The coconut industry has deep historical and economic importance in Sri Lanka, but coconut palms are vulnerable to water stress exacerbated by environmental challenges. This study explored using Sunn hemp (Crotalaria juncea L.) in major coconut-growing soils in Sri Lanka to improve resilience to water stress. The study was conducted at the Coconut Research Institute of Sri Lanka to evaluate the growth of Sunn hemp in prominent coconut soils—gravel, loamy, and sandy—to determine its cover crop potential. Sunn hemp was planted in pots with the three soil types, arranged in a randomized, complete design with 48 replicates. Growth parameters like plant height, shoot/root dry weight, root length, and leaf area were measured at 2, 4, 6, and 8 weeks after planting. Soil type significantly impacted all growth parameters. After 8 weeks, sandy soil showed the highest plant height and root length, while loamy soil showed the highest shoot/root dry weight and leaf area, followed by sandy and gravel soils. Nitrogen content at 6 and 8 weeks was highest in loamy soil plants. In summary, Sunn hemp produces more biomass in sandy soils, while loamy soils promote greater nutrient accumulation and growth. This suggests the suitability of Sunn hemp as a cover crop across major coconut-growing soils in Sri Lanka, improving resilience.

Tomato powdery mildew, fruit rot, and twig blight are all managed with Deltamethrin. Its residues could still be present in the crops, posing a health risk. The pesticide residue analysis, dissipation rate, and safety assessments were thus examined in green tomatoes. The analytical method for residue analysis was validated according to international standards. Tomato fruits and soil were used to study the dissipation of Deltamethrin 100 EC (11% w/w) at 12.5 g a.i ha⁻¹ for the recommended dose (RD) and 25.0 g a.i ha⁻¹ for the double of the recommended dose (DD). Ethyl acetate was used to extract residues from tomato fruit, and PSA and magnesium sulphate were used for cleanup.The fruits had recoveries ranging from 83% to 93% and the soil sample from 81.67% to 89.6%, with the limit of detection (LOQ) estimated at 0.01 mg kg⁻¹. The matrix effect (ME) was calculated to be less than 20% for the tomato fruits and the soil.Half-lives for RD and DD were 1.95 and 1.84 days, respectively. All sampling days for both doses had dietary exposures of residues below the maximum permissible intake (MPI) of 0.16 mg person⁻¹ day⁻¹. The most effective method of decontaminating tomato residue containing Deltamethrin is blanching.

Bangladesh’s coastal regions are rich in saline water resources. The majority of these resources are still not being used to their full potential. In the southern Bangladeshi region of Patuakhali, research was conducted to investigate the effects of mulching and drip irrigation on tomato yield, quality, and blossom-end rot (BER) at different soil salinity thresholds. There were four distinct treatments applied: T₁= drip irrigation with polythene mulch, T₂ = drip irrigation with straw mulch, T₃ = drip irrigation without mulch, and T₄ = standard procedure. While soil salinity was much greater in treatment T₃ (1.19–8.42 dS/m) fallowed by T₄ (1.23–8.63 dS/m), T1 treatments had the lowest level of salinity and the highest moisture retention during every development stage of the crops, ranging from 1.28–4.29 dS/m. Treatment T₃ exhibited the highest soil salinity levels (ranging from 1.19 to 8.42 dS/m), followed by T₄ with a range of 1.23 to 8.63 dS/m. In contrast, T₁ treatments consistently maintained the lowest salinity levels (ranging from 1.28 to 4.29 dS/m) and the highest moisture retention throughout all stages of crop development. In terms of yield, drip irrigation with no mulch treatment (T₃) provided the lowest output (13.37 t/ha), whereas polyethylene mulching treatment (T₁) produced the maximum yield (46.04 t/ha). According to the study, conserving moisture in tomato fields and reducing soil salinity may both be achieved with drip irrigation combined with polythene mulch. The research suggests that employing drip irrigation in conjunction with polythene mulch could effectively preserve moisture in tomato fields and concurrently decrease soil salinity.

The chemical reinforcement of sandy soils is usually carried out to improve their properties and meet specific engineering requirements. Nevertheless, conventional reinforcement agents are often expensive; the process is energy-intensive and causes serious environmental issues. Therefore, developing a cost-effective, room-temperature-based method that uses recyclable chemicals is necessary. In the current study, poly (styrene-co-methyl methacrylate) (PS-PMMA) is used as a stabilizer to reinforce sandy soil. The copolymer-reinforced sand samples were prepared using the one-step bulk polymerization method at room temperature. The mechanical strength of the copolymer-reinforced sand samples depends on the ratio of the PS-PMMA copolymer to the sand. The higher the copolymer-to-sand ratio, the higher the sample’s compressive strength. The sand (70 wt.%)-PS-PMMA (30 wt.%) sample exhibited the highest compressive strength of 1900 psi. The copolymer matrix enwraps the sand particles to form a stable structure with high compressive strengths.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

The coastal area of Bohai Bay of China has a wide distribution of salt-accumulated soils which could pose a problem to the sustainable development of the local ecology. As a result, the land remains largely degraded and unsuitable for biophysical and agricultural purposes. In this study, we characterized the soil and native plants in the area, to properly understand and identify species with satisfactory adaptation to saline soil and of high economic or ecological value that could be further developed or domesticated, using appropriate cultivation techniques. The goal was to determine the salinity parameters of the soil, identify the inhabiting plant species and contribute to the ecosystem data base for the Bay area. A field survey involving soil and plant sampling and analyses was conducted in Yanshan and Haixing Counties of Hebei Province, China, to estimate the level of salt ions as well as plant species population and type. The mean electrical conductivity (EC) of the soils ranged from 0.47 in more remote locations to 23.8 ds/m in locations closer to the coastline and the total salt ions from 0.05 to 8.8 g/kg, respectively. Each of the salinity parameters, except HCO₃⁻ showed wide variations as judged from the coefficient of variation (CV) values. The EC, as well as chloride, sulphate, Mg and Na ions increased significantly towards the coastline but the HCO₃⁻ ion showed a relatively even distribution across sampling points. Sodium was the most abundant cation and chloride and sulphate the most abundant anions. Therefore, the most dominant salinity-inducing salt that should be properly managed for sustainable ecosystem health was sodium chloride. Based on the EC readings, the most remote location from the coastline was non-saline but otherwise, the salinity ranged from slightly to strongly-very strongly saline towards the coast. There were considerably wide variations in the number and distribution of plant species across sampling locations, but most were dominated entirely Phragmites australis, Setaria viridis and Sueda salsa. Other species identified were Aeluropus littoralis, Chloris virgata, Heteropappus altaicus, Imperata cylindrica, Puccinellia distans, Puccinellia tenuiflora and Scorzonera austriaca. On average, the sampling points furthest from the coast produced the most biomass, and the point with the highest elevation had the most diverse species composition. Among species, Digitaria sanguinalis produced the highest dry mass, followed by Lolium perenne and H. altaicus, but there were considerable variations in biomass yield across sampling locations, with the location nearest the coastline having no vegetation. The observed variations in soil and vegetation should be strongly considered by planners to allow for the sustainable development of the Bahai bay area.