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Vollst. Titel : Johann Jacob Griesingers vollständiges Bienen-Magazin : in welchem von der Bienenpflege überhaupt, der Bienen natürlichen Generation, Ursprung und Präparation ihres Honig- und Waabenbaues, denen Gebrechen, und was der Bienenzieglung hinderlich und schädlich, hingegen derselben vorträglich und beförderlich seye; wie durch Natur gemäße Tractation die sicherste Producta von einer wohlangelegten Bienenhaltung erlanget werden können, von dem Bienen-Recht, und dem gesamten Bienenwesen, wie auch vom Honig und Wachs, aus eigener und anderer gegründeter Erfahrung, durch Exempel mit ganz neuen Entdeckungen erläutert, auf das vollständigste gehandelt wird
Wenige Zeit nach der Ausgabe des dritten Theils meiner kleinen vermischten Schriften, die den unverdienten Beyfall einiger gelehrten und großen Patrioten erhielten, wurde mir eine baldige neu zu vermehrende und besonders zu druckende Auflage etlicher darinne befindlichen Abhandlungen auf das fördersamste empfohlen. ...
As a cognitively-mediated response, autonomous adaptation at farm-gate levels constitutes reactionary actions by farmers against climate impacts. These actions are shaped by interacting factors such as household characteristics, livelihood scope and resources. It is driven by the goal of adapting cultivated farmlands to climate and for sustaining crop yields. Thus, interest in balancing adaptation goals with protection of vegetation conditions is less of a priority. Lack of research interest in understanding the gap between objectives of reactionary adaptation and protection of surface conditions (vegetation canopies) is a gap in research. In many studies, farm-gate level adaptation is described as a set of zero-feedback actions in response to climate impacts. This perception conceals the stress and impact-engendering attribute of reactionary adaptation. Inspired towards addressing this conceptual gap; this study investigates impact of farmers’ reactionary adaptation on vegetation cover in Keffi, Nasarawa, Nigeria. A twenty-year time-series NDVI and rainfall datasets are linearly regressed to examine the extent of NDVI-rainfall sensitivity. A weak linear relationship between NDVI and rainfall in Keffi for the period, 1999-2018 is observed. At a regression slope of 0.001, R squared, R2=0.129 (implying that only about 13% of the variability in NDVI in Keffi are explained by rainfall amount) and a bivariate regression coefficient, r=0.359; statistical evidence shows that rainfall amount are not significant predictors of NDVI in Keffi. In investigating the possible interference of non-rainfall factors on vegetation productivity (NDVI) in Keffi; a residual trend (RESTREND) analysis was carried out. Regression of residuals from NDVI-Rainfall linear regression produced a R=0.192 with a negative and downwards slope. The downward character of the RESTREND slope is suggestive of non-rainfall factors contained in the residuals. In validating the RESTREND analysis, a comparative analysis between observed and predicted NDVI derived from a reference NDVI value of 0.46 was carried out. The NDVI value of 0.46, is empirically assumed to be average NDVI value expected at a minimum rainfall amount of 850mm/year reported in tropical Savanna ecosystems. Using this empirical relationship, NDVI values were predicted for Keffi. Even at higher rainfall amounts≈1340mm/year, amounts were unable to produce corresponding higher NDVI values; rather a more plausible correlation between reference-derived predicted NDVI values and rainfall was obtained. A further analysis with predicted NDVI values, based on 1999 NDVI value in Keffi returned higher NDVI units than observed NDVI values. This strengthens the attribution of the possible interference of rainfall-NDVI sensitivity by non-rainfall factors like human activities on vegetation productivity. Surface soil analysis to exclude potential impacts of soil nutrients and moisture deficiency on vegetation productivity, showed that soil had insignificant effect on vegetation dynamics. Further inferential analysis, using the inter-annual NDVI and the reclassified bi-decadal NDVI maps showed that spatial vegetation distribution in Keffi were driven by farmers inter-annual rotational cultivation footprints than rainfall variability. With a three-class categorization, “gain, loss and significant loss”, the spatial distribution of vegetation in Keffi between (1999-2008) and (2009-2018) was assessed. Temporal condition (stressed and healthy) across the three classes supports the attribution of farmers’ reactionary adaptation and cultivation practices on the dynamic spatial vegetation distribution. Between 1999 -2018, an increase in areas with significant vegetation loss (42%), so with a decrease of -25% in areas with healthy vegetation was observed. The character of vegetation cover across the two decadal time slices, reflects landuse intensity and unsustainable farming practices. Preferences for modification of cultivation practices and changes in seed by farmers exerts positive feedbacks on vegetation cover. Higher statistical measures, 38.4% (yearly cropping) and 44% (shifting cultivation with less fallow periods) were observed in the chi-square analysis. These measures were higher than 2.0% relating to shifting cultivation with more fallow periods. While 11.6% farmers noted cultural practices as reasons for preferred cultivation methods, 48.4% farmers attributed climate as reason behind cultivation modification. This was higher than 24.4% who linked issues of tenure rights to cultivation practices. With preferences for yield- breaching strategies, the non-receding cultivation and shorter fallow practices in Keffi triggers feedback on vegetation dynamics. Evidence from this study shows that the NDVI-rainfall functional sensitivity in Keffi is plausibly dampened by effects of reactionary farm-gate level adaptation practices.
