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Predictive analytics in the agriculture industry
This document discusses how predictive analytics can help the agriculture industry, specifically in Nova Scotia. It begins with an introduction of the speaker and defines predictive analytics as using statistical models to predict future events. It then discusses applications of predictive analytics like marketing and fraud detection. Specifically, it explains how predictive analytics can help precision agriculture by optimizing crop yields, managing water and inputs, and intelligently applying pesticides and fungicides. As an example, it outlines how predictive analytics could help detect and control apple scab in Kings County, Nova Scotia, reducing costs by over $13,000 while increasing apple production and profits. Overall, the document argues predictive analytics provides economic benefits and a competitive advantage for the agriculture industry in Nova Scotia.
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Predictive analytics in the agriculture industry
- 1. PREDICTIVE ANALYTICS & THEAGRICULTURE INDUSTRY
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- 3. Wesley Booth Wolfville native Enrolled@ Acadia University Entrepreneur
- 4. Why Am IHere?
- 5. Agenda What is PredictiveAnalytics? Applications of Predictive Analytics How Can This Help Agriculture? Precision Agriculture Relationship – Predictive Analytics & Precision Agriculture Example – Apple Scab The Why Invest? Economic Reasons Impact
- 6. What Is PredictiveAnalytics?
- 7. Predictive Analytics Describes arange of analytical and statistical techniques used for developing models that may be used to predict future events or behaviors.
- 8. Applications Of PredictiveAnalytics Direct marketing Google AdWords Fraud detection Insurance Cross-sell & upsell Retail Customer relationship management Salesforce
- 11. Defined Today 1997 An integrated information andproduction-based farming system that is designed to increase long term, site-specific and whole farm production efficiency, productivity and profitability while minimizing unintended impacts on the wildlife and the environment. Using real-time data on weather, soil and air quality, crop maturity and even equipment and labor costs and availability, predictive analytics can be used to make smarter decisions.
- 12. The Relationship How CanPredictive Analytics Be Used In Precision Agriculture?
- 13. Predictive Analytics –Precision Agriculture Optimizing crop yields 90% Water management 70% of crop loss is due to weather of world’s fresh water is used for irrigation Minimizing large variable costs Animal feed Labour Intelligent pesticide, herbicide, & fungicide spraying
- 14. Example Using Predictive AnalyticsTo Detect & Control Apple Scab
- 15. Apple Scab -Problem Economic threat Crop loss & quality control Resistance development
- 16. Apple Scab –Solution Optimize fungicide spray timing Minimize number of sprays Improve crop quality Reduce risk
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- 18. Economic Impact –Kings County Apple Scab Example Cost 2010 ($) With Predictive Analytics ($) Cost Reduction Hired Labour 115,776 115,776*(.90) = 104,198.40 $11,577.60 Fungicides 20,151 20,151*(.90) = 18,135.90 $2,015.10 Total Variable Costs 270,961 257,368.30 $13,592.70 Total Apple Production 47,120 47,120*(1.10) = 51,832 4,712 (increase) Variable Cost/bu 5.75 4.97 $0.78 Total Provincial Cost 14,375,000 12,425,000 Nova Scotia produces 2.5 million bu/year $1,950,000
- 19. Other Reasons Competitive advantage Atlantic Food & Horticulture Research Centre Access to key resources – Acadia University Acadia Centre for Rural Innovation Data Analytics Institute Potential spin-off projects Young & dedicated leadership
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Editor's Notes
- #4 Grew up in Wolfville – attendedWolfville School from Primary – Grade 9 & graduated from Horton High School in 2009.Initially started at University of Ottawa - moved back home during 2nd year & decided to transfer to Acadia.Currently in my final year - planning to graduate in May with a Bachelor of Business Administration - Major in Marketing.Started 1st venture at age 14 – landscape & construction. Have done various consulting work for both Acadia University and local small businesses.Recently started working with Danny Silver & Geoff Mason on developing a predictive analytic start-up here in the Valley.
