Agricultural Research

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.