The fermentation of oolong tea is a critical process that determines its quality and flavor. Current fermentation control relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. In this study, using Tieguanyin oolong tea as the research object, features including the tea water loss rate, aroma, image color, and texture were obtained using weight sensors, a tin oxide-type gas sensor, and a visual acquisition system. Support vector regression (SVR), random forest (RF) machine learning, and long short-term memory (LSTM) deep learning algorithms were employed to establish models for assessing the fermentation degree based on both single features and fused multi-source features, respectively. The results showed that in the test set of the fermentation degree models based on single features, the mean absolute error (MAE) ranged from 4.537 to 6.732, the root mean square error (RMSE) ranged from 5.980 to 9.416, and the coefficient of determination (R2) values varied between 0.898 and 0.959. In contrast, the data fusion models demonstrated superior performance, with the MAE reduced to 2.232–2.783, the RMSE reduced to 2.693–3.969, and R2 increased to 0.982–0.991, confirming that feature fusion enhanced characterization accuracy. Finally, the Sparrow Search Algorithm (SSA) was applied to optimize the data fusion models. After optimization, the models exhibited a MAE ranging from 1.703 to 2.078, a RMSE from 2.258 to 3.230, and R2 values between 0.988 and 0.994 on the test set. The application of the SSA further enhanced model accuracy, with the Fusion-SSA-LSTM model demonstrating the best performance. The research results enable online real-time monitoring of the fermentation degree of Tieguanyin oolong tea, which contributes to the automated production of Tieguanyin oolong tea.
Using convolutional neural networks (CNNs) to detect plant diseases has proven to reach high accuracy in the classification of infected and non-infected plant images. However, most of the existing researches are based on RGB images due to the availability and the comparably low cost of image collection. The limited spectral information restricts the detectability of plant diseases, especially in the early stage where often symptoms of pathogen infection have not yet become visible. To this end, in this study, hyperspectral imaging (HSI) data are combined with deep learning models to test the classification ability of two soybean fungal diseases: Asian soybean rust (Phakopsora pachyhizi) and soybean stem rust (Sclerotinia scleroriorum). Different CNNs employing 2D, 3D convolution, and hybrid approaches are compared. The influences of the depth of the convolutional layer and the regularization techniques are also discussed. Besides, image augmentation methods are investigated to overcome the problem of data scarcity. The results indicate the 6-convolutional-layer depth hybrid model to have the best capacity in classifying Asian soybean rust in the early-mid to mid-late stage when there are over 2 % visible symptoms but a limited detectability in the early stages when there are below 2 % visible symptoms on leaves. On the other hand, the optimized CNN model shows a limited capability to detect both diseases when there are no visible symptoms observable. Overall, this study suggests a hybrid 2D-3D convolutional model with augmentation and regularization methods has a high potential in the early detection of fungal diseases. This research is expected to contribute to a new cropping system that vastly reduces the chemical-synthesis plant protection products, where a continuous pathogen disease monitoring plays a key to manage the crop stands.
Winter wheat cultivation faces yield reductions in the North China Plain due to drought and excessive nitrogen fertilizer use, exacerbated by climate change. This study employed a life cycle assessment approach, integrating economic and material input-output data, to evaluate the eco-efficiency of reduced irrigation and nitrogen fertilizer inputs. Field experiments were conducted with four irrigation regimes at the jointing stage (W0: no irrigation; W1: 75 mm), heading stage (W2: additional 75 mm), and filling stage (W3: additional 75 mm), in combination with three nitrogen fertilization levels (conventional, N250: 250 kg ha−1; 20 % reduction, N200: 200 kg ha−1; 40 % reduction, and N150: 150 kg ha−1). The interactive effects on environmental benefits were comprehensively assessed. Results showed irrigation frequency had higher effect on yield than nitrogen application, with nitrogen reduction causing a maximum yield loss of 11.7 %, while reduced irrigation led to 34.0–48.9 % yield losses. Under conditions of sufficient water availability, total environmental costs were inversely correlated with wheat yield and did not increase with higher irrigation frequency. Specifically, increasing irrigation frequency reduced total environmental costs by an average of 32.4 %, 26.9 %, and 23.7 % under N250, N200, and N150 fertilization levels, respectively. Nitrogen fertilizer inputs represented the largest contributor to environmental costs, accounting for 25.6–60.1 % of the total environmental burden. Nitrogen reduction strategies enhanced overall eco-efficiency and lowered environmental costs, whereas water-saving measures involving reduced irrigation decreased eco-efficiency and increased environmental costs. The optimal strategy for high-quality wheat production involved applying 150 kg ha−1 nitrogen and irrigating twice (W2), balancing yield, sustainability, and eco-efficiency. This approach effectively balances yield, environmental sustainability, and eco-efficiency, providing a practical solution to address the environmental challenges of wheat production in the region.
