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Scientific Information for Sound Agricultural Decision Making – Food and Fertilizer

Scientific Information for Sound Agricultural Decision Making – Food and Fertilizer . Upendra Singh Senior Scientist - Systems Modeler Resource Development Division and Feisal Beig Senior Specialist - Marketing Market Development Division. Goals.

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Scientific Information for Sound Agricultural Decision Making – Food and Fertilizer

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  1. Scientific Information for Sound Agricultural Decision Making – Food and Fertilizer Upendra Singh Senior Scientist - Systems Modeler Resource Development Division and Feisal Beig Senior Specialist - Marketing Market Development Division

  2. Goals • Maintaining and improving soil fertility to meet food demands of growing population. • Increasing the productivity of the land currently under production to avoid encroaching on wilderness areas. • Mitigating climate change by improved use efficiencies/reduced environmental hazards. • Improving livelihoods of farmers. • Improving human nutrition. • Unbiased information systems.

  3. Industry Response • Products • Vast majority of crop nutrition products exist in forms that have remained largely unchanged for a half-century • Unlike traditional commodity products, new “knowledge base” products incorporate extensive research and have characteristics that are protected by intellectual property rules. E.g. “smart” release, bio-fertilizers, “enriched” organic, anti-explosives, low Cd P fertilizers, biotechnology components

  4. Industry Response • Production Technology 1. Cost consideration 2. Energy Use (in last 15 years from 10 to 8 MWh per ton ammonia produced) 3. Greenhouse gas emission (N2O, CFC): Signatory on UNEP International Declaration on Cleaner Production 4. “Clean air” – NH3, SO2, F2 5. Environmental credits 6. Designs, databases – competitive, regulate

  5. Best Available Technique (BAT) Assessment, Cost vs. Benefit Considerations

  6. Anthropogenic Sources for N2O Emission (Kroeze, 1999)

  7. Industry Response • Production Technology 1. Cost consideration 2. Energy Use (in last 15 years from 10 to 8 MWh per ton ammonia) 3. Greenhouse gas emission (N2O, CFC): Signatory on UNEP International Declaration on Cleaner Production 4. “Clean air” – NH3, SO2, F2 5. Environmental credits 6. Designs, databases – competitive, regulate

  8. Research Support • Nutrient Management : – site specific recommendation, precision agriculture – deep-placement – control release fertilizers – inhibitors  Improve - Productivity (Food, feed, fiber and bioenergy) - NUE, WUE, C Sequestration  Reduce - Nutrient Losses - erosion, leaching, N2O volatilization - Methane (cultivars and feed additives)

  9. Research Support • International Nitrogen Initiative - A balanced approach increase fertilizer use efficiency while safeguarding the key role that nitrogen plays in meeting the world's growing food demand • Research and extension by independent research centers - Developed countries knowledge base is well established - Developing countries (major agricultural growth markets) less investment - Industry investment in independent research centers.

  10. Education and Policy • Government and FAO Advisory - Early years expertise highly valued in decision-making, at both a policy level and field- level - Information from industry sources has been viewed with increasing cynicism by other stakeholders, and governments have been forced to maintain their distance from potentially valuable sources of expertise

  11. Education and Policy • Outsourcing of agronomic research to independent institutes - Increase the credibility of the results. - However, this has reduced the interaction between industry decision-makers, policymakers and researchers. - This disconnect has probably adversely affected decision-making across the board to varying degrees in different agribusiness sectors (Regional Conference for Africa)

  12. Organization for Economic Co-operation and Development (OECD), Agri-Environmental Indicators and Economic Instruments • OECD indicators focus on simple measures such as kg ha-1 product applied with no consideration of initial soil conditions, climate, crop, etc. • OECD economists often suggest taxes on these indicators as a way to deal with excessive nutrients in the environment.

  13. OECD Agri-Environmental Indicators and Economic Instruments Ignores good science in a number of ways: - Fails to address the management of nutrients from manures and other sources; - having no effect on the quality of management of the fertilizer that is applied and potentially diminishing crop quality and soil fertility - Dynamic nutrient balance. • Proposed indicators are too difficult to implement in a policy framework.

  14. Meeting Market Demands • Competitive producers deliver commodities that are in demand • What crops can be cultivated profitably at any specified location and season at prescribed commodity prices • Constraints - 12 mil ha forest cut annually - 40% more rice, wheat, etc without recourse to additional land and water resources  DSS tools, input data

  15. Nutrient Component Nutrient Database System • Nutrient DSS • select target area • display base map • query nutrient • information • productivity analysis • print out Graphical User Interface (ArcView)

  16. P Dilemma in Sub-Saharan Africa • P deficiency widespread • P fertilizer use = 2.5 kg P2O5/ha.

  17. Phosphate Rock Decision Support System (PRDSS) • IFDC/IAEA-FAO developed PRDSS • Database: PR characteristics, deposit, geological, climatic, soils, crops and economics • Identify agronomic and economic feasibility of direct PR application • Link with GIS: Feasibility of mining and transportation Decision: use local PR vs. imported P fertilizer

  18. Enabling Research Prediction Research Understanding Research Customer Recommendations Prediction/Diagnosis Decision Aids Existing Knowledge Base Prices, Market Trends Distribution, Consumption Production, Import, Export Natural Resource Inventory • Databases • Soil • Climate • Crop • Inputs • Output Outputs Inputs Food crop Cash Crop Fertilizers Pesticides Seeds

  19. Soil profile data bases (national, FAO) • Climate data (global climate change models, climate forecast) • Remote sensing data • Crop simulation models, expert knowledge (post-harvest handling, processing) and GIS • Market information system (inputs, outputs (crops), cash and futures market data, credits, loans) • Decision aids for economic and impact assessment Components of Integrated Decision Support Toolbox

  20. DSS Response • Food habits changing faster than farming can change • Corporate participation – better alignment of farming and consumption pattern • Opportunities for improvement in developing countries - currently low productivity but low labor = globally competitive

  21. Selected DSS Users • Crop Insurance Data mining - Discovery (conditional logic, affinities and associations, etc) - Predictive Modeling (probabilities and forecasts) - Forensic Analysis (deviation detection) Scenarios for spot check

  22. Fertilizer Industry DSS Use Demand Forecasting (existing and future) Consider changes in: -demographics, - incomes, - purchasing power, - food habits - cropping patterns - agricultural technology including bio-technology and precision agriculture, seeds, CPP technology - information technology, - educational levels, - infrastructure, - prices of inputs and outputs, - investments in agriculture, etc.

  23. Fertilizer Industry DSS Use • Demand Forecast (continued) - Changes in markets due to a universal trend of decontrol, deregulation, privatization, introduction of open and competitive markets dominated by the private sector. - Changes in business brought about by the ease in communication, dissemination of information, etc.

  24. Fertilizer Industry DSS Use • Supply Forecast Consider - Changes in fertilizer technology (products but more so production efficiencies and economies of scale have changed), - Agroindustrial profile - materials sources, - prices, - substitutes, - increase in competition, etc  Improving the decision making process.

  25. Market Society to Network Society • Easy access to information • Knowledge – universities, research and extension • Purchasing power – competition • Sales – local, export, processing • Development work – technology, health, legal • Feedback and power of scale

  26. Acknowledgement • Thankful for contribution from IFA on how policymakers in the food value chain use science originating from industry and its related research base • Dr Arvin Mosier – information on GECAFS-DSS • GECAFS/USDA-ARS/UF for the invitation and financial support

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