Tool for the Establishment of Agro-Management Zones Using GIS Techniques for Precision Farming in Egypt
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Data
2.2.1. Soil Samples
2.2.2. Deriving of Soil Productivity Maps
2.2.3. Field Management Practices—Salhiya Site
2.3. Statistical Analyses
- A.
- A simple correlation procedure was applied by computing simple correlation coefficients matrix between peanut yield and soil characteristics [53]. Correlation analysis is utilized to quantify the degree to which yield data and soil variables are related.
- B.
- Multiple linear regression (MLR) accompanied with (R2), which refers to partial coefficient of determination, was calculated for yield to evaluate the relative contribution of each of soil properties and to simulate the prediction model for peanut yield (Y) with a measure of goodness of fit.
- C.
- Multiple Linear Regression analysis using a Stepwise selection procedure was used to determine each variable accounting for the yield variability majority as, multiple linear regression involves the fitting of a response to more than one predictor variable [53]. The stepwise selection method can be a forward, backward, or mixed selection method. In the current study, stepwise forward selection method computed a sequence of multiple linear regressions in the iterations of stepwise procedure by adding one variable predictor to the prediction equation at each stage of the procedure similar to a forward selection procedure. The tradeoff or eliminating variable to obtain another for the extra computational effort is the capacity to erase non-significant indicators as variables are added in addition to the ability to include new indicators taking after erasure. The additional variable was the one which incited the best diminishment in the error total of squares. It was additionally the variable which had the most partial correlation with the one of the soil properties as a dependent variable for fixed estimations of those variables included. In addition, it was the variable which had the highest F value.
- D.
- Ranking sum equation was used to manipulate weights for ranked soil criteria [22,24]. It calculates a ranked weight for a number (n) of different criteria (k) using Equation (3). Table 2 shows the weight vectors for various numbers of criteria according to Equation (1):
2.4. Kriging
2.5. Accuracy Assessment of Mapping Peanut Productivity
3. Results and Discussion
3.1. Soil Properties
3.1.1. Morphological, Physical, and Chemical Attributes
3.1.2. Contents of Available Nutrients in the Site
3.1.3. Salhiya Digital Soil Maps of Available Nutrients and Soil Units
- Soil unit 1 is characterized by non-saline soil with ECe ranging from 0 to 2 dS m−1, low % CaCO3 less than 3%, and sandy clay loam texture.
- Soil unit 2 is characterized by non-saline ECe, sandy clay, and low CaCO3.
- Soil map unit 3 is characterized by very slightly saline ECe ranging from 2–4 dS m−1, low CaCO3, and sandy clay loam texture.
- Soil map unit 4 is characterized by slightly saline soil with ECe more than 4 dS m−1, low CaCO3 content, and sandy clay loam texture.
- Zone 1: this area represents soils having a high content of available Phosphorus, Potassium, Iron, and Manganese.
- Zone 2: this area had fair amounts of soil fertility elements with high soil content of available K, Fe, Mn and medium soil content of available P.
- Zone 3: this area had a flat elevated area with medium soil content of available P and K in addition to high soil content of available Fe.
- Zone 4: characterized by a flat area with medium soil content of available Phosphorus, medium soil content of available K, and medium soil content of available Fe.
- Zone 5: relatively elevated soils and contains low content of most soil fertility elements such as available Phosphorus, low Fe, and moderate amount of available K.
- Zone 6: characterized by nutrient stress due to the low contents of most fertility elements such as available P, K, and Fe with high elevated soils.
