Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Estimating the Environmental Impact of Agriculture by means of Geospatial and Big Data Analysis: The Case of Catalonia


Published on

Intensive farming has been linked to significant degradation of land, water and air. A common body of knowledge is needed, to allow an effective monitoring of cropping systems, fertilization and water demands, and impacts of climate change, with a focus on sustainability and protection of the physical environment. In this presentation, we describe AgriBigCAT, an online software platform that uses geo-physical information from various diverse sources, employing geospatial and big data analysis, together with web technologies, in order to estimate the impact of the agricultural sector on the environment, considering land, water, biodiversity and natural areas requiring protection, such as forests and wetlands. This platform can assist both the farmers' decision-taking processes and the administration planning and policy making, with the ultimate objective of meeting the challenge of increasing food production at a lower environmental impact. Presentation at the EnviroInfo 2017 Conference in Luxembourg.

Published in: Environment
  • Be the first to comment

Estimating the Environmental Impact of Agriculture by means of Geospatial and Big Data Analysis: The Case of Catalonia

  1. 1. 1 EnviroInfo Conference 2017 Estimating the Impact of Agriculture on the Environment by means of Geospatial and Big Data Analysis: The Case of Catalonia Andreas Kamilaris 13th September, 2017 Luxembourg
  2. 2. Problem 2 Intensive farming linked to excessive accumulation of nutrients and contaminants in the soil. Significant groundwater pollution with nitrates. Emission of acidifying greenhouse gases. Catalonia is one of the European regions with the highest livestock density. Need to assess environmental impact of agriculture and potential risks
  3. 3. Motivation 3 Need for a common body of knowledge. Effective monitoring of cropping and animal production systems, fertilization and water demands. Estimations of impacts, including climate change. Focus on sustainability and protection of the physical environment. Decision-making assistant tool for policymakers
  4. 4. Research Questions 4 How can we accurately measure the environmental impact of agriculture in Catalunya using big data analysis? Which solutions can we find to avoid the negative effects of animal manure on the environment?
  5. 5. 5 Technologies
  6. 6. Methodology 6 1. Collect datasets from sensors used in agriculture and weather monitoring. 2. Develop a Big Database for storing this information for easy retrieval and analysis. 3. Use the datasets as layers into a geospatial analysis tool/application. 4. Apply Big Data Analysis to estimate environmental impact and find viable solutions. 5. Enhance analysis with real-time info from Web of Things sensors (e.g. weather, hazards, alerts).
  7. 7. 7 Data Sources • GPS sensors • Physical sensors • Weather stations • Web data from online web services • Crowdsourcing-based techniques from mobile phones • Static historical information: databases and statistics
  8. 8. Datasets 8 • Farmers & Animal types/numbers • Climatic conditions (temperature, humidity, evapotranspiration) • Infrastructures (transportation network, pipelines system) • Areas of natural interest, areas that require protection • Forests • Agricultural parcels • Air quality • Soil characteristics • Manure management units • Statistics of the population • Biodiversity (animals, birds, micro-organisms) • Water (lakes, rivers, precipitation)
  9. 9. Geospatial Analysis 9 • Collection of data sources – use as layers • Geospatial application in ArcGIS Data layers Tools for spatial analysis GIS visualization
  10. 10. 10 Animal Concentration per Municipality Hotspots of Farms in Catalonia Shortest Paths between Farms for Manure Collection Methane Emissions per Municipality Geospatial Analysis
  11. 11. Architecture
  12. 12. Web Application
  13. 13. Web Application
  14. 14. Web Application
  15. 15. 15 • Calculation of animal manure produced annually in Catalunya. • Estimation of gases produced: • Carbon dioxide, Methane, Nitrous oxide • Ammonia, Odor • IPPCC (TIER1-2) Vs. Relevant Literature (TIER2) AgriBigCAT Online Policy Tool
  16. 16. AgriBigCAT Online Policy Tool 16 Emissions calculator GIS visualization in the Web browser Farms involved in the results Query Results
  17. 17. Pigs 29% Dairy cows 6% Poultry 6% Beef Cattle 19% Sheep 12% Goats 10% Rabbits 9% Horses 8% Pigs 15% Dairy cows 0% Poultry 72% Beef Cattle 2% Sheep 2% Rabbits 6% Volume of Farms* Volume of Animals* * Based on data provided by the Department of Agriculture of Catalonia. AgriBigCAT Analysis
  18. 18. Pigs, 86.6, 52% Dairy cows, 15.79, 10%Poultry, 0.64, 0% Beef Cattle, 56.37, 34% Sheep, 6.51, 4% AgriBigCAT Analysis Volume of Methane* produced (Tones/Year) Pigs 72% Dairy cows 14% Poultry 8% Sheep 3% Volume of Manure* * Based on IPCC TIER1 guidelines.
  19. 19. Volume of GHG Emissions Beef cattle farms Sheep farms Dairy cow farms Pigs farms Pigs 52% Dairy cows 10% Beef Cattle 34% Sheep 4% AgriBigCAT Analysis
  20. 20. AgriCatVIZ Visualization Tool Data layers Weather conditions and forecasting
  21. 21. 21 Cultivations per municipality Stations of meteorology and manure management Forests and annual precipitationTransportation and pipelines network AgriCatVIZ Visualization Tool
  22. 22. 22 Conclusion Employing geospatial and big data analysis, together with web technologies, in order to estimate the impact of the agricultural sector on the environment. Decision-making assistant tool for policymakers
  23. 23. 23 Current Work Nitrate management of livestock agriculture according to the yearly needs of the local farms that cultivate crops, in fertilizer. Livestock Farm Crop Farm1x1 sq. km grid cell 74,090 grid cells, 20,526 crop farms, 31,595 livestock farms
  24. 24. 24 Current Work Multiple objectives: 1. Satisfy needs of crop farmers in fertilizer 2. Reduce the transportation costs for livestock farmers to transport manure Approach: Employ nature-inspired techniques • Ant Colony Optimization • Particle Swarm Optimization • Genetic Algorithm
  25. 25. 25 Future Work Decision-making assistance for the Ministry of Agriculture, Government of Catalonia, to decide where to install manure management plants.
  26. 26. 26 Future Work Open-source code of the AgriBigCAT software: • How to create geospatial layers • How to perform geospatial analysis • How to publish geospatial maps • How to create web-based visualizations • How to connect maps with web interfaces • How to get authentication, access control, privacy • How to perform big data analysis and visualize results
  27. 27. 27 Many thanks for your attention! Andreas Kamilaris
  28. 28. 28 Additional Material: Big Data Aspects  Volume: Datasets, layers, estimations/calculations, spatial analysis.  Velocity: Weather information, precipitation patterns.  Variety: Different sources of information involved, i.e. historical data, satellite data, real-time web feeds, Internet of Things sensor data.  Veracity: Trusted source/origin, i.e. Ministry of Agriculture, AccuWeather predictions, international satellites, research project outcomes.  Valorization: Analysis, simulation, modeling.