and NE) to determine the level of calibration needed for APEX before using the model to generate estimates of P loads appropriate for evaluating a P Index.
This document evaluates roadside vegetation for erosion control in West Virginia. It summarizes:
1) Typical issues with roadsides in WV include rock falls, bare slopes, erosion, and poor vegetative growth due to variable soil types, elevation changes, and steep slopes.
2) The study aimed to determine the effectiveness of current reclamation methods and improve practices to provide better vegetative cover to prevent erosion. Sites across multiple locations, soil types, seed mixtures, slopes, aspects, and climates were sampled.
3) Results showed 30% of sites had excellent cover over 90% while 24% had poor cover under 50%. Only 55% met the 70% cover requirement. Soil nutrient levels and
Provides a guideline for soil sampling and processing techniques.
Get more: http://worldagroforestry.org/research/land-health/spectral-diagnostics-laboratory
This document discusses issues with the methodology for measuring active carbon (POX-C) in soils. Specifically, it finds that active carbon measurements are dependent on the amount of soil sample used and how finely the sample is ground. Statistical analysis showed that both the sample weight class and grind size significantly impacted results. While decreasing sample weight or increasing grind size increased measured active carbon, this makes values incomparable between different sample preparations. The document concludes that more work is needed to understand what active carbon analysis actually measures and that it may only be useful for comparing similar soils when a standard sample weight is used.
Soil sampling analysis for various techniquejafar61180
1) Soil sampling and testing is important to determine nutrient levels and pH to provide optimal fertilizer recommendations for plant growth.
2) A soil sample should be taken before planting, fertilizing, or 3 months after fertilizing from 15 spots that are dug 15cm deep and mixed well.
3) Nutrient deficiencies in plants can be diagnosed from symptoms like yellowing, stunted growth, or spotting; and soil pH is measured to determine acidity or alkalinity levels that also impact plant growth.
The document discusses comparing the Pennsylvania Phosphorus Index to the TopoSWAT model. It summarizes research objectives to establish watershed networks for evaluating nutrient management tools, identify priority concerns within physiographic provinces, and use water quality data to refine the P Index. It then describes challenges with the P Index, experimental versus modeling approaches, and initial results comparing TopoSWAT and P Index outputs for an agricultural watershed in Pennsylvania. Next steps focus on determining appropriate comparison methods to improve the P Index.
This document provides a summary of a presentation on soil testing. It discusses:
1) The objectives of soil testing programs are to provide an index of nutrient availability, predict fertilizer response, and provide fertilizer recommendations.
2) The phases of soil testing include collecting soil samples, extracting and analyzing nutrients, interpreting results, and making fertilizer recommendations.
3) Proper soil sampling involves taking composited samples at a depth of 20cm, using augers or probes, drying the samples, grinding them to pass through a 2mm sieve.
This document provides information about soil testing procedures. It discusses (1) what soil testing is and why it is needed, (2) how and when to take soil samples, (3) common soil testing kits and sampling methods, (4) how to prepare soil samples and conduct soil testing, (5) key parameters tested like pH, nutrients, and organic carbon, (6) how to interpret soil analysis results, and (7) advantages and disadvantages of soil testing. The overall purpose is to explain the full soil testing process from sampling to analysis and interpretation.
This document summarizes a study that tested objective and subjective methods for measuring land area, soil fertility, and crop production in Ethiopia. The study involved collecting over 3,700 soil samples from 1,799 fields across 85 areas, which were tested using several methods including spectral and conventional analysis. The objective data showed variation in soil properties within and between areas. Comparisons found farmers' subjective assessments of soil quality did not capture the full variation and sometimes overestimated quality. The results suggest spectral soil analysis could improve soil data collection but challenges include cost, lab capacity, and scaling to different regions and crop cycles.
Sam Mullins - Updates for the H2Ohio programJohn Blue
Updates for the H2Ohio program - Sam Mullins, Ohio Department of Agriculture, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Jason Haines (@PenderSuper) and Larry Stowell (@paceturf) MLSN and Growth Potential slides from the 2017 Golf Industry Show (#GIS17).
MLSN (Minimum Levels for Sustainable Nutrition) guidelines are a new way of determining what types and amounts of fertilizer are necessary, without compromising turfgrass health and quality. Growth Potential (GP) aids in fertilizer budgeting by estimating nutrient needs based upon local climate. Turf professionals can realize significant cost savings and reduce environmental impacts by taking a fresh look at fertilizer inputs.
