Ants Colony Optimization Algorithm in the Hopfield Neural Network for Agricultural Soil Fertility Reverse Analysis

The Boolean Satisfiability Problem (BSAT) is one of the most important decision problems in mathematical logic and computational sciences for determining whether or not a solution to a Boolean formula.. Hopfield neural network (HNN) is one of the major type artificial neural network (NN) popularly known for it used in solving various optimization and decision problems based on its energy minimization machinism. The existing models that incorporate standalone network projected non-versatile framework as fundamental Hopfield type of neural network (HNN) employs random search in its training stages and sometimes get trapped at local optimal solution. In this study, Ants Colony Optimzation Algorithm (ACO) as a novel variant of probabilistic metaheuristic algorithm (MA) inspired by the behavior of real Ants, has been incorporated in the training phase of Hopfield types of the neural network (HNN) to accelerate the training process for Random Boolean kSatisfiability reverse analysis (RANkSATRA) based for logic mining. The proposed hybrid model has been evaluated according to robustness and accuracy of the induced logic obtained based on the agricultural soil fertility data set (ASFDS). Based on the experimental simulation results, it reveals that the ACO can effectively work with the Hopfield type of neural network (HNN) for Random 3 Satisfiability Reverse Analysis with 87.5 % classification accuracy

Modelling the Potential Risk of Infection Associated with Listeria monocytogenes in Irrigation Water and Agricultural Soil in Two District Municipalities in South Africa

Listeria monocytogenes (L. monocytogenes) is the etiologic agent of listeriosis which significantly affects immunocompromised individuals. The potential risk of infection attributed to L. monocytogenes in irrigation water and agricultural soil, which are key transmission pathways of microbial hazards to the human population, was evaluated using the quantitative microbial risk assessment modelling. A Monte Carlo simulation with 10,000 iterations was used to characterize the risks. High counts of L. monocytogenes in irrigation water (mean: 11.96 × 102 CFU/100 mL; range: 0.00 to 56.67 × 102 CFU/100 mL) and agricultural soil samples (mean: 19.64 × 102 CFU/g; range: 1.33 × 102 to 62.33 × 102 CFU/g) were documented. Consequently, a high annual infection risk of 5.50 × 10−2 (0.00 to 48.30 × 10−2), 54.50 × 10−2 (9.10 × 10−3 to 1.00) and 70.50 × 10−2 (3.60 × 10−2 to 1.00) was observed for adults exposed to contaminated irrigation water, adults exposed to contaminated agricultural soil and children exposed to agricultural soil, respectively. This study, therefore, documents a huge public health threat attributed to the high probability of infection in humans exposed to L. monocytogenes in irrigation water and agricultural soil in Amathole and Chris Hani District Municipalities in the Eastern Cape province of South Africa.

Microbial Responses to the Reduction of Chemical Fertilizers in the Rhizosphere Soil of Flue-Cured Tobacco

The overuse of chemical fertilizers has resulted in the degradation of the physicochemical properties and negative changes in the microbial profiles of agricultural soil. These changes have disequilibrated the balance in agricultural ecology, which has resulted in overloaded land with low fertility and planting obstacles. To protect the agricultural soil from the effects of unsustainable fertilization strategies, experiments of the reduction of nitrogen fertilization at 10, 20, and 30% were implemented. In this study, the bacterial responses to the reduction of nitrogen fertilizer were investigated. The bacterial communities of the fertilizer-reducing treatments (D10F, D20F, and D30F) were different from those of the control group (CK). The alpha diversity was significantly increased in D20F compared to that of the CK. The analysis of beta diversity revealed variation of the bacterial communities between fertilizer-reducing treatments and CK, when the clusters of D10F, D20F, and D30F were separated. Chemical fertilizers played dominant roles in changing the bacterial community of D20F. Meanwhile, pH, soil organic matter, and six enzymes (soil sucrase, catalase, polyphenol oxidase, urease, acid phosphatase, and nitrite reductase) were responsible for the variation of the bacterial communities in fertilizer-reducing treatments. Moreover, four of the top 20 genera (unidentified JG30-KF-AS9, JG30-KF-CM45, Streptomyces, and Elsterales) were considered as key bacteria, which contributed to the variation of bacterial communities between fertilizer-reducing treatments and CK. These findings provide a theoretical basis for a fertilizer-reducing strategy in sustainable agriculture, and potentially contribute to the utilization of agricultural resources through screening plant beneficial bacteria from native low-fertility soil.

