8/14/2014 Precision Management of Nutrients, Water, & Seeds: A Holistic Approach To identify and demonstrate the most productive, efficient, profitable and sustainable variable rate-water, -nutrient and –seed management strategies for precision crop management Raj Khosla Colorado State University Info Ag 2014 St. Louis, MO Holistic crop management approach Management Zones Active remote-sensing (micro-management) A sub-region of a field that expresses a homogeneous combination of yield limiting factors Management zones (macro-management) R. Khosla, Colorado State University 1 8/14/2014 Management Zones Management Zones… 1. Bare soil imagery • Soil organic matter • Moisture content and • Other stable soil properties (bulk density, texture, compaction, etc) Management Zones… Management Zones… 2. Field topography 3. Farmer’s experience Elevation map Grain yields are correlated with topography R. Khosla, Colorado State University 2 8/14/2014 Management Zones… Management Zones… The three data layers 3. Farmer’s experience Aerial Imagery Topography Farmer’s experience • Previous management • Familiarity with field • Intuition • Observations • History of land use • Tillage • Pests and weeds are stacked as GIS layers to delineate the zone Traits such as dark color, lowlying topography, and historic high yields were designated as a zone of potentially high productivity or high zone Low Productivity (Zone 3) High Productivity (Zone 1) Medium Productivity (Zone 2) Mean grain yield across MZs 12 12 8 4 0 9 6 3 0 Low Medium High Management zones Overall findings 20 Grain yield (Mg ha -1) Grain yield (Mg ha -1) Grain yield (Mg ha -1) 16 Precision Nutrient Management Across Soil Zones… has shown to enhance: 15 10 (i) overall grain yield of the field, 5 (ii) nutrient use efficiency, 0 Low Medium High Management zones Low Medium High Management zones (iii) net $ returns to farmers and (iv) reduces overall nutrient losses from the field. • What’s the problem? R. Khosla, Colorado State University 3 8/14/2014 Crop Based Management N Rate (kg ha-1) = (135.3 x (NDVIRef. / NDVITarget)2) – (134.8 x (NDVIRef. / NDVITarget)) + 1 NDVI 0.41 NDVI 0.41 NDVI 0.41 2 N Rate (kg ha-1) = Crop (135.3properties x (NDVIRef. + Soil / NDVI Properties Target) ) – (134.8 x (NDVIRef. / NDVITarget)) + 1 NDVI ~96 lb/a ~96 lb/a Medium High 0.41 NDVI ~92 lb/a ~144 lb/a 0.41 NDVI ~96 lb/a ~37 lb/a 0.41 Low Crop Sensing + Soil Sensing Make better and most efficient nutrient management decisions Holistic crop management approach Fertilizer Crop sensing Water Precision water management MZ R. Khosla, Colorado State University 4 8/14/2014 Variability in soil water holding capacity at the field scale Sand varies from 36 % to 78% (more than double) Clay varies from 10 to 28 % (almost triple) Source: http://www.soilsampling.com/services/soil‐characteristics/electrical‐conductivity‐mapping A Is there spatial variability? B System dating to 1905 Sand: 39% 5% Silt: 55% 75% Clay: 6% 20% R. Khosla, Colorado State University 5 07‐07‐2012 00:12:00 07‐07‐2012 09:42:00 07‐07‐2012 19:12:00 07‐08‐2012 04:42:00 07‐08‐2012 14:12:00 07‐08‐2012 23:42:00 07‐09‐2012 10:12:00 07‐09‐2012 20:12:00 07‐10‐2012 05:42:00 07‐10‐2012 15:12:00 07‐11‐2012 00:42:00 07‐11‐2012 10:12:00 07‐11‐2012 19:42:00 07‐12‐2012 05:12:00 07‐12‐2012 14:42:00 07‐13‐2012 00:12:00 07‐13‐2012 09:42:00 07‐13‐2012 19:12:00 07‐14‐2012 04:42:00 07‐14‐2012 14:12:00 07‐14‐2012 23:42:00 07‐15‐2012 09:12:00 07‐15‐2012 18:42:00 07‐16‐2012 04:12:00 07‐16‐2012 13:42:00 07‐16‐2012 23:12:00 07‐17‐2012 08:42:00 07‐17‐2012 18:12:00 07‐18‐2012 03:42:00 07‐18‐2012 13:12:00 07‐18‐2012 22:42:00 07‐19‐2012 08:12:00 07‐19‐2012 17:42:00 07‐20‐2012 03:12:00 07‐20‐2012 12:42:00 07‐20‐2012 22:12:00 07‐21‐2012 07:42:00 07‐21‐2012 17:12:00 07‐22‐2012 02:42:00 07‐22‐2012 12:12:00 07‐22‐2012 21:42:00 07‐23‐2012 07:12:00 07‐23‐2012 16:42:00 07‐24‐2012 02:12:00 07‐24‐2012 11:42:00 07‐24‐2012 21:12:00 07‐25‐2012 06:42:00 07‐25‐2012 16:12:00 Precision irrigation based on FC and MAD 25% 50% 75% R. Khosla, Colorado State University Capacitance 8/14/2014 Crop responds to drought stress Quantifying the spatial variability of soil moisture content • How much of the field is represented by the average? • Is there a spatial pattern? • How strong is the temporal variability? March June Adapted from Tom Trout’s presentation, USDA-ARS How much of the field is represented by the average? 