This document provides information about soil testing and fertilizer recommendations. It discusses the objectives, phases and process of soil testing programs including sample collection, extraction of nutrients, interpretation of results, and fertilizer recommendations. The key phases involve collecting representative soil samples, extracting available nutrients using common extractants, interpreting results based on nutrient categories and critical levels, and providing fertilizer guidelines based on soil fertility levels and target crop yields.
B Sc Agri II Sc,Sf & Nm, U 3 Soil Fertility EvaluationRai University
This document discusses soil fertility evaluation in India. It provides key landmarks in soil testing research from 1953-1980s. It describes various approaches used for soil testing and fertility evaluation including soil testing, plant analysis, biological tests, and soil test crop response correlation. The goal of soil fertility evaluation is to precisely predict fertilizer requirements for crops through calibration of soil test methods and establishing critical limits.
Predicting crop yield and response to Nutrients from soil spectra at WCSS 201...CIAT
This document presents a study that uses soil spectroscopy to predict crop yields and response to fertilizers in sub-Saharan Africa. The study collected soil spectra and yield data from plots in Tanzania and Malawi. Statistical models like partial least squares regression and random forest were used to determine how much of the variance in yield and response could be explained by soil spectra alone and in combination with other soil, topographic, and weather data. The models were able to explain up to 65% of the variance in yield, with soil spectra and additional data like rainfall and topography each contributing. The study aims to refine yield predictions and help smallholder farmers apply optimal fertilizer levels based on cheap soil spectral analyses.
- The document summarizes Muhammad Adeel's internship experience sampling soils and testing soils and water at the Soil and Water Testing Laboratory in Layyah, Pakistan.
- The lab's equipment was old and results were not always satisfactory due to lack of funding, but it had necessary equipment for testing soil properties like pH, organic matter, phosphorus, and texture.
- Adeel learned techniques for collecting soil and water samples and testing them in the lab to determine nutrients, contaminants, and other properties following standard procedures.
- He gained hands-on experience that contributed to his education, and he expressed gratitude to the laboratory staff and his teachers.
Dr. Steve Culman - Tri-State Recommendations (as they relate to 2019 disrupti...John Blue
Tri-State Recommendations (as they relate to 2019 disruptions) - Dr. Steve Culman, OSU Soil Fertility Extension Specialist, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
This document provides information about soil and sediment sampling. It discusses basic principles of soil sampling including objectives of soil monitoring and parts of a monitoring plan. It covers site characterization, selection of sampling approach and factors that affect sample reliability. The document also addresses selection of area, sampling point, parameters and equipment for sampling. Finally, it discusses guidelines for handling and storage of soil samples including preservation techniques, as well as pre-treatment and extraction of contaminants from soil.
Jordan Hoewischer - OACI Farmer Certification ProgramJohn Blue
OACI Farmer Certification Program - Jordan Hoewischer, Ohio Farm Bureau, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
Fred Yoder - No-till and Climate Change: Fact, Fiction, and IgnoranceJohn Blue
No-till and Climate Change: Fact, Fiction, and Ignorance - Fred Yoder, Former President, National Corn Growers Association, from the 2020 Conservation Tillage and Technology Conference, held March 3-4, 2020, Ada, OH, USA.
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This document evaluates roadside vegetation for erosion control in West Virginia. It summarizes:
1) Typical issues with roadsides in WV include rock falls, bare slopes, erosion, and poor vegetative growth due to variable soil types, elevation changes, and steep slopes.
2) The study aimed to determine the effectiveness of current reclamation methods and improve practices to provide better vegetative cover to prevent erosion. Sites across multiple locations, soil types, seed mixtures, slopes, aspects, and climates were sampled.
3) Results showed 30% of sites had excellent cover over 90% while 24% had poor cover under 50%. Only 55% met the 70% cover requirement. Soil nutrient levels and
Provides a guideline for soil sampling and processing techniques.
Get more: http://worldagroforestry.org/research/land-health/spectral-diagnostics-laboratory
This document discusses issues with the methodology for measuring active carbon (POX-C) in soils. Specifically, it finds that active carbon measurements are dependent on the amount of soil sample used and how finely the sample is ground. Statistical analysis showed that both the sample weight class and grind size significantly impacted results. While decreasing sample weight or increasing grind size increased measured active carbon, this makes values incomparable between different sample preparations. The document concludes that more work is needed to understand what active carbon analysis actually measures and that it may only be useful for comparing similar soils when a standard sample weight is used.
Soil sampling analysis for various techniquejafar61180
1) Soil sampling and testing is important to determine nutrient levels and pH to provide optimal fertilizer recommendations for plant growth.
