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Modeling Wheat Yield Gaps in European Russia Using the SWAT Model

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Modeling Wheat Yield Gaps in European Russia Using the SWAT Model Modeling wheat yield gaps in European Russia using the SWAT model Schierhorn, Florian Faramarzi, Monireh Prishchepov, Alexander V. Koch, Friedrich Müller, Daniel SWAT conference 2013 | 17 – 19 July Rising demand for agricultural products Population o 2.5 billion more by 2070 o Growing affluence Diets o Meat guardian.co.uk o Processed food Bioenergy guardian.co.uk o Traditional (80%) o Modern (20%) guardian.co.uk 2 Two options to increase agricultural production 1. Expansion of agricultural land o Most fertile land already used o Further expansion associated with high environmental tradeoffs 2. Intensification on existing agricultural land o More inputs per unit of land o Better technology 3 Low yields due to yield gap? Objectives • Simulation of post-Soviet wheat yields using SWAT [->capturing of inter-anual yield variations] • Simulation of wheat yield potentials • Sensitivity analysis of growing period (& PHU) and crop parameters on yield Study area 30°E 40°E 50°E 60°E 70°E Vologda Kirov Perm Udmurtia European Russia MariEl Nizhny Chuvash Tatarstan Moscow*# Bashkortostan Ulyanovsk Ryazan Mordovia Tula Samara 50°N Penza Bryansk Oryol Lipetsk Tambov Saratov Kursk Voronezh 546 subbasins -> 30 *# Belgorod Kazakhstan 50°N selected for individual calibration Volgograd Ukraine Rostov Spring wheat Subbasin Krasnodar Stravropol Provinces (oblast) 40°E 50°E 60°E Model setup Calibration: 1996-2006 / 3yrs warm-up Validation: 1991-1994 / 3yrs warm-up Study area size: 4 million km² square kilometers Hargreaves method for ET-estimation Dominant soil and land use property Winter wheat & spring wheat dominant Assignment of ONE representative subbasin per province (oblast) -> 30 regions to be calibrated indi Data • Landuse: Binary cropland map (100 ha) • Soil: Harmonized World Soil Database (10 km) • Climate: Daily weather data from Schuol and Abbaspour (2007) • Wheat yield: ROSSTAT 2012 (provinces) • Management: • Growing period – USDA • N fertilizer – ROSSTAT 2012 Calibration, Validation, and scenarios analysis • Wheat yields were calibrated for each province • Simulated wheat yields were compared with anual wheat yield data reported at provincial level • After satisfactory calibration/validation, two scenarios: A) sufficient N fertilizer, B) sufficient N fertilizer + Water were constructed [to access yield potentials] Initial parameter ranges for calibration Parameter MIN MAX Parameter MIN MAX v__ESCO.hru 0.01 1 v__USLE_K(1).sol 0 0.65 v__EPCO.hru 0.01 1 v__USLE_K(2).sol 0 0.65 v__OV_N.hru 0.01 0.8 v__SOL_EC(1).sol 0 100 v__LAT_TTIME.hru 0 180 v__SOL_EC(2).sol 0 100 v__LAT_SED.hru 0 5000 v__SHALLST.gw 0 1000 v__CANMX.hru 0 1 v__DEEPST.gw 0 1290 v__SOL_BD(1).sol 1.1 1.7 v__GW_DELAY.gw 0 500 v__SOL_BD(2).sol 1.1 1.7 v__ALPHA_BF.gw 0 1 v__SOL_CBN(1).sol 1 3 v__GWQMN.gw 0 5000 v__SOL_CBN(2).sol 0 2 v__GW_REVAP.gw 0.02 0.2 v__SOL_ALB(1).sol 0 0.25 v__RCHRG_DP.gw 0 1.1 v__SOL_ALB(2).sol 0 0.25 v__GWHT.gw 0 25 v__ANION_EXCL.sol 0.2 0.8 v__GW_SPYLD.gw 0 0.4 v__SOL_K(1).sol 0 2000 v__SHALLST_N.gw 0 10 v__SOL_K(2).sol 0 2000 v__GWSOLP.gw 0 1000 v__SOL_CRK.sol 