The agricultural sector is seriously impacted by climate change, leading to potential risks to food security. In terms of global food production, maize ranks third. As a result, crop production and food security depend critically on assessing the effects of climate change and developing measures to adapt maize. Regarding adaptability, changing planting dates and using different agrochemicals are more effective than other management. Crop models are part of a global decision support system to help farmers maximize yields despite unpredictable weather patterns. To mitigate yield loss and protect the ecosystem, it is essential to use efficient maize-sowing practices in the field. This experiment was carried out to identify the most favorable sowing dates that maximize yield while ensuring the crop’s productivity and the integrity of the surrounding ecosystem remain intact. The main aim of this experiment was to mitigate the different climatic conditions by exogenous application of salicylic acid (SA) and sodium nitroprusside (SNP) on pigments and sugar content in maize under different sowing dates. A field experiment was carried out in the School of Agriculture, Lovely Professional University, Punjab, India, during the spring season of 2022. The experiment dealt with various maize crops, PMH-10, sourced from the Punjab Agricultural University (PAU), Punjab. The experiment was conducted in an open-air environment. The experimental setup was laid out in a split-plot design. The results stated that foliar application of salicylic acid and sodium nitroprusside successfully influenced high-temperature tolerance and low temperature at the reproductive phase and initial vegetative stages with other growing climatic conditions of maize in early and late sowings when controlled by increasing the chlorophyll index, carotenoids content, and sugar content of maize.

Studies have emphasized that the presence of heavy metals in freshwater fish represents a global public health issue. Nigeria, being a developing nation with less emphasis on the quality of seafood consumed by the residents, ranks this study very vital. The policy implication of this study is the advancement of a healthy population in contemporary Nigeria. Hence, this study assessed heavy metal concentration in two fish species, Clarias gariepinus (Catfish) and Oreochromis niloticus (Nile Tilapia), in the Anambra River. The sample included twenty fishes, of which eighteen were collected from the three sampling locations (the fish ports of Anambra River), namely Otu-nsugbe, Otuocha, and Ikemivite) while two control samples were collected from a pond about 200 m away from the river. The levels of heavy metals were determined using Varian AA 240 atomic absorption spectrophotometer (AAS). The results showed that the concentrations of heavy metals (cadmium and arsenic) in the sampled fishes from Anambra River exceeded the joint World Health Organization and Food and Agriculture Organization (FAO/WHO) standard for fish and fish product consumption, while the concentration of chromium, mercury, and lead are within the permissible limit. The study also showed the distribution of the heavy metals in the fish organ varies among fish species. Heavy metals occur higher in Clarias garepinus than in Oreochromis niloticus, while tissue preference for heavy metal accumulation is in the order of gill > liver > muscle. It was recorded from this study that the heavy metal concentration in the fish from the pond is generally higher than the fish from the river for some metals. The high level of heavy metals in the sampled fish was attributed to heavy metals contamination of the river as a result of various anthropogenic activities such as mining, burning of fossil fuel and emission from the exhaust of boats/vehicles, overuse of fertilizers and pesticides, discharge of effluent, sewage, and hospital waste. This study concluded that long-term consumption of fish from the river may pose health risks to the consumers due to the possible bioaccumulation of heavy metals, especially cadmium and arsenic. It was recommended that continuous monitoring of heavy metal levels in the fish and water, public awareness, and appropriate legislative provisions should be put in place to ensure that harvested fish and fish products may be safe for human consumption.

Heavy metals (iron, copper, and zinc) were quantified in purple crab (Platyxanthus orbignyi) tissues collected in winter (September 2021), spring (November 2021), and summer (March 2022) at three beaches (Tres Hermanas, Fundición, and El Diablo) in Ilo Harbour (Moquegua), South Peru. The rank order of heavy metal concentrations in purple crab tissues and sediments was similar; iron (Fe) was followed by Copper (Cu), and this last one was followed by Zinc (Zn). The heavy metal concentrations in tissue crabs from the three beaches differed from each other spatially and seasonally. In addition, Fundición Beach was the zone with the highest concentration of those three metals during the summer.

In this study, the AquaCrop model was employed to analyze the impact of projected future climate changes on the water usage and biomass production of potato crops in Babylon, Iraq, under varying irrigation methods. The irrigation techniques evaluated included sprinkler irrigation, surface drip irrigation, and subsurface drip irrigation at depths of 10 cm and 20 cm. The study involved simulating and forecasting conditions for the year 2050, comparing them to current conditions. The model measured and predicted the evapotranspiration (ETa) and actual biomass of potato crops for 2050 using the RCP 8.5 scenarios, which outline different trajectories for greenhouse gas emissions. The AquaCrop model was calibrated and validated using statistical measures such as the R2, RMSE, CV, EF, and D, achieving a 99% accuracy level in its performance. The findings suggest that using drip irrigation systems and applying the AquaCrop model significantly mitigates the adverse effects of environmental stress on desert soils and enhances sustainable agricultural practices in arid regions.

Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

A high concentration of hydrogen sulfide (H2S) released from pig farming is one of the major environmental problems affecting surrounding communities. In modern pig farms, the bioscrubber is used to eliminate H2S, which is found to be driven mainly by the sulfur-oxidizing bacteria (SOB) community. Therefore, in this study, molecular biology techniques such as next-generation sequencing (NGS) and DNA microarray are proposed to study the linkage between enzyme activity and the abundance of the SOB community. The starting sludge (SFP1) and recirculating sludge (SFP2) samples were collected from the bioscrubber reactor in the pig farm. The abundance of microbial populations between the two sampling sites was considered together with the gene expression results of both soxABXYZ and fccAB. Based on the NGS analysis, the members of phylum Proteobacteria such as Halothiobacillus, Acidithiobacillus, Thiothrix, Novosphingobium, Sulfuricurvum, Sulfurovum, Sulfurimonas, Acinetobacter, Thiobacillus, Magnetospirillum, Arcobacter, and Paracoccus were predominantly found in SFP2. The presence of Cyanobacteria in SFP pig farms is associated with increased biogas yields. The microarray results showed that the expression of soxAXBYZ and fccAB genes involved in the oxidation of sulfide to sulfate was increased in Halothiobacillus, Paracoccus, Acidithiobacillus, Magnetospirillum, Sphingobium, Thiobacillus, Sulfuricurvum, Sulfuricurvum, Arcobacter, and Thiothrix. Both NGS and DNA microarray data supported the functional roles of SOB in odor elimination and the oxidation of H2S through the function of soxABXYZ and fccAB. The results also identified the key microbes for H2S odor treatment, which can be utilized to monitor the stability of biological treatment systems and the toxicity of sulfide minerals by oxidation.

The total soluble protein-mediated morphological traits in mustard treated with Thiourea and Salicylic acid were investigated. In addition, it tested the hypothesis that the growth regulator salicylic acid protects the photosynthetic apparatus by up-regulating morphological traits. Under natural environmental conditions, seeds were sown in the field, and seed emergence was recorded. For three days after the 15-day stage, plants in the area were treated with thiourea and salicylic acid and allowed to grow for 90 days. Plants were harvested to assess various morphological traits. A follow-up application of SA and Thiourea plants improved plant height, leaf area, internodal length, leaf number, and accelerated plant activity. The up-regulation of morphological traits may have occurred in SA and Thiourea-mediated plants. After treatments, the level of total soluble protein was estimated in the leaves at proposed day intervals.

Over time, predictive models tend to become more accurate but also more complex, thus achieving better predictive accuracy. When the data is improved by increasing its quantity and availability, the models are also better, which implies that the data must be processed to filter and adapt it for initial analysis and then modeling. This work aims to apply the Random Forest model to predict PM10 particles. For this purpose, data were obtained from environmental monitoring stations in Mexico City, which operates 29 stations of which 12 belong to the State of Mexico. The pollutants analyzed were CO carbon monoxide, NO nitrogen oxide, and PM10 particulate matter equal to or less than 10 μg.m-3, NOx nitrogen oxide, NO2 nitrogen dioxide, SO2 sulfur dioxide, O3 ozone, and PM2.5 particulate matter equal to or less than 2.5 μg.m-3. The result was that when calculating the certainty of our model, we have a value of 80.40% when calculating the deviation from the mean, using 15 reference variables.

The project aims to remove reactive blue dye from the effluent of textile industries by utilizing coal-associated soil as an adsorbent, as it possesses effective physical properties and distinguishing characteristics. In comparison to other separation techniques, the adsorption method is the most effective, cost-effective, and straightforward. A batch adsorption investigation was carried out to examine the various adsorption-influencing factors, including solution pH, adsorbent dosage, contact time, temperature, and dye concentration. Contact time of 30 min, an adsorbent dosage of 10g.100 mL-1, a solution pH of 7, a temperature of 30°C, and an initial dye concentration of 100 mg.L-1 were found to be optimal for dye adsorption. Using two distinct kinetic models, the evaluation of kinetic studies revealed that the pseudo-second-order provided the greatest fit, with a higher R2 value than the pseudo-first-order. The thermodynamic parameters Gibbs free energy (ΔG°), entropy (ΔS°), and enthalpy (ΔH°) indicated that the current adsorption system was exothermic and spontaneous. Further study of the adsorption isotherm revealed that the Langmuir isotherm model provided the best fit, with an R2 value of 0.977%.

This research investigates the potential of two Plant Growth-Promoting Bacteria (PGPB) strains, EP1-AS and EP1-BM, isolated from the halophyte Euphorbia prostrata, to enhance plant growth and provide abiotic stress resilience. The study addresses the urgent need for sustainable agricultural practices in the face of challenges like soil salinization and heavy metal contamination. The investigation comprehensively analyzes the heavy metal and salt tolerance of the PGPB strains, revealing their potential applications in promoting plant growth under adverse environmental conditions. The research further explores the impact of these PGPB strains on wheat plants subjected to varying concentrations of heavy metals and salts. Results indicate that both PGPB strains, especially EP1-BM, exhibit significant tolerance to heavy metals and salt stress. EP1-BM demonstrates remarkable resilience even under high concentrations of these stressors. The study extends its findings to in vitro testing on wheat plants, revealing the positive influence of PGPB strains on germination, shoot length, and root length in the presence of salt and heavy metals. This research underscores the significance of understanding plant-microbe interactions, particularly in the context of promoting sustainable agriculture in challenging environments. The identified resilience of PGPB strains, especially EP1-BM, suggests their potential application as bio-remediators and plant growth promoters in soils affected by salinity and heavy metal stress. The promising results observed will be followed-up field trials. They will highlight the translational potential of these PGPB strains, offering a novel avenue for developing biofertilizer formulations with a cautious approach to safety concerns. Overall, this study contributes valuable insights into harnessing the untapped potential of resilient plants and their associated microbial communities for sustainable agriculture. It addresses key global challenges outlined by the United Nations Sustainable Development Goals.