Cloud Computing Technology for Precision Nitrogen Management in Corn

Transcription

1 Cornell Adapt-N Training Webinar: Cloud Computing Technology for Precision Nitrogen Management in Corn 4/3/2014 Presenters: Bianca Moebius-Clune, Greg Levow, & Harold van Es 10:30-11:00am ET/ 9:30-10:00CT: connect to Webinar at host sites, local introductions 11:00ET/10:00CT: Start of Webinar presentations-will be recorded for later reference Adapt-N.cals.cornell.edu

2 2014 Cornell Adapt-N Training Webinar: Cloud Computing Technology for Precision Nitrogen Management in Corn 1. N Concerns & Challenges - Why Adapt-N? 2. On-Farm Trial Results, What s New for 2014? 4. Inner Workings of the Adapt-N Tool 5. Becoming an Expert User of Adapt-N 6. Complementary Technologies and Future Road Map for Adapt-N

3 1. N Concerns & Challenges - Why Adapt-N?

4 Nitrogen Concerns in Corn Production Systems Agronomic/Economic ~ $5 billion/yr of N fertilizer applied to corn; large input costs for farmer N use efficiency low (30-70%) High uncertainty, and sensitivity to climate change Environmental (negative externalities) Largest energy input into cropping system Greenhouse gas emissions (esp. N 2 O) High groundwater nitrate levels Hypoxia/anoxia in estuaries

5 Agricultural GHG Emissions (2008) CH4 N2O CO2 20% 36% 44% Del Grosso et al.,gbc 2010 Nitrogen application to US agricultural lands in 2008 had a greater global warming impact than the entire US aviation industry (EPA, 2010)

6 6 The Hypoxia Problem Riverine N Yield in the MRB LUMCON, Rabalais, 2013) Area of Gulf Dead Zone (NOAA, 2013) David et al. JEQ (2010) Gulf Hypoxia Action Plan Goal: 45% reduction in riverine total N Iowa Nutrient Reduction Strategy (2013):..the target load reductions for nonpoint sources is 41% of the statewide total nitrogen [..] to meet the Gulf Hypoxia Action Plan goal.

7 NO3-N LEACHING LOSSES (kg ha-1) N Losses Increase Exponentially With N Rate Beyond a Critical Value Sand 1993 Sand 1994 Clay 1993 Clay FERTILIZER N RATE (kg ha-1) Snyder et al., 2009, based on data by Bouwman et al., 2002 van Es et al., 2002 Key Points: Environmental loss can be minimized while optimum yield is supported Finding sweet spot is critical 7

8 Yield (bu/ac) What IS the Optimum N Rate? Corn Response to N Application N Rate (lbs/ac)

9 Reasons for Tendency to Over-Fertilize: Coffee Shop Wisdom on Risk and Risk Perception Economic: With uncertainty around the optimum N rate, the profit losses from under-fertilizing (yield penalty) are greater than those from overfertilizing (unnecessary fertilizer expense). Retailers have incentives to suggest high rates or encourage poor application timing (fall, early spring) Psychological: Under-fertilization results in highly visible leaf yellowing, while overfertilization is not noticeable. Farmers therefore tend towards the higher rates. Consultants cannot afford to be wrong with low recommendation Confusion about multitude of recommendation systems

10 Many sources of variation in N availability and crop needs generalized recommendations are too simplistic Organic amendments (manure, compost, etc.) Crop rotations Soil type differences Soil organic matter contents Soil and crop management (tillage, planting date, cultivar-yield, etc.) Weather (Sogbedji et al., 2001; Kahabka et al, 2004; Kay et al., 2006; Tremblay et al., 2012; Xie et al., 2013) Temperature Precipitation! Interactions are complex and nonlinear!

11 Soil, Weather and Nitrogen

12 Corn N Uptake - Illinois Average yield = 230 bu/ac Bender et al. Better Crops 2013 (1)

13 Crop N uptake pattern corn N uptake Plant N Spring Summer Fall

14 SOM mineralization provides part of corn N needs, depending on soil type, OM%, etc. corn N uptake Soil or plant N High OM soil mineral N, normal year Low OM Amount of N Fertilizer Needed Spring Summer Fall

15 Need for supplemental N fertilizer is also strongly affected by early season weather soil or plant N Critical Time Period N builds up in soil before corn uptake corn N uptake High OM in normal year soil mineral N, normal year Amount of N Fertilizer Needed in year with wet spring soil mineral N, wet spring Spring Summer Fall

16 Early application of N risks major losses fertilizer or manure soil mineral N normal year N excess in normal year soil or plant N corn N uptake N deficiency in year with wet spring soil mineral N, wet spring Spring Summer Fall N deficiency

17 Importance of Timing A precise N rate CANNOT be determined before the growing season Adaptive recommendations are most useful for in-season N applications Main Factors: snow spring rains summer drought intervention nonintervention high low fall winter spring summer fall

18 Greater Sidedress Flexibility with High-Clearance Equipment Purdue Univ. 18

19 Precipitation is highly localized and variable. June Precipitation Iowa

20 Why the Adapt-N Approach? Opportunities to optimize crop returns and reduce environmental impacts Research-based, unbiased, independent, field tested Cost-effective cloud-based technology with continuous soil-crop monitoring, record keeping, etc.

