Mechanization and Maize Productivity in Tanzania’s Ruvuma Region: A Python-Based Analysis on Adoption and Yield Impact
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Sampling Methodology
2.3. Data Collection
2.4. Data Analysis
Protectioni) + β4 (Harvestingi) + β5 (Dryingi) + ϵi.
2.5. Software and Workflow
3. Results
3.1. Socioeconomic Profile and Mechanization Context
3.2. Mechanization Adoption Patterns
3.3. Impact of Mechanization on Maize Yields
3.4. Differences in Yields in Relation to Farm Scale
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Farm Scale | Acreage | Hectares | Characteristics |
---|---|---|---|
Small-scale | <12 acres | ≤4.86 hectares | Subsistence-focused; reliance on manual labor |
Medium-scale | 12–50 acres | 4.86–20.23 hectares | Partial mechanization for key activities |
Large-scale | >50 acres | >20.23 hectares | Commercial operations with full mechanization |
Category | Variables/Activities | Description |
---|---|---|
Demographics | Farm size | Categorized as - Small-scale: <12 acres - Medium-scale: 12–50 acres - Large-scale: >50 acres |
Mechanization practices | Land preparation | Scored as - 0 (Manual): Hand tools - 0.5 (Partial): Occasional tractor rental - 1 (Full): Ownership/consistent access |
Planting | Scored as - 0: Manual sowing - 0.5: Semi-mechanized (e.g., seed drills) - 1: Fully mechanized planters | |
Plant protection | Scored as - 0: Manual pesticide application - 0.5: Partial use of sprayers - 1: Mechanized sprayers/drones | |
Harvesting | Scored as - 0: Manual harvesting - 0.5: Partial use of harvesters - 1: Combine harvesters | |
Drying | Scored as - 0: Sun-drying on ground - 0.5: Raised racks - 1: Mechanical dryers | |
Maize yield | Yield per acre (2021–2022) | Self-reported yield (tons/acre), cross-validated with cooperative records. - Methods: Post-harvest timing, visual aids (grain sacks) |
Step | Description | Tools/Methods |
---|---|---|
1. Data cleaning and pre-processing | • Missing data handled via imputation (mean and median) or removal. • Categorical variables (e.g., farm size) encoded into numerical values (small = 0, medium = 1, and large = 2). | pandas (dropna, fillna), scikit-learn (SimpleImputer) |
2. Exploratory data analysis (EDA) | • Descriptive statistics (mean, SD, and frequencies) calculated. • Visualizations (histograms and scatter plots) generated to explore mechanization–yield relationships and outliers. | matplotlib, seaborn, numpy |
3. Hypothesis testing | • T-tests: Compared small-scale vs. medium/large-scale yields. • ANOVA: Tested yield differences across all farm sizes. | scipy.stats (ttest_ind, f_oneway), post-hoc Tukey HSD |
4. Regression modeling | • Multiple linear regression (MLR) to assess mechanization’s impact on yield, controlling for farm size and scale. | statsmodels (OLS), variance inflation factor (VIF) checks |
Stage | Small-Scale | Medium-Scale | Large-Scale |
---|---|---|---|
Land preparation | 70 | 94 | 100 |
Planting | 44 | 89 | 100 |
Plant protection | 23 | 100 | 93 |
Harvesting | 0 | 41 | 95 |
Drying | 0 | 43 | 80 |
Variable | Coefficient | Standard Error | t-Statistic | p-Value |
---|---|---|---|---|
Intercept | 1.18 | 0.026 | 45.178 | 0.000 |
Land preparation | 0.75 | 0.040 | 18.45 | 0.000 |
Planting | 0.52 | 0.042 | 12.28 | 0.000 |
Plant protection | 0.35 | 0.037 | 9.30 | 0.000 |
Harvesting | 0.0128 | 0.060 | 0.212 | 0.832 |
Drying | −0.0014 | 0.061 | −0.023 | 0.981 |
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Majebele, J.J.; Yang, M.; Mateen, M.; Tola, A.A. Mechanization and Maize Productivity in Tanzania’s Ruvuma Region: A Python-Based Analysis on Adoption and Yield Impact. Agriculture 2025, 15, 1412. https://doi.org/10.3390/agriculture15131412
Majebele JJ, Yang M, Mateen M, Tola AA. Mechanization and Maize Productivity in Tanzania’s Ruvuma Region: A Python-Based Analysis on Adoption and Yield Impact. Agriculture. 2025; 15(13):1412. https://doi.org/10.3390/agriculture15131412
Chicago/Turabian StyleMajebele, James Jackson, Minli Yang, Muhammad Mateen, and Abreham Arebe Tola. 2025. "Mechanization and Maize Productivity in Tanzania’s Ruvuma Region: A Python-Based Analysis on Adoption and Yield Impact" Agriculture 15, no. 13: 1412. https://doi.org/10.3390/agriculture15131412
APA StyleMajebele, J. J., Yang, M., Mateen, M., & Tola, A. A. (2025). Mechanization and Maize Productivity in Tanzania’s Ruvuma Region: A Python-Based Analysis on Adoption and Yield Impact. Agriculture, 15(13), 1412. https://doi.org/10.3390/agriculture15131412