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Mapping suitable niche for cactus and legumes in diversified farming in drylands

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Presentation by Chandrashekhar Biradar and team.
16-18 October 2019. Hyderabad, India. TRUST: Humans, Machines & Ecosystems. This year’s Convention was hosted by The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). The Platform is led by the International Center for Tropical Agriculture (CIAT) and the International Food Policy Research Institute (IFPRI).

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Mapping suitable niche for cactus and legumes in diversified farming in drylands

  1. 1. Mapping suitable niche for cactus and legumes in diversified farming in drylands Chandrashekhar Biradar and team
  2. 2. #BDPHYDERABAD2019 Current Diets vs Planetary Health EAT Lancet Report Moving from narrow sense economic benefit to a new ecologically sound functional system for well being…
  3. 3. #BDPHYDERABAD2019 EAT Lancet Report …with diversified cropping systems, conservation, rotation, nutrition focus >> “more health per acre” Dryland Cereals Dryland Pulses Dryland Livestock Current Diets vs Planetary Health
  4. 4. #BDPHYDERABAD2019 4 Geotag Agrotag Aggregate Analytics Design DecisionResilient Agroecosystems Digital augmentation for demand driven decisions for economically viable and ecologically sustainable agroecosystems Farming Systems Dynamics Ecological Intensification Crops/system Diversification Interoperable Synergies Technology Scaling and Ex-ante Potential Risks and shocks Spatially informed designs for decisions Market access Rural welfare
  5. 5. #BDPHYDERABAD2019 Daal/Falafal Water used 1,250 liters Chicken 4,325 Mutton 5,520 Beef 13,000 Changing diet pattern >> cropping systems Crop diversification for future smart foods There is a need for paradigm shift from more calories per acre to more health per acre. >> Sustainable living
  6. 6. #BDPHYDERABAD2019 6 Crops of resilient systems • Diversified farming systems • Sustainable food and nutrition • High resource use efficiency • Rebuilding living soils • Crops of resilient systems Crop type Total Soil loses Bare soil 29.10 Cotton 10.91 Cereals 5.94 Ceraisl + beans 3.93 Opuntia ficus-indica 1.98 Perennial grass 0.03
  7. 7. #BDPHYDERABAD2019 Nutrition Rich Food: Fruits, Juices, Vegetables, Pickles, Fodder Why cactus is important? Cosmetic oilSoaps/shampoo Natural die anti-wrinkle, and anti-age purposes
  8. 8. #BDPHYDERABAD2019 Cactus with date palm Cactus with cluster bean Degraded wastelands Cactus with citrus Cactus with chickpea Cactus/trees/crop Cactus in different farming systems Cactus with Napier Cactus with Barley
  9. 9. #BDPHYDERABAD2019 Image download Atmospheric correction Save RED and NIR band to tmp folder, names of files: *DOY_RED.tif *DOY_NIR.tif Read two tif files from tmp folder, do NDVI calculation Save NDVI file to folder “basename_DOY_ndvi.tif” Add column to existing csv Module-1 Module-3 Module-2 Shapefile all_fields_fergana .shp Field : “ID” Do 10-day interpolation, add columns GEOTIFF of field IDs Zonal statistics Pre- existing data Empty CSV with field IDs in folder named after recent year? Do classification Update columns in CSV (classification, probabilities) Output: Saveto“annual”folders Processing Inputorexisting database Save final csv for classification “fergana_croptype_YY YY.csv” Save final csv for interpolated NDVI “fergana_NDVI_ts_int erpolated_YYYY.csv” Save updated, historic data base for VCI as CSV or R Object? “historic_vci_db_YYY Y.csv” Do table join: Update annual shapefile with classifications, probabilities, and VCI indicator (seaosnal and end-or-seaosn each) 1x1 km grid as shapefile Compute MIN and MAX NDVI multi-annual reference for each grid Update columns (VCI deviation +/-) Save final csv for VCI “fergana_vci_YYYY.csv ” Front-end system File Geodatabase Front-end Web-mapping service (hosted or referenced) Front end Pooled data sets and RF models (seasonal and annual) Mean Save atmospherically corrected images file to folder “basename_DOY.tif” Big-data, Machine Learning and AI SVM, BT, LR, RF, DT, MLP Multi-mode classification algorithms
