Analyses of likely demand for small mechanisation

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Analyses of likely demand for small mechanisation




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Presentations text content in Analyses of likely demand for small mechanisation

Slide1

Analyses of likely demand for small mechanisationFocus group discussions and farm survey in Kenya and Tanzania

M.

Misiko.

,

F. Baudron. and D. Tirfessa

CIMMYT Addis Ababa, Ethiopia

m.misiko@cgiar.org

FACASI

Project Review and Planning Meeting

Sportsmans

Arms Hotel,

Nanyuki

, Kenya

March 11-14, 2014

Slide2

Introduction

Need

to reduce

drudgery; make

farm work more attractive and efficient (

FAO

2006

)

Consequences of mechanisation decline in

SSA

include

disproportionate marginalisation of already disadvantaged groups including

women

Necessity of accessing appropriate

mechanisation and energy saving technologies such as conservation agriculture (CA

)

Rate of demand of food, feed, processing resources require drastic changes in power provision

Slide3

Objective of the baseline

FGD

– “to map power demand and use trends, by studying forms of drudgery and sources of

power”

Questionnaire

survey: “to understand and map the potential demand for, and trends in farm mechanisation”

Slide4

Sites:

Meru

and

Mbulu

Districts (Tanzania)

Laikipia

and

Bungoma

Counties (Kenya)

Slide5

Methodology

Methods:

8

focus group

discussions

; key informant interviews

Tools:

semi-structured guide

, with tables

Sampling: farmers

, service providers,

extension, fabricators

Purposive selection

– knowledge

on

mechanisation,

local

farming

Gender based sessions

– between

7 and 13

participants

Data analyses: percentages, content analyses

Reliability

:

findings consistent with

other studies e.g.

FAO

, 2005

;

FAO

,

2006

Slide6

FGD study process

Participants identified and analysed key tasks and estimated:

frequencies of performing tasks e.g. daily, weekly or seasonally

percent of tasks done by each gender

sources of power for each task i.e. human muscle, animal draft or machine

general trend of source of power in the recent 10 years

mechanisation ownership i.e. owned individually, collectively or hired

Slide7

Methodology – cont.Questionnaire survey complemented

FGD

Sample:

household heads/ knowledgeable members, 400 interviews (Kenya

,

Tanzania)

Systematic sampling:

incomplete frames for scientific random sample

representative transect

routes

used

every

fourth farmer on alternate sides of the

track interviewed

Excel – data entry and analyses

Slide8

Findings

Slide9

Drudgery? “when you bend too long, repeatedly… low benefits e.g. weeding”

Traditional hand tools,

Jijiga

Ethiopia

Slide10

Selected challenges related to mechanisation in FACASI

sites

Available machinery, esp. tractors, few and far between

Or

dedicated for

commercial scale

production e.g.

Laikipia

,

Mumias

Little

multi-functioning, beyond the

tillage, large scale transport

Weak smallholder

business orientations e.g.

perceptions that animal traction was cheaper or even

more adapted to existing social, economic and environmental conditions

lack of evidence or promotion among smallholders

Low

mechanisation or related skills and equipment

Inappropriate institutional arrangements (along value chains):

for technology

i

) generation ii) multiplication iii) delivery

Slide11

Evidence of

m

achinery: not necessarily for

smallholder sustainable systems intensification

Slide12

Household activities

Activities

were usually scattered,

routine

Limited mechanisation refers

to harvesting of

fuelwood

by power-saws,

and

ownership of animal

carts

Slide13

Processing

Processing was not

large scale,

yet it required extensive

travelling

e.g

Tanzania’s

Meru

women walked on average

5+km

to grind cereals

Slide14

Timber and/ or construction

Timber

/ construction – most important growth in

mechanisation

Perceived

as most expensive,

often

required skilled

labour, seasonal/ occasional

Slide15

Transport

More male participation, higher mechanisation e.g. bicycles

, animal

carts, motorcycles

Slide16

Slide17

Agricultural production

Weeding – ranked as leading

drudgery

No change or decrease in mechanisation

Slide18

Bungoma

Laikipia

Mbulu

Meru

Contribution of Women and Children to Farming

Women often supervise children

Hired labour often performed ‘men’s’ duties – e.g. Tillage (FGD)

Slide19

Mechanisation trends – averages

More mechanisation in non-farm activities

Muscle power disproportionately high

Slide20

47%

32%

22%

58%

30%

12%

43%

33%

23%

32%

21%

46%

Frequency

Total human power (man-days)

Bungoma

Laikipia

Mbulu

Meru

Delineating typologies based on labour invested in farming

Slide21

Sites

Type

Total human power (man-days)

Total draught power (pair-days)

Total tractor power (tractor-day)

Proportion of hired labour (%)