2) A soil sample should be taken before planting, fertilizing, or 3 months after fertilizing from 15 spots that are dug 15cm deep and mixed well.
3) Nutrient deficiencies in plants can be diagnosed from symptoms like yellowing, stunted growth, or spotting; and soil pH is measured to determine acidity or alkalinity levels that also impact plant growth.
The document discusses comparing the Pennsylvania Phosphorus Index to the TopoSWAT model. It summarizes research objectives to establish watershed networks for evaluating nutrient management tools, identify priority concerns within physiographic provinces, and use water quality data to refine the P Index. It then describes challenges with the P Index, experimental versus modeling approaches, and initial results comparing TopoSWAT and P Index outputs for an agricultural watershed in Pennsylvania. Next steps focus on determining appropriate comparison methods to improve the P Index.
This document provides a summary of a presentation on soil testing. It discusses:
1) The objectives of soil testing programs are to provide an index of nutrient availability, predict fertilizer response, and provide fertilizer recommendations.
2) The phases of soil testing include collecting soil samples, extracting and analyzing nutrients, interpreting results, and making fertilizer recommendations.
3) Proper soil sampling involves taking composited samples at a depth of 20cm, using augers or probes, drying the samples, grinding them to pass through a 2mm sieve.
This document provides information about soil testing procedures. It discusses (1) what soil testing is and why it is needed, (2) how and when to take soil samples, (3) common soil testing kits and sampling methods, (4) how to prepare soil samples and conduct soil testing, (5) key parameters tested like pH, nutrients, and organic carbon, (6) how to interpret soil analysis results, and (7) advantages and disadvantages of soil testing. The overall purpose is to explain the full soil testing process from sampling to analysis and interpretation.
This document summarizes a study that tested objective and subjective methods for measuring land area, soil fertility, and crop production in Ethiopia. The study involved collecting over 3,700 soil samples from 1,799 fields across 85 areas, which were tested using several methods including spectral and conventional analysis. The objective data showed variation in soil properties within and between areas. Comparisons found farmers' subjective assessments of soil quality did not capture the full variation and sometimes overestimated quality. The results suggest spectral soil analysis could improve soil data collection but challenges include cost, lab capacity, and scaling to different regions and crop cycles.
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Results of geophysics and pneumatic injection pilot tests during 2003 – 2007 yielded significant positive results for injection delivery design and contaminant mass treatment, resulting in permanent shut-down of an existing groundwater Pump & Treat system.
Accessible source areas were subsequently removed (2011) by soil excavation and treated with the placement of Emulsified Vegetable Oil EVO and zero-valent iron ZVI to accelerate treatment of impacted groundwater in overburden and weathered fractured bedrock. Post pilot test and post remediation groundwater monitoring has included analyses of CVOCs, organic fatty acids, dissolved gases and QuantArray® -Chlor to quantify key microorganisms (e.g., Dehalococcoides, Dehalobacter, etc.) and functional genes (e.g., vinyl chloride reductase, methane monooxygenase, etc.) to assess potential for reductive dechlorination and aerobic cometabolism of CVOCs.
In 2022, the first commercial application of MetaArray™ was performed at the site. MetaArray™ utilizes statistical analysis, such as principal component analysis and multivariate analysis to provide evidence that reductive dechlorination is active or even that it is slowing. This creates actionable data allowing users to save money by making important site management decisions earlier.
The results of the MetaArray™ analysis’ support vector machine (SVM) identified groundwater monitoring wells with a 80% confidence that were characterized as either Limited for Reductive Decholorination or had a High Reductive Reduction Dechlorination potential. The results of MetaArray™ will be used to further optimize the site’s post remediation monitoring program for monitored natural attenuation.
2. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
2
3. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
3
• Traditionally viewed as limiting function when
managing field average
• When planning VR we often grid sample and
interpolate between points
4. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
4
• Traditionally viewed as limiting function
when managing field average
• When planning VR we often grid sample and
interpolate between points
• We’re pretty good at soil analysis
5. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
5
• Traditionally viewed as limiting function
when managing field average
• When planning VR we often grid sample and
interpolate between points
• We’re pretty good at soil analysis
• We have a long way to go here
6. What do we need and what do we have for VR?
•~40 - 70% report VR nutrient
management– what’s the
agronomic basis?
