An approach to understanding persistent yield variation-A case study in North China Plain

European Journal of Agronomy

Volumn 77, Issue , 2016, Pages 10-19

  Zhao, Yi ^a   Chen, XinPing ^b   Lobell, David B ^a  

^a STANFORD UNIVERSITY   (United States)

^b CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

China; Maize; Remote sensing; Survey

Indexed keywords

CROP YIELD; CULTIVAR; FARMERS KNOWLEDGE; FIELD SURVEY; FOOD SECURITY; HIGH YIELDING VARIETY; IRRIGATION SYSTEM; MAIZE; REMOTE SENSING; WATER QUALITY; WHEAT;

CHINA; HEBEI; NORTH CHINA PLAIN; QUZHOU;

TRITICUM AESTIVUM; ZEA MAYS;

EID: 84962282130     PISSN: 11610301     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eja.2016.03.006     Document Type: Article

References (38)

1. Affholder F., Poeydebat C., Corbeels M., Scopel E., Tittonell P. The yield gap of major food crops in family agriculture in the tropics: assessment and analysis through field surveys and modelling. Field Crops Res. 2013, 143:106-118.
2. Bai J., Xu Z., Qiu H., Liu H. Optimising seed portfolios to cope ex ante with risks from bad weather: evidence from a recent maize farmer survey in China. Aust. J. Agric. Resour. Econ. 2015, 59:242-257.
3. Cai Z.C., Qin S.W. Dynamics of crop yields and soil organic carbon in a long-term fertilization experiment in the Huang-Huai-Hai Plain of China. Geoderma 2006, 136:708-715.
4. Cerrato M.E., Blackmer A.M. Comparison of models for describing; corn yield response to nitrogen fertilizer. Agron. J. 1990, 82:138.
5. Chen X.-P., Cui Z.-L., Vitousek P.M., Cassman K.G., Matson P.A., Bai J.-S., Meng Q.-F., et al. Integrated soil-crop system management for food security. Proc. Natl. Acad. Sci. U. S. A. 2011, 108:6399-6404.
6. Cutler, F., original by L.B. and A., and R. port by A.L. and Weiner, M., 2014. randomForest: Breiman and Cutler's random forests for classification and regression. Available at: (accessed 29.04.15). http://cran.r-project.org/web/packages/randomForest/index.html.
7. Dai X., Huo Z., Wang H. Simulation for response of crop yield to soil moisture and salinity with artificial neural network. Field Crops Res. 2011, 121:441-449.
8. Dang Y.P., Pringle M.J., Schmidt M., Dalal R.C., Apan A. Identifying the spatial variability of soil constraints using multi-year remote sensing. Field Crops Res. 2011, 123:248-258.
9. Derksen S., Keselman H. Backward. forward and stepwise automated subset-aelection algorithms-frequency of obtaining authentic and noise variables. Br. J. Math. Stat. Psychol. 1992, 45:265-282.
10. Fermont A.M., van Asten P.J.A., Tittonell P., van Wijk M.T., Giller K.E. Closing the cassava yield gap: an analysis from smallholder farms in East Africa. Field Crops Res. 2009, 112:24-36.
11. Fernandez-Cornejo J., Hendricks C., Mishra A. Technology adoption and off-farm household income: the case of herbicide-tolerant soybeans. J. Agric. Appl. Econ. 2005, 37:549-563.
12. Gong W., Yan X., Wang J., Hu T., Gong Y. Long-term manure and fertilizer effects on soil organic matter fractions and microbes under a wheat-maize cropping system in northern China. Geoderma 2009, 149:318-324.
13. Ha N., Feike T., Back H., Xiao H., Bahrs E. The effect of simple nitrogen fertilizer recommendation strategies on product carbon footprint and gross margin of wheat and maize production in the North China Plain. J. Environ. Manage. 2015, 163:146-154.
14. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning 2009, Springer, New York.
15. Hawkins E., Fricker T.E., Challinor A.J., Ferro C.A.T., Ho C.K., Osborne T.M. Increasing influence of heat stress on French maize yields from the 1960 to the 2030. Global Change Biol. 2013, 19:937-947.
16. van Ittersum M.K., Cassman K.G., Grassini P., Wolf J., Tittonell P., Hochman Z. Yield gap analysis with local to global relevance-a review. Field Crops Res. 2013, 143:4-17.