3.1.4. Correlation between Peanut Yield and Soil Characteristics
3.2. Soil Productivity Assessment
3.2.1. Mapping Soil Productivity
3.2.2. Accuracy Assessment of Mapping Peanut Productivity
3.3. Nitrogen Management Zones
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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(a) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Land Characteristics | Class, Degree of Limitation, and Rating Scale | |||||||||
Rank of Groups | Rank of Soil Layers | 100–95 | 95–85 | 85–60 | 60–40 | 40–25 | 25–0 | |||
1 | Soilsalinity | 1 | ECe dS m−1 | 0–2 | 2–4 | 4–6 | 6–8 | 8–12 | >12 | |
2 | ESP | 8–10 | 10–15 | 15–20 | 15–20 | -- | >20 | |||
3 | pH | 6.8–7.0 | 7.0–7.5 | 7.5–8 | 8–8.2 | -- | >8.2 | |||
4 | O.M.% | >2 | 2–1.2 | 1.2–0.8 | <0.8 | -- | -- | |||
2 | Soil Fertility | 1 | Available P mg kg−1 | 6–5 | 5–4 | 4–3 | 3–2 | <2 | -- | |
2 | Available K mg kg−1 | 60–50 | 50–45 | 45–40 | 40–30 | <30 | -- | |||
3 | Available Fe mg kg−1 | 4–3.5 | 3.5–3 | 3–2.5 | 2.5–2.1 | <2 | -- | |||
4 | Available Mn mg kg−1 | 2–1.9 | 1.9–1.7 | 1.7–1.5 | <1.5 | -- | -- | |||
5 | Available Zn mg kg−1 | >1.5 | 1.5–1 | 1–0.9 | <0.9 | -- | -- | |||
3 | Soil physicalcharacteristics | 1 | Texture | L, SCL | SC, Cl, | C < 60, LS | C > 60 v, | S, cS | Cm, SicM | |
Si, SL | SiC, fS | |||||||||
2 | CaCO3 % | 0–12 | 12–25 | 25–35 | 35–50 | -- | >50 | |||
3 | Coarse fragments % | 0–1 | 1–3 | 3–15 | 15–35 | -- | >35 | |||
4 | Topography | 1 | Slope % | 0–1 | 1 -2 | 2–4 | 4–6 | -- | >6 | |
2 | DEM (m) | 0–15 | 15–30 | 30–45 | 45–60 | > 60 | -- | |||
3 | Location | 3–6 towers | 2nd tower | 1st tower | 7 pivot tower | -- | -- | |||
5 | Climate | 1 | Humidity % | 60–50 | 70–80<50 | 60–70 | >80 | -- | -- | |
2 | Day length | 15–13 | <13 | -- | -- | -- | -- | |||
(b) | ||||||||||
Field Practice | Measure Units | Application Rate | ||||||||
Before Plantation | After Plantation | |||||||||
1st Day | 15 Days | 30 Days | 45–65 Days | |||||||
Irrigation water | mm ha−1 | 750 | ||||||||
Ammonium sulfate | N unit ha−1 | 35.7 | 35.7 | 35.7 | ||||||
Super phosphate | kg ha−1 | 55 | ||||||||
Potassium sulfate | kg ha−1 | 42 | ||||||||
Micro nutrients Fe, zn, and mn (1, 1.5, 1) | 0.5 g L−1 | 7.15 L ha−1 | 950 L ha−1 |
Criteria | Criterion Weights | |||
---|---|---|---|---|
n | w1 | w2 | w3 | w4 |
2 | 0.66 | 0.33 | ||
3 | 0.50 | 0.33 | 0.17 | |
4 | 0.40 | 0.30 | 0.20 | 0.10 |
Sample No | GPS Map Location | Sand% | Silt% | Clay% | Textural Class | CaCO3 g kg−1 | S.P. | Elevationmeter |
---|---|---|---|---|---|---|---|---|
1 | 1 | 54 | 8 | 38 | sandy clay | 8.8 | 37 | 12 |
2 | 2 | 51 | 10 | 39 | sandy clay | 9. 8 | 36 | 16 |
3 | 3 | 51 | 16 | 33 | sandy clay loam | 15.1 | 30 | 18 |
4 | 4 | 55 | 10 | 35 | sandy clay | 36.1 | 37 | 15 |
