Use of Global Sensitivity Analysis for CROPGRO Cotton Model Development

Pathak, Tapan B.; Fraisse, Clyde W.; Jones, James W.; Messina, Carlos; Hoogenboom, Gerrit

doi:10.13031/2013.24082

Cited by 51 publications

(8 citation statements)

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“…The model parameters were varied in such a way that the resultant RMSE was low (<0.5), the coefficient of agreement was high (>0.85), and the coefficient of determination (R 2 ) was high (>0.85). Finally, selected model parameter values were compared to the previously published studies (Pathak et al, 2007;Ortiz et al, 2009;Pathak et al, 2012;Thorp et al, 2014b). In this procedure, first the cultivarspecific parameters affecting crop phenology (table 4) were adjusted until the simulated crop phenology stages matched reasonably well with observed data.…”

Section: Csm-cropgro-cotton Model Calibration Validation and Evaluamentioning

confidence: 99%

Evaluation of the CSM-CROPGRO-Cotton Model for the Texas Rolling Plains Region and Simulation of Deficit Irrigation Strategies for Increasing Water Use Efficiency

2015

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Cotton is one of the major crops cultivated in the Texas Rolling Plains region, and it is a major contributor to the regional economy. Cotton cultivation in this region is facing severe challenges due to an increase in the frequency of droughts and a projected decrease in rainfall in the future. Development and evaluation of deficit irrigation strategies for this region could potentially conserve water while maintaining cotton yields. In this study, the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) CROPGRO-Cotton was extensively tested and then used for evaluating various deficit irrigation strategies for this region. The model inputs were obtained from field experiments conducted at Chillicothe, Texas, during four growing seasons: 2008-2010 and 2012. The model was first calibrated using the data from a 100% evapotranspiration (ET) replacement irrigation scheduling experiment conducted in 2012 and then validated on three other irrigation scheduling treatments (75% ET replacement, soil moisture based, and tensiometer based) conducted in the same year. The model was further evaluated using the data from cotton tillage and irrigation experiments conducted in an adjacent field during 2008-2010. The model calibration, validation, and evaluation results were satisfactory except under dry conditions (0% ET replacement and 33% ET replacement). Simulated maximum seed cotton yields under normal and dry weather conditions were achieved at 100% and 110% ET replacement, respectively. Percentage decrease in seed cotton yield was marginal (3.5% to 8.8%) when the amount of irrigation water applied was decreased from 100% to 66% ET replacement under normal rainfall conditions. However, under less than normal rainfall (drier) conditions, the percentage decrease in seed cotton yield was substantial (about 17.5%) when the irrigation strategy was switched from 100% to 70% ET replacement. The simulations demonstrate that adopting deficit irrigation practices under normal weather conditions can conserve water without adversely affecting seed cotton yields. However, under dry conditions, there is a risk of increased yield loss, and therefore producers should consider that risk when adopting deficit irrigation strategies.

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Section: Csm-cropgro-cotton Model Calibration Validation and Evaluamentioning

confidence: 99%

Evaluation of the CSM-CROPGRO-Cotton Model for the Texas Rolling Plains Region and Simulation of Deficit Irrigation Strategies for Increasing Water Use Efficiency

2015

Trans. ASABE

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“…The DSSAT CSM can simulate crop growth, development, and yield in response to variability in weather conditions, soil properties, and management practices (Jones et al, 2003;Hoogenboom et al, 2012Hoogenboom et al, , 2015Thorp et al, 2014). The DSSAT CSM CROPGRO-Cotton model has been extensively used by researchers worldwide for various applications (Buttar et al, 2007;Pathak et al, 2007;Ortiz et al, 2009;Pereira et al, 2009;Thorp et al, 2010Thorp et al, , 2014Modala et al, 2015;Adhikari et al, 2016Adhikari et al, , 2017Mauget et al, 2017;Loison et al, 2017;Amin et al, 2018;Spivey et al, 2018).…”

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confidence: 99%

Determining Optimum Irrigation Termination Periods for Cotton Production in the Texas High Plains

