Yield and composition of soybeans as influenced by soil pH, phosphorus, zinc, and copper

Abstract

Abstract The absorption and utilization of most elements by plants are strongly influenced by factors other than elemental concentration in the soil solution such as soil pH, organic matter, CEC and interactions with other nutrients. This field study was conducted to determine the response of soybean cultivars with different tolerance to P induced growth disorders to applied P, Zn, and Cu at different soil pH levels. Both Wright (tolerant) and Hutton (sensitive) soybean cultivars showed a significant response to applied Zn. The greatest response was observed for Hutton although Wright had seed yields as great or greater than those for Hutton. Yield of both cultivars was significantly correlated with leaf Zn concentration and the P/Zn ratio. The highest yields for both cultivars were associated with leaf Zn concentrations of approximately 26 μg/g and P/Zn ratios of 115. Applied P did not affect yield or P absorption of either cultivar. Copper fertilization increased Cu concentration in plant tissue by 38% to 58% but did not influence seed yields. Seed yield and Zn absorption of both cultivars was reduced by increased pH with yield decreasing at a faster rate when Zn was not applied than when plants received Zn fertilizer. Copper concentrations in plant tissue were not affected by increased soil pH. Zinc concentration of leaf tissue was also affected by a P‐Zn interaction. Wright contained much greater concentrations of leaf Zn, Fe, Cu, and several other nutrients than did Hutton indicating a greater ability to absorb these elements under conditions of high soil pH and P levels. This ability may account for the difference in tolerance to P induced growth disorders that has been observed between these cultivars. Within each pH treatment, Zn absorption was highly correlated to extractable soil Zn but when soil pH was not taken into account neither DTPA nor Mehlich No. 1 ex‐tractable soil Zn adequately predicted Zn absorption. Better results were obtained by using prediction equations determined by multiple linear regression with extractable soil Zn and pH as the independent variables. These equations accounted for 80% to 95% of the variation in leaf Zn concentration.