Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach
Abstract
:1. Introduction
2. Data and Methodology
2.1. Data Acquisition
2.2. Data Pre-Processing
2.3. Stochastic Frontier Algorithm
- Y = the yield or any variable representing the productivity per unit area.
- Xi = the vector of inputs used in production.
- βi = the estimated coefficient of the ith input.
- u = the error term.
- Y = the yield or any variable representing the productivity per unit.
- Xi = the vector of inputs (the same as Equation (1));
- βi = the estimated coefficient of the ith input.
- vi = an asymmetrical random term or stochastic noise, assumed with a normal distribution
- ui = the individual farm level technical inefficiency assumed to be half-normally distributed.
- Yi= Output/Yield (quintals per hectare)
- X1 = Total human labor (Man-hours)
- X2 = Total animal labor (Hours)
- X3 = Total machine labor (Hours)
- X4 = Total Fertilizer (kg.)
- X5 = Total insecticide (Rupees).
- f = the Cobb–Douglas type production function.
- TE = the technical efficiency of an individual farm (0 < TEi ≤ 1).
2.4. Machine Learning Algorithms for the Prediction of Efficiency Classes
3. Results and Discussion
3.1. The Status of Paddy Farming in India
3.2. Regional Disparity in Productivity and Input Use
3.3. The Stochastic Frontier Approach of Technical Efficiency Estimation
3.4. Machine Learning Models for Efficiency Group Prediction
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
State | Punjab | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.167 | 0.770 | 0.190 | 0.455 | 0.900 | 0.969 | 0.667 | 0.936 | 0.900 | 0.985 | 0.800 | 0.968 |
Recall | 0.100 | 0.877 | 0.267 | 0.484 | 1.000 | 0.710 | 0.979 | 0.825 | 0.976 | 0.710 | 0.979 | 0.947 |
Sensitivity | 0.100 | 0.877 | 0.267 | 0.484 | 1.000 | 0.875 | 0.909 | 0.744 | 0.900 | 0.877 | 0.923 | 0.909 |
Specificity | 0.881 | 0.452 | 0.638 | 0.684 | 0.900 | 0.969 | 0.667 | 0.936 | 0.900 | 0.985 | 0.800 | 0.968 |
State | Andhra Pradesh | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | NA | 0.508 | 0.682 | 0.000 | NA | 0.855 | 0.864 | 0.200 | NA | 0.873 | 0.864 | 0.600 |
Recall | NA | 0.564 | 0.682 | 0.000 | 1.000 | 0.823 | 0.741 | 0.842 | 0.987 | 0.919 | 0.889 | 0.790 |
Sensitivity | NA | 0.564 | 0.682 | 0.000 | NA | 0.810 | 0.731 | 0.250 | 0.000 | 0.906 | 0.864 | 0.429 |
Specificity | 1.000 | 0.516 | 0.741 | 0.947 | NA | 0.855 | 0.864 | 0.200 | NA | 0.873 | 0.864 | 0.600 |
States | Assam | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.536 | 0.333 | 1.000 | 0.000 | 0.871 | 0.818 | 0.000 | 0.000 | 0.903 | 0.818 | 1.000 | 0.400 |
