Effect of Project Monitoring and Evaluation on Success of Agriculture Project: A Case of the Scientific Intercropping Project in Rwamagana District, Rwanda

1. Introduction

Monitoring and evaluation (M&E) is now treated as a core project management function. It tracks progress against plan, supplies evidence for decisions and supports accountability and learning throughout the project cycle.¹ Its value is greatest in agriculture, where projects operate under climate, market and capacity constraints that demand continuous tracking and timely adjustment. Agriculture is central to rural livelihoods in Rwanda, and the sector has attracted substantial project investment.

M&E systems are used across the developing world to link project activities to national and organisational goals. Studies in Tanzania document both the integration of M&E into government planning and the difficulties of practising it in public organisations.² In Rwanda, M&E practices have been adopted in public and private development projects, but implementation challenges remain and stakeholder engagement in M&E is often weak.³ Beneficiary participation in M&E has been associated with stronger skills and self-reliance in a World Bank-funded urban development project in Musanze District.⁴

Even so, the performance of projects in Rwanda remains uneven. Studies report schedule and budget overruns linked to weak risk management⁵ and low stakeholder participation in planning and budgeting,⁶ while soil erosion and drought add risk to crop production in the Eastern Province.⁷ Weak monitoring and evaluation is repeatedly identified as a reason why such problems are detected late or not corrected.⁸

A growing body of research in Kenya, Ethiopia and Rwanda links M&E practices to the performance of agricultural, health, NGO and local government projects.⁹ Few studies, however, examine scientific intercropping, in which complementary crops are grown together on the same plot under agronomic guidance. This study addresses that gap using the Scientific Intercropping Project in Rwamagana District, which was piloted by One Acre Fund from 2020 to 2024 and has since been extended to other districts.

The general objective was to assess the effect of project M&E on the success of the Scientific Intercropping Project in Rwamagana District. The specific objectives were to evaluate the effect of (i) M&E planning, (ii) M&E tools, (iii) M&E data collection and (iv) M&E reporting on project success. For each dimension the study tested a null hypothesis of no significant effect (H01 to H04) against the alternative that the effect is significant (Ha1 to Ha4).

The findings matter for three groups. For researchers, they extend M&E scholarship to a specific agronomic practice in a Rwandan district. For project managers and donors, they show which parts of an M&E system are most closely tied to success and where investment is likely to pay off most. For policymakers, they provide evidence for supporting M&E capacity in agricultural projects that are being scaled across districts.

2. Literature review and theoretical framework

2.1 Conceptual review

Monitoring and evaluation are linked processes. Monitoring systematically tracks activities, resources and timelines against plan, while evaluation examines the outcomes, sustainability and relevance of an intervention.¹⁰ Together they provide the evidence on which managers adjust projects and report to stakeholders. This study treats M&E as four dimensions and project success as the outcome.

M&E planning is the design of a structured approach to monitoring and evaluation. It involves defining objectives, setting measurable indicators, choosing tools and data collection methods, and allocating budget and resources.¹¹ M&E tools are the instruments and frameworks used to collect, analyse and present project information. They range from checklists and observation sheets to logical frameworks, scorecards, indicator frameworks and real-time digital data platforms.¹²

M&E data collection is the gathering and interpretation of project information against defined indicators in order to track progress, identify trends and judge whether objectives are being met.¹³ M&E reporting communicates the results of monitoring and evaluation, including progress, achievements, challenges and suggested actions, and depends on regular reporting cycles and clear indicators.¹⁴ Project success is understood here as completion within the planned time and budget, to the expected quality and scope, with intended benefits for the farming communities served.

2.2 Theoretical underpinnings

The study draws on four theories, each linked to one M&E dimension. Theory of Change explains how and why an intended change is expected to happen by mapping long-term goals back to the preconditions and actions needed to reach them.¹⁵ It therefore informs M&E planning, which sets goals, objectives, indicators and budgets. The Resource-Based View holds that lasting organisational performance rests on valuable and hard-to-imitate resources and capabilities.¹⁶ Well-designed M&E tools can be seen as such resources because they improve data accuracy and decision-making.

