An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach
2. Materials and Methods
2.1. Location of the Study Area
2.2. Data Type and Characteristics
- Baseline—farms using traditional methods comprising application of saved local seed varieties (supa shinyanga, mbawa mbili, supa pamba, Kabangala, tule na bwana; kisegese; mwarabu, rangi mbili; ngome, zambia), no fertilizer and higher seed rate between 75–100 kg/ha is used as farmers prefer broadcast planting method. Weeding is done manually and typically done twice before harvest, and no specific spacing is applied. Continuous flooding is dominant with neither irrigation nor water control.
- Alt.1—applying the traditional practices (Baseline), but farmers use improved varieties (mainly SARO5 and IR64) instead of local varieties. Farmers in this group prefer transplanting of seedlings instead of broadcasting, which is done between 21–35 days with limited fertilizer application, and no specific spacing is applied.
- Alt.2—farms supplemented with improved varieties, transplanting of seedlings (no specific spacing is applied), and application of fertilizer at the rate of 50 kg bags per ha. The main types of fertilizers used are Urea and NPK, and occasionally, farmers use organic fertilizers.
- Alt.3 and Alt.4—this dedicated group of farmers apply some but not all the SRI practices, i.e., SRI partial adopters (Alt.3) and those claiming to use all the specified SRI practices (Alt.4). The specific practices under SRI in Tanzania involve (1) stepwise selection and preparation of quality viable seeds; (2) nursery plot development and careful management; (3) land/field leveling for easy infield water management; (4) transplanting one young seedling (at two leaves) per hill while using 25 cm × 25 cm or 25 cm × 30 cm spacing; (5) quickly transplanting within 30 min of gently removing seedlings from their nursery and not inverting the seedlings; (6) wetting and drying of the field (water control) to improve soil aeration and promote root elongation; (7) timely weeding done every 10–12 days after transplanting and repeated in the same interval until harvest; and (8) intensive application of fertilizer, especially one which is rich in nitrogen and phosphorus. A pictorial demonstration of some necessary steps involved in SRI practices in Tanzania is shown in Appendix A. Also, it should be noted that rice production under SRI is done twice a year. Therefore, yield under SRI scenarios included harvests for both rain and dry seasons as the farmers under SRI have gone a step further to use the water from rivers for irrigation during the dry season.
2.3. Yield Data
2.4. Price Data
2.5. Cost of Production Per Scenario
2.6. Monte Carlo Simulation for Economic Comparison between Rice Farming Systems
2.7. Scenario Ranking
Conflicts of Interest
Appendix A. Important Steps in SRI Farming System
|Seed: Improved ***||8.9||17.8||8.9||17.8||8.9||17.8||17.8||35.6|
|Leveling and puddling||31.1||44.4||31.1||44.4|
|Marking transplanting grids||22.2||35.6||22.2||35.6|
|Weeding: 1st round||44.4||111.1||44.4||111.1||44.4||111.1||66.7||133.3||66.7||133.3|
|Weeding: 2nd round||35.6||44.4||35.6||44.4||35.6||44.4||44.4||66.7||44.4||66.7|
|Weeding: 3rd round||35.6||44.4||44.4||66.7||44.4||66.7|
|Field wetting and drying (water control)||35.6||53.3|
|Fertilizer: 1st round DAP||26.7||48.9||26.7||48.9||44.4||57.8|
|Fertilizer: 2nd round UREA||26.7||48.9||26.7||48.9||44.4||57.8|
|Fertilizer: 3rd round UREA||26.7||48.9||26.7||48.9||44.4||57.8|
Appendix C. Probability Distribution Functions (PDF) Charts for the Simulated Sample (in Red) vs. Observed Yields and Prices Sampled (in Black)
Appendix D. PDFs and CDFs of Production Cost per ha (US$/ha) for Rice under Different Farming Systems (Alt.0–4) in Tanzania
Appendix E. SRI Success Story of Mwanaidi H. Hussen Who Is One of the First Farmers to Adopt the Technology
- In the year 2015, I was awarded a prize of 5,500,000 TZS by Morogoro agricultural Authority as the best farmer of the year.
- In terms of food security my family has never suffered from food shortage anymore.
- I am now capable of sending my kids to English medium schools and afford the costs.
- I have renovated my house and installed with electricity plus tap water.
- I also conduct SRI pieces of training to my fellow farmers. Taking care of one young orphan boy.
- I built a small fish pond and a vegetable garden around my house, which gives me a small amount of money for my family …”
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|Statistics||Low Supply||High Supply|
|No. of observations||45||45||45||45|
|~||A tilde represents the stochastic variable|
|i||Rice farming alternatives (Baseline, Alt.1, Alt.2, Alt.3, Alt.4)|
|ai||Hectares (ha) allocated for each alternative i|
|Stochastic rice yield per ha for alternative i|
|Stochastic production for alternative i which is the product of hectares and yield|
|ω||Rice variety (local and improved)|
|Stochastic rice price influenced by seasonal volatility for variety ω (Local_P1, Local_P2, Improved_P1, Improved_P2)|
|Stochastic receipt/revenue which is a product of stochastic production and price|
|Variable cost (US$/ha) given by the summation of all costs included in rice production per each scenario in a range of Min and Max [including seed, plow, harrow, planting, weeding, bird scaring, fertilizer, post-emergence herbicides, harvesting/threshing, postharvest handling, and storage]|
|The fixed cost equated to zero for this analysis [F = 0]|
|Stochastic Total production cost for each rice farming system computed as|
|Net income which is calculated as the receipt minus total cost|
|Sy||Fraction deviations from a mean or sorted array of random yields for scenario i|
|Sp||Fraction deviations from a mean or sorted array of the random price for variety ω|
|P(Sy)||Cumulative probability function for the Sy values|
|P(Sp)||Cumulative probability function for the Sp values|
|CUSDyp||Simetar function to simulate correlated uniform standard deviates of random variables.|
|EMP()||Simetar function used to simulate an MVE distribution.|
|Scenarios||Mean||SD||CV||Min||Max||Probability (NCI < 0)|
|Income during harvesting season (April–September)|
|Income during low supply season (October–March)|
|Annual net income|
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Kadigi, I.L.; Mutabazi, K.D.; Philip, D.; Richardson, J.W.; Bizimana, J.-C.; Mbungu, W.; Mahoo, H.F.; Sieber, S. An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach. Sustainability 2020, 12, 6528. https://doi.org/10.3390/su12166528
Kadigi IL, Mutabazi KD, Philip D, Richardson JW, Bizimana J-C, Mbungu W, Mahoo HF, Sieber S. An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach. Sustainability. 2020; 12(16):6528. https://doi.org/10.3390/su12166528Chicago/Turabian Style
Kadigi, Ibrahim L., Khamaldin D. Mutabazi, Damas Philip, James W. Richardson, Jean-Claude Bizimana, Winfred Mbungu, Henry F. Mahoo, and Stefan Sieber. 2020. "An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach" Sustainability 12, no. 16: 6528. https://doi.org/10.3390/su12166528