Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model

Timsina, Jagadish; Godwin, D. C.; Humphreys, E.; Yadvinder‐Singh,; Bijay‐Singh,; Kukal, S. S.; Smith, D. J.

doi:10.1016/j.agwat.2008.04.009

Cited by 104 publications

(51 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, the closer the r 2 value is to one showed a good match between two variables. Similar results have been reported by Timsina et al, 2008 andAndarzian et al, 2014. Aforementioned indexes imply the robustness of the model in simulating wheat yields and harvest index of wheat.…”

Section: Model Validationsupporting

confidence: 77%

“…In India, the DSSAT-CSM-CERES-Wheat v4.0 model was calibrated using the historical weather data of a 36 year period to estimate the long-term mean and variability of potential yield, drainage, runoff, evapotranspiration, crop water productivity and irrigation water productivity (Timsina et al, 2008). This capability of DSSAT model makes it more suitable to predict response of the complex system affected by many factors such as crop growth and crop yield in wake of variable soil potential (Solaimani, 2009).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Growth and Yield of Late Sown Wheat Using DSSAT (v4.5) Model under Western Zone of Haryana

Pannu¹,

Singh²,

Rajeev³

2017

Int.J.Curr.Microbiol.App.Sci

View full text Add to dashboard Cite

Section: Model Validationsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Prediction of Growth and Yield of Late Sown Wheat Using DSSAT (v4.5) Model under Western Zone of Haryana

Pannu¹,

Singh²,

Rajeev³

2017

Int.J.Curr.Microbiol.App.Sci

View full text Add to dashboard Cite

“…They reported that the calibrated model responded very well to different levels of irrigation, ranging from dryland to full irrigation, and simulated wheat yield (aboveground biomass) accurately. The DSSAT CSM has also been used by various other researchers (He et al, 2013;Timsina et al, 2008;Panda et al, 2003) to develop irrigation scheduling tools for wheat. All of the above-mentioned researchers adequately tested their models against field measurements before addressing the research questions related to crop and water management.…”

mentioning

confidence: 99%

Simulated Effects of Winter Wheat Cover Crop on Cotton Production Systems of the Texas Rolling Plains

Adhikari¹,

Omani²,

Ale³

et al. 2017

Transactions of the ASABE

View full text Add to dashboard Cite

ABSTRACT. Interest in cover crops has been increasing in the Texas (1) irrigated cotton without a cover crop (CwoC-I), (2) irrigated cotton with winter wheat as a cover crop (CwC-I), (3) dryland cotton without a cover crop (CwoC-D), and (4) dryland cotton with a winter wheat cover crop (CwC-D) at the Texas A&M AgriLife Research Station at Chillicothe from 2011 to 2015. The average percent error (PE) between the CSM-CROPGRO-Cotton simulated and measured seed cotton yield was -10.1% and -1.0% during the calibration and evaluation periods, respectively, and the percent root mean square error (%RMSE) was 11.9% during calibration and 27.6% during evaluation. For simulation of aboveground biomass by the CSM-CERES-Wheat model, the PE and %RMSE were 8.9% and 9.1%, respectively, during calibration and -0.9% and 21.8%, respectively, during evaluation. Results from the long-term (2001-2015) simulations indicated that there was no substantial reduction in average seed cotton yield and soil water due to growing winter wheat as a cover crop.

show abstract

“…Moderate water deficit type was defined as -20% < αi < 20%, while light water deficit type was defined as αi ≤ -20% ). The normalized root mean squared error (NRMSE) was used to measure the difference between the simulated and observed yields for model calibration (Timsina et al, 2008):…”

Section: Methodsmentioning

confidence: 99%

“…This crop model simulates growth and yield as a function of the climate, soil, crop cultivar, and management practices. DSSAT has been widely used to analyze yield gap trends under climate change and the impact of N on the crop yield gap during recent years (Rinaldi, 2004;Lobell and Monasterio, 2007;Timsina et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Effect of nitrogen and water deficit type on the yield gap between the potential and attainable wheat yield

Liu

Wang

Thorp

et al. 2015

Chilean J. Agric. Res.

View full text Add to dashboard Cite

Water deficit and N fertilizer are the two primary limiting factors for wheat yield in the North China plain, the most important winter wheat (Triticum aestivum L.) production area in China. Analyzing the yield gap between the potential yield and the attainable yield can quantify the potential for increasing wheat production and exploring the limiting factors to yield gap in the high-yielding farming region of North China Plain. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to identify methods to increase the grain yield and decrease the gap. In order to explore the impact of N and cultivars on wheat yield in the different drought types, the climate conditions during 1981 to 2011 growing seasons was categorized into low, moderate, and severe water deficit classes according to the anomaly percentage of the water deficit rate during the entire wheat growing season. There are differences (P < 0.0001) in the variations of the potential yields among three cultivars over 30 yr. For all three water deficit types, the more recent cultivars Jimai22 and Shijiazhuang8 had higher yields compared to the older 'Jinan17'. As the N fertilizer rate increased, the yield gap decreased more substantially during the low water deficit years because of the significant increase in attainable yield. Overall, the yield gaps were smaller with less water stress. Replacement of cultivars and appropriate N fertilizer application based on the forecasted drought types can narrow the yield gap effectively.

show abstract

Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model

Cited by 104 publications

References 29 publications

Prediction of Growth and Yield of Late Sown Wheat Using DSSAT (v4.5) Model under Western Zone of Haryana

Prediction of Growth and Yield of Late Sown Wheat Using DSSAT (v4.5) Model under Western Zone of Haryana

Simulated Effects of Winter Wheat Cover Crop on Cotton Production Systems of the Texas Rolling Plains

Effect of nitrogen and water deficit type on the yield gap between the potential and attainable wheat yield

Contact Info

Product

Resources

About