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Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture.

Ice is commonly used to preserve fish, but ice crushers are not always available in rural and remote areas of Bangladesh. A low-cost hand-operated ice crusher machine was developed in the Department of Farm Power and Machinery at Bangladesh Agricultural University. The machine yielded a throughput capacity of 21.6 kg min-1 with an average loss of 13.1%. The average time to crush a 10.6 kg ice block was 21.6 seconds. The total deformation, strain energy, maximum shear stress, and structural error were estimated as 0.0046 mm, 0.0031 MJ, 28 MPa, and 0.0025 MJ, respectively, for a single spike of the crusher. The average revolution per minute of the crushing cylinder was 493.6, with a maximum value of 634.7, and the maximum vibration frequency was 10.7 Hz. The maximum noise level during crushing operation was 92.9 dB, which was within the acceptable range. The machine's ergonomic assessment after operation yielded a satisfactory result. On average, the operators' blood pressure (systolic and diastolic), pulse rate, and respiration rate were 138.4 and 74.2 mm (Hg), 105.4 per minute, and 20 per minute, respectively. The results of the performance evaluation indicate that the device appears to be a suitable input for marginal fish farmers with inadequate access to electricity. [Fundam Appl Agric 2022; 7(1.000): 60-74]

This study was conducted to assess and identify the problems of handling reaper and their effects on the farmers adoption in the haor region of Kishoreganj and Brahmanbaria district through personal interviews with a well-structured questionnaire and focus group discussion. Six different characteristics were selected namely, age, level of education, annual family income, farm size, access to information sources, and extent of machine used to study the adoption of reaper by 44 selected farmers. Results of statistical analysis with SPSS software indicated that education, access to information sources, and extent of different machinery use played a very significant role in the adoption of the reaper. It was found that the higher secondary educated respondents scored 11.43, the high extent of media contact respondents scored 14.57 and the medium extent of machinery users scored 8.65 in the case of measuring the satisfaction level within the range of -18 to +18. Based on data from the selected farmers, the result revealed that 82% of them were satisfied. This study also identified some constraints of reaper operation that were obtained from the respondents like slipping and skidding problems on muddy soil, problems in cutting over inclined crops, problems in cutting over flooded crops, problems in cutting over matured crops, etc. Some suggestions were made by the respondents for further improvement of reaper like wheel bit should be more ridge or iron wheel or plastic wheel should be used to overcome the slipping or skidding problem, a binding mechanism should be attached to save both time and labor. [Fundam Appl Agric 2021; 6(1.000): 86-94]

This study was conducted to analyze the financial parameters of a mechanical rice transplanter operation for local service providers and entrepreneurs with hybrid rice variety. Two walking type mechanical rice transplanter of model Daedong DP 480 and DP 488 were used in the experiment. Field data from farmers and local service providers and also some necessary secondary data were used to establish the results of this research. The experiment was carried out in two different cropping seasons, Boro-2018 at Bangladesh Agricultural University, Mymensingh and in Aman-2018 at Mothbaria village of Dumuria Upazila, Khulna. The effective field capacity of Daedong DP 480 transplanter was found 0.173 ha/h which was used in Boro-2018 season and in Aman season, Daedong DP 488 transplanter was used; the field capacity of which was found 0.189 ha/h. Considering 10% discount factor, the NPV of DP 480 transplanter was found 7358.13 USD with IRR of 59% and for DP 488, NPV was 8037.97 USD at 63% IRR. The NPV and IRR indicate the financial viability of rice transplanter entrepreneurship as the IRR is much higher than the bank interest rate. The payback period of DP 480 was found 1.60 years whereas the DP 488 will return the capital in 1.51 years. By analyzing the BCR of the two rice transplanters, use of transplanter was also found profitable for entrepreneurs for custom hire services as the BCR of DP 480 and DP 488 transplanters were found 1.62 and 1.66, respectively. The minimum tenure for the economic use of the transplanters was found 20.36 ha and 21.01 ha per year. Evaluating the economy of total procedure of mechanical transplanting, the machine transplanting was found to be more economic over manual transplanting with both hybrid and inbred rice varieties when it is subjected to cover up more than 10.1ha and 6.7 ha per year, respectively. [Fundam Appl Agric 2020; 5(1.000): 124-132]

Brucellosis is endemic in Bangladesh both in humans and in animals. A number of reasons complicate the diagnosis, as bovine brucellosis can be diagnosed by various serological tests. But the tests have a limitation; when the organism remains intracellular, the disease goes into chronic stage and the antibody titres may decline. The present study was conducted for isolation and detection of Brucella spp. by polymerase chain reaction (PCR) from seronegative cows. A total of 360 dairy cows from three geographical regions were screened serologically by Rose Bengal Plate Test (RBPT) where 24 samples were serologically positive and the rest of the samples were serologically negative. Among the 24 seropositive individuals, 11 were culture positive and 6 were culture positive from serologically negative dairy cows. The overall seroprevalence of brucellosis in cattle was 6.6% and in disease condition a higher prevalence was recorded in abortion (28.07%) followed by infertility (13.33%). To confirm the Brucella spp. in seronegative dairy cattle, the isolates were extracted and PCR was conducted, which produced 905 bp amplicon size of 6 Brucella spp. from milk sample. So, for the detection or eradication of brucellosis, a bacteriological test and a PCR technique should be performed with the serological test of milk.

The yield of rice largely depends on transplanting techniques. Mechanical transplanting is gaining popularity as a cost-saving and on-time operation with less labor orientation in rice cultivation. This experiment was performed to investigate the relation of rice yield (g m−2) with four mechanical rice transplanting parameters (i.e., seedling density in the tray (nos.cm−2), missing hill percentage, floating hill percentage, and seedling number per hill), and to develop an Artificial Neural Networks (ANN) model to predict the yield from the transplanting parameters. A regression analysis was also conducted to validate the accuracy of the trained ANN model. This study reveals that the dependency of yield on these four parameters is not ignorable and significant. The ANN model performed an accurate match in predicting yield from transplanting parameters with an R2 value of 0.994 and adjusted R2 of 0.993. The ANN model possessed an RMSE of 4.577 in predicting yield which lied in the allowable range of 10 %. The ANN model showed better accuracy in predicting yield than the regression model and pretended to be an alternative to numerical models in predicting yield. The findings of this study showed that ANN-based models would be an alternative to the regression model and a more accurate method of yield prediction based on the transplanting field parameters.