5 | 6 | 48 | 11 | 41 | sandy clay | 61 | 35 | 20 |
6 | 8 | 56 | 7 | 37 | sandy clay | 12.9 | 32 | 15 |
7 | 9 | 52 | 17 | 31 | sandy clay loam | 15.1 | 32 | 12 |
8 | 10 | 54 | 11 | 35 | sandy clay loam | 15.1 | 31 | 14 |
9 | 13 | 54 | 9 | 37 | sandy clay | 8.8 | 32 | 13 |
10 | 14 | 63 | 9 | 28 | sandy clay loam | 23.7 | 36 | 14 |
11 | 16 | 55 | 7 | 38 | sandy clay loam | 19.1 | 32 | 16 |
12 | 21 | 64 | 5 | 31 | sandy clay loam | 14.6 | 35 | 13 |
13 | 23 | 51 | 13 | 36 | sandy clay | 12 | 35 | 14 |
14 | 25 | 53 | 10 | 37 | sandy clay | 52.9 | 30 | 18 |
15 | 27 | 51 | 9 | 40 | sandy clay | 38.8 | 33 | 18 |
16 | 30 | 55 | 10 | 35 | sandy clay | 11.8 | 37 | 18 |
17 | 31 | 52 | 9 | 39 | sandy clay | 20 | 35 | 16 |
18 | 32 | 50 | 11 | 39 | sandy clay | 5.9 | 29 | 14 |
19 | 33 | 49 | 14 | 37 | sandy clay | 25.9 | 33 | 14 |
20 | 36 | 56 | 8 | 36 | sandy clay | 11 | 33 | 16 |
Sample | GPS Map Location | ECe | pH | Ca | Mg | K | Na | Cl | HCO3 | ESP |
---|---|---|---|---|---|---|---|---|---|---|
No. | No. | dS m−1 | - | meq L−1 | ||||||
1 | 1 | 3.01 | 7.5 | 2.987 | 2.195 | 51.01 | 4.715 | 13.04 | 1.64 | 3 |
2 | 2 | 5.13 | 7.4 | 2.986 | 2.292 | 53.47 | 12.97 | 12.54 | 1.74 | 10 |
3 | 3 | 5.17 | 7.6 | 3.779 | 2.263 | 93.84 | 2.734 | 9.28 | 2.22 | 1 |
4 | 4 | 4.82 | 7.8 | 4.434 | 2.068 | 63.38 | 6.491 | 10.54 | 1.46 | 4 |
5 | 6 | 5.59 | 7.5 | 2.596 | 1.951 | 49.76 | 11.06 | 10.78 | 1.36 | 9 |
6 | 8 | 1.94 | 7.5 | 0.475 | 0.115 | 29.96 | 5.509 | 12.74 | 1.42 | 12 |
7 | 9 | 2.43 | 7.4 | 0.842 | 0.493 | 36.67 | 4.211 | 11.24 | 1.74 | 6 |
8 | 10 | 2.39 | 7.9 | 0.848 | 0.496 | 34.91 | 5.268 | 12.24 | 1.792 | 8 |
9 | 13 | 4.73 | 7.3 | 1.986 | 1.549 | 47.28 | 10.94 | 13.54 | 2.54 | 10 |
10 | 14 | 2.56 | 7.9 | 0.758 | 0.917 | 34.91 | 5.487 | 8.78 | 1.32 | 7 |
11 | 16 | 2.27 | 7.3 | 1.336 | 0.885 | 38.61 | 3.379 | 10.28 | 1.46 | 3 |
12 | 21 | 2.29 | 7.1 | 0.876 | 0.538 | 34.95 | 4.237 | 11.74 | 1.64 | 6 |
13 | 23 | 3.31 | 7.2 | 1.443 | 1.075 | 42.33 | 6.318 | 13.74 | 2.22 | 7 |
14 | 25 | 4.63 | 7.4 | 0.846 | 0.495 | 34.91 | 4.211 | 11.28 | 2.26 | 6 |
15 | 27 | 3.87 | 7.5 | 0.947 | 0.758 | 29.96 | 3.259 | 11.54 | 1.26 | 4 |
16 | 30 | 2.31 | 7.2 | 0.971 | 0.606 | 36.15 | 3.279 | 13.24 | 1.32 | 4 |
17 | 31 | 4.16 | 7.3 | 1.821 | 1.105 | 48.52 | 11.09 | 12.04 | 1.64 | 11 |
18 | 32 | 2.42 | 7.7 | 0.994 | 0.617 | 36.15 | 3.139 | 14.24 | 3.12 | 4 |
19 | 33 | 2.54 | 7.9 | 0.415 | 0.085 | 29.96 | 4.458 | 9.78 | 1.36 | 11 |
20 | 36 | 2.61 | 8.2 | 0.846 | 0.495 | 34.91 | 4.171 | 11.04 | 1.36 | 6 |
Sample | GPS Map Location | K | P | Fe | Mn | Zn |
---|---|---|---|---|---|---|
No | No | mg kg−1soil | ||||
1 | 1 | 325.6 | 11.7 | 2.4 | 2.8 | 0.4 |
2 | 2 | 337.3 | 10.5 | 2 | 0.8 | 0.5 |
3 | 3 | 384.8 | 9 | 5.5 | 6.9 | 1 |
