Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Keynote Dave Harris

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Workshop Trade-off Analysis - CGIAR_19 Feb 2013_Keynote Dave Harris

  1. 1. Wide boundaries for rural systems:implications for household decision-making and adoption of agricultural technology. Dave Harris ICRISAT Nairobi 19th February 2013
  2. 2. Outline1. Concepts for Research with Development Outcomes2. Sustainable Intensification3. Profitability and Technology;4. Profitability, Land and Household Per Capita Income;5. “Intensificationability” – the potential for HHs to benefit from intensification.6. Decision-making.
  3. 3. CGIAR Drylands System - Core Concepts
  4. 4. ICRISAT Strategic Plan:Inclusive Market-Oriented Development (IMOD)
  5. 5. Sustainable Intensification (SI)General consensus (CGIAR-CRPs, USAID, etc) that this is the way forward forrural households to:  Reduce / get people out of poverty  Improve food security
  6. 6. Three Propositions1. No adoption = no impact (= no Developmental Outcomes)2. Intensification = more investment (cash, credit, labour, effort, etc)3. More investment = more exposure to risk (more to lose)
  7. 7. With the key concepts and the three propositions in mind, we need to:• Develop better understanding of, and relationships between, risk, resilience, vulnerability, food security, sustainable intensification, investment, profitability, off-farm opportunities, surpluses, markets etc.
  8. 8. (Sustainable) Intensification (SI)Some questions: Ignoring sustainability for now, can rural households intensify their agricultural enterprises by adopting improved technology? Are there limits to how much they can intensify? What are the consequences (impacts) of intensification for rural households?
  9. 9. Productivity versus Profitability We all concentrate on increasing the productivity of (rainfed) crops, cropping systems, etc. However, it is the net return (profitability) from investments (cash, labour, time, etc) that may be important to a farming household and is likely to influence adoption of new technologies.
  10. 10. Literature survey of net returns from improved rainfed technology. Values converted to 2005 Purchasing Power Parity for comparisons across time and between countries. Technologies exist that can substantially increase profit 2000 Net returns ($/ha/season) 1500 Median values: Base = $186 There seem to be limits 1000 Improved = $558 500 0 -500 Cases Base Improved
  11. 11. Profitability, Land and Household Per Capita IncomeThe amount of land required for any household to achieve a given value ofincome per person from crop production depends on: the profitability of anycropping enterprise and the number of people in the household.To achieve a threshold of $1.25 / person / day, the relationship is: y = (365/x) * n * 1.25Where:y = land required per HH (hectares)x = net returns from the enterprise ($ / ha / year)n = number of persons in the HH
  12. 12. Land required per household for a given Net Return to produce $1.25/person/day (1 season/year) Base Improved $186/ha/season $558/ha/season 60 N=6 50 Land required for $1.25 (ha/HH) 40 30 N=4 20 N=2 10 N=1 0 0 200 400 600 800 1000 1200 1400 Net return ($/ha/yr)
  13. 13. “Intensificationability”80 % of farms in SSA are now below 2 ha (Nagayets, 2005).
  14. 14. Maintaining net income per hectare as farm size increases and effect of off-farm income for a family of five in relation to an IPL of $1.25/person/day (one season per year). Net income from crops ($/ha/season) 5000 4500 4000 Nr New tech $558/ha Nr/ha/seasonIPL 3500 Nr/ha/seasonIPL70% 3000 Nr/ha/seasonIPL30% Income/HH/season from $558/ha $2281/year required for 2500 a family of 5 to have 2000 $1.25/person/day 1500 1000 500 0 0 1 2 3 4 5 Farm size (hectares)
  15. 15. Degree to which communities can benefit from intensification - examples 80 Makueni 10.44 70 Impact of intensification depends on D1 Tanz. 11.19% HHs with $1.25/p/d 60 where you are, who you are 50 and what you have Kadoma 9.61 Lawra-Jirapa 6.18 40 30 Tougou 4.4 20 10 R. Valley 0.68 0 0 100 200 300 400 500 600 700 800 Net returns ($/ha/season)Values are the slopes of the lines x 102
  16. 16. Questions:Do we have technologies appropriate for Dryland environments?• Almost certainly, although fine-tuning is still required and there is need for consideration of climate change.Do we have technologies appropriate for Dryland rural households?• Not so sure because we know very little about what criteria rural households use to make decisions about investments.
  17. 17. Agricultural technologies What can be done What ‘farmers’ can do What ‘farmers’ will do: 1. Will it work? 2. What’s the ‘cost’? 3. What’s the risk? 4. Is it worth my while? 5. Is it my best option?
  18. 18. Some issues, processes, phenomena, etc., influencing decision-making (Daniel Kahneman) Prospect theory Halo effect Risk aversion Conflict (between alternatives) Judgment heuristics Asymmetry of knowns/unknownsIntuition Sequence of exposure Consensus Overconfidence Intensity matching Question substitution (heuristics) Familiar narratives Content versus reliability Anchors (expectations) Comfort zones Suggestion Availability (inf. recall ease)Natural tendencies Availability cascade (policy, Understanding probability public opinion) Impressions Base rates Representativeness Cognitive ease/strain Stereotyping Conjunction fallacy Opinions Narrative fallacies PlausibilityHunches Loss aversion Hindsight bias Mental effort Confirmation bias Associative coherence Fear of ridicule Perception of risk Common bias in groups Association of ideas Familiarity Regression to the meanPriming Attitude Mood Affect heuristic (feel/think) Experience NormalityRepetition State of mind Surprise Personal world view Morality ValuesCulture Sequence of questioning
