Theses and Dissertations

Issuing Body

Mississippi State University


Reddy, K. Raja

Committee Member

Bi, Guihong

Committee Member

Henry, W. Brien

Committee Member

Irby, Trent

Committee Member

Krutz, L. Jason

Date of Degree


Document Type

Dissertation - Open Access


Plant and Soil Sciences (Agronomy)

Degree Name

Doctor of Philosophy


College of Agriculture and Life Sciences


Department of Plant and Soil Sciences


With the increasing scarcity of water resources, soil moisture stress is the single most threat to global soybean production causing extensive yield losses. The objectives of this study were to investigate soil moisture stress effects on all aspects of soybean growth and development processes and to develop functional algorithms that could be used for field management decisions and in soybean crop modeling. To fulfill these objectives, six experiments were conducted; one in vitro osmotic stress study on seed germination, four studies by imposing five soil moisture treatments, 100, 80, 60, 40, and 20% of daily evapotranspiration of the control at different growth stages using sunlit plant growth chambers, and one transgenerational study on seed germination at different osmotic levels and offspring growth at three irrigation treatments (100, 66, and 33% based on field capacity) for plants grown at different soil moisture levels. Two cultivars from maturity group V, Asgrow AG5332 and Progeny P5333RY, with different growth habits were used in all these studies. Midday leaf water potential, plant height, mainstem nodes, gas-exchange traits, canopy reflectance, and several yield components including pod weight, seed yield, and seed quality were measured. Soil moisture stress decreased biomass, net photosynthesis, yield, individual seed weight, maximum seed germination, protein, fatty acids, sucrose, N, and P and increased oil, stachyose, Fe, Mg, Zn, Cu, and B contents. Overall, Asgrow AG5332 was more tolerant to drought stress than Progeny P5333RY. Soil moisture stress induced changes in seed quality that were correlated with seed germination and seedling vigor in the F1 generation. These data can be used to build a model-based decision support system capable of predicting yield under field conditions.



Soybean drought||root morphology||seed quality||soybean canopy reflectance||pod distribution