The study is conducted to determine the correlation between climatic parameters and rice yield. The present study is also undertaken to analyze the land cover change in Sylhet district between 2013 and 2018 using LANDSAT-8 images. Local climate and rice yield data are collected from BMD (Bangladesh Meteorological Department) and BRRI (Bangladesh Rice Research Institute) and BBS (Bangladesh Bureau of Statistics). ArcGIS 10.5 and SPSS software are used to show the vegetation condition and correlation coefficient between rice yield and climatic variables respectively. It is revealed from the result that rainfall is negatively correlated with Aman and Boro (local and HYV) rice whereas temperature and relative humidity showed a positive correlation with local Aman and Boro rice. On the other hand, relative humidity showed a strong linear relationship with HYV Boro rice. Finally, both temperature and relative humidity have substantial effects on yields in the Boro rice. Furthermore, vegetation condition is observed through NDVI and found the moderate-high vegetation in 2013. After that NDVI value is fluctuating which evidently signifies the rapid vegetation cover change due to a flash flood, flood and other climate changing aspects. Additionally, Forested and high land vegetation’s are endangered rapidly. Some adaptation strategies should be followed to minimize the effects of natural calamities for improving better vegetation condition.

Heavy metal contamination in Bangladesh has become widespread due to both industrialisation and natural geological processes, posing risks to public health and agriculture. This study aimed to assess the degree of heavy metal pollution in agricultural land of Khulna in Bangladesh, specifically comparing areas near a dumping site with the site further away. Soil samples were analysed for five heavy metals: zinc (Zn), copper (Cu), lead (Pb), cadmium (Cd), and iron (Fe), using indices like contamination factor (CF), pollution load index (PLI), geo-accumulation index (I_geo), and potential ecological risk index (PERI). We observed a significant variation (p < 0.05) in heavy metal concentrations between two sites. Pearson correlations indicated significant relationships among most metals in the near dumping site, suggesting that their concentrations tend to increase. The PLI showed lower pollution (PLI < 1) at the away site compared to the nearby. The CF values indicated moderate level of contamination (1 ≤ CF < 3) at the away site, while considerable contamination (3 ≤ CF < 6) was recorded at the near dumping site. The PERI were higher (95 ≤ PERI < 190) for near dumping site compared to the site away (PERI < 95), indicating a higher ecological risk at near dumping site. I_geo revealed moderate to strong pollution levels for Pb and Cd near the dumping site, whereas away site were unpolluted to moderately polluted. Overall, the findings highlighted cropland near dumping site experiences significantly higher heavy metal pollution than away site, underscoring the need for sustainable remediation and continuous monitoring to protect the environment and public health.

At present greenhouse farming has become more popular in contrast to traditional farming because of its adjustment capability of the environmental parameters such as temperature, humidity, light intensity, and soil moisture according to the requirements of the crops. Continuous monitoring and controlling facilities of the greenhouse system allow the farmers a good maintenance system with good quality and high yield of the crops. In this paper, an Arduino microcontroller was used in a greenhouse system for an automatic monitoring system for cultivation incorporating various sensors such as a temperature-humidity sensor, and soil moisture sensor to collect parameters for monitoring the environment of the greenhouse. The collected data were used to control the temperature using cooling fans which facilitated the greenhouse controlling the environment. For storage and processing the data the controller code was generated in the Arduino programming language, and finally inserted into the Arduino UNO R3 microcontroller. A solar power system with a rechargeable battery was installed as a source of energy to ensure continuous power supply to the greenhouse system. Implementation of a greenhouse with a microclimatic parameter monitoring and controlling system will result in mitigating land and labor requirement problems for small-scale farmers, and gardeners as well as supplying suitable data for agricultural researchers.

The overall goal of this study was to examine the effects of climate change on the yield of four distinct crops (Aus, Aman, Boro and Wheat) in Sylhet by using secondary climate data from 1970 to 2020. The study's other goal is to assess the impact of river water levels on crop productivity in Sylhet over time. Data on crop productivity, weather variability and river water levels were gathered from the various fields. Yield vs. climatic correlation was discovered in the study, and this correlation varied according to season. To estimate the impact of climate change on rice yield, a multiple regression model is used. Climate variables in the model were found to account for 11% of the overall variation in Aus rice yield. The relationship between relative humidity and maximum temperature is positive and statistically significant. Other variables had no effect on yield because they were not significant. Furthermore, Regression results indicated that climate variables account for 60.6 percent of the overall variation in Aman rice output. Relative humidity, on the other hand, can undermine the yield. Climate variables account for 53.5 percent of the overall variation in Boro rice output, according to the findings. As a result, an increase in rainfall may have a negative impact on Boro rice yield. Maximum and minimum temperatures might have a favorable impact on Boro rice yield. Increases in maximum temperature, on the other hand, can considerably boost Wheat yield while decreases in minimum temperature can diminish Wheat yield. On the other side, the results of the regression analysis suggest that river water level has a minor impact on Aus, Aman, and Boro yield. However, as the model demonstrates, the river water level can have an impact on wheat yield. The impact of temperature and rainfall on water level was also investigated in this study because the regression model failed to produce positive results. Surprisingly, the model performs well, despite the fact that maximum temperatures have a negative impact on water levels in the Aus and Aman seasons. This shows that if warmer temperatures aid raises Aus and Aman yields, then the water level cannot sabotage the yield rise. Rainfall has a favorable impact on the water levels in the Aus, Aman, and Boro seasons, but has a negative impact on the water levels in the Wheat season. Int. J. Agril. Res. Innov. Tech. 12(2): 18-26, December 2022