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This study aims to evaluate the impact of training on the use of modern agricultural inputs in comparison to the recommended amount for wheat and maize production in Gozamin and Machakil Woredas in Northwest Ethiopia. The study employs a mixed-methods research design. The propensity score matching and thematic methods of analysis were used to analyze the quantitative and qualitative data, respectively. The study finds that trainees reduced the excess application of wheat seed by 11.66% to 15% ha-1. In Machakil, they tended to reduce excess NPSB application by 21% and increase urea by 52.17% for wheat. But, the NPSB rate was found to increase by 100.4% above the recommended rate in Gozamin. For maize, the trainees increased urea by 12.42% ha-1 in both Woredas. The study recommends the provision of consecutive practical training and demonstrations till farmers get convinced that applying the recommended rates of inputs significantly enhances crop productivity. [ABSTRACT FROM AUTHOR]

Although accurate forecasting of the production of major cereal crops is important to make policy decisions related to their supply, demand and import/export, very little work has been done in Ethiopia. Therefore, this study aimed to develop the best performing trend models for area, production, and yield of wheat (Triticum aestivum L.), teff [Eragrostis tef (Zucc.) Trotter], and maize (Zea mays L.) in Ethiopia, and to provide forecasts for 2019 to 2023. For each crop, the best-performing model was selected among linear, quadratic, exponential growth, and S-curve trend models using five performance measures (mean absolute percent error [MAPE], mean absolute deviation [MAD], mean squared deviation [MSD], coefficient of efficiency [E], and index of agreement [d]). Quadratic trend model was the best for area of wheat and teff, production of wheat, teff, and maize, and wheat and maize yields. S-curve and Linear trend models were the best for area of maize and yield of teff, respectively. Forecasts for 2019 to 2023 using the best-performing model revealed that cultivated area for these three cereals will decrease by 3,581 ha in Ethiopia, but their combined total production will increase by 4,896,630 tons, and productivity of wheat, teff, and maize will increase by 20.3%, 13.2%, and 13.8%, respectively, which is slightly higher than the projected population growth (10.9%) during this time. Although these results indicate that Ethiopia is on track to meet the second SDG, government policymakers and other stakeholders can use these findings to ensure sustainable food and nutrition security in Ethiopia. [ABSTRACT FROM AUTHOR]

Maize and wheat are important stable food and cash crops in Ethiopia. However, the productivity and quality of these crops are highly limited by high weed infestations. Identification and quantification of weeds are very important. The survey was conducted to identify and document weed species in study areas. Therefore, the objectives were to determine the distribution of weeds, to document major cereal crops weed species and to identify newly introduced weeds in study areas. The study was carried out in Siltie zone (Sankura and Lanfuro districts) and Gurage zone (Mareko and Meskan districts) during 2022 cropping season. A total of 22 fields for each crop were randomly assessed within minimum of 3 kilometres distance. The selected field was assessed by using 0.5 m × 0.5 m quadrants. The assessment in farms was performed with a simple random sampling technique by throwing quadrants. There were three random sampling sites per field taken. The survey result revealed that thirteen weed families and twenty nine weed species were identified in maize field while fifteen weed families and thirty weed species in wheat field. The family Poaceae, Asteraceae and Solanaceae were dominant families in both maize and wheat fields. The frequency (0.05 up to 0.95%) and the dominance (0.09% up to 38.55%) of weed species were recorded in maize field, while the frequency (0.05 up to 0.95%) and the dominance (0.05% up to 57.81) of weed species in wheat field. Galinsoga parviflora, Phalaris minor and Guzotia scabra weeds in maize field and Galinsoga parviflora, Avena fatua, Guzotia scabra, Phalaris minor and Cyperus species weeds in wheat field were the most frequent and dominant weeds in four selected districts. [ABSTRACT FROM AUTHOR]

Ethiopia's economy is dominated by agriculture which is mainly rain-fed and subsistence. Climate change is expected to have an adverse impact particularly on crop production. Previous studies have shown large discrepancies in the magnitude and sometimes in the direction of the impact on crop production. We assessed the impact of climate change on growth and yield of maize and wheat in Ethiopia using a multi-crop model ensemble. The multi-model ensemble (n = 48) was set up using the agroecosystem modelling framework Expert-N. The framework is modular which facilitates combining different submodels for plant growth and soil processes. The multi-model ensemble was driven by climate change projections representing the mid of the century (2021–2050) from ten contrasting climate models downscaled to finer resolution. The contributions of different sources of uncertainty in crop yield prediction were quantified. The sensitivity of crop yield to elevated CO2, increased temperature, changes in precipitations and N fertilizer were also assessed. Our results indicate that grain yields were very sensitive to changes in [CO2], temperature and N fertilizer amounts where the responses were higher for wheat than maize. The response to change in precipitation was weak, which we attribute to the high water holding capacity of the soils due to high organic carbon contents at the study sites. This may provide the sufficient buffering capacity for extended time periods with low amounts of precipitation. Under the changing climate, wheat productivity will be a major challenge with a 36 to 40% reduction in grain yield by 2050 while the impact on maize was modest. A major part of the uncertainty in the projected impact could be attributed to differences in the crop growth models. A considerable fraction of the uncertainty could also be traced back to different soil water dynamics modeling approaches in the model ensemble, which is often ignored. Uncertainties varied among the studied crop species and cultivars as well. The study highlights significant impacts of climate change on wheat yield in Ethiopia whereby differences in crop growth models causes the large part of the uncertainties. [ABSTRACT FROM AUTHOR]