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, research hypotheses or working propositions, data, and methodology can be aligned in one specific discipline.
Geography is especially suitable for this kind of exercise because it naturally connects space, place, environment, mobility, population, land use, inequality, climate, regional development and spatial perception. It also supports quantitative, qualitative and mixed methods designs, making it ideal for showing how the same geographic issue can be studied through spatial indicators, surveys, maps, remote sensing, interviews, field observation and integrated designs.
In geography, theoretical starting points often include human geography frameworks, spatial theory, place and sense-of-place approaches, political ecology, mobility theory, urban theory, environment–society frameworks, regional development models and GIS- and spatial-analysis traditions. These frameworks guide the construction of questions and hypotheses about location, movement, inequality, accessibility, territoriality, environmental exposure, regional difference or spatial behavior and the corresponding constructs are measured through spatial coordinates, distance measures, mapping layers, demographic and socioeconomic indicators, field observations, narratives of place or combined spatial and qualitative 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.
Agriculture – quantitative research
Descriptive questions
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RQ: What is the average maize yield per hectare among smallholder farms in rainfed regions?
RH: The average maize yield per hectare among smallholder farms in rainfed regions is below the regional agronomic benchmark.
D: Maize yield per hectare (continuous/ratio); farm size category (categorical: smallholder); production system (categorical: rainfed); region (categorical).
M: Descriptive statistics, one-sample t-test against benchmark, confidence intervals, weighted mean estimation if survey design is used.
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RQ: What proportion of dairy farms use digital herd-monitoring tools?
RH: Fewer than half of dairy farms use digital herd-monitoring tools.
D: Use of digital herd-monitoring tools (binary); farm type (categorical: dairy); herd size (count/continuous).
M: Frequencies, proportions, binomial test, confidence intervals, survey-weighted estimation.
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RQ: How much nitrogen fertilizer is applied on average to wheat fields in irrigated production systems?
RH: The average nitrogen fertilizer application rate in irrigated wheat systems exceeds the recommended agronomic rate.
D: Nitrogen application rate (continuous/ratio); crop type (categorical: wheat); irrigation status (binary/categorical).
M: Descriptive statistics, one-sample t-test or Wilcoxon signed-rank test, confidence intervals, density plots.
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RQ: What is the average annual milk output per cow in medium-scale dairy operations?
RH: The average annual milk output per cow in medium-scale dairy operations is above 5,000 liters.
D: Annual milk output per cow (continuous/ratio); farm scale (categorical: medium-scale); livestock type (categorical: dairy cattle).
M: Descriptive statistics, one-sample t-test, confidence intervals, distributional summaries.
Comparative questions
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RQ: Do farms using drip irrigation differ from farms using flood irrigation in tomato yield per hectare?
RH: Farms using drip irrigation achieve higher tomato yield per hectare than farms using flood irrigation.
D: Tomato yield per hectare (continuous); irrigation type (categorical: drip/flood); farm ID (categorical).
M: Independent-samples t-test, Mann–Whitney U test, OLS regression with treatment indicator, ANCOVA if controls are added.
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RQ: Are pesticide application costs different between conventional and integrated pest management vegetable farms?
RH: Farms using integrated pest management have lower pesticide application costs than conventional vegetable farms.
D: Pesticide cost per hectare (continuous); production system (categorical: conventional/IPM); crop type (categorical).
M: t-test, Mann–Whitney U test, OLS regression, generalized linear models for skewed costs.
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RQ: Do improved seed adopters and non-adopters differ in rice yield?
RH: Farmers adopting improved seed varieties have higher rice yield than non-adopters.
D: Rice yield (continuous); improved seed adoption (binary); plot size (continuous); region (categorical).
M: t-test, ANCOVA, OLS regression, propensity score matching as alternative.
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RQ: Do organic and conventional orchards differ in soil organic matter content?
RH: Organic orchards have higher soil organic matter content than conventional orchards.
D: Soil organic matter (%) (continuous); orchard system (categorical: organic/conventional); orchard age (continuous).
M: t-test, ANOVA, OLS regression, mixed-effects model if repeated soil samples are nested within orchards.
