Research Vanguard

WHY ON-FARM EXPERIMENTS?

Experiments conducted at the station under the researcher's
control produce impressive yield numbers. But when those same technologies reach smallholder farms, the results often disappoint. On-farm experiments exist to close that gap. They are one of the most important tools in applied agricultural research.

THE PROBLEM
The gap between on-station yield and farmer yield is large. However, it is not a mystery. Walk into any National Agricultural Research Station (NARS) in East Africa and you will find maize yields of 6 t/ha or more. Ask a smallholder farmer in the same region what they harvest, and the answer is closer to 1.5 t/ha. The gap is real, large, and well-documented.

But this is not primarily a story about farmer ignorance or poor management. It is a story about what research stations are, and what they are not. Stations offer controlled conditions, optimal inputs, and professional management. They are not designed to replicate the constraints, variability, and resource limitations of a smallholder farm. When research happens only at the station, we are testing technologies under conditions that most farmers will never experience.

Maize yield gap — East Africa
Setting Yield
Genetic potential 8.5 t/ha
On-station (researcher-managed) 6.2 t/ha
On-farm (researcher-managed) 4.8 t/ha
On-farm (farmer-managed) 3.2 t/ha
Average farmer yields 1.5 t/ha

Each step down reflects real-world management constraints — not variety failure.

“A technology that only works at a research station is not a technology for farmers.”

Five main reasons why on-farm experiments matter
1 Real soils, real variability: On-station soils are carefully managed, fertilised, and relatively uniform. Farmer fields have variable pH, texture, organic matter, and drainage, often within a single plot. Technologies tested only on-station may succeed partly because of the soil, not because of the technology itself. On-farm experiments expose interventions to the variability that farmers actually face. Farmers' fields have been described as notoriously heterogeneous.

2 Real management constraints: Farmers face competing labour demands at planting and weeding time. They cannot always apply inputs at the optimal moment. They may split fertiliser doses because cash is short. On-farm experiments, especially farmer-managed trials, expose technologies to these real constraints, revealing whether a variety or practice can survive imperfect management, which is the only kind of management most farmers can afford.

3 Farmers as evaluators, not just recipients: Breeders optimise for yield. Farmers evaluate varieties on taste, storability, cooking quality, ease of threshing, stover quality for feed, drought tolerance, and market acceptance. In on-farm experiments, especially participatory varietal selection, farmers assess these traits directly. The result is a recommendation that farmers will actually adopt, rather than one they will try once and abandon.

4 Evidence that extension and policy-makers can use: An extension officer advising a smallholder needs to know what that farmer will achieve, not what a researcher achieved at a station. Multi-environment trial data from 15–20 farmer-managed sites, spanning diverse soils and rainfall zones, is what actually underpins credible technology recommendations. Station data alone cannot do this job.

5 Bridging the adoption gap: Technologies with strong station performance but poor on-farm performance stall in extension systems for years. On-farm trials identify this failure mode early, before significant resources are invested in scaling something that will not work under real conditions. When on-farm results are strong, extension officers can speak from evidence, not from optimism.

\ Research & Analysis \AgriculturalResearch
\FarmerExperiments \On-farmResearch \Researchdesigns

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Research Methodology in Agricultural Sciences by Jayne Njeri Mugwe and Steven Runo is now out!

A comprehensive guide covering research journey - from idea development to communication.

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Research Methodology in Agricultural Sciences Provides end-to-end practical guidance from idea development to data analysis, reporting, and publication. Facilitates active learning with key takeaways

Research Methodology in Agricultural Sciences.
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CHAPTER 11: RESEARCH DESIGNS FOR IMPACT EVALUATION

Impact Evaluation is a research activity

Many students write objectives like:
• “To assess the impact of training…”
• “To evaluate the effect of policy…”
• “To determine the influence of technology…”
• “To assess the influence of ….

At first glance, these objectives sound academic and impressive. However, on closer inspection, we see that they carry a cause-and-effect evaluation. They are impact-oriented objectives that require a rigorous methodological design.

Impact evaluation seeks to determine whether a program, intervention, technology or policy actually caused measurable change, often referred to as an outcome. It goes beyond description. It asks deeper questions such as:
1. Did the intervention work?
2. What changed because of it?
3. Could the change have happened anyway?
4. Was the intervention truly responsible for the observed outcomes?