Julia Radwan-Pragłowska, Paulina Bąk, Łukasz Janus
et al.
Excessive blood loss is a leading cause of mortality among soldiers and accident victims. The wound healing process typically ranges from three weeks to several months, with disruptions in healing stages potentially prolonging recovery time. Chronic wounds may persist for years, creating a favorable environment for microbial growth. Chitosan, a derivative of chitin—the second most abundant biopolymer in nature—is obtained through deacetylation and exhibits mucoadhesive, analgesic, antioxidant, biodegradable, non-toxic, and biocompatible properties. Due to its hemostatic and regenerative support capabilities, chitosan is widely applied in the food, cosmetic, and agricultural industries; environmental protection; and as a key component in dressings for chronic wound healing. Notably, its antibacterial properties make it a promising candidate for novel biomaterials to replace traditional antibiotics and prevent the emergence of drug-resistant strains. The primary aim of this study was the chemical cross-linking of chitosan with the amino acids L-aspartic and L-glutamic acid in the presence of periclase (magnesium oxide) under microwave radiation conditions. Subsequent research stages involved the analysis of the samples’ physicochemical properties using SEM, FT-IR, XPS, atomic absorption spectrometry, swelling behavior (in water, SBF, and blood), porosity, and density. Biological assessments included biodegradation, cytotoxicity, and antibacterial activity against Escherichia coli and Staphylococcus aureus. The obtained results confirmed the high potential of the newly developed hemostatic agents for effective hemorrhage management under non-sterile conditions.
The goal of this research was to examine tail dependence structures between selected commodity futures returns. Tail dependence, called also extremal dependence, was evaluated for the pairs of commodities coming from the same sector (energy or agricultural). The study covers the years 2018-2023, embracing the COVID-19 pandemic and the outbreak of the Russia-Ukraine war. To achieve the goal, bivariate dynamic models were applied to percentage log returns of commodity futures. Marginal distributions were described using the ARMA-GARCH models. Joint distributions were constructed using the symmetrized Joe-Clayton copula, which allowed to model asymmetric dependence in the tails of a distribution. Time variation of the copula parameters, here equal to tail dependence coefficients, was described using the evolution equations [Patton 2006]. In the energy sector, the dependence in both tails of analyzed distributions was relatively strong, dynamic and higher in the lower tail than in the upper tail. On the contrary, the agricultural sector lacks common patterns of tail dependency. This feature of the agricultural sector creates an opportunity for investors or risk managers to build well-diversified portfolios.
Usa Wannasingha Humphries, Muhammad Waqas, Phyo Thandar Hlaing
et al.
Climate change (CC) is causing a significant threat to agriculture, a sector complicatedly tied to natural resources. Changes in precipitation patterns, atmospheric water content, and rising temperatures intensely affect global agriculture, especially in tropical regions. In this intense CC scenario, potential evapotranspiration (PET) and crop water requirement (CWR) are critical components of agricultural water management. This study evaluates the future impact of CC on precipitation, CWR, and PET in different provinces of Thailand's northern and northeastern regions. Three bias correction methods (Delta (DT), Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) were employed for precipitation downscaling from the CanESM5 CMIP6-GCM across selected 13 coffee farms with different coffee species. Arabica and Robusta coffee were carefully selected for this analysis. The DT method demonstrated superiority, exhibiting lower RMSE and higher correlation coefficients than EQM and QM. Farm-specific assessments illuminated water demand's complex dynamics during critical growth stages, showcasing variable CWR and PET. During the blooming stage in N-F1, CWR ranged from 16.7 to 33.7 mm/stage, highlighting the variability in water needs. Projected CC impacts on Arabica and Robusta coffee farms in Chiang Rai and Sisaket presented challenges, emphasizing farm-specific strategies to address potential water deficits or surpluses during critical growth phases. Projected 2023, 2028, and 2033 precipitation demonstrated incongruities with CWR and PET. The findings emphasize the crucial role of farm-specific adaptive strategies in mitigating the impacts of changing precipitation patterns on coffee cultivation.