Mbulu

1

69.1 ± 19.2

10.1 ± 9.7

0.0 ± 0.0

15.3 ± 24.6

2

146.9 ± 31.2

26.5 ± 20.7

0.3 ± 1.1

16.4 ± 23.8

3

345.0 ± 176.2

58.4 ± 62.8

0.0 ± 0.0

19.4 ± 26.0

Meru

1

53.3 ± 21.1

15.1 ± 12.4

0.3 ± 0.8

35.5 ± 27.7

2

138.8 ± 23.5

27.6 ± 22.7

0.5 ± 0.7

45.2 ± 30.8

3

326.2 ± 153.2

47.1 ± 53.2

1.3 ± 1.7

52.9 ± 37.0

Bungoma

1

88.1 ± 33.1

3.7 ± 8.6

0.0 ± 0.3

15.9 ± 28.6

2

203.0 ± 31.0

11.0 ± 15.8

0.9 ± 2.1

32.5 ± 38.6

3

516.2 ± 265.0

26.5 ± 29.6

3.4 ± 5.0

51.3 ± 31.7

Laikipia

1

97.6 ± 39.2

0.7 ± 1.7

1.9 ± 3.1

30.8 ± 37.6

2

196.6 ± 28.8

1.3 ± 2.6

1.5 ± 2.8

47.9 ± 34.8

3

456.4 ± 198.9

2.1 ± 6.9

2.6 ± 4.3

40.9 ± 33.4

Table 2. Draught power does not necessarily reduce reliance on human power

Slide22

Social class and machinery

Limited (5-10%) smallholders reliance on

tractor

for tillage

Smallholders

did not (

independently)

own draft animals or (tillage)

equipment - they

primarily

hired

,

joined

forces,

or relied on

goodwill

 

Beyond

tillage, most

farm work was

manually

done

Incompatibility

of existing machines with

farming

styles

,

or for

drudgery tasks

such as weeding, harvesting, planting, or

processing

Trade-offs

Slide23

Labour distribution through the year

Bungoma

Laikipia

Mbulu

Meru

Proportion of the total labour (in men equivalent) invested in farming

Poor farmers labour peaks with season, more than richer farmers

Slide24

Grain production (t farm

-1

)

Bungoma

Laikipia

Mbulu

Meru

Farm productivity increases with increasing power

Slide25

Women’s share of agricultural labour decreases with increasing access to farm power

Type 1

Type 2

Type 3

Women participate in men’s activities, which tend to be more mechanised

Multifunctional machines reduce women chores e.g. carrying produce

Machinery in CA reduces weeding needs

Slide26

Evolving contexts, changing nature of power demand

Lowest

mechanisation in

activities handled more by women

mechanisation mostly

corresponded with level of male

involvement

Tillage

has become

lighter, especially because:

land

parcels

are

smaller, and

repeatedly tilled

However farm-work is not necessarily decreasing:

Increasing task feminisation, and by hand hoe

Emergence of

new weeds

, due to CC, need for more

weeding

Harder to mechanise

on scattered, small, uneven, inaccessible plots

Power

saws have

increased yet:

trees

rarely grow as old as in the 1970s or 80s when only axes or simple saws were

used

Oxen only used seasonally – see next slide

not necessarily profitable

– idle, feeding oxen, yet

preference

is not

necessarily waning

Slide27

Bungoma

Laikipia

Mbulu

Meru

Animal

Draught

Power

Land

preparation

Land

preparation

Harvesting

/

Threshing

Land

preparation

Manuring

Land

preparation

Manuring

Sowing

Land

preparation

Proportion of the total DAP

invested

in

farming

Slide28

Mechanisation entry points

Joint ownership was non-existent,

individual ownership is difficult:

business

models

can

revolve around

hiring

services

Versatile, accessible, handiness:

from

non-motorised tools,

transitioning

into

two-wheelers, etc

Convenience

- portability, repair

,

refuel, ‘asymmetrical’ tasks

Off-farm

tasks, not on-farm activities were more mechanised e.g. Table 1

Slide29

So what?

Demand exists

, in different forms

Entry

points

for small (agricultural) mechanisation include

off-farm

Change

perceptions

about animal

power, target off season activities

Transition

e.g. fabrication, dual-haul equipment (tractors, animals)

2WT efficiency trials – including animal traction as ‘control’

Income

enterprises

e.g. horticultural irrigation, intensification

Facilitate

commercial clusters

/ Agri.

Innov

. Platforms:

socially

acceptable business

models e.g. versatile machines for rural hire

Policy -

institutional amendments

, mechanisms:

enabling access among

90% of farmers who cannot own

2WT

eliminating cross-

sectoral

policy

conflicts

Gender

and decision making

Slide30

Conclusions

Small mechanisation demand:

not about mere increment of machine numbers, but rather prioritising entry points while seeking

multi-functionality

Create demand through interactive leaning:

integrated-trials for illustrating ‘

convenience

visual educational resources e.g. internet, video, photography, brochures

Institutional support mechanisms are key

Slide31

Acknowledgements

Australian Centre for International Agricultural Research (ACIAR)

The

International Maize and Wheat Improvement Centre (CIMMYT)

Selian

Agricultural Research Institute (SARI)

Kenya Network for Dissemination of Agricultural Technologies (KENDAT).

Car and General’s Abel

Gikenyi

and Farm Concern’s Steve

Ngeru

SIMLESA Programme and partner institutions

Farmers

and partners in the Project sites

Slide32

Thank you!

Slide33


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