RESUMEN
Biohydrogen (H2) is an efficient form of renewable energy generated from various biological organisms. Specifically, primitive plants such as algae which are photosynthetic organisms can produce several commercial products, including biofuels due to their simple form, short life span, efficient photosynthetic capacity, and ability to grow in non-potable water sources. But these algae are often neglected and considered waste. Several studies have revealed the importance and role of algal species in generating biofuels, especially biohydrogen. Considerable research has been conducted in order to understand hydrogen production from algal sources. This review emphasizes the photolysis of water-based hydrogen production in algae apart from the metabolites fermentation process. The influence of physico-chemical factors, including oxygen scavengers, nanoparticles, and hydrogenases, was highlighted in this review to enhance H2 production from algal species. Also, several algal species used for hydrogen production are summarized in detail. Overall, this review intends to summarize the developments in hydrogen production from algal species keeping in view of excellent prospects. This knowledge certainly would provide a good opportunity for the industrial production of hydrogen using algal species, which is one of the most concerned areas in the energy sector.
RESUMEN
Rhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water deficit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artificial neural network (ANN)-based prediction of DRR incidence considering DRR incidence data from previous reports and weather factors. ANN-based prediction using the backpropagation algorithm showed that the combination of total rainfall from November to January of the chickpea-growing season and average maximum temperature of the months October and November is crucial in determining DRR occurrence in chickpea fields. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven different locations in India with combination of low soil moisture and pathogen stress treatments confirmed the impact of low soil moisture on DRR incidence under different agroclimatic zones and helped in determining the correlation of soil factors with DRR incidence. Soil phosphorus, potassium, organic carbon, and clay content were positively correlated with DRR incidence, while soil silt content was negatively correlated. Our results establish the role of edaphic and other weather factors in chickpea DRR disease incidence. Our ANN-based model will allow the location-specific prediction of DRR incidence, enabling efficient decision-making in chickpea cultivation to minimize yield loss.
