Workshop crop suitability modeling GMS

1,063 views
895 views

Published on

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,063
On SlideShare
0
From Embeds
0
Number of Embeds
319
Actions
Shares
0
Downloads
16
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Can you please take area per altitude line out? This is very important is shows that there is no more area available further up and that coffee will compete even more with protected areas. PES discussion.If you cannot, explain to what does it pertain: current or 2050? It simply shows the area available at each altitude current and future. Just area per altitude.
  • The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most sophisticated crop simulation models currently available. Its advantages are the possibility to include specific information on weather, soils, plants, management and interactions of these factors.We ran DSSAT with available bean and maize variety calibration sets (2 fertilizer levels, 2 varieties, 2 soils, common smallholder conditions and management) to simulate current average yield and future expected yields. Results for current yields where ground-proofed through expert consultation throughout the region. In addition, field trials with recently introduced bean varieties with higher drought tolerance were conducted in order to obtain calibration data sets for more precise predictions.
  • Workshop crop suitability modeling GMS

    1. 1. Ecocrop modeling Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS) 10 – 11 March, 2014, Hanoi, Vietnam Eitzinger Anton, Giang Linh, Lefroy Rod Laderach Peter, Carmona Stephania
    2. 2. outline • What is Ecocrop? • FAO Ecocrop plant database • Suitability modeling with Ecocrop • Modeling Ecocrop with DIVA GIS • Calibrating ecological ranges (using literature) • Projecting suitability into the future
    3. 3. • The database was developed 1992 by the Land and Water Development Division of FAO (AGLL) as a tool to identify plant species for given environments and uses, and as an information system contributing to a Land Use Planning concept. • In October 2000 Ecocrop went on-line under its own URL www.ecocrop.fao.org. The database now held information on more than 2000 species. • In 2001 Hijmans developed the basic mechanistic model (also named EcoCrop) to calculate crop suitability index using FAO Ecocrop database in DIVA GIS. • In 2011, CIAT (Ramirez-Villegas et al.) further developed the model, providing calibration and evaluation procedures.
    4. 4. • http://ecocrop.fao.org
    5. 5. • Common bean
    6. 6. • database held information on more than 2000 species
    7. 7. Suitability modeling with Ecocrop EcoCrop, originally by Hijman et al. (2001), was further developed, providing calibration and evaluation procedures (Ramirez-Villegas et al. 2011). It evaluates on monthly basis if there are adequate climatic conditions within a growing season for temperature and precipitation… …and calculates the climatic suitability of the resulting interaction between rainfall and temperature… How does it work?
    8. 8. What happens when Ecocrop model runs? 1 2 3 4 5 6 7 8 9 10 11 12 1 kilometer grid cells (climate environments) The suitability of a location (grid cell) for a crop is evaluated for each of the 12 potential growing seasons. Growing season 0 24 100 80
    9. 9. For temperature suitability Ktmp: absolute temperature that will kill the plant Tmin: minimum average temperature at which the plant will grow Topmin: minimum average temperature at which the plant will grow optimally Topmax: maximum average temperature at which the plant will grow optimally Tmax: maximum average temperature at which the plant will cease to grow For rainfall suitability Rmin: minimum rainfall (mm) during the growing season Ropmin: optimal minimum rainfall (mm) during the growing season Ropmax: optimal maximum rainfall (mm) during the growing season Rmax: maximum rainfall (mm) during the growing season Length of the growing season Gmin: minimun days of growing season Gmax: maximum days of growing season
    10. 10. • Growing season: xx days (average of Gmin/Gmax) • Temperature suitability (between 0 – 100%) • Rainfall suitability (between 0 – 100%) • Total suitability = TempSUIT * RainSUIT If the average minimum temperature in one of these months is 4C or less above Ktmp, it is assumed that, on average, KTMP will be reached on one day of the month, and the crop will die. The temperature suitability of that month is thus 0%. If this is not the case, the temperature suitability is evaluated for that month using the other temperature parameters. The overall temperature suitability of a grid cell for a crop, for any growing season, is the lowest suitability score for any of the consecutive number of months needed to complete the growing season The evaluation for rainfall is similar as for temperature, except that there is no “killing” rainfall and there is one evaluation for the total growing period (the number of months defined by Gmin and Gmax) and not for each month. The output is the highest suitability score (percentage) for a growing season starting in any month of the year.
    11. 11. Results from GMS study