Recall | 0.484 | 0.273 | 0.333 | 0.000 | 0.957 | 0.869 | 1.000 | 1.000 | 0.957 | 0.967 | 1.000 | 1.000 |
Sensitivity | 0.484 | 0.273 | 0.333 | 0.000 | 0.931 | 0.692 | NA | NA | 0.933 | 0.900 | 1.000 | 1.000 |
Specificity | 0.723 | 0.803 | 1.000 | 0.864 | 0.871 | 0.818 | 0.000 | 0.000 | 0.903 | 0.818 | 1.000 | 0.400 |
States | Bihar | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.111 | 0.529 | 0.300 | 0.000 | 0.500 | 0.773 | 0.467 | 0.538 | 0.667 | 0.727 | 0.800 | 0.846 |
Recall | 0.083 | 0.409 | 0.200 | 0.000 | 0.875 | 0.987 | 0.787 | 1.000 | 0.900 | 1.000 | 0.766 | 1.000 |
Sensitivity | 0.083 | 0.409 | 0.200 | 0.000 | 0.546 | 0.944 | 0.412 | 1.000 | 0.667 | 1.000 | 0.522 | 1.000 |
Specificity | 0.800 | 0.892 | 0.851 | 0.987 | 0.500 | 0.773 | 0.467 | 0.538 | 0.667 | 0.727 | 0.800 | 0.846 |
States | Chhattisgarh | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.200 | 0.455 | 0.227 | 0.286 | 0.500 | 0.688 | 0.650 | 0.500 | 0.625 | 0.938 | 0.700 | 0.625 |
Recall | 0.125 | 0.313 | 0.250 | 0.250 | 0.977 | 0.985 | 0.897 | 0.684 | 0.977 | 0.955 | 0.956 | 0.895 |
Sensitivity | 0.125 | 0.313 | 0.250 | 0.250 | 0.667 | 0.917 | 0.650 | 0.400 | 0.714 | 0.833 | 0.824 | 0.714 |
Specificity | 0.955 | 0.910 | 0.750 | 0.737 | 0.500 | 0.688 | 0.650 | 0.500 | 0.625 | 0.938 | 0.700 | 0.625 |
States | Gujarat | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | NA | 0.535 | 0.438 | 0.250 | 0.000 | 0.900 | 0.833 | 0.286 | 0.000 | 0.833 | 0.917 | 0.714 |
Recall | 0.000 | 0.767 | 0.583 | 0.286 | 1.000 | 0.830 | 0.969 | 0.700 | 1.000 | 0.906 | 0.922 | 0.900 |
Sensitivity | 0.000 | 0.767 | 0.583 | 0.286 | 0.750 | 0.909 | 0.250 | NA | 0.833 | 0.815 | 0.714 | |
Specificity | 1.000 | 0.623 | 0.719 | 0.700 | 0.000 | 0.900 | 0.833 | 0.286 | 0.000 | 0.833 | 0.917 | 0.714 |
States | Kerala | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.638 | 0.273 | 0.444 | 0.556 | 0.952 | 0.600 | 0.692 | 0.714 | 0.952 | 1.000 | 0.846 | 1.000 |
Recall | 0.714 | 0.200 | 0.615 | 0.714 | 0.778 | 1.000 | 0.889 | 0.800 | 0.889 | 1.000 | 0.917 | 0.800 |
Sensitivity | 0.714 | 0.200 | 0.615 | 0.714 | 0.833 | 1.000 | 0.692 | 0.556 | 0.909 | 1.000 | 0.786 | 0.636 |
Specificity | 0.528 | 0.882 | 0.722 | 0.800 | 0.952 | 0.600 | 0.692 | 0.714 | 0.952 | 1.000 | 0.846 | 1.000 |
States | Tamil Nadu | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.000 | 0.393 | 0.125 | NA | 0.200 | 0.786 | 0.455 | 0.000 | 0.600 | 0.893 | 0.455 | 1.000 |
Recall | 0.000 | 0.393 | 0.091 | 0.000 | 1.000 | 0.978 | 0.868 | 1.000 | 1.000 | 0.966 | 0.868 | 1.000 |
