Alternate wetting and drying water management can reduce phosphorus availability under lowland rice cultivation irrespective of nitrogen level.

The limited availability of phosphorus (P) in the soil, which is affected by soil moisture, has a significant impact on crop production. However, we still do not fully understand how water management and nitrogen (N) addition affect the availability of P in paddy soil. An evaluation of the effects of two water management strategies that is continuous flooding (CF) and alternate wetting and drying (AWD) irrigation along with various nitrogenous fertilizer addition rates (equivalent to 0, 100%, 133%, and 166% recommended dose of N addition) on P availability in paddy soil took place over the course of a 2-year field experiment. The results showed that water management had a significant influence on ferrous iron, microbial biomass P, and soil-available P. However, the addition of N did not affect the availability of P in the soil. When N was added at various rates, AWD consistently reduced the amount of soil-available P compared to CF. This was primarily because AWD increased microbial biomass, which immobilized P and decreased the content of ferrous iron. As a result, the soil's ability to absorb P increased, leading to a decrease in the amount of P available. In conclusion, AWD decreases the amount of available P in paddy soil compared to CF.

Critical weather limits for paddy rice under diverse ecosystems of India.

Rice yields are largely influenced by variability in weather. Here, we demonstrate the effect of weather variables viz., maximum and minimum temperatures, rainfall, morning and evening relative humidity, bright sunshine hours on the yield of rice cv. Swarna, grown across five rice ecologies of India through field experiments during kharif (wet) season (Jun-Sept.). Critical thresholds of weather elements were identified for achieving above average, average and below average yield for each ecology. The investigation could determine how different weather elements individually and collectively affect rice yield in different rice ecosystems of India. While a sudden increase in minimum temperature by 8-10 °C (> 30 °C) during reproductive period resulted in 40-50 per cent yield reduction at Mohanpur, a sudden decrease (< 20 °C) caused yield decline at Dapoli. The higher yields may be attributed to a significant difference in bright sunshine hours between reproductive phases of above-average and below-average yield years (ranging from 2.8 to 7.8 hours during P5 stages and 1.7 to 5.1 during P4 stages). Rice cultivar Swarna performed differently at various sowing dates in a location as well as across locations (6650 kg ha-1 at Dapoli to 1101 kg ha-1 at Samastipur). It was also found that across all locations, the above average yield could be associated with higher range of maximum temperature compared to that of below average yield. Principal component analysis explained 77 per cent of cumulative variance among the variables at first growth stage, whereas 70 per cent at second growth stage followed by 74 per cent and 66 per cent at subsequent growth stages. We found that coastal locations, in contrast to inland ones, could maximize the yield potential of the cultivar Swarna, due to the longer duration of days between panicle initiation to physiological maturity. We anticipate that the location-specific thresholds of weather factors will encourage rice production techniques that are climate resilient.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.