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Present Status of Software for Site Specific Fertilizer Recommendations and Future Outlook for improving its Scope and Precision.

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Presentation on theme: "Present Status of Software for Site Specific Fertilizer Recommendations and Future Outlook for improving its Scope and Precision."— Presentation transcript:

1 Present Status of Software for Site Specific Fertilizer Recommendations and Future Outlook for improving its Scope and Precision

2 Crop Production Regions in Punjab CPR-1 (Cotton-Wheat) Multan, Lodhran, Khanewal, Vehari, Muzaffargarh, Rajanpur, Bahawalpur, Bahawalnagar, Rahim Yar Khan, D.G.Khan CPR-2 (Rice-Wheat) Sheikhupura, Gujranwala, Hafizabad, Sialkot, Narowal, Gujrat, Mandi Baha-ud-Din CPR-3 (Mixed Crops) Sahiwal, Pakpattan, Okara, Kasur, Lahore, Faisalabad, Toba Tek Singh, Jhang, Chiniot, Sargodha CPR-4 ( Thal) (Pulses-Wheat) Layyah, Bhakkar, Khushab, Mianwali CPR-5 (Rainfed) (Maize-Wheat/Oilseeds) Attock, Chakwal, Jhelum, Rawalpindi, Islamabad

3 Soil Fertility status of Punjab soils Organic Matter (%) Available Phosphorus (%) Available Potassium (%) Low Medium HighLow Medium HighLow Medium High CPR-I CPR-II CPR-III CPR-IV CPR-V

4 MICRONUTRIENT DEFICIENCY (%)

5 Summarized N balance Sheet Nitrogen (Kg/ha) Year CPR-I CPR-II CPR-III CPR-IV CPR-V Punjab

6 Summarized P balance Sheet Phosphorus (Kg/ha) Year CPR-I CPR-II CPR-III CPR-IV CPR-V Punjab

7 Summarized K balance Sheet Potassium (Kg/ha) Year CPR-I CPR-II CPR-III CPR-IV CPR-V Punjab

8 Use of N, P & K (Kg/ ha) in Punjab Year NitrogenPhosphorusPotassium

9 CPR Wise Use of N, P & K (kg/ha) during CPRNPK CPR CPR CPR CPR CPR Punjab

10 District Wise Use of N, P & K (kg/ha) during District NPK Multan M. Garh Gujranwala M. B. Din Okara Sargodha Layyah Bhakkar Jhelum Chakwal

11 Fertilizer Recommendations for Wheat over the Years Irrigated Rain-fed Soil TypepoorMediumFertile Progressive Farmers<350 mm mm>500 mm Year N: P: K : 85: : 111: 57114: 67: 5784: 57: :57: 5784: 57: 57114: 57: : 114: 62114: 84: 6284: 57: 62158: 114: 6257: 57: 6284: 57: 64114: 84: : 114: 62104: 84: 6279: 57: 62158: 114: : 114: 62104: 84: 6279: 57: 62158: 114: 6257: 57: 6284: 57: 62114: 57: : 114: 62104: 84: 6279: 57: 62158: 114: : 114: 62104: 84: 6279: 57: 62158: 114: : 114: 62104: 84: 6279: 57: 62158: 114: : 114: 62104: 84: 6279: 57: 62 57: 57: 30114: 114: : 114: 62104: 84: 6279: 57: 62 References: Agriculture department, Govt. of Punjab; Agricultural Research Council

12 Nutrient off take vs. yield of major crops in Punjab

13 Percent increase in Yield by Balanced Fertilizer

14 Projected Impact of Balanced Fertilization in Punjab, on 50 % area, Impact WheatRicesugarcaneMaizeCottonTotal Area (000 ha) % of cultivated area of Punjab Additional Yield (000 tons) Financial Gain (Rs 000 million) Variation in Fert. Consumption (000 tons) :N :P :K Additional Fertilizer cost (Rs 000 million) Net Gain (Rs 000 million) Note: Calculation covered 100% wheat area; 84% rice area; 95.5% sugarcane area; 68.5% maize and 81.1% cotton area of Punjab

15  Soil Test Based Fertilizer Recommendations:  To improve the yield at the same level of inputs OR  To decrease the amount of inputs at the same level of yield WE MUST MOVE TO :

16 OBJECTIVES OF PROJECT  Collection and synthesis of available fertilizer trials data.  Software development for site-specific recommendations and dissemination through CDs and access to web.  Diagnostic survey for low adoption of fertilizer use technology and imbalance use of nitrogen and phosphorus.

17 COLLECTION AND PROCESSING OF DATA Collection of relevant reports, research papers, hand outs and personal communication for last 10 years. Extraction of fertilizer trials data (yield with soil analysis) separately for each district. Selection of treatments with balanced NP graded doses Processing of data for modeling and development of software Verification of predictions with original treatments Uploading of soft ware on the web.

18 Prediction Models on the Web  Prediction models for soil analysis based fertilizer recommendations(N&P) have been completed for all the districts of Punjab for wheat crop and relevant districts for rice(12 districts) and maize(7 districts) crops which are available on Web:   Similar models for Sugarcane crop will be on the web up to mid September.  Models for cotton crop will be completed before the end of this year.

19 Involvement of stakeholders  August 2011: Internal review by Vice Chancellor and his team  September 2011: Meeting of stakeholders i.e. Agri. Research, Agri. Extension, Fertilizer Companies, UAF experts  October 2011: Review by Secretary Agriculture and his team  November 2011: Stakeholders workshop – Attended by Field staff of Agri. Extension, Research and Fertilizer companies working in Multan and their heads, Farmers from Multan, UAF experts.

