agriculture industry
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2024 ◽  
Vol 84 ◽  
Author(s):  
A. Zaman ◽  
N. Roohi ◽  
M. Irfan

Abstract Livestock is a fundamental part of the agriculture industry in Pakistan and contributes more than 11.53% to GDP. Among livestock species, the buffaloes are regarded as the black gold of Pakistan. Being the highest milk producers globally, Nili-Ravi buffaloes are the most famous ones. Buffaloes are affected by many endemic diseases, and "Hemorrhagic septicemia" (HS) is one of them. This study was designed to ascertain the effects of experimental exposure ofP. multocida B:2 (oral) and its immunogens, i.e., LPS (oral and intravenous) and OMP (oral and subcutaneous) on reproductive hormonal profiles in Nili-Ravi buffaloes. Repeated serum samples were collected from the jugular vein of experimental animals for 21 days (0, 02, 04, 08, 12, 16, 20, 24, 36, 48, 72, 120, 168, 216, 264, 360, 456 and 504 hours). Hormonal assays to determine the serum concentrations of Gonadotropin-releasing hormone (GnRH), Follicle-stimulating hormone (FSH), Luteinizing hormone (LH), Estrogen (E2) and progesterone (P4) were performed using (MyBioSource) commercial Elisa kits. The hormonal profile of all treatment groups of the buffalo heifers exhibited significant (P<0.05) variations as compared to the control group (G-1). These results indicate suppression in Nili-Ravi buffaloes' reproductive hormonal profile on exposure to P. multocida B:2 and its immunogens. This influence warrants that exposure to H.S may be a possible reason for delayed puberty and poor reproduction performance in Nili-Ravi buffaloes.


Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 166
Author(s):  
Ranran Wang ◽  
Wei Bian ◽  
Zhuran Hu ◽  
Lirong Wang ◽  
Chunhong Yuan ◽  
...  

Bacillus velezensis is a kind of beneficial bacteria that is widely used in agriculture industry. Bacillus velezensis was irradiated with corona discharge generated by a needle-array high-voltage electrode. The results showed an improvement of activity of Bacillus velezensis by the corona discharge treatment was confirmed at an optimum input energy. Mutation of the Bacillus velezensis by the corona discharge treatment was also confirmed through an rRNA sequence alignment analysis. The enzyme activity of the mutated bacteria was greatly improved, which was a positive effect that can meet the production demand.


2022 ◽  
Vol 14 (2) ◽  
pp. 274
Author(s):  
Mohamed Marzhar Anuar ◽  
Alfian Abdul Halin ◽  
Thinagaran Perumal ◽  
Bahareh Kalantar

In recent years complex food security issues caused by climatic changes, limitations in human labour, and increasing production costs require a strategic approach in addressing problems. The emergence of artificial intelligence due to the capability of recent advances in computing architectures could become a new alternative to existing solutions. Deep learning algorithms in computer vision for image classification and object detection can facilitate the agriculture industry, especially in paddy cultivation, to alleviate human efforts in laborious, burdensome, and repetitive tasks. Optimal planting density is a crucial factor for paddy cultivation as it will influence the quality and quantity of production. There have been several studies involving planting density using computer vision and remote sensing approaches. While most of the studies have shown promising results, they have disadvantages and show room for improvement. One of the disadvantages is that the studies aim to detect and count all the paddy seedlings to determine planting density. The defective paddy seedlings’ locations are not pointed out to help farmers during the sowing process. In this work we aimed to explore several deep convolutional neural networks (DCNN) models to determine which one performs the best for defective paddy seedling detection using aerial imagery. Thus, we evaluated the accuracy, robustness, and inference latency of one- and two-stage pretrained object detectors combined with state-of-the-art feature extractors such as EfficientNet, ResNet50, and MobilenetV2 as a backbone. We also investigated the effect of transfer learning with fine-tuning on the performance of the aforementioned pretrained models. Experimental results showed that our proposed methods were capable of detecting the defective paddy rice seedlings with the highest precision and an F1-Score of 0.83 and 0.77, respectively, using a one-stage pretrained object detector called EfficientDet-D1 EficientNet.


2022 ◽  
pp. 191-212
Author(s):  
Anjila Saleem ◽  
Javed Ali ◽  
Mohd Yasir Arafat

Numerous nations hail the agriculture sector as a critical source of wealth creation, and past researches have shown the importance of entrepreneurship in the agriculture industry. However, there is a substantial difference in men and women's rates of taking entrepreneurial initiatives. Prior research has overlooked the significance of entrepreneurial inclination in creating agricultural start-ups from the perspective of gender. The primary objective of this study is to investigate the factors that influence women entrepreneurs working in the business endeavours of the agriculture sector. Using logistic regression, the study looked at a representative interview of 581 samples with individuals (18–65 years of age) from GEM countries. This model demonstrates the connection between the variables' qualities reliant on the data and the determinants. The chapter suggests that policymakers consider the consequences of promoting women's entrepreneurship in the agricultural industry and evolve the policies accordingly.


Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1784
Author(s):  
Houda Ben Slama ◽  
Ali Chenari Bouket ◽  
Faizah N. Alenezi ◽  
Zeinab Pourhassan ◽  
Patrycja Golińska ◽  
...  