  10. 10. #BDPHYDERABAD2019 #/km2 Dynamics of Cropping Systems ▪ Integrated Agro-Ecosystems ▪ Sustainable Intensification and Diversification ▪ Input Use Efficiency-Conservation Agriculture ▪ Thematic Land-Water-Climate Resilience Agricultural Intensification Cropping Intensity Increase in Arable Land 72% 21% 7% Length of the crop fallows, start-date, end-date (Biradar et al., 2015)
  11. 11. #BDPHYDERABAD2019 11 0.1 0.3 0.5 0.7 0.9 0.1 0.2 0.3 0.4 0.5 fitted EVI fitted NDVI EVI NDVI Linear (fitted EVI) Linear (fitted NDVI) Timely Tracking for precision decisions >> near real-time mapping and monitoring
  12. 12. #BDPHYDERABAD2019 12 Fallows in Double cropped area Fallows in Single cropped area Mapping farm typology for site specific interventions
  13. 13. #BDPHYDERABAD2019 Legumes and Cactus as integrated crops From 2000 to current (real-time mapping) Mapping Realtime Rice-Fallows Soil Moisture and Water Harvesting Variety Suitability Agro-Tagging
  14. 14. #BDPHYDERABAD2019 Mapping suitable niche Rainfall Anomaly Mean Rainfall Rainfall trend Suitability based on rainfall variation • 32% of India is in the ‘high (3%) to moderate suitable (29%)’ category. • Precipitation anomaly suggests high suitability coerced in western and east-central part. • A decreasing rate of suitability with increase of aridity was found. 𝑆𝑃𝐼 = 𝑥𝑖 − 𝜇 𝑥 𝜎𝑥 𝑇 𝑚𝑘 = ෍ 𝑖=1 𝑛−1 ෍ 𝑗=𝑖+1 𝑛 𝑠𝑔𝑛 𝑥𝑗 − 𝑥𝑖 𝑆 = ෍ 𝑖=1 𝑛 𝑊𝑖 𝑆𝑖
  15. 15. #BDPHYDERABAD2019 Precipitation 400-800 mm/yr Optimum 800-1000 mm/yr Suitable 250-400 mm/yr Less Suitable < 250 and > 1000 mm/yr Not Suitable Min. temperature more than 5 °C Optimum >-5°C to < 5 °C Less suitable <5°C Not suitable Max. Temperature > 41°C Suitable 5 to 41°C Optimum < 5°C Less Suitable Mean Temperature > 23°C Less suitable 18-23°C Optimum 15 -18°C Suitable 10 -15°C Less suitable Less than 10°C Not suitable Annual relative humidity >60% Optimum 40-60% Suitable <40% Not suitable Soil Salinity <2 Optimum 2 to 4 Suitable 4 to 7 Less suitable more than 7 Not suitable Soil Texture clay (heavy/light), clay loam Not suitable Silt/sandy clay loam Less suitable Loam, sandy loam, loamy sand Suitable sandy Optimum Soil PH 5 to 8 Optimum <5 and >8 Not suitable Soil organic matter 1 to 2 Optimal 0.5-1 Suitable < 0.5 Less suitable Absent Not suitable Parameters for Multi-criteria analysis (AHP) Conventional Approach Cloud computing: GEE
  16. 16. #BDPHYDERABAD2019 https://geoagro.users.earthengine.app/view/cactus Pixel suitability State level suitability Dynamic and on-the fly suitability mapping
  17. 17. #BDPHYDERABAD2019 Adjust the parameters or choose presets to map the variety specific
  18. 18. #BDPHYDERABAD2019 Oct 2018 Dry Moist Wet Water Real-time rice fallows Real-time Soil moisture Static map Static Rice fallows Realtime monitoring to target site specific interventions Corresponding soil moisture Oct 2018 Nov 2018 Dry Moist Wet Water Real-time rice fallows Real-time Soil moisture Static map Static Rice fallows Corresponding soil moisture Oct 2018 Nov 2018 Dec 2018 Dry Moist Wet Water 2018/2019 <30 days 31-60 61-90 91-120Rice varieties> Short Mid Long duration rice varieties Corresponding soil moisture
  19. 19. #BDPHYDERABAD2019 19 Compound productivity Single commodity Productivity(return) Planting multiple crops for monthly income while main crop continue to grow (Salad greens, radish, leafy amaranth, cilantro, dill, spinach in Cotton crop)
  20. 20. bigdata.cgiar.org Thank You c.biradar@cgiar.org Chandrashekhar Biradar, PhD Principal Scientist (Agro-Ecosystems) ICARDA Head-Geoinformatics Units icarda.org geoagro.icarda.org Acknowledgements Prasenjit Acharya, Mounir Louhaichi, Surajit Ghosh, Sawsan Hassan, Nigamananda Swain, Khaled, Alshamma, Enrico Bonaiuti, Ashutosh Sarker, Jacques Wery

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