Identifying irrigation and nitrogen best management practices for sweet corn production on sandy soils using CERES-Maize model

He, Jianqiang; Dukes, Michael D.; Hochmuth, George; Jones, James W.; Graham, Wendy D.

doi:10.1016/j.agwat.2012.02.007

Cited by 57 publications

(36 citation statements)

References 25 publications

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“…These models are useful for supplementing http field experiments for identifying best management strategies in a cropping sequence using soil and weather parameters (He et al, 2012). Crop growth models such as those in Decision Support System for Agro technology Transfer (DSSAT) have been used successfully in many places around the world for a wide range of conditions and applications (Tsuji et al, 1998;Jones et al, 2003;Hoogenboom et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…CERES (Crop Estimation through Resource and Environment Synthesis)-Rice and -Maize are process-based models embedded in DSSAT simulate the main processes of crop growth and development such as phenological development, canopy leaf area growth, dry matter accumulation and grain yield. The CERES-Rice andMaize models were evaluated by many researchers across locations (Sarkar and Kar, 2006;Timsina and Humphreys, 2006;O'Neal et al, 2002;Behera and Panda, 2009;Liu et al, 2011;He et al, 2012;Salmerón et al, 2012;Jeong et al, 2014;Ngwira et al, 2014) with good agreements between predicted and observed values. Even though simulation results generally will have some uncertainties associated with inputs and model parameters, but still the simulation models can be effectively utilized as a scientific tool to increase the resource use efficiency of cropping systems (Timsina and Connor, 2001;Sarkar and Kar, 2008;Timsina and Humphreys, 2006;Timsina et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Kadiyala

Jones

Mylavarapu

et al. 2015

Agricultural Water Management

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Kadiyala

Jones

Mylavarapu

et al. 2015

Agricultural Water Management

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“…Hence, the estimation of a unique set of parameters that optimizes a goodness-of-fi t criterion given the observations is not possible (Romanowicz and Beven, 2006). Based on He et al (2012), we assumed that the parameters followed normal distributions. Many parameter sets are generated from specifi ed prior distributions of parameters and then used to simulate outputs by Monte Carlo simulation.…”

Section: Parameter Estimationmentioning

confidence: 99%

Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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Dynamic simulation models can increase research effi ciency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, diffi culties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fi tted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from fi ve growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling effi ciency, correlation coeffi cient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass. IntroductionDynamic simulation models can increase research effi ciency by allowing the analyst to search for strategies and analyze system performance, improve risk management, and interpret fi eld experiments that deal with crop responses to soil, management, genetic or environmental factors (Keating et al., 1999).Sugarcane (Saccharum spp.) is of major social and economic importance in Brazil. Worldwide, there have been several models developed specifi cally for sugarcane crop simulation (Pereira and Machado, 1986;Jones et al., 1989;Langellier and Martine, 2007;Keating et al., 1999;Thorburn et al., 2005;Inman-Bamber, 1991;Singels et al., 2008).Some of the physiological development and growth parameters that appear in the functions vary among sugarcane cultivars, meaning that they have to be estimated from data in order to predict growth and yield. Some of the parameters cannot be measured directly in typical experiments; instead, they have to be estimated based on data that are measured in experiments. Recent literature contains relatively little work on parameter estimation for crop models (Ahuja and Ma, 2011; Makowski et al., 2006). Makowski et al. (2006) point out the importance of raising the quality of calibration in crop models with automatic procedures for parameter adjustment. This would help ensure that the data are always used appropriately and in the same way for parameter estimation (Wallach et al., 2001), but such procedures are not available for direct use with existing sugarcane models.A new sugarcane model was developed ...

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“…Well-calibrated and tested models can be used to estimate these losses (Tonitto et al, 2007) and provide information to decision support tools designed to estimate N rates that, if implemented, should reduce N losses from these croplands with minimal impact on maize yields or farmer profitability (Thorp et al, 2006;He et al, 2012). In this study, we report on the calibration and testing of a daily time-step, process-based model of soil and crop N and water dynamics (PNM model) that has been revised to simulate these processes for maize production systems on artificially drained fields.…”

Section: Discussionmentioning

confidence: 99%

Drainage and Nitrate Leaching from Artificially Drained Maize Fields Simulated by the Precision Nitrogen Management Model

et al. 2016

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Environmental nitrogen (N) losses (e.g., nitrate leaching, denitrification, and ammonia volatilization) frequently occur in maize (Zea mays L.) agroecosystems. Decision support systems, designed to optimize the application of N fertilizer in these systems, have been developed using physically based models such as the Precision Nitrogen Management (PNM) model of soil and crop processes, which is an integral component of Adapt-N, a decision support tool providing N fertilizer recommendations for maize production. Such models can also be used to estimate N losses associated with particular management practices and over a range of current climates and future climate projections. The objectives of this study were to update the PNM model to include an option for simulating soil-water processes in artificially drained soils, and to calibrate the revised PNM model and test it against multiyear field studies in New York and Minnesota with different soils and management practices. Minimal calibration was required for the model. Denitrification rate constants were calibrated by minimizing the error between simulated and observed nitrate leaching for each study site. The normalized root mean squared error of cumulative daily drainage for the validation sets ranged from 10 to 23%. For cumulative daily nitrate leaching, the normalized root mean squared error ranged from 11 to 28% for the validation sets. The minimal calibration required and relatively simple data inputs make the PNM model a broadly applicable tool for simulating water and N flows in maize systems. , 1997;Cassman et al., 2002Cassman et al., , 2003van Es et al., 2002). To increase the sustainability of maize production and maintain important ecosystem services under a changing climate, we need to better understand how climate and management affect the multiple N-loss pathways at the field, landscape, and regional scales. Such understanding is crucial for improved decision support systems for N management in maize agroecosystems.The interaction of climate, management practices, and soil physical and biological properties significantly affects both the magni- ). These interactions are complex, nonlinear, and dynamic; therefore, estimating the fate of N under different climate and management scenarios in cropping systems is a formidable challenge (Smil, 1999;Jayasundara et al., 2007). Well-calibrated and tested dynamic simulation models of soil and crop processes have potential for estimating the fate of N, including the environmental impact of N practices in crop production across a broad range of environments and production systems (Shaffer and Ma, 2001;Laurent and Ruelland, 2011;Grizzetti et al., 2015). We have developed the Precision Nitrogen Management (PNM) dynamic simulation model to estimate N-leaching losses from the rootzone, total N losses, aboveground biomass production, and crop N uptake in maize production systems (Melkonian et al., 2007).The PNM model is a daily time-step, process-based model that was built on two model components: LEACHN (...

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Identifying irrigation and nitrogen best management practices for sweet corn production on sandy soils using CERES-Maize model

Cited by 57 publications

References 25 publications

Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Process-based simple model for simulating sugarcane growth and production

Drainage and Nitrate Leaching from Artificially Drained Maize Fields Simulated by the Precision Nitrogen Management Model

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