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Purpose Information on carbon dioxide (CO 2 ) emission from different organic sources and their temperature sensitivity to decomposition is scarce in Bangladesh. Therefore, this study quantified the rates of CO 2 emission and carbon (C) degradation constants from different organic material mixed soils at variable temperatures in two laboratory experiments. Methods The first experiment was conducted at room temperature for 26 weeks to study CO 2 emission and C mineralization using vermicompost, chicken manure, cow dung, rice straw, and rice husk biochar. Weekly CO 2 emission was measured by alkali absorption followed by acid titration. The second experiment comprised two factors, viz. four organic materials (vermicompost, chicken manure, cow dung, and rice straw) and six temperature regimes (25, 30, 35, 40, 45, and 50°C). Organic materials at 2.5 g C kg -1 soil were mixed in both experiments. Results CO 2 emission reached the peak at 5th weeks of incubation and then decreased with irregular fashion until 21st week. The C emission loss followed in the order of chicken manure [ rice straw [ vermicompost [ cow dung [ rice husk biochar, and C degradation constants indicated the slower decomposition of rice husk biochar compared to cow dung, vermicompost, chicken manure, and rice straw. Temperature positively enhanced the mineralization of organic materials in the order of 50 [ 45 [ 40 [ 35 [ 30 [ 25°C, which contributed to higher availability of soil phosphorus. Conclusions High temperature increased mineralization of tested organic materials. Because of slower decomposition rice husk biochar, cow dung and vermicompost application can be considered as climate-smart soil management practices that might help in reducing CO 2 emission from soil.

The North-West (NW) region of Bangladesh is pivotal for the country’s agricultural development, mainly in producing irrigated Boro rice. However, increasing cost of irrigation water, fertilizers, labour and other inputs, and the spatio-temporal variation in actual yield, market price and profitability of rice, have added uncertainty to the sustainability of Boro rice cultivation. In this study, we evaluated the productivity, profitability, and prospect of Boro rice production using comprehensive field data collected directly from 420 farmers’ fields over two consecutive seasons (2015–16 and 2016–17), across seven geographically distributed locations in the NW region. We also analyzed the risk and return trade of popular Boro rice cultivars using Monte-Carlo simulation. The results show that there were significant (p≤0.05) variations in rice yield between sites, irrigation pump-types, and rice varieties, with Hybrid rice and BRRI dhan29 producing highest yields (6.0–7.5 t/ha). Due to different pricing systems, the cost of irrigation water varied from site to site and from year to year, but always comprised the highest input cost (20–25% of total production). The total paid-out cost, gross benefit, and gross income of rice significantly (p≤0.05) differed between sites, type of irrigation pumps, rice varieties, transplanting dates, and two cropping years. The variations in observed yield and profitability reveal considerable scope to improve rice production systems. Market variation in the price of rice affected overall profitability significantly. Probability and risk analysis results show that Minikit and BRRI dhan29 are the most stable varieties for yield and profitability. Hybrid rice, which has the maximum attainable yield among the cultivated rice varieties, also has the risk of negative net income. Based on the analysis, we discussed ways to improve yield and profitability and the prospect of Boro rice cultivation in the region. The study provides valuable information for policy-makers to sustain irrigated rice cultivation in both the NW region and nationally.

Agricultural productivity is affected by air temperature and CO 2 concentration. The relationships among grain yields of dry season irrigated rice (Boro) varieties (BRRI dhan28, BRRI dhan29 and BRRI dhan58) with increased temperatures and CO 2 concentrations were investigated for futuristic crop management in six regions of Bangladesh using CERES-Rice model (DSSATv4.6). Maximum and minimum temperature increase rates considered were 0˚C, +1˚C, +2˚C, +3˚C and +4˚C and CO 2 concentrations were ambient (380), 421, 538, 670 and 936 ppm. At ambient temperature and CO 2 concentration, attainable grain yields varied from 6506 to 8076 kg·ha −1 depending on rice varieties. In general, grain yield reduction would be the highest (13% -23%) if temperature rises by 4˚C and growth duration reduction would be 23 -33 days. Grain yield reductions with 1˚C, 2˚C and 3˚C rise in temperature are likely to be compensated by increased CO 2 levels of 421, 538 and 670 ppm, respectively. In future, the highest reduction in grain yield and growth duration would be in cooler region and the least in warmer saline region of the country. Appropriate adaptive techniques like shifting in planting dates, water and nitrogen fertilizer management would be needed to overcome climate change impacts on rice production.

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In the changing climatic condition, temperature is the most vulnerable parameter and is projecting a trend of increase in the future. Crop growth and development process depend largely on air temperature. This study aims to determine the role of increasing air temperature in yield, crop water requirement (CWR), and other agronomic parameters of irrigated rice. Ceres-rice model associated in the Decision Support System for Agrotechnology Transfer (DSSAT) was used in 15 different locations of Bangladesh. Grain yield, growth duration, and crop water requirement of widely cultivated irrigated rice (Boro rice) variety BRRI dhan28 were analysed in normal temperature and elevated air temperature by 1°C, 2°C, 3°C, and 4°C. The result revealed detrimental effect of elevated temperature on growth duration and grain yield. The estimated highest growth duration reduction of 30 days was found in Moulvibazar for 4°C temperature rise. The grain yield reduction was projected by 0–17%, 16–35%, 31–49%, and 39–61% from the normal condition if the seasonal mean temperature increased by 1°C, 2°C, 3°C, and 4°C, respectively. The country average crop water requirement was found to be 405 mm of which the highest 445 mm and the lowest 358 mm were recorded in Moulvibazar and Chandpur, respectively. The study revealed that the country average rice CWR reduced by 5%, 8%, 12%, and 17% over the normal condition for 1°C, 2°C, 3°C, and 4°C rising temperature, respectively. For 1°C temperature rise, BRRI dhan28 life span shortened by 6.4 days, grain yield reduced by 695 kg, and estimated CWR decreased by 14 mm. The projected declining CWR indicated that irrigated rice will require less irrigation water, but it will cause considerable yield loss under elevated temperature. Though elevated temperature will save huge irrigation water used in country-wide Boro rice cultivation, the crop developers need to introduce new heat-tolerant cultivar to minimize yield loss.

A TTENTION deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems.To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously published values in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.