FAO-IIASA Agro-Ecological Zoning v3 FAO-IIASA Agro-Ecological Zoning v3.0: Model system, Applications and Data Portal 17 December 2014, Investment Days, FAO-HQ, Rome Günther Fischer International Institute for Applied Systems Analysis, Laxenburg, Austria

Content Background GAEZ v3 modules and databases Examples of national and global AEZ applications Data Portal update to GAEZ v4

Land & Water: Some key issues Population increase by 2050 expected to be +50% globally; +60% in less developed countries; more than doubling in Sub-Saharan Africa. Agriculture is the largest user of water; the sector is highly dependent on water resources, accounting for 70% of total water withdrawals; some 40% of the global food crop is derived from irrigated agriculture. Agriculture is in competition with other water users and has impacted negatively on the environment. Food and water supply are key human sectors exposed to climate change. Climate-change impacts are already being felt in many countries; further global warming will be unavoidable. Agriculture is a major source and sink of greenhouse gases via land use changes, land management and livestock production.

Land Resources & Agro-ecological Zoning: FAO and IIASA have developed a spatial analysis system that enables rational land-use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and production potentials of land. The AEZ methodology follows an environmental approach; it provides a standardized framework for analyzing synergies and trade-offs of alternative uses of agro-resources (land, water, technology) for producing food and energy, while preserving environmental quality. The AEZ analysis yields knowledge about current and future production potentials of land, helps identify land and water limitations and provides insight into current yield and production gaps and their causes. 4

What is the GAEZ Data Portal Interactive web application to report on the current state and trends of agricultural production and crop suitability; designed to maintain a comprehensive multi-dimensional, multi-temporal and multi-purpose database; developed using standards and innovative technology.

GAEZ v3.0 Modules Climate, Water, Soil, Terrain, Land Use, Protection, Market Access M1 Agro-climatic Analysis M2,3 Agro-climatic constraints, yields M4 Soil & Terrain Suitability M5 Agro-ecological Suitability & Potential Yield M6 Actual Yield & Production Agricultural Land and ProductionStatistics M7 Yield & Production Gaps

Sub-themes in GAEZ Portal Steps in GAEZ Data Compilation Themes in GAEZ Portal Sub-themes in GAEZ Portal A B Soil Resources Water Resources Terrain Resources Land Use/Cover Protected Areas Population, Accessibility Land Resources Data base Land and Water Resources 1 Agro-climatic Analysis Agro-Climatic Resources Thermal regimes Moisture regimes Growing period 2 Agro-climatic yields Climate yield constraints Crop calendars Agro-ecological suitability & productivity Assessment of Crop Potentials Agricultural Suitability and Potential Yield 3 Downscaling of Crop Statistics Actual Yields and Production Aggregate value of crop production and yield Crop harvested area, yield and production 4 Estimation of Yield and Production Gaps Yield and Production Gaps Yield gap Production gap 5 7

Reference HadCM3 2080s Annual P/PET Ratio CSIRO 2080s CGCM2 2080s

IEF-LUC/IIASA Biofuels Length of growing period (LGP) The agro-climatic potential productivity of land depends largely on the number of days during the year when temperature regime and moisture supply are conducive to crop growth and development. This period is termed the length of the growing period (LGP).

Automatic crop calendar in AEZ JAN JUN DEC attainable yield Water deficit considered AEZ choice: Optimum sowing date/crop-cycle combination (Max yield – rad/temp) Length of growing period (rain-fed) Note: For each grid-cell and LUT the algorithm tests all possible starting dates and determines the highest attainable yield, which then defines the respective outcome for that location .

IIASA and FAO with other partners have produced a new harmonized world soil database (HWSD) by combining the major regional soil/SOTER maps/databases produced over the last 10 years and using soil profile information derived from WISE and other sources.

Attainable Rain-fed Yields, high input level Maize Wheat Cassava Oil palm Sugar cane Soybean

13 Average cereal yields of year 2000 production (tons/ha) Potential cereal yields (tons/ha), using year 2000 crop shares 13

Ratio of Actual to Potential Cereal Output

CLIMATE CHANGE RISKS: IMPACT ON CROP YIELDS AND IRRIGATION WATER DEMAND

Climate Change Scenario Projected by PRECIS ( 30 year average annual temperature & precipitation, Baseline (1961-1990) vs 2020s(2011-2040), 2050s(2041-2070), 2080s(2071-2100) A1B scenario) High resolution (50x50km) PRECIS has provided 90 years scenario projections for future climate change. Among them, the A1B scenario shows the projection under the relatively high CO2 emission scenario. The comparison of the A1B baseline (1961-1990) with projections for 2020s-2080s shows the changes. For example, average temperature in Northeastern China and Northern Xinjiang province may increase by 5 ℃ , and precipitation may increase significantly in the Northwestern China, probably by 30%.

