Better Fertilizer Decisions

Transcription

1 Better Fertilizer Decisions Tom Bruulsema, Phosphorus Program Director With acknowledgments to Rob Norton (IPNI), Mark Conyers, Mike Bell, Nigel Wilhelm, Chris Dyson, Graeme Watmuff, Richard Bell, Chris Dowling, Charlie Walker and Ken Peverill.

2 The International Plant Nutrition Institute is supported by leading fertilizer manufacturers. Its mission is to promote scientific information on responsible management of plant nutrition.

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5 How can these legacy data be curated and used? Challenge # 1 Where is it? Refereed published research papers Earlier papers would report site by site mean responses. Later papers were often multi-site but primary data often lacking Within the datasets used by fertilizer companies as part of their decision support systems on recommendations. Unpublished and grey literature. In offices, file cabinets, Range of media and formats

6 Searchable data repository Challenge #2 - Development of a minimum and common data set Location & soil type (ASC) Crop species, variety, Sowing dates, harvest dates. Treatments described (4Rs) Statistically valid Yields recorded Able to fit Y 0 and Y max to generate relative yield Mitscherlich, quadratic, parabolic, square root. RY% = 100*Y 0 /Y max Recognized soil test & recorded sample depth!!!!! Metadata associated with experiment (weather).

7 Australian Crop Nutrition Database A standard Microsoft Access data entry template developed Wheat Barley Triticale Sorghum Maize Oats Nitrogen Phosphorus Cereals Oilseeds Pulses Potassium Sulphur Nitrogen Phosphorus Canola Sunflower Safflower Linseed/Flax Mustard Cotton Linola Potassium Nitrogen Sulphur Phosphorus XXXX Potassium Sulphur Data were checked for consistency and imported into a central MySQL Australian Crop Nutrition Database Lupin Chickpeas Faba bean Field pea Soybean Peanut Lentil Narbon bean Phosphorus Potassium Sulphur

8 Searchable data repository BFDC Interrogator Challenge #3 Make the data accessible, searchable and able to derive statistically valid soil test critical values. Develop methodologies to search the database on-line. Watmuff et al. (2013) Crop & Pasture Science 64, Dyson and Conyers (2013) Crop & Pasture Science, 64,

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12 Method Extractant Soil/solution ratio Olsen P 0.5M NaHCO 3, ph 8.5 Colwell P (modified Olsen) 0.5M NaHCO 3, ph 8.5 Extraction period Reference 1:20 30 min Olsen SR, Cole CV, Watanabe FS, Dean LA (1954) Estimation of available phosphorus in soils by extraction with sodium bicarbonate. US Department of Agriculture, Circular No :100 16h Colwell JD (1963) The estimation of the phosphorus fertiliser requirements of wheat in southern New South Wales by soil analysis. Aus. J. of Exp. Agri. and Animal Husb. 3,

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15 Research Data Stewardship: What Are the Costs/Limitations Associated with Data Sharing? Costs to develop the BDFC Nutrient Database & Interrogator Direct investment Grains Research and Development Corporation Grower levy plus federal fund, $1.5 M/3 years Matched with In-kind costs to input the data collaborators Time and experience - $2.5 M/3 years Total of $4 M AUD to collate, curate and present the data. Experimental costs for historic data collaborators Costs to run 6000 field experiments - $60 M SUNK COST

16 Issues faced in the development of BFDC Accessing data Ageing demographic of soil fertility specialists Desire by them to leave their data for posterity Aware that not all data provided was suitable 6000 of 10,000 sourced experiments Relatively poor metadata especially soil descriptions. Insufficient data to fit Y 0 and Y max Not everyone believed in soil tests Aware that not all data that was suitable was provided. Maybe only 30% of commercial agronomy field work included What about trials with no response not reported? Laboratory analytical standards pre-aspac. Institutional imperialism

17 Issues faced in the development of BFDC Ownership of Intellectual Property Not as big an issue as was first expected high level of good will with confidence in the leadership of the project Senior industry figures and science leaders led Sufficient integrity to engender confidence. Much of the data were provide with funding from Government or farmers contributions so who REALLY owns the data? What is the commercial value of the data? Real or imagined? Who owns data once it is published Reuse in systematic reviews & meta-analyses Acknowledgement?

18 What were the benefits database itself A national web-based database of crop nutrient response trials and an interrogator interface for future users of the database National Nutrition database & BFDC data interrogator on-line Database revealed gaps in the current crop/soil/nutrient matrix. Being addressed through current research projects Protocols for future research based on the minimum data set requirements and metadata. Repository for future research (MPCN II) GRDC requires that any nutrition research now collects and provides the datasets to the National Database. Wake up call for agencies about public records Capacity for ex-post analyses of the data Example is the rotational effect of P critical value

19 What were the benefits derived data. Develop nationally and regionally agreed soil test crop response calibrations for N, P, K, S Traceability of critical values How do you know that.? Values are regional and crop specific Moved away from critical values to critical ranges Confidence limits indicate the reliability of the value Process for splitting/lumping data pools Critical soil test criteria published in Crop & Pasture Science Embedded in DSS and assessed through Fertcare 80% of all soil tests have DSS using BFDC. Training of advisors in Soil Fertility 101

20 What were the benefits beyond data! National forum for soil testing and interpretation. Collegiality amongst participants across disciplines, organisations and geography view to succession planning. Publications Provide growers with confidence in the critical ranges provided. Nothing was lost by sharing

21 4R: right means sustainable

22 Nutrient Stewardship Metrics for Sustainable Crop Nutrition Enablers (process metrics) Outcomes (impact metrics) Extension & professionals Infrastructure Research & innovation Stakeholder engagement Actions (adoption metrics) Cropland area under 4R (at various levels) [Requires regional definition of 4R] 1. Farmland productivity 2. Soil health 3. Nutrient use efficiency 4. Water quality 5. Air quality 6. Greenhouse gases 7. Food & nutrition security 8. Biodiversity 9. Economic value

23 How does BFDC improve recommendations? 4R principle: what s right is site-specific. Diversity of grower goals, financial circumstances, and risk of P loss Avoidance of redundancy in safety margins Ability to develop and share on-farm calibration datasets through precision ag information technology Key Potential Benefit: improving awareness (among producers, crop advisers, retailers, engos, policymakers) of the relationships of soil test nutrient levels to: 1. crop response, and 2. risk of nutrient loss.

24 Summary 1. Better Fertilizer Decisions for Crops is a great example of a data project with benefits to many. 2. In Australia it has made soil test calibration data accessible and facilitated the development of customized fertilizer recommendations. 3. Imagine how we could improve public perception of North American crop nutrition if we did something similar. Crop response metrics: yield, NUE, soil tests Environmental response metrics: water quality, air quality and GHGs