The study examined cocoa farmers’ utilization of agricultural information in the Asante Akim North Municipality of Ghana. A multistage sampling technique was employed to select 388 cocoa farmers, and data were analyzed using descriptive statistics and a multivariate probit (MVP) model. The findings showed that pest and disease information had the highest utilization (60.82%), followed by agrochemical information (60.05%), harvesting and storage information (59.02%), and market information (57.47%), while climate change information recorded the highest extent of utilization (mean = 3.82), indicating that it was often utilized. Farmers primarily relied on COCOBOD extension services, personal experience, and information from friends and fellow farmers, while radio, television, social media, and community leaders played relatively limited roles. The study further found that socioeconomic characteristics such as age, sex, education, training participation, farmer‐based organization (FBO) membership, distance to market, and access to credit are significantly associated with farmers’ use of agricultural information. Major barriers to information access and utilization included inadequate extension agents, low financial capacity, illiteracy, and language barriers. The study recommends that COCOBOD and agricultural extension agencies strengthen decentralized extension delivery, promote FBOs as local information hubs, and expand local‐language communication through accessible channels such as radio to improve agricultural information dissemination and utilization among cocoa farmers.
Maize is one of the leading agricultural commodities widely cultivated in Indonesia. The increasing demand for maize in the country has driven the need for land extensification, including the utilization of suboptimal areas. The objective of this study was to identify maize genotypes that are adapted to acidic dryland conditions. The genetic materials used in this research consisted of 100 genotypes (94 hybrids derived from line × tester crosses and 6 check varieties). The multitrait genotype–ideotype distance index (MGIDI) analysis was performed to select the best genotypes based on the observed traits. The results indicated that five genotypes were adaptive to acidic dryland conditions, namely, B4B × L15, NK Sumo, Sukmaraga × L15, NK Perkasa, and MS3 × L15. These genotypes had an average plant height of 172.4 cm, stem diameter of 17.9 mm, an anthesis–silking interval (ASI) of 1.9 days, a plant appearance score of 2.3 (good), a stay‐green proportion above 40%, an ear appearance score of 2.0 (good), a grain weight per ear of 118.8 g, and a yield potential of 7.88 tons/ha. The MGIDI method showed potential for identifying superior maize genotypes adapted to acidic dryland conditions, with the selected genotypes demonstrating promising overall performance in the tested environment. Because the evaluation was conducted at a single location within one growing season, the observed performance reflects adaptation to that specific test environment. Further multilocation and multiseason trials are required to confirm their stability and broader adaptation, as well as to assess their potential for advancement as candidates for new, improved maize varieties.
Soybean classification is essential for ensuring quality and standardization, determining commercial value, and optimizing processes throughout the agricultural supply chain. Due to the complexity of the parameters assessed, advanced technologies such as artificial intelligence (AI) and machine learning have emerged as promising tools to improve the accuracy and automation of these analyses. This study conducted a systematic literature review (SLR) following the PRISMA protocol guidelines. The review was conducted using the Scopus database, with no temporal restriction, covering studies indexed up to August 2024. A total of 56 studies were selected and analyzed. The objective was to identify the main technologies and methods used to classify soybeans using AI. The review mapped the most commonly used technologies, including RGB, near‐infrared (NIR), and hyperspectral imaging, and identified a growing dominance of deep learning approaches, particularly convolutional neural networks (CNNs). RGB imaging is the most widely used technique, while hyperspectral imaging supports more detailed internal quality assessments. Despite the high reported accuracies, recurring limitations across the reviewed studies include dataset variability and the predominance of controlled laboratory conditions. These findings provide a structured overview of current research trends, highlight key challenges for real‐world applications, and suggest practical directions for selecting appropriate AI techniques in soybean seed classification.