Moisture Supply Index (100*P/ET0, Δ MSI) NSFC PRECIS 1961-90 PRECIS A2 2050 Moisture Supply Index (100*P/ET0, Δ MSI) PRECIS B2 2050 PRECIS A1b 2050

Cropping Systems under Irrigated Conditions (2080s, PRECIS, A1B) NSFC Cropping Systems under Irrigated Conditions (2080s, PRECIS, A1B) 650km 700km 450km Triple rice cropping baseline Triple rice cropping 2080s Limited double cropping baseline Limited double cropping 2080s Triple cropping baseline Triple cropping 2080s Double cropping 2080s 350km Double cropping baseline Under the A1B scenario in 2080s, the cropping systems will move northwards, increasing potential multi-cropping and sown area. This could increase the potential crop production in especially in Northeast and Northwest China. The AEZ analysis demonstrates that the cropping system may change significantly and multi-cropping zones will shift by several hundred kilometers.

Multi-cropping Potential Cereal Yield Changes from Baseline to PRECIS A1b 2050s (ratio) Multi-cropping class fixed to Baseline Multi-cropping class dynamic for A1b2050s For PRECIS A1B climate in 2050s, the maximum potential multi-cropping cereal yield is estimated to be lower than for Baseline in southern China. This will be more than compensated by increased multi-cropping yield potential in North and Northeast China . We can estimate the total cereal potential yield on each land unit based on the chosen multi-cropping pattern. The highest cereal potential yield appears in Jiang-huai basin, the yield is over 19 tons per hectare.

China: Climate Change Impacts on Crop Yields and Water Requirements HadCM3, IPCC A2 Scenario Policy relevant findings: Climate change requires substantial adaptation of cropping systems in China’s regions; Crop production potential is shifting northwards with climate change; Positive temperature effects may be limited by soil moisture deficits and more frequent extreme event; Crop water requirements projected to increase more than10% by 2050; a growing fraction to be supplied by irrigation; High risk that water stress will increase with climate change. Magnitude of effects varies with GCM and scenario. The impacts of climate change on China’s agriculture will largely depend on the consequences for regional water resources. With climate change the share of irrigation in total crop water requirements as well as the total amount of water to be supplemented by irrigation increases, varying with scenario/ climate model.

THE GLOBAL LAND RUSH: LARGE-SCALE ACQUISITION OF LAND

Methodology for estimating ‘fair’ land values Assemble global/regional land resources database; in this study we use GAEZ v3.0 (FAO-IIASA, 2012). Assess suitability for major crops and estimate attainable agro- ecological yield and production (in this study: maize, sorghum, wheat, soybean, groundnut, oil palm, sugar cane, cassava, cotton). Estimate for each crop and location the required inputs (seed, fertilizers, labor, machinery, and other costs) using a generic set of production technologies. Construct ‘umbrella’ crop based on assessed major crops, selecting in each grid cell the one maximizing estimated net output value. Calculate ‘fair’ land values on the basis of estimated attainable output values minus location-specific reference production costs and transportation costs. Summarize grid-cell outcomes and tabulate by current land cover/use category, protection status and land value class.

Potential Productivity of ‘umbrella’ crop under rain-fed conditions (GK$/ha) Note: The aggregate result shown represents the best of nine major crops (wheat, maize, sorghum, soybean, groundnut, oil palm, sugar cane, cassava, cotton) under rain-fed conditions, assuming good management and high input level.

Comparative Advantage of Sugarcane and Sorghum IEF-LUC/IIASA Biofuels Comparative Advantage of Sugarcane and Sorghum Sugarcane The diagram shows the output value of sugarcane and sorghum relative to the ‘umbrella’ crop (maize, sorghum, wheat, soybean, groundnut, oil palm, sugar cane, cassava, cotton). Sorghum Source: IIASA, 2011 24 24

Spatial Distribution of ‘No-Go’ Areas: Closed Forest and Protected Areas Source: WDPA, 2009; IIASA, 2010.

Transport Costs to nearest Port (US$/ton) Source: World Bank (2011, unpublished).

Net Value of Production of ‘umbrella’ crop ($/ha) Net value excluding transportation costs to seaport Net value including transportation costs to seaport Source: Calculations by authors based on GAEZ v3.0 database, 2011

Grass/Wood Land outside ‘No-go’ areas suitable for ‘umbrella’ crop (mill. ha) Region Total Grass/ Wood Land excl. ‘No-Go’ VS+S Grass/Wood Land NVP > 1000 Grass/Wood Land NVPT>1000 Grass/Wood Land Northern America 502 21 5 2 Europe & Russia 512 61 4 1 Australia & N.Zealand 457 29 3 Latin America 606 182 152 57 Sub-saharan Africa 855 246 194 85 North Africa & West Asia 87 6 0.0 Asia (excl. W.Asia) 563 32 17 8 World Total 3587 578 378 156 Source: Calculations by authors based on FAO-IIASA GAEZ v3.0 database, 2011 Note: Extents of land currently classified as grass/wood land outside ‘No-Go’ areas. The table shows (i) total extents, (ii) land very suitable and suitable for rain-fed cultivation of at least one of nine major agricultural crops (maize, sorghum, wheat, soybean, groundnut, oil palm, sugar cane, cassava, cotton), (iiI) of which with NVP (excl. transport) > 1000 $/ha, and (iv) NVP (incl. transport) > 1000 $/ha.