•High resolution
characterization of spatially
variable nutrient need
• Spatial distribution of nutrient
availability (soil testing)
•Interpretation of soil test
results with matching precision
•Recommendations developed
for VR application
•We can vary fertilizer at a
pretty fine resolution
•We can’t (precisely) map need
at the same resolution
•We don’t develop
recommendations at that
resolution
6
7. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
7
8. Soil Sampling for Precision Ag
10 13 11 22
7
9 15 3512
45 13 18
?
10 13 11 22
• Precision ag breaks a field into
smaller management zones
• Grid Sampling (grid point and grid
cell):
• Typically involves interpolation of grid
sample data
• Previous management has altered soil
nutrient levels
• Combined small fields into large field
• Directed sampling:
• Break field into zones and collect
“average” sample for each zone
• Requires other data: yield maps, remotely
sensed images, or other sources of spatial
data
• Requires experience with field
9. Most VRP likely based on interpolated grid soil data
•Interpolation estimates
unknown value between two
sample points
•Samples must be collected
close enough that they are
correlated
•r > 0.3
•Requires samples on ¼ acre
grid or less
9
Lauzon, J.D., I.P. O’Halloran, D.J. Fallow, A.P. von Bertoldi, and D. Aspinall. 2005. Spatial Variability of Soil Test
Phosphorus, Potassium, and pH of Ontario Soils. Agronomy Journal 97(2): 524–532.
10. How does interpolation perform?
•Often field average is closer to true value
than coarse sampling (>1/4 acre)
•At small scale soil properties tend to be
stochastic
•Random such that they can be predicted
accurately, but not necessarily precisely
•On average the estimated values are right,
at each spot they are off further than the
field average
10
¼ acre grid
1 acre grid
Courtesy John Spargo, Penn State Soil Testing Lab
11. High Precision
Low Accuracy
Low Precision
High Accuracy
Reminder: Accuracy v. Precision
http://en.wikipedia.org/wiki/File:High_precision_Low_accuracy.svg
11
12. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
12
13. Soil analysis
•Soil testing should
provide
•Nutrient in soil solution
(intensity) and in stored
pools (quantity)
•Buffer capacity (Q/I).
13
14. Soil analysis
•Soil testing should
provide
•Nutrient in soil solution
(intensity) and in stored
pools (quantity)
•Buffer capacity (Q/I).
14
•Multiple factors affect Q/I
•Soil testing can only
provide an index of
nutrient supplying capacity
of a soil.
16. Soil Chemical Analysis
•There are multiple soil tests
that use different procedures
and chemicals
•Each extracts different
nutrient amounts and forms
from soil pools
•Require local correlation and
calibration to be useful
16
17. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
17
18. Soil test interpretation &
Fertilizer recommendations
•Correlation
•Relative yield versus soil test
value
•Plant response to application
of element
• Requires check plot and sufficient plot
•Conduct experiment at
multiple sites (multiple soil
concentrations)
18
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 50 100 150 200
RelativeYield
Soil Test Result
Soil critical concentration
Relative Yield = 0.95
19. Soil test interpretation &
Fertilizer recommendations
•Calibration
•Amount of applied nutrient
versus soil test value
•Multiple fertilizer rates at one
site (one soil test value)
•Conduct at multiple sites
•Build fertilizer
recommendations for different
soil test values
19
0
20
40
60
80
100
120
140
160
180
200
0 50 100 150
Yield(bu/acre)
Fertilizer Rate (lbs/acre)
Yield response to fertilizer rate at different
sites
Soil Test = 30
Soil Test = 20
Soil Test = 10
21. Soil Testing
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
21
22. Soil Test Calibration: How much fertilizer?
1. Sufficiency approach
• When soil test level is below optimum,
apply only enough nutrients to meet
crop needs
2. Buildup and maintenance
approach
• Rapidly build low soil test
concentrations to optimum level
• Replace nutrients removed by crop at
higher soil test levels where response is
not expected
3. Hybrid Approach
22What about BCSR?
-100
0
100
200
300
400
500
600
700
0 10 20 30 40 50 60 70
FertilizerPrate(lb-P2O5/a)
Initial soil test P (lb/a)
Fertilizer P rate to move STP10 units CornP2O5 Rec
23. Calibration: Fertilizer Recommendation Systems
23
0
50
100
150
200
250
0 5 10 15 20 25 30
Fertilizerrate(lb-P2O5/a)
Mehlich 3 Soil P (mg/kg)
Sufficiency only recommendations
Sufficiency
0
50
100
150
200
250
0 5 10 15 20 25 30
Fertilizerrate(lb-P2O5/a)
Mehlich 3 Soil P (mg/kg)
