Agricultural Research最新文献

The present study evaluates the effect of the probiotic bacterium Bacillus subtilis and the baker’s yeast Saccharomyces cerevisiae, either single or in combination, on hemato-immunological indices, digestive enzyme activities and disease resistance in Indian major carp, Labeo rohita (Hamilton 1822). Fish (22 ± 1.0 g) were administered three doses of probiotics on alternate days by oral and intraperitoneal injection at 1 × 10⁶ cfu/mL concentration. The specified parameters were examined on the 10th and 20th day after administration in all the groups. Further, the control and treatment groups were both challenged with a lethal strain of Aeromonas veronii intraperitoneally at 1 × 10⁸ cfu/mL concentration after 20 days, following which the clinical signs and survivability rate were documented in all the groups regularly. Overall, an improvement in hematological indices, enzymatic activity and immunological parameters was observed in groups supplemented with probiotics as compared to the control group. Furthermore, when challenged with A. veronii, groups that received oral and injection [CI (B. subtilis + S. cerevisiae I/P injection) and CO (B. subtilis + S. cerevisiae oral)] had the best survivability (85.7%) followed by BS-I (B. subtilis I/P injection) and SC-O (S. cerevisiae oral) with 71.4% and lastly by SC-I (S. cerevisiae I/P injection) and BS-O (B. subtilis oral) with 57.1%. The findings suggest that supplementation of B. subtilis and S. cerevisiae can help to enhance fish health and increase aquaculture production.

The silkworm Bombyx mori produces natural proteins known as silk fibroin filaments, which are encased in the sericin polymers found in the silk thread used in the cocoon. The exceptional quality of bivoltine silk produced in Jammu and Kashmir is the source of reliance for sericulture farmers. The present study is the first study to investigate the genetic superiority among different bivoltine silkworm Bombyx mori L. strains, viz. M-43, SK-6, SK-7, Sanish-8, CSR-27 and BHR-3, at molecular level based on their fibroin gene expression and has been studied to ascertain fibroin synthesizing potential and to study correlation of expression analysis with economic parameters. A maximum larval weight was recorded in M-43 and minimum in CSR-27 strains of silkworm. The cocoon reeling parameters also showed significant variations in different strains of silkworm with strain M-43 recording maximum average filament length and raw silk percentage. The molecular characterization of the silkworm strains was evaluated following standard protocols to estimate the fibrion gene expression and protein profiling of the silk gland. Silkworm strain M-43 showed maximum fibrion gene expression followed by BHR-3. RT expression, heavy chain and light chain as well as the protein expression showed positive correlation with all the rearing parameters of silkworm under study except denier showed negative correlation. Among the six silkworm strains, M-43 and BHR₃ showed better results in molecular expression and economic parameters, hence can be utilized in future breeding programmes to develop fibroin-rich strains for better silk productivity and can also help us to establish silkworm gene bank for promoting sustainable sericulture worldwide.

For the development of sustainable agriculture and prosperity, it is important to breed new wheat genotypes that can produce stable yields even under increasingly adverse environmental conditions. In this study, the interactions between genotype and environment (G × E) on yield stability of thirty-five wheat genotypes under different conditions were investigated in a randomized complete block design with three replicates each. Analysis of variance revealed significant differences (p < 0.01) among genotypes, environments and their interactions, suggesting a high degree of variability in performance under these test conditions. A two-dimensional GGE biplot was used to illustrate how the different genotypes performed in the different environments responsible for 96.15 and 3.24% difference in GEI for yield per plant. Stable and high yielding genotypes such as G4, G10, G34 and G35 were also identified. The application of the AMMI model for the analysis of genotype-by-environment data showed that G34 performed best in several variables. The most promising genotypes with high average yield with high stability under terminal heat stress conditions are, in rank order, G34, G33, G32 and G31. The application of the AMMI model for the analysis of genotype-by-environment data showed that G34 performed best in several variables. The most promising genotypes with high average yield with high stability under terminal heat stress conditions were, in rank order, G34, G33, G32 and G31. Based on the AEC line, G33 and G31 were more stable, while G1 and G29 were less stable. The complex relationships between the genotypes and the environmental conditions were efficiently visualized by GGE and AMMI biplots, allowing a classification of the genotypes into three categories. The evaluation procedure was simplified by this graph which helped to clarify how well a genotype adapts and is commercially cultivated in various adverse environmental conditions.

