Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data

Garibay, Victoria M.; Kothari, Kritika; Ale, Srinivasulu; Gitz, Dennis C.; Morgan, Gaylon D.; Munster, C. L.

doi:10.1016/j.agwat.2019.105695

Cited by 24 publications

(6 citation statements)

References 20 publications

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“…This overestimation, although high, is close to the calibration period (0.6%–11%) and is in agreement with several studies that have adopted similar approach for calibrating the models (Klein et al 2012; Bao et al 2017; Adnan et al 2019) There are several factors that are not accounted for in the models used and could have attributed toward the overestimation of crop yield. These factors include, but are not limited to, several biotic and abiotic stresses (Garibay et al 2019) as well as pedo‐climatic conditions (Brilli et al 2017).…”

Section: Resultsmentioning

confidence: 99%

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

Sharda

Mekonnen

Ray

et al. 2020

J American Water Resour Assoc

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With a long‐term goal to optimize use of groundwater in the Ogallala Aquifer Region (OAR) to sustain food production systems, this study was conducted to calibrate Decision Support System for Agrotechnology Transfer (DSSAT) and AquaCrop crop modeling platforms to simulate maize production at a regional scale using historic datasets. Calibration of the models with local crop growth data and crop management practices is important, but usually this in‐season crop growth information is not available. This study determined the possibility of using maize variety trial data for the evaluation of the CSM‐Crop Estimation through Resources and Environmental Synthesis‐Maize and AquaCrop models in the OAR. The models were calibrated and tested in three counties in Nebraska. Both the models were then used to simulate irrigated maize yield during 1988 to 2015 for all three counties. The criteria for evaluating the performance of these crop models included statistical parameters and graphical analysis. The performance of both models were then compared with the observed yield from field variety test results and historic National Agricultural Statistical Service yields. The results indicated that difference between yield of calibrated DSSAT model and observed yield was less than 10% and AquaCrop root mean square error ranged from 740 to 1,820 kg/ha. Long‐term comparison between observed and simulated Nebraska county yields also indicated confidence in calibrating crop models with typical end of season yield data and using these models for studying crop production at regional scales when detailed in‐season crop growth observed data are not available.

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Section: Resultsmentioning

confidence: 99%

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

Sharda

Mekonnen

Ray

et al. 2020

J American Water Resour Assoc

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“…The crop yield is widely used as the observed/reference variable to calibrate the parameters of a crop growth simulation model(Mubeen et al, 2020). Alternatively, the combinations of above ground biomass, canopy development and leaf area index were also used to calibrate the model parameters(Garibay et al, 2019;Wang et al, 2020). In this study, the parameters of DSSAT model are calibrated to match the observed yields.…”

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confidence: 99%

Investigating the impact of compound extremes on crop yield response of cotton: A case study

Kandula

Manikanta

Umamahesh

2023

Preprint

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Agricultural productivity is significantly affected by weather and climate extremes such as droughts and heat waves. Despite several studies addressing the impact of individual and compound extremes on crop yields, there needs to be more comprehensive understanding of their duration thresholds beyond which the crop declines significantly. Hence, in this study, an analysis was carried out to understand the duration thresholds of the compound and individual extremes under varying scenarios for cotton. Future yields were simulated using the bias-corrected projections from 8 climate models and DSSAT-CROPGRO-Cotton crop simulation model. Future yields are predicted for three different scenarios i) rainfed with constant [CO2] (S1), ii) rainfed with elevated [CO2] (S2) and iii) full irrigation with elevated [CO2] (S3). The results reveal that the overall cotton yields in the study area are likely to decrease by the end of the century under all scenarios (SSP585 > SSP370 > SSP245). The duration threshold (change point) increases from S1 to S3, i.e., S1 < S2 < S3. Due to Coincidental Heatwaves and Droughts and Heatwaves alone in the SSP585 scenario, a decline in the crop yield of about 33%, 23%, and 15% is observed in the S1, S2, and S3 scenarios, respectively.

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“…The results indicate that climate warming affects cotton development and yield, and climate warming has increased oasis seed cotton yields in arid regions of northwestern China. The CROPGRO-Cotton module within the Cropping System Model (CSM) is widely used around the world (Adhikari et al, 2016;Amin et al, 2018;Amouzou et al, 2018;Garibay et al, 2019;Ur et al, 2017). Li et al (2018) established the CSM-CROPGRO-Cotton model of improved cotton growth under drip irrigation with plasticfilm mulching based on experimental data for 2015 and 2016.…”

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Cotton growth model under drip irrigation with film mulching: A case study of Xinjiang, China

Wang

2021

Agronomy Journal

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Crop growth depends on the cumulative temperature of the crop growing season, and growing degree days (GDD) is presently widely used as an index of the effective accumulated temperature. However, there are few reports on the relationship model between cotton (Gossypium hirsutum L.) growth and GDD. Based on meteorological data and published document from the Xinjiang region in the past 12 yr, we used GDD to replace days after sowing to establish a model for the growth of cotton under drip irrigation with film mulch in Xinjiang, China. In order to analyze the effects of temperature on cotton growth under the different regions and management measures, a correction coefficient method was proposed to obtain the uniform growth law. Then a standardized logistic model was used to analyze growth indices based on GDD, and the relationships between maximum leaf area index (LAI max ), maximum drymatter accumulation (DMA max ), the harvest index (HI), and total irrigation amount are discussed. Finally, the irrigation amount during the whole growth period is used as an independent variable to predict seed-cotton yield. The results showed that plant height and growth rate were highest when GDD increased to about 1,100 and 530 C, respectively. The rates of increase in LAI and DMA were highest when GDD increased to about 900 and 1,000˚C, respectively. Maximum leaf area index was 4.65 and HI was near optimal when irrigation amount for the entire growing season was 500 mm. The GDD could thus improve the accuracy of yield prediction. This study provides an available method for the further establishment of a universal cotton model. INTRODUCTIONGlobal warming has a significant impact on China's agricultural production by altering crop growth. According to the

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Determining water-use-efficient irrigation strategies for cotton using the DSSAT CSM CROPGRO-cotton model evaluated with in-season data

Cited by 24 publications

References 20 publications

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

Use of Multiple Environment Variety Trials Data to Simulate Maize Yields in the Ogallala Aquifer Region: A Two Model Approach

Investigating the impact of compound extremes on crop yield response of cotton: A case study

Cotton growth model under drip irrigation with film mulching: A case study of Xinjiang, China

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