Testing and improving the maize models in DSSAT: Development, growth, yield, and N uptake

Yakoub, A.; Lloveras, J.; Biau, A.; Lindquist, John L.; Lizaso, Jon

doi:10.1016/j.fcr.2017.07.002

Cited by 37 publications

(27 citation statements)

References 48 publications

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“…Recently, the relationship (eq. 2) was further evaluated for higher AGB levels of 27 Mg ha -1 by Ciampitti et al (2012) and 40 Mg ha -1 by Yakoub et al (2017), which demonstrated its validity for a wide range of biomass levels (up to 40 Mg ha -1 ). The method was incorporated into the model and compared with the growth stage-based method (eq.…”

Section: Soil Water and Crop N Uptake Simulation Processes In Rzwqmmentioning

confidence: 99%

“…These changes in GNC from previous to current maize hybrids are not accounted for in most crop models, which were developed using data from prior maize cultivars (e.g., CERES-Maize). For example, Yakoub et al (2017) compared the relationship between GNC and shoot N uptake based on with the original GNC estimation method in the CSM-IXIM maize model and found that the method significantly improved GNC simulations and crop N uptake under a range of N fertilizer treatments at Lleida in northeast Spain.…”

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confidence: 99%

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Modeling N Concentration and Uptake for Maize Hybrids under Growth Stage-Based Deficit Irrigations

Fang¹,

Ma²,

Trout³

et al. 2017

Transactions of the ASABE

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Abstract. Current maize hybrids have lower critical aboveground biomass nitrogen (N) concentration (TCNP) and grain N concentration (GNC) compared to older hybrids, but few crop models have incorporated this trend. The objective of this study was to evaluate alternative algorithms for calculating TCNP (biomass-based method) and GNC (grain N demand based on N dilution curve) for predicting crop N concentration and N uptake for a current maize hybrid in the CERES-Maize model as implemented in the Root Zone Water Quality Model (RZWQM). Experimental data were obtained from a field study on maize irrigated to meet various percentages (40% to 100%) of evapotranspiration demand at both vegetative and reproductive stages in 2012 and 2013 in Greeley, Colorado. The original RZWQM showed little response of aboveground N concentration (AGBNC) to the irrigation treatments and overpredicted GNC in both years. As a result, crop N uptake was generally overpredicted, with root mean square error (RMSE) values of 28 to 60 kg N ha-1 for the two years. Adjusted coefficients in the original TCNP and GNC algorithms (RZWQM_ADJ) effectively reduced the overpredicted GNC but with less improvement in response to the irrigation treatments in 2013 compared with the original RZWQM simulations. The RZWQM with modified TCNP and GNC algorithms simulated lower GNC and AGBNC than the original version, significantly improved the responses to the irrigation treatments, and captured the variations in measured GNC among seasons. The corresponding crop N uptake simulations improved more in 2012 than in 2013, with lower RMSE values of 16 to 32 kg N ha-1 than the original and RZWQM_ADJ versions. The better-predicted grain N uptake by the alternative algorithms could be helpful to making better crop N management decisions under different deficit irrigation conditions. Keywords: CERES-Maize, Crop N concentration, Crop N demand, Crop N uptake, Deficit irrigation, Maize hybrid, RZWQM.

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Section: Soil Water and Crop N Uptake Simulation Processes In Rzwqmmentioning

confidence: 99%

mentioning

confidence: 99%

Modeling N Concentration and Uptake for Maize Hybrids under Growth Stage-Based Deficit Irrigations

Fang¹,

Ma²,

Trout³

et al. 2017

Transactions of the ASABE

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show abstract

“…Some maize crop models (e.g., CERES-Maize; Jones and Kiniry, 1986) can simulate grain-N removal, but they require calibration and copious amounts of data inputs (i.e., daily weather, soil properties, cultivar coefficients, and management practices) to be useful for predicting grain-N removal in individual fields. Additionally, previous studies have shown that these models performed relatively poor at reproducing measured GNC in field-grown maize (e.g., Liu et al, 2010;Yakoub et al, 2017). Hence, at issue is how reliable the estimation of grain-N removal can be in the absence of measured GNC.…”

Section: Introductionmentioning

confidence: 99%

Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region

Tenorio

Eagle

McLellan

et al. 2019

Field Crops Research

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Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years.

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“…The calibration of CSM-CERES-maize model mainly focused on determining the variety and genetic parameters of summer maize cultivated in the experiments. The genetic parameters need to be calibrated and validated in this study included P1, P2, P5, G2, G3, and PHINT [39], and the detailed descriptions are indicated in Table 4. These parameters were adjusted using the generalized likelihood uncertainty estimation (GLUE) procedure of DSSAT set to 10,000 iterations.…”

Section: Calibration and Validation Of Summer Maize Genetic Parametersmentioning

confidence: 99%

Simulated Assessment of Summer Maize Drought Loss Sensitivity in Huaibei Plain, China

et al. 2019

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In an agricultural drought risk system, crop drought loss sensitivity evaluation is a fundamental link for quantitative agricultural drought loss risk assessment. Summer maize growth processes under various drought patterns were simulated using the Cropping System Model (CSM)-CERES-maize, which was calibrated and validated based on pit experiments conducted in the Huaibei Plain during 2016 and 2017 seasons. Then S-shaped maize drought loss sensitivity curve was built for fitting the relationship between drought hazard index intensity at a given stage and the corresponding dry matter accumulation and grain yield loss rate, respectively. Drought stress reduced summer maize evapotranspiration, dry matter, and yield accumulation, and the reductions increased with the drought intensity at each stage. Moreover, the losses caused by drought at different stages were significantly different. When maize plants were exposed to a severe water deficit at the jointing stage, the dry matter and grain yield formation were greatly affected. Therefore, maize growth was more sensitive to drought stress at the jointing stage when the stress was serious. Furthermore, when plants encountered a relatively slight drought during the seedling or jointing stage, which represented as a lower soil water deficit intensity, the grain yield loss rates approached the maximum for the sensitivity curves of these two stages. Therefore, summer maize tolerance to water deficit at the seedling and jointing stages were weak, and yield formation was more sensitive to water deficit during these two stages when the deficit was relatively slight.

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Testing and improving the maize models in DSSAT: Development, growth, yield, and N uptake

Cited by 37 publications

References 48 publications

Modeling N Concentration and Uptake for Maize Hybrids under Growth Stage-Based Deficit Irrigations

Modeling N Concentration and Uptake for Maize Hybrids under Growth Stage-Based Deficit Irrigations

Assessing variation in maize grain nitrogen concentration and its implications for estimating nitrogen balance in the US North Central region

Simulated Assessment of Summer Maize Drought Loss Sensitivity in Huaibei Plain, China

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