White lupin (Lupinus albus L.), the neglected multipurpose crop: Its production and utilization in the mixed crop-livestock farming system of Ethiopia

A survey was conducted to generate holistic information on the production and utilization of local white lupin in two lupin growing districts, namely, Mecha and Sekela, representing mid and high altitude areas, respectively in North-western Ethiopia. During the survey, two types of participatory rural appraisal (PRA) techniques, namely, individual farmer interview (61 farmers from Mecha and 51 from Sekela) and group discussion (with 20 farmers from each district) were employed. There are significant differences (P<0.05) between the two study districts for the variables like total land holding, frequency of ploughing during lupin planting, days to maturity, lupin productivity, and number of days of soaking lupin in running water. However, there are no significant differences (P>0.05) between the two study districts for the variables like land allocated for lupin cultivation, lupin seed rate, lupin soaking at home, lupin consumption per family per week and proportion of lupin used for household consumption. The use of the crop as livestock feed is negligible due to its high alkaloid content. It is concluded that the local white lupin in Ethiopia is a valuable multipurpose crop which is being cultivated in the midst of very serious shortage of cropland. Its ability to maintain soil fertility and serve as a source of food in seasons of food scarcity makes it an important crop. However, its bitter taste due to its high alkaloid content remains to be a big challenge and any lupin improvement strategy has to focus on minimizing the alkaloid content of the crop.

Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record (1989–2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of −0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and −0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ~ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.