A case study in cloud computing

Transcription

1 A case study in cloud computing Greg Levow President & Chief Operating Officer Agronomic Technology Corp November 3, 2014

2 Topics: What is the cloud? What it means for companies and users Benefits and risks Impact on agriculture Adapt-N: a case study

3 What is the cloud? The shift of computing power from the user s device to a remote shared computing resource that can be dynamically scaled and reallocated.

4 What is the cloud?

5 Infrastructure impact on the company Traditional Data Centers Dedicated network staff Up front purchases (CAPEX) Logistics, power concerns Redundancy is very expensive and complex Image: Data Center Journal

6 Infrastructure impact on the company Cloud-Based Resources Software-controlled Pay on a usage basis (OPEX) Easily scale up/down Redundancy is still complex, less expensive Amazon Web Services

7 Great benefits for the user Access tools from any device Data recovery simpler if device is lost Capabilities aren t limited by device memory or speed Very easy to collaborate with other cloud users

8 Massive potential for agriculture Every activity on the farm will become measurable Virtually unlimited analytical potential The divide between what s happening in a field and our ability to access information about those things will shrink Tools will become available to do things that couldn t have been dreamed about a decade ago

9 Risks the cloud enables for agriculture Just because a tool is in the cloud, doesn t mean it works Once a user s data is in the cloud, he no longer physically controls it Providers of cloud based tools in ag may have a bias in using data for undeclared purposes

10 Adapt-N Case Study How Adapt-N leverages the benefits of cloud technology for its users, while mitigating the potential risks

11 Adapt-N Background Nitrogen recommendation software for corn production Developed over 10 years at Cornell University Dr. Harold van Es Dr. Art DeGateano Dr. Jeff Melkonian Dr. Bianca Moebius-Clune Based on lab and field research on space-time aspects of N response dating back to the 1970s, LEACHM, etc. Supported by > $5MM in grant funding

12 Testing, calibration, and use Collaborator testing across the country since 2008 On-Farm strip trials from 2011 to 2013 Commercialized by Agronomic Technology Corp (ATC) via public-private partnership ATC put Adapt-N into a cloud based architecture Launched April, 2014 Customer fields across 28 states Used by growers, retailers, co-ops, and independent agronomists

13 User Inputs: Adapt-N Simulations: High-Resolution Climate Data (Precip, Temp, Solar Radiation) 13 Interrelated Software Models Crop growth, N uptake, N loss, manure, etc. 2,000 proprietary soil dictionary records Results for every zone: RECOMMENDATION Daily recommendations PDF reports Interactive graphs N-Alerts Prior-season analysis

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41 What happens in a variable rate environment? The cloud becomes even more critical.

42 Data Layers: Field location Soil type Coland clay loam Spillville loam Zook silty clay loam

43 Data Layers: Field location Soil type Organic matter Moderate OM High OM Low OM

44 Data Layers: Field location Soil type Organic matter Variable rate seeding Yield history And more Creates hundreds or thousands of unique combinations of inputs Requires massive computing power

45 How Adapt-N leverages the cloud Parallel processing capability for thousands of zones Server resources auto-scale to meet changes in demand Also lets us scale at a macro level

46 How Adapt-N leverages the cloud Parallel processing capability for thousands of zones Server resources auto-scale to meet changes in demand Also lets us scale at a macro level Geographically redundant servers and automatically replicated databases support reliability/disaster recovery Our ability to pay for this infrastructure on a usage basis lets us keep our prices low for users Robust data encryption and security to protect user data

47 Adapt-N Model Calibration and Testing Over 100 replicated strip trials in 10 states Adapt-N vs. Grower Rate, plus N response trials (4-6 rates)

48 On-Farm Trial Performance Comparison Iowa New York Combined (Adapt-N rate Grower Rate) (n=104) N fertilizer input (lb/ac) Yield (bu/ac) Profit ($/ac) +$26 +$17 +$12 +$26 +$32 +$94 +$35 Trials with greater profit 78% 74% 67% 86% 79% 82% 77% * *Adjusting for verified model improvements and optimal tool use, 88% of trials would have resulted in greater profit.

49 Other Adapt-N Features Current Manure Irrigation Nitrogen alerts Past season analysis Pre-season planning Farm/Grower Dashboards Future Cover crops module Nitrogen stabilizers Irrigation guidance Soil health component Models for other crops, nutrients, and more

50 Avoiding the risks the cloud poses for agriculture

51 Risks the cloud enables for agriculture 1. Just because a tool is in the cloud, doesn t mean it works Look for: tools based on real science with published methodology and results (both good and bad)

52 Risks the cloud enables for agriculture 2. Once a user s data is in the cloud, he no longer physically controls it Look for: commitments the company makes regarding its care of user data. Look beyond the privacy policy to understand current and future intent.

53 Risks the cloud enables for agriculture 3. Providers of cloud based tools in ag may have a bias in using data for undeclared purposes Look for: what else the company provides, and think through potential conflicts of interest. Does the company stand to gain in other ways from your use of their cloud based tool?

54 It is incumbent on the industry to maintain rigorous, transparent scientific approaches to developing new tools It is incumbent on the user to consider the risks before relying on a cloud based tool to support farm decision making

55 Greg Levow President & Chief Operating Officer Agronomic Technology Corp