Goal-setting theory argues that specific and challenging goals raise motivation and performance.¹⁷ It supports the expectation that planned, targeted data collection with clear quality standards will improve results. Double-loop learning goes beyond correcting errors within existing rules and questions the assumptions that govern action.¹⁸ It underpins M&E reporting, where results and feedback should prompt reflection and changes in approach rather than only record progress.

2.3 Empirical review

M&E planning. Studies of agricultural and development projects consistently associate sound M&E planning with better results. In Kilifi County, Kenya, a strong M&E policy, adequate budget and staff capacity were necessary for the performance of smallholder farming projects.¹⁹ In Kirinyaga County, M&E capacity building and performance measurement both had positive and significant effects on the sustainability of agricultural projects.²⁰ In Ethiopian NGOs, planning, technical competence, financial planning and stakeholder involvement significantly affected project success.²¹ In Musanze District, M&E practices explained 93.4% of the variation in the performance of a local government market building project.²²

M&E tools. Evidence from Rwanda links tools and systems to performance. In the Rwanda-Israel Horticultural Centre of Excellence project, M&E planning, data management and data dissemination each had significant positive effects on performance.²³ In the Rwanda Dairy Development Project, M&E design, quality assurance, capacity building, information sharing and budget together explained 75.3% of the variation in performance.²⁴ Staff technical skills and reporting also predicted the performance of an income-generating project run by an NGO in Rwanda,²⁵ and planning, staff capability and data quality were associated with the success of health projects in Nyeri County.²⁶

M&E data collection. Research in Baringo County found that planning, participatory M&E, stakeholder involvement and timely feedback were important for the performance of agricultural projects.²⁷ Dube and Mutundu²⁸ stressed the frequency of data collection, stakeholder engagement and the use of local knowledge in agricultural NGOs, and Uwanyirigira et al.²⁹ reported that systematic data collection and participatory approaches improved NGO project performance in Rwanda.

M&E reporting. Regular monitoring and performance reporting improved the performance of a dairy community processing centre in Rwanda,³⁰ and clear indicators and regular reporting cycles improved decisions in health-funded projects.³¹ Okafor³² likewise found that M&E systems, including planning, staff skills and data systems, contributed significantly to project success in Nigeria.

2.4 Conceptual framework and research gap

Figure 1 shows the framework. The four M&E dimensions are the independent variables and project success is the dependent variable. Most earlier studies examined broad agricultural, health or NGO projects and did not address scientific intercropping, so context-specific evidence for Rwamagana District is missing. This study provides it.

Figure 1: Conceptual framework

3. Methodology

3.1 Research design and study setting

A descriptive and correlational design was used, combining quantitative and qualitative methods.³³ The descriptive component documented M&E practices and perceived project success, and the correlational component examined how the M&E dimensions relate to success. The study was carried out in Rwamagana District, Eastern Province, Rwanda, and the project period examined was 2020 to 2024. Primary data were collected in 2025.

3.2 Population and sample

The target population was 371 people in four groups directly involved in or affected by M&E in the project: 12 managerial staff, 43 field staff, 211 beneficiaries and 105 local stakeholders (government officials and agricultural specialists). The Yamane³⁴ formula, n = N / (1 + N e²) with a 5% margin of error, gave a sample of 193. Stratified random sampling was used, with simple random selection within each group,³⁵ and the sample was allocated across groups as shown in Table 1.

Table 1: Population and sample

Source: Researcher, 2025

3.3 Data collection instruments

A structured questionnaire measured each variable with four statements (five for project success) on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree).³⁶ The planning items covered goals, objectives, indicators and budget; the tools items covered checklists, logical frameworks, database systems and dashboards; the data collection items covered planning, adherence to plan, quality checks and use of data in decisions; the reporting items covered timeliness, use of data, clarity and feedback; and the success items covered timeliness, budget, quality, scope and benefits to farming communities. Open-ended interviews were held with project managerial staff (n = 12), and excerpts from managerial and field staff are used to illustrate the survey results. Project reports and official records were reviewed as secondary data.

