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Background Ethiopian policy makers, government planners, and farmers all demand up-to-date information on maize yield and production. The Kaffa Zone is the country's most important maize-producing region. The Central Statistical Agency's manual gathering of field data and data processing for crop predictions takes a long time to complete before official conclusions are issued. In various investigations, satellite remote sensing data has been shown to be an accurate predictor of maize yield. With station data from 2008 to 2017, the goal of this study was to develop a maize yield forecast model in the Kaffa Zone using time series data from the Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index, actual evapotranspiration, potential evapotranspiration, and Climate Hazards Group Infrared Precipitation. The indicators' correctness in describing the production was checked using official grain yield data from Ethiopia's Central Statistical Office. Crop masking was applied on cropland, and agro ecological zones suited for the crop of interest were used to change the crop. Throughout the long wet season, correlation studies were utilized to investigate correlations between crop productivity, spectral indices, and agro climatic factors for the maize harvest. There were indicators established that demonstrated a strong relationship between maize yield and other factors. Results The Normalized Difference Vegetation Index Average and Climatic Hazards Group Infrared Precipitation with station data rainfall exhibit substantial associations with maize productivity, with correlations of 84 percent and 89 percent, respectively. To put it another way, these variables have a significant beneficial impact on maize yield. The derived spectro-agro meteorological yield model (r2 = 0.89, RMSE = 1.54qha−1, and 16.7% coefficient of variation) matched the Central Statistical Agency's expected Zone level yields satisfactorily. Conclusion As a result, remote sensing and geographic information system-based maize yield forecasts improved data quality and timeliness while also distinguishing yield production levels/areas and simplifying decision-making for decision-makers, demonstrating the clear potential of spectro-agro meteorological factors for maize yield forecasting, particularly in Ethiopia.

Climate variability adversely affects rural households in Ethiopia as they depend on rain-fed agriculture, which is highly vulnerable to climate fluctuations and severe events such as drought and pests. In view of this, we have assessed the impacts of climate variability on rural household’s livelihoods in agricultural land in Tarchazuria district of Dawuro Zone. A total of 270 samples of household heads were selected using a multistage sampling technique with sample size allocation procedures of the simple random sampling method. Simple linear regression, the standard precipitation index, the coefficient of variance, and descriptive statistics were used to analyze climatic data such as rainfall and temperature. Two livelihood vulnerability analysis approaches, such as composite index and Livelihood Vulnerability Index-Intergovernmental Panel on Climate Change (LVI-IPCC) approaches, were used to analyze indices for socioeconomic and biophysical indicators. The study revealed that the variability patterns of rainfall and increasing temperatures had been detrimental effects on rural households' livelihoods. The result showed households of overall standardized, average scores of Wara Gesa (0.60) had high livelihood vulnerability with dominant major components of natural, physical, social capital, and livelihood strategies to climate-induced natural hazards than Mela Gelda (0.56). The LVI-IPCC analysis results also revealed that the rural households in Mela Gelda were more exposed to climate variability than Wara Gesa and slightly sensitive to climate variability, considering the health and knowledge and skills, natural capitals, and financial capitals of the households. Therefore, interventions including road infrastructure construction, integrated with watershed management, early warning information system, providing training, livelihood diversification, and SWC measures' practices should be a better response to climate variability-induced natural hazards. Keywords: Households;  Livelihood Vulnerability Index; climate variability; Tarchazuria District   Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

The main objective of this study was to select potential solid waste landfill areas suitable for Bonga Town that are environmentally sound. The key data were LANDSAT 8 and SPOT-6 satellite images with a spatial resolution of 15 and 1.5 m, respectively; a digital elevation model with a spatial resolution of 30 m; and a ground control point, which was collected through a ground point survey and a topographic map of the study area. Each parameter was subjected to a peer review according to the analytical hierarchy process. Once the weights were established, the weighted overlap analysis was determined, which combined these criteria and classified them into high, medium, less suitable and inappropriate regions of the study area. The results show that 75.65% of the study area is not suitable for the solid waste landfill, 18.86% less suitable, 5.17% moderately adequate and 0.3% very adequate. Therefore, the ability to use inaccessible geological information framework and detection innovations for mandatory discrimination evidence for a reasonably robust waste dump will minimise opportunities and natural human well-being problems.

Drought is one of the most overwhelming natural disasters that has a widespread impact on ecosystems, economies, and societies around the world. It has been a major concern for farmers in the South Wollo Zone, so better monitoring and assessment of agricultural drought with the help of earth observation data is critical. The main objective of this research is to characterize the spatiotemporal variation, frequency, and trends of agricultural drought from 2001 to 2021 using the earth observation-derived vegetation health index (VHI) and standardized precipitation evapotranspiration index (SPEI). The VHI and SPEI were developed using the following variables: potential evapotranspiration (MOD16A2GF), climatic hazards group infrared precipitation with stations (CHIRPS), surface temperature of the land and emissivity (MOD11A2), and normalized difference vegetation index (MOD13Q1 NDVI). As a result, SPEI and VHI were used to characterize the spatiotemporal agricultural drought variation in the South Wollo zone. Additionally, the Mann-Kendall (MK) trends analysis and Pearson correlation were used to identify the trends in the agricultural drought and the relationship between VHI and SPEI, respectively. SPEI and VHI were validated using crop yield data. According to the findings, there were agricultural droughts of varying severity in 2002, 2004, 2009, 2010, 2014, and 2015. Furthermore, the study found a significant increase in drought frequency over the years 2001–2021. The results demonstrated that there was a decreasing SPEI (87.5%) and VHI (57.4%) slope during July. The comparison between the SPEI and VHI was positive and significant on the seasonal scale (r = 0.56, p = 0.01). The regression analysis results showed that detrended crop yields agreed well with VHI (R2/r = 0.49/0.70, P < 0.01) and SPEI (R2/r = 0.34/0.58, P < 0.05). Hence, the findings of the present study illustrated the effectiveness and utility of the SPEI and VHI for agricultural drought evaluation, monitoring, and early warnings in the South Wollo administrative zone.