Evaluating Fertilizer Use Efficiency and Spatial Correlation of Its Determinants in China: A Geographically Weighted Regression Approach
Abstract
:1. Introduction
2. Literature Review
3. Methodology and Data Source
3.1. Calculation of Fertilizer Use Efficiency
3.2. Spatial Correlation Test
3.3. Geographical Weighted Regression Model
3.4. Data Sources
4. Results
4.1. Model Choice
4.2. Estimation Results of SFA
4.3. Results of FUE
4.4. Spatial Autocorrelation Test of FUE
4.5. Results of GWR
5. Discussion
5.1. Education Level
5.2. Non-Agricultural Employment Ratio
5.3. Disaster
5.4. Farmers’ Income
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
FUE | fertilizer use efficiency |
GWR | geographical weighted regression |
SFA | stochastic frontier analysis |
NUE | nitrogen use efficiency |
CS-PNM | canopy sensor-based precision N management |
DEA | data envelopment analysis |
EKC | environmental Kuznets curve |
PGDP | per capita gross domestic product |
TE | technical efficiency |
GNP | gross national product |
OLS | ordinary least square |
CO2 | carbon dioxide |
CV | cross-validation |
AIC | Akaike information criterion |
VIF | variance inflation factor |
C-D | Cobb–Douglas |
LR | likelihood ratio |
References
- Wang, Z.; Xiao, H. Analysis of chemical fertilizer on the growth of grain output. Issues Agric. Econ. 2008, 8, 65–68. [Google Scholar]
- Yang, J.; Lin, Y. Spatiotemporal evolution and driving factors of fertilizer reduction control in Zhejiang Province. Sci. Total Environ. 2019, 660, 650–659. [Google Scholar] [CrossRef] [PubMed]
- Vitousek, P.M.; Naylor, R.; Crews, T.; David, M.B.; Drinkwater, L.E.; Holland, E.; Johnes, P.J.; Katzenberger, J.; Martinelli, L.A.; Matson, P.A.; et al. Nutrient imbalances in agricultural development. Science 2009, 324, 1519–1560. [Google Scholar] [CrossRef] [PubMed]
- Yang, X.; Lu, Y.; Ding, Y. Optimising nitrogen fertilisation: A key to improving nitrogen-use efficiency and minimising nitrate leaching losses in an intensive wheat/maize rotation (2008–2014). Field Crop Res. 2017, 206, 1–10. [Google Scholar] [CrossRef]
- Bai, X.; Wang, Y.; Huo, X.; Salim, R.; Bloch, H.; Zhang, H. Assessing fertilizer use efficiency and its determinants for apple production in China. Ecol. Indic. 2019, 104, 268–278. [Google Scholar] [CrossRef]
- Duan, Y.; Xu, M.; Gao, S.; Yang, X.; Huang, S.; Liu, H.; Wang, B. Nitrogen use efficiency in a wheat–corn cropping system from 15 years of manure and fertilizer applications. Field Crop Res. 2014, 157, 47–56. [Google Scholar] [CrossRef]
- Aguilera, E.; Lassaletta, L.; Gattinger, A.; Gimeno, B. Managing soil carbon for climate change mitigation and adaptation in Mediterranean cropping systems: A meta-analysis. Agric. Ecosyst. Environ. 2013, 168, 25–36. [Google Scholar] [CrossRef]
- Cao, Q.; Miao, Y.; Feng, G.; Gao, X.; Liu, B.; Liu, Y.; Li, F.; Khosla, R.; Mulla, D.; Zhang, F.; et al. Improving nitrogen use efficiency with minimal environmental risks using an active canopy sensor in a wheat-maize cropping system. Field Crop Res. 2017, 214, 365–372. [Google Scholar] [CrossRef]
- Domagalski, J.; Lin, C.; Luo, Y.; Kang, J.; Munn, M. Eutrophication study at the Panjiakou-Daheiting reservoir system, northern Hebei province, People’s Republic of China of China: ChlorophyII-a model and sources of phosphorus and nitrogen. Agric. Water Manag. 2007, 94, 43–53. [Google Scholar] [CrossRef]