Abstract
This study aimed to evaluate the effectiveness of Laney p' chart in overcoming the limitations of conventional p-chart in cigar quality control, especially in handling overdispersion of production data. Overdispersion often occurs in agricultural processes with large sample sizes, resulting in narrow control limits and false alarms. The study was conducted at PT Taru Martani, using cigar quality data from three main production units from August 2021 to July 2022. A quantitative descriptive approach was used to analyze the proportion of product defects. Initial analysis with conventional p-chart showed that 29,140 units in the Cocoon Unit, 23,602 units in the Rolling Unit, and 5,987 units in the Dry Cigar Unit were out of control due to overdispersion. After the Laney p' chart application, the control limits were expanded to 234.7%, significantly reducing false alarms and increasing sensitivity to actual variations in the data. The analysis showed that Laney p' chart was more effective in identifying relevant process variations. The process in the Dry Cigar Unit continued to show instability, likely due to humidity and raw material quality fluctuations. These findings highlight the importance of environmental control and raw material stability in maintaining product quality. This study provided practical contributions to the quality control of high-value agricultural products. It is recommended that further studies explore the integration of other statistical methods and study deeply the relationship between external factors and product quality.
Keywords: Agricultural products, cigars, Laney p' chart, overdispersion, quality control
Abstrak
Penelitian ini bertujuan untuk mengevaluasi efektivitas Laney p' chart dalam mengatasi keterbatasan p-chart konvensional pada pengendalian mutu cerutu, khususnya dalam menangani overdispersi data produksi. Overdispersi sering kali muncul dalam proses agrikultur dengan ukuran sampel besar, menghasilkan batas kendali yang sempit dan alarm palsu. Studi dilakukan di PT Taru Martani, menggunakan data mutu cerutu dari tiga unit produksi utama selama Agustus 2021 hingga Juli 2022. Pendekatan deskriptif kuantitatif digunakan untuk menganalisis proporsi kecacatan produk. Analisis awal dengan p-chart konvensional menunjukkan bahwa 29.140 unit di Unit Kepompong, 23.602 unit di Unit Pelintingan, dan 5.987 unit di Unit Cerutu Kering berada di luar kendali akibat overdispersi. Setelah penerapan Laney p' chart, batas kendali diperluas hingga 234,7%, mengurangi alarm palsu secara signifikan dan meningkatkan sensitivitas terhadap variasi nyata dalam data. Hasil analisis menunjukkan Laney p' chart lebih efektif dalam mengidentifikasi variasi proses yang relevan. Proses di Unit Cerutu Kering, misalnya, tetap menunjukkan ketidakstabilan, kemungkinan akibat fluktuasi kelembaban dan kualitas bahan baku. Temuan ini menyoroti pentingnya pengendalian lingkungan dan stabilitas bahan baku dalam menjaga mutu produk. Penelitian ini memberikan kontribusi praktis dalam pengendalian mutu produk agrikultur bernilai tinggi. Disarankan agar studi lanjutan mengeksplorasi integrasi metode statistik lain dan mempelajari hubungan lebih dalam antara faktor eksternal dengan mutu produk.
Kata kunci: Cerutu, Laney p' chart, overdispersi, pengendalian mutu, produk agrikultur
Vinay Vijayakumar, Yiannis Ampatzidis, John K. Schueller
et al.
In this study, the previous work, present status, benefits, and limitations of the state-of-the-art technologies used in smart spraying technologies in precision weed management are reviewed. A total of 116 articles were identified from Google Scholar and Scopus to study the research work in the field of smart sprayers and precision weed management. The articles were examined based on the relevance, research focus, novelties, measured parameters, used technologies, and field of applications. Smart sprayers based on machine vision (MV) and artificial intelligence (AI) are keys to improving crop productivity and meeting the food demands of the future by reducing the yield losses due to weeds and working towards a sustainable future in agriculture. Many papers published in recent years have focused more on the machine vision, weed detection, and classification aspects of the weeding robot. Very few studies have attempted to discuss the components of a smart weeding machine, non-chemical-based weeders, the components of spraying systems, their controls, underlying fluid mechanics, and the field trials of these weeding robots. This article reviews conventional weeding techniques, machine-vision-based weeding robots, and spraying systems proposed or constructed in the last twenty-five years. Key technologies such as non-chemical-based weeding machines, image preprocessing, feature extraction, and weed detection based on machine learning (ML) and deep learning (DL) for smart sprayers are discussed. The fundamental components of a smart spraying system are also discussed, and previous works are compared to highlight the key components, the spraying accuracy, and the major advantages and disadvantages. The fluid mechanics of the spraying system and its associated controls involved are also presented. There are still many bottlenecks in weed detection systems and smart spraying systems. The results of the systematic review provide an understanding of the progress made in the field of robotic weed detection, herbicide and non-herbicide-based weed management, the use of machine vision, and the limitations of the current spraying systems.