    12. 12. Crop climate- suitability change by 2050 s of D:_modeling_OUTPUTsearun-1.gdbbanana2chg in zones of D:Anton_DAPA_Projects_ongoingSEA-CCAFSgeodatagms_mask.shp KHM 2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 LAO MMR THA VNM CHN Histograms of D:_modeling_OUTPUTsearun-1.gdbpotato2chg in zones of D:Anton_DAPA_Projects_ongoingSEA-CCA KHM 1,000 900 800 700 600 500 400 300 200 100 0 LAO MMR THA VNM CHN Histogram: Banana Potato Cambodia Laos Myanmar Thailand Vietnam China Cambodia Laos Myanmar Thailand Vietnam China
    13. 13. www.ciat.cgiar.org Science to cultivate change Use and Interpretation of EcoCrop • Purely Climatic Suitability: • Does not include soils • Does not include pests and diseases • Rainfall does not equal available water: • Irrigation • Soil water management (SOM, mulch, etc.) • Topography and soil type affect drainage • Phenology: Different requirements at different stages of growth (especially for perennials) • What is “most suitable” not necessarily the best to grow – markets, labour, farming system, etc.
    14. 14. www.ciat.cgiar.org Science to cultivate change Maize in Lao PDR • Maize in Lao PDR Maize - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 NorthernRegionPhongsaly LuangnamthaOudomxay Bokeo LuangprabangHuaphanhXayabury CentralRegion Vientiane.C XiengkhuangVientiane Borikham xay Khammuane Savannakhet SouthernRegionSaravan Sekong ChampasackAttapeu
    15. 15. www.ciat.cgiar.org Science to cultivate change Sugarcane in Lao PDR Sugar Cane - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 N orthern R egionPhongsaly Luangnam thaO udom xay Bokeo LuangprabangH uaphanhXayabury C entralR egion Vientiane.C XiengkhuangVientiane Borikham xay Kham m uane Savannakhet Southern R egionSaravan Sekong C ham pasackAttapeu
    16. 16. www.ciat.cgiar.org Science to cultivate change Rubber and Oil Palm in Thailand
    17. 17. Other approaches for crop modeling
    18. 18. • Maximum entropy methods are very general ways to predict probability distributions given constraints on their moments • Predict species’ distributions based on environmental covariates What is Entropy Maximization? • You can think of Maxent as having two parts: a constraint • component and an entropy component • The output is a probability distribution that sums to 1 • For species distributions this gives the relative probability of observing the species in each cell • Cells with environmental variables close to the means of the presence locations have high probabilities MaxEnt model
    19. 19. B 20 Input: Crop evidence (GPS points) 19 bioclimatic variables of current (worldclim) & future climate Output: Probability of distribution of coffee (0 to 1) MaxEnt model
    20. 20. Bioclimatic variables for suitability modeling • Bio1 = Annual mean temperature • Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp)) • Bio3 = Isothermality (Bio2/Bio7) (* 100) • Bio4 = Temperature seasonality (standard deviation *100) • Bio5 = Maximum temperature of warmest month • Bio6 = Minimum temperature of coldest month • Bio7 = Temperature Annual Range (Bio5 – Bi06) • Bio8 = Mean Temperature of Wettest Quarter • Bio9 = Mean Temperature of Driest Quarter • Bio10 = Mean Temperature of Warmest Quarter • Bio11 = Mean Temperature of Coldest Quarter • Bio12 = Annual Precipitation • Bio13 = Precipitation of Wettest Month • Bio14 = Precipitation of Driest Month • Bio15 = Precipitation Seasonality (Coefficient of Variation) • Bio16 = Precipitation of Wettest Quarter • Bio17 = Precipitation of Driest Quarter • Bio18 = Precipitation of Warmest Quarter • Bio19 = Precipitation of Coldest Quarter derived from monthly temperature & precipitation
    21. 21. Coffee suitability - Maxent Results Nicaragua
    22. 22. B Results Variable Adjusted R2 R2 due to variable % of total variability Present mean Change by 2050s Locations with decreasing suitability (n=89.8 % of all observations) BIO 14 – Precipitación del mes más seco 0.0817 0.0817 24.8 24.49 mm -3.27 mm BIO 04 – Estacionalidad de temperatura 0.1776 0.0959 29.1 0.83 0.166 BIO 12 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mm BIO 11 - Temperatura media del cuarto más frío 0.2633 0.0576 17.5 20.11 ºC 1.86 ºC BIO 19 - Precipitación del cuarto más frío 0.2993 0.0155 4.7 169.13 mm -7.08 mm BIO 05 - Temperatura máxima del mes más cálido 0.3198 0.0102 3.1 28.45 ºC 2.30 ºC BIO 13 - Precipitación del mes más húmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mm Otros - - 6.2 Coffee suitability - Maxent Results Nicaragua
    23. 23. Decision Support System for Agro technology Transfer (DSSAT) +
    24. 24. Decision support system modelling (for benchmark sites) Agronomic management Expert & farmer survey Integrated crop-soil modeling 160 LDSF sample sites Baseline domains Impact 2030 A1b Experimental [n] cultivars [n] fertilizer application [n] seasons Application domains Analysis of biophysical systems and simulating crop yield in relation to management factors. Combine these models with field observations that allow adjustment of the models in the course of the growing season . Future 24 GCM A1B (IPCC) Current worldClim Validation with available station data Daily weather generator MarkSIM Weather station data (daily) Climate data yield soil management
    25. 25. Conclusions crop models • Ecocrop, when there is a lack on crop information, for global or regional assessment • Maxent, perennial crops with presence only data (coordinates) available • DSSAT, only for few crops (beans, maize, …), high data input demand and calibrated field experiments are necessary • We need to communicate uncertainty of model predictions Empirical models Mechanistic models

    ×