0 1 v__HLIFE_NGW.gw 0 365 v__CN2.mgt 65 90 r__FRT_KG{x,x}.mgt -0.3 0.3 Crop related parameters were excluded for calibration! Parameter 129 139 201 220 221 235 251 279 300 308 379 425 FRT_KG{12,3}.mgt 8 9 14 10 8 7 5 11 18 1 5 1 SOL_CBN(2).sol 7 1 8 26 1 8 6 12 1 6 12 9 FRT_KG{9,3}.mgt 20 2 3 1 15 15 19 1 20 2 1 3 FRT_KG{6,3}.mgt 9 7 48 4 5 11 1 3 11 4 6 15 FRT_KG{11,3}.mgt 5 15 21 9 6 9 2 8 27 3 13 11 FRT_KG{14,3}.mgt 10 12 15 12 2 1 10 2 23 12 11 20 + FRT_KG{8,3}.mgt 29 3 1 6 16 14 3 5 31 16 2 4 ESCO.hru 6 13 4 3 14 4 16 41 2 23 3 2 Parameter sensitivity Parameter FRT_KG{10,3}.mgt 3 10 6 8 4 34 8 7 10 13 25 5 CN2.mgt 48 5 2 2 13 6 14 10 5 14 9 8 FRT_KG{7,3}.mgt 2 4 5 5 19 12 4 4 24 43 8 6 FRT_KG{13,3}.mgt 11 16 12 11 7 3 15 6 35 5 10 16 SOL_CBN(1).sol 12 6 11 28 12 10 12 13 3 9 23 13 FRT_KG{15,3}.mgt 1 17 23 7 3 18 7 14 47 8 7 17 FRT_KG{4,3}.mgt 4 11 9 14 21 25 9 37 28 10 4 12 ANION_EXCL.sol 26 8 10 19 10 5 13 25 8 15 29 18 EPCO.hru 35 20 7 13 11 2 18 15 25 7 17 19 FRT_KG{5,3}.mgt 44 23 13 16 9 21 11 9 9 36 14 7 CANMX.hru 15 24 16 15 17 13 29 46 19 17 16 44 SOL_BD(1).sol 16 14 43 35 18 40 17 28 6 21 21 36 SOL_BD(2).sol 14 18 32 18 47 16 47 23 4 32 30 14 SOL_K(1).sol 42 34 26 41 29 28 23 27 7 11 18 10 GWSOLP.gw 23 32 28 17 27 23 24 32 12 22 19 43 FRT_KG{3,3}.mgt 25 19 27 23 37 24 31 18 30 44 24 23 GWQMN.gw 18 29 24 45 30 20 38 30 16 18 33 34 SOL_K(2).sol 39 33 22 30 31 42 26 16 41 19 40 27 LAT_SED.hru 37 30 33 36 28 37 20 20 17 47 42 22 SHALLST_N.gw 13 40 40 20 25 19 22 39 43 46 41 24 FRT_KG{1,3}.mgt 28 31 25 47 24 38 39 42 26 20 31 26 SOL_EC(1).sol 24 22 38 43 40 26 27 22 36 48 26 25 DEEPST.gw 40 25 44 37 20 32 42 19 32 45 15 30 GWHT.gw 30 37 29 24 33 30 44 17 46 29 45 21 LAT_TTIME.hru 22 36 46 32 46 17 46 38 14 26 32 46 FRT_KG{2,3}.mgt 21 46 31 27 42 33 25 24 48 24 37 45 SOL_ALB(1).sol 19 43 18 40 43 36 40 40 15 38 43 29 SOL_EC(2).sol 36 26 30 46 34 22 35 21 33 37 47 37 GW_REVAP.gw 46 27 34 31 41 46 21 26 40 27 38 31 HLIFE_NGW.gw 45 35 20 21 45 31 32 45 38 30 35 38 OV_N.hru 41 48 39 22 26 48 45 29 13 35 44 28 GW_SPYLD.gw 38 42 36 33 22 47 41 35 21 40 28 40 SOL_ALB(2).sol 17 41 42 39 35 29 43 47 45 28 22 35 USLE_K(1).sol 47 28 47 25 38 27 30 43 34 34 36 41 - RCHRG_DP.gw 31 47 41 42 23 35 33 48 29 31 34 39 ALPHA_BF.gw 43 44 35 38 39 39 34 31 39 42 20 33 USLE_K(2).sol 27 21 37 29 44 45 48 36 44 39 27 42 SHALLST.gw 32 38 19 34 48 41 37 33 42 33 39 48 SOL_CRK.sol 34 39 45 44 36 43 28 34 22 25 48 47 GW_DELAY.gw 33 45 17 48 32 44 36 44 37 41 46 32 Calibration and Validation results Calibration Validation 2 3500 4 8000 10 kg N 3000 3.5 300 kg N 7000 1.5 NoRainfed irrigation 2500 3 NoRainfed irrigation 6000 2.5 5000 2000 1 2 4000 1500 1.5 3000 1000 0.5 1 2000 500 0.5 1000 0 0 0 0 1 10 19 28 37 46 55 64 73 82 91 100 109 1 11 21 31 41 51 61 71 81 91 101 111 PHU: 2009 YLD = 0.7 t/ha PHU: 2009 YLD = 1.2 t/ha 4 16000 4 12000 3.5 300 kg N 14000 3.5 300 kg N 10000 3 Irrigation 12000 3 Irrigation 8000 2.5 10000 2.5 2 8000 2 6000 1.5 6000 1.5 4000 1 4000 1 2000 0.5 2000 0.5 0 0 0 0 1 11 21 31 41 51 61 71 81 91 101 111 1 11 21 31 41 51 61 71 PHU: 2009 YLD = 4.7 t/ha PHU: 1409 YLD = 3.4 t/ha Yield gap for sufficient N & Water supply Bashkortostan Belgorod Bryansk Chuvash Kirov Krasnodar 8 6 4 2 0 Kursk Lipetsk MariEl Mordovia Moscow Nizhny 8 6 4 2 0 Oryol Penza Perm Rostov Ryazan