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Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
Real-time nitrogen management in rice
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Real-time nitrogen management in rice

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  • 1. Shantappa Duttarganvi M.Sc. (Agri) Dept of Agronomy, 2009
  • 2.  Introduction  Need of Real-time N management  Tools  Basic approaches in Real-time N management  Challenges of Real-time N management  Strategies of N management  Variable rate N application  Conclusion
  • 3. Continued improvement in cropping system management Better prediction of soil N mineralization Improved timing of N application Improved manure management Improved fertilizers
  • 4. . “Match the agricultural inputs and practices to localized conditions within field to do the right thing, in the right place, at right time and in right way”. (Pierce et al., 1994) The precision agriculture concept wheel based on GPS
  • 5. . Real-time N management Diagrammatic representation of the relationships between various terms related to Precision Agriculture
  • 6. SSNM provides two equally effective options  Real-time N management  Fixed time N management
  • 7. Site-specific nutrient management (SSNM) 1. Establish a yield target – the crop’s total needs 2. Effectively use existing nutrients Feeding crop needs! 3. Fill deficit between total needs and indigenous supply
  • 8. What is Real-time N management ?
  • 9. Nitrogen is the nutrient that most often limits crop production. (Pathak et al., 2005)  Crop use nitrogen inefficiently, generally more than 50% of N applied is not assimilated by plants. (Dobermann and cassman, 2004)   Leaching, runoff and denitrification are the processes that result in loss of N from soil-plant system creating the potential for N deficiency in crop. (Nowak et al., 1998)
  • 10.  Worldwide nitrogen use efficiency for cereal grains and row crops estimated at only 33 %.  Unaccounted 67 % represents a $ 28 billion annual loss of fertilizer N. FAO., 2006
  • 11.  To apply nutrients at optimal rates  To achieve high yield and high efficiency of nutrient use by the rice crop  Estimating the total fertilizer N required for rice in a typical season  Formulating the dynamic N management to distribute fertilizer N to best match the crop need for nitrogen
  • 12.  LCC  SPAD  Optical sensor or crop canopy spectral reflectance  GIS
  • 13.  To develop Site-specific N Management based on crop N status monitoring - Canopy reflectance of light - Chlorophyll content
  • 14.   IRRI- 1996 The leaf color chart (LCC) is an easy-to-use and inexpensive diagnostic tool for monitoring the relative greenness of a rice leaf as an indicator of the plant N status. Alam et al., 2005
  • 15. How to use the LCC  Select at least 10 disease-free rice plants  Select the topmost fully expanded leaf and compare the leaf color with the color panels of the LCC and do not detach or destroy the leaf  Measure the leaf color under the shade of your body  Determine the average LCC reading for the selected leaves
  • 16. Year 7 Mean Range Mean 120 kg N ha-1 11.2 - 30.4 20.8 15.0 - 48.2 30.9 10.2 – 42.7 27.4 29.3 – 53.6 42.7 9.8 – 51.5 28.1 21.1 – 51.1 42.1 120 kg N ha-1 7.0 – 25.4 15.4 18.2 – 50.8 29.1 9.2 – 31.2 19.8 18.9 – 58.3 38.9 LCC 4 (20 kg N ha-1 as basal) 25 Range LCC 4 (no basal N) 2002 Treatments 7 RE (%) LCC 4 (20 kg N ha-1 as basal) 2001 AE (kg grain/kg N) LCC 4 (no basal N) 2000 No. of sites 8.5 – 41.8 21.6 18.2 – 56.3 45.4 120 kg N ha-1 3.8 – 22.5 11.3 16.7 – 61.7 39.8 LCC 4 (20 kg N ha-1 as basal) 8.3 - 33. 8 19.2 26.3 – 88.8 58.3 Yadvinder Singh et al., 2004
  • 17. Treatments Grain yield (kg ha-1) 2000 Straw yield (kg ha-1) 2001 Net income (RS ha-1) Benefit cost ratio 2000 2001 2000 2001 2000 2001 Nitrogen management Control N0 N 3008 2617 4793 4440 4001 3428 1.32 1.28 LCC value 3 4557 3151 6624 4518 10646 4702 1.79 1.35 LCC value 4 5769 4297 7702 6315 16152 11164 2.17 1.81 LCC value 5 5456 3802 7592 5508 14489 7898 2.02 1.56 Recommend ed N 4342 2917 6608 4466 9123 3394 1.66 1.25 CD (P=0.05) 336 572 879 820 NA NA NA NA Budhar (2005)
