A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Method
2.3. Analysis of Results
3. Results and Discussion
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Data | Type | Source | References |
---|---|---|---|
Digital Elevation Model | Raster Image | Topodata–Inpe | Valeriano [32] |
Multispectral Image (NDVI) | Raster Image | Copernicus–ESA | |
Spectral Image (Thermal) | Raster Image | USDA–NASA | |
Slope Shape | Raster Image | Topodata–Inpe | Valeriano [32] |
Hierarchical Categories | Declivity (%) | Flow Power (W/m) | Terrain Shape (Topodata Classes) | Temperature (Celsius) |
---|---|---|---|---|
Very bad | >25 (1) | 0–2 (1) | 8 and 9 (1) | >20.13 (1) |
Bad | 16–25 (2) | 2–4 (2) | 6 and 7 (2) | 19.90–20.13 (2) |
Medium | 12–16 (3) | 4–6 (3) | 4 and 5 (3) | 19.67–19.90 (3) |
Good | 0–2 and 6–12 (4) | 6–8 (2) | 1 and 3 (2) | 19.44–19.67 (4) |
Very Good | 3–6 (5) | >8 (1) | 2 (1) | 19.00–19.44 (5) |
Plot | Productivity (Benefited Bags.hectare−1) | Standard Deviation (Benefited Bags.hectare−1) | |||
---|---|---|---|---|---|
2016 | 2018 | 2020 | Medium | ||
D | 106 | 101 | 102 | 61.80 | 2.65 |
C | 102 | 96 | 90 | 57.50 | 5.76 |
G | 111 | 82 | 88 | 56.17 | 15.23 |
F | 100 | 80 | 87 | 53.40 | 10.15 |
E | 101 | 78 | 82 | 52.26 | 12.47 |
B | 72 | 51 | 76 | 39.73 | 13.36 |
A | 70 | 54 | 66 | 38.07 | 8.46 |
Medium | 95 | 77 | 84 | 85.33 | |
Standard Deviation | 16.49 | 19.04 | 11.37 |
Variable | R2 | Equation |
---|---|---|
Terrain Shape | 0.7723 | y = −7.6959x + 98.66 |
Water Flow Power | 0.4144 | y = 8.3527ln(x) + 35.082 |
NDVI | 0.3908 | y = 303.46x − 206.23 |
Slope | 0.1073 | y = −0.7107x2 + 7.1845x + 35.273 |
Data/Plot | A | B | C | D | E | F | G |
---|---|---|---|---|---|---|---|
Terrain Shape | 7.77 | 6.91 | 5.35 | 4.81 | 5.46 | 6.64 | 6.16 |
Flow Power | 4.29 | 3.7 | 3.41 | 2.06 | 3.36 | 4.08 | 3.95 |
Declivity | 4 | 4 | 5 | 4 | 5 | 5 | 5 |
Temperature | 4.45 | 4.49 | 3.28 | 4.68 | 4.39 | 4.23 | 4.66 |
T Factor | 0.70 | 0.80 | 0.93 | 0.90 | 1.07 | 1.19 | 0.95 |
NDVI medium 2018 | 0.8514 | 0.8242 | 0.8621 | 0.8788 | 0.8328 | 0.8384 | 0.8522 |
NDVI medium 2020 | 0.8461 | 0.8120 | 0.8742 | 0.7991 | 0.7640 | 0.8613 | 0.8024 |
NDVI medium 2016 | 0.8066 | 0.8076 | 0.8539 | 0.8621 | 0.7822 | 0.8388 | 0.8226 |
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Cunha, J.E.F.; Martins, G.D.; Fraga Júnior, E.F.; Camboim, S.P.; Bravo, J.V.M. A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements. Agriculture 2023, 13, 2083. https://doi.org/10.3390/agriculture13112083
Cunha JEF, Martins GD, Fraga Júnior EF, Camboim SP, Bravo JVM. A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements. Agriculture. 2023; 13(11):2083. https://doi.org/10.3390/agriculture13112083
Chicago/Turabian StyleCunha, Jorge Eduardo F., George Deroco Martins, Eusímio Felisbino Fraga Júnior, Silvana P. Camboim, and João Vitor M. Bravo. 2023. "A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements" Agriculture 13, no. 11: 2083. https://doi.org/10.3390/agriculture13112083
APA StyleCunha, J. E. F., Martins, G. D., Fraga Júnior, E. F., Camboim, S. P., & Bravo, J. V. M. (2023). A Multicriteria Model for Estimating Coffea arabica L. Productive Potential Based on the Observation of Landscape Elements. Agriculture, 13(11), 2083. https://doi.org/10.3390/agriculture13112083