Relational / correlational questions
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RQ: Is fertilizer application rate associated with wheat yield among commercial farms?
RH: Higher fertilizer application rates are positively associated with wheat yield up to a moderate threshold.
D: Fertilizer rate (continuous); wheat yield (continuous); farm size (continuous); region (categorical).
M: Pearson/Spearman correlation, OLS regression, polynomial regression for nonlinearity, GAM as alternative.
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RQ: Is herd size associated with annual veterinary expenditure in dairy farms?
RH: Larger herd size is associated with higher annual veterinary expenditure.
D: Herd size (count); veterinary expenditure (continuous/cost); farm type (categorical).
M: Correlation, OLS regression, log-linear model, generalized linear models for skewed expenditure.
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RQ: Is rainfall variability associated with maize yield instability over time?
RH: Greater rainfall variability is associated with greater maize yield instability over time.
D: Rainfall variability index (continuous); maize yield instability measure (continuous); year (time); district (categorical).
M: Panel regression, time-series cross-sectional models, correlation, random-effects/fixed-effects models.
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RQ: Is farmer education associated with adoption of conservation agriculture practices?
RH: Higher farmer education is associated with higher odds of adopting conservation agriculture practices.
D: Farmer education level (ordinal); conservation agriculture adoption (binary); age (continuous); farm size (continuous).
M: Logistic regression, chi-square test, ordinal/logit models, multilevel logistic regression if clustered by district.
Causal / experimental-style questions
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RQ: What is the effect of a biofertilizer treatment on soybean yield compared with a control treatment?
RH: Soybean plots receiving the biofertilizer treatment will produce higher yields than control plots.
D: Treatment assignment (binary/categorical); soybean yield (continuous); plot ID (categorical); block ID (categorical).
M: Randomized complete block design ANOVA, linear mixed-effects model, ANCOVA if baseline soil fertility is included.
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RQ: Does supplemental irrigation increase onion yield during dry-season production?
RH: Onion plots receiving supplemental irrigation will achieve higher yields than non-irrigated plots.
D: Irrigation treatment (binary); onion yield (continuous); plot/block identifiers (categorical); soil moisture (continuous).
M: Field experiment, ANOVA, mixed-effects model, regression with treatment and moisture covariates.
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RQ: What is the effect of a farmer training intervention on correct pesticide use one season later?
RH: Farmers receiving the training intervention will demonstrate higher correct pesticide-use scores than farmers in the control group.
D: Training status (binary); pesticide-use knowledge/practice score (continuous or ordinal); pre/post measures if available.
M: Randomized or quasi-experimental design, ANCOVA, difference-in-differences, mixed-effects model.
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RQ: Does mulching reduce soil moisture loss compared with bare-soil cultivation under field conditions?
RH: Mulched plots will show lower soil moisture loss than bare-soil plots under comparable field conditions.
D: Mulch treatment (binary/categorical); soil moisture repeated measures (continuous); time (longitudinal); plot ID.
M: Repeated-measures ANOVA, linear mixed-effects model, growth-curve modeling as alternative.
Agriculture – qualitative research
Farm management and decision-making
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RQ: How do smallholder farmers describe decision-making around crop diversification under uncertain weather conditions?
WP / RH: Farmers are likely to describe diversification decisions as shaped by risk, labor capacity, and market uncertainty rather than agronomic advice alone.
D: In-depth interviews, farm histories, field notes, seasonal planning records; key dimensions: risk, labor, diversification.
M: Thematic analysis, qualitative content analysis, comparative case study, grounded coding.
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RQ: How do livestock farmers interpret the trade-off between productivity and animal welfare in daily management?
WP / RH: Farmers are likely to frame the trade-off as a practical negotiation between care, cost, and output.
D: Interviews, on-farm observation, management records, reflective notes.
M: Thematic analysis, case study, ethnographic observation, framework analysis.
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RQ: How do fruit growers make sense of harvest timing decisions under volatile market prices?
WP / RH: Growers are likely to describe harvest timing as influenced by price expectations, perishability, labor availability, and weather risk.
D: Semi-structured interviews, harvest logs, field observations, trader communication records.