3.4 Validity and reliability

The instrument was reviewed by the supervisor and an expert panel and tested in a pilot study of 19 respondents in Rwamagana District, about 10% of the sample. The content validity index was 0.875 (21 of 24 items judged relevant), above the 0.70 threshold. Cronbach’s alpha ranged from 0.813 to 0.832 across the five scales (Table 2), indicating good internal consistency.

Table 2: Reliability of the measurement scales

Source: Field data, 2025 (pilot study)

3.5 Data analysis

Data were analysed in SPSS version 28. Descriptive statistics (frequencies, percentages, means and standard deviations) summarised the responses. Means of 3.6 to 4.2 were read as high agreement, and a standard deviation of 0.5 or less as consistent responses. Pearson correlation examined bivariate relationships, and the following multiple regression model estimated the effects: Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε, where Y is project success and X1 to X4 are M&E planning, tools, data collection and reporting. Normality of the dependent variable was supported (Kolmogorov–Smirnov p = 0.073; Shapiro–Wilk p = 0.067).

3.6 Data processing and ethical considerations

Completed questionnaires were edited for completeness, coded numerically and tabulated before analysis. Participants were told the purpose of the study, took part voluntarily, gave informed consent before data collection and could withdraw at any time without consequence. Responses were kept confidential and were reported only in aggregate or without identifying details.

4. Results

4.1 Response rate and respondent profile

Of the 193 questionnaires distributed, 181 were completed and returned, a response rate of 93.78%. Men made up 58.6% of respondents and women 41.4%. Most respondents were aged 30 to 49 (75.1%) and held a bachelor’s degree (68.5%), so they were well placed to comment on M&E practice (Table 3).

Table 3: Profile of respondents (N = 181)

Source: Field data, 2025

4.2 Descriptive statistics

Respondents agreed that all four M&E dimensions and project success were present to a high degree, with composite means between 4.01 and 4.05 (Table 4). Item means ranged from 3.92 to 4.12, and most standard deviations were at or below 0.5, indicating consistent views.

Table 4: Descriptive statistics by variable (N = 181)

Source: Field data, 2025

For M&E planning, respondents agreed that project goals were clearly set (M = 4.08, SD = 0.470), that planning objectives were well defined (M = 4.01, SD = 0.483) and that budget and resources were adequately allocated (M = 4.07, SD = 0.483). The identification of key indicators in the M&E plan had the lowest mean in this group (M = 3.97, SD = 0.515) and the only standard deviation above 0.5, which suggests that views on indicators were somewhat more mixed than views on goals and budget.

For M&E tools, the highest-rated item was the use of performance dashboards to show progress (M = 4.09, SD = 0.504), followed by logical frameworks (M = 4.06, SD = 0.502), database management systems (M = 4.04, SD = 0.457) and checklists (M = 3.99, SD = 0.434). The slightly higher standard deviations for dashboards and logical frameworks may reflect differences in how consistently these tools were applied across project components.

For M&E data collection, respondents agreed that collection was carried out according to plan (M = 4.04, SD = 0.536), that data quality was checked regularly (M = 4.01, SD = 0.489), that data were analysed for decision-making (M = 4.00, SD = 0.548) and that collection was well planned and organised (M = 3.98, SD = 0.516). Three of the four standard deviations exceed 0.5, so this dimension had the most varied responses, which fits the field staff comment that analysis was sometimes difficult.

