Potential of conventional and new breeding technologies to manage soybean diseases
Abstract Malnutrition is a serious issue in developing countries. It affects about 165 million children under the age of five and causes about 13 million deaths annually. Among various nutritional deficiencies, protein deficiency in diets is a major contributing factor. One of the befitting ways to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
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| Series: | Phytopathology Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42483-025-00338-0 |
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| Summary: | Abstract Malnutrition is a serious issue in developing countries. It affects about 165 million children under the age of five and causes about 13 million deaths annually. Among various nutritional deficiencies, protein deficiency in diets is a major contributing factor. One of the befitting ways to overcome malnutrition is to increase reliance on plant-based protein. Soybean, being rich in protein, oil, dietary fiber, and minerals, offers an enduring solution to this problem and serves as a raw material for different industries. However, sustainable soybean production is threatened by pathogens like bacteria, fungi, nematodes, and viruses. The use of chemical fungicides at the farm level is a preferred method to control plant pathogens, but their indiscriminate application causes environmental harm and increases production costs, emphasizing the need for eco-friendly disease management. This review highlights important soybean diseases caused by bacteria, fungi, viruses, and nematodes, along with their potential losses and various strategies to mitigate these diseases. Role of conventional breeding, marker-assisted breeding, mutation breeding, transgenic approaches, and emerging plant breeding technologies to expedite the incorporation of disease resistance in soybean. Additionally, this review explores possible applications of nanotechnology in disease diagnosis and management in soybean, offering solutions to reduce dependence on costly and labor-intensive diagnostic and disease management practices. |
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| ISSN: | 2524-4167 |