This post is part of the continuing mini-series on RQ–RH–D–M across fields. Its purpose is to provide a compact, practical toolkit showing how research questions (RQ), research hypotheses (RH) or working propositions, data (D), and methodology (M) can be aligned in one specific discipline.

Agriculture is an especially useful domain for this kind of exercise because it naturally combines diverse elements such as field experiments, farm-level comparisons, environmental monitoring, farmer behavior, technology adoption, sustainability and rural livelihoods. The field is well suited to quantitative, qualitative and mixed methods designs, which makes it ideal for demonstrating how the same substantive issue can be approached through different forms of evidence.

In agriculture, research questions and hypotheses are commonly derived from frameworks related to production ecology, cropping systems theory, soil–plant–water relationships, agroecology, diffusion and adoption of agricultural innovations, sustainable intensification, risk and resilience frameworks and farmer decision-making models. These frameworks help define constructs such as productivity, input efficiency, soil quality, adoption readiness, sustainability, vulnerability or food-system performance, which are then measured through yield data, soil indicators, agronomic observations, management records, survey scales, interview responses or integrated farm-level datasets.

Note: The entries in the Methodology are intentionally general and indicative. They are meant to illustrate plausible methodological directions, not to exhaust the full range of possible methods, model variants or analytic choices available to the researcher. Researchers are not expected to apply all of the methodological tools listed in column Methodology in a single study. The entries are intended to indicate suitable methodological options or families of approaches from which the researcher selects those that best fit the research question, hypothesis, data, and design.