For M&E reporting, respondents agreed that reports were timely and structured (M = 4.03, SD = 0.427), that the use of collected data was evident (M = 3.99, SD = 0.434) and that results were presented clearly (M = 4.01, SD = 0.387). Feedback on reports being collected and considered had the highest mean of any item (M = 4.12, SD = 0.360). For project success, respondents agreed that the project reached its objectives within planned timelines (M = 3.97, SD = 0.446), stayed within budget (M = 4.02, SD = 0.364), met quality standards (M = 3.92, SD = 0.482), covered its scope (M = 4.07, SD = 0.395) and benefited farming communities (M = 4.08, SD = 0.470).

4.3 Qualitative insights

Interviews supported the survey results. A managerial staff member explained that the M&E framework was drafted through internal workshops so that objectives and indicators matched national agricultural goals, that budgets for M&E were planned in detail, and that close collaboration with field staff and local stakeholders strengthened ownership. Field staff described using checklists on field visits, displaying the logical framework in every sector office and entering and retrieving data through a database, which prevented loss of information during reporting periods.

On data collection, a field staff member described a schedule agreed in advance, training of field agents on the method and weekly double-checking of data for accuracy and completeness, while noting that analysis was sometimes difficult. A managerial staff member said that quarterly reports were written so that non-technical readers could follow them, included photographs, charts and field stories, and incorporated partner feedback, which improved quality over time. Managers also credited clear milestones and constant communication with field teams for delivery within time and budget and for the quality of outputs such as improved seed varieties and training materials.

4.4 Project outcome indicators

Project records for 2024 show a different picture of reach than the perception data. Only 14% of farmers had received training in scientific intercropping and 14.3% had adopted the practices, although 85.3% said they were willing to adopt in future.³⁷ These figures indicate substantial potential for scaling if support is available, and they are discussed in Section 5.

4.5 Correlation analysis

All four M&E dimensions were positively and significantly correlated with project success (p < 0.01). The correlations were strongest for M&E tools (r = 0.815), followed by data collection (r = 0.769), planning (r = 0.697) and reporting (r = 0.693). The dimensions were also moderately to strongly correlated with each other (r = 0.532 to 0.687), but tolerance and VIF values showed that multicollinearity was not a problem (Table 5 and Table 6).

Table 5: Pearson correlations (N = 181)

Source: Field data, 2025. ** p < 0.01 (two-tailed).

4.6 Regression analysis

The model was significant overall, F(4, 176) = 209.862, p < 0.001. The four M&E dimensions together explained 82.9% of the variance in project success (R = 0.910, R² = 0.829, adjusted R² = 0.825, standard error of the estimate = 0.169). The Durbin-Watson statistic of 1.824 indicates no serious autocorrelation in the residuals. Table 6 gives the coefficients.

Table 6: Multiple regression of project success on M&E dimensions (N = 181)

Source: Field data, 2025. Dependent variable: project success.

Holding the other dimensions constant, a one-unit increase in M&E tools was associated with a 0.468-unit increase in project success, making tools the strongest predictor. Reporting (B = 0.249), data collection (B = 0.230) and planning (B = 0.114) followed in that order. The standardised coefficients give the same ranking except that data collection (β = 0.280) ranks above reporting (β = 0.238).

4.7 Hypothesis testing

All p-values were below 0.05, so all four null hypotheses were rejected (Table 7).

Table 7: Hypothesis test results

Source: Field data, 2025

5. Discussion

The four M&E dimensions together explained 82.9% of the variance in perceived project success. This is higher than the 75.3% reported for the Rwanda Dairy Development Project³⁸ and below the 93.4% reported for the Musanze market building project,³⁹ which places the result within the range of Rwandan studies and confirms that M&E is closely tied to project performance.

M&E tools had the strongest effect. Checklists, logical frameworks, database systems and dashboards give staff a common structure for tracking activities and make information available for decisions. This is consistent with Niyongira and Kwena,⁴⁰ who found that data management and dissemination strongly influenced performance in a Rwandan horticulture project, and with the view that effective tools support sustainable development projects.⁴¹ The Resource-Based View helps explain this: where tools are well designed and used, they become a capability that other projects cannot easily copy.