Countries with large extents of potential highly productive land Including transport cost to port Without transport cost to port Million hectares Note: The diagrams show extents of land currently classified as grass/woodland outside ‘No-Go’ areas and with an estimated NVP exceeding 1000 US$/ha, based on assessment of nine major agricultural crops (maize, sorghum, wheat, soybean, groundnut, oil palm, sugar cane, cassava, cotton). Source: Calculations by authors based on FAO-IIASA GAEZ v3.0 database, 2011 Million hectares

NEXUS THINKING ENERGY FOOD WATER ON - Preparing land Growing crops Raising livestock Harvesting produce Drying, processing Storing food products Transport, distribution Preparing food Food/Land Use System NEXUS THINKING ENERGY FOOD WATER ON Biomass , crop residues, biofuel feedstocks, land Fertilizer, irrigation, fuel, processing, transportation Irrigation, food processing, sanitation, health risk Runoff, pollution, storage, purification, flood protection Energy System - Extracting resources Harnessing hydro, wind, solar, biomass energy Generating and transmitting electricity Production, refinement and distribution of transport fuels Storing, buffering Water System - Manage renewable surface- and groundwater resources Distribute water supply for human consumption Collect sewage Treat wastewater to protect human and ecological health Transfer between basins Desalination Hydropower, power plant cooling, extraction, (bio)fuels Water pumping, delivery, water treatment, energy for desalination

Case study of Mauritius: Climate, Land, Energy and Water Strategies (CLEWS) The Climate, Land, Energy and Water Strategies project (CLEWS) deals with integration of water, energy and land-use models to quantify resource use, greenhouse gas emissions and costs associated with meeting energy, water and food security goals. For this purpose the WEAP water model, the LEAP energy model and the AEZ land production planning tool were applied in an integrated fashion to determine (a) crop suitability under rain-fed and irrigated conditions for current and future projected climate, (b) potentials of bio-fuel feedstock crops, (c) the viability and impact of crop changes, and (d) measures to ensure adequate water supplies in the face of an observed and projected trend of decreasing rainfall.

AEZ Analysis Provides standardized framework and database for land resources appraisal and for analyzing alternatives of land and water resources use. Contains an automatic crop calendar search for assessing historical variability and enabling simulations for adaptation to climate change. Estimates land suitability and productivity of a large number of food, feed and energy crops across a wide range of environmental settings. Computes crop water requirements and irrigation demand and indicates trade-offs among crops and between rain-fed and irrigated uses. Produces comprehensive resource accounts for current land use, reveals apparent yield gaps and resource limitations, and can identify hot- spots for land use change and intensification.

Change of Mean Annual Runoff, mm/year Note: The map shows the multi-model ensemble mean of annual runoff over a 30-year period 1971-2000 using outputs of six hydrological models and five CMIP5 climate models at 30 arcmin latitude/longitude, bias-corrected for impact and adaptation analysis by the ISI-MIP project. Note: The map shows the ratio of multi-model ensemble mean annual runoff over a 30-year period respectively for 2041-2070 and 1971-2000 using outputs of six hydrological models and five CMIP5 climate models (for RCP8.5) at 30 arcmin latitude/longitude. Change of Mean Annual Runoff, 2000-2050, RCP 8.5

Per Caput Water Resources Water Challenges: Per Caput Water Resources 1971-2000 Note: Classes indicate severity of water security challenges regarding water availability. (Mean annual runoff per caput, period 1971-2000 and 2041-2070 for UNmed / RCP6p0). 2041-2070

Update of Data Portal to GAEZ v4 Update of climate database (GPCC v6, CRU TS3.2, WATCH Forcing Data); use of daily temperature and rainfall data Update of land cover/use data to 2010 CMIP5 climate scenarios (bias-corrected output from five global climate models for the four IPCC RCPs) Link to spatial outputs from hydrological modeling (e.g integration of FAO GlobWat procedures into GAEZ framework) Tabulation of crop summary results by country and major river basin Integration of AGROMAPS sub-national crop statistics Spatial attribution of actual crop production (‘Downscaling’) using statistical data of 2009-11 and calculation of apparent yield gaps.

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