- Gu, B.J.; Ge, Y.; Chang, S.X.; Luo, W.D.; Chang, J. Nitrate in groundwater of China: Sources and driving forces. Glob. Environ. Chang. 2013, 23, 1112–1121. [Google Scholar] [CrossRef]
- Erisman, J.W.; Galloway, J.N.; Seitzinger, S.; Bleeker, A.; Dise, N.B.; Petrescu, A.M.; Leach, A.M.; Vries, W. Consequences of human modification of the global nitrogen cycle. Philos. Trans. R. Soc. B 2013, 368, 20130116. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wu, Y.; Xi, X.; Tang, X.; Luo, D.; Gu, B.; Lam, S.; Vitousek, P.; Chen, D. Policy distortions, farm size, and the overuse of agricultural chemicals in China. Proc. Natl. Acad. Sci. USA 2018, 115, 7010–7015. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lassaletta, L.; Billen, G.; Grizzetti, B.; Anglade, J.; Garnier, J. 50 year trends in nitrogen use efficiency of world cropping systems: The relationship between yield and nitrogen input to cropland. Environ. Resour. Lett. 2014, 9, 105011. [Google Scholar] [CrossRef]
- Liang, S.; Li, Y.; Zhang, X.; Sun, Z.; Sun, N.; Duan, Y.; Xu, M.; Wu, L. Response of crop yield and nitrogen use efficiency for wheat-maize cropping system to future climate change in northern China. Agric. Forest Meteorol. 2018, 262, 310–321. [Google Scholar] [CrossRef]
- Wu, Y. Chemical fertilizer use efficiency and its determinants in China’s farming sector. China Agric. Econ. Rev. 2011, 3, 117–130. [Google Scholar] [CrossRef]
- Ma, L.; Feng, S.; Reidsma, P.; Qu, F.; Heerink, N. Identifying entry points to improve fertilizer use efficiency in Taihu Basin, China. Land Use Policy 2014, 37, 52–59. [Google Scholar] [CrossRef]
- Ladha, J.K.; Chakraborty, D. Nitrogen and cereal production: Opportunities for enhanced efficiency and reduced N losses. In Proceedings of the 2016 International Nitrogen Initiative Conference, Solutions to Improve Nitrogen Use Efficiency for the World, Melbourne, Australia, 4–8 December 2016. [Google Scholar]
- Rasool, G.; Guo, X.; Wang, A.; Ali, M.U.; Chen, S.; Zhang, S.; Wu, Q.; Ullah, M.S. Coupling fertigation and buried straw layer improves fertilizer use efficiency, fruit yield, and quality of greenhouse tomato. Agric. Water Manag. 2020, 239, 106239. [Google Scholar] [CrossRef]
- Liang, L.; Shi, W. Poly-γ-glutamic acid improves water-stable aggregates, nitrogen and phosphorus uptake efficiency, water-fertilizer productivity, and economic benefit in barren desertified soils of Northwest China. Agric. Water Manag. 2020, 245, 106551. [Google Scholar] [CrossRef]
- Gutierrez, E.; Aguilera, E.; Lozano, S.; Guzman, G. A two-stage DEA approach for quantifying and analysing the inefficiency of conventional and organic rain-fed cereals in Spain. J. Clean. Prod. 2017, 149, 335–348. [Google Scholar] [CrossRef]
- Reinhard, S.; Lovell, C.A.K.; Thijssen, G. Econometric estimation of technical and environmental efficiency: An application to Dutch dairy farms. Am. J. Agric. Econ. 1999, 81, 44–66. [Google Scholar] [CrossRef]
- Godoy-Durán, Á.; Galdeano-Gómez, E.; Pérez-Mesa, J.C.; Piedra-Muñoz, L. Assessing eco-efficiency and the determinants of horticultural family farming in southeast Spain. J. Environ. Manag. 2017, 204, 594–604. [Google Scholar] [CrossRef] [PubMed]
