Publications of the model and its applications

1,Yang et al, 2004. Hybrid-Maize - A maize simulation model that combines two crop modeling approaches.

2, Yang et al, 2003. Comparison and hybridization of two approaches for maize simulation.

3, Dobermann at al, 2003 Understanding corn yield potential in different environments.

4, Dobermann and Yang, 2003. Growing corn in a computer - The Hybrid-Maize simulation model and its application to production agriculture (Presented in the 2003 Research Highlights of the Dept of Agronomy and Horticulture, UNL).

5, Yang et al, 2003. A simulation approach for evaluating maize yield potentail in difference environments (Presented in the 2003 ASA Annual Meeting).

6, Walters and Dobermann, 2004. Plant population and fertilization impacts on irrigated corn in Nebraska.

7, Dobermann and Waterls, 2004. What was my attainable yield potential for corn in 2003?

8, Yang et al, 2004. Test of the Hybrid-Maize model for simulation of soil moisture dynamics and maize response to water defici.

9, Dobermann et al, 2004. In-season prediction of attainable maize yield using the Hybrid-Maize model (Poster presented in the VIII ESA Congress: European Agriculture in A Global Context. KVL, Copenhagen, Danmark, 11-15 July 2004).

10, Use of Hybrid--Maize to improve management decisions

11, Yang et al, 2006. Features, Applications, and Limitations of the Hybrid-Maize Simulation Model.

12, Liu et al, 2008. Adaptability of Hybrid-Maize model and potential productivity estimation of spring maize on dry highland of Loess Plateau

13, Ping et al, 2008. Site-Specifi c Nitrogen and Plant Density Management in Irrigated Maize

14, Grassini et al, 2009. Limits to maize productivity in Western Corn-Belt: A simulation analysis for fully irrigated and rainfed conditions

15, Lobell et al, 2009. Crop Yield Gaps: Their Importance, Magnitudes, and Causes.

16, Raymont et al, 2009. Reducing Corn Yield Variability and Enhancing Yield Through the Use of Corn-Specific Growth Models

17, Bai at al, 2010. Evaluation of NASA Satellite- and Model-Derived Weather Data for Simulation of Maize Yield Potential in China.

18, Cassman et al, 2010. Crop yield potential, yield trend, and global food security in a changing climate.

19, Fischer et al, 2010. Breeding and Cereal Yield Progress.

20, Timsina et al, 2010. Rice-maize systems of South Asia: current status, future prospects and research priorities for nutrient management

21, Chen et al, 2011. Integrated soil-crop system management for food security.

22, Grassini et al, 2011. High-yield irrigated maize in the Western U.S. Corn Belt: II. Irrigation management and crop water productivity.

23, Grassini et al, 2011. High-yield irrigated maize in the Western U.S. Corn Belt: I. On-farm yield, yield potential, and impact of agronomic practices

24, Setiyono et al, 2011. Maize-N: A Decision Tool for Nitrogen Management in Maize.

25, Walters et al, 2011. Moving towards a global approach to nitrogen management in maize.

26, Chen et al, 2012. Factors affecting summer maize yield under climate change in Shandong Province in the Huanghuaihai Region of China

27, Grassini et al, 2012. Evaluation of water productivity and irrigation efficiency in Nebraska cron production

28, Liu et al, 2012. Application of the Hybrid-Maize model for limits to maize productivity analysis in a semiarid environment

29, Meng et al, 2012. Alternative cropping systems for sustainable water and nitrogen use in the North China Plain

30, Yang et al, 2012. Progress of Crop Model Research

31, Chen et al, 2013. Modern maize hybrids in Northeast China exhibit increased yield potential and resource use efficiency despite adverse climate change

32, Fan et al, 2013. Current Status and Future Perspectives to Increase Nutrient- and Water-Use Efficiency in Food Production Systems in China

33, Meng et al, 2013. Understanding production potentials and yield gaps in intensive maize production in China

34, Schulthess et al, 2013. Mapping field-scale yield gaps for maize: An example from Bangladesh

35, Sibley et al, 2013. Testing Remote Sensing Approaches for Assessing Yield Variability among Maize Fields

36, Van Wart et al, 2013. Impact of derived global weather data on simulated crop yields

37, Van Wart et al, 2013. Estimating crop yield potential at regional to national scales

38, Bu et al, 2014. Attainable yield achieved for plastic film-mulched maize in response to nitrogen deficit.

