Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming.

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems.

BananaLSD: A banana leaf images dataset for classification of banana leaf diseases using machine learning.

Bananas, one of the most widely consumed fruits globally, are highly susceptible to various leaf spot diseases, leading to significant economic losses in banana production. In this article, we present the Banana Leaf Spot Diseases (BananaLSD) dataset, an extensive collection of images showcasing three prevalent diseases affecting banana leaves: Sigatoka, Cordana, and Pestalotiopsis. The dataset was used to develop the BananaSqueezeNet model [1]. The BananaLSD dataset contains 937 images of banana leaves collected from banana fields, which were then further augmented to generate another 1600 images. The images were acquired using three smartphone cameras in diverse real-world conditions. The dataset has potential for reuse in the development of machine learning models that can help farmers identify symptoms early. It can be useful for researchers working on leaf spot diseases and serve as motivation for further researches.

Canopy spectral reflectance indices correlate with yield traits variability in bread wheat genotypes under drought stress.

Drought stress is a major issue impacting wheat growth and yield worldwide, and it is getting worse as the world's climate changes. Thus, selection for drought-adaptive traits and drought-tolerant genotypes are essential components in wheat breeding programs. The goal of this study was to explore how spectral reflectance indices (SRIs) and yield traits in wheat genotypes changed in irrigated and water-limited environments. In two wheat-growing seasons, we evaluated 56 preselected wheat genotypes for SRIs, stay green (SG), canopy temperature depression (CTD), biological yield (BY), grain yield (GY), and yield contributing traits under control and drought stress, and the SRIs and yield traits exhibited higher heritability (H2) across the growing years. Diverse SRIs associated with SG, pigment content, hydration status, and aboveground biomass demonstrated a consistent response to drought and a strong association with GY. Under drought stress, GY had stronger phenotypic correlations with SG, CTD, and yield components than in control conditions. Three primary clusters emerged from the hierarchical cluster analysis, with cluster I (15 genotypes) showing minimal changes in SRIs and yield traits, indicating a relatively higher level of drought tolerance than clusters II (26 genotypes) and III (15 genotypes). The genotypes were appropriately assigned to distinct clusters, and linear discriminant analysis (LDA) demonstrated that the clusters differed significantly. It was found that the top five components explained 73% of the variation in traits in the principal component analysis, and that vegetation and water-based indices, as well as yield traits, were the most important factors in explaining genotypic drought tolerance variation. Based on the current study's findings, it can be concluded that proximal canopy reflectance sensing could be used to screen wheat genotypes for drought tolerance in water-starved environments.

Changes of Land Use and Land Cover with the Diversity of Fishes, Aquatic Plants, and Bird's Species at Wetland Ecosystem.

Bangladesh is rich in wetland biodiversity with aquatic plants, fishes, and birds. Mohanganj Upazila is known as the capital of lower Bangladesh. The present study focuses on the changes of land use and land cover (LULC) with a diversity of species that are being least concerned (LC), vulnerable (VU), and endangered (EN). Over the last two decades, the wetland species of Mohanganj were gradually declined. Our results showed that 19 fish, 4 aquatic plants, and 7 bird species were LC in 2015. Among the fish and aquatic plant species, 6 fish species (Wallago attu, Ompok pabda, Channa punctate, Chitala chitala, Salmostoma phulo, and Corica soborna) and 2 aquatic plant species (Nymphaea nouchali and Nymphaea lotus) were VU during the dry and rainy season of 2017 and 2019, respectively. In the dry season of 2019, 4 fish species (W. attu, O. pabda, C. punctate, and Ch. chitala), 2 aquatic plant species (N. nouchali and N. lotus), and 7 bird species (Anas platyrhynchos, Ardeola grayii, Gyps bengalensis, Alcedo atthis, Phalacrocorax fuscicollis, Porphyrio porphyria, and Larus ridibundus) were EN. Among the species, W. attu, N. nouchaii, G. bengalensis, P. porphyria, and L. ridibundus were extremely endangered categories. Changes in LULC, the establishment of settlements for the increasing population, indiscriminate use of pesticides, environmental pollutions, and climate change are the potential reasons for declining trends of wetland biodiversity. Stern actions on land use policy, expansion of organic agriculture, bioremediation of industrial effluents, and adoption of sustainable environmental policies should be taken by the Government of Bangladesh for immediate conservation of wetland biodiversity.