  19. 19. Modeling risks and returns from use of N – Mwingi, Eastern Kenya, using APSIM and weather data from 1962-2006 (KPC Rao) Risk and return with fertilizer application 0 Kg/ha 20KgN/ha 40kgN/ha 60 kgN/ha 80 kgN/ha Average Yield (kg/ha) 1213 2185 2612 2666 2674 Best yield (kg/ha) 2802 3399 3447 3475 3511 Optimistic Yield(kg/ha) 1568 2497 3005 3104 3136 Expected Yield (kg/ha) 1207 2209 2806 2853 2874 Pessimistic Yield (kg/ha) 694 1861 2298 2466 2482 Worst Yield (kg/ha) 0 903 522 472 438 % years with >10 kg 87% 83% 74% 74% grain/kg N Value cost ratio >2 73% 61% 52% 42%
  20. 20. Full-time farmers? ‘ DIRT POOR: The key to tackling hunger in Africa is enriching its soil. The big debate is about how to do it.’ 29 MARCH 2012 | VOL 483 | NATURE | 525 “Eneless Beyadi appears through a forest of maize clutching an armful ofvegetables and flashing a broad smile. Beyadi cultivates about half a hectare ofplots in the village of Nankhunda, high on the Zomba plateau in southernMalawi. She gets up at 4 a.m. every day to tend her gardens, as she lovinglycalls them, before heading off to teach at a school.”
  21. 21. Opportunities – even in MalawiNo. Enterprise Turnover a Operating costs c Net income f Returns to labour g (MK/month) b (MK/month) (MK/month) (MK/day)1 Brewing local gin( kachasu) 2947 2144 1324 402 Selling goat hides 2900 2259 2435 783 Selling fried fish (kanyenya) 3600 3076 1052 444 Trading maize and flour ADMARC maize 100 78 57 31 flour 662-805 547-340 350-531 48-1635 Selling cooked food (zophikaphika) 868 750 469 506 Selling snuff 284 241 97 367 Trading maize bran (madeya) wet season (town) 480 416 35 wet season (village) 100 85 28 dry season 1400 904 528 Tailoring 3300 2410 2203 379 Village shop-keeping 8000 6771 1625 2610 Village carpentry 675-1180 263-402 647-1152 61-6811 Building houses 1200 546 1166 5012 Agricultural labour (ganyu) land preparation - - 676 25-40 weeding - - 312 2613 Permanent labour - - 1024 2814 Estate labour - - 526 2215 Selling firewood 263 0.75 262 1416 Moulding bricks - - - 2917 Selling thatching grass 500 31 469 5018 Making baskets 1170 841 1003 2519 Making mats 144 196 137 720 Making granaries (nkhokwe) 195 195 195 3021 Making hoe and axe handles 20 48 18 922 Selling herbal medicine 667 171 667 208
  22. 22. Timing of opportunities in relation to croppingNo. Enterprise Place of trade Customers Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep 1 Brewing local gin Residence Villagers 2 Selling goat hides Residence Tannery 3 Selling fried fish Local villages Villagers 4 Trading ADMARC maize Local markets Traders 5 Trading maize flour Town Townsfolk 6 Selling cooked food Village school Schoolchildren 7 Selling snuff Residence Villagers 8 Trading maize bran Local villages Cattle-owners 9 Tailoring Local markets Villagers10 Village shop-keeping Home village Villagers11 Village carpentry Nearby village Villagers12 Building houses Local villages Villagers13 Labouring: land preparation Local villages Villagers14 Labouring: weeding Local villages Villagers15 Permanent labour Nearby village One household16 Estate labour Mindale estate Tea plantation17 Selling firewood Residence Villagers18 Moulding bricks Home village Villagers19 Selling thatching grass Residence Villagers20 Making baskets Local markets Villagers21 Making mats Residence Villagers22 Making granaries Local villages Villagers23 Making hoe and axe handles Residence Villagers24 Selling herbal medicine Residence Villagers, townsfolk
  23. 23. Back to Sustainability 7.5 “… improved non-farm employment Per capita income (x 1000Rs) 7 Base 6.5 opportunities in the village increase More non-farm 6 household welfare in terms of increase 5.5 in household income but reduce the 5 households’ incentive to use labour for 4.5 4 soil and water conservation leading to 1 2 3 4 5 6 7 8 9 10 higher levels of soil erosion and rapid Year land degradation in the watershed. This indicates that returns to labour are higher in non-farm than on-farm 2100 2000 employment.”Soil loss (tonnes) 1900 1800 S. Nedumaran ‘Tradeoff between Non- 1700 farm Income and On-farm Conservation 1600 1500 Base Investments in the Semi-Arid Tropics of 1400 India’ 1 2 3 4 5 6 7 8 9 10 Year
  24. 24. (Some) ConclusionsAll the core concepts with which we are concerned - risk, resilience, vulnerability, foodsecurity, sustainable intensification, investment, net income, etc. – are more relevant in alivelihoods context that goes beyond merely agriculture and natural resources management.More consideration, and better understanding, of the wider context in which smallholderagriculture operates will help in targeting of technology, may improve its adoption andapplication to produce Development Outcomes.However, agricultural intensification (for example) may not be as attractive an option as wewould like, and we need to consider the consequences of such an outcome.
  25. 25. Thank you!

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