17. de Janvry A., Sadoulet E., Zhu N. The Role of Non-Farm Incomes in Reducing Rural Poverty and Inequality in China 2005, Department of Agricultural & Resource Economics, UCB, Available at: http://escholarship.org/uc/item/7ts2z766 (accessed 30.01.15).
18. Ju X.-T., Xing G.-X., Chen X.-P., Zhang S.-L., Zhang L.-J., Liu X.-J., Cui Z.-L., et al. Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proc. Natl. Acad. Sci. U. S. A. 2009, 106:3041-3046.
19. Kang Y., Chen M., Wan S. Effects of drip irrigation with saline water on waxy maize (Zea mays L. var. ceratina Kulesh) in North China Plain. Agric. Water Manage. 2010, 97:1303-1309.
20. Liang W., Carberry P., Wang G., Lü R., Lü H., Xia A. Quantifying the yield gap in wheat-maize cropping systems of the Hebei Plain, China. Field Crops Res. 2011, 124:180-185.
21. Lobell D.B., Ortiz-Monasterio J.I., Asner G.P., Naylor R.L., Falcon W.P. Combining field surveys remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agron. J. 2005, 97:241-249.
22. Lobell D.B., Ortiz-Monasterio J.I., Gurrola F.C., Valenzuela L. Identification of saline soils with multiyear remote sensing of crop yields. Soil Sci. Soc. Am. J. 2007, 71:777.
23. Lobell D.B., Cassman K.G., Field C.B. Crop yield gaps: their importance magnitudes, and causes. Annu. Rev. Environ. Resour. 2009, 34:179-204.
24. Lobell D.B., Roberts M.J., Schlenker W., Braun N., Little B.B., Rejesus R.M., Hammer G.L. Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest. Science 2014, 344:516-519.
25. Ma W., Mao Z., Yu Z., van Mensvoort M.E.F., Driessen P.M. Effects of saline water irrigation on soil salinity and yield of winter wheat-maize in North China Plain. Irrig. Drain. Syst. 2008, 22:3-18.
26. Meng Q., Hou P., Wu L., Chen X., Cui Z., Zhang F. Understanding production potentials and yield gaps in intensive maize production in China. Field Crops Res. 2013, 143:91-97.
27. Miao Y., Mulla D.J., Robert P.C. Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precis. Agric. 2006, 7:117-135.
28. Neumann K., Verburg P.H., Stehfest E., Müller C. The yield gap of global grain production: a spatial analysis. Agric. Syst. 2010, 103:316-326.
29. Park S.J., Hwang C.S., Vlek P.L.G. Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agric. Syst. 2005, 85:59-81.
30. Prost L., Makowski D., Jeuffroy M.-H. Comparison of stepwise selection and Bayesian model averaging for yield gap analysis. Ecol. Modell. 2008, 219:66-76.
31. Sheng F., Xiuling C. Using shallow saline groundwater for irrigation and regulating for soil salt-water regime. Irrig. Drain. Syst. 1997, 11:1-14.
32. Taylor J.E., Lopez-Feldman A. Does migration make rural households more productive? Evidence from Mexico. J. Dev. Stud. 2010, 46:68-90.
33. Taylor J.E., Rozelle S., de Brauw A. Migration and incomes in source communities: a new economics of migration perspective from China. Econ. Dev. Cult. Change 2003, 52:75-101.
34. Tittonell P., Shepherd K.D., Vanlauwe B., Giller K.E. Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya-an application of classification and regression tree analysis. Agric. Ecosyst. Environ. 2008, 123:137-150.
35. Whittingham M.J., Stephens P.A., Bradbury R.B., Freckleton R.P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 2006, 75:1182-1189.
36. Zhao Y., Chen X., Cui Z., Lobell D.B. Using satellite remote sensing to understand maize yield gaps in the North China Plain. Field Crops Res. 2015, 183:31-42.
37. Zhen L., Zoebisch M.A., Chen G., Feng Z. Sustainability of farmers soil fertility management practices: a case study in the North China Plain. J. Environ. Manage. 2006, 79:409-419.
38. Zheng H., Chen L., Han X., Zhao X., Ma Y. Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: the importance of phosphorus application rates under drought conditions. Agric. Ecosyst. Environ. 2009, 132:98-105.