Example of Build and Maintain
Recommendations
Buildup
Maintenance
ActualCriticalLevel
ActualCriticalLevel
24. How should we make precise recommendations?
•Build and maintain ignores
soil buffer capacity
•To move STP 1 lb/acre
•Low STP required 10 – 25 lb-
P2O5/acre
•Optimum STP or above
required ~5 lb-P2O5/acre
•Initial STP <6
•600 – 200 lb/a P2O5 to move
soil test +10 lb/a
•Soils don’t pay interest
(Thom and Dollarhide, 2002)
0
5
10
15
20
25
0 50 100 150 200 250 300
Lb-P2O5added/lb-STPchange
Initial Soil Test P (lb/acre)
25. Soil nutrient storage – buffer capacity
25
This Photo by Unknown Author is licensed under CC BY-NC-ND
26. How should we make precise recommendations?
•Precision ag -- frequent soil
testing and sufficiency rates
•We need to know the yield
maximizing (sufficiency) rate
•Sufficiency rate < build &
maintain
•Sufficiency probably < crop
removal
•buffer capacity makes up
difference
(Thom and Dollarhide, 2002)
0
50
100
150
200
250
0 5 10 15 20 25 30
Fertilizerrate(lb-P2O5/a)
Mehlich 3 Soil P (mg/kg)
Example of Build and Maintain
Recommendations
Buildup
Maintenance
Sufficiency
27. Designing soil testing for precision
•Four separate activities:
1. Soil sampling
2. Soil analysis
3. Interpretation of results
4. Recommendations
•How do we spatially
characterize response
potential?
•Do we need additional
points of information
besides current soil test?
•What would this data look
like?
•Philosophical approach to
precision ag?
27
28. How do we move forward?
•I would argue we need new
research design and analysis
methods to get precise
•Five P rates, randomized in
five blocks, at four locations,
for three years…
28
We don’t need more
data; we need different
data!
29. Spatial variability in soil test correlation
•Reduce plot size to limit
variability
•Two treatments: sufficient or
none
•One phosphorus application
rate using APP in 2x2
•29 kg ha-1 P (60 lb/acre P2O5)
•56 kg ha-1 N (50 lb/acre)
balanced using UAN
29
30. Early growth response: All sites
• 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 =
)*+,-../
)*+,-..0
×100%
•Biomass in kg/ha
•Red line indicates University
critical level
•Green line indicates 95%
Relative Biomass
•Majority of the time starter P
increased biomass V4-V6
30
31. Delta Yield Paired T-Test: Princeton
•Delta Yield = YP-Y0
•Found to be highly
significant
•Mean difference 9.4 bu/a
31
32. On average soil test correctly predicted yield response
32
Regardless of soil test we had a yield
response in about 50% of the plots
0
20
40
60
80
100
120
140
0 5 10 15 20 25 30 35
RelativeYield
Mehlich 3 Phosphorus (mg kg-1)
34. Soil testing for SSM: New challenges
34
0%
20%
40%
60%
80%
100%
120%
0 10 20 30 40 50 60
RelativeYield
Soil test phosphorus
Where do we place the critical level? •We have focused on mapping
soil P status spatially
•Correlation and calibration
were designed to make
accurate recommendations
•What if in addition to soil P
concentration varying, the
critical level varies?
•spatially and temporally?
35. Grid sampling
•Interpolated soil sample maps (>1/4 acre
grid) are unreliable AT BEST.
•There is nothing wrong with the grid
approach, the problem is interpolation of the
data
•More frequent sampling is better use of
money
•If you insist on grid sampling, then shift grid
over time to get denser sample map
•Look at soil test range, median, average, and
deviation – just don’t interpolate
35
36. Zone sampling
•Intensively sampled zones
might work
•Use topography, soil texture,
or even historic grid samples
• Look at summary statistics from grid
data by zone
•Use yield to test zones – but
not necessarily to create zones
•We’re working on guidance
for zone development using
free software (e.g. QGIS,
MZA)
36
37. Future opportunities
•Even with a decent soil test
map (grid or zone) our
recommendations are very
coarse and were intended to
be an average
• Use your technology to insert
check strips (High, Low, None)
within your prescription and
evaluate recommendations
yourself
•To truly practice precision ag
you need to be closer to
sufficiency rates
37
0
50
100
150
200
250
0 5 10 15 20 25 30
Fertilizerrate(lb-P2O5/a)
Mehlich 3 Soil P (mg/kg)
Example of Build and Maintain
Recommendations
Buildup
Maintenance
Sufficiency