- Angulo-Meza, L.; González-Araya, M.; Iriarte, A.; Rebolledo-Leiva, R.; De Mello, J.C.S. A multiobjective DEA model to assess the eco-efficiency of agricultural practices within the CF + DEA method. Comput. Electron. Agric. 2019, 161, 151–161. [Google Scholar] [CrossRef]
- Expósito, A.; Velasco, F. Exploring environmental efficiency of the European agricultural sector in the use of mineral fertilizers. J. Clean. Prod. 2020, 253, 119971. [Google Scholar] [CrossRef]
- Reinhard, S.; Lovell, C.A.K.; Thijssen, G.J. Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur. J. Oper. Res. 2000, 121, 287–303. [Google Scholar] [CrossRef]
- Benedetti, I.; Branca, G.; Zucaro, R. Evaluating input use efficiency in agriculture through a stochastic frontier production: An application on a case study in Apulia (Italy). J. Clean. Prod. 2019, 236, 117609. [Google Scholar] [CrossRef]
- Loureiro, M. Farmers’ health and agricultural productivity. Agric. Econ. 2009, 40, 381–388. [Google Scholar] [CrossRef]
- Nasrin, M.; Bauer, S.; Arman, M. Dataset on measuring perception about fertilizer subsidy policy and factors behind differential farm level fertilizer usage in Bangladesh. Data Brief 2019, 22, 851–858. [Google Scholar] [CrossRef]
- Naseem, A.; Kelly, V.A. Macro Trends and Determinates of Fertilizer Use in Sub-Saharan Africa; Technical Report No. 1096-2016-88389; Food Security International Development Working Papers: East Lansing, MI, USA, 1999. [Google Scholar]
- Zhang, B.; Bai, X. Fertilizer use efficiency and its affecting factors in apple production in Loess Plateau. J. Arid Land Res. Environ. 2017, 31, 55–61. [Google Scholar]
- Zhang, X.; Davidson, E.A.; Mauzerall, D.L.; Searchinger, T.D.; Dumas, P.; Shen, Y. Managing nitrogen for sustainable development. Nature 2015, 528, 51–59. [Google Scholar] [CrossRef] [Green Version]
- Gu, B.; Ju, X.; Wu, Y.; Erisman, J.W.; Bleeker, A.; Reis, S.; Sutton, M.A.; Lam, S.K.; Smith, P.; Oenema, O.; et al. Cleaning up nitrogen pollution may reduce future carbon sinks. Glob. Environ. Chang. 2018, 48, 56–66. [Google Scholar] [CrossRef] [Green Version]
- Lamb, R. Fertilizer use, risk, and off-farm labor markets in the semi-arid tropics of India. Am. J. Agric. Econ. 2003, 85, 359–371. [Google Scholar] [CrossRef] [Green Version]
- Shi, J.; Zhu, J.; Luan, J. Fertilizer use efficiency of wheat production in China and its determinants. Agrotech. Econ. 2015, 11, 69–78. [Google Scholar]
- Ma, J. Fertilizer use of food crops and its influencing factors. J. Agrotech. Econ. 2006, 6, 36–42. [Google Scholar]
- Wang, Y.; Chen, W.; Kang, Y.; Li, W.; Guo, F. Spatial correlation of factors affecting CO2 emission at provincial level in China: A geographically weighted regression approach. J. Clean. Prod. 2018, 184, 929–937. [Google Scholar] [CrossRef]
- Koh, E.H.; Lee, E.; Lee, K.K. Application of geographically weighted regression models to predict spatial characteristics of nitrate contamination: Implications for an effective groundwater management strategy. J. Environ. Manag. 2020, 268, 110646. [Google Scholar] [CrossRef] [PubMed]
- Zhou, Q.; Wang, C.; Fang, S. Application of geographically weighted regression (GWR) in the analysis of the cause of haze pollution in China. Atmos. Pollut. Res. 2019, 10, 835–846. [Google Scholar] [CrossRef]
