Exploiting spatial & temporal variability in the Prairies Chris Holzapfel, MSc, PAg
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Exploiting spatial & temporal variability in the Prairies Chris Holzapfel, MSc, PAg
Exploiting spatial & temporal variability in the Prairies Chris Holzapfel, MSc, PAg Indian Head Agricultural Research Foundation [email protected] Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 1 Objectives • Focus on characteristics of Prairie landscape that create potential for site-specific management (fertilization) – – – – Major causes of soil and yield variability Zone management and delineation Addressing temporal variability Opportunities for the future Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 2 definitions Spatial Variability • occurs when a quantity that is measured at different spatial locations at the same time exhibits values that differ across the locations Temporal Variability • occurs when a quantity that is measured at different times in the same location exhibits values that differ over time Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 3 definitions Precision Agriculture • The application of technologies and agronomic principles to manage spatial and temporal variability associated with all aspects of agricultural production for the purpose of improving crop performance and environmental quality Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 4 Focus on Nutrient Management • 4R Nutrient Stewardship (IPNI) • • • • Right Source Right Rate Right Place Right Time Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 5 Trends in Precision Agriculture • One of the fastest growing sectors in the agriculture industry Current trends in the U.S. Midwest Whipker and Akridge. 2008. Precision Agricultural Services: Dealership Survey Results – Sponsored by CropLife Magazine and Purdue University url: http://ageconsearch.umn.edu/bitstream/46427/2/08-09.pdf • Results based on responses of 275 crop input dealerships Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 6 Precision Agriculture Trends (Services offered) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 7 US Trends in Precision Ag (Variable Rate Services) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 8 Trends in Precision Agriculture (Market Area) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 9 Canadian Prairies Alberta Manitoba Saskatchewan Canadian Shield www.nrcan.gc.ca Beaverlodge i rd Co lle n ra Lacombe g re Melfort ion Saskatoon Lethbridge Winnipeg Swift Current Dec-15, 2009 Brandon MB Agronomists Conference University of Manitoba, Winnipeg MB Gray Dark Gray Black Dark Brown Brown 10 Spatial Variability – Major Causes • • • • • Topography Soil texture Soil fertility Erosion Salinity Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 11 Spatial Variability - Characterization • Elevation (DEM) • Conductivity (Veris, EM38) • Fertility (soil sampling) • Soil Color (aerial photos) • Biomass (NDVI – satellite imagery) • Grain Yield • etc. Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 12 Example of Spatial Variability • Yields within a <5 ac area at Indian Head (2008) for adjacent passes in a canola field with no treatments applied 70 62 60 Yield (bus/ac) 50 46 49 49 49 53 53 55 46 40 30 20 NO TREATMENTS APPLIED 52 10 avg 51.4 stdev 4.79 cv 9.3% 0 1 2 3 4 5 6 7 8 9 10 Pass # Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 13 Management Zones to Address Spatial Variability Definition: • Management zone: a “sub-region of a field that expresses a homogeneous combination of yield limiting factors for which a single rate of a specific crop input is appropriate” Doerge, T.A., 1999. J. Prod. Agric. 12:54-61. Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 14 Management Zones: Basic Approach 1. Data Collection / Mapping – collect information to characterize spatial distribution of parameter of interest 2. Clustering – divide field into zones based on variability in parameter of interest (supervised vs unsupervised) 3. Verification – confirm that productivity and/or nutrient requirements differ between zones 4. Apply Zone Management – adjust inputs or production practices to optimize production for each zone Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 15 Zone Delineation Soil Sampling / Fertility • Requires multiple, geo-referenced samples per field for variable-rate fertilization – grid-based vs landscape directed • Advantages – maps can be prepared for each nutrient and be used for variable rate fertilization or to help explain observed yield variability • Disadvantages – expensive (labor & capitol) – high variability (especially for N) – does not account for non-nutrient factors affecting productivity (ie: moisture, organic matter) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 16 Management Zone Delineation Soil Nutrients (K) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 17 Management Zone Delineation Soil Nutrients (P2O5) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 18 Management Zone Delineation Soil Nutrients (NO3-N) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 19 Zone Delineation Topography / DEM • Variable-rate fertilizer based on differences in relative field elevation – rates based on moisture availability • Advantages – zones static, relatively straight-forward approach, only done once • Disadvantages – can be expensive (RTK) – dry year vs wet year affects productivity of zones (high vs low fertilizer in low areas?) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 20 Management Zone Delineation Topography Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 21 Management Zone Delineation Topography Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 22 Management Zone Delineation Topography Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 23 Zone Delineation Remote Sensing • Ground-based – Airborne – Spaceborne – includes conductivity (EM38), aerial photography (soil color), satellite imagery (NDVI), etc. • Advantages – large data volume (minimal or no interpolation) – cost effective – image archives • Disadvantages – high computational requirements – difficult to interpret (ie: identifying causes of observed variability in NDVI) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 24 Management Zone Delineation Remote Sensing (NDVI) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 25 Management Zone Delineation Remote Sensing (NDVI) Site 1: Mean Yield per Management Zone 30 27 25.2 Yield (bu/ac) 25 22.2 21.4 20 Zone 1 (Low) 15 Zone 2 (High) 10 5 0 2001 (Peas) 2002 (Wheat) Year (crop) *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 26 Management Zone Delineation Grain Yield Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 27 Unsupervised Classification (Basnyat et al. 2005. Can J. Soil Sci. 85:319-328) • Acquire classification data (ie: NDVI) • Separate spatial data into zones of similar values using the fuzzy K-means algorithm “Fuzme” (Minasny and McBratney 2002) • Assess uniqueness of zones using significant differences in grain yield between zones • Increase or decrease number of zones until maximum number of zones with significantly different grain yields is achieved Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 28 Standardized Grain Yields for Zones Delineated from NDVI Comparing standardized mean grain yield (year 1998) differences under zonal combination. Management ZoneZ Iteration 1 (5 Zones) Iteration 2 (4 Zones) Iteration 3 (3 Zones) 1 -0.36aY -0.36a -0.36a 2 0.12b 0.12b 0.11b 3 0.18b 0.18b 0.72c 4 0.59c 0.72c 5 0.95c ZManagement zones delineated using Fuzme (increase zone number indicates increasing NDVI value YMean yields within columns followed by different letters are statistically different at P≤ 0.05 *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 29 Standardized Grain Yields for Zones Delineated from Various Parameters Comparing standardized mean grain yield (year 1998) differences under zone delineated using different variables of interest. Zone NDVI OC 1z -0.36a -0.3a 2z 0.11b 3z 0.72c ON TC ResN ELEV EM38 BASIN 0.03a -0.58a 0.11a -0.48a -0.37a -0.12a -0.05a -0.14ab -0.04ab -0.03a -0.01b 0.26b 0.02a 0.12a 0.16ac 0.3c -0.31a 0.3bc -0.29ac 0.04a a,b,c in the same column indicates statistically significantly different yields zzone 1, 2, and 3 represents the progressively higher mean value of the variable of interest NDVI: normalized difference vegetation