  • 18. Year Different methods LCC(4) based N management AE (%) RE (%) Grain Total N yield(t/ha) applied (kg N/ha) 2000 86 20.8 0.31 6.63 95 27.4 0.43 FFP 6.53 120 28.1 0.43 -B, LCC 6.89 79 15.4 0.29 +B,LCC 7.20 91 19.8 0.39 FFP 2002 6.59 +B,LCC 2001 -B, LCC 6.84 120 21.6 0.45 -B, LCC 6.76 71 11.3 0.40 +B,LCC 7.01 91 19.2 0.58 FFP 6.69 127 16.4 0.53 Singh
  • 19.  The LCC is a cheap  Farmers can easily use the LCC to qualitatively assess foliar N status and adjust N topdressing accordingly  It helps to manage N for large area leading to improved fertilizer N use efficiency  It reduces the risks associated with fertilizer N application  It saves nearly 26% fertilizer N  It helps to synchronize N supply and crop demand
  • 20. It is a simple, quick and non destructive in situ tool for measuring relative content of chlorophyll in leaf that is directly proportional to leaf N content.
  • 21. 1. 2. 3. 4. 5. SPAD readings are taken at 9-15 day intervals, starting from 14 DAT for transplanted rice and 21DAS for wet direct seeded rice, Periodic readings continue up to the first (10%) flowering. The youngest fully expanded leaf of a plant is used for SPAD measurement. Readings are taken on one side of the midrib of the leaf blade. A mean of 10-15 readings per field or plot is taken as the measured SPAD value. Whenever SPAD values fall below the critical values, N fertilizer should be applied immediately to avoid yield loss.
  • 22.  Nitrogen fertilizer efficiency  Rice cultivar  Position of leaf on plant  Deficiencies of P, Zn, Mn and Fe
  • 23. N Management Grain yield(Mg/ha) Total N uptake(kg/ha) Recovery efficiency(%) Agronomic efficiency(%) Well fertilized plot 5.05 131.4 35.7 9.1 SPAD based 5.05 106.4 61.6 22.7 Fixed timing 5.17 120.1 57.6 18.0 Control 3.0 51.0 - - Hussain et al. (2000)
  • 24. Rice grain yield, N uptake, total fertilizer N applied, and recovery and agronomic efficiency using different need based fertilizer N management criteria N management treatment Grain yield Mg/ha Total N uptake Kg/ha 0 4.4 60 - - T2- Recommended splits 120 6.1 111 42 14.3 T3- N30 at SPAD <35, N30 basal 60 4.9 86 43 8.2 T4- N30 at SPAD<35, no basal 30 5.1 75 50 22.4 T5- N30 at SPAD < 37.5, N30 basal 90 5.8 88 31 15.4 T6- N30 at SPAD<37.5, no basal 90 6.4 93 37 21.8 T1- Zero N (control) Total N applied Kg/ha RE (%) AE (%) Singh et al. (2002)
  • 25. Treatment N used (kg ha-1) Grain yield (t ha-1) AEN FP-N Philippines Control 0 3.7 - - Farmer’s practice 126 6.0 18.2 41.0 SPAD-35 150 6.7 19.7 44.7 0 5.3 - - Farmer’s practice 125 6.4 8.8 51.6 SPAD-35 60 7.1 51.0 118.4 0 2.8 - - Farmer’s practice 120 4.0 9.8 33.0 SPAD-35 70 4.0 17.8 57.5 India Control Vietnam Control Balasubramanian (2000)
  • 26. Treatment Biometric observations 1000-grain weight Harvest index Filled grain (%) T1 –control 20.40 71.66 0.34 T2 -NPK recommended 22.13 99.70 0.26 T3 –LCC 2 21.80 90.86 0.37 T4 -LCC 3 21.70 89.30 0.34 T5 -LCC 4 22.80 99.73 0.33 T6 -LCC 5 22.23 98.93 0.29 T7 -CM 35 21.80 88.96 0.27 T8 –CM 37 21.66 89.94 0.30 T9 -CM 40 22.23 96.50 0.29 Balaji and Jawahar (2007)
  • 27. Total N applied (kg ha-1) Grain yield (t ha-1) Total N uptake (kg ha-1) AEN REN 0 5.2 59 - - 120 9.1 132 32 61 180 9.6 170 25 62 115 (SPAD 35) 9.7 142 39 72 135 (SPAD 37) 9.5 143 32 60 Peng et al.(1996)
  • 28.  The chlorophyll meter is faster than tissue testing for N.  Samples can be taken often and can be repeated if results are questionable.  Chlorophyll content can be measured at any time to determine the crop N status.  The chlorophyll meter allows “fine tuning” of N management to field condition.  The Chlorophyll Meter would also help people who are not highly trained to make N recommendations.
