On-farm trials

1. On-farm trials Some biometric guidelines

2. Topics • Introduction • Types of on-farm experiment • Specifying objectives • Choice of farms and villages • Choice of treatments • Measurements • Analysis • Conclusions

3. 1. Introduction • Participatory on-farm trials • some different skills required from on-station trials • be prepared to be bold in the design • This presentation highlights aspects that are different from on-station trials

4. Are these guidelines relevant? • Is your on-farm trial: • genuinely involving the farmer in the research • seeking to resolve research questions • i.e. it is research and not purely demonstration • Show on the next slides why on-station designs can not usually simply be transferred

5. An example of the transfer of on-station ideas • Propose that each farmer is to be a replicate • Six replicates are usually adequate for on-station trials, therefore propose six farmers for this trial • Suggest 4 treatments, simpler than on-station where 8 to 10 are often used • So, total of 24 plots - like a small on-station experiment

6. Simple design? Simple design, no new concepts need to be learned. The analysis can also proceed as for an on-station experiment What could go wrong??

7. What could go wrong? • On-station the blocks (or reps) are not of direct interest • Measurements are not therefore made at the block level • Here the farmers are of interest • Data are collected at the “farmer level” • usually with a questionnaire • And six farmers is a very small sample

8. What else could go wrong? • On-station trials usually control most of the variables that are not included in the treatments • same soil type • same date of planting • same weeding strategy • etc • On-farm trials often leave such aspects to the farmer. So: • yields are very different • data may need to be analysed in subsets • little is learned from such a small trial

9. 2. Types of on-farm experiment • Researcher designed and managed • often equivalent to “hiring” the farmer’s field • design principles are primarily as for on-station trials • can be important, but not covered in this guide • Researcher designed and farmer managed • Farmers involved in both design and management. Researchers monitor • Note these categories are purely indicative. Many intermediate possibilities are available

10. Biometric perspective • Data are normally collected at plot level • like an experiment • and at a farmer level • like a survey • Hence the design and analysis of these trials will reflect ideas from both experiments and surveys

11. Specifying the objectives • Early on-farm trials were mainly to broaden the validity of conclusions, beyond the research institute • This is still valid • Also include genuine participation. Research programs can incorporate farmers’ innovations. • Specify objectives clearly from all points of view • researcher • farmer • NGO, etc • check the objectives regularly, during the planning phase

12. Objectives, continued • Trials are to resolve specific research questions • be impartial to the need to include • participatory • on-farm • in the proposal, for funding • most research programs need studies of different types, but not everything needs to be on-farm. • Keep objectives simple for each study • if there are varied objectives consider 2 or more studies, with different levels of farmer involvement

13. Choice of farms and villages • Consider mainly as a “sample survey”. • Sometimes an initial survey (or census) is useful • Consider using a stratified scheme • Take care if • villages remain associated for long periods • only “good” farmers are included. • Though this may be appropriate for some farmer designed studies?

14. Choosing the treatments and units • Same choices for treatment structure as on-station • unstructured, i.e. just “treatments” • controls (or baseline treatments) may be needed • factorial structure remains an important concept • how many and what levels for quantitative factors • Some differences for discussion: • Who chooses the treatments? • Same number of plots for each farm? • Same control(s) for each farm? • Who chooses where to site the plots?

15. How many treatments • No rules • 4 or less is a rule • each farmer is a “rep” is a rule • Arguments for many treatments include • experiments are costly so make them informative • problems are complex • And against • complex designs more likely to fail • not enough space on each farm • farmers may object to a large experiment

16. How many treatments per farm? • In many experiments: • if there are 4 treatments • then each farm has 4 plots • each farm has one repetition of all treatments • like a randomised complete block design with each farmer being a block! • There are other possibilities • more plots (e.g. 5) per farm, with some within-farm repetition • less plots (e.g. 3) per farm, i.e. an incomplete block design • different number of plots per farm • different number of treatments per farm, perhaps some chosen individually by the farmer • All sensible designs can be analysed

17. Replication and resources • For a given number of plots it is better • to have more farms and less replication within farm • than fewer farms with more replication within farm • So many farmers, with each having one replication, is sensible • Only problem is the difficulty of studying the farmer by treatment interaction • could add one plot extra per farm • and always measure characteristics of each farm • see the Section on analysis

18. Crop experiments: plot size • No rules! • “Plots must be large in on-farm trials” is a rule • General guideline is, for a given area, to have more small plots rather than fewer large ones. • Against this, the case for large plots includes: • they are more realistic if farmers are to be interviewed • labour measurements often need large plots • guard rows need a smaller proportion of the plot • treatments can be applied more easily

19. Crop experiments: plot layout • Use farmers knowledge of their fields • to locate plots • to avoid odd patches • Check on criteria to choose plot locations • researchers usually like homogeneity • farmers often exploit variability • so, check back to the objectives! • Check on block layout • general concepts as for on-station • requirements of interview process also to be considered

20. Livestock studies • Experimental unit • usually an animal, sometimes a group of animals • check there are sufficient units (particularly when each unit is a group of animals) • can sometimes use a single animal for more than one treatment • Blocking • if blocking unit is farmer, then different block size per farm. This is ok. • Age and breed are other possible blocking factors. • These are also useful if just one animal per farm • Different levels of variation • between farm, between animal within farm, within animal • statistical advice on analysis can be useful

21. Measurements • On-station type, e.g. yield • check how much detail is needed • Management type • important • used to understand the variability from • plot to plot • farm to farm • Discussion type • important • from questionnaires or participatory methods

22. Analysis • Of discussion type information • mainly at farmer level • Of yield type information • mainly at plot level • uses management data to understand variability • Combining the two levels • often expect a farmer by treatment interaction • variation at different levels • between farms • between plots, within farms • still aim to understand as much variation as possible

23. Approaches to the analysis • Could be • simple analyses on subgroups of the data • or attempts to model the whole data set • often try to relate crop responses to management, environmental and social variables • Objective is often to • understand reasons for farmer assessments • could derive decision trees • or maps of recommendation domains

24. Be prepared to: • Split the data into subsets • usually groups of similar farmers • Omit particular plots, or farms • Use additional information at both farm and plot level • Investigate farmer by treatment interaction • Report on interesting farmers individually

25. Further topics • Other important areas include • good system for data management • good data presentation • Guidelines are as for other studies • In conclusion • Like undertaking a sample survey • It is very easy to do a collaborative on-farm trial • as long as you can define “collaborative” yourself • But difficult and time-consuming to do good one.