Sensitivity | 0.000 | 0.393 | 0.091 | 0.000 | 1.000 | 0.917 | 0.500 | NA | 1.000 | 0.893 | 0.500 | 1.000 |
Specificity | 0.959 | 0.809 | 0.816 | 1.000 | 0.200 | 0.786 | 0.455 | 0.000 | 0.600 | 0.893 | 0.455 | 1.000 |
States | Uttar Pradesh | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.353 | 0.333 | 0.320 | 0.682 | 0.333 | 0.875 | 0.684 | 0.867 | 0.733 | 1.000 | 0.474 | 0.800 |
Recall | 0.400 | 0.313 | 0.421 | 1.000 | 0.757 | 0.980 | 0.558 | 0.333 | 0.811 | 0.960 | 0.907 | 0.778 |
Sensitivity | 0.400 | 0.313 | 0.421 | 1.000 | 0.357 | 0.875 | 0.406 | 0.684 | 0.611 | 0.800 | 0.692 | 0.857 |
Specificity | 0.703 | 0.901 | 0.605 | 0.222 | 0.333 | 0.875 | 0.684 | 0.867 | 0.733 | 1.000 | 0.474 | 0.800 |
States | West Bengal | |||||||||||
Model | KNN | SVM | RF | |||||||||
Class | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE | Low TE | Medium TE | High TE | Very High TE |
Precision | 0.300 | 0.385 | 0.333 | 0.000 | 0.467 | 0.667 | 0.077 | 0.000 | 0.667 | 0.667 | 0.846 | 0.000 |
Recall | 0.400 | 0.278 | 0.154 | 0.000 | 0.703 | 0.929 | 0.980 | 0.952 | 0.946 | 0.960 | 0.959 | 0.905 |
Sensitivity | 0.400 | 0.278 | 0.154 | 0.000 | 0.389 | 0.632 | 0.500 | 0.000 | 0.833 | 0.750 | 0.846 | 0.000 |
Specificity | 0.622 | 0.919 | 0.918 | 0.952 | 0.467 | 0.667 | 0.077 | 0.000 | 0.667 | 0.667 | 0.846 | 0.000 |
Abbreviation | State Name |
---|---|
AP | Andhra Pradesh |
AS | Assam |
BH | Bihar |
CG | Chhattisgarh |
GJ | Gujarat |
KL | Kerala |
PB | Punjab |
TN | Tamil Nadu |
UP | Uttar Pradesh |
WB | West Bengal |
Abbreviation | Full Form |
---|---|
TE | Technical Efficiency |
DES | Department of Economics and Statistics |
CACP | Commission for Agricultural Cost and Prices |
FAO | Food and Agriculture Organization |
KNN | K- Nearest Neighbor |
SVM | Support Vector Machine |
RF | Random Forest |
ANN | Artificial Neural Network |
MLR | Multiple Linear Regression |
SVR | Support Vector Regression |
MANN | Modular Artificial Neural Networks |
RGB | Red, Green, Blue |
UAV | Unmanned Aerial Vehicle |
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Efficiency Class | Efficiency Score Range |
---|---|
Very High | 1.0 to 0.90 |
High | 0.90 to 0.80 |
Medium | 0.80 to 0.70 |
Low | <0.70 |
Author | Classifiers/Predictors | Crop | Classification/Prediction Problem | Machine Learning Algorithm | Model Selection Parameters |
---|---|---|---|---|---|
Gopal, M et al. [40] | Weather data, irrigation, planting area, fertilization | Paddy | paddy crop yield | ANN, SVR, KNN, RF | RF: RMSE = 0.085, MAE = 0.055, R = 0.93 |
