Precision Agriculture Technologies: An Opportunity for new Approaches to N management John Schmidt Department of Agronomy Kansas State University

Annual Precipitation

Major Kansas Watersheds

Alluvial soils and the Great Bend Sand Plains N Study sites Source: USDA NRCS

Grain yield at Ellinwood (2001 and 2002) * * (P>F = 0.10)

Grain yield at Manhattan, Rossville, Scandia (2001 and 2002) Golden Harvest 9164Bt was planted at these sites. (P>F = 0.10) * * * *

Grain yield at St John and Pretty Prairie (2001 and 2002) 30 lb N acre -1 applied as starter St John * * 11 lb N acre -1 applied as starter 45 lb N acre -1 applied through pivot Pretty Prairie * (P>F = 0.10)

Fertilizer required for optimum yield KSU Rec.Required (kg N ha -1 ) Ellinwood Manhattan Rossville Scandia Pretty Prairie St John

Post-harvest soil NO 3 -N at Pretty Prairie (2001) East 11 lb N acre -1 applied as starter 45 lb N acre -1 applied through pivot *N *Depth x N *N West 11 lb N acre -1 applied as starter 65 lb N acre -1 applied through pivot *N

Combining water and N management Improving evaluation of soil moisture content for irrigation scheduling Emphasize economic loss of leached N KSU’s current N recommendation is too high for high- yielding corn (environmental risk on sands) Opportunity for site-specific management (not necessarily variable input management) Management implications

Characterize textural discontinuities with Veris’ electrical conductivity, verify with additional sampling An additional site with lysimeters and tensiometers to evaluate a soil with textural discontinuities Whole-field N response evaluation combined with some additional small-plot sites, producer collaboration Modeling N leaching potential to assess risk of various soil types, lead to better spatial interpretation Next step for N management research

Precision Agriculture Technologies and Smaller Research Budgets: An Opportunity for a Paradigm Shift in Soil Fertility Research John Schmidt Department of Agronomy Kansas State University

Yield modeling simulations based on current fertilizer recommendations Implications for P management Direction of current research Collaboration with producers, especially KARA group New paradigm for soil fertility research (developing fertilizer recommendations) Phosphorus fertility / fertilizer management

Yield models implied by traditional fertilizer recommendations and a framework for including nontraditional information Kastens, Schmidt, and Dhuyvetter Recent SSSAJ article

Yield models, if they exist, are rarely provided to clientele Depend on soil test value for fertilizer in question, does not allow for interaction with other independent variables Many labs’ fertilizer recommendations are based on a 1-yr time horizon Soil Labs’ recommendations are inadequate

Fertilizer P recommendations for wheat in NW Kansas

Yield goal at 10 % above 10-yr NW KS CRD wheat yield of 38 bu acre -1 Simulation means (10,000 observations) and CV: MeanCV STN40 lb acre STP16 mg kg Yield38 bu acre Wheat price: $3.22 bu -1 Fertilizer N price: $0.19 lb -1 Fertilizer P price: $0.23 lb -1 Simulation Assumptions

KSU-based model rec. compared to KSU’s actual rec.

Predicted yield response from KSU-based model

Assume crop removal to be 0.6 lbs P 2 O 5 bu -1 Time path depends on transformation rate lbs of excess (above removal) P 2 O 5 to increase STP by 1 mg kg -1 Literature suggests a constant rate of 10 – 20 Assumed a non-linear transformation rate based on some work in Missouri … Long-run steady-state STP implied by lab-based models

Optimal fertilizer P as a function of time (initial STP = 5)

Soil test P as a function of time

Annually amortized profit as a function of time

The scientific basis for the long-run steady-state STP needs improved (discrepancy among labs) We should be able to manage STP for time horizon > 1 yr Need to better describe the fertilizer to STP transformation Validate assumed yield responses inherent to P fertilizer recommendations Current P research project, collaboration with KARA group Implications for P fertility research

Kansas Agricultural Research Association (KARA) Whole-field research collaboration with producers

Kansas Agricultural Research Association (KARA) Successfully distributed and implemented detailed field protocol Producers responsible for collecting soil samples, applying fertilizer, and collecting yield data I’m responsible for data analysis, any small-plot studies, soil analyses KARA provides research awards to participants Whole-field research collaboration with producers

Multidisciplinary effort Modeling to direct research effort Improved yield response models that include non-traditional data, could be field- or site-specific Active participation by producers Less clean data, more messy data Alternative methods of analyses / interpretation A shift in the soil fertility research paradigm