This is what makes impact evaluation different from descriptive research.

For example:
A descriptive study may tell us that farmers attended a training program. An impact evaluation tries to determine whether the training actually improved:
• Productivity e.g., yields,
• Incomes,
• Farming practices,
• or livelihoods.
The focus is on causality. This type of research is common in agriculture, public health, education, development projects, and policy studies.

What makes a study truly Impact-Oriented?

A strong impact evaluation usually includes three important elements.
1. A clear intervention: There must be something being evaluated, such as a training program, policy, treatment, technology, or educational initiative. This intervention must be clearly defined and operationalized so that its scope, content, and implementation are unambiguous within the study.

2. Measurable outcomes: Researchers must identify observable and measurable changes, such as income changes, academic performance, productivity levels, health improvements, or food availability. These outcomes must also be clearly operationalized to ensure they can be consistently measured and meaningfully interpreted.

3. A Basis for comparison: Impact evaluation requires comparison. This may involve before-and-after measurements, treatment and control groups, or long-term tracking. A key element of this comparison is the counterfactual: the estimated scenario of what would have happened in the absence of the intervention. Without such a comparison framework, it becomes difficult to determine whether the intervention actually caused the observed outcomes, rather than other external factors.

Why Research Design Matters
Because impact evaluation seeks to establish causality, it often requires stronger research designs than ordinary descriptive studies.
Common approaches include:
1. True experiments (Randomized controlled Trials)
2. Quasi-experimental designs
3. Before-and-after studies
4. Mixed methods research
5. Longitudinal data with controls

However, students should choose designs that realistically fit: available resources, time, data access, and ethical considerations. A highly ambitious design that cannot be implemented properly may weaken the entire study.

Before presenting an impact-oriented objective, ask yourself:
• Can my methods truly measure impact?
• Do I have comparison data?
• Can I establish causality?
• Is my design realistic?

Strong research about methodological alignment, ethical responsibility, and producing trustworthy knowledge. Impact evaluation is thus a serious research activity. When done properly, it can provide powerful evidence that improves programs, policies, and lives.

Recommended further reading on impact evaluation and the use of propensity score matching

1. Mugwe, J.N., Runo, S. (2026). Research Designs for Impact Evaluation Studies. In: Research Methodology in Agricultural Sciences. Springer, Singapore. https://doi.org/10.1007/978-981-95-1892-0_11
2. Leroy JL, Frongillo EA, Kase BE, et al (2022) Strengthening causal inference from randomised controlled trials of complex interventions. BMJ Glob Health 7:8597. https://doi.org/10.1136/BMJGH-2022-008597
3. Gertler PJ, Martinez S, Premand P, et al (2016) Impact Evaluation in Practice, Second Edition. Impact Evaluation in Practice, Second Edition. https://doi.org/10.1596/978-1-4648-0779-4
4. White, H., Sabarwal, S., & de Hoop, T. (2014). Randomized Controlled Trials (RCTs): Methodological Briefs – Impact Evaluation No. 7. UNICEF Office of Research – Innocenti
5. White, H., & Sabarwal, S. (2014). Quasi-Experimental Design and Methods: Methodological Briefs – Impact Evaluation No. 8. UNICEF Office of Research – Innocenti..

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Research Methodology in Agricultural Sciences by Jayne Mugwe and Steve Runo

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CHAPTER 3: FROM RESEARCH TOPIC TO RESEARCH OBJECTIVES AND HYPOTHESIS

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"Research methodology in Agricultural sciences". You can access this link https://link.springer.com/book/10.1007/978-981-95-1892-0

Key Features

1. Covers both hard-science experiments (e.g., CRD, RCBD, and L*D) and social science methodologies (e.g., surveys, mixed-methods, and impact evaluations).

2. Provides field layouts, on-farm experimentation, sampling, and qualitative/participatory research.

3. Provides actionable techniques for quantitative and qualitative data analysis, including SPSS for survey data.

4. Includes four chapters on writing a thesis and journal papers, selecting journals, and disseminating findings

28 chapters
689 pages
333 Illustrations (293 in color)

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CHAPTER 2: THE RESEARCH PROCESS, KEY CONCEPTS AND TERMINOLOGIES

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Chapter 1: FOUNDATIONS OF AGRICULTURAL RESEARCH