Ale¹,

Omani²,

Himanshu³

et al. 2020

Transactions of the ASABE

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HighlightsIrrigation water use efficiency was consistently higher under deficit irrigation as compared to full irrigation.Irrigation water use was always less than the annual allowable pumping limit under deficit irrigation.The first/second week of September was ideal for terminating irrigation under full/deficit irrigation in normal years.Ideal irrigation termination periods in wet/dry years were a week earlier/later than those in normal years.Abstract. Cotton ( L.) production in the Texas High Plains (THP) region relies heavily on irrigation with groundwater from the underlying Ogallala Aquifer. However, rapidly declining groundwater levels in the aquifer and increasing pumping costs pose challenges for sustainability of irrigated cotton production in this region. Adoption of efficient irrigation strategies, such as terminating irrigation at an appropriate time in the growing season, could enable producers to increase irrigation water use efficiency (IWUE) while maintaining desired yield goals. The objective of this study was to determine optimum irrigation termination periods for cotton production in the THP under full and deficit irrigation conditions using the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Cotton model, which was evaluated in a prior study in the THP using measured data from an IWUE field experiment at Halfway, Texas. The treatment factors in the field experiment included irrigation capacities of 0 mm d-1 (low, L), 3.2 mm d-1 (medium, M), and 6.4 mm d-1 (high, H), applied during the vegetative, reproductive, and maturation growth stages. This study focused on a full irrigation (HHH) treatment and three deficit irrigation (LMH, LHM, and LMM) treatments. Eight irrigation termination dates with a one-week interval between 15 August and 30 September were simulated, and the impact of irrigation termination date on cotton IWUE and seed cotton yield were studied by dividing the 39-year (1978 to 2016) simulation period into dry, normal, and wet years based on the precipitation received from 1 April to the simulated irrigation termination date. Results indicated that the simulated IWUE was consistently higher under the LHM, LMH, and LMM treatments when compared to the HHH treatment. Based on the simulated average seed cotton yield and IWUE, optimum irrigation termination periods for cotton were found to be the first week of September (about 118 days after planting, DAP) for the HHH and LMH treatments and the second week of September (125 DAP) for the LHM and LMM treatments in normal years. In wet years, optimum irrigation termination periods were a week earlier than those in normal years and a week later in dry years for the HHH, LHM, and LMM treatments. For the LMH treatment, the optimum irrigation termination period in wet years was the same as that in normal years and two weeks later in dry years. The results from this study along with field-specific, late-season information will assist THP cotton producers in making appropriate irrigation termination decisions for improving economic productivity of the Ogallala Aquifer and thereby ensuring water security for agriculture. However, the recommendations from this study should be used with caution, as the optimum irrigation termination periods could potentially change with changes in cultivar characteristics, soil type, climate, and, crop management practices. Keywords: CROPGRO-Cotton, Deficit irrigation, DSSAT, Full irrigation, Irrigation water use efficiency, Seed cotton yield.

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“…A 2-year field study conducted by Bednarz et al (2000) found that seed cotton yields were not influenced by population density. Their literature review, and that of Musunuru (2003), also notes a number of previous field trials indicating that cotton yields appear relatively stable over a range of plant densities. Woodward et al's (2013) focus was on disease incidence and fiber quality effects, and yield impacts due to seeding density were not reported.…”

Section: Discussionmentioning

confidence: 88%

Optimizing Dryland Crop Management to Regional Climate via Simulation. Part I: U.S. Southern High Plains Cotton Production

Mauget

Marek

Adhikari

et al. 2020

Front. Sustain. Food Syst.

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Over semi-arid agricultural regions such as the U.S. Southern High Plains (SHP) producers of dryland crops need to know which management practices increase yields and decrease production risk. Here, a modeling approach is used to explore management options (MO) that increase dryland cotton yields and estimate those practice's yield risk effects under current SHP climate conditions. To simulate current dryland yield variability, dense distributions of lint yield outcomes were generated using the CROPGRO-Cotton crop model driven by weather inputs from 21 SHP weather stations during 2005-2016. Management effects were explored by repeating simulations over 32 MOs defined by 4 planting dates, 4 planting densities, and applying or not applying nitrogen. Both earlier planting date and decreased plant density increased median simulated yields, with earlier planting having the greatest positive yield effects. The simulated MO that produced the highest median lint yields planted on the earliest planting date (May 15), at the lowest density (3 plants m −1), and applied no nitrogen. Recent SHP field studies generally confirm the earlier planting date effect, but suggest insignificant yield effects for different seeding rates. Even so, negligible yield effects and lower input costs favor lower seeding densities from a profit standpoint. These crop simulations demonstrate a modeling-based method for climate-related agricultural risk management, and suggest mid-May planting dates and low plant densities as part of management practices that increase yields and profits in dryland SHP cotton production.

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Use of Global Sensitivity Analysis for CROPGRO Cotton Model Development

Cited by 51 publications

References 0 publications

Evaluation of the CSM-CROPGRO-Cotton Model for the Texas Rolling Plains Region and Simulation of Deficit Irrigation Strategies for Increasing Water Use Efficiency

Evaluation of the CSM-CROPGRO-Cotton Model for the Texas Rolling Plains Region and Simulation of Deficit Irrigation Strategies for Increasing Water Use Efficiency

Determining Optimum Irrigation Termination Periods for Cotton Production in the Texas High Plains

Optimizing Dryland Crop Management to Regional Climate via Simulation. Part I: U.S. Southern High Plains Cotton Production

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