Abstract
Current problems of world including food security, water scarcity, soil erosion, climate changes, population demand and environmental safety can be challenged by agriculture science by introduction of biotech crops, new farming practices and new crop protection methods. The efficiency of crops is improved by a novel technique like bioalgalization for soil amendments. In this aspect, Spirulina is applied to soils along with biofertilizers, organic manure and vermicompost anticipating enhanced soil mineral status to help the growth and yield of crops. The present experiment was carried out with field studies on Amaranthus, Green gram and Tomato using different combinations and concentrations of Spirulina with biofertilizer, vermicompost and organic manure and different treatments to estimate the NPK status in plants and in soils prior and after the studies. There was 10–20 fold increase of protein content in yield of tomato when compared with reference value of 0.9/100 g with different concentrations of Spirulina. The soil nitrogen levels were found to be increased in experimental set up of green gram seeds soaked in Spirulina, 5 g concentration resulted in N content as (0.84 ± 0.04%) compared to control (0.03 ± 0.02%). In experimental method of biofertilizer and Spirulina combination Phosphorus content of soil after harvest of Amaranthus plants was 44.5 ± 0.70 mg/100 g and the control value was 37 ± 0.70 mg/100 g. In post-harvest soil of tomato plants the potassium (K) levels were increased to 184.5 ± 2.1 mg/100 g from the control value of 44 ± 0.70 mg/100 g in 3 h of soaking experimental group. Bioalgalization is a promising technology to prevent soil erosion and pollution caused by use of heavy chemical fertilizers and also helps to improve soil fertility.
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Anitha, L., Bramari, G.S., Kalpana, P. (2020). Bioalgalization—A Novel Approach for Soil Amendment to Improve Fertility. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_49
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