Gopal, M et al. [39] | Weather data, irrigation, planting area, fertilization | Paddy | paddy fields yield | ANN, MLR, SVR, KNN RF | ANN-MLR: R = 0.99, RMSE = 0.051 MAE = 0.041 |
Shidnal, S et al. [31] | RGB leaf images | Paddy | nutrient deficiencies (P, N, K) | ANN | Accuracy = 77% |
Khosla, E et al. [36] | Weather data | Rice, maize, millet, ragi | kharif crops yield | MANN, SVR | Overall RMSE = 79.85% |
Amaratunga, V et al. [35] | Weather data | Paddy | Paddy yield | ANN | R = 0.78–1.00, MSE = 0.040–0.204 |
Wan, L et al. [33] | Multispectral images from UAV | Rice | rice grain yield | RF | RMSE = 62.77 kg·ha−1 , MAPE = 0.32 |
Ramesh, S et al. [26] | RGB images | Rice | Recognition and classification of rice infected leaves | KNN, ANN | ANN: Accuracy = 90%, Recall = 88% |
Gomez Selvaraj et al. [50] | Satellite spectral data, Multispectral images from UAV, RGB images from UAV | Banana | Detection of banana diseases in different African | RF, SVM | RF: Accuracy = 97%, omissions error = 10%; commission error = 10%. Kappa coefficient = 0.96 |
Gao, J et al. [51] | RGB images from UAV | Wheat | Detection of weeds in early season maize fields | RF | Overall Accuracy = 0.945, Kappa = 0.912 |
States | Production Percentage to All India Production | Area as a Percentage of All India Area | Average Productivity (kg/Hectare) |
---|---|---|---|
Punjab | 10.56 | 6.59 | 3998.00 |
Andhra Pradesh | 6.79 | 4.78 | 3540.00 |
Haryana | 4.06 | 3.15 | 3213.00 |
West Bengal | 13.95 | 12.49 | 2784.00 |
Kerala | 0.40 | 0.39 | 2550.00 |
Karnataka | 2.37 | 2.35 | 2519.00 |
Bihar | 7.51 | 7.59 | 2467.00 |
Uttarakhand | 0.57 | 0.59 | 2414.00 |
Gujarat | 1.76 | 1.90 | 2306.00 |
Uttar Pradesh | 12.54 | 13.62 | 2295.00 |
Jharkhand | 3.50 | 3.90 | 2241.00 |
Odisha | 7.59 | 8.76 | 2160.00 |
Chhattisgarh | 7.34 | 8.71 | 2101.00 |
Maharashtra | 2.83 | 3.49 | 2025.00 |
Himachal Pradesh | 0.13 | 0.17 | 1968.00 |
Assam | 4.31 | 5.61 | 1916.00 |
Madhya Pradesh | 3.85 | 5.20 | 1847.00 |
Tamil Nadu | 2.16 | 3.28 | 1642.00 |
States | Operational Cost (%) | Fixed Cost (%) |
---|---|---|
Andhra Pradesh | 60.9 | 39.1 |
Assam | 74.4 | 25.6 |
Bihar | 67.7 | 32.3 |
Chhattisgarh | 68.6 | 31.4 |
Gujarat | 72.6 | 27.4 |
Haryana | 55.6 | 44.4 |
Himachal Pradesh | 69.6 | 30.4 |
Jharkhand | 66.3 | 33.7 |
Karnataka | 62.3 | 37.7 |
Kerala | 73.8 | 26.2 |
Madhya Pradesh | 71.7 | 28.3 |
Maharashtra | 79.7 | 20.3 |
Odisha | 75.7 | 24.3 |
Punjab | 47.2 | 52.8 |
Tamil Nadu | 71.5 | 28.5 |
Uttar Pradesh | 66.8 | 33.2 |
Uttarakhand | 67.9 | 32.1 |
West Bengal | 75.3 | 24.7 |
