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- Author or Editor: C.A. Sims x
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The influence of bilateral cordon (BC) and cane training systems and level of pruning severity on vegetative and reproductive characteristics of Vitis hybrid `Suwannee' were determined from 1987 to 1989. In 1987, yield and quality were similar on BC- and cane-trained vines. In 1988, shoot count, yield, and quality were similar regardless of training system and pruning severity (50, 70, or 90 nodes per vine). When data from both training systems were combined, yield was related to the number of shoots. Vines pruned more severely compensated by producing more shoots from non-count (non-node) positions on the canes, cordon, or spurs. Similarly, in 1989 yield and berry quality were not affected by training system or levels of pruning severity (50, 70, 90, or 110 nodes), although berry weight was affected by training system, and shoot count and shoot length were affected by level of pruning severity. Interactive effects of training system and pruning level were not significant in either year. An analysis of combined data for 1989 indicated that yield was affected by the number of nodes and shoots. Thus, `Suwannee' may be trained to the BC system, which is more readily adapted to mechanization. Pruning to a specific number of nodes per vine was not critical.
Even though research and education systems have transformed agriculture from a traditional to a high-technology sector, soil erosion still remains as a major universal problem to agricultural productivity. The Universal Soil Loss Equation (USLE) and its replacement, the Revised Universal Soil Loss Equation (RUSLE) are the most widely used of all soil erosion prediction models. Of the five factors in RUSLE, the cover and management (C) factor is the most important one from the standpoint of conservation planning because land use changes meant to reduce erosion are represented here. Even though the RUSLE is based on the USLE, this modern erosion prediction model is highly improved and updated. Alcorn State Univ. entered into a cooperative agreement with the NRCS of the USDA in 1988 to conduct C-factor research on vegetable and fruit crops. The main objective of this research is to collect plant growth and residue data that are used to populated databases needed to develop C-factors in RUSLE, and used in databases for other erosion prediction and natural resource models. The enormous data collected on leaf area index (LAI), canopy cover, lower and upper biomass, rate of residue decomposition, C:N ratio of samples of residues and destructive harvest and other gorwth parameters of canopy and rhizosphere made the project the largest data bank on horticultural crops. The philosophy and methodology of data collection will be presented.