index, OC: soil organic carbon at 0-15 cm depth; ON: soil organic N at 0-15 cm depth; TC: soil total carbon at 0-5 cm depth; ResN: average of residual N at 0-120 cm depth (1998-99); ELEV: Elevation of the field; EM38: EM measurement of the field; and BASIN: Three basin delineated using drainage pattern *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 30 Zones Delineated from Various Parameters Zone 1 2 3 E LE V A T IO N ON Dec-15, 2009 EM HYDRO TC OC MB Agronomists Conference University of Manitoba, Winnipeg MB 31 Management Zones Based on NDVI N W E S Zone 1 2 3 1:6418 • Zone 1 (low) – 46 ha (37.4%) • Zone 2 (mid) – 69 ha (56.1%) • Zone 3 (high) – 8 ha (6.5%) *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 32 Zone Stability Spatio-temporal difference in normalized grain yield Grain Yield Management Zone 1998 1999 1 -0.36a (0.68)Z -0.64a (0.98) 2 0.11b(0.21) 0.31b(0.74) 3 0.72c(0.91) 1.05c(0.48) Z Standard errors of mean in parenthesis Means followed by same letter within each column are not significantly different. *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 33 Wheat Response to N in Different Management Zones (NDVI) 2 W H EAT Standardized Yield 1 0 Management Options -1 Variable Rate Zone 1 Variable Rate Zone 2 Variable Rate Zone 3 Uniform Rate -2 -3 0 50 100 N Fertilizer (kg ha-1) 150 *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 34 Canola Response to N in Different Management Zones (NDVI) 2 C AN O LA Standardized Yield 1 0 Management Option -1 Variable Rate Zone 1 Variable Rate Zone 2 Variable Rate Zone 3 Uniform Rate -2 -3 0 20 40 60 80 N Fertilizer (kg ha-1) 100 120 *Lafond et al. 2006. Final Report: Indian Head Precision Farming Project Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 35 Temporal Variability – Major Causes • • • • Precipitation Temperature Disease Insects Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 36 Wheat Yield Variability in Canada (1910-1975) Sakamato et al. 1980. Climate and global grain yield variability. Climatic Change. 2:349-361 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 37 Wheat Yield Variability in Canada (1961-2007) Avg S 25.8 5.4 CV 20.8 *Food and Agriculture Organization – FAOSTAT (2009) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 38 Regional Grain Yield Variability 1955-2007 Source R.E. Karamanos, R.H. McKenzie, Yantai Gan, G.P. Lafond, C.A. Jones and S.S. Malhi. 2010. Fertilizer management for maximum yield of common crops in the Northern Great Plains of North America. in S.S. Malhi et al. (eds) Recent Trends in Soil Science and Agronomy Research in the Northern Great Plains of North America, Research Singpost (in press) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 39 Historical Wheat Yields 3.0 3.5 (d) 2.5 2.5 Wheat gain yield, Mg ha-1 Wheat grain yield, Mg ha-1 3.0 2.0 1.5 1.0 AB 0.5 0.0 MT 2.0 1.5 1.0 0.5 0.0 01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 3.0 3.0 (b) (e) SK 2.0 2.5 Wheat gain yield, Mg ha-1 2.5 Wheat grain yield, Mg ha-1 • Average (2-yr moving) wheat grain yields for western Canada (1955-2007) and the northern states (1901-2007) (Karamanos et al. 2010) (a) 1.5 1.0 2.0 1.5 1.0 0.5 0.5 0.0 0.0 01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06 3.0 4.0 (c) MB 2.6 2.4 3.5 3.0 Wheat gain yield, Mg ha-1 2.8 Seed yield, kg ha-1 ND 2.2 2.0 1.8 1.6 1.4 1.2 MN (f) 2.5 2.0 1.5 1.0 1.0 0.5 0.8 0.6 0.0 60 Dec-15, 2009 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 MB Agronomists Conference University of Manitoba, Winnipeg MB 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 02 05 40 Long-Term Wheat Yields (MB) 3.0 (c) 2.8 2.6 Seed yield, kg ha-1 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 60 Dec-15, 2009 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 MB Agronomists Conference University of Manitoba, Winnipeg MB 94 96 98 00 02 04 41 Historical Barley Yields 3.5 4.0 (a) 3.0 Barley gain yield, Mg ha-1 -1 Barley grain yield, Mg ha 3.0 2.5 2.0 1.5 2.5 AB 0.5 2.0 1.5 1.0 0.0 0.5 0.0 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06 3.5 4.0 (b) 2.5 3.5 3.0 Barley gain yield, Mg ha-1 -1 (e) SK 3.0 2.0 1.5 1.0 2.0 1.5 0.5 0.0 0.0 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06 