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Crop yield estimates are an important data output of agricultural monitoring systems. In sub-Saharan Africa, large input requirements of crop growth models, fragmented agricultural systems, and small field sizes are substantial challenges to accurately estimate crop yield. Multisensor data fusion can be a valuable source of high spatial and temporal resolution data to meet the requirements of crop growth models in Africa. In this study, we estimated crop yield in smallholder agricultural systems of Ethiopia by assimilating Landsat and MODIS fused data in the simple algorithm for crop yield estimation (SAFY) model. Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) adapted for fragmented agricultural landscapes was used for data fusion. We assimilated LAI and phenology information derived from Landsat–MODIS fusion, MODIS and field data for comparison. The model was validated with in situ LAI and yield measured in rice and maize fields during the 2019 growing season. Data fusion minimized the yield estimation error (rRMSE = 16% for maize and rRMSE = 23% in rice) more than MODIS (rRMSE = 20% for maize and rRMSE = 35% in rice) because of its higher LAI and phenology estimation accuracy. Data fusion improved the calibration accuracy of the field and crop-specific model parameters and better captured the spatial variability of yield, which is vital for crop production monitoring and food security in smallholder agricultural systems in Africa. Considering the promising results, further investigation into the transferability of the approach to other smallholder agricultural landscapes and hybridization with machine learning is needed for large-area applications.

AbstractThe present study is undertaken at the lower Manair River basin, the central part of Telangana in southern India, as the Government authorities has done rapid developmental activities over the last seven years demanded identifying change detection in environmental indicators. Geospatial tools, viz., remote sensing and geographic information system, are used to classify and identify land-use/land cover (LULC) changes during 2015–2021 based on the images collected from Landsat 8 OLI/TIRS multispectral and multi-temporal satellite data set. Change detection of environmental indicators such as LULC changes, land surface temperature (LST), and normalized difference vegetation index (NDVI) are crucial parameters that play a vital role in identifying vulnerable areas, which helps in planning and utilizing natural resources. The results revealed that the agricultural cropland filled in the top class with an area of 1124.70 km2 (33.82%) in 2015, amplified to 1442.95 km2 (43.39%) in 2021. The built-up area has marginally raised by 581.45 km2 (17.49%) in the study region. A negative correlation is observed between LST and NDVI. LULC patterns are changing, and its imprint echoed on a surge of LST. It establishes a foundation for evidence-based policymaking and sustainable growth, emphasizing the importance of proactive measures in preserving the delicate balance between development and environmental conservation.

AbstractThis research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km2, respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area.

AbstractIn Ethiopia, agricultural output commercialization plays a crucial role in maintaining the share of the sector in the economic growth of the country. This study was designed to analyze determinants of potato market participation among smallholder farmers in the Mida Kegn district of West Shoa Zone. A stage sampling procedure was employed to select 150 sample respondents randomly from the district. To accomplish the study, data were accessed from both primary and secondary data sources. Primary data were accessed through a structured questionnaire. The descriptive result indicated that 38% and 62% of sample households were categorized as non-participant and participant farmers in the potato output market, respectively. Moreover, the result shows that about 54.46% of potato produce was supplied to the market from total produce. The result of the double-hurdle model analysis showed that experience and land allocated for potatoes influenced potato commercialization positively, whereas family size, dependency ratio, and distance to all-weather roads were affected negatively. Consequently, the study suggests that all concerned bodies should focus on facilitating farmers to participate in off/non-farm activities, supporting potato producers through the provision of training and access to improved seed, proper utilization of land resources, and giving enough family planning training by health extension workers.

Human-induced land degradation in biodiverse regions like Mount Elgon threatens vital ecosystems. This study employs drone mapping and community insights to assess land use changes, degradation, and restoration in Mount Elgon, Uganda. Drone monitoring (2020–2023) covered six sites, complemented by household surveys (n = 499), Focus Group Discussions (FDGs), and interviews. Drone imagery shows agriculture and planted forest as dominant land use types, gradually replacing tropical high forest, bushland, and grassland. Drone image results showed that smallholder subsistence farming is leading to and enhancing degradation. Landslides and encroachment into the park were detected in three of the six sites. Trenches were the most adopted Soil and Water Conservation (SWC) measure. The trench adoption varied by location and crop type, creating restoration potential, notably in Elgon, Nabyoko, and Shiteka. Interviews and FGDs revealed adoption of trenches, grass strips, and afforestation as remedies to land degradation. Complex interactions exists among land use, degradation, and SWC measures in the upper Manafwa watershed, underscoring the urgency of addressing landslides and encroachment into the forest. Community-based initiatives are vital for hands-on SWC training, emphasizing long-term benefits. Collaboration among government, local communities, and NGOs is crucial to enforce conservation and restore Mt. Elgon National Park, while encouraging diversified income sources can reduce land dependency and mitigate degradation risks.

AbstractThe aim of this investigation was to identify honeybee flora species, identify the constraints and opportunities of honey production in three purposively selected districts of North Wollo, Amhara, Ethiopia from September to November 2021. In total, 384 beekeepers were purposively selected for the interview based on accessibility and beekeeping potentials. Structured and semi-structured questionnaires, key informant interview and focal group discussions were used for data collection. Beekeepers indicated that the major source of bee colony was buying, catching swarm and gift from others with decreasing order. Beekeepers kept their bee colony in the backyard area. They keep traditional, movable frame and top bar hive, respectively, in descending order. Majority of the beekeepers follow traditional production systems. Twenty-nine honeybee flora species and twelve poisonous plants are also identified. Use of agrochemicals, lack of beekeeping equipment, diseases, pests and predators were the main challenges to beekeeping identified in the study area. Conversely, the suitability of an environment with different agro-ecology, the availability of diversified honeybee flora and the attractive price of honey are the best-observed opportunities to increase honey production and productivity. In conclusion, great attention should be given to training and extension programs for beekeepers focusing on honeybee management, pest and predator prevention, and/or control methods. Supporting beekeeping equipments, strict rules and regulations on the use of agrochemicals should be necessary. Further research should be done on the abundance of honey bee flora and the level and effect of poisonous honeybee floras in order to increase the productivity and income of beekeepers.