4 | 4 | 534.1 | 6.5 | 5.1 | 6.4 | 0.7 |
5 | 6 | 319.8 | 11.1 | 3.5 | 8.3 | 1 |
6 | 8 | 228.5 | 10.2 | 2.2 | 2.1 | 0.2 |
7 | 9 | 251 | 6.8 | 3.1 | 5.4 | 0.6 |
8 | 10 | 251 | 9.8 | 2.6 | 3.5 | 0.1 |
9 | 13 | 308.2 | 13.5 | 1.5 | 0.6 | 0.8 |
10 | 14 | 245.3 | 7.8 | 3.2 | 7.5 | 0.7 |
11 | 16 | 268 | 10.3 | 2.5 | 5.3 | 0.8 |
12 | 21 | 251 | 8.7 | 2.3 | 0.3 | 0.1 |
13 | 23 | 285.1 | 12.5 | 2.3 | 7.2 | 0.5 |
14 | 25 | 251 | 13 | 3 | 7 | 0.8 |
15 | 27 | 228.5 | 9.6 | 2.2 | 2.5 | 0.4 |
16 | 30 | 256.6 | 10.9 | 3.2 | 1.7 | 0.7 |
17 | 31 | 314 | 9.8 | 3.9 | 7.7 | 0.4 |
18 | 32 | 256.6 | 13.8 | 3 | 2.7 | 0.9 |
19 | 33 | 228.5 | 9.8 | 7.6 | 4.7 | 1.8 |
20 | 36 | 251 | 8.4 | 8.8 | 2.6 | 0.2 |
Different Units Based on Soil Properties | Hectare | % | |
---|---|---|---|
Unit 1 | Non-saline ECe, low CaCO3, scl texture | 0.7 | 1.11 |
Unit 2 | Non-saline ECe, low CaCO3, scl texture | 51 | 75.56 |
Unit 3 | Very slightly saline ECe, low CaCO3, scl texture | 7.5 | 11.48 |
Unit 4 | Slightly saline ECe, low CaCO3, scl texture | 7.8 | 11.85 |
Soil Characteristics | Texture | ECe | pH | OM | SP | CaCO3 | K available | P available | Fe | Mn | Zn |
---|---|---|---|---|---|---|---|---|---|---|---|
Texture | 1 | ||||||||||
ECe | 0.11 | 1 | |||||||||
pH | 0.08 | −0.30 | 1 | ||||||||
OM | −0.02 | 0.38 | 0.03 | 1 | |||||||
SP | 0.44 | 0.24 | −0.35 | 0.38 | 1 | ||||||
CaCO3 | −0.18 | 0.48 | 0.09 | −0.11 | −0.23 | 1 | |||||
K available | 0.12 | 0.91 | −0.31 | 0.32 | 0.23 | 0.42 | 1 | ||||
Pavailable | −0.01 | 0.45 | −0.50 | 0.45 | 0.39 | −0.34 | 0.31 | 1 | |||
Fe | −0.17 | −0.08 | 0.30 | 0.41 | 0.02 | −0.05 | −0.10 | −0.14 | 1 | ||
Mn | −0.22 | −0.02 | 0.02 | −0.09 | −0.22 | 0.18 | 0.01 | −0.20 | 0.14 | 1 | |
Zn | −0.17 | −0.08 | 0.30 | 0.41 | 0.02 | −0.05 | −0.10 | −0.14 | 1.00 | 0.14 | 1 |
Factors | Intercept | Slope | Generated Model | R |
---|---|---|---|---|
SP | 63.2 | 1.91 | Y = 1.91 × SP − 63.2 | 0.77 |
OM | 80.62 | −10.6 | Y = −10.6 × OM + 80.62 | 0.73 |
ECe | 13.13 | 0.7 | Y = 0.7 ×ECe + 2.13 | 0.60 |
CaCO3 | −245.67 | 3.43 | Y = 3.43 × CaCO3− 245.67 | 0.54 |
pH | 14.5 | 2 | Y = 2 × pH − 14.5 | 0.40 |
Yield Fitted Model | Factors | R2 | RMSE |
---|---|---|---|
2 | 0.61 | 11.27 | |
2 | 0.63 | 9.42 | |
3 | 0.83 | 6.71 | |
5 | 0.92 | 5.35 |
Soil Properties | Unit | High Production | Low Production |
---|---|---|---|
ECe | dS m−1 | 0.3 to 3.9 | 1.3 to 5.2 |
Soil pH | - | 7.1 to 7.8 | 7.7 to 8.2 |
Available potassium | mg kg−1 | 20 to 30 | 1.5 to 1.6 |
Fe | mg kg−1 | 1 to 2.3 | 0.9 to 0.5 |
Mn | mg kg−1 | 1.7 to 2.4 | 1.1 to 0.2 |
CaCO3 | g kg−1 | 1.1 to 4.3 | ≥5.6 |
Production Levels | Production Levels | Zones Test | ||
---|---|---|---|---|
High | Moderate | Low | ||
High production | 13 | 1 | 0 | 14 |
