Effects of K Scheduling on Soybean Multicropped with Sweet Corn and Cabbage: Crop Yields and Tissue and Soil K

Abstract

Soybean [Glycine max (L.) Merr.) has been grown recently in central Florida on vegetable‐producing soils which normally lie idle during the hot summer months. The objectives of this study were to determine the K requirements of a cabbage (Brasska oleraceae L. var. capitata)‐sweet corn (Zea mays L.)‐soybean intensively managed cropping sequence on a tile‐drained Immokalee vine sand (sandy, siliceous, hyperthermic Arenic Haplaquods). Eight different K fertilization schedules (varying in rate and timing) were evaluated in a randomized complete block design with six replications over two complete triple‐cropping cycles. Yields of soybean and sweet corn were not increased by addition of 108 lb K2O/acre whenever the previous crop also received K; direct application of 216 lb K2O/acre to cabbage increased cabbage yields regardless of K application to previous sweet corn or soybean crops. Critical soil test K levels below which yields of sweet corn and soybean were increased by a 108 lb K2O/acre application were found to be 25 and 32 ppm K, respectively. Soil test K increased from preplant to soybean bloom without K addition, indicating that K from residues of the previous crop may be an important source of K for the subsequent crop and may account for the low critical values recorded. In 1982, leaf K at bloom and pod‐fill accounted for 50 and 60%, respectively, of the variation in soybean yield. Sidedress K applied at bloom increased leaf K concentration at pod‐fill from 0.65% to 0.92%. Due to the high variability in plant stand and crop growth of the 1983 no‐till soybean crop, no differences in leaf K concentration at late bloom were observed. Increased seed size accounted for an average 23% of the increase in soybean grain yield from the side‐dress K application at soybean bloom. It was concluded that yields of soybean, cabbage, and sweet corn in a multiple cropping sequence depend upon K fertilizer scheduling and can be predicted by soil testing and/or plant analysis.