- Wu, D. Spatially and temporally varying relationships between ecological footprint and influencing factors in China’s provinces using Geographically Weighted Regression. J. Clean. Prod. 2020, 261, 121089. [Google Scholar] [CrossRef]
- Robinson, D.P.; Lloyd, C.D.; McKinley, J.M. Increasing the accuracy of nitrogen dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics. Int. J. Appl. Earth Obs. 2013, 21, 374–383. [Google Scholar] [CrossRef]
- Battese, G.E.; Coelli, T.J. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir. Econ. 1995, 20, 325–332. [Google Scholar] [CrossRef] [Green Version]
- Griffith, D.A. Spatial Autocorrelation: A Primer Resource Publications in Geography; Association of American Geographers: Washington, DC, USA, 1987. [Google Scholar]
- Brunsdon, C.; Fotheringham, A.S.; Charlton, M. Geographically weighted regression: A method for exploring spatial non-stationarity. Geogr. Anal. 1996, 28, 281–298. [Google Scholar] [CrossRef]
- Fotheringham, A.S. Spatial variations in school performance: A local analysis using geographically weighted regression. Geogr. Environ. Mode 2001, 5, 43–66. [Google Scholar] [CrossRef]
- Chen, W.; Shen, Y.; Wang, Y.; Wu, Q. How do industrial land price variations affect industrial diffusion? Evidence from a spatial analysis of China. Land Use Policy 2018, 71, 384–394. [Google Scholar] [CrossRef]
- Bowman, A.W. An alternative method of cross-validation for the smoothing of density estimates. Biometrika 1984, 71, 353–360. [Google Scholar] [CrossRef]
- Bai, X.; Salim, R.; Bloch, H. Environmental efficiency of apple production in China. Agri. Res. Econ. Rev. 2019, 48, 199–220. [Google Scholar]
- Singbo, A.G.; Oude, L.A.; Emvalomatis, G. Estimating shadow prices and efficiency analysis of productive inputs and pesticide use of vegetable production. Eur. J. Oper. Res. 2015, 245, 265–272. [Google Scholar] [CrossRef]
- Wang, S.; Liu, Y.; Tian, X.; Yan, B. The estimation of fertilizer use efficiency in agriculture and the improving ways. J. Environ. Econ. 2017, 3, 101–114. [Google Scholar]
- Yang, J.; Lin, Y. Driving factors of total-factor substitution efficiency of chemical fertilizer input and related environmental regulation policy: A case study of Zhejiang Province. Environ. Pollut. 2020, 263, 114541. [Google Scholar] [CrossRef]
- Yang, Q.; Zhu, Y.; Wang, J. Adoption of drip fertigation system and technical efficiency of cherry tomato farmers in Southern China. J. Clean. Prod. 2020, 275, 123980. [Google Scholar] [CrossRef]
Null Hypothesis | Degree of Freedom (k) | LR Test | Threshold χ20.05(k) | Decision |
---|---|---|---|---|
C-D production function H0: β7 = β8 = β9 = …= β27 = 0 | 21 | 72.174 | 32.670 | Reject |
No technical progress H0: β6 = β12 = β17 = β21 = β24 = β26 = β27 = 0 | 7 | 780.048 | 14.067 | Reject |
Neutral technical progress H0: β17 = β21 = β24 = β26 = β27 = 0 | 5 | 116.652 | 11.070 | Reject |
Heteroscedastic variance of stochastic errorH0: δ1 = 0 | 3 | 6.326 | 7.045 | Accept |
Heteroscedastic variance of inefficiency errorH0: δ2 = 0 | 4 | 70.245 | 8.761 | Reject |
Variable | Coefficient | Standard Error | Variable | Coefficient | Standard Error |
---|---|---|---|---|---|
Constant(β0) | −0.189 | 0.151 | lnFer*Time(β17) | 0.029 ** | 0.014 |
lnFer(β1) | 1.506 | 1.334 | lnLa*lnAr(β18) | −0.149 | 0.089 |
lnLa(β2) | 3.269 *** | 1.228 | lnLa*lnMe(β19) | −0.822 *** | 0.204 |
