The N required by soybeans [Glycine max L.) Merr.] is furnished by soil and by symbiotically fixed N. The latter is associated with available photosynthate. A determinate soybean cultivar, ‘Bragg’, was grown in sand in the greenhouse in 1974 and outdoors in 1975 to further evaluate the effect of growth stages and NH4+‐N and NO3‐‐N on 14C translocation, N fixation as measured by acetylene reduction by intact nodules, and accumulation of dry matter and nitrogen.Plants exported very little 14C to the nodules from photosynthesis of 14CO2, and acetylene reduction was very low when significant sink demand by pods became evident. Inorganic N supplied throughout the season or 10 days prior to sampling reduced 14C in nodules and acetylene reduction by nodules. NO3‐‐N generally decreased acetylene reduction more than NH4+‐N. 14C transport to plant parts other than nodules was not influenced by inorganic N. There is indication that in the determinate cultivar of soybeans used, more photosynthate was transferred to nodules during the vegetative and early reproductive stages than that reported previously for indeterminate cultivars. The data further emphasize the high demand for N at the pod‐fill stage where 333 mg N per plant (23% of total) was required during a 20‐day period, mid‐pod to late pod fill stage, and at the same time N fixation was declining.
Sweet sorghum has become a promising alternative feedstock for biofuel production because it can be grown under reduced inputs, responds to stress more efficiently than traditional crops, and has large biomass production potential. A three-year field study was conducted to evaluate three cultivars of sweet sorghum as bioenergy crops in the Southeast United States (Fort Valley, Georgia): Dale, M81 E and Theis. Parameters evaluated were: plant density, stalk height, and diameter, number of nodes, biomass yield, juice yield, °Bx, sugar production, and theoretical ethanol yields. Yields were measured at 85, 99, and 113 days after planting. Plant fresh weight was the highest for Theis (1096 g) and the lowest for Dale (896 g). M81 E reported the highest stalk dry weight (27 Mg ha−1) and Theis reported the lowest (21 Mg ha−1). Theis ranked the highest °Bx (14.9), whereas M81 E was the lowest (13.2). Juice yield was the greatest for M81 E (10915 L ha−1) and the lowest for Dale (6724 L ha−1). Theoretical conservative sugar yield was the greatest for Theis (13 Mg ha−1) and the lowest for Dale (9 Mg ha−1). Theoretical ethanol yield was the greatest for Theis (7619 L ha−1) and the lowest for Dale (5077 L ha−1).
Composting broiler litter may increase the amount of stable organic components and reduce contamination of ground‐ and surface‐water with N and P from excessive land applications. Limited research has been done comparing field‐scale losses of nutrients from broiler litter applied to hayfields. This project determined field‐scale N and P runoff losses from fresh and composted litter applied to hayfields. Two rates of broiler litter, 10 Mg ha−1 yr−1 (1X) and 20 Mg ha−1 yr−1 (2X), and a mix of 10 Mg ha−1 yr−1 of broiler litter and 50 Mg ha−1 yr−1 of composted litter (1X + C), were split‐applied in April and September for 2 yr. Surface runoff and subsurface flow were monitored for inorganic and total N and P. Nitrate concentrations in subsurface flow remained below the USEPA standard of 10 mg L−1 for all treatments. Average dissolved reactive P (DRP) concentrations were statistically higher under the 1X + C treatment, followed by the 2X and 1X treatments, reaching a maximum of 8.5 mg L−1 under the 1X + C treatment. Differences between field and plot‐scale results were most likely controlled by the timing of application and occurrence of the first rainfall event. Concentrations of resin‐extractable P (Pr) in soil increased under all treatments, indicating accumulation of P after only 2.5 yr of application. In this research, the amount of P applied was the principal determinant of the DRP concentration in the surface runoff. Composting broiler litter increased the amount of stable organic components.
Artificial neural networks (ANN) and traditional regression models were developed for prediction of thermal properties of sweet sorghum bagasse as a function of moisture content and room temperature. Predictions were made for three thermal properties: 1) thermal conductivity, 2) volumetric specific heat, and 3) thermal diffusivity. Each thermal property had five levels of moisture content (8.52%, 12.93%, 18.94%, 24.63%, and 28.62%, w. b.) and room temperature as inputs. Data were sub-partitioned for training, testing, and validation of models. Backpropagation (BP) and Kalman Filter (KF) learning algorithms were employed to develop nonparametric models between input and output data sets. Statistical indices including correlation coefficient (R) between actual and predicted outputs were produced for selecting the suitable models. Prediction plots for thermal properties indicated that the ANN models had better accuracy from unseen patterns as compared to regression models. In general, ANN models were able to strongly generalize and interpolate unseen patterns within the domain of training.
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