M: Thematic analysis, narrative analysis, case-oriented coding, document-assisted qualitative analysis.
Farmer experience and rural livelihoods
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RQ: How do young farmers describe the barriers to entering commercial agriculture?
WP / RH: Young farmers are likely to describe entry barriers through land access, credit constraints, social legitimacy, and uncertainty about profitability.
D: Interviews, life histories, rural youth focus groups, policy or program documents.
M: Narrative inquiry, thematic analysis, phenomenological analysis, qualitative case comparison.
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RQ: How do women farmers describe their role in household agricultural decision-making?
WP / RH: Women farmers are likely to describe decision-making as shared in principle but uneven in actual control over land, inputs, and income.
D: Interviews, household narratives, observation notes, informal decision records.
M: Thematic analysis, feminist qualitative analysis, case study, narrative inquiry.
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RQ: How do farming households experience seasonal food insecurity between harvests?
WP / RH: Households are likely to describe seasonal food insecurity as a shifting combination of income shortage, storage depletion, and coping strategies.
D: Household interviews, food diaries, observation notes, community discussions.
M: Phenomenological analysis, thematic analysis, qualitative longitudinal inquiry, case study.
Sustainability, adaptation, and environmental change
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RQ: How do farmers interpret climate change in relation to local farming practice?
WP / RH: Farmers are likely to interpret climate change through observed shifts in rainfall, heat, pests, and seasonal reliability rather than through abstract scientific terminology.
D: Interviews, community discussions, seasonal calendars, observation notes.
M: Thematic analysis, environmental ethnography, discourse analysis, participatory qualitative methods.
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RQ: How do producers describe the experience of shifting from conventional to organic farming systems?
WP / RH: Producers are likely to describe the transition as involving uncertainty, learning costs, changing identities, and market experimentation.
D: Interviews, farm transition narratives, certification documents, field notes.
M: Narrative inquiry, thematic analysis, case study, longitudinal qualitative comparison.
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RQ: How do pastoralists describe adaptation to changing grazing conditions?
WP / RH: Pastoralists are likely to frame adaptation through mobility, negotiation over access, herd adjustment, and environmental memory.
D: Interviews, route maps, group discussions, observation notes, local records.
M: Ethnographic case study, thematic analysis, participatory mapping with qualitative interpretation, narrative analysis.
Policy, extension, and institutional support
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RQ: How do farmers experience agricultural extension services during input subsidy programs?
WP / RH: Farmers are likely to describe extension support as unevenly useful depending on timing, trust, relevance, and bureaucratic accessibility.
D: Farmer interviews, extension meeting observations, policy documents, advisory materials.
M: Thematic analysis, institutional ethnography, policy implementation case study, framework analysis.
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RQ: How do extension officers interpret the challenges of promoting sustainable farming practices?
WP / RH: Extension officers are likely to describe promotion challenges through farmer trust, resource limits, local fit, and incentive structures.
D: Interviews, field notes, training manuals, service records.
M: Thematic analysis, qualitative content analysis, case study, practitioner inquiry.
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RQ: How do farmers describe their interactions with formal agricultural credit institutions?
WP / RH: Farmers are likely to describe credit access as shaped by paperwork burdens, risk perception, collateral demands, and prior relationships.
D: Interviews, loan application narratives, institutional documents, observation notes.
M: Thematic analysis, narrative inquiry, institutional case study, discourse-informed qualitative analysis.
Technology adoption and innovation
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RQ: How do farmers describe the process of deciding whether to adopt precision agriculture tools?
WP / RH: Farmers are likely to describe adoption decisions through expected payoff, complexity, peer influence, and trust in service providers.
D: Interviews, adoption histories, demonstration-day observations, extension materials.
M: Thematic analysis, innovation-focused case study, narrative inquiry, framework analysis.
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RQ: How do greenhouse growers interpret sensor-based irrigation technologies in daily practice?
WP / RH: Growers are likely to interpret sensor-based irrigation as useful when it improves control, but burdensome when it increases dependence on technical troubleshooting.
D: Interviews, greenhouse observations, sensor logs used qualitatively, grower notes.
M: Thematic analysis, practice-based case study, qualitative content analysis, ethnographic observation.