States | Human Labor | Animal Labor | Machine Labor | Seed | Fertilizer | Insecticide | Irrigation |
---|---|---|---|---|---|---|---|
Andhra Pradesh | 47.8 | 1.6 | 20.3 | 4.1 | 14.5 | 5.6 | 2.3 |
Assam | 57.5 | 24.0 | 9.2 | 2.7 | 2.0 | 0.1 | 1.1 |
Bihar | 56.0 | 0.4 | 13.6 | 6.6 | 10.1 | 0.1 | 10.0 |
Chhattisgarh | 44.2 | 8.8 | 20.2 | 5.0 | 9.7 | 3.1 | 1.2 |
Gujarat | 47.5 | 0.6 | 15.2 | 11.9 | 11.2 | 2.5 | 6.1 |
Haryana | 52.3 | 0.0 | 12.9 | 3.0 | 10.2 | 5.0 | 14.0 |
Himachal Pradesh | 74.2 | 7.9 | 6.5 | 6.6 | 1.1 | 1.5 | 0.2 |
Jharkhand | 58.7 | 4.4 | 14.3 | 8.2 | 10.7 | 0.0 | 0.1 |
Karnataka | 44.2 | 10.2 | 11.2 | 6.5 | 14.0 | 4.9 | 2.2 |
Kerala | 56.2 | 0.0 | 19.6 | 5.6 | 8.3 | 3.3 | 0.2 |
Madhya Pradesh | 41.7 | 9.0 | 19.2 | 6.0 | 9.4 | 3.5 | 2.3 |
Maharashtra | 51.6 | 10.1 | 10.9 | 4.9 | 6.0 | 1.0 | 2.6 |
Odisha | 66.4 | 7.1 | 11.3 | 2.6 | 6.0 | 0.8 | 0.3 |
Punjab | 45.5 | 0.1 | 17.7 | 4.8 | 9.2 | 12.3 | 6.7 |
Tamil Nadu | 41.5 | 0.2 | 18.2 | 12.7 | 10.8 | 2.8 | 7.4 |
Uttar Pradesh | 49.4 | 1.6 | 11.5 | 10.4 | 11.5 | 0.9 | 12.3 |
Uttarakhand | 46.2 | 10.6 | 14.5 | 11.0 | 10.0 | 2.4 | 1.9 |
West Bengal | 64.0 | 2.7 | 8.4 | 3.5 | 8.9 | 2.9 | 5.1 |
State | Particulars | Yield (Qtls/ha) | Fertilizer (kg/ha) | Insecticides (Rs/ha) | Human Labor (Person Hours/ha) | Animal Labor (Hours/ha) | Machine Labor (Hours/ha) | Irrigation (Hours/ha) |
---|---|---|---|---|---|---|---|---|
Andhra Pradesh | Average | 60.08 | 241.75 | 2810.16 | 541.35 | 22.27 | 23.95 | 293.82 |
Minimum | 12.5 | 66.5 | 93.2 | 177.83 | 0.5 | 0.91 | 2.47 | |
Maximum | 110.89 | 590.78 | 22,500 | 1325 | 164.34 | 150 | 1206.25 | |
Coefficient of Variation | 0.21 | 0.35 | 0.91 | 0.39 | 1.26 | 0.89 | 0.77 | |
Assam | Average | 33.65 | 47.4 | 829.94 | 668.98 | 180.43 | 65.03 | 92.03 |
Minimum | 16.68 | 4.83 | 89.55 | 321.16 | 3.9 | 12.64 | 11.56 | |
Maximum | 69 | 349.89 | 1940.3 | 1415.92 | 424.53 | 156.72 | 229.85 | |
Coefficient of Variation | 0.27 | 0.92 | 0.6 | 0.26 | 0.5 | 0.43 | 0.64 | |
Bihar | Average | 31.31 | 110.6 | 490.18 | 597.48 | 56.83 | 13.2 | 37.79 |
Minimum | 15.91 | 23 | 297.62 | 274.82 | 15.66 | 9 | 4.4 | |
Maximum | 52.44 | 244.03 | 851.85 | 1192 | 90 | 30.11 | 80 | |
Coefficient of Variation | 0.19 | 0.35 | 0.42 | 0.21 | 0.68 | 0.34 | 0.4 | |
Chhattisgarh | Average | 34.86 | 120.76 | 1158.67 | 456.14 | 39.61 | 21.84 | 40.36 |
Minimum | 13.16 | 31.94 | 53.33 | 99.32 | 1.44 | 15.56 | 8 | |
Maximum | 48.15 | 220 | 2912.81 | 1040.83 | 192.42 | 30.42 | 141.29 | |
Coefficient of Variation | 0.19 | 0.33 | 0.6 | 0.4 | 0.94 | 0.21 | 0.63 | |