  • 29. Variations in reflectance are employed on a variable rate applicator
  • 30. Crop that needs N is - lighter in color - smaller in size and - reflects light differently than a crop that has sufficient N
  • 31. Optical sensor  Optical sensor used rapidly through measurement of visible and near infrared spectral response from plant canopies to detect the nitrogen stress.  It can not work properly when the crop is too young  It can not work in transplanted rice in early stages
  • 32.  Grid soil sampling  Residual Soil-nitrate N values  N availability maps  N fertilizer recommendation maps
  • 33. Paul and Subramanian (2006)
  • 34. Based on Remote sensing  Develop Site-specific optional N rate recommendations based on condition of specific N response curves  Aerial or satellite photos or digital images
  • 35. Major challenges  To retain the success of approach  To build on what has been already achieved using this approach while reducing the complexity of the technology as it is disseminated to the farmers  The nutrient needs of rice are highly variable  Differ from field to field  Differ year to year
  • 36. OPPORTUNITIES  Supply nutrients to optimally match the location specific needs of the crop for an achievable yield goal  Provides basis for plant based approach to nutrient management
  • 37.  Assessing variability One cannot manage what one does not know  Spatial variability (high degree is needed)  Temporal variability (difficult to manage)  Management of maps Condition maps  Prescription maps  Performance maps 
  • 38. Soil supply and plant demand vary in space and time Higher the spatial dependence, higher the potential for precision Field variability should be accurately identified and reliably interpreted
  • 39.  Economics Whether the documented agronomic benefits – translated into value through market mechanism.  Environment Whether precision management can improve soil, water, and ecological sustainability of our agriculture system?.  Technology transfer Whether bundle of enabling technologies and agronomic principles will work on individual farm?.
  • 40.  Prevention strategies Application of N inputs prior to or early in the N uptake phase of plant growth to avoid nutrient deficiencies.  Intervention strategies N inputs are applied to meet N requirements as determined by the nutrient status of soil or plants during the rapid N uptake phase of growing plants.  Hybrid strategies Combination of both strategies.
  • 41. Feeding the plant need for nitrogen Nitrogen Plant demand is related to growth stage Split apply N fertilizer to match plant demand
  • 42. Variable rate N fertilizer demand is a function of year to year climate differences (Rainfall & Temp). Point - to - point soil differences  Nutrient content of manure  Soil tests and crop needs  Water quality concerns
  • 43.  Uniform N rates  Variable N rates N use Efficiency, kg grain/kg N 28-39 39-50 52-62 62-73 Murrell and Murrell (2002)
  • 44. 40 ha field divided into 9 zones Frequency of zones 9 Whole field year 1, 47 kg grain/kg N 8 8 Variable rate year 3, 53 kg grain/kg N 7 13% increase in fertilizer N efficiency 6 5 4 4 3 2 2 2 1 2 1 1 0 0 28-39 39-50 50-62 N use efficiency, kg grain/kg applied N 62-73 Murrell and Murrell (2002)
  • 45. General guidelines for determining the early application of N before 14 DAT or 21 DAS of rice  Typically apply 20 to 30 kg N ha−1 in seasons with yield response between 1 and 3 t ha-1 Apply about 25 to 30% of the total N in seasons with yield response >3 t ha−1 .  Increase the N application up to 30 to 50% of the total N when old seedlings (>24 days old) and short-duration varieties are used.  Reduce or eliminate early N application when high-quality organic materials and composts are applied.  Eliminate early application when yield response is ≤1 t ha−1 .  Do not use the LCC with the early N application. www.irri.org/irrc/ssnm