Exploring standardized precipitation evapotranspiration index for drought assessment in Bangladesh.

Drought is a critical issue, and it has a pressing, negative impact on agriculture, ecosystems, livelihoods, food security, and sustainability. The problem has been studied globally, but its regional or even local dimension is sometimes overlooked. Local-level drought assessment is necessary for developing adaptation and mitigation strategies for that particular region. Keeping this in understanding, an attempt was made to create a detailed assessment of drought characteristics at the local scale in Bangladesh. Standardized precipitation evapotranspiration (SPEI) is a new drought index that mainly considers the rainfall and evapotranspiration data set. Globally, SPEI has become a useful drought index, but its local scale application is not common. SPEIbase (0.5° grid data) for 110 years (1901-2011) was utilized to overcome the lack of long-term climate data in Bangladesh. Available weather data (1955-2011) from Bangladesh Meteorology Department (BMD) were analyzed to calculate SPEIweather station using the SPEI calculator. The drivers for climate change-induced droughts were characterized by residual temperature and residual rainfall data from different BMD stations. Grid data (SPEIbase) of 26 stations of BMD were used for drought mapping. The findings revealed that the frequency and intensity of drought are higher in the northwestern part of the country which makes it vulnerable to both extreme and severe droughts. Based on the results, the SPEI-based drought intensity and frequency analyses were carried out, emphasizing Rangpur (northwest region) as a hot spot, to get an insight of drought assessment in Bangladesh. The findings of this study revealed that SPEI could be a valuable tool to understand the evolution and evaluation of the drought induced by climate change in the country. The study also justified the immediate need for drought risk reduction strategies that should lead to relevant policy formulations and agricultural innovations for developing drought adaptation, mitigation, and resilience mechanisms in Bangladesh.

Monitoring the drastic growth of ship breaking yards in Sitakunda: a threat to the coastal environment of Bangladesh.

The vast coastal and marine resources that occur along the southern edge of Bangladesh make it one of the most productive areas of the world. However, due to growing anthropogenic impacts, this area is under considerable environmental pressure from both physical and chemical stress factors. Ship breaking, or the dismantling and demolition of out-of-service ocean-going vessels, has become increasingly common in many coastal areas. To investigate the extent of ship breaking activities in Bangladesh along the Sitakunda coast, various spatial and non-spatial data were obtained, including remote sensing imagery, statistical records and published reports. Impacts to coastal and marine life were documented. Available data show that ship breaking activities cause significant physical disturbance and release toxic materials into the environment, resulting in adverse effects to numerous marine taxonomic groups such as fish, mammals, birds, reptiles, plants, phytoplankton, zooplankton and benthic invertebrates. Landsat imagery illustrates that the negatively impacted coastal area has grown 308.7 % from 367 ha in 1989 to 1,133 ha in 2010. Physicochemical and biological properties of coastal soil and water indicate substantially elevated pollution that poses a risk of local, regional and even global contamination through sea water and atmospheric transport. While damage to the coastal environment of Bangladesh is a recognized hazard that must be addressed, the economic benefits of ship breaking through job creation and fulfilling the domestic demand for recycled steel must be considered. Rather than an outright ban on beach breaking of ships, the enterprise must be recognized as a true and influential industry that should be held responsible for developing an economically viable and environmentally proactive growth strategy. Evolution of the industry toward a sustainable system can be aided through reasonable and enforceable legislative and judicial action that takes a balanced approach, but does not diminish the value of coastal conservation.