Aqly Tyasna Fiqhry, Tri Nugraha Budi Santoso, Fani Ardiani
Several factors, including the less-than-optimal altitude of Arabica coffee planting influence the low productivity of Arabica coffee in Temanggung Regency. The lack of rejuvenation of production plants means that old coffee plants have low productivity; apart from these two factors, farmers do not have good plant management skills. This research further examines the influence of altitude on Arabica coffee production. This research was carried out in Temanggung Regency, with the sub-districts that were the sample for this research being Ngadirejo District, with an altitude range of 900-1150 m asl, Parakan District, with an altitude range of 1150-1400 m asl, and Kledung District with an altitude range of more than 1400 m asl. The sampling method employed in this study is a purposive sampling technique; the researcher directly determines the location and source of research information. A total of 28 participants responded to this study, with an assessment sample taken of 5% of the population of Arabica coffee plantations that bear fruit. The analysis used the linear regression method of a fixed variable, namely height, and independent variables, namely production, productivity, and evaluation, with a significance level of 5%. The research results show that altitude does influence productivity. If altitude increases, productivity will also increase.
Evi Winingsih, Denok Setiawati, Titin Indah Pratiwi
Career is an important aspect of human life and its stability is determined by Career Decision-making Self Efficacy (CDSE). This study aims to compare high school students' CDSE between those living in industrial and agrarian areas. Subjects in this study were 309 high school students and 309 vocational students from both areas. Data were collected using the CDSE-SF instrument developed by Nancy E. Betz. Mann Whitney comparative test was used to analyze the data in this study. The results show that there were differences in the CDSE significance level of students from both areas. Accordingly, the results find that three of the five aspects of senior high school students' CDSE show their The results of data analysis showed significant differences in the five aspects of student CDSE in industrial and agricultural areas.The different value between both students occurs due to the difference in goal and career directions which is highly possible because of several factors that were not discussed in this study.
Purpose This paper aims to explore patterns of business financing and adoption of blockchain technology in the agricultural industry. The adoption of blockchain technology in terms of recording, storing, validating and securing data can solve a variety of agricultural problems such as agricultural business financing. If the banking and insurance industries are connected in real-time to activity data in the agricultural industry, they can create better credit ratings and profile models. So, finally, all parties in the agricultural industry have a greater chance to get business financing from banks. Design/methodology/approach This paper uses a case study research approach with a framework of analysis of the theory of adoption of technology, organization and environment (TOE) and the theory of “mindfulness of adoption”. The case study method has advantages when verification is still questioned or the application of certain theories in practice as phenomena and contexts that occur in the field in accordance with the application of blockchain technology into a relatively new business, both technically and practically in the field. Findings The findings indicate that there are no barriers to the availability of blockchain technology for technology adoption. The characteristics of this technology are very suitable for solving financing and supply chain business problems in the agricultural industry. However, the adoption of blockchain technology in agriculture shows that there is complexity in the organizational context involving internal and external organizations. The number of organizations and small parties involved in the agricultural process challenges the adoption of blockchain technology as new technology. Then, the external environment of technology, especially government regulations in developing countries, is still an obstacle to the adoption of blockchain technology. Research limitations/implications This study faces several limitations, namely, the limited case of implementation of the blockchain technology due to the novelty of technology and government regulation. So that further research related to the adoption of blockchain technology needs to be done using field data such as surveys. Research related to the connectivity of the banking industry and other financial institutions also needs to be explored further, especially in creating a data-based credit risk model of the blockchain system. Originality/value On the practical side, case studies of technology adoption and its relationship with the financing of agricultural business are still little explored so this study contributes to exploring the application of blockchain technology in the agricultural industry. The adoption of blockchain technology has an impact not only on farmers but also on all parties involved in the supply chain including banks, insurance and other financial institutions. In addition, the distributed data exchange business model using blockchain technology is a new business model in the agriculture industry.
Disposal of poultry sludge is one of the great challenges facing cities because of very strict requirements for landfilling and the scarcity of space for landfills. The present study was therefore aimed at evaluating the physical and chemical properties of poultry sludge and its suitability for reuse in agricultural and non-agricultural applications. Three samples were collected from sludge at the wastewater treatment plant of Al-Thuraya slaughterhouse in Al-Mafraq District, Jordan. The physical and biochemical properties of these samples were analyzed. Also, elemental composition and heat value were determined. The results indicated that poultry sludge had a slightly alkaline pH and a total moisture content of 20%, as well as an average total solid of 80%. The dry solid sludge had a volatile solid content of 94.9% and 5.1% of ash. Also, dry sludge had a high protein content (62 %) followed by carbohydrate (20%) and fiber (17%), with fat being around 1%. The major elements in the sludge were carbon (65.5%) followed by nitrogen (16%), phosphorous (5 %) and sulfur (2%). Heavy metal concentrations in dry sludge ranged from 0.01 to 2 mg/kg. These heavy metal concentrations were well below the safe limits recommended by legislators for sludge used as a fertilizer. The findings from this study revealed that dry poultry sludge offers a wide range of potential uses as fertilizer, animal feed and a source of energy, and it should be considered as a potentially valuable and sustainable resource rather than a waste product.