  • 46. Principles of N management When is fertilizer N needed? Match early application of N with low initial demand of the crop for N Apply only a moderate amount of fertilizer N to young rice Ensure sufficient supply of N to the crop at active tillering and panicle initiation Use the LCC to assess leaf N status and adjust applications to match crop needs for N A standardized leaf color chart (LCC)
  • 47. Example of a real-time N recommendation for rice Active tillering Transplanting -20 -10 0 10 20 30 Panicle initiation (PI) 40 50 Harvest Heading 60 70 80 90 100 DAT Take LCC readings every 7 days Early Within 14 DAT 30 kg N/ha 0 to 20 kg N/ha * 21–50 DAT If LCC < 3.5 ** 45 kg N/ha High-yielding season If LCC < 3.5 ** 23 kg N/ha Low-yielding season Yield target = 7 t/ha Yield target = 5 t/ha * Early N is not essential but up to 20 kg N/ha can be applied when NPK fertilizers are used to supply P and K. ** Leaf color is nearer to LCC reading 3 than 4 with standardized IRRI LCC 23 kg N/ha = 1 bag urea/ha; 45 kg N/ha = 2 bags urea/ha. www.irri.org/irrc/ssnm
  • 48. Dobermann et al. (1998)
  • 49. Effect of Nitrogen regimes on grain yield, straw yield and dry matter production of rice Treatment N consumed (kg/ha) T1 - Control Output ( t/ha) Grain yield Straw yield Dry matter yield 3.77 7.34 10.45 T2 -NPK recommended 125 4.68 14.92 17.47 T3 - LCC 2 80 5.18 9.02 13.73 T4 -LCC 3 80 5.16 10.04 14.70 T5 -LCC 4 110 6.36 14.33 19.65 T6 -LCC 5 130 5.78 16.36 21.26 T7 -CM 35 80 5.26 13.41 17.77 T8 -CM 37 80 5.48 12.07 17.82 T9 -CM 40 130 5.64 14.37 19.34 Balaji and Jawahar (2007)
  • 50. Nitrogen use efficiency as influenced by different LCC and SPAD values Treatment Nitrogen use efficiency Agronomic (%) Physiological (%) Economic (%) - - 0.44 T2 -NPK recommended 7.32 17.49 0.43 T3 -LCC 2 17.69 23.55 0.49 T4 -LCC 3 17.37 22.82 0.48 T5 -LCC 4 23.54 31.75 0.54 T6 -LCC 5 15.50 25.81 0.48 T7 -SPAD 35 18.69 24.58 0.50 T8 -SPAD 37 21.44 27.83 0.52 T9 -SPAD 40 14.42 24.57 0.47 T1 -control Balaji and Jawahar (2007)
  • 51. Where and when Real-time N management will pay off in terms of either profitability or environmental benefits? Where N inputs are high (Fiez et al., 1994) Where residual N is temporally stable and /or high residual N is predictable (Cattanach et al., 1996) Where crop quality is affected by excess N in soil (Lenz et al., 1996) Where crop yield spatial variability is high and predictable (Long et al., 1996)
  • 52. Contd….  Where net mineralization is high and consistently related to soil and landscape properties (Pan et al., 1997)  Where N application is not restricted in time (Evan et al., 1996)  Where leaching potential is very high during the crop N uptake period of the plant growth (Malzer et al., 1995)
  • 53. Tool / Tactics Benefit : cost Limitations Site specific N management High Has to developed for every site Chlorophyll meter High Initial high cost Leaf color chart Very high Minimum limitations Plant analysis High Facilities need to be developed Controlled- released fertilizer Low Nitrification inhibitor Low Low profitability and lack of interest by industry Fertilizer placement High Lack of equipment, labour intensive Foliar N application High Lack of equipment, risk involved Breeding strategy Very high Varieties yet to be developed N – fixation in non legumes High Technology yet to be developed for field scale Models and decision support system Medium Tools are not available Remote sensing tools Low Geographic information system Low Resource-conserving technology High Integrated crop management high Technology need to be fine-tuned Technology needs to be evaluated for long- term impacts Ladha et al. (2005)

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