Modeling the impacts of climate change on irrigated corn production in the Central Great Plains

Islam, Adlul; Ahuja, Lajpat R.; Garcia, Luis A.; Ma, Liwang; Saseendran, A. S.; Trout, Thomas J.

doi:10.1016/j.agwat.2012.04.004

Cited by 105 publications

(68 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In general, the days to maturity for soybean were 112.5 and 105.9 days under baseline and future scenarios, respectively; comparatively, the days to maturity for corn were 133.3 and 118.9 days for baseline and future, respectively. Decreases in the length of soybean and corn days to maturity were, respectively, 2.6 and 5.7 days per°C increase of temperature (see supplementary material for detailed information), consistent with the finding of another RZWQM2 simulation which found a 5 days shorter physiological maturity in corn grown in Colorado for every 1.0°C increase in temperature (Islam et al 2012). In the present study, a strong linear relationship existed between temperature rise and decrease in days to maturity for soybean (R 2 =0.90) and corn (R 2 =0.96; see supplementary material for detailed information).…”

Section: Temperaturesupporting

confidence: 73%

“…RZWQM2 is an agricultural system model which considers hydrology, plant nutrition and growth, pesticide transport and transformation and agricultural management practices. RZWQM2 has been shown to be effective in simulating ET, tile drainage, nitrogen losses and crop production, as well as the impacts of climate change thereon, at various locations in the United States (Islam et al 2012;Ko et al 2012;Ma et al 1999;Qi et al 2011aQi et al , 2012. Therefore, the objective of this study was to apply RZWQM2, calibrated and validated by Qi et al (2011b), to assess the impacts of climate change on tile drainage, NO 3 -N losses and crop yield in a subsurface-drained field located near Gilmore City, IA.…”

Section: List Of Acronymsmentioning

confidence: 99%

“…The results in each scenario were expressed as percentage changes with respect to the baseline and as cumulative distribution functions (CDFs) for yields (Islam et al 2012). The nonparametric Kolmogorov-Smirnov (K-S) test was employed to determine whether the baseline CDF differed significantly from each of the projection's CDFs.…”

Section: Rzwqm2 Simulationsmentioning

confidence: 99%

“…Representing the largest absolute deviation of the two empirical distribution functions and therefore the average yield changes compared with the baseline, the D value indicates a significant difference when it exceeds 0.4. The WUE was calculated as the yield per unit of actual evapotranspiration (Islam et al 2012). Soil derived N is defined as total N uptake minus N fixation and N application rate (Malone and Ma 2009).…”

Section: Rzwqm2 Simulationsmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the impacts of climate change on nitrogen losses and crop yield in a subsurface drained field