39, Hou et al, 2014. Temporal and spatial variation in accumulated temperature requirements of maize.

40, Meng et al, 2014. The benefits of recent warming for maize production in high latitude China

41, Zhao, Yi, Chen, Xinping, Cui, Zhenling, and Lobell, David B. 2015. Using satellite remote sensing to understand maize yield gaps in the North China Plain. Field Crops Research. 183, 31-42

42, Liu, Xing, Andresen, Jeff, Yang, Haishun, and Niyogi, Dev. 2015. Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt. Earth Interactions. 19, 1-16.

43, Bu, Lingduo, Chen, Xinping, Li, Shiqing, Liu, Jianliang, Zhu, Lin, Luo, Shasha, Lee Hill, Robert, and Zhao, Ying. 2015. The effect of adapting cultivars on the water use efficiency of dryland maize (Zea mays L.) in northwestern China. Agricultural Water Management. 148, 1-9

44, Morell, Francisco J., Yang, H. S., Cassman, Kenneth G., van Wart, Justin, Elmore, Roger W., Licht, Mark, Coulter, Jeffrey A., Ciampitti, Ignacio A., Pittelkow, Cameron M., and Brouder, Sylvie M. 2016. Can crop simulation models be used to predict local to regional maize yields and total production in the US Corn Belt? Field Crops Research. 192, 1-12

45, Jin, Zhenong, Zhuang, Qianlai, Tan, Zeli, Dukes, Jeffrey S., Zheng, Bangyou, and Melillo, Jerry M. 2016. Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches. Global Change Biology. 22, 3112-3126

46, Carr, Tony, Yang, Haishun, and Ray, Chittaranjan. 2016. Temporal Variations of Water Productivity in Irrigated Corn: An Analysis of Factors Influencing Yield and Water Use across Central Nebraska. PLOS One. 11, e0161944-DOI:10.1371/journal.pone.0161944

47, van Ittersum and, M. K., van Bussel, L. G. J., Wolf, J, Grassini, P, van Wart, JP, Guilpart, Nicolas, Claessens, L, de Groot, H, Wiebe Keith, Mason-D'Croz Daniel, Yang, H. S., Boogaard, H, van Oort, P. A. J., van Loon, MP, Saito, K., Adimo, O, Adjei-Nsiah, S, Agali, A, Bala, A, Chikowo, R, Kaizzi, K, Kouressy, M, Makoi, JHJR, Ouattara, K, Tesfaye, K, and Cassman, K. G. 2016. Can sub-Saharan Africa feed itself? PANS.

48, Meng, Qingfeng, Chen, Xinping, Lobell, David B., Cui, Zhenling, Zhang, Yi, Yang, Haishun, and Zhang, Fusuo. 2016. Growing sensitivity of maize to water scarcity under climate change. Nature - Scientific reports. 6, 19605.

49, Farmaha, BS., Lobell, DB., Boone, KE., Cassman, KG., Yang, H. S., and Grassini, P. 2016. Contribution of persistent factors to yield gaps in high-yield irrigated maize. Field Crops Research. 186, 124-132.

50, Witt, C, Pasuquin, JM, and Dobermann, A. 2006. Towards a Site-Specific Nutrient Management Approach for Maize in Asia. Better Crops. 90, 28-31

51, Yang, Haishun, Grassini, Patricio, Cassman, Kenneth G., Aiken, Robert M., and Coyne, Patrick I. 2017. Improvements to the Hybrid-Maize model for simulating maize yields in harsh rainfed environments.  Field Crops Research. 204, 180-190.