A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a Hydroclimatic Forecast—Case Studies for Western Bahia, Brazil
Abstract
:1. Introduction
2. Materials and Methods
2.1. Region Description
2.2. Dataset Acquisition and Processing
2.3. LPM Formulation
2.3.1. Decision Variables
2.3.2. Objective Function
2.3.3. Constraints and Solution Method
3. Case Studies
3.1. Farms and Municipalities Characterization
3.2. Crop Characterization
3.3. Model’s Constraints Setup
4. Results
4.1. LPM Application at Farms
4.2. LPM Application at Municipalities
5. Discussion
Strategy for LPM Effectiveness
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Farm or Municipality | Irrigated Area | Rainfed Area | Water Grant | Evaluated Crop | Season | Labor Availability | Tractor Availability | Truck Availability | Sprayer Availability | Harvester Availability |
---|---|---|---|---|---|---|---|---|---|---|
(Hectares) | (Hectares) | (m³ day−1) | (h day−1) | (h day−1) | (h day−1) | (h day−1) | (h day−1) | |||
Sama f | 1221.10 | 878.50 | 39,297.50 | Soybean | 2018/19 | 160 | 16 | 16 | 32 | 16 |
Floryl f | 950.00 | 120.00 | 68,437.00 | Maize 1−2; Soybean | 2019/20 | Not applicable | ||||
DRB MOR f | 4267.00 | 646.00 | 101,760.00 | Cotton; Bean 1−2; Maize 1−2; Soybean; Wheat | 2017 aw; 2017 ss; 2018 aw; 2018 ss; 2019 aw; 2019 ss | |||||
DECDS f | 3299.00 | 0.00 | 156,552.00 | |||||||
Busato f | 4246.80 | 676.00 | 23,240.00 | Cotton; Maize 1−2; Soybean | 2019/20 | |||||
Barreiras m | 42,760.00 | 291,948.00 | 4,586,256.52 | Cotton; Bean 1−2; Maize 1−2; Soybean | 2018 ss; 2019 aw; 2019 ss; 2020 aw; 2020 ss; 2021 aw | |||||
Correntina m | 15,008.00 | 408,022.00 | 2,444,307.23 | |||||||
Santa Rita de Cássia m | 60.00 | 7293.00 | 44,000.00 |
Farm or Municipality | Minimum ETo | Maximum ETo | Minimum Rainfall | Maximum Rainfall |
---|---|---|---|---|
(mm fortnight−1) | (mm fortnight−1) | (mm fortnight−1) | (mm fortnight−1) | |
Sama f | 45.2 | 96.9 | 0.0 | 172.8 |
Floryl f | 42.7 | 86.7 | 0.0 | 142.8 |
DRB MOR f | 46.2 | 107.5 | 0.0 | 142.8 |
DECDS f | 43.1 | 89.9 | 0.0 | 232.2 |
Busato f | 41.1 | 94.7 | 0.0 | 168.8 |
Barreiras m | 54.7 | 88.6 | 0.2 | 103.7 |
Correntina m | 54.5 | 84.8 | 0.2 | 108.7 |
Santa Rita de Cássia m | 59.0 | 91.6 | 0.0 | 101.5 |
Crop | Cycle Type | Cycle Duration | Initial Kc | Average Kc | Final Kc | Kr [44] | Range Sowing Time |
---|---|---|---|---|---|---|---|
(Fortnight, or 15 Days) | (Fortnight–Month) [42] | ||||||
Soybean | short | 8 | 0.60 | 0.70 | 0.80 | 0.80 | 1–10 to 2–02 |
average | 9 | ||||||
long | 10 | ||||||
Maize 1st season | average | 9 | 0.65 | 1.00 | 0.60 | 0.88 | 1–10 to 2–02 |
long | 12 | 0.60 | 0.50 | ||||
Maize 2nd Season | average | 9 | 0.65 | 1.00 | 0.60 | 0.88 | 1–05 to 2–06 |
long | 12 | 0.60 | 0.50 | ||||
Cotton | long | 14 | 0.50 | 0.90 | 0.38 | 0.80 | 1–11 to 2–02 |
Bean 1st season | average | 7 | 0.70 | 1.20 | 0.60 | 0.87 | 1–10 to 2–02 |
Bean 2nd season | average | 7 | 0.70 | 1.20 | 0.60 | 0.87 | 1–04 to 2–06 |
Wheat | average | 8 | 0.70 | 1.20 | 0.40 | 0.85 | 1–08 to 2–09 |
Analysis | Farms | Municipalities | ||||||
---|---|---|---|---|---|---|---|---|
Sama | Floryl | DRB MOR | DECSD | Busato | Barreiras | Correntina | Santa Rita de Cássia | |
Original | Ia; Ra; CwdI | - | - | - | - | Ia; Ra; CwdI; CwdR | Ia; Ra; CwdI; CwdR | Ia; Ra; CwdI; CwdR |
Irrigated original | Ia; CwdI; L; M | Ia; CwdI | Ia; CwdI | Ia; CwdI | Ia; CwdI | Ia; CwdI | Ia; CwdI | Ia; CwdI |
Irrigated rain delay | ||||||||
Irrigated rain reduction | ||||||||
Rainfed original | Ra; CwdR; L; M | Ra; CwdR | Ra; CwdR | - | Ra; CwdR | Ra; CwdR | Ra; CwdR | Ra; CwdR |
Rainfed rain delay | ||||||||
Rainfed rain reduction |
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Boninsenha, I.; Mantovani, E.C.; Costa, M.H.; da Silva Júnior, A.G. A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a Hydroclimatic Forecast—Case Studies for Western Bahia, Brazil. Water 2022, 14, 3625. https://doi.org/10.3390/w14223625
Boninsenha I, Mantovani EC, Costa MH, da Silva Júnior AG. A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a Hydroclimatic Forecast—Case Studies for Western Bahia, Brazil. Water. 2022; 14(22):3625. https://doi.org/10.3390/w14223625
Chicago/Turabian StyleBoninsenha, Igor, Everardo Chartuni Mantovani, Marcos Heil Costa, and Aziz Galvão da Silva Júnior. 2022. "A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a Hydroclimatic Forecast—Case Studies for Western Bahia, Brazil" Water 14, no. 22: 3625. https://doi.org/10.3390/w14223625
APA StyleBoninsenha, I., Mantovani, E. C., Costa, M. H., & da Silva Júnior, A. G. (2022). A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a Hydroclimatic Forecast—Case Studies for Western Bahia, Brazil. Water, 14(22), 3625. https://doi.org/10.3390/w14223625