ABSTRACT
The study of the use of organic [FYM] and inorganic [NPK] nutrient sources with biofertiliser on wheat-fallow and wheat-maize cropping system under rainfed environment revealed significant increase in biometric parameters of wheat during winter and summer seasons of two years. During both the seasons, application of 1/2 NPK + 1/2 FYM + Biopower [brand] produced the highest grain yield [3684 kg/ha] and [3781 kg/ha] of wheat with the maximum N uptake of 357 kg/ha, P uptake of 51 kg/ha and K uptake of 215 kg/ha. Wheat-maize cropping system was found to be profitable economically with integrated use of mineral and organic and/or Biopower under rainfed conditions of Pakistan
Subject(s)
Soil , Triticum , Zea mays , ManureABSTRACT
Disparity in the root morphology of six rice (Oryza sativa L.) genotypes varying in potassium (K) efficiency was studied with three K levels: 5 mg/L (low), 10 mg/L (moderate) and 40 mg/L (adequate) in hydroponic culture. Morphological parameters included root length, surface area, volume and count of lateral roots, as well as fine (diameter<0.2 mm) and thick (diameter>0.2 mm) roots. The results indicate that the root growth of all genotypes was reduced under low K, but moderate K deficiency increased the root length of the efficient genotypes. At deficient and moderate K levels, all the efficient rice genotypes developed more fine roots (diameter<0.2 mm) than the inefficient ones. Both fine root count and root surface area were found to be the best parameters to portray K stress in rice. In accordance with the root morphology, higher K concentrations were noted in shoots of the efficient genotypes when grown at moderate and deficient K levels, indicating that root morphology parameters are involved in root uptake for K and in the translocation of K up to shoots. K deficiency affected not only the root morphology, but also the root ultra-structure. The roots of high-efficient genotypes had stronger tolerance to K deficient stress for root membrane damage, and could maintain the developed root architecture to adapt to the low K growth medium.
Subject(s)
Genotype , Oryza , Metabolism , Plant Roots , Potassium , MetabolismABSTRACT
The effect of Al and Cd on the growth, photosynthesis, and accumulation of Al, Cd and plant nutrients in two soybean genotypes were determined using hydroponic culture. There were six treatments: pH 6.5; pH 4.0; pH 6.5+1.0 micromol/L Cd; pH 4.0+1.0 micromol/L Cd; pH 4.0+150 micromol/L Al; pH 4.0+1.0 micromol/L Cd+150 micromol/L Al. The low pH (4.0) and Al treatments caused marked reduction in root length, shoot height, dry weight, chlorophyll content (SPAD value) and photosynthetic rate. Al-sensitive cv. Zhechun 2 accumulated comparatively more Al and Cd in plants than Al-tolerant cv. Liao 1. Compared with pH 6.5, pH 4.0 resulted in significant increase in Cd and Al concentration in plants. Combined application of Cd and Al enhanced their accumulation in roots, but caused a reduction in shoots. The concentrations of all 10 nutrients (P, K, Ca, Mg, Fe, Mn, Cu, Zn and B), except Mo were also increased when plants were exposed to pH lower than pH 6.5. Al addition caused a reduction in the concentration of most nutrients in plant roots and shoots; but K, Mn and Zn in roots were increased. Treatments with Cd alone or together with Al reduced the concentrations of all the plant nutrients in plants. Al-sensitive genotype Zhechun 2 has lower nutrient concentration than Al-tolerant genotype Liao 1. The current findings imply that Al and Cd are synergistic in their effect on plant growth, physiological traits and nutrient uptake.
Subject(s)
Aluminum , Toxicity , Cadmium , Toxicity , Hydrogen-Ion Concentration , Photosynthesis , Glycine max , MetabolismABSTRACT
<p><b>OBJECTIVE</b>During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance.</p><p><b>METHODS</b>Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network.</p><p><b>RESULTS</b>Within the range of experimental conditions tested, it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of sulfate to an acceptable manner.</p><p><b>CONCLUSION</b>Apart from experimentation, ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASObased denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies, better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and, as a consequence, better effluent quality.</p>