A study was conducted with the goal of developing an algorithm for use in sensors to monitor available soil N. For this purpose, three different soils were selected. The soils were studied for electrical conductivity (EC) at four different moisture levels and four levels of N. The selection of moisture levels was based on optimum moisture levels between tillage moisture and field capacity. The results revealed a significant relationship between electrical conductivity and moisture level of the soil as well as between electrical conductivity and soil N content. Based on these relations, a polynomial model was developed between the EC of each selected soil sample and moisture content as well as N levels. The regression model for moisture content-based EC determination had coefficients of determination of 0.985, 0.988, and 0.981 for clay loam, sandy loam, and sandy loam soils, respectively. Similarly, the regression model for N content-based EC determination had coefficients of determination of 0.9832, 0.9, and 0.99 for clay loam, sandy loam, and sandy loam soils, respectively. An algorithm developed using a polynomial relationship between the EC of each selected soil sample at all moisture and N levels can be used to develop a sensor for site-specific N application.

A study was conducted to evaluate ethyl acetate, soy biodiesel, and 1-octanol as surfactants for stabilization of ethanol–diesel fuel blends. The evaluation was based on the temperature stability and engine performance of ethanol–diesel fuel blends stabilized by selected surfactants. Among the three selected surfactants, 1-octanol was most effective in stabilizing ethanol–diesel fuel blends. Out of 20 ethanol–diesel fuel blends prepared with 1-octanol as surfactant, only 3 blends showed distinct phase separation; the rest 17 remained stable in the entire temperature range of 0–45 °C. At the rated engine speed, eight ethanol–diesel fuel blends showed similar power-producing capability as that of diesel. The engine running on diesel developed a power of 3.71 kW, while the engine brake power with ethanol–diesel fuel blends was in the range of 3.67–3.74 kW at the rated load condition. The tested fuel blends resulted in slightly higher fuel consumption and increased emission of unburnt hydrocarbons as compared to the diesel fuel. The tested fuel blends had comparable thermal efficiency and acceptable UBHC and NO_x emissions compared to diesel.

Nitrogen responses vary under diverse agronomic management practices, influencing vegetation indices (VIs) and productivity across different ecological conditions. However, the proper quantification of these responses under various crop establishment methods with varied nitrogen levels is rarely studied. Therefore, a field experiment was conducted to investigate the impact of varying nitrogen levels on VIs, growth parameters, yield attributes, yield, and economic aspects of transplanted rice (TR) and direct-seeded rice (DSR). The experiment was conducted in the randomized block design consisted seven N levels, which included 0% recommended dose of nitrogen (RDN) or no nitrogen (N₀), 33.33% RDN (N₁), 66.66% RDN (N₂), 100% RDN (N₃), 133.33% RDN (N₄), 166.66% RDN (N₅) and 200% RDN (N₆), and replicated thrice. The plots with higher N levels demonstrated increased values of VIs and treatment N₃ (120 kg N ha⁻¹), N₄ (160 kg N ha⁻¹), N₅ (200 kg N ha⁻¹), and N₆ (240 kg N ha⁻¹) showed no statistically significant differences in NDVI (normalized difference vegetation index), RVI (ratio vegetation index), NDRE (normalized difference red edge), and GNDVI (green normalized difference vegetation index) values across the various growth stages of rice. The application of treatment N₄ resulted in the highest number of panicles m⁻² (348.2 in TR, 376.8 in DSR), filled grains panicle⁻¹ (74.55 in TR, 62.43 in DSR), and a 1,000-grain weight of 26.92 g in TR and 26.76 g in DSR. The maximum yield (4.89 t ha⁻¹) was obtained in transplanted rice at treatment N₄ and, 8.15% yield reduction was noted in DSR for the same treatment, which was statistically equivalent to N₃, but significantly superior to other N levels. Conversely, in DSR with RDN (120 kg N ha⁻¹), the cost–benefit ratio surpassed that of TR by 16.96%, signifying DSR’s adaptability for more profitable rice cultivation in the region. This research provides valuable insights into optimizing nitrogen management practices for TR and DSR, thereby enhancing rice crop performance and economic returns.