Remembering and measuring data
Farmers in Europe will tell you how much time they spend
filling in forms reporting every aspect of their farm enterprise. They need to
have every bit of data available at their fingertips to satisfy demands from
banks, the EU, shareholders, ministries of agriculture, food standards
organisations etc. and therefore have to have sophisticated record keeping
systems. In other parts of the world, where ‘farming’ often means smallholders
using traditional practices, requirements to keep records and provide data are
very different. Yet sometimes these farmers are also asked to provide
information about their farms. These requests come from governments compiling
statistics on aspects such as food production, and from researchers with many
different aims. So, how is information on farm input and output collected, and how
does it work?
Image 1: Examples of farm
inputs and outputs that need measuring
The common procedure for obtaining the data is simple: ask
the farmer. Think of a smallholder who grows maize as a staple crop. We often
find surveys with questionnaires containing questions such as these:
of maize did you plant last year?
did you spend weeding it?
maize grain and stover did you produce?
This is an attempt to obtain actual quantities by recall. In
most cases the information was never written down by the farmer, and therefore to
answer these questions they have to ‘remember’ what the answer is. So is the
resulting data valid, accurate and useful? This has been investigated and some
insights gained. But before explaining what has been discovered, I find it’s
useful to think of analogies with someone trying to collect data from me.
What sort of answer would you give if asked:
time did you spend using a computer last year?
This is, in many ways, similar to the questions on maize production asked of a farmer who depends on it. I depend on the computer for my work. I spend a lot of time in front of it; both for work and other things. But how much time did I spend on it in the last year? I have no idea. I could try to come up with an estimate (hours per day, days in the year), but it is likely to be inaccurate and far from the truth. If I did come up with a number, would I tell the data collector? I might be ashamed about how much time I spend in front of the computer - and perhaps lower it to show I’m smart enough to balance work with other areas of my life. Or perhaps the opposite – increase it to show what an important worker I must be. The same thing is likely to be true for many other quantities used for measuring things that are, or should be important in our lives – for instance the time I spent on public transport in January, how far I walked last year, what I ate last week, how much I earned last year.
The following four points illustrate four critical factors
that need to be in place to get a good answer to these quantitative recall
I need to understand
the question. For example, does ‘public transport’ include commercial flights?
Bikes hired from the street? What about a hired car?
I need to have
known the quantity at the time it happened. Even at the end of last January,
I didn’t know how far I walked that month. I am not even sure how far I walked
yesterday! I can add up the walk to
work, the walk to the shops and taking the dog out. But how far did I walk
around the house and office? And is it really 1km to the shops, as I like to
I need to remember
the quantity when data is collected. I might remember a special meal last week,
but otherwise I don’t remember lunch on Tuesday, or supper on Wednesday, or
what it all adds up to.
I need to be prepared to give an honest answer to the data collector. If
you have a salary and tax bill, you probably know what your earned last year,
but who are your prepared to tell?
Exactly the same applies to smallholder farmers answering
questions about their farm inputs and outputs. It’s sometimes not easy to get the
question clear (e.g. does ‘my farm’ include land that was borrowed or rented?).
While some quantities are probably known accurately (e.g. income from an
important cash crop), others are never measured or assessed (e.g. firewood
collected on a farm). Quantities that
are important at the time may be remembered (e.g. the number of labourers hired
to plough fields), but others forgotten. Whether a farmer is prepared to reveal
the answer depends on many factors including trust in the data collector and who
they represent, as well as the belief that the answer given will have some type
of consequence for them.
We can predict when each of these factors is likely to be a
problem and hence predict when the collection of quantitative data by recall is
likely to be effective. When it is not, then we need an alternative. Take the
example of firewood collected from the farm during last year. It is probably
something that happened many times, but irregularly - with different people
involved and different amounts collected each time. The wood that is collected
is never arranged in standard ‘units’ (piles, containers) and is burned as
needed - so there is never a pile with the year’s supply all in one place. If we want an accurate estimate of this firewood,
we probably need a simple protocol such as:
Provide the farmer with a spring balance, bit of
rope, pencil and suitably robust notebook.
Agree that every time anyone brings firewood
from the farm, it is bundled, weighed and the weight written down.
The researcher comes to collect data from the
notebook every month.
Whether anyone agrees to this will depend on whether they think it is useful. The monthly visits (rather than one at the end of the year).
Image 2: Weight crops in the
field at harvest time – an alternative approach to recall
These ideas have recently been written up in a small guide
by staff at Stats4SD and colleagues in the World Agroforestry Centre (ICRAF) –
If you are planning any type of survey that involves asking farmers about
quantities of inputs or outputs, then it would be worth looking at it to make
sure your effort will generate reliable data.
Author: Ric Coe
Ric’s main focus is on improving the quality and effectiveness of research for development using the application of statistical principles and ideas. He is particularly interested in research design, including the design of complex integrative research projects.
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