Abstract Owing to climate change, population growth and tenurial changes, the past decade has witnessed high interest among migrant and settler pastoralist groups in the vegetal-rich customary lands of the Agogo Traditional Area. This has resulted in lease grants of large land areas to pastoralists by traditional authorities and usufruct families, for reasons of ensuring optimum use and gaining the highest returns from lands. This paper examines the implications of consequent competing interests over land resources between farmers and herders on indigenous farmer’s agricultural investment decisions. The study uses qualitative methods and empirical evidence is given by primary data from semi-structured interviews and focus group discussions in the case study area. Results indicated that land owners exploit lapses in customary land administration systems to allocate lands in exchange for money, to pastoralists while neglecting indigenous farmers’ land use rights. Thus, indigenous farmers report land tenure insecurity and a sense of deprivation from their customary lands. Despite tenure insecurity concerns, farmer’s agricultural investment decisions remain unchanged because such changes in investment decisions may reduce incomes and compromise their livelihoods. The findings herein contradict theoretical expectations and provide new perspectives for understanding the relationship between tenure (in)security and investment decisions.
Nutrition. Foods and food supply, Agricultural industries
Nichola Eliza Davies Calvani, Jan Šlapeta, Emily Onizawa
et al.
Bovine trichomonosis, caused by infection with the protozoan parasite Tritrichomonas foetus, is globally recognised as a cause of reproductive failure in cattle. Maintained in clinically normal bulls, T. foetus infection results in infertility and abortion in infected cows. In Australia’s Northern Territory (NT), logistical limitations associated with extensive livestock production inhibit wide-scale testing and diagnosis, allowing the parasite to persist undetected. In the present study, T. foetus was detected in 18/109 preputial cultures collected from bulls on a property in the NT with a history of low birth rates and reproductive failure using real-time PCR testing. Of the T. foetus-positive samples, 13/18 were genotyped using the internal transcribed spacer regions (ITS1 and ITS2) and the 5.8S rDNA unit. Selected samples were further characterised using the protein-coding genes of cysteine proteases (CP-1, 2, 4–9) and cytosolic malate dehydrogenase 1 (MDH-1) to determine if the isolates were ‘bovineʼ, ‘felineʼ or ‘Southern Africaʼ genotypes. All samples were 100% identical to the T. foetus ‘bovine’ genotype across all markers. This is the first reported case of trichomonosis in Australian cattle since 1988 and is a reminder that T. foetus should be considered whenever reproductive failure occurs in extensive cattle systems.
Fusarium oxysporum is a pathogen that causes wilt in oil palm and can cause oil palm sprout decay. The study aimed to determine the effectivity of mangosteen peel extract (Garcinia mangostana L.) in inhibiting the growth of Fusarium oxysporum in vitro and in vivo. The research was conducted in November 2017 to June 2018 at the Politeknik Negeri Lampung. The method used was a completely randomized design (CRD) consisting of 5 treatments, namely the concentration of mangosteen peel extract 0% (control), 15%, 30%, 45%,and 60%. Data were analyzed using analysis of variance (ANOVA) and further tests of mean values using the LSD test. The results showed that mangosteen peel extract affected the percentage of inhibitory zone extract in Fusarium oxysporum in vitro at concentrations of 15%, 30%, and 45% respectively at 25,92%, 29,06% and 35,95%. The treatment of mangosteen peel extract also affected the percentage of disease incidence and number of leaves in in vivo testing.
The aim of the study is to examine which part of agricultural and food trade between Visegrad countries and the United Kingdom is threatened by Brexit. On 23 June 2016, the United Kingdom voted in a referendum to leave the European Union, but this has not yet taken place, though it should have happened by 29 March 2019. Therefore, it remains uncertain and the conditions the exit remain to be seen. In the absence of a final agreement, it is only possible to determine currently competitive sectors that could remain in this situation in the future, too. Competitiveness studies can provide guidance to determine expected effects. For products with a lower competitiveness value, turnover is expected to decrease due to changing regulations or increasing duties. Based on the long-term analysis of agri-food trade values of the parties, it is clear that markets are sufficiently diversified. So British withdrawal will not result in significant consequences in the case of Visegrad countries. In terms of trade relations, highly processed products are expected to be competitive in the future.