Global Yield Gap Atlas (GYGA) University of Nebraska (UNL) Wageningen University & Alterra Kenneth Cassman Patricio Grassini Martin van Ittersum Lenny van Bussel Joost Wolf Justin van Wart Haishun Yang Hendrik Boogaard Hugo de GrootDaniel van Kraalingen Regional coordinators and partners Lieven Claessens (ICRISAT) Kazuki Saito (Africa Rice) Funding sources: Gates Foundation (SSA, S Asia) UNL Water for Food Institute (N & S Amer) USAID (N Africa, Middle East)
EUROPE: - ca. 30 countries : Global Yield Gap Atlas:
70% 30% Can agriculture reliably and sustainably provision an urban population of 6+ billion? 4 Source:
Why yield gap analysis? Currently not possible to provide reliable answers to critical questions of policy makers and R&D organizations: Food production potential for a region or country (on existing farm land, if farmers adopted best management practices)? Will it be possible for country/region X to be self-sufficient in food production by 2030 or 2050? Under different climate and socio- economic scenarios? When and where can we predict crop yields to stagnate because they reach biophysical yield ceilings? What are the causes of yield gaps and how to overcome them? How can we target options for sustainable intensification? What are the regions to target experimentation and what are extrapolation domains?
Previous yield gap studies Regional studies: crop growth models, experiments, best management practices local relevance, but not possible to compare them mutually, due to inconsistent concepts and methods Global studies: statistical procedures or generic crop growth models consistent, but generally too coarse, lacking local detail and hence agronomic relevance van Ittersum et al., Field Crops Research 143, 2013
Motivation and focus on Sub-Saharan Africa GYGA aspirations: Food and water security for a population exceeding 9 billion by 2050 while conserving natural resources = high(er) and stable yields on currently used arable land suitable for sustainable intensification Especially relevant for smallholder systems in SSA: 80% of food produced in SSA from smallholder agriculture (IFAD, 2011) Food production not keeping pace with population growth More to food security than production alone (distribution, demand, waste, governance, population)… Major options in SSA for improving productivity and environmental outputs simultaneously
Sustainable intensification in SSA smallholder context Smallholder production systems extremely diverse: Agro-ecology (climate, soil, landform) Socio-economic conditions (e.g. access to land, labour, inputs, markets) No silver bullet intervention for sustainable intensification! Rather best fit approach from basket of options (Giller et al., 2011)
Examples from the basket of options Integrated Soil Fertility Management (Tittonell & Giller, 2013; Giller et al., 2011; Khan et al., 2010; Vanlauwe et al., 2010; Altieri et al., 2012) Crop-livestock integration, dual-purpose crops (Valbuena et al., 2012; Homann et al.; Claessens et al., 2009) Fertilizer micro-dosing Seed technologies (hybrids, seed priming,…) New crops and crop rotations/combinations/intercropping (e.g. banana-coffee (van Asten) sorghum-legumes (Atakos et al., 2013) Small scale irrigation/mechanization Soil water management (e.g. tied ridges, terracing) Conservation agriculture (e.g. mulching, zero-tillage, rotation with legume,…) Agroforestry
Importance of soils for SI in SSA Degraded and poorly responsive soils cover large parts of SSA and represent the majority of poor farmers fields Where natural resources are degraded, yield gaps become poverty traps (Tittonell & Giller, 2013) African form of sustainable intensification needs to be targeted to ag. systems responsiveness to limited amounts of intervention (inputs, technologies from basket, policies)
Proposed index for soil suitability/responsiveness Inherent soil properties contributing to yield potential: WHC (texture, bulk density, infiltration, soil temperature) Rooting depth not limiting Slope (runoff/erosion) not limiting Properties that are, in principle, amenable to modification through management and inputs: Soil fertility/health (SOM of topsoil as proxy?) Measure of physical and chemical degradation + (ir)reversibility pH, salinity, toxicity,… Classify each (quantified) property and combine in (weighted) matrix for Soil Suitability Index
Possible sources of soil data ISRIC-AfSIS suitability/constraints maps Soil rooting conditions Soil nutrient availability and retention capacity Soil salinity, toxicity, workability AfSIS 1km soil property maps of Africa: Texture SOC pH CEC Bulk Density AfSIS Land Degradation Surveillance Framework 60 sentinel sites, 19,200 soil samples,….
ISRIC/AfSIS: 3D regression kriging with ~12,000 legacy profiles (including ISRIC-WISE) 1km resolution SOC pH Texture CEC Bulk Density WRB groups
Yield gap analysis: bottom-up protocol Climate zones Crop-specific harvested areas Weather station buffer zones Soil types and cropping systems Crop model simulations Actual yields Yield gaps Ewert et al., 2011 Aggregation and upscaling van Wart et al, 2013
5% GYGA upscaling method 20% 15% 30% 25% 5% CZ1 CZ2 CZ3 CZ4 ST1 ST2 ST3 Select soil type in harvested area as near to the selected weather stations as possible In case two soil types have similar dominant level, these two will be selected ST4
Linking Soil Suitability with Yield Gap Assessment GYGA yield gap assessment will give indication about yield gaps and stability of potential/water limited yield over time Overlaying soil suitability index with yield gaps will identify zones where (lack of) soil quality can explain a large part of the yield gap: High soil suitability with large (stable) yield gap = high potential for sustainable intensification (productivity side)
Conclusions Targeting sustainable intensification options is and important component of global future security studies Focus on soil suitability especially relevant for smallholder systems in SSA New sources of high resolution soil data can help in constructing a soil suitability index tuned towards the basket of intervention/adaptation options Combined with GYGA approaches to yield gap assessment, best bet areas/systems for sustainable intensification can be identified: extrapolation domains for OFRA