Samara 8 6 4 2 0 Saratov Stravropol Tambov Tatarstan Tula Udmurtia 8 6 4 2 Wheat (t/ha) yield 0 1995 2000 2005 1995 2000 2005 Ulyanovsk Volgograd Vologda Voronezh 7 8 6 6 4 YGN&Water 2 5 0 1995 2000 2005 1995 2000 2005 1995 2000 2005 1995 2000 2005 4 YGN 3 Yield potential for sufficient N & WATER supply 2 Wheat yield (t/ha) yield Wheat Yield potential for sufficient N supply 1 Reported 0 Average wheat yields, 2001-2006 30°E 40°E 50°E 60°E 70°E Vologda Kirov Perm Spring wheat Udmurtia European Russia MariEl Nizhny Chuvash Tatarstan Moscow*# Bashkortostan Ulyanovsk Ryazan Mordovia Tula Samara 50°N Penza Bryansk Oryol Lipetsk Tambov Saratov Kursk Voronezh Average wheat yields *# Belgorod 50°N reported 2001-2006 Volgograd t/ha 2.17 - 2.43 1.10 - 1.18 2.44 - 2.53 Ukraine Rostov 1.19 - 1.35 2.54 - 2.94 1.36 - 1.48 2.95 - 3.15 1.49 - 1.87 3.16 - 4.31 Krasnodar Stravropol 1.88 - 2.16 40°E 50°E 60°E Yield gap for sufficent N supply 30°E 40°E 50°E 60°E 70°E Vologda Kirov Perm Spring wheat Udmurtia European Russia MariEl Nizhny Chuvash Tatarstan Moscow*# Bashkortostan Ulyanovsk Ryazan Mordovia Tula Samara 50°N Penza Bryansk Oryol Lipetsk Tambov Saratov Kursk Voronezh Average yield gap for sufficient *# Belgorod N supply, 2001-2006 50°N Volgograd t/ha 2.18 - 2.31 1.16 - 1.43 2.32 - 2.37 Ukraine Rostov 1.44 - 1.75 2.38 - 2.52 1.76 - 1.80 2.53 - 2.82 1.81 - 1.97 2.83 - 3.03 Krasnodar Stravropol 1.98 - 2.17 40°E 50°E 60°E Yield gap for sufficent N supply & water supply 30°E 40°E 50°E 60°E 70°E Vologda Kirov Perm Spring wheat Udmurtia European Russia MariEl Nizhny Chuvash Tatarstan Moscow*# Bashkortostan Ulyanovsk Ryazan Mordovia Tula Samara 50°N Penza Bryansk Oryol Lipetsk Tambov Saratov Kursk Voronezh Average yield gap for sufficient *# Belgorod 50°N N & Water supply, 2001-2006 Volgograd t/ha 3.21 - 3.38 1.79 - 2.57 3.39 - 3.44 Ukraine Rostov 2.58 - 2.74 3.45 - 3.72 2.75 - 2.89 3.73 - 3.80 2.90 - 3.11 3.81 - 4.56 Krasnodar Stravropol 3.12 - 3.20 40°E 50°E 60°E Yield gap for entire European Russia: 2.1 t/ha -> under sufficient N supply 3.2 t/ha -> under sufficient N & Water supply Yield sensitivity to growing period Bashkortostan Chuvash Kirov MariEl 7 6 5 4 3 Mordovia Nizhny Penza Perm 7 6 5 4 3 Samara Saratov Tatarstan Udmurtia 7 6 5 4 3 1995 2000 2005 Spring wheat yield (t/ha) Spring wheat Ulyanovsk Volgograd Vologda 7 6 5 4 3 1995 2000 2005 1995 2000 2005 1995 2000 2005 95PPU of simulated yield, average growing period 95PPU of simulated yield, short growing period 95PPU of simulated yield, long growing period Yield sensitivity to crop parameter Mordovia Nizhny Penza 8 6 4 2 0 Samara Saratov Tatarstan 8 6 4 2 0 1995 2000 2005 Ulyanovsk Volgograd 8 Spring wheat yield (t/ha) Spring wheat 6 4 2 0 1995 2000 2005 1995 2000 2005 95PPU of simulated yield for sufficient N & Water supply, Variation of crop parameters 95PPU of simulated yield for sufficient N & Water supply, SWAT default crop parameters Reported Conclusion • Russia has large potential to increase crop yields • SWAT crop growth module reproduces accurate yields despite the large scale & data scarcity • Yield gaps are heterogeneously distributed • Information on crop characteristics (PHU in particular) are essential for accurate yield gap estimations 20 Thank you very much.
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