1.8 1.6 1.4 1 Stress days (water logged) 1.2 No stress 0.8 0.6 0.4 0.2 Stress days (drought) 0 FC: Field Capacity, MAD: Max Allowable Depletion Date 6 8/14/2014 Field 3100 at Agriculture Research, Development and Education Center of Colorado State University Source: Timothy Gish, Hydrolab, ARS-USDA Probe readings Soil moisture in space (Data at 42” deep) 0.00 2.25 0 - 8– 484 2.26 – 2.50 8 485 - 9 000 2.51 – 3.00 9 001 - 10 (in/ft) 000 3.01 – 3.50 10 010 - 11 000 3.51 – 4.00 11 010 - 12 000 4.01 – 4.51 12 010 - 13 000 6” 18” June July Aug. Sept. 30” 42” 54” R. Khosla, Colorado State University 7 8/14/2014 Stability over Time What are the next steps? May 1. Delineation of water management zones r r r r 12 dates June r July r r r r Aug. r r r r r r Correlation Coefficient = r (0 to 1.0) 0=different, 1.0 = 100% match 0 1 5 10 15 Time interval (weeks) Temporal variability May June r r r r July r r Temporal variability Aug. r r r r June July r r r May Aug. r r r r r r r r r r r r 0 1 2 5 10 Time interval (weeks) R. Khosla, Colorado State University 15 0 1 2 5 10 15 Time interval (weeks) 8 8/14/2014 0 .8 0 .6 0.4 0.2 AtAt156”cm AtAt4518” cm AtAt7530” cm 42”cm AtAt105 At 54” At 135 cm 0.0 Average correlation (r) 1 .0 Site 1 0 1 2 5 10 15 Time interval (weeks) 0.5 m (mS/m) EM38 at 1.0 [ARDEC_April18th2012_vertical_point].[CV_0_5M] [ARDEC_April18th2012_vertical_point].[CV_1_0M] 0 - 13,4 27,5 - 39,3 13,4 -- 41,2 14,5 39,3 14,5 -- 42,7 15,4 41,2 Effect of VRI on yield 15,4 -- 44,3 16,4 42,7 16,4 -- 46,6 17,4 44,3 17,4 -- 50,3 18,9 46,6 18,9 -- 73,1 40,4 50,3 R. Khosla, Colorado State University 9 8/14/2014 150 100 Strong evidence of spatial and temporal variability in precision levelled field. Identify surrogate data layers to delineate water 50 0 Yield (Bu/ac) Effect of VRI on yield management zones a a a a ab ab b 40% 60% 80% 100% Percentage of full irrigation Holistic crop management approach Variable rate seeding Fertilizer VRS based on Yield map Crop sensing MZ Water Long-term (> 7 years) yield history Doerge et al. 2006 Precision water management Yield maps are cheap VR Planting More profitable when high variability Seeds Lowenberg-DeBoer 1998 R. Khosla, Colorado State University High Low 10 8/14/2014 Variable rate seeding Variable rate seeding Better use of available resources Several factors can explain yield o Intra-crop competition for: P2O5 K 2O NO3- K 2O P2O5 NO3- Reduced crop-to-crop competition P2O5 K2O NO3- K 2O NO3 P2O5 K 2O P2O5 NO3 P2O5 K 2O Soil moisture content • Sunlight o Inter-row moisture influencing disease occurrence NO3- K 2O K 2O K 2O P- 2O5 NO3- Nutrients • o Crop competition to weeds NO3- NO3- P2O5 - • NO3- P2O5 Cu P K Zn o Germination rate P Mo N K B Mg Ca S N Fe Mn Cl o Interplay of all factors K 2O P2O5 P2O5 Variable rate seeding Uniform seed rate Variable rate seeding Yield map Yield map Example of reasons that could explain antagonist effect: • Crust formation reducing germination rate • Limited water holding capacity Variable seed rate Increased Increased • Fine texture • Low organic matter content Decreased R. Khosla, Colorado State University • High salinity Maintaining population or coupling higher population with higherpopulation irrigation could Increasing could have have been been aa better better option option Decreased 11 8/14/2014 Variable rate seeding Variable rate seeding Information from the soil is important Medium yield Higher yield Topography 100 Electric conductivity 150 Yield (bu/ac) 200 Lower yield Soil survey Yield (historic) 20000 30000 40000 50000 Plant population (plants/ac) Holistic crop management approach Fertilizer Water Precision water management Crop sensing MZ Thank you VR Planting [email protected] Seeds R. Khosla, Colorado State University 12
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