World population growth and modernization have engendered multiple environmental problems: the propagation of humans and crop diseases and the development of multi-drug-resistant fungi, bacteria and viruses. Thus, a considerable shift towards eco-friendly products has been seen in medicine, pharmacy, agriculture and several other vital sectors. Nowadays, studies on endophytic fungi and their biotechnological potentials are in high demand due to their substantial, cost-effective and eco-friendly contributions in the discovery of an array of secondary metabolites. For this review, we provide a brief overview of plant–endophytic fungi interactions and we also state the history of the discovery of the untapped potentialities of fungal secondary metabolites. Then, we highlight the huge importance of the discovered metabolites and their versatile applications in several vital fields including medicine, pharmacy, agriculture, industry and bioremediation. We then focus on the challenges and on the possible methods and techniques that can be used to help in the discovery of novel secondary metabolites. The latter range from endophytic selection and culture media optimization to more in-depth strategies such as omics, ribosome engineering and epigenetic remodeling.


Author(s):  
علي يوسف عكاشة ◽  
خليل ابراهيم أبو زقية ◽  
عادل محمد أبو كيل

Radioactive background is very important with regard to the exposure of the population to radiation, many countries of the world measure the rate of exposure caused by natural radiation for different purposes, where radioactive pollution represents an important problem as a result of the spread and the frequent use of radioactive materials in different applications, such as medicine, agriculture, industry, and others, and some industrial facilities in the city of Misurata like Libyan Iron Company use some techniques that depend on radioactive sources. In this study, over a whole year with its four seasons, the levels of radiation background were evaluated in the area surrounding the Libyan Iron and Steel Company in the Qasr Ahmed region in Misurata city and within the company’s. It is measured for primary and secondary directions for a distance of 8 km. The radiation levels outside and inside the company’s perimeter were within the natural limits of the radiation background in the area, radiation levels do not different between the monitoring points that were measured within the company’s borders from those that were measured in the area surrounding the company. The radiation levels are not affected by the measurement season, and therefore that the obsession of radioactive contamination that some people have is unfounded and that the situation from this aspect is reassuring.


Minerals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1369
Author(s):  
Tülay Türk ◽  
Zeynep Üçerler ◽  
Fırat Burat ◽  
Gülay Bulut ◽  
Murat Olgaç Kangal

Potassium, which is included in certain contents in the structure of K-feldspar minerals, has a very important function in the growth of plants. Turkey hosts the largest feldspar reserves in the world and is by far the leader in feldspar mining. The production of potassium salts from local natural sources can provide great contributions both socially and economically in the agriculture industry along with glass production, cleaning materials, paint, bleaching powders, and general laboratory purposes. In this study, potassium extraction from K-feldspar ore with an 8.42% K2O content was studied using chloridizing (CaCl2) roasting followed by water leaching. Initially, to produce wollastonite and calcite concentrates, froth flotation tests were conducted on wollastonite-calcite ore after comminution. Thus, wollastonite and calcite concentrates with purities of 99.4% and 91.96% were successfully produced. Then, a calcite concentrate was combined with hydrochloric acid (HCl) under optimal conditions of a 1 mol/L HCl acid concentration, a 60 °C leaching temperature, and a 10 min leaching time to produce CaCl2. To bring out the importance of roasting before the dissolution process, different parameters such as roasting temperature, duration, and feldspar—CaCl2 ratios were tested. Under optimal conditions (a 900 °C roasting temperature, a 60 min duration, and a 1:1.5 feldspar—CaCl2 ratio), 98.6% of the potassium was successfully extracted by the water leaching process described in this article.


2021 ◽  
Vol 9 ◽  
Author(s):  
Deng Yue ◽  
Apurbo Sarkar ◽  
Cui Yu ◽  
Lu Qian ◽  
Zhao Minjuan

The impacts of widespread carbon emission trends possessed tremendous pressure for global food security, sustainable development, and ecosystems. Several temporal and spatial patterns of green technology have been adopted to reduce carbon emissions in different regions of China. In China, agriculture industries may have colossal importance for reducing carbon emissions. On the basis of the data from 1998 to 2018, the study uses the heterogeneous stochastic frontier model to quantify the carbon emission reduction potential of agricultural green technology progress in eastern, central, and western regions of China by using the heterogeneous stochastic frontier model. We also analyze the coefficient of variation and its spatial and temporal evolution pattern of carbon intensity decline potential index and explore the potential factors related to the agriculture green technology progress of China. The finding of the study revealed that the carbon emission rate in the agriculture industry of China is very high, whereas adopting green technology is slower because of economic and policy-related factors—the carbon emission of green technological progress. In terms of spatial variations, the changes in various regions were consistent with the overall fluctuating rate compared with the state of another country, but an increasing trend has been traced within the “east-central-west” regions. The overall regional differences are gradually trending, but differences between regions mainly cause them. The increase in the structure of the agricultural agriculture industry, the level of labor, and the increase in administrative environmental regulations will weaken the obstacles to the carbon emission reduction potential of green technological progress. The increase in urbanization, the level of the agricultural economy, and economic and environmental regulations will increase the carbon emission reduction potential of green technological progress. It is necessary to actively promote exchanges and cooperation in green agricultural technology and advanced management concepts, accelerate the optimization and upgrading of the industrial structure, and achieve the goal of peaking carbon emissions through regional coordinated development. Regionally, the overall external environment and the level of green technology progress in the western region need to be improved in all respects. The central and eastern regions need to focus on combining different policy tools to transform them from hindrance to promotion.


2021 ◽  
Vol 31 (1) ◽  
pp. 1-14
Author(s):  
Firas Mohammed Aswad ◽  
Ali Noori Kareem ◽  
Ahmed Mahmood Khudhur ◽  
Bashar Ahmed Khalaf ◽  
Salama A. Mostafa

Abstract Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system.


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