Gujarat | Average | 37.21 | 153.6 | 1095.93 | 833.9 | 37.41 | 24.88 | 65.06 |
Minimum | 0.87 | 13.89 | 91.4 | 170.83 | 3.19 | 3.51 | 0.67 | |
Maximum | 77.05 | 435.76 | 4017.22 | 2634.92 | 100 | 90.44 | 383.33 | |
Coefficient of Variation | 0.43 | 0.45 | 0.96 | 0.44 | 0.83 | 0.78 | 1.07 | |
Himachal Pradesh | Average | 22.72 | 64.58 | 1226.39 | 423.48 | 86.47 | 20.45 | 65.73 |
Minimum | 6.25 | 14.38 | 388.89 | 200 | 1.39 | 5.56 | 40.62 | |
Maximum | 52.5 | 191.67 | 4900 | 954.16 | 172.92 | 44.58 | 103.12 | |
Coefficient of Variation | 0.49 | 0.9 | 0.69 | 0.32 | 0.41 | 0.49 | 0.39 | |
Kerala | Average | 41.01 | 139.79 | 1994.45 | 460.38 | 6.54 | 16.71 | 25.53 |
Minimum | 7.21 | 10.06 | 56.25 | 74.17 | 3.02 | 14.54 | 12.73 | |
Maximum | 89.2 | 442.94 | 13550 | 1383.32 | 7.44 | 19.67 | 35.42 | |
Coefficient of Variation | 0.4 | 0.59 | 1.02 | 0.53 | 0.3 | 0.12 | 0.27 | |
Odisha | Average | 36.77 | 93.22 | 642.26 | 965.91 | 154.52 | 26.74 | 12.62 |
Minimum | 15.28 | 23.16 | 25.96 | 507.22 | 1.6 | 0.35 | 0.62 | |
Maximum | 56.18 | 198.49 | 4250 | 1408.67 | 365 | 58.33 | 32.81 | |
Coefficient of Variation | 0.17 | 0.26 | 1.18 | 0.17 | 0.72 | 0.52 | 0.75 | |
Punjab | Average | 67.13 | 183.1 | 4122.04 | 363.94 | 2.08 | 24.43 | 251.7 |
Minimum | 23.64 | 64.94 | 500 | 247.71 | 0.09 | 1.33 | 26.67 | |
Maximum | 109 | 344.03 | 11,644.78 | 779.69 | 46.51 | 58.81 | 612.5 | |
Coefficient of Variation | 0.21 | 0.25 | 0.57 | 0.22 | 2.73 | 0.35 | 0.32 | |
Tamil Nadu | Average | 47.96 | 228.17 | 1577.93 | 508.96 | 7.17 | 20.97 | 224.8 |
Minimum | 13.92 | 103.57 | 129.95 | 168.25 | 0.63 | 3.47 | 38.33 | |
Maximum | 93.75 | 741.67 | 4938.02 | 1281.25 | 40 | 98.77 | 876.47 | |
Coefficient of Variation | 0.22 | 0.27 | 0.65 | 0.36 | 0.92 | 0.65 | 0.59 | |
Uttar Pradesh | Average | 36.19 | 164.7 | 1832.87 | 683.84 | 48.61 | 18.19 | 65.91 |
Minimum | 12.5 | 28.75 | 294.64 | 291.86 | 3.03 | 5.49 | 10.39 | |
Maximum | 64.29 | 327.42 | 8767.12 | 1396.43 | 137.14 | 370.83 | 790.62 | |
Coefficient of Variation | 0.21 | 0.34 | 1.08 | 0.28 | 0.8 | 1.75 | 0.73 | |
West Bengal | Average | 46.86 | 171.94 | 1781.97 | 1021.94 | 46.15 | 40.18 | 101.57 |
Minimum | 23.58 | 14.62 | 30.61 | 448.13 | 0.37 | 1.71 | 1.28 | |
Maximum | 70.8 | 533 | 9066.07 | 2109.09 | 245.37 | 146.34 | 450 | |
Coefficient of Variation | 0.19 | 0.39 | 0.98 | 0.26 | 0.89 | 0.7 | 0.88 |
Variables/States | Punjab | Bihar | Uttar Pradesh | West Bengal | Odisha | Andhra Pradesh | Tamil Nadu | Kerala | Assam | Gujarat | Chhattisgarh |
---|---|---|---|---|---|---|---|---|---|---|---|