21 Nitrogen Management with Cloud Computational Tools Move from generalized to site and time-specific recommendations Allows for adaptive, real-time management using up-todate data - Weather - Local soils and crop management Universal process-based approach - Incorporates system complexity and variability through relevant processes Real-time and post-season evaluations Universal access through web services Allows for progressive improvement and refinement

22 Basically. What does Adapt-N do? It makes soil-crop simulations based on daily time step It simulates water and nitrogen dynamics in the soil (soil, management, weather, etc.) It simulates corn growth (soil, management, weather) Soil and crop models interact It estimates supplemental N needs It provides additional information on: N and crop dynamics Environmental impact - nitrate leaching and (soon) N 2 O losses Additional diagnostic information End-of season situation analysis and what-if scenarios

23 What factors does Adapt-N include in making a recommendation? High resolution (2.5x2.5 mi grid) daily precipitation & temperature data Soils: texture/soil type, slope, rooting depth, % organic matter (NRCS databases) N fertilizer applications: rate, type, timing, placement Cultivar: Silage, grain, or sweet corn; planting date, maturity class Population and expected yield Tillage: fall or spring plowing; conservation tillage/residue management Manure applications: date, rate, N analysis, incorporation info Rotations: soy, corn - silage or grain, or sod - last 3 yrs, % legume, surface killed or incorporated Irrigation amounts and dates Fertilizer and grain prices & risks Simulates soil and crop processes

24 High Resolution Climate Data (2.5x2.5 mi) Critical Input to Adapt-N Tool Iowa, June 14,

25 Adapt-N Interface Defining your location for simulation

26 Adapt-N Interface Viewing your locations

27 Adapt-N Interface Entering Soils Information

28 Adapt-N Interface Entering Soil Information

29 Adapt-N Interface Entering Crop Information

30 Adapt-N Interface Entering N Fertilizer Inputs

31 Adapt-N Interface Entering Manure Inputs

32 Adapt-N Interface Entering Irrigation Inputs

33 Adapt-N Interface

34 Adapt-N Interface

35 Adapt-N Graphs: Mineralization and Losses Coming in 2014: Estimates of Nitrous Oxide Losses

36 Adapt-N Graphs: Temperature and Crop

37 Adapt-N Graphs: Precipitation 37

38 Adapt-N Graphs: Current N status (for PSNT equivalent, divide by 4)

39 1. N Concerns & Challenges - Why Adapt-N? 2. On-Farm Trial Results, What s New for 2014? 4. Inner Workings of the Adapt-N Tool 5. Becoming an Expert User of Adapt-N 6. Complementary Technologies and Future Road Map for Adapt-N Download the next section of the webinar here:

40 For more information Adapt-N, Cornell University: More information on the team, development of the tool, current research, publications, additional webinars, a blog, etc. are provided at Adapt-N tool access: The new Adapt-N interface is provided through a publicprivate partnership between Cornell University and Agronomic Technology Corporation.

41 Acknowledgements Thanks to our Collaborators Thanks to all contributors of data and feedback for tool improvement: MGT Envirotec IA; Cornell Cooperative Extension; WNY Crop Management Association; Miner Institute; Willsboro Research Farm; Champlain Valley Agronomics; Cook's Consulting; Crop-Tech Consulting; GK Concepts Consulting; IPM Institute; AgFlex; and others who have contributed. Also, we would like to thank the many scientists whose work provided the foundations on which the Adapt-N tool is based. In particular: Jeff Wagenet, John Hutson. Thomas Sinclair, and Russell Muchow, and Jean Sogbedji for their work on the two dynamic simulation models that were combined to create the Precision Nitrogen Management Model at the core of Adapt-N. Thanks to our funders Funding and resources for the beta-testing and development Adapt-N have been provided by: Cornell University Department of Crop and Soil Sciences, Earth and Atmospheric Sciences, College of Agriculture and Life Sciences, Cornell Cooperative Extension, Hatch and Smith Lever Funds, New York Farm Viability Institute, USDA-NRCS Conservation Innovation Grants program, USDA-NIFA Agriculture and Food Research Initiative, USDA-NIFA Special Grant on Computational Agriculture (Rep. Maurice Hinchey), Northern NY Agricultural Development Program, MGT Envirotec, International Plant Nutrition Institute, Walton Family Foundation, McKnight Foundation, Northeast Sustainable Agriculture Research and Education (NE-SARE)