The Global Yield Gap Atlas (GYGA) www.yieldgap.org Dr Patricio Grassini University of Nebraska-Lincoln & Water for Food Institute Fellow e-mail: pgrassini2@unl.edu
Global population trend Global population around 9.7 billion by year 2050 (+30% increase relative to 2
Increasing calorie intake per capita COUNTRY MEAT CONSUMPTION POPULATION INCREASE NEXT 35 YEARS ---kg/person/yr--- --- million people-- - US 90 +67 European Union 65 +5 China 50-28 Sub-Saharan Africa <10 +1161 South-east Asia <10 +159 Source: OECD-FAO Agricultural Outlook; U.N. World Population Prospects Population increase + diet change requires 50-70% food production increase in next 35 years
Food security: don t worry, no problem (?) The commercial maize productivity simulation is driven by the estimate from private sector sources that hybrid maize yields can be expected to increase by 2.5 percent per year at least until the 2030s. this productivity change would affect about 80 percent of world production in 2010. The effects on world maize prices are dramatic: prices increase only 12 percent, instead of 101 percent, between 2010 and 2050. The effect on malnourished children is also not insignificant, with a 3.2 percent decline relative to the baseline in 2050. Nelson, Rosegrant, et al., 2010. Food Security, Farming and Climate Change to 2050, IFPRI. Rosegrant et al., 2013. Water and food in the bioeconomy: challenges and opportunities for development. Agric. Economics, 44, 139-150 Evenson, R.E., 1999. Global and local implications of biotechnology and climate change for future food supplies. PNAS 96, 5921-5928. Dyson, T., 1999 World food trends and prospects to 2025. PNAS 96, 5929 5936
Hype versus reality in yield trends Average US maize yield (kg ha -1 ) 35000 30000 25000 20000 15000 10000 5000 SOLID LINE: linear regression (1965-2011) y = -220114 + 114 x r 2 = 0.87 (P<0.01) 0 1965 1980 1995 2010 2025 2040 2055 Year (13) (15) (14,16) (12) (12) Extrapolation of 1965-2011 linear regression (12) Nelson et al. (2010) & Rosegrant et al. (2013), IMPACT model, IFPRI, Washington D.C. (13, 14) Reilly & Fuglie (1998) & Heisey (2009), USDA-ERS (15) Edgerton (Plant Phys., 2009), Monsanto Co. (16) Hertel (Bioscience, 2010), GTAP model, Purdue Univ. Source: Grassini et al., Nature Communications (2014)
Global trends in cereal crop yields Global crop yields have to increase 1.2-1.3% annually from NOW until 2050 (Bruinsma, 2009; Fischer, 2009). However, rates of increase in cereal crop yields have been remarkably linear during the past 50 years. 2013: 1.2% Grain yield (Mg ha -1 ) 5 4 3 2 1970: 2.5% Maize 65 kg ha -1 y -1 Rice 52 kg ha -1 y -1 Wheat 39 kg ha -1 y -1 1 1960 1970 1980 1990 2000 2010 Year Source: FAOSTAT, 1965-2013
Slowdown of yield gain in intensive crop systems Evidence of yield plateaus or abrupt decreases in rate of yield gain, including rice in eastern Asia and wheat in northwest Europe, which account for 31% of total global rice, wheat and maize production. Crop yield (Mg ha -1 ) 10 RICE 8 California 6 4 2 Rep of Korea China Indonesia India 10 8 6 4 2 WHEAT MAIZE? North-west Europe (Netherlands, UK, France) China India 12 10 8 6 4 2 U.S. irrigated China India Italy 0 1970 1980 1990 2000 2010 Year 0 1970 1980 1990 2000 2010 Year 0 1970 1980 1990 2000 2010 Year Source: Grassini et al., Nature Communications (2014)
Global Cropland Trends Staple-crop area includes cereals, oilseed, pulses, sugar, root, fiber, and tuber crops. Crop harvested area (Mha) 1200 1100 1000 900 550 500 450 400 1965-1981 b = 5.9 Mha y -1 1965-1980 b = 3.9 Mha y -1 Staple crops area 1981-2002 b = 1.5 Mha y -1 Rice + w heat + m aize area 1970 1980 1990 2000 2010 Year 2002-2013 b = 11 Mha y -1 2003-2013 b = 6.1 Mha y -1 Nearly all of the increase in crop area since 2002 has occurred in South America, Asia, and Sub-Saharan Africa Source: Grassini et al., Nature Communications (2014)
Photo: KG Cassma
Food crises and political instability FAO food price index between 2011 and 2011. Red dashed vertical lines correspond to beginning dates of food riots and protests associated with the major recent unrest in North Africa and the Middle East. The overall death toll is reported. Source: Lagi et al., 2011
Crop grain yield (Mg ha -1 ) Yield potential, farm yield, and yield gaps Determined by: Radiation Temperature [CO 2 ] Cultivar Rainfall & soil (in rainfed crops) Yield Potential Limited by: Poor Fertility Poor management Insects, weeds diseases Yield gap Farm yield Modified from: van Ittersum and Rabbinge, Field Crops Research (1997)
Average farm yield (% of yield potential) Yield gaps across crop systems Yp = Yield potential simulated using crop simulation models. 100 Yp = 14.0 t ha -1 Gap 15% Yp = 3.6 t ha -1 Gap 50% Yp = 8.8 t ha -1 Gap 80% 80 60 40 20 0 Irrigated Maize, USA Rainfed Wheat, Australia Rainfed Maize, Sub-Saharan Africa Adapted from: van Ittersum, Cassman, Grassini, et al. Field Crops Research (2013) & Global Yield Gap Atlas (www.yieldgap.org)
Why a Global Yield Gap Atlas (GYGA)? We need to provide reliable answers about key questions on food security: What is the food production potential for a region or country, on existing cropland area, if producers adopted best management practices? Will it be possible for country X to be selfsufficient in food production by 2030 or 2050? If yes, will there be surplus capacity for export? How much? If not, how much land is available and suitable to expand production? If insufficient land is available, how much food imports will be required?
