Climate change is a major environmental and socioeconomic challenge in Ethiopia in recent decades. The study site is one of the climate change prone areas affected by climate variability and extreme events. Therefore, a better understanding of area-specific and adaptation is crucial to develop and implement proper adaptation strategies that can alleviate the adverse effects of climate change. Therefore, this work was aimed to identify determinants of farmers' adoption of climate change adaptation strategies in Gondar Zuria District of northwestern Ethiopia. Primary data were collected through semi-structured questionnaires, observation, and interviews. Besides, the secondary data were also obtained from journal articles, reports, governmental offices, and the internet. The Multinomial and Binary logistic regression models with the help of the Statistical Package for Social Sciences (SPSS) (21^th edition) were used to analyze the data. The multinomial logistic regression model was used to estimate the influence of the socioeconomic characteristics of sample households on the farmer's decision to choose climate change adaptation strategies. The result showed that age, gender, family size, farm income, and farm size had a significant influence on the farmers' choice of climate change adaptation strategies. The result also revealed that crop failure, severe soil erosion and shortages of water are major climate change-related problems than others. In order to alleviate these problems, farmers have implemented mixed farming, mixed cropping, early and late planting (changing sowing period), use of drought-resistant crop varieties, application of soil and water conservation techniques, shifting to non-farm income activities and use of irrigation. In contrast, access to climate information, total annual farm income, and market access variables are significant adoption determinants of climate change adaptation strategies by farmers' in the study site. Therefore, we recommend future adaptation-related plans should focus on improving climate change information access, improving market access and enhancing research on the use of rainwater harvesting technology.

Global freshwater resources are getting scarcer and scarcer due to the ever-increasing population, climate change, and other human activities. Hence, assessing the consumption of freshwater by different consumers is a key to efficiently utilize the resource. In this study, the Water Footprint Assessment (WFA) tool was used to determine the water footprint (WF) of Center Pivot (CP) irrigated cotton and mung-bean production using two approaches, namely, CROPWAT and field-data based methods. Based on the CROPWAT-based estimates, the average total WF of cotton was found to be 2745 m3/ton. Out of this, the green and blue WF contributed to an average of 35% and 65 %, respectively. For mung-bean, the total WF was 6561m3/ton, of which blue WF covered around 93 %. Comparison of the blue WF from CROPWAT and field-data based estimates showed a good agreement (nRMSE = 4.5 %, nMBE = 10.7 % and relative error/RE/ranging from 0.8 to 17% for cotton and 12.6% for mung-bean) and no significant difference (p = 0.456) was obtained between the two estimates. The effect of planting date on the WF estimation also showed a small variation of 0.7%–6.6 % for cotton and up to 12% for mung-bean. However, major reductions were obtained on the blue WF of cotton and mung-bean as a result of changing planting dates by about two months prior to the baseline planting dates. In this study, it is concluded that WF assessment could be satisfactorily estimated using CROPWAT model if supported with field obtained information such as soil, crop, and weather data. Another finding of the present study was that, changing planting dates close to the major rainy months could substantially contribute to reducing the blue WF in similar climates.

Globally, more than 2 million tons of pesticides are used every year, and many farmers use pesticides in violation of the recommendations, which may lead to adverse health effects. The study aimed to assess the knowledge and practices of farmers regarding the handling of chemical pesticides. A semi-structured questionnaire was developed and cross-sectional data were collected from 306 sample farmers using face-to-face interviews. Descriptive statistics and χ2 tests were used to analyze the collected data. Farmers listed the 10 most pesticide-consuming crops and 22 common pesticides used in their farms, including abamectin (“extremely hazardous”) and P,p′-DDT. Among the participants, 53.6% and 58.8% of the farmers received training and had awareness of the negative impact of pesticides, respectively. Of the total sample respondents, 54.9% never used personal protective equipment and 50.3% never read pesticide labels. Moreover, 77.1% and 74% of respondents stored pesticides anywhere in the living room and incorrectly disposed of empty containers, respectively, while 34.7% of the farmers harvested crops within 7 d after spraying. The use of personal protective equipment is significantly associated with farmers’ education (χ2 = 6.923; P = 0.031) and experience of pesticide use (χ2 = 9.487; P = 0.023). Generally, most of the farmers had poor practice in handling pesticides, and hence, it needs consistent follow-up and recurrent training to minimize the negative impact of pesticides, and to adopt integrated pest management as pest control method.

The breaching of embankments have devastating consequences for the economic, human, cultural, and environmental assets. One of the most widely used approaches for understanding the characteristics of embankment breaching is through laboratory and field-scale experiments. Despite the advancements in instrumentation and measurement techniques of embankment breaching experiments, there is a lack of comprehensive documentation. In this review, the advancements and state-of-the-art instrumentation and measurement techniques employed in overtopping-induced embankment breaching of laboratory and field-scale experiments are discussed. The key parameters commonly measured in experimental modeling are breach morphological changes, reservoir and flow depth, velocity, breach outflow, and pore water pressure. Instrumentation for breach morphological change detection, including mechanical, photography, photogrammetry, electronic sensors, and laser technologies, are reviewed. The various flow velocity measuring techniques, such as Particle Tracking Velocimetry (PTV), Particle Imaging Velocimetry (PIV), acoustic, and radar-based techniques, are discussed. Instrumentation for water level, flow rate, and pore pressure measurements are also briefly documented. The challenges and constraints encountered during embankment breaching experiments are discussed. The review further suggests future perspectives in improving the accuracy of breach detection, velocity, and pore pressure measurement techniques. Additionally, improving scale effects by incorporating geotechnical factors is also recommended.

Consistent and well-timed information regarding the extent of irrigated areas is very important for the development of the agricultural sector. This study investigated the reliability of traditional estimates of irrigated area coverages from three micro-dam irrigation schemes. High resolution Google Earth (GE) and medium resolution satellite imagery coupled with an intensive ground survey were employed to determine irrigation area coverages. Landsat-8 image pre-processing and classification was carried out in ENVI 5.0 software. In comparison to the Landsat-8 based estimates, the traditional method over-estimated 87% of the total 14 cases considered. The level of discrepancies in scheme area coverages range from a minimum of -20% (underestimated) to a maximum of 224%. Besides this, the traditional estimates were ranging from -1.8 to 238% of GE-based estimates. Whereas, the Landsat-8 based estimates slightly overestimated it by 1.8% -16% of GE-based estimates. On the other hand, the traditional estimates tend to underestimate/ignore crops with smaller area coverages and overestimate crops with relatively larger area coverages. The results hinted that the using medium and low resolution satellite imagery could improve the data collection and reporting from irrigated agriculture in developing countries.