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RQ: How do small-scale producers experience mobile-phone-based market information systems?
WP / RH: Producers are likely to describe these systems as helpful for awareness but uneven in real usefulness because of timing, trust, and negotiation realities.
D: Interviews, usage stories, message samples, local market observation.
M: Thematic analysis, case study, narrative analysis, digital ethnography.
Agriculture – mixed methods
Technology adoption and farm performance
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RQ: How is adoption of improved seed associated with maize yield, and how do farmers explain the conditions under which improved seed performs well or poorly?
RH / WP: Improved seed adoption will be associated with higher maize yield; farmers are likely to explain performance through rainfall, soil quality, and input affordability; qualitative findings are expected to clarify why adopters experience uneven yield outcomes.
D: Quantitative: maize yield, improved seed adoption status, fertilizer use, rainfall measures; Qualitative: farmer interviews, field observations, adoption histories.
M: Explanatory sequential design, regression or propensity-adjusted models plus thematic analysis, joint display integration.
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RQ: What is the relationship between use of digital farm tools and productivity indicators, and how do farmers describe the practical value of those tools?
RH / WP: Use of digital farm tools will be associated with higher productivity indicators; farmers are likely to describe value through decision speed, record-keeping, and management confidence; integration is expected to show when tool use translates into measurable gains.
D: Quantitative: digital tool use, yield/productivity metrics, farm size, input intensity; Qualitative: interviews, tool-use narratives, observation notes.
M: Convergent mixed methods design, regression analysis plus thematic analysis, merged interpretation through joint displays.
Sustainability and adaptation
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RQ: How are conservation agriculture practices associated with soil and yield outcomes, and how do farmers interpret the benefits and trade-offs of adopting those practices?
RH / WP: Conservation agriculture adoption will be associated with improved soil indicators and stable yields; farmers are likely to describe both ecological benefits and practical constraints; integration is expected to explain why measured benefits do not always lead to broader adoption.
D: Quantitative: soil organic matter, yield, adoption status, plot characteristics; Qualitative: farmer interviews, observation notes, practice histories.
M: Convergent or explanatory sequential mixed methods design, group comparison/regression plus thematic analysis, integrated case interpretation.
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RQ: How does rainfall variability relate to crop yield instability, and how do farmers describe their adaptation strategies under changing seasonal conditions?
RH / WP: Greater rainfall variability will be associated with greater yield instability; farmers are likely to describe adaptation through planting shifts, crop choice, and risk spreading; integration is expected to connect climatic variability with lived adaptation strategies.
D: Quantitative: rainfall variability measures, yield time series, location identifiers; Qualitative: interviews, seasonal calendars, local adaptation narratives.
M: Explanatory sequential design, panel/time-series cross-sectional analysis plus thematic analysis, integrated interpretation.
Rural livelihoods, institutions, and inclusion
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RQ: How is access to agricultural credit associated with farm investment, and how do farmers describe their experience with formal lending systems?
RH / WP: Better access to agricultural credit will be associated with higher farm investment; farmers are likely to describe lending experiences through paperwork, collateral, trust, and timing; qualitative findings are expected to explain why formal access does not always generate actual investment.
D: Quantitative: credit access, loan amount, investment expenditure, farm characteristics; Qualitative: interviews, application narratives, institutional documents.
M: Explanatory sequential design, regression/logit models plus thematic analysis, matrix-based integration.
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RQ: What differences exist in extension-service use between male and female farmers, and how do farmers describe the barriers behind those differences?
RH / WP: Female farmers will report lower use of extension services than male farmers; farmers are likely to describe barriers through timing, mobility, recognition, and social norms; integration is expected to explain the mechanisms behind observed gender gaps.
D: Quantitative: extension-service use, gender, age, farm size, location; Qualitative: interviews, focus groups, local service records.
M: Convergent mixed methods design, group comparison/logistic regression plus thematic analysis, integrated interpretation using joint displays.
Director of Wellington based My Statistical Consultant Ltd company. Retired Associate Professor in Statistics.
Has a PhD in Statistics and over 45 years experience as a university professor, consultant, international researcher and government advisor.