M&E reporting and data collection also had significant effects. Reporting that is timely, clear and responsive to feedback supports transparency and corrective action, which fits double-loop learning and the findings of Harelimana and Umugwaneza⁴² and Rutikanga and Gachili.⁴³ The highest-rated item in the survey, that feedback on reports was considered, points the same way. Planned and quality-checked data collection, as described by field staff, echoes the emphasis on frequency, engagement and local knowledge in Dube and Mutundu⁴⁴ and Uwanyirigira et al.⁴⁵

M&E planning had the smallest, though still significant, effect (B = 0.114). Earlier studies emphasise planning, budget and stakeholder involvement.⁴⁶ A plausible reading, which this study did not test, is that planning works partly through the other dimensions: it correlated strongly with data collection (r = 0.687), so part of its influence may be captured by them in the model.

The pattern of results fits the theoretical framework. The significant effect of planning supports Theory of Change, because projects that state their goals, indicators and resources in advance have a clear pathway against which to track change. The strong effect of tools supports the Resource-Based View. The effect of data collection is consistent with goal-setting theory, since specific and planned collection targets give field teams something concrete to work towards. The effect of reporting supports double-loop learning, because reports that invite and use feedback allow teams to revisit their assumptions as well as their activities.

Several practical implications follow. Investment in a small set of well-used tools, such as a logical framework, a database and a simple dashboard, appears more likely to pay off than additional planning documents alone. Training for field staff in data analysis is warranted, since this was the area with the most varied responses. Donors and implementers can also treat feedback on reports as a low-cost routine that was already rated highly in this project and is consistent with better outcomes.

The outcome indicators in Section 4.4 call for caution. Respondents rated the project as successful, yet only 14% of farmers had been trained and 14.3% had adopted the practices. Perceived success on timeliness, budget, quality and scope is therefore not the same as reach among farmers. The strong willingness to adopt (85.3%) suggests that the main constraint is coverage and support, not interest.

6. Conclusion and recommendations

M&E planning, tools, data collection and reporting each have a positive and statistically significant effect on the success of the Scientific Intercropping Project in Rwamagana District, and together they explain most of the variation in perceived success. M&E tools matter most, followed by reporting, data collection and planning. The study concludes that well-designed M&E systems are essential to the success of agricultural projects.

Based on these findings, the following actions are recommended:

1. Project managers should plan M&E with clearly defined and communicated indicators at every level, alongside goals and budgets, and involve stakeholders from the planning stage.

2. Project coordinators should invest in standardising M&E tools and train staff to use logical frameworks, dashboards and data systems.

3. Field staff should receive regular training on data collection and analysis, and field-level data verification should be built into routine work.

4. Local leaders and stakeholders should contribute feedback to M&E reports and help disseminate results to strengthen local ownership.

5. Beneficiaries and community groups should take part in feedback loops so that the project can adapt in real time. Training and adoption targets should be tracked alongside perception measures.

Limitations and future research

The data are cross-sectional and based on perceptions, and the same respondents rated both M&E and project success, which may inflate the relationship. Mean scores were clustered near 4.0, which limits variation. Objective outcome measures, such as yield or adoption data, were not used in the regression. Future studies could use longitudinal designs and objective indicators, compare government-led and NGO-led M&E systems, examine community participation in feedback, and test how digital technology in M&E affects project success.

Declarations

Ethics and consent. Ethical approval for the study was obtained from the University of Kigali. Participants were informed about the study's purpose, took part voluntarily, and gave consent before data collection. Their responses were kept confidential, and they could withdraw at any time. Respondents did not receive any payment or incentive for taking part.

Use of generative AI. I used generative AI (Claude, developed by Anthropic) to condense my master’s dissertation into this article, draft the abstract, and edit the text. I reviewed and verified all content, including the data, analysis, and references, and take full responsibility for the manuscript.

Acknowledgements. The author thanks the supervisor, the management, staff, beneficiaries, and local stakeholders of the Scientific Intercropping Project in Rwamagana District for their cooperation.

Endnotes

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