Global Yield Gap Atlas (GYGA): coordinating team University of Nebraska (USA) Wageningen University (The Netherland Kenneth Cassman Patricio Grassini Martin van Ittersum Lenny van Bussel Joost Wolf Nicolas Guilpart Haishun Yang Hendrik Boogaard Hugo de Groot Funding From: Bill & Melinda Gates Foundation UNL Water for Food Institute USAID Wageningen University
Principles of the Global Yield Gap Atlas Consistent, transparent, and reproducible approach to determine yield potential and yield gaps, with local and global relevance Based on a strong agronomic foundation A bottom-up approach: yield potential is simulated for specific locations using validated crop models based on local weather, soil, and management data Scaling up from point to region/country assisted by a climate-zone scheme ( from field to globe )
GYGA climate zonation Climate zones are based on a matrix of three climate variables: 1. Growing degree days 2. Aridity index 3. Temperature seasonality Source: Van Wart, van Bussel, Wolf, Licker, Grassini, et al. Field Crops Research (2013)
Yield gap analysis: protocol Climate zones Crop-specific harvested areas Weather station buffer zones Soil types and cropping systems Crop model simulations Actual yields Sources: Grassini et al. & van Bussel et al., Field Crops Research (2015) Yield gaps and water productivity
Global coverage of cropland; currently >50 countries* * Including countries completed and in progress
www.yieldgap.org
Example National Assessment: Yield gaps and potential production of maize in Argentina Aramburu-Merlos, Monzon, Mercau, Taboada, Andrade, Hall, Jobaggy, Cassman, Grassini (2015). Potential for crop production in Argentina through closure of existing yield gaps. Field Crops Research 184, 145-154.
Climate zones in Argentina
Relevant climate zones for maize production
Selected weather stations Each circle indicates a weather station 15 weather stations are sufficient to cover 78% of maize producing area in Argentina (3 million ha)
Simulated rainfed maize yield potential Website: www.yieldgap.org
Actual farm maize yield
Yield gap (as % of potential) for rainfed maize Large gaps in areas recently brought into crop ARGENTINA production Potential: 11.6 t/ha Actual yield: 6.8 t/ha Gap: 4.8 t/ha (41% of potential)
Additional exports due to intensification on existing cropland area in Argentina Current gap (% of potential) Current Production (Mt) Attainable Production (Mt)* Additional Production (Mt) Additional Exports (Million US$) # Maize 41% 25 34 9 (1%) + 2,250 U$S 1,350- (9%) ++ Soybea n 31% 46 55 9 (4%) + 4,500 U$S 2,700- (9%) ++ Wheat 39% 14 19 6 (1%) + 1,800 U$S 1,200- (4%) ++ * Attainable production = 80% of water-limited yield x current cropland area # Given a price (US$/t) range for maize (150-250), soybean (300-500), and wheat (200-300) + As % of global production for each crop ++ As % of global exports for each crop
Global coverage of cropland; currently >50 countries* * Including countries completed and in progress
Global Yield Gap Atlas (GYGA) Solid foundation for studies that deal with food security, climate change, land use, environmental footprint, etc. at different spatial scales GYGA spatial framework can be used to define extrapolation domains for technology transfer ( take agronomy to scale ) and identify best opportunities for sustainable crop intensification ( biggest bang for the buck ) GYGA weather soils Other data layers demographics land use climate zones yield gaps infrastructure groundwater Governments Donors Private sector CGIAR It is feasible to get an estimate of yield gap and environmental footprint for every field in the world. This is a stone s throw away for GYGA, and feasible depending our ability to generate/collect high-quality data
Thanks for your attention! www.yieldgap.org
References van Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z. 2013. Yield gap analysis with local to global relevance a review. Field Crops Res. 143:4-17 van Bussel LGJ, Grassini P, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, Saito K, Cassman KG, van Ittersum MK, 2015. From field to atlas: Upscaling of location-specific yield gap estimates. Field Crops Res. 177, 98-108 Van Wart J, Grassini P, Yang HS, Claessens L, Jarvis A, Cassman KG, 2015. Creating long-term weather data from thin air for crop simulation modelling. Agric. Forest Meteoro. 208, 49-58. Van Wart J, van Bussel LGJ, Wolf J, Licker R, Grassini P, Nelson A, Boogaard H, Gerber J, Mueller ND, Claessens L, Cassman KG, van Ittersum MK. 2013. Reviewing the use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143, 44-55 Grassini P, van Bussel LGJ, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, van Ittersum MK, Cassman KG, 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res. 177, 49-63.