4.0 (c) 4.0 3.5 Barley gain yield, Mg ha-1 -1 3.0 01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06 4.5 MB 3.5 Barley grain yield, Mg ha ND 2.5 1.0 0.5 2.5 2.0 1.5 1.0 MN (f) 3.0 2.5 2.0 1.5 1.0 0.5 0.5 0.0 0.0 60 Dec-15, 2009 MT (d) 1.0 Barley grain yield, Mg ha • Average (2-yr moving) barley grain yields for western Canada (1955-2007) and the northern states (1901-2007) (Karamanos et al. 2010) 3.5 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 MB Agronomists Conference University of Manitoba, Winnipeg MB 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 02 05 42 Long-Term Barley Yields (MB) 4.0 (c) Barley grain yield, Mg ha-1 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 60 62 Dec-15, 2009 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 MB Agronomists Conference University of Manitoba, Winnipeg MB 96 98 00 02 04 43 Historical Canola Yields • Average (2-yr moving) canola grain yields for western Canada (1955-2007) and the northern states (19902007) (Karamanos et al. 2010) 1.8 2.5 (a) AB MB 1.6 Canola grain yield, Mg ha-1 Canola grain yield, Mg ha-1 2.0 1.5 1.0 0.5 (c) 1.4 1.2 1.0 0.8 0.0 0.6 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 2.0 Canola grain yield, Mg ha-1 1.6 SK (b) 2.5 (d) 2 Canola grain yield, Mg ha-1 1.8 1.4 1.2 1.0 0.8 0.6 1.5 1 0.5 0.4 Montana North Dakota Minnesota 0.2 0.0 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06 Dec-15, 2009 0 1990 1992 1994 1996 1998 MB Agronomists Conference University of Manitoba, Winnipeg MB 2000 N_ST 2002 2004 2006 2008 44 Long-Term Canola Yields (MB) 1.8 (c) Canola grain yield, Mg ha-1 1.6 1.4 1.2 1.0 0.8 0.6 60 Dec-15, 2009 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 MB Agronomists Conference University of Manitoba, Winnipeg MB 94 96 98 00 02 04 45 Swift Current Old Rotation Study 1967-2009 *data contributed by Robert Zentner – AAFC Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 46 Swift Current, SK Fallow Wheat Yields Mean 32.9 Mean 34.5 St. Dev. 10.2 St. Dev. 11.3 CV (%) 31.0 CV (%) 32.7 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 47 Swift Current, SK Stubble Wheat Yields Mean 18.3 Mean 24.8 St. Dev. 7.9 St. Dev. 10.5 CV (%) 43.3 CV (%) 42.4 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 48 Swift Current, SK Wheat Yield Response to N & P Fertilizer Mean 4.7 Mean 40.7 St. Dev. 9.6 St. Dev. 52.6 CV (%) 204.9 CV (%) 129.1 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 49 Scott, SK – Rotation C 1913-2009 *data contributed by Stu Brandt – AAFC (retired) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 50 Scott, SK Fallow Wheat Yields Mean 31.6 Mean 38.2 St. Dev. 11.2 St. Dev. 13.3 CV (%) 35.6 CV (%) 34.9 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 51 Scott, SK Stubble Wheat Yields Mean 21.2 Mean 33.4 St. Dev. 8.5 St. Dev. 11.3 CV (%) 40.2 CV (%) 33.7 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 52 Scott, SK Wheat Yield Response to N & P Fertilizer Mean 23.0 Mean 81.6 St. Dev. 22.9 St. Dev. 73.2 CV (%) 99.6 CV (%) 89.8 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 53 Indian Head, SK – Long-Term Rotations 1958-2007 *data contributed by Guy Lafond – AAFC Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 54 Indian Head, SK Fallow Wheat Yields Mean 28.4 Mean 37.2 St. Dev. 9.3 St. Dev. 9.3 CV (%) 32.7 CV (%) 25.1 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 55 Indian Head, SK Stubble Wheat Yields Mean 14.1 Mean 30.7 St. Dev. 5.5 St. Dev. 10.8 CV (%) 39.0 CV (%) 35.1 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 56 Indian Head, SK Wheat Yield Response to N & P Fertilizer Mean 42.4 Mean 144.1 St. Dev. 53.4 St. Dev. 113.8 CV (%) 126.1 CV (%) 79.0 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 57 Addressing Temporal Variability in Nutrient Requirements • Temporal variability in grain yields / fertilizer requirements a challenge largely overlooked by precision agriculture efforts • Spring soil moisture estimates can be inadequate due to importance of in-season precipitation • Splitting N applications (ie: topdressing a portion in late spring / early summer) adds flexibility and allows growing conditions to be reassessed later in the growing season Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 58 Active, Optical Sensors for FineTuning N Rates in Real Time • Active optical sensor that measures canopy reflectance of emitted red & NIR light • Utilizes measurements of highN reference crops to estimate response to topdressed N • Integratable w/ application equipment to direct VR applications of agrochemicals (ie: liquid fertilizer) Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 59 Integrated Sensing & Application Individual Sensors • In the RT200TM VRA system, post-emergent N rates are based on the mean NDVI of 6 boom-mounted sensors Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 60 Spring Wheat 2009 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 61 N Topdress VRA Example Spring Wheat Yield Potential Estimates 4500 INPUTS • NDVI_yp0 = 0.65 • NDVI_NR = 0.71 • NDVI_max = 0.79 • GDD0 = 476 Yield Potential (kg/ha) 4000 3500 3000 YP_0 2500 YP_N YP_MAX 2000 1500 1000 500 0 0.025 0.2 0.375 0.55 0.725 0.9 Post-Emergent N Recommendation 1 20 17 US gpa UAN Estimated YP • YP0 – 43 bus ac-1 • YPN – 53 bus ac-1 • YPMAX – 59 bus ac- 14 11 UAN rate 8 5 2 -1 0.025 Dec-15, 2009 0.2 0.375 0.55 0.725 NDVI MB Agronomists Conference University of Manitoba, Winnipeg MB 0.9 62 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 63 Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 64 Mean yield = 64 bus/ac Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 65 Real-time Sensing Performance, Pros and Cons N Inputs (based on 27 field-scale trials - IHARF) • • • FP nitrogen > GS nitrogen 74% of the time (17% lower) FP nitrogen ≤ GS nitrogen 26% of the time (9% higher) N inputs reduced by 10% overall with GreenSeeker™ Yield (based on 27 field-scale trials - IHARF) • • • FP yield = GS yield 86% of the time FP yield < GS yield 7% of the time FP yield > GS yield 7% of the time Pros • • • user friendly / easy to implement added flexibility (weather, economic considerations) accounts for temporal variability Cons • • • economic gains must cover cost of post-emergent application limitations of topdressing N apply (ie: needs rain) cannot predict environmental conditions after sensing / topdressing Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 66 Integration of Management Zones and Real-time Sensing N • Integration of zone management and realtime sensing is possible and may become important in foreseeable future • Enhanced ability to exploit both spatial and temporal variability in soils and crops Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB W E S Zone 1 2 3 1:6418 67 RT Commander Pro Job Setup Import of Rx Zone Modification Maps Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 68 RT Commander Pro Job Setup Choice of rate modification strategy Dec-15, 2009 Rx Zone Map layer selection MB Agronomists Conference University of Manitoba, Winnipeg MB 69 User-Defined Rate Modification Map Rx Multiplier Values by management zone 1.50 Dec-15, 2009 1.0 .5 MB Agronomists Conference University of Manitoba, Winnipeg MB 70 Field NDVI Map Highest Vigor Lowest Vigor Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB Medium Vigor 71 Comparison of Rx Maps Standard NDVI-Based Rx Zone Modified Rx 1.50x Average Rx Rate 9.4 GPA Dec-15, 2009 1.0x .5x Average Rx Rate 10.5 GPA MB Agronomists Conference University of Manitoba, Winnipeg MB 72 Summary & Conclusions • Moisture predominant driver of both spatial and temporal variability • Variability in Prairies sufficiently high to justify precise management of crop inputs (ie: fertilizer) • Zone management embraced as a basis for variable rate fertilizer but does not adequately account for temporal variability • Real-time sensing and topdressing improves ability to address temporal variability but has important limitations • Integration of zone management and real-time sensing is worthy of further consideration Dec-15, 2009 MB Agronomists Conference University of Manitoba, Winnipeg MB 73 Acknowledgements • • • • • • • Bob Zentner Guy Lafond Stu Brandt Brian McConkey Alan Moulin Prakash Basnyat Yann Pelcat Dec-15, 2009 • • • • • AAFC MII program AAFC ETAA program CARDS IHARF Numerous Private Industry Partners MB Agronomists Conference University of Manitoba, Winnipeg MB 74