Simulating Inbred‐Maize Yields with CERES‐IM

Rasse, Daniel P.; Ritchie, J. T.; Wilhelm, Wallace; Wei, Jun; Martin, E. C.

doi:10.2134/agronj2000.924672x

Cited by 17 publications

(18 citation statements)

References 25 publications

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“…Maximum light interception values registered in this study were low as compared with records obtained for hybrids cropped in a similar environment (Maddonni et al, 2001) but represented an expected level for inbred lines because of their usually reduced leaf growth (Rasse et al, 2000). Maximum light interception values registered in this study were low as compared with records obtained for hybrids cropped in a similar environment (Maddonni et al, 2001) but represented an expected level for inbred lines because of their usually reduced leaf growth (Rasse et al, 2000).…”

Section: Discussion Canopy Characteristics Light Interception and Bcontrasting

confidence: 56%

“…In spite of the agreement between inbreds and results from hybrids in the initial response to N of leaf area production, no treatment reached the critical LAI in our experiments, with the concomitant disadvantage for light capture. Maximum light interception values registered in this study were low as compared with records obtained for hybrids cropped in a similar environment (Maddonni et al, 2001) but represented an expected level for inbred lines because of their usually reduced leaf growth (Rasse et al, 2000). The negative effects of low N availability on leaf senescence additionally conditioned light interception efficiency, particularly after flowering.…”

Section: Discussion Canopy Characteristics Light Interception and Bcontrasting

confidence: 51%

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Genotypic Variability in Morphological and Physiological Traits among Maize Inbred Lines—Nitrogen Responses

et al. 2006

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A better understanding of the physiological processes related to nitrogen (N) metabolism in maize (Zea mays L.) inbred lines is important for increasing the efficiency of breeding programs targeting low-N environments. This study analyzed the response to contrasting N availability of morphophysiological traits in a set of 12 maize inbred lines, from different origins (USA and Argentina) and breeding eras (from 1952 onward). Traits included in the analysis were related to canopy structure, light interception, shoot biomass production, and grain yield. Our results indicate that (i) the start of N effects on canopy size was more related to a threshold crop leaf area index (about 2) than to a given leaf stage (i.e., V n ), (ii) the light attenuation coefficient value was not affected by N availability, (iii) variations in kernel number per plant were explained by prolificacy (r 2 5 0.59), and (iv) differences in harvest index were related to kernel number per plant (r 2 5 0.77). The most important finding of our research was the detection in some inbreds of a particular response of kernel number to plant growth rate around silking, different from the general model established for hybrids. In these inbreds an additional effect of N availability was detected as reduced kernel set at a given plant growth rate under N deficient conditions (i.e., reduced reproductive efficiency). This result highlights the need of more research on reproductive sink development in this type of germplasm.

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Section: Discussion Canopy Characteristics Light Interception and Bcontrasting

confidence: 56%

Section: Discussion Canopy Characteristics Light Interception and Bcontrasting

confidence: 51%

Genotypic Variability in Morphological and Physiological Traits among Maize Inbred Lines—Nitrogen Responses

et al. 2006

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“…One's perception of model accuracy is highly dependent on scale. For example, Rasse et al (2000) pointed out that experimental plot data are not as buffered against pest damage and individual management errors as averaged county grain yields. They cited two contrasting studies: one that reported accurate grain yield simulation using averaged county grain yields (Kiniry et al, 1997) and another that reported poor simulation of year‐to‐year grain yield variability using research plot data (Otegui et al, 1996).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of GPFARM for Dryland Cropping Systems in Eastern Colorado

Andales¹,

Ahuja²,

Peterson

2003

Agronomy Journal

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GPFARM is an ARS decision support system for strategic (long‐term) planning. This study evaluated its performance for comparing alternative dryland no‐till cropping systems and established limits of accuracy for eastern Colorado, using data collected in 1987 through 1999 from an ongoing long‐term experiment at three locations along a gradient of potential evapotranspiration (PET) (Sterling, low PET; Stratton, medium PET; and Walsh, high PET). The crop rotations, which included winter wheat (Triticum aestivum L.), corn (Zea mays L.), sorghum [Sorghum bicolor (L.) Moench], proso millet (Panicum miliaceum L.), and varying fallow periods, were wheat–fallow, wheat–corn–fallow, and wheat–corn–millet–fallow at Sterling and Stratton and wheat–fallow, wheat–sorghum–fallow, and wheat–sorghum–millet–fallow at Walsh. The ranges of relative error (RE) of simulated mean and root mean square error (RMSE) were total soil profile water content (RE: 0 to 23%; RMSE: 38 to 76 mm water), dry mass grain yield (RE: −27 to 84%; RMSE: 419 to 2567 kg ha−1), dry mass crop residue (RE: −5 to 42%; RMSE: 859 to 1845 kg ha−1), and total soil profile residual nitrate N (RE: −42 to 32%; RMSE: 26 to 78 kg ha−1). GPFARM simulations agreed with observed trends and showed that productivity and water use efficiency increased with cropping intensification and that Stratton was the most productive and Walsh the least. GPFARM (v. 2.01) was less suited for year‐to‐year grain yield prediction under dryland conditions but has potential as a tool for studying long‐term interactions between environment and crop management system. Future development and applications of GPFARM must account for crop‐specific responses to stress, detailed hydrology, better understanding of root uptake processes, and spatial variability to give more accurate grain yield predictions in water‐stressed environments.

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“…Leaves partially removed before completing expansion will continue growing. On the same date, leaf weight is reduced by 10.5% and stem weight by 5.0% (Rasse et al, 2000). On the day of male plant removal, the remaining plant population density is reduced according to the loss of male plants and the initial proportion of male and female plants in the fi eld (Table 1).…”

Section: Development Of Modifi Ed Ceres-maizementioning

confidence: 99%

Simulating Source‐Limited and Sink‐Limited Kernel Set with CERES‐Maize

2007

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CERES‐Maize simulates kernel set as a source‐limited process based on average plant growth rate during the lag phase after flowering. Yet the number of kernels formed by maize (Zea mays L.) also depends on timely interaction between male and female flowers, which can limit formation of reproductive sinks under some conditions. Failure to account for sink‐limited kernel set may contribute to simulation error observed under conditions that affect dynamics of pollen shed or silking, but do not alter crop growth rate. We developed algorithms for a Flowering Model to simulate sink‐limited kernel set from flowering dynamics. This model was calibrated against kernel production in hybrid seed production fields and then linked to CERES‐Maize. The Modified CERES‐Maize was calibrated against two years of field data involving three hybrids, eight population densities, and seven N levels. Integrating the capacity to simulate sink‐limited kernel set with source‐limited kernel set increased simulation accuracy dramatically, relative to original CERES‐Maize. For 13 commercial fields tested, Modified CERES‐Maize decreased simulation error for kernels per plant from 17.1 to 2.3%, improved r2 between measured and simulated values from 0.77 to 0.87, and decreased simulation error indicators mean error, root mean square error, and mean square deviation by 85, 40, and 64%, respectively. Modified CERES‐Maize accounts for a much greater range of variability in the biological processes controlling kernel set.

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Simulating Inbred‐Maize Yields with CERES‐IM

Cited by 17 publications

References 25 publications

Genotypic Variability in Morphological and Physiological Traits among Maize Inbred Lines—Nitrogen Responses

Genotypic Variability in Morphological and Physiological Traits among Maize Inbred Lines—Nitrogen Responses

Evaluation of GPFARM for Dryland Cropping Systems in Eastern Colorado

Simulating Source‐Limited and Sink‐Limited Kernel Set with CERES‐Maize

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