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CROP PHYSIOLOGY & METABOLISM

Predicting Potential Kernel Set in Maize from Simple Flowering Characteristics

^a Dep. of Agricultural and Biosystems Engineering, Iowa State Univ., Ames, IA 50011
^b Dep. of Agronomy, Iowa State Univ., Ames, IA 50011

^* Corresponding author (westgate{at}iastate.edu)

Accurate prediction of kernel number per plant is critical for yield simulation in maize (Zea mays L.). Model predictions of kernel number generally are based on empirical relationships between final kernel number and carbohydrate supply or plant growth at silking, without regard to flowering dynamics. The objective of this study was to develop a model for predicting kernel set from seasonal dynamics of pollen shed and silk exsertion. Hybrids were planted in isolated plots at four ratios of male fertile (MF) and male sterile (MS) plants. Dynamics of pollen shed in each plot was described by a Gauss curve and by a population index (P_ind) derived from the percentage of plants at beginning, maximum, and ending pollen shed. Percentage of plants at silking followed a sigmoid curve, whereas the number of silks exposed daily per ear followed a monomolecular model. Pollen shed and silk exsertion curves were translated to potential kernel set using a published relationship. Excellent agreement was observed between predicted and measured number of florets per hectare available for pollination. The model overpredicted kernel number per hectare at high pollen shed densities. Predicted kernels per hectare were within 5% of actual kernel numbers at low pollen shed densities when delayed development of subapical ears and asynchronous pollination within ears were considered. These results indicate kernel set in maize can be predicted from simple measures of the flowering process. They confirm that kernel set is limited primarily by factors other than pollen amount when all plants in the population are MF.

Abbreviations: IPAR, intercepted photosynthetically active radiation • MF, male fertile • MS, male sterile • P_ind, population index • RMSE, root mean square of error

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