1. Wide boundaries for rural systems:
implications for household decision-making and adoption of
agricultural technology.
Dave Harris
ICRISAT Nairobi
19th February 2013

2. Outline
1. Concepts for Research with Development Outcomes
2. Sustainable Intensification
3. 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. CGIAR Drylands System - Core Concepts

4. ICRISAT Strategic Plan:
Inclusive Market-Oriented Development (IMOD)

5. Sustainable Intensification (SI)
General consensus (CGIAR-CRPs, USAID, etc) that this is the way forward for
rural households to:
 Reduce / get people out of poverty
 Improve food security

6. Three Propositions
1. 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. 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. (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. 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. 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. Profitability, Land and Household Per Capita Income
The amount of land required for any household to achieve a given value of
income per person from crop production depends on: the profitability of any
cropping enterprise and the number of people in the household.
To achieve a threshold of $1.25 / person / day, the relationship is:
y = (365/x) * n * 1.25
Where:
y = land required per HH (hectares)
x = net returns from the enterprise ($ / ha / year)
n = number of persons in the HH

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. “Intensificationability”
80 % of farms in SSA are now below 2 ha
(Nagayets, 2005).

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. 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. 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. 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. Some issues, processes, phenomena, etc., influencing decision-making (Daniel Kahneman)
Prospect theory Halo effect Risk aversion
Conflict (between alternatives) Judgment heuristics Asymmetry of knowns/unknowns
Intuition 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 Plausibility
Hunches 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 mean
Priming Attitude Mood
Affect heuristic (feel/think) Experience Normality
Repetition State of mind Surprise
Personal world view Morality Values
Culture Sequence of questioning

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. 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 of
vegetables and flashing a broad smile. Beyadi cultivates about half a hectare of
plots in the village of Nankhunda, high on the Zomba plateau in southern
Malawi. She gets up at 4 a.m. every day to tend her gardens, as she lovingly
calls them, before heading off to teach at a school.”

21. Opportunities – even in Malawi
No. 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 40
2 Selling goat hides 2900 2259 2435 78
3 Selling fried fish (kanyenya) 3600 3076 1052 44
4 Trading maize and flour
ADMARC maize 100 78 57 31
flour 662-805 547-340 350-531 48-163
5 Selling cooked food (zophikaphika) 868 750 469 50
6 Selling snuff 284 241 97 36
7 Trading maize bran (madeya)
wet season (town) 480 416 35
wet season (village) 100 85 28
dry season 1400 904 52
8 Tailoring 3300 2410 2203 37
9 Village shop-keeping 8000 6771 1625 26
10 Village carpentry 675-1180 263-402 647-1152 61-68
11 Building houses 1200 546 1166 50
12 Agricultural labour (ganyu)
land preparation - - 676 25-40
weeding - - 312 26
13 Permanent labour - - 1024 28
14 Estate labour - - 526 22
15 Selling firewood 263 0.75 262 14
16 Moulding bricks - - - 29
17 Selling thatching grass 500 31 469 50
18 Making baskets 1170 841 1003 25
19 Making mats 144 196 137 7
20 Making granaries (nkhokwe) 195 195 195 30
21 Making hoe and axe handles 20 48 18 9
22 Selling herbal medicine 667 171 667 208

22. Timing of opportunities in relation to cropping
No. 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 Villagers
10 Village shop-keeping Home village Villagers
11 Village carpentry Nearby village Villagers
12 Building houses Local villages Villagers
13 Labouring: land preparation Local villages Villagers
14 Labouring: weeding Local villages Villagers
15 Permanent labour Nearby village One household
16 Estate labour Mindale estate Tea plantation
17 Selling firewood Residence Villagers
18 Moulding bricks Home village Villagers
19 Selling thatching grass Residence Villagers
20 Making baskets Local markets Villagers
21 Making mats Residence Villagers
22 Making granaries Local villages Villagers
23 Making hoe and axe handles Residence Villagers
24 Selling herbal medicine Residence Villagers,
townsfolk

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. (Some) Conclusions
All the core concepts with which we are concerned - risk, resilience, vulnerability, food
security, sustainable intensification, investment, net income, etc. – are more relevant in a
livelihoods context that goes beyond merely agriculture and natural resources management.
More consideration, and better understanding, of the wider context in which smallholder
agriculture operates will help in targeting of technology, may improve its adoption and
application to produce Development Outcomes.
However, agricultural intensification (for example) may not be as attractive an option as we
would like, and we need to consider the consequences of such an outcome.

25. Thank you!