Agronomy Journal Journal of Natural Resources and Life Sciences Education
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Published in Agron. J. 96:1222-1230 (2004).
© American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA

Agronomic Modeling

Simulating Seed Number in Grain Sorghum from Increases in Plant Dry Weight

T. J. Gerika,*, W. D. Rosenthala, R. L. Vanderlipb and L. J. Wadec

a Texas Agric. Exp. Stn., Texas A&M Univ. Syst., Blackland Res. and Ext. Cent., 720 East Blackland Rd., Temple, TX 76502
b Dep. of Agron., Kansas State Univ., Manhattan, KS 66506
c School of Plant Biol., Univ. of Western Australia, Crawley, WA 6009, Australia

* Corresponding author (t-gerik{at}tamu.edu)

Received for publication May 30, 2002. Simulation of seed number for crop models is important in identifying cultural practices, which enhance yield stability. Field and crop simulation studies examined the relationship between dry weight accumulation and seed number per plant to potentially improve the capability of the grain sorghum [Sorghum bicolor (L.) Moench] model, SORKAM, to simulate seed number. The best estimates of seed numbers were obtained from plant dry weight accumulated during the 360 growing degree day intervals encompassing panicle branch–spikelet formation (PBSI) and panicle elongation through anthesis (EAI). Comparison of observed vs. simulated seed numbers using SORKAM's original equations accounted for 57% of the variation in seed number, but it underestimated high seed numbers. Accumulated plant dry weight for the PBSI and EAI intervals accounted for 49 and 64% of the variation in seed number, respectively. Simulation of seed numbers improved when the more sensitive water stress coefficient (for leaf area, WATCOle) was applied to the interval (PBSI or EAI) experiencing the highest water stress while the less sensitive water stress coefficient (for dry weight, WATCOdw) was applied to the interval experiencing the lowest water stress. The slope from the regression of observed on simulated seed numbers was 0.80 (r2 = 0.57) for SORKAM with the WATCOle switch compared with 0.59 (r2 = 0.57) in the original SORKAM model. Hence, the timing and recovery of water stress during the panicle development period was important in estimating seed number of sorghum.

Abbreviations: E, SORKAM model containing Eq. [14], from A–480 to A+120 (600 GDD), approximating the time interval used to simulate seed number • EA, SORKAM with Eq. [17], from A–180 to A+180 (360 GDD), approximating the second half of the panicle development—panicle elongation through anthesis • EAI, interval encompassing panicle elongation through anthesis • GDD, growing degree days • PBS, SORKAM with Eq. [16], from A–540 to A–180 (360 GDD), approximating the first half of the panicle development—appearance of primary branches and spikelets appear on the developing panicle • PBSI, interval encompassing panicle branch and spikelet appearance • PBS/EA, SORKAM containing the empirically derived equations [i.e., representing panicle branch—spikelet appearance interval (A–540 to A–180) and panicle elongation–anthesis interval (A–180 to A+180)] with a switch to select the equations to simulate seed number. The switch compares the mean WATCO values for the two intervals and then directs the model to apply the equation associated with the interval where water stress was more severe (i.e., where the WATCO value was smaller) to simulate seed numbers for the whole 720-GDD interval • RMSE, root mean squared error • SORKAM, grain sorghum plant growth model • WATCO, water stress coefficient • WATCOdw, water stress coefficient for dry weight • WATCOle, water stress coefficient for leaf area • WATCOdw/le, the SORKAM with its original equations for estimating seed number but containing a switch that applies the water stress coefficient, WATCOdw or WATCOle, during the intervals of panicle branch–spikelet appearance (A–540 to A–180) and panicle elongation–anthesis (A–180 to A+180) depending on levels of water stress. The switch compares the mean WATCOle values of the two intervals and then applies WATCOle to the interval where water stress was more severe and WATCOdw to the interval where water stress was less severe. If water stress levels are equal, WATCOle is applied to both intervals







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