Common bean ( Phaseolus vulgaris L.) production is a vital component of agriculture and food security in the West Hararghe Zone. Nevertheless, postharvest losses caused by seed‐borne fungal pathogens are a major obstacle to productivity. This study aimed to identify the mycoflora associated with stored common bean varieties and to evaluate their effects on seed germination. Using purposive sampling, 45 samples of three common bean cultivars (Nasir, Awash‐1, and Awash‐2) were collected from three key districts (Anchar, Guba Koricha, and Chiro). The isolation and identification of seed borne fungi associated with common bean seed were done by using direct plating technique. The Nasir variety from Guba Koricha exhibited the highest mean moisture level (13.56%), while the minimum mean (9.20%) was identified in the Awash‐1 variety from the Chiro district. Fungal isolation and identification were conducted using the standard agar plate method, while pathogenicity was assessed through seed inoculation and germination assays. Out of 2250 plated seeds, 1267 were infected, resulting in an average infection rate of 56.3% (ranging from 40% to 88%). A total of nine fungal genera was isolated and identified into 15 fungal species. Fusarium was the most dominant genus by isolate count (385), followed by Aspergillus (346), Alternaria (164), Penicillium (86), Macrophomina (85), and Rhizoctonia (77). However, Alternaria alternata showed the highest frequency of occurrence, appearing in 86.7% of the seed samples, followed by Fusarium spp. at 75.6%. Among the studied districts, Guba Koricha exhibited the highest level of fungal infection, particularly in the Nasir variety, which reached an infection rate of 77.6%. Analysis of variance (ANOVA) confirmed that four pathogenic isolates ( Macrophomina phaseolina , Rhizoctonia solani , Fusarium oxysporum , and Alternaria alternata ) successfully induced root rot symptoms. Among these, Rhizoctonia spp. demonstrated the highest virulence, resulting in significantly greater disease incidence (DI) of up to 95% ( p ≤ 0.05), compared to only 0%–40% in the control groups. Furthermore, all inoculated treatments exhibited higher percent severity index (PSI) values than the uninoculated controls. The study concludes that a diverse array of seed‐borne fungi, dominated by Fusarium , Aspergillus , and Alternaria , are strongly associated with stored common bean seeds in selected districts of the West Hararghe Zone. This contamination adversely affects both seed viability and seedling health. These findings underscore the urgent need for improved seed storage practices and the development of effective, location‐specific disease management strategies. Future studies should incorporate molecular tools for species identification to confirm taxonomic accuracy and assess the genetic diversity of key fungal pathogens in stored seeds, thereby safeguarding crop quality.
Nutrient management is important for maintaining productivity and quality in soilless vegetable production. This study independently evaluated vermicompost (VC) and mineral fertilizer rate in Experiment 1 and harvest time and mineral fertilizer rate in Experiment 2 for Napa cabbage ( Brassica pekinensis cv. Rado 67) under greenhouse conditions. Two independent 2 × 4 factorial experiments were conducted using a completely randomized design (CRD). Experiment 1 evaluated VC (0 and 100 g pot −1 ) and four mineral fertilizer rates, whereas Experiment 2 evaluated harvest time (50 and 60 days after transplanting [DAT]) and the same fertilizer rates under a uniform VC application of 100 g pot −1 . In Experiment 1, VC and mineral fertilizer rate significantly affected plant growth, yield, and selected quality attributes. The highest yield was obtained with 100 g pot −1 VC and F 4 (80–40–60 kg ha −1 N–P 2 O 5 –K 2 O), reaching 233.63 g pot −1 . In Experiment 2, harvest time and fertilizer rate significantly affected plant growth and total yield, with a significant harvest time × fertilizer rate interaction. The highest treatment‐combination yield was obtained at 60 DAT with F4, reaching 564.00 g grow bag −1 . Under the highest‐yielding treatment in Experiment 1, nitrate concentration was 109.80 mg NO 3 − kg −1 fresh weight, while Escherichia coli and coliforms were below the detection limit (<1 most probable number [MPN] g −1 ). Overall, VC and mineral fertilizer affected crop performance in Experiment 1, whereas harvest time and mineral fertilizer rate affected productivity in Experiment 2 under the tested conditions.