20 Training of Field officers  Training of nominees of DOs Agri. Extension for use of Fertilizer Prediction models for imparting awareness to the farming community of their district.  Training of nominees of Soil Fertility Laboratories in Punjab for using the Fertilizer Prediction Models for giving recommendations on the basis of Soil analysis.

21 Validation Fertilizer Trials, Multan  Stakeholders Conference in Nov, 2011  Dr. Shahid Mahmood Dir. Soil Fertility- Focal Person  Plan of trials: 0-0, 50-25, 75-40, , , , ,  Allocation of trials: Soil Fertility-8, Agri. Extension- 9, Engro Fertilizer-3, FFC-2, Jaffer Brother-3, Fatima Fertilizer-6  Results of some trials discussed:

22 Munawwar Hussain, Moza Mansoor Pur, Jalalpur Pirwala Wheat Yield (Kg/ha) Applied N (Kg/ha) Applied P (Kg/ha) Predicted N (Kg/ha) Predicted P (Kg/ha)

23 Lal Muhammad, Haveli Lang, Moza Thelan, Jalalpur Pirwala Wheat Yield (Kg/ha) Applied N (Kg/ha) Applied P (Kg/ha) Predicted N (Kg/ha) Predicted P (Kg/ha)

24 Umar Farooq Chattha, Moza Baghdar Wala, Makhdoom Rasheed Wheat Yield (Kg/ha) Applied N (Kg/ha) Applied P (Kg/ha) Predicted N (Kg/ha) Predicted P (Kg/ha)

25 Muhammad Aleem, Shah Pur Ubha, Shujabad Wheat Yield (Kg/ha) Applied N (Kg/ha) Applied P (Kg/ha) Predicted N (Kg/ha) Predicted P (Kg/ha)

26 Muhammad Ishaque, Chak 86-M, Jalalpur Pirwala Wheat Yield (Kg/ha) Applied N (Kg/ha) Applied P (Kg/ha) Predicted N (Kg/ha) Predicted P (Kg/ha)

27 Mohammad Nasir, Moza Jalalabad, Makhdoom Rasheed Wheat Yield (Kg/ha) Applied N (Kg/ha) Applied P (Kg/ha) Predicted N (Kg/ha) Predicted P (Kg/ha)

28 DEFICIENCIES IN THE DATA 1.Uneven distribution of trials leading to less no of observations in certain districts: i.Wheat: ii.Rice iii.Maize iv.Sugarcane

29 Number of trials for wheat DistrictNo of trials DistrictsNo of Trials DistrictsNo of Trials Faisalabad171Muzaffargarh44Layyah23 Bahawalnagar143Narowal42D.G.Khan23 Multan132T.T.Singh39kasur20 Bahawalpur117Hafizabad34Okara19 Sheikhupura115Khanewal32Lahore16 Rawalpindi94Chakwal26Pakpattan9 Gujrat90Jhelum25Rajanpur7

30 Number of trials for Rice DistrictNo of TrialsDistrictNo of Trials Sheikhupura95Sialkot33 Faisalabad91Sargodha28 Gujranwala64Hafizabad27 Kasur49Jhang26 Narowal47Lahore20 Gujrat45M.B.din10

31 Number of Trials for Maize DistrictNo of TrialsDistrictNo of Trials Faisalabad71Okara26 T.T.Singh63Pak Pattan8 Rawalpindi51Sargodha9 Sahiwal45Kasur6 Attock36Jhang2

32 Number of Trials for Sugarcane DistrictNo of TrialsDistrictNo of Trials Faisalabad23M.B.Din4 Sheikhupura12Jhang2 Sargodha11Pak Pattan2 T.T.Singh6R.Y.Khan2 Sahiwal4Kasur1

33 2. Few points for the Prediction curve: WheatRice Basmati Coarse Maize Hybrid Non Hybrid SugarcaneCotton Non-BT BT 0: 0: 0 150: 120: : 57: : 114: : 120:60 240: 114:60 Arid Region 60: 40: : 0 : : 50: :100: :100: 60 0: 0: 0 90: 90: : 90: : 90: : 114: : 90: 60 0: 0: 0 150: 0: : 50: : 100: 0 150: 100: : 150: : 100: 75 0: 0: 0 120: 90: : 0: : 75: : 150: : 150: : 150: : 150: 120 0: 0: 0 150: 0: : 75: : 150: : 150: : 150: 75 0: 0: 0 125: 125: : 0: : 125: : 150: : 250: : 125: 125 0: 0: 0 120: 0: : 40: : 80: :120:60 160: 40: : 80: : 80: : 80: 60 0: 0: 0 125:100: : 0: 0 250: 50: :100: :150: :100: :100:125

34 What we have What we need ( Few points for Yield vs N/P) ( More points for Yield vs N/P)

35 3. Fertilizer trials- Yield but no soil analysis 4. Salt affected soils- Area affected 2.68 mha in Punjab and within irrigated areas, the figure is 1.7 mha but no data available regarding salinity effect on fertilizer use efficiency 5. Rainfed Agriculture- 2.9 mha but no data available for quantifying the relation of moisture content in the soil with fertilizer use efficiency

36 These Deficiencies lead to:  Low precision of prediction models(Around 80%)  Low reliability for prediction in rainfed areas( 60 to 70 %)  No prediction for salt affected soils

37 Recommendations 1. Divert the resources to improve the fertilizer use efficiency: –Conduct soil analysis based demand oriented trials –Conduct soil moisture based fertilizer trials in rainfed areas –Conduct fertilizer trials in salt affected areas 2. Improve the efficiency of soil testing laboratories

38 FUTURE PLANS Fertilizer prediction models for Fruits plants, vegetables and minor crops Addition of K and micronutrients Establishment of Farmers Facilitation Centre at University of Agriculture, Faisalabad Site specific fertilizer recommendations to site specific crop production technology Commercialization

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