Your search found 8 records
1 Hoogenboom, G.; Huck, M. G.; Hillel, D. 1987. Modification and testing of a model simulating root and shoot growth as related to soil water dynamics. In Hillel, D. (Ed.), Advances in irrigation. Vol.4. Orlando, FL, USA: Academic Press. pp.331-386.
(Location: IWMI-HQ Call no: 631.7 G000 HIL Record No: H05926)
2 Hoogenboom, G.; Jones, J. W.; Boote, K. J. 1991. A decision support system for prediction of crop yield, evapotranspiration, and irrigation management. In Ritter, W. F. (Ed.), Irrigation and drainage: Proceedings of the 1991 National Conference sponsored by the Irrigation and Drainage Division of the American Society of Civil Engineers and the Hawaii Section, ASCE, Honolulu, Hawaii, July 22-26, 1991. New York, NY, USA: ASCE. pp.198-204.
Decision support tools ; Irrigation management ; Crop yield ; Evapotranspiration ; Computer models ; Simulation models
(Location: IWMI-HQ Call no: 631.7 G430 RIT Record No: H019886)
Computer simulation models have been developed which predict growth, development, and yield for grain legumes. These models can be applied for yield prediction as well as water management decisions. The soybean and peanut models, SOYGRO and PNUTGRO, were used to generate long-term yield, evapotranspiration, and irrigation demand for Gainesville, FL, and Tifton, GA, for six different soils. High yield levels were a function of location and total irrigation, while low yields were a function of location and soil water holding characteristics.
3 Heinemann, A. B.; Hoogenboom, G.; de Faria, R. T. 2002. Determination of spatial water requirements at county and regional levels using crop models and GIS: an example for the State of Parana, Brazil. Agricultural Water Management, 52(3):177-196.
Water requirements ; Irrigation requirements ; Runoff ; Nitrogen ; Leaching ; Plant growth ; Climate ; Calibration ; Models ; GIS ; River basins ; Irrigation management ; Planning ; Decision support tools ; Computer techniques ; Irrigated farming ; Maize ; Soyabeans / Brazil / Parana / Tibagi River Basin
(Location: IWMI-HQ Call no: PER Record No: H029515)
4 Gijsman, A. J.; Hoogenboom, G.; Parton, W. J.; Kerridge, P. C. 2002. Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter-residue module from CENTURY. Agronomy Journal, 94:462-474.
(Location: IWMI-HQ Call no: P 6054 Record No: H030374)
5 Boken, V. K.; Hoogenboom, G.; Hook, J. E.; Thomas, D. L.; Guerra, L. C.; Harrison, K. A. 2004. Agricultural water use estimation using geospatial modeling and a geographic information system. Agricultural Water Management, 67(3):185-199.
Irrigated farming ; GIS ; Models ; Cotton ; Maize ; Water use ; Conflict / USA / Georgia / Alabama / Florida
(Location: IWMI-HQ Call no: PER Record No: H035179)
6 Boken, V. K.; Hoogenboom, G.; Easson, G. L. 2009. Applying pattern recognition to satellite data for detecting irrigated lands: a case study for Georgia, United States. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.409-419. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042432)
7 Nangia, Vinay; Ahmad, Mobin-ud-Din; Du, J.; Changrong, Y.; Hoogenboom, G.; Xurong, M.; Wenqing, H.; Shuang, L.; Qin, L. 2009. Modeling the effects of conservation agriculture on land and water productivity of rainfed maize in the Yellow River Basin, China. In Humphreys, E.; Bayot, R. S. (Eds.). Increasing the productivity and sustainability of rainfed cropping systems of poor smallholder farmers: proceedings of the CGIAR Challenge Program on Water and Food, International Workshop on Rainfed Cropping Systems, Tamale, Ghana, 22-25 September 2008. Colombo, Sri Lanka: CGIAR Challenge Program on Water and Food. pp.147-166.
Tillage ; Simulation models ; Water productivity ; Soil water ; Water balance ; Maize ; Crop yield / China / Yellow River Basin
(Location: IWMI HQ Call no: 631 G000 HUM Record No: H042440)
http://www.dfid.gov.uk/r4d/PDF/Outputs/WaterfoodCP/CPWF_Proceedings_Rainfed_Workshop%5B1%5D.pdf
https://vlibrary.iwmi.org/pdf/H042440.pdf
https://vlibrary.iwmi.org/pdf/H042440.pdf
(0.47 MB) (8.92MB)
In the dryland regions of North China, water is the limiting factor for rainfed crop production. Conservation agriculture (featuring reduced or zero tillage, mulching, crop rotations and cover crops) has been proposed to improve soil and water conservation and enhance yields in these areas. Conservation agriculture systems typically result in increased crop water availability and agro-ecosystem productivity, and reduced soil erosion. To evaluate the potential of conservation agriculture to improve soil water balance and agricultural productivity, the DSSAT crop model was calibrated using the data of a field experiment in Shouyang County in the semi-arid northeastern part of the Yellow River Basin. The average annual precipitation at the site is 472 mm, 75% of which falls during the growing season. The site had a maizefallow-maize rotation. We used data from two crop seasons (2005 and 2006) and four treatments for calibration and analysis. The treatments were: conventional tillage (CT), no-till with straw mulching (NTSM), all-straw incorporated (ASRT) and one-third residue left on the surface with no-till (RRT). The calibration results gave satisfactory agreement between field observed and model predicted values for crop yield for all treatments except RRT, and for soil water content of different layers in the 150cm soil profile for all treatments. The difference between observed and predicted values was in the range of 3-25% for maize yield and RMSE was in the range of 0.03-0.06cm3/cm3 for soil water content measured periodically each cropping season. While these results are encouraging, more rigorous calibration and independent model evaluation are warranted prior to making recommendations based on model simulations. Medium-term simulations (1995-2004) were conducted for three of the treatments using the calibrated model. The NTSM and ASRT treatments had similar or higher yields (by up to 36%), higher crop water productivity by up to 28% and reduced runoff of up to 93% or 43 mm compared to CT.
8 Asseng, S.; Ewert, F.; Martre, P.; Rotter, R. P.; Lobell, D. B.; Cammarano, D.; Kimball, B. A.; Ottman, M. J.; Wall, G. W.; White, J. W.; Reynolds, M. P.; Alderman, P. D.; Prasad, P. V. V.; Aggarwal, Pramod Kumar; Anothai, J.; Basso, B.; Biernath, C.; Challinor, A. J.; De Sanctis, G.; Doltra, J.; Fereres, E.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L. A.; Izaurralde, R. C.; Jabloun, M.; Jones, C. D.; Kersebaum, K. C.; Koehler, A-K.; Muller, C.; Kumar, S. N.; Nendel, C.; O’Leary, G.; Olesen, J. E.; Palosuo, T.; Priesack, E.; Rezaei, E. E.; Ruane, A. C.; Semenov, M. A.; Shcherbak, I.; Stockle, C.; Stratonovitch, P.; Streck, T.; Supit, I; Tao, F.; Thorburn, P. J.; Waha, K.; Wang, E.; Wallach, D.; Wolf, J.; Zhao, Z.; Zhu, Y. 2015. Rising temperatures reduce global wheat production. Nature Climate Change, 5:143-147. [doi: https://doi.org/10.1038/nclimate2470]
(Location: IWMI HQ Call no: e-copy only Record No: H046906)
Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
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