lnAr(β3) | −0.017 | 0.502 | lnLa*lnPe(β20) | −0.240 | 0.154 |
lnMe(β4) | −2.787 *** | 0.991 | lnLa*Time(β21) | 0.053 *** | 0.014 |
lnPe(β5) | 1.792 *** | 0.690 | lnAr*lnMe(β22) | 0.221 *** | 0.080 |
Time(β6) | 0.309 *** | 0.059 | lnAr*lnPe(β23) | −0.038 | 0.048 |
lnFer*lnFer(β7) | −0.001 | 0.185 | lnAr*Time(β24) | 0.006 | 0.010 |
lnLa*lnLa(β8) | 0.011 | 0.163 | lnMe*lnPe(β25) | 0.277 ** | 0.120 |
lnAr*lnAr(β9) | −0.021 | 0.064 | lnMe*Time(β26) | −0.076 *** | 0.013 |
lnMe*lnMe(β10) | 0.416 *** | 0.144 | lnPe*Time(β27) | −0.016 ** | 0.007 |
lnPe*lnPe(β11) | 0.001 | 0.106 | Inefficiency variance (σu2) | ||
Time*Time(β12) | 0.007 *** | 0.001 | edu | 0.034 | 0.102 |
lnFer*lnla(β13) | 1.436 *** | 0.311 | income | 0.0003 *** | 0.000 |
lnFer*lnAr(β14) | 0.004 | 0.097 | nonagr | 3.118 *** | 0.954 |
lnFer*lnMe(β15) | −0.582 ** | 0.253 | disa | −0.112 | 0.592 |
lnFer*lnPe(β16) | −0.542 *** | 0.137 | constant | −3.916 *** | 0.855 |
look likelihood | 146.913 | ||||
Hausman test | Chi-square = 64.31 | p-value = 0.000 | |||
Mean TE 0.816 | (min, max) | (0.163, 0.983) |
Province | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.316 | 0.323 | 0.336 | 0.342 | 0.353 | 0.364 | 0.373 | 0.386 | 0.385 | 0.394 | 0.102 | 0.334 |
Tianjin | 0.278 | 0.273 | 0.286 | 0.295 | 0.316 | 0.327 | 0.387 | 0.421 | 0.524 | 0.645 | 0.084 | 0.349 |
Hebei | 0.873 | 0.899 | 0.907 | 0.931 | 0.940 | 0.906 | 0.896 | 0.716 | 0.535 | 0.901 | 0.440 | 0.813 |
Shanxi | 0.765 | 0.749 | 0.947 | 0.963 | 0.962 | 0.926 | 0.865 | 0.761 | 0.566 | 0.682 | 0.382 | 0.779 |
Inner Mongolia | 0.921 | 0.905 | 0.831 | 0.840 | 0.865 | 0.890 | 0.833 | 0.580 | 0.388 | 0.894 | 0.191 | 0.740 |
Liaoning | 0.932 | 0.880 | 0.784 | 0.922 | 0.806 | 0.746 | 0.696 | 0.524 | 0.480 | 0.943 | 0.243 | 0.723 |
Jilin | 0.899 | 0.896 | 0.770 | 0.650 | 0.663 | 0.716 | 0.439 | 0.362 | 0.137 | 0.822 | 0.018 | 0.579 |
Heilongjiang | 0.897 | 0.718 | 0.718 | 0.474 | 0.682 | 0.685 | 0.808 | 0.578 | 0.225 | 0.848 | 0.101 | 0.612 |
Shanghai | 0.274 | 0.276 | 0.283 | 0.243 | 0.247 | 0.249 | 0.251 | 0.252 | 0.253 | 0.342 | 0.062 | 0.248 |
Jiangsu | 0.863 | 0.836 | 0.809 | 0.778 | 0.795 | 0.699 | 0.463 | 0.333 | 0.216 | 0.896 | 0.163 | 0.623 |
Zhejiang | 0.812 | 0.862 | 0.810 | 0.808 | 0.832 | 0.577 | 0.462 | 0.351 | 0.280 | 0.871 | 0.192 | 0.623 |
Anhui | 0.910 | 0.962 | 0.928 | 0.960 | 0.953 | 0.867 | 0.786 | 0.695 | 0.519 | 0.954 | 0.408 | 0.813 |
Fujian | 0.951 | 0.901 | 0.861 | 0.904 | 0.874 | 0.808 | 0.761 | 0.620 | 0.506 | 0.966 | 0.331 | 0.771 |
Jiangxi | 0.950 | 0.924 | 0.826 | 0.645 | 0.505 | 0.223 | 0.643 | 0.488 | 0.417 | 0.964 | 0.231 | 0.620 |
Shandong | 0.859 | 0.942 | 0.931 | 0.907 | 0.871 | 0.649 | 0.642 | 0.532 | 0.424 | 0.837 | 0.304 | 0.718 |
Henan | 0.962 | 0.963 | 0.957 | 0.974 | 0.960 | 0.726 | 0.846 | 0.812 | 0.694 | 0.891 | 0.523 | 0.846 |
Hubei | 0.779 | 0.825 | 0.760 | 0.877 | 0.911 | 0.824 | 0.519 | 0.283 | 0.102 | 0.911 | 0.125 | 0.629 |
Hunan | 0.938 | 0.940 | 0.899 | 0.969 | 0.961 | 0.933 | 0.839 | 0.755 | 0.671 | 0.963 | 0.519 | 0.853 |
Guangdong | 0.982 | 0.965 | 0.926 | 0.943 | 0.940 | 0.915 | 0.824 | 0.773 | 0.676 | 0.887 | 0.491 | 0.847 |
Guangxi | 0.978 | 0.970 | 0.914 | 0.956 | 0.945 | 0.896 | 0.850 | 0.783 | 0.731 | 0.982 | 0.594 | 0.873 |
