Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant (p < 0.001), with 84%-95% accuracy in all the indexes. The majority of LCVIs showed significant (p < 0.05) relationships with yield components and FGCC. The time-series NDVI also demonstrated a significant relationship with cane yield (R² = 0.73-0.85) at the age of 10 months and above. The accuracy level of LCVIs varies with varieties and crop types, but the Normalized Difference Phenology Index (NDPI), Soil Adjusted Vegetation Index (SAVI), and NDVI were identified as the most consistent and effective LCVIs for sugarcane monitoring. Therefore, the accuracy of LCVIs was dependable and can be used effectively in monitoring sugarcane plantations to tackle the problem of continuous decline in the yield of the crop.

Agriculture is the pillar of Ethiopia’s economy, employing over 80% of the population. However, it remains highly vulnerable to the impacts of climate change. Promoting climate-smart agricultural practices is therefore critical. Quinoa (Chenopodium quinoa Willd.), a highly nutritious pseudo-cereal with broad adaptability and efficient water use, presents a promising alternative crop for Ethiopia’s diverse agro-ecologies. Its tolerance to drought and salinity makes it an ideal candidate for enhancing food and nutritional security under changing climate conditions. Therefore, this study aimed to evaluate the GEI for seed yield and yield-related traits and identify the stable and high-yielding genotypes of quinoa across six agro-ecological locations in Ethiopia—Negelle Arsi, Haramaya, Holeta, Kulumsa, Melkassa, and Ziway—representing three agro-climatic classifications (lowland, midland and highland). Thirteen genotypes were studied using a randomized complete block design (RCBD) with three replications. The G × E interaction main effect revealed significant GEI effects, particularly for plant height, days to maturity, and grain yield. Among the tested genotypes, ‘Brightest Brilliant Rainbow’, ‘Salcedo-INIA-Puno-Peru’, and ‘Multi-Hued’ demonstrated superior performance across multiple environments. Stability analyses were performed using the AMMI, ASV, and GGE biplot approaches. The analyses revealed significant variation among the genotypes for both adaptability and stability. The AMMI results indicated that environmental effects contributed more to yield variability than genotypic or interaction effects. The AMMI model identified four promising genotypes: ‘Salcedo-INIA-Puno-Peru’, ‘Brightest Brilliant Rainbow’, ‘Multi-Hued’, and ‘Bio-bio’. According to the ASV, the most stable genotypes were ‘Amarilla Sacaca’, ‘Amarilla Maranganí’, ‘Bio-bio’, ‘Cherry Vanilla’, ‘Multi-Hued’, and ‘Brightest Brilliant Rainbow’. The GGE biplot identified ‘Brightest Brilliant Rainbow’ and the Holeta location as the most ideal genotype and testing environment, respectively. ‘Multi-Hued’ and ‘Brightest Brilliant Rainbow’ emerged as the winning genotypes in the only defined mega-environment. Notably, ‘Brightest Brilliant Rainbow’ exhibited a high protein content, ranging from 14.8% at Kulumsa to 17.8% at Melkassa. Overall, ‘Brightest Brilliant Rainbow’ and ‘Salcedo-INIA-Puno-Peru’ were identified as high-performing, stable genotypes suitable for cultivation across diverse Ethiopian environments. These findings confirm quinoa’s adaptability to the broader agro-ecologies of Ethiopia and enable selection of stable, widely adapted varieties through the G × E interaction and stability analyses using AMMI, ASV, and a GGE biplot.