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Ximing Cai, Dingbao Wang, and Romain Laurent

the effects of various climate change scenarios to corn yields in central Illinois. SWAP is a one-dimensional, physically based model that can estimate water (based on Richard’s equation) availability, heat, and solute transport in both the saturated and unsaturated zones of soil and includes modules for simulating crop water requirements and crop growth. In this study, a soil profile of a depth of 180 cm, which is the average soil depth in the study area, is divided into 28 compartments with

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Tom Osborne, Julia Slingo, David Lawrence, and Tim Wheeler

1. Introduction Crop growth, development, and yield are affected by numerous environmental variables, notably rainfall (via soil water fluctuations), temperature, humidity, and the chemical composition of the atmosphere itself (e.g., CO 2 concentration). These factors, in combination with the control of day length on the development of some crop species, have led to the distribution of crops across the globe seen today. Currently crops occupy approximately 12% of the land surface ( Ramankutty

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Yingbin He, Yanmin Yao, Huajun Tang, Youqi Chen, Jianping Li, Peng Yang, Zhongxin Chen, Xiaoping Xin, Limin Wang, Dandan Li, and Hui Deng

soybeans concentrated on the response of soybean growth and final yield to climatic factors ( Sinclair and Rawlins 1993 ; Lal et al. 1999 ; Bhatia et al. 2008 ). However, to guide agricultural production, some countries have introduced agro-climatic crop regionalization aiming at avoiding low-efficiency crops cultivation ( Brown and Chapman 1960a , b , 1961 ; Nuttonson 1965 ; Li 1987 ). The regionalization was based on identification of agro-climatic suitability ( Ogunkun 1993 ; Satyavathi and

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Keith J. Harding, Tracy E. Twine, and Yaqiong Lu

irrigation affects the hydroclimate of the Great Plains. In addition, because most of the water applied for irrigation is evapotranspired rather than lost to runoff or drainage ( DeAngelis et al. 2010 ; Moore and Rojstaczer 2001 ), it is imperative to understand the cumulative atmospheric effect of irrigation on the Ogallala. In this study, we examine how irrigation affects precipitation within the confines of the Ogallala Aquifer using a high-resolution regional climate model with dynamic crop growth

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Akarsh Asoka and Vimal Mishra

inversely linked with evapotranspiration. Also, Sun et al. (2019) found that different cropping systems and irrigation influence evapotranspiration and groundwater level in north China Plain. Despite the strong linkage between seasonal crop growth and evapotranspiration, efforts to examine the causes of groundwater depletion in India have been primarily limited to anthropogenic pumping ( Asoka et al. 2017 ; Rodell et al. 2009 ; Tiwari et al. 2009 ). The role of crop growth in different seasons is

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Xing Liu, Jeff Andresen, Haishun Yang, and Dev Niyogi

. , and J. R. Kiniry , 1986 : CERES-Maize: A Simulation Model of Maize Growth and Development . Texas A&M Univeristy Press, 194 pp . Jones , C. A. , P. T. Dyke , J. R. Williams , J. R. Kiniry , V. W. Benson , and R. H. Griggs , 1991 : EPIC: An operational model for evaluation of agricultural sustainability . Agric. Syst. , 37 , 341 – 350 , doi: 10.1016/0308-521X(91)90057-H . Jones , J. W. , and Coauthors , 2003 : The DSSAT cropping system model . Eur. J. Agron. , 18

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Tobias Siegfried, Stefan Sobolowski, Pradeep Raj, Ram Fishman, Victor Vasquez, Kapil Narula, Upmanu Lall, and Vijay Modi

Asia, Telangana has witnessed a dramatic increase in irrigated agriculture over the last 30 yr, especially in terms of the growth in total area irrigated. The food crop mix in Telangana is dominated by rice, maize, wheat, jowar (sorghum), and bajra (pearl millet). Total irrigation water requirements for these five major crops have increased by more than 50% over the same period. Figure 2 shows the development of sourcewise and total irrigated area for the Telangana region (data sources: World

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Jeffrey A. Andresen, Robert F. Dale, Jerald J. Fletcher, and Paul V. Preckel

48 JOURNAL OF CLIMATE VOLUME2Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index* JEFFREY A. ANDRESENJoint Agricultural Weather Facility, USDA-World Agricultural Outlook Board, Washington. D.C. ROBERT F. DALEDepartment of Agronomy, Purdue University. West Lafayette, Indiana JERALD J. FLETCHER AND PAUL

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Tobias Dalhaus and Robert Finger

) extracted information on vegetation phases from a crop model, Leblois et al. (2014a) and Conradt et al. (2015b) used growing degree days to specify vegetation periods. Kumar et al. (2016) identified heat-sensitive growth stages from experimental data based on observed phenological phases. Second, the start and end dates of these windows of index measurement are usually fixed; that is, they are identical in every year (e.g., start and end dates of months). This choice ignores the fact that the

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Richard T. McNider, John R. Christy, Don Moss, Kevin Doty, Cameron Handyside, Ashutosh Limaye, Axel Garcia y Garcia, and Gerrit Hoogenboom

final kernel growth stage the plant will sacrifice water and nutrients to support the kernel at the expense of the leaves and stalk. But if the full complement of kernels is not set early during reproductive development or if moisture is insufficient for full pollination, the final yield will be significantly impacted even if plenty of soil moisture is available near the final maturity date ( Ritchie et al. 1998 ). Other crops such as soybeans may not be as sensitive since their phenological window

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