Rice biofortification: breeding and genomic approaches for genetic enhancement of grain zinc and iron contents.

Rice is a highly consumed staple cereal cultivated predominantly in Asian countries, which share 90% of global rice production. Rice is a primary calorie provider for more than 3.5 billion people across the world. Preference and consumption of polished rice have increased manifold, which resulted in the loss of inherent nutrition. The prevalence of micronutrient deficiencies (Zn and Fe) are major human health challenges in the 21st century. Biofortification of staples is a sustainable approach to alleviating malnutrition. Globally, significant progress has been made in rice for enhancing grain Zn, Fe, and protein. To date, 37 biofortified Fe, Zn, Protein and Provitamin A rich rice varieties are available for commercial cultivation (16 from India and 21 from the rest of the world; Fe > 10 mg/kg, Zn > 24 mg/kg, protein > 10% in polished rice as India target while Zn > 28 mg/kg in polished rice as international target). However, understanding the micronutrient genetics, mechanisms of uptake, translocation, and bioavailability are the prime areas that need to be strengthened. The successful development of these lines through integrated-genomic technologies can accelerate deployment and scaling in future breeding programs to address the key challenges of malnutrition and hidden hunger.

Prediction of ammonia contaminants in the aquaculture ponds using soft computing coupled with wavelet analysis.

Intensive aquaculture practices generate highly polluted organic effluents such as biological oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron, and chlorides. In recent years, Inland aquaculture ponds in the western delta region of Andhra Pradesh have been intensively expanding and are more concerned about negative environmental impact. This paper presents the water quality analysis of aquaculture waters in 64 random locations in the western delta region of Andhra Pradesh. The average water quality index (WQI) was 126, with WQI values ranging from 21 to 456. Approximately 78% of the water samples were very poor and unsafe for drinking and domestic usage. The mean ammonia content in aquaculture water was 0.15 mg/L, and 78% of the samples were above the acceptable limit set by the World Health Organization (WHO) of 0.5 mg/L. The quantity of ammonia in the water ranged from 0.05 to 2.8 mg/L. The results show that ammonia levels exceed the permissible limits and are a significant concern in aquaculture waters due to toxicity. This paper also presents an intelligent soft computing approach to predicting ammonia levels in aquaculture ponds, using two novel approaches, such as the pelican optimization algorithm (POA) and POA coupled with discrete wavelet analysis (DWT-POA). The modified and enhanced POA with DWT can converge to higher performance when compared to standard POA, with an average percentage error of 1.964 and a coefficient of determination (R2) value of 0.822. Moreover, it was found that prediction models were reliable with good accuracy and simple to execute. Furthermore, these prediction models could help stakeholders and policymakers to make a real-time prediction of ammonia levels in intensive farming inland aquaculture ponds.