Food waste is a significant factor that directly affects both the environment and human health. Utilizing composted organic food waste to create potting media, especially when supplemented with Trichoderma asperellum and Talaromyces tratensis offers an eco-friendly solution. In this context, the present study aimed to repurpose composted organic food waste into potting media, simultaneously evaluating the efficacy of antagonistic fungi in mitigating root and stem-end rot diseases affecting Chinese kale. For this purpose, 10 distinct formulations of potting mixtures were developed and employed for their efficacy with the aforementioned vegetable. The findings indicated that the media containing composted organic food waste, when used at a ratio of 1 part by volume, notably enhanced its growth. Furthermore, the media composed of an equal blend of composted organic food waste and black chaff exhibited optimal results. This was closely followed by a mixture consisting of composted organic food waste and chopped coconut husks in an identical 1:1 ratio. Moreover, the incorporation of the antagonistic fungus T. asperellum into the potting media was observed to be highly effective against Sclerotium rolfsii, particularly under greenhouse conditions. As an outcome of this intervention, the growth trajectory of Chinese kale mirrored that achieved using chemical fungicides. It is evident from these observations that T. asperellum plays a pivotal role in the biological control of plant diseases.

Technological change in agriculture in climate risk-exposed developing countries is required for at least two reasons. First, increased climate risk increases the need for new agricultural technologies that are more robust to such variability. Second, the need to feed the growing population creates the need for land-use intensification. The purpose of this study is to assess technological change in terms of the adoption and intensity of drought-tolerant teff use and its impact on farm households’ welfare in a semiarid economy of northern Ethiopia. Determinants of the adoption and extent of adoption of drought-tolerant teff are estimated using correlated random effect double-hurdle models. A control function approach was used to fix the endogeneity associated with access to technology. Household fixed-effect model is used to estimate welfare impact of area used for drought-tolerant teff. The results show that although the adoption of drought-tolerant teff is access constrained, it contributes significantly to household welfare. Strengthen distribution effort of the technology in the rainfall stress areas would have an implication on food security and emerging a resilient farming system.

Remote sensing (RS) plays a crucial role in land use classification, providing essential information to address various environmental issues. The incorporation of machine learning techniques into remote sensing, including random forests (RFs), support vector machines (SVMs), and artificial neural networks (ANN)s, has garnered significant attention due to its potential for efficient land cover classification in remotely sensed images. However, applying machine learning in the context of agricultural land classification presents challenges, with limited research exploring these techniques for this specific purpose. This study aims to investigate the performance of machine learning techniques in the southern prairie region of Saskatchewan, focusing on agricultural land classifications. Utilizing Sentinel-2 satellite images, publicly available from the European Space Agency, a total of 133,080 samples were analyzed through stratified random sampling, with 70% allocated to training and 30% to testing subsets. Accuracy assessment involved various indicators. Results indicate that random forests exhibit the highest overall accuracy, whereas support vector machines demonstrate the lowest accuracy. Artificial neural networks, on the other hand, display distinct advantages compared to other machine learning techniques. This research contributes valuable insights into the application of machine learning for agricultural land use classifications, emphasizing the need for further exploration and refinement in this challenging domain.

Water is a critical resource in irrigated agriculture, and its efficient management, based on water balance and meteorological data, requires data collection and transmission systems with high reliability and the capacity to accurately represent the physical phenomena occurring in the agricultural environment. Thus, aiming to manage irrigation using smart devices, this article presents the development of the hardware and software of a complete automatic meteorological station based on IoT, from the calibration of the sensor elements, to the development of the final device, which operates with the routine of reading variables at intervals of 10 s, followed by online storage of average and extreme data every 10 min, followed by estimation of daily evapotranspiration using the Penman–Monteith method. The device has a user communication interface via a messaging application, through which the current weather condition can be requested remotely and receive daily data collected. The final version had its operation analyzed continuously, for a period of one year, together with a commercial automatic station, where a correlation was found between its estimates (R²) of evapotranspiration of 0.93, together with an average absolute error of 0.30 mm, obtaining an excellent classification in the capacity to estimate evapotranspiration.