Wang

Xue

et al. 2015

Climatic Change

View full text Add to dashboard Cite

The effect of climate change on crop production and nitrate-nitrogen (NO3-N) pollution from subsurface drained fields is of a great concern. Using the calibrated and validated RZWQM2 (coupled with CERESMaize and CROPGRO in DSSAT), the potential effects of climate change and elevated atmospheric CO2 concentrations (CO2) on tile drainage volume, NO3-N losses, and crop production were assessed integrally for the first time for a corn-soybean rotation cropping system near Gilmore City, Iowa. RZWQM2 simulated results under 20-year observed historical weather data (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) and ambient CO2 were compared to those under 20-year projected future meteorological data (2045)(2046)(2047)(2048)(2049)(2050)(2051)(2052)(2053)(2054)(2055)(2056)(2057)(2058)(2059)(2060)(2061)(2062)(2063)(2064) and elevated CO2, with all management practices unchanged. The results showed that, under the future climate, tile drainage, NO3-N loss and flow-weighted average NO3-N concentration (FWANC) increased by 4.2 cm year−1 (+14.5 %), 11.6 kg N ha−1 year−1 (+33.7 %) and 2.0 mg L−1 (+16.4 %), respectively. Yields increased by 875 kg ha−1 (+28.0 %) for soybean [Glycine max (L.) Merr.] but decreased by 1380 kg ha−1(−14.7 %) for corn (Zea mays L.). The yield of the C3 soybean increased mostly due to CO2enrichment but increased temperature had negligible effect. However, the yield of C4 corn decreased largely because of fewer days to physiological maturity due to increased temperature and limited benefit of elevated CO2 to corn yield under subhumid climate. Relative humidity, short wave radiation and wind speed had small or negligible impacts on FWANC or grain yields. With the predicted trend, this study suggests that to mitigate NO3-N pollution from subsurface drained corn-soybean field in Iowa is a more challenging task in the future without changing current management practices. This study also demonstrates the advantage of an agricultural system model in assessing climate change impacts on water quality and crop production. Further investigation on management practice adaptation is needed. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. Abstract The effect of climate change on crop production and nitrate-nitrogen (NO 3 -N) pollution from subsurface drained fields is of a great concern. Using the calibrated and validated RZWQM2 (coupled with CERES-Maize and CROPGRO in DSSAT), the potential effects of climate change and elevated atmospheric CO 2 concentrations (CO 2 ) on tile drainage volume, NO 3 -N losses, and crop production were assessed integrally for the first time for a corn-soybean rotation cropping system near Gilmore City, Iowa. RZWQM2 simulated results under 20-year observed historical weather data (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006...

show abstract

Section: Temperaturesupporting

confidence: 73%

Section: List Of Acronymsmentioning

confidence: 99%

Section: Rzwqm2 Simulationsmentioning

confidence: 99%

Section: Rzwqm2 Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the impacts of climate change on nitrogen losses and crop yield in a subsurface drained field

Wang

Xue

et al. 2015

Climatic Change

View full text Add to dashboard Cite

show abstract

“…It is a strategy aimed at procuring a maximum yield with less irrigation water; or an optimum net economical return is achieved with limited resources. The influence of DI on these factors is a function of, individually or collectively, the phenological stage of implementation -growth stage DI, available soil water, the irrigation system and the genotype used [7,8]. The adoption of DI as a water management strategy has received considerable commendations from the scientific community because of improved WUE [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Review of Growth Stage Deficit Irrigation Effecting Sticky Maize Production

Ha¹

2017

GeoScience Engineering

View full text Add to dashboard Cite

The shortage of water resources influences the future sustainability of sticky Maize (Zea mays L.) production. Deficit irrigation (DI) -a water management strategy -has gained much attention from scientists because of enhanced water use efficiency (WUE). Nonetheless, in reality, when applying this technique, its impact on yield and economic returns should be considered. Through an analytical literature review, this study examined the effect of growth stage DI on Maize production factors, i.e. yield, WUE, and economic returns. The results revealed that Maize's WUE could be improved with the lowest reduction in yield as water stress was imposed during the vegetative or maturation growth stages. Therefore, the profitable returns could be reached even if the yield was reduced; however, the economic return was sensitive to commodity prices. The present review addressed that the Maize flexible capacities under growth stage water stress presented an opportunity for the optimization of irrigated water and profit preservation by accurately judging the managing time of irrigation implementation.

show abstract

Evapotranspiration, Transpiration, and Evaporation Processes and Modeling in the Soil–Residue–Canopy System

Irmak,

Kukal

2022

Modeling Processes and Their Interactions in Cropping Systems

View full text Add to dashboard Cite

Modeling the impacts of climate change on irrigated corn production in the Central Great Plains

Cited by 105 publications

References 43 publications

Modeling the impacts of climate change on nitrogen losses and crop yield in a subsurface drained field

Modeling the impacts of climate change on nitrogen losses and crop yield in a subsurface drained field

A Review of Growth Stage Deficit Irrigation Effecting Sticky Maize Production

Evapotranspiration, Transpiration, and Evaporation Processes and Modeling in the Soil–Residue–Canopy System

Contact Info

Product

Resources

About