- #8 Definition is from Techopdia
- #9 Most examples of predictive analytic use in business are from marketing; however predictive analytics can be applied to many situations where large amounts of useful data are available. Companies use predictive analytics to directly target consumers such as Google AdWords.Insurance companies use predictive analytics to filter out false claims.Companies with multiple product lines can use predictive analytics to suggest what products their customers may also find of interest.Predictive analytics can help sales teams manage their leads more effectively by ranking them according to the likelihood of getting a response.
- #12 1997 – U.S. House of RepresentativesToday – I.B.M.Predictive analytics complements precision agriculture well & with recent improvements in data collection it can be used in a variety of situations throughout the agricultural supply chain and business model.
- #14 Predictive analytics can take advantage of data that is now available thanks to precision agriculture. By combining real-time weather data with sensory data collected by devices throughout fields, farmers can make better informed decisions with regard to planting, fertilizing, and harvesting crops.According to the U.S. Department of Agriculture, 90% of all crop loss is caused by weather. By building specific predictive weather models for each field, farmers would be able to optimize their crop yields while reducing waste.According to the U.N. 70% of the world's fresh water is used for irrigation. If a farmer can predict more accurately if, and exactly where, it will rain within the next 48 hours, farmers won't waste water irrigating a field that won't need it, therefore helping conserve an increasingly precious resource. Sending labor into the field is time consuming and costly. Through the understanding of different variables, such as humidity, frost and rain forecasts, better decisions can be made in advance about where field workers should work.If the data is combined with real-time market information, farmers can mitigate price fluctuations better. The volatility on the market of crops can be substantial. Speculations can increase the price of crops or bring it down. Using predictive analytics the price of a certain crop can be determined upfront in a certain location. This will help the farmer to get the right price for the right crop at the right moment in time at the right location.
- #16 Most serious fungal disease to apples in Canada and is a significant economic threat to all apple growing regions including Kings County. Can result in 100% crop loss in any given year.New fungicides pose medium to high risks for resistance development –all the more reason to spray as least as possible.Apple scab management is most often based on repeated fungicide applications that result in high costs in terms of money for the fungicide sprays and in time dedicated to scab management. Because of increasing pressure on apple growers to reduce pesticide use and reduce production costs, while maintaining a high level of crop quality, we believe it is crucial and timely to simplify and optimize apple scab management
- #17 With the availability of real-time data it is now possible for farmers to be notified through a mobile app when to spray based on parameters such as time, temperature, relative humidity, leaf wetness, day light, and rainfall.These parameters could be used to build predictive models that would alert farmers when the probability of apple scab forming is too high. This would ensure accurate timing of fungicide applications. This could provide farmers with an improved early-warning solution for apple scab.Mobile app would be important for not only receiving warnings but also providing recommendations for a plan of action – how to best protect their orchard.Reduced-Risk Strategy for Apple Scab Managementhttp://www.agr.gc.ca/eng/?id=1299079705240Released April 2012
- #19 Numbers are based on a 10% decrease in hired labour & fungicide application. This decreased the total variable costs while increasing apple production due to improved new yields (10% increase).Variable cost is reduced by almost $1.00 ($0.78 to be exact).Reduction of almost $2 million provincial wide ($1,950,000 to be exact).Data taken from 2010 Nova Scotia Apple Industry – Cost of Production Study (Total of 19 orchards).
- #20 Competitive AdvantageThe Atlantic Food & Horticulture Research Centre in Kentville provides access to industry experts and research opportunities. Educational resources such as Acadia University & the Nova Scotia Agricultural College in Truro ensure the opportunity for growth giving access to top talent.AcadiaCentre for Rural InnovationAcadia is currently working on a Data Analytics Institute that will focus on rural issues such as agriculture environment, energy, industry and tourism. This Data Analytics Institute will form part of the Acadia Centre for Rural Innovation (ARCI). A project such as this would be an excellent way to utilize the new institute as well as the ARCI. Young & dedicated leadershipBoth Geoff & I are in our early 20’s – have no reason to leave the Valley.
- #21 Who has the first question?