Organic agriculture is a pivotal solution for achieving sustainable agricultural development and enhancing sustainability in the production of safe and healthy food systems. Consequently, policymakers, planners, and researchers worldwide have endeavored to expand it by focusing on key drivers and significant areas. However, organic farms have experienced recent declines in some countries. This study aimed to identify best strategies for developing healthy and organic production based on the perspectives of producers, who are crucial stakeholders in Mazandaran province, Iran. This region faces challenges with diminishing organic farming. The study employed a mixed-methods design, focusing on the strategic analysis of healthy and organic crop production. The study’s participants included healthy and organic farmers as well as subject-matter experts. In the qualitative phase, 34 development strategies were derived from 11 in-depth interviews with healthy and organic farmers using conventional content analysis. These strategies were then evaluated based on three criteria. In the quantitative phase, which involved a survey of 102 subject-matter experts, the strategies were weighted and ranked according to efficiency, feasibility and adaptability. The analytic network process (ANP) method was employed for this purpose. The results highlighted strategies such as “using less harmful toxins and fertilizers (organic and biological fertilizers),” “organizing educational and promotiDonal events to enhance consumer awareness,” and “ensuring the availability of essential inputs for producing healthy and organic crops” as the most significant, with the highest weights and rankings. These findings offer valuable insights for decision-makers engaged in the development of organic agriculture.

Aquaponics is an emerging area of agricultural sciences that combines aquaculture and hydroponics in a symbiotic way to increase crop production. Though it offers a lot of advantages over traditional techniques, including chemical-free and soil-less farming, its commercial application suffers from some problems such as the lack of experienced manpower. To operate a stable smart aquaponic system, it is critical to estimate the fish size properly. In this context, the use of dedicated hardware for real-time aquaponic monitoring can greatly resolve the issue of inexperienced handlers. In this article, we present a complete methodology to train a deep neural network to perform fish size estimation in real time. To achieve high accuracy, a novel implementation of swish function is presented. This novel version is far more hardware efficient than the original one, while being extremely accurate. Moreover, we present a deep learning accelerator that can classify 40 million fish samples in a second. The dedicated real-time system is about 1600 times faster than the one based on general-purpose computers. The proposed neuromorphic accelerator consumes about 2600 slice registers on a low-end model of Virtex 6 FPGA series.

Papaveraceae encompasses some of the most significant and widely utilized plants within traditional medicine, pharmaceuticals, and food industries. This study aims to delineate the diversity of root endophytic fungi associated with Glaucium fimbrilligerum, G. contortuplicatum, Chelidonium majus, Papaver macrostomum, P. chelidoniifolium, P. pavoninum, and P. rhoeas from Iran. Molecular identification involved amplification and sequencing of LSU (partial large subunit nrDNA), ITS (internal transcribed spacer), TEF-1α (translation elongation factor), and TUB (β-tubulin) genomic regions. A total of 32 isolates of endophytic fungi were identified. Approximately 96.87% of these fungi belonged to Ascomycota, with only Irpex laceratus isolated and identified from G. fimbrilligerum as Basidiomycota. Fusarium was the predominant genus, representing 34.37% of endophytic fungi, followed by Sarocladium, Alternaria, and Cladosporium at 18.75%, 9.375%, and 6.25%, respectively. F. proliferatum was found to colonize 71.42% of plants, including P. macrostomum, P. chelidoniifolium, P. pavoninum, P. rhoeas, and C. majus. The fungus isolate S. strictum was recovered from 42.85% of various plant species, including G. fimbrilligerum, G. contortuplicatum, and P. chelidoniifolium. Moreover, this investigation represents the inaugural documentation of endophytic behavior exhibited by the fungi A. japonica and S. implicatum within plant species, as no previous literature has reported such occurrences. Consequently, this study unveils novel insights into the population of endophytic fungi associated with seven medicinal plants belonging to the Papaveraceae family. These findings not only enrich our understanding of fungal biodiversity but also hold implications for species conservation efforts and advancements in elucidating the intricate dynamics of plant-microbiome interactions within their native ecosystems.