Moderate production | 1 | 11 | 3 | 15 |
Low production | 0 | 0 | 8 | 8 |
Total | 14 | 12 | 11 | 37 |
Production Levels | Omission Error | Commission Error | Omission and Commission Errors | Producer’s Accuracy | User’s Accuracy |
---|---|---|---|---|---|
High | 7.14 | 0 | 7.14 | 92.31% | 85.71% |
Moderate | 0 | 6.67 | 6.67 | 90.90% | 83.33% |
Low | 0 | 27.27 | 27.27 | 80.00% | 72.72% |
Classes Peanut Soil Productivity | Total Examined Cells | Classified Cells | Correctly Classified Cells | Accuracy | |
---|---|---|---|---|---|
Producers | Users | ||||
High production | 14 | 13 | 12 | 92.31% | 85.71% |
Moderate production | 12 | 11 | 10 | 90.90% | 83.33% |
Low production | 11 | 10 | 8 | 80.00% | 72.72% |
Totals | 37 | 34 | 30 | 88.24% | 81.08% |
Overall classification accuracy = 86.49% | |||||
Overall kappa statistics = 79.8% |
Zone n° | Zone Area | Soil Available P | Required P (kg ha−1) | Price EGP ha−1 | Price EGP/Zone | ||
---|---|---|---|---|---|---|---|
(ha) | mg kg−1 | Kg ha−1 | Unit | Fertilizer (Super Phosphate) | |||
Zone 1 | 3 | 13 | 1169 | 90 | 181 | 809 | 2362.6 |
Zone 2 | 15 | 12 | 1123 | 93 | 183 | 826 | 11,795.1 |
Zone 3 | 9 | 11 | 1081 | 133 | 267 | 1197 | 8049.5 |
Zone 4 | 17 | 10 | 990 | 143 | 288 | 1295 | 16,833.4 |
Zone 5 | 22 | 7 | 900 | 155 | 305 | 1392 | 29,634.1 |
Zone 6 | 1 | 4 | 809 | 162 | 309 | 1490 | 1671.3 |
Total | 67 | 70,346.0 |
Zone no. | Zone Area | Available K in Soil | Required K (kg ha−1) | Price EGP ha−1 | Price EGP/Zone | ||
---|---|---|---|---|---|---|---|
(ha) | mg kg−1 | kg ha−1 | Unit | Fertilizer (Potassium Sulfate) | |||
Zone 1 | 3 | 280 | 58 | 68 | 452 | 677 | 2822 |
Zone 2 | 15 | 250 | 54 | 79 | 524 | 786 | 12,402 |
Zone 3 | 9 | 240 | 49 | 89 | 597 | 895 | 10,761 |
Zone 4 | 17 | 220 | 45 | 100 | 669 | 1004 | 21,695 |
Zone 5 | 22 | 200 | 31 | 133 | 887 | 1331 | 30,986 |
Zone 6 | 1 | 180 | 18 | 166 | 998 | 1657 | 1501 |
Total | 67 | 80,168 |
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Elsharkawy, M.M.; Sheta, A.E.A.S.; D’Antonio, P.; Abdelwahed, M.S.; Scopa, A. Tool for the Establishment of Agro-Management Zones Using GIS Techniques for Precision Farming in Egypt. Sustainability 2022, 14, 5437. https://doi.org/10.3390/su14095437
Elsharkawy MM, Sheta AEAS, D’Antonio P, Abdelwahed MS, Scopa A. Tool for the Establishment of Agro-Management Zones Using GIS Techniques for Precision Farming in Egypt. Sustainability. 2022; 14(9):5437. https://doi.org/10.3390/su14095437
Chicago/Turabian StyleElsharkawy, Mohamed M., Abd El Aziz S. Sheta, Paola D’Antonio, Mohammed S. Abdelwahed, and Antonio Scopa. 2022. "Tool for the Establishment of Agro-Management Zones Using GIS Techniques for Precision Farming in Egypt" Sustainability 14, no. 9: 5437. https://doi.org/10.3390/su14095437