(Intercept) | 4.363 *** (0.389) | 2.527 *** (0.274) | 2.817 *** (0.257) | 4.081 *** (0.212) | 0.872 *** (0.206) | 3.994 *** (0.212) | 2.641 *** (0.293) | 4.571 *** (0.268) | 3.448 *** (0.321) | 2.408 *** (0.451) | 3.744 *** (0.984) |
Area under crop (hectare) | −0.011 ns (0.065) | −0.178 *** (0.045) | 0.167 *** (0.039) | −0.013 ns (0.029) | −0.524 *** (0.031) | −0.009 ns (0.033) | −0.244 *** (0.046) | 0.059 ns (0.046) | −0.089 * (0.042) | −0.345 *** (0.077) | −0.016 ns (0.586) |
Human labor (man-hours) | −0.271 *** (0.062) | 0.145 *** (0.043) | 0.076 * (0.036) | −0.028 ns (0.031) | 0.127 *** (0.027) | −0.014 ns (0.027) | 0.193 *** (0.035) | −0.169 *** (0.043) | 0.076 ns (0.050) | 0.125 * (0.068) | 0.044 ns (0.399) |
Mechanical labor (Hours) | −0.004 ns (0.004) | 0.001 ns (0.003) | 0.003 ns (0.003) | −0.005 * (0.002) | −0.008 *** (0.001) | −0.004 ns (0.003) | 0.014 *** (0.003) | 0.002 ns (0.013) | −0.012 ** (0.004) | 0.003 ns (0.009) | −0.012 ns (0.020) |
Fertilizer (kg.) | 0.061 *** (0.010) | 0.049 * (0.024) | 0.087 *** (0.025) | 0.019 *** (0.005) | 0.420 *** (0.020) | 0.065 * (0.028) | 0.042 ns (0.044) | 0.080 *** (0.014) | −0.007 * (0.003) | 0.126 *** (0.023) | −0.076 ns (0.607) |
Irrigation (Hours) | 0.152 *** (0.033) | −0.011 *** (0.003) | 0.001 (0.004) | 0.004 * (0.002) | −0.003 *** (0.003) | 0.005 * (0.002) | 0.002 ns (0.003) | −0.014 ns (0.014) | 0.039 *** (0.004) | 0.022 * (0.010) | −0.003 ns (0.047) |
Insecticide (Rupees) | 0.064 *** (0.014) | 0.024 *** (0.004) | 0.011 *** (0.002) | 0.010 *** (0.002) | 0.005 *** (0.001) | 0.002 *** (0.003) | 0.008 * (0.004) | 0.020 *** (0.005) | −0.004 ns (0.004) | 0.057 *** (0.008) | 0.028 ns (0.019) |
Sigma Square (σ2) | 0.100 *** (0.011) | 0.045 *** (0.009) | 0.063 *** (0.010) | 0.084 *** (0.007) | 0.011 *** (0.003) | 0.116 *** (0.011) | 0.121 *** (0.012) | 0.286 *** (0.038) | 0.140 *** (0.014) | 0.505 *** (0.085) | 0.097 ns (0.460) |
Gamma (γ) | 0.971 *** (0.012) | 0.617 *** (0.167) | 0.548 *** (0.144) | 0.890 *** (0.022) | 0.282 *** (0.365) | 0.924 *** (0.023) | 0.953 *** (0.017) | 0.909 *** (0.039) | 0.909 *** (0.029) | 0.968 *** (0.028) | 0.990 ns (0.974) |
Sigma Square U (σ2U) | 0.097 *** (0.011) | 0.028 * (0.013) | 0.035 * (0.014) | 0.075 *** (0.007) | 0.003 *** (0.005) | 0.107 *** (0.012) | 0.116 *** (0.013) | 0.260 *** (0.044) | 0.128 *** (0.016) | 0.489 *** (0.092) | 0.096 ns (0.443) |
Sigma Square V (σ2v) | 0.003 ** (0.001) | 0.017 *** (0.004) | 0.029 *** (0.005) | 0.009 *** (0.002) | 0.008 *** (0.002) | 0.009 *** (0.002) | 0.006 ** (0.002) | 0.026 ** (0.009) | 0.013 *** (0.003) | 0.016 ns (0.012) | 0.001 ns (0.096) |