Hainan | 0.835 | 0.710 | 0.476 | 0.584 | 0.463 | 0.594 | 0.306 | 0.299 | 0.227 | 0.918 | 0.105 | 0.502 |
Chongqing | 0.985 | 0.973 | 0.959 | 0.976 | 0.958 | 0.947 | 0.846 | 0.793 | 0.682 | 0.931 | 0.539 | 0.872 |
Sichuan | 0.973 | 0.977 | 0.964 | 0.979 | 0.977 | 0.953 | 0.912 | 0.858 | 0.812 | 0.978 | 0.691 | 0.916 |
Guizhou | 0.989 | 0.992 | 0.981 | 0.988 | 0.928 | 0.957 | 0.920 | 0.943 | 0.955 | 0.962 | 0.830 | 0.950 |
Yunnan | 0.980 | 0.980 | 0.970 | 0.954 | 0.898 | 0.969 | 0.937 | 0.904 | 0.780 | 0.967 | 0.668 | 0.910 |
Tibet | 0.956 | 0.901 | 0.957 | 0.905 | 0.947 | 0.856 | 0.647 | 0.676 | 0.506 | 0.972 | 0.564 | 0.808 |
Shaanxi | 0.969 | 0.980 | 0.975 | 0.979 | 0.985 | 0.930 | 0.916 | 0.909 | 0.873 | 0.907 | 0.677 | 0.918 |
Gansu | 0.909 | 0.874 | 0.867 | 0.946 | 0.970 | 0.955 | 0.948 | 0.836 | 0.697 | 0.771 | 0.566 | 0.849 |
Qinghai | 0.948 | 0.952 | 0.877 | 0.927 | 0.970 | 0.922 | 0.805 | 0.736 | 0.567 | 0.983 | 0.494 | 0.835 |
Ningxia | 0.920 | 0.943 | 0.909 | 0.916 | 0.930 | 0.802 | 0.646 | 0.429 | 0.265 | 0.726 | 0.186 | 0.697 |
Xinjiang | 0.951 | 0.883 | 0.844 | 0.944 | 0.904 | 0.786 | 0.673 | 0.754 | 0.393 | 0.564 | 0.245 | 0.722 |
mean | 0.857 | 0.844 | 0.815 | 0.822 | 0.816 | 0.761 | 0.701 | 0.618 | 0.500 | 0.847 | 0.357 | 0.722 |
Index/Year | 2007 | 2010 | 2014 | 2017 |
---|---|---|---|---|
Moran’s I | 0.235 | 0.247 | 0.333 | 0.444 |
p-value | 0.003 *** | 0.004 *** | 0.000 *** | 0.000 *** |
Z-value | 2.700 | 2.662 | 3.332 | 4.349 |
Parameter | Year | |||||||
---|---|---|---|---|---|---|---|---|
2007 | 2010 | 2014 | 2017 | |||||
Min. | Max. | Min. | Max. | Min. | Max. | Min. | Max. | |
Intercept | 1.233719 *** | 1.234685 *** | 1.066785 *** | 1.117555 *** | 0.853885 *** | 0.855233 *** | 1.043769 *** | 1.062382 *** |
Education level | −0.002037 | −0.001865 | −0.005770 | 0.002720 | 0.021067 | 0.021201 | −0.043542 | −0.041685 |
Non-agriculture employment ratio | 0.109457 | 0.110484 | 0.525146 *** | 0.813680*** | 0.277719 | 0.279154 | 0.601153** | 0.604344 ** |
Disaster ratio | −0.066262 | −0.065314 | −0.019600 | 0.131110 | 0.013681 | 0.014561 | −0.473800 * | −0.470500 * |
Farmers’ income | −0.087294 *** | −0.087285 *** | −0.095688 *** | −0.087224 *** | −0.05095 *** | −0.05094 *** | -0.043317 *** | −0.043266 *** |
Bandwidth | 431.058 | 45.520 | 431.058 | 185.083 | ||||
AIC | −28.619 | −22.526 | −15.936 | −11.376 | ||||
Adjusted R2 | 0.571 | 0.574 | 0.423 | 0.414 |
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Bai, X.; Zhang, T.; Tian, S. Evaluating Fertilizer Use Efficiency and Spatial Correlation of Its Determinants in China: A Geographically Weighted Regression Approach. Int. J. Environ. Res. Public Health 2020, 17, 8830. https://doi.org/10.3390/ijerph17238830
Bai X, Zhang T, Tian S. Evaluating Fertilizer Use Efficiency and Spatial Correlation of Its Determinants in China: A Geographically Weighted Regression Approach. International Journal of Environmental Research and Public Health. 2020; 17(23):8830. https://doi.org/10.3390/ijerph17238830
Chicago/Turabian StyleBai, Xiuguang, Tianwen Zhang, and Shujuan Tian. 2020. "Evaluating Fertilizer Use Efficiency and Spatial Correlation of Its Determinants in China: A Geographically Weighted Regression Approach" International Journal of Environmental Research and Public Health 17, no. 23: 8830. https://doi.org/10.3390/ijerph17238830