Proposal of a Nomenclature for Hydrogeological Instability Risks and Case Studies of Conservative Soil Tillage for Environmental Protection

In order to implement environmental protection, within the Soil Cadastre, previously proposed as a multipurpose inventory that aims to promote sustainable soil uses, the hydrogeological instability caused by human activities is the focus of this work. These activities can be aimed at sustainable agricultural soil use or the building of roads to allow the access to the fields. The soil’s hydrogeological instability causes the unsustainable use and management of a cadastral parcel. Therefore, the aim of this work is to propose a nomenclature for hydrogeological instability risks, as well as the best practices of conservative soil tillage in case studies, in order to reduce environmental impact. According to the proposed Soil Cadastre, the missing environmental sustainability of a parcel and the reason for this must be communicated to the field owner or manager. In a hilly area of inland Western Sicily, four main risk types of hydrogeological instability were identified: hydrogeological instability (caused only by natural factors); hydraulic-pedological farming instability (crop not suitable for the field for missing or insufficient soil drainage and landslides); hydraulic-infrastructural instability (built up infrastructures unsuitable for the site); hydraulic-infrastructural-pedological-management instability (field improvements changing the downflow line and crop operations not suitable for the soil and climate parameters). The farm owner or manager must be informed about the risk type affecting their fields in order to perform the best practices (i.e., conservative soil tillage), for implementing or restoring a sustainable soil use or management in each cadastral parcel.

Microbial Diversity of Reconstituted, Degraded, and Agricultural Soils Assessed by 16S rDNA Multi-Amplicon Sequencing

The microbial diversity is, among soil key factors, responsible for soil fertility and nutrient biogeochemical cycles, and can be modified upon changes in main soil physicochemical properties and soil pollution. Over the years, many restoration techniques have been applied to restore degraded soils. However, the effect of these approaches on soil microbial diversity is less understood and thus requires more investigation. In this study, we analyzed the impact, on soil microbial diversity of a patented novel technology, used to restore degraded soils. Soil samples were collected from three nearby sites located in Borgotrebbia, Piacenza, Italy, and categorized as reconstituted, degraded, and agricultural soils. After total soil DNA extraction, 16S rDNA multi-amplicon sequencing was carried out using an Ion GeneStudio S5 System to compare soils’ bacterial community profiles. Sequenced reads were processed to assign taxonomy and then key microbial community differences were identified across the sampling sites. Species diversity featured significant abatement at all rank levels in the degraded soil when compared to the agricultural control. The 5 year restoration technique showed full recovery of this index at the genus level but not at the phylum level, displaying a rank-dependent gradient of restored richness. In parallel, the abundance of genes involved in the nitrogen (N) biogeochemical cycle was assessed using quantitative Real-Time PCR (qPCR). Total DNA content was significantly higher (p < 0.05) in degraded (μ = 12.69 ± 2.58 μg g−1) and reconstituted (μ = 11.73 ± 1.65 μg g−1) soil samples when compared to the agricultural soil samples (μ = 2.39 ± 0.50 μg g−1). The taxonomic diversity of each soil site was significantly different, with some instances unique of the agricultural soil even at the phylum level. The analysis of N functional genes showed that the relative abundance of bacterial amoA (p < 0.05) and nosZ (p < 0.01) genes were significantly lower in the agricultural than in the reconstituted and degraded soils. We concluded that the application of the soil reconstitution technique appears to enhance the active microbial community, with distinct diversity and functionality towards genes involved in N biogeochemical cycle, as compared to both the degraded and the agricultural soil.