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:
· What area of maize did you plant last year?
· How long did you spend weeding it?
· How much 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:
· How much 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 questions:
1. I need to understand the question. For example, does ‘public transport’ include commercial flights? Bikes hired from the street? What about a hired car?
2. 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 tell people?
3. 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.
4. 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) – see https://stats4sd.org/resources/464. 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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