Remote Sensing for large-scale agricultural investment areas in Ethiopia – agricultural monitoring based on Earth observation time-series

Ethiopia is known to be currently food insecure and suffering from considerable food deficits. The Government of Ethiopia strives to increase the agricultural production and its efficiency. Therefore, Ethiopia has been promoting large-scale agricultural investment (LSAI) to transform the agricultural sector. However, the progress by agricultural development has been limited. Investors only developed a small fraction of the transferred land. Therefore, there is a great need for monitoring of the implementation and actual state of land use of every LSAI project. The use of remote sensing can substantially support agricultural monitoring. In this study, Earth observation time series are analyzed to examine the land used for agricultural production and to differentiate crop types grown within the three study areas. Current land use/land cover (LULC) is analyzed using Sentinel-2 time series to identify cropland areas. In a second step, remote-sensing time-series of Sentinel-1 and Sentinel-2 are used to differentiate among 20 different crop types grown in the region. The developed classification methods have been applied to derive information products for three study regions in Ethiopia including the LSAI areas within the provinces of Amhara, Benishangul, and Gambella. The methods and derived information products on LULC and crop types will be made available to GIZ and regional experts to support agricultural monitoring of developed land in Ethiopia

A Prospective Study of Causes of Illness and Death in Preterm Infants in Ethiopia: The SIP Study Protocol

Background With nearly 15 million annual preterm births globally, preterm birth is the most common cause of neonatal death. Forty to 60 % of neonatal deaths are directly or indirectly associated with preterm mortality. As countries aim to meet the Sustainable Development Goals to reduce neonatal mortality, significant reductions in preterm mortality are needed. This study aims to identify the common causes of preterm illness and their contribution to preterm mortality in low-resource settings. This article will describe the methods used to undertake the study. Methods This is a prospective, multi-centre, descriptive clinical study. Socio-demographic, obstetric, and maternal factors, and clinical and laboratory findings will be documented. The major causes of preterm mortality will be identified using clinical, laboratory, imaging, and autopsy methods and use the national Ethiopian guidelines on management of preterm infants including required investigations to reach final diagnoses. The study will document the clinical and management protocols followed in these settings. The approach consists of clinical examinations and monitoring, laboratory investigations, and determination of primary and contributory causes of mortality through both clinical means and by post-mortem examinations. An independent panel of experts will validate the primary and contributory causes of mortality. To obtain the estimated sample size of 5000 preterm births, the study will be undertaken in five hospitals in three regions of Ethiopia, which are geographically distributed across the country. All preterm infants who are either born or transferred to these hospitals will be eligible for the study. Three methods (last menstrual period, physical examination using the New Ballard Score, and ultrasound) will be used to determine gestational age. All clinical procedures will be conducted per hospital protocol and informed consent will be taken from parents or caretakers prior to their participation in the study as well as for autopsy if the infant dies. Discussion This study will determine the major causes of death and illness among hospitalized preterm infants in a low-resource setting. The result will inform policy makers and implementers of areas that can be prioritized in order to contribute to a significant reduction in neonatal mortality

Bovine tuberculosis at a cattle-small ruminant-human interface in Meskan, Gurage region, Central Ethiopia

ABSTRACT: BACKGROUND: Bovine tuberculosis (BTB) is endemic in Ethiopian cattle. The aim of this study was to assess BTB prevalence at an intensive contact interface in Meskan Woreda (district) in cattle, small ruminants and suspected TB-lymphadenitis (TBLN) human patients. METHODS: The comparative intradermal test (CIDT) was carried out for all animals involved in the cross-sectional study and results interpreted using a < 4 mm and a < 2 mm cut-off. One PPD positive goat was slaughtered and lymph nodes subjected to culture and molecular typing. In the same villages, people with lymphadenitis were subjected to clinical examination. Fine needle aspirates (FNA) were taken from suspected TBLN and analyzed by smear microscopy and molecular typing. RESULTS: A total of 1214 cattle and 406 small ruminants were tested for BTB. In cattle, overall individual prevalence (< 2 mm cut-off) was 6.8% (CI: 5.4-8.5%) with 100% herd prevalence. Only three small ruminants (2 sheep and 1 goat) were reactors. The overall individual prevalence in small ruminants (< 2 mm cut-off) was 0.4% (CI: 0.03-5.1%) with 25% herd prevalence. Cattle from owners with PPD positive small ruminants were all PPD negative. 83% of the owners kept their sheep and goats inside their house at night and 5% drank regularly goat milk.FNAs were taken from 33 TBLN suspected cases out of a total of 127 screened individuals with lymph node swellings. Based on cytology results, 12 were confirmed TBLN cases. Nine out of 33 cultures were AFB positive. Culture positive samples were subjected to molecular typing and they all yielded M. tuberculosis. M. tuberculosis was also isolated from the goat that was slaughtered. CONCLUSIONS: This study highlighted a low BTB prevalence in sheep and goats despite intensive contact with cattle reactors. TBLN in humans was caused entirely by M. tuberculosis, the human pathogen. M. tuberculosis seems to circulate also in livestock but their role at the interface is unknow

Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia

Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management