Lambda (λ) | 5.799 *** (1.200) | 1.269 ** (0.449) | 1.101 *** (0.320) | 2.846 *** (0.327) | 0.626 *** (0.565) | 3.476 *** (0.562) | 4.514 *** (0.885) | 3.165 *** (0.748) | 3.160 *** (0.554) | 5.484 * (2.433) | 9.737 ns (459.040) |
Log Likelihood | 79.409 | 154.593 | 86.508 | 165.151 | 425.631 | 66.746 | 55.821 | −78.10 | 17.204 | −65.366 | 58.189 |
Mean Technical Efficiency | 0.801 | 0.879 | 0.868 | 0.819 | 0.958 | 0.793 | 0.784 | 0.699 | 0.768 | 0.639 | 0.801 |
Number of Observations | 260 | 401 | 487 | 596 | 449 | 422 | 317 | 248 | 448 | 129 | 149 |
Mean Accuracy from 10 Resamples | Mean Kappa Values from 10 Resamples | |||||
---|---|---|---|---|---|---|
State/Models | KNN | SVM | Random Forest | KNN | SVM | Random Forest |
PB | 0.306 | 0.595 | 0.729 | 0.072 | 0.451 | 0.646 |
BH | 0.514 | 0.802 | 0.857 | 0.086 | 0.62 | 0.744 |
UP | 0.685 | 0.848 | 0.943 | 0.247 | 0.644 | 0.882 |
WB | 0.449 | 0.797 | 0.916 | 0.17 | 0.689 | 0.876 |
AP | 0.399 | 0.767 | 0.843 | 0.149 | 0.671 | 0.784 |
TN | 0.34 | 0.518 | 0.701 | 0.104 | 0.335 | 0.597 |
KL | 0.478 | 0.611 | 0.800 | 0.200 | 0.38 | 0.706 |
AS | 0.353 | 0.779 | 0.874 | 0.094 | 0.692 | 0.828 |
GJ | 0.579 | 0.632 | 0.786 | 0.086 | 0.186 | 0.628 |
CG | 0.334 | 0.531 | 0.795 | 0.108 | 0.361 | 0.725 |
States | Accuracy | 95% CI | NIR | Kappa |
---|---|---|---|---|
PB | 0.730 | (0.589,0.844) | 0.289 *** | 0.638 |
BH | 0.910 | (0.824,0.963) | 0.539 *** | 0.834 |
UP | 0.885 | (0.804,0.942) | 0.677 *** | 0.740 |
WB | 0.863 | (0.787,0.919) | 0.470 *** | 0.801 |
AP | 0.880 | (0.789,0.941) | 0.361 *** | 0.835 |
TN | 0.776 | (0.634,0.882) | 0.449 *** | 0.667 |
KL | 0.710 | (0.581,0.818) | 0.301 *** | 0.614 |
AS | 0.875 | (0.787,0.936) | 0.352 *** | 0.827 |
GJ | 0.667 | (0.447,0.844) | 0.625 NS | 0.407 |
CG | 0.704 | (0.498,0.863) | 0.296 *** | 0.598 |
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Bhoi, P.B.; Wali, V.S.; Swain, D.K.; Sharma, K.; Bhoi, A.K.; Bacco, M.; Barsocchi, P. Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach. Agriculture 2021, 11, 837. https://doi.org/10.3390/agriculture11090837
Bhoi PB, Wali VS, Swain DK, Sharma K, Bhoi AK, Bacco M, Barsocchi P. Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach. Agriculture. 2021; 11(9):837. https://doi.org/10.3390/agriculture11090837
Chicago/Turabian StyleBhoi, Priya Brata, Veeresh S. Wali, Deepak Kumar Swain, Kalpana Sharma, Akash Kumar Bhoi, Manlio Bacco, and Paolo Barsocchi. 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach" Agriculture 11, no. 9: 837. https://doi.org/10.3390/agriculture11090837