Using a crop growth simulation model for evaluating irrigation practices

Kundu, S.S.; Skogerboe, Gaylord V.; Walker, Wynn R.

doi:10.1016/0378-3774(82)90047-6

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…(Abd-El-Baki et al 2017) used a numerical crop model to determine the optimum irrigation depth for the tomato crop. (Kundu et al 1982) used the CORNGRO crop model to determine the optimum soil moisture depletion and replenishment levels and timing and amount of irrigation during different crop growth stages of corn. (Mubeen et al 2016) used the CSM-CERES-Maize model to optimize the irrigation conditions.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Sugarcane Growth and Yield Under Optimized Subsurface Irrigation System

Gunarathna

Kumari

2021

Preprint

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The optimized subsurface irrigation system (OPSIS) is a newly developed subsurface irrigation system to irrigate upland crops. Although it is already evaluated for few crops using field experiments, further evaluation is required. Since field experiments are tedious, expensive, we need an alternative approach to evaluate the OPSIS. A well-calibrated and validated crop model is a fast-alternative option for developing and evaluating agronomic practices. Therefore, this study aimed to develop the modeling capabilities of APSIM to simulate the OPSIS. We conducted field experiments for three growing seasons (main crop and two ratoons), two planting seasons (spring and summer planting), and two separate crops to collect necessary data for calibration and validation processes. We scripted a new module named "OPSIS" to couple OPSIS to the APSIM engine. We parameterized, calibrated, and validated the APSIM to simulate the growth and yield of sugarcane with OPSIS. After firm parameterization and calibration, APSIM-Sugar can successfully simulate the growth and yield of sugarcane with OPSIS. However, the simulation of soil moisture dynamics and irrigation water use were not up to the standards. Although it gives quite reasonable results for growth and yield simulations of sugarcane, further studies are suggested to develop the simulation accuracy of soil water dynamics and irrigation water use through the OPSIS.

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Section: Introductionmentioning

confidence: 99%

Simulation of Sugarcane Growth and Yield Under Optimized Subsurface Irrigation System

Gunarathna

Kumari

2021

Preprint

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“…Recommendations can be generated under a series of field experiments and subsequently demonstrated or disseminated to farmers. However, field experiments are expensive and time-consuming (Kundu et al ., 1982; Knörzer et al ., 2011). Crop simulation models have been developed based on the theory of crop physiological ecology (Graves et al ., 2002).…”

Section: Introductionmentioning

confidence: 99%

Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators

Buddhaboon

Jintrawet

Hoogenboom³

2018

J. Agric. Sci.

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Prior to applying the cropping system model-CERES-Rice model to deep water rice (DWR), it is important to estimate the rice genetic coefficients (GC). The goal of the current study was to compare two methods for estimating GC using a GC calculator (GENCALC) and generalized likelihood uncertainty estimation (GLUE) for three flooded rice (FDR) varieties. Data from a field experiment on the effect of planting date and variety on FDR production was conducted in 2009 on a DWR area in Bang Taen His Majesty's Private Development Project, Prachin Buri, Thailand. The experimental design was split-plot with four main plots (planting dates) and three sub-plots (FDR varieties) with four replications. The simulated values for anthesis date, maturity date and grain weight using GENCALC produced normalized root mean square errors (RMSEn) of 3.97, 3.69 and 3.68, while using GLUE produced RMSEn of 3.67, 2.50 and 3.68, respectively. The simulated grain number and grain yield under GENCALC GC were not significantly different from the observed values but were higher than simulated values for GLUE GC. Simulated values of above-ground biomass for both GENCALC (11 727 kg/ha) and GLUE GC (11 544 kg/ha) were overestimated compared to observed values (8512 kg/ha). In addition, good agreements of leaf N values were found with D-index values of 0.94 and 0.96 using GENACALC and GLUE GC simulations, respectively. Therefore, the GENCALC and GLUE GC estimators of DSSAT can both be used for estimating GC of FDR in the DWR area in Thailand and similar agro-ecosystems in Southeast Asia.

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“…Some examples of these sophisticated models for simulating the growth of the grain corn plant would include the pioneering ELCROS model of Brouwer and de Wit (1969), revised by de Wit et al (1978), Russo and Knapp (1976), and the CORNGRO model of Childs et al (1977), revised by Kundu et al (1982). These elaborate models are excellent for summarizing knowledge regarding the environmental and physiological processes involved in the growth of corn, but their input requirements are usually too demanding of special site-specific plant and environmental information to be easily used in regional applications.…”

Section: Introductionmentioning

confidence: 99%

Climatic change and grain corn yields in the North American Great Plains

et al. 1986

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A basic parametric crop yield model (YIELD) that uses climatic and environmental data to calculate yield and associated parameters for grain corn (maize) was applied to a transect through the North American Great Plains. This paper continues our examination of the impact of probable climatic change scenarios on crop evapotranspiration and krigation requirements (Terjung et al., 1984). This study of grain corn yields showed highest yield for the first (or primary) harvest under full irrigation occurring under a sunny and cold scenario in Austin, TX, sunny and cool in Kansas City, KS, and sunny and warm in Bismarck, ND. Lowest irrigated yield was found with cloudy and hot and very dry climate change scenarios. Under rainfed-only conditions, minima were obtained under the sunny-hot and -warm scenarios and very dry conditions.

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Using a crop growth simulation model for evaluating irrigation practices

Cited by 7 publications

References 6 publications

Simulation of Sugarcane Growth and Yield Under Optimized Subsurface Irrigation System

Simulation of Sugarcane Growth and Yield Under Optimized Subsurface Irrigation System

Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators

Climatic change and grain corn yields in the North American Great Plains

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