Traditional beekeeping has been playing important socio-economic roles in Ethiopia for millennia. The country is situated in northeast Africa, where ranges of major evolutionary lineages of Apis mellifera adjoin. However, studies on the classification and distribution of subspecies and lineages of honey bees in the country are partly inconsistent, either proposing multiple subspecies and lineages or a unique A. m. simensis. This study was conducted with the aim of elucidating Ethiopian honey bees in reference to African subspecies and major global lineages using wing geometric morphometrics and COI-COII mitochondrial DNA analyses. For this purpose, 660 worker bees were collected from 66 colonies representing highland, midland, and lowland zones in different locations. Both methods indicated that the samples from this study form a distinct cluster together with A. m. simensis reference. In addition, forewing venation patterns showed that most of the Ethiopian samples are separate from all reference subspecies, except A. m. simensis. Analysis of COI-COII sequences revealed five DraI haplotypes (Y2, Y1, A1, and O5’), of which one was new denoted as Y3. Moreover, centroid size strongly associated with elevation. In conclusion, the results supported that Ethiopian honey bees are distinct both at lineage and subspecies levels; however, there is an indication of lineage O in the north.
Grüne Maden schonen Umwelt : studentisches Projekt Green Grubs stellt proteinreiches Tierfutter her
(2019)
This study presents a global scale analysis of cropping intensity, crop duration and fallow land extent computed by using the global dataset on monthly irrigated and rainfed crop areas MIRCA2000. MIRCA2000 was mainly derived from census data and crop calendars from literature. Global cropland extent was 16 million km2 around the year 2000 of which 4.4 million km2 (28%) was fallow, resulting in an average cropping intensity of 0.82 for total cropland extent and of 1.13 when excluding fallow land. The lowest cropping intensities related to total cropland extent were found for Southern Africa (0.45), Central America (0.49) and Middle Africa (0.54), while highest cropping intensities were computed for Eastern Asia (1.04) and Southern Asia (1.0). In remote or arid regions where shifting cultivation is practiced, fallow periods last 3–10 years or even longer. In contrast, crops are harvested two or more times per year in highly populated, often irrigated tropical or subtropical lowlands where multi-cropping systems are common. This indicates that intensification of agricultural land use is a strategy that may be able to significantly improve global food security. There exist large uncertainties regarding extent of cropland, harvested crop area and therefore cropping intensity at larger scales. Satellite imagery and remote sensing techniques provide opportunities for decreasing these uncertainties and to improve the MIRCA2000 inventory.
The Global Irrigation Model (GIM) is used within the framework of the global hydrological model WaterGAP to calculate monthly irrigation crop water use. Results on a 0.5 degrees grid include, consumption (ICU) and, via division by irrigation efficiencies, water withdrawal (IWU). The model distinguishes up to two cropping periods of rice and non-rice crops, each grown for 150 days, using a grid of area equipped for irrigation (AEI). Historical development of AEI and fraction of area actually irrigated (AAI) was previously considered via scaling of cell-specific results with country-specific factors for each year. In this study, GIM was adapted to use the new Historical Irrigation Data set (HID) with cell-specific AEI for 14 time slices between 1900 and 2005. AEI grids were temporally interpolated, and using the optional grid of AAI/AEI, results for years 1901-2014 were generated (runs "HID-ACT"). Thus, new installation or abandonment of irrigation infrastructure in new grid cells can be represented in a spatially explicit manner. For evaluated years 1910, 1960, 1995, and 2005, ICU from HID-ACT was superior to country-specific scaled results (run "HID-ACTHIST") in representing historical development of the spatial pattern. Compared to US state-level reference data, spatial patterns were better, while country totals were not always better. For calculating the cropping periods, 30-years climate means are needed, the choice of which is relevant. Four chosen periods before 1981-2010 all resulted in considerable, pertaining changes of ICU spatial pattern, and various percent changes in country totals. This might be because of already present climate change.
Agriculture of crops provides more than 85% of the energy in human diet, while also securing income of more than 2.6 billion people. To investigate past, present and future changes in the domain of food security, water resources and water use, nutrient cycles, and land management it is required to know the agricultural land use, in particular which crop grows where and when. The current global land use or land cover data sets are based on remote sensing and agricultural census statistics. In general, these only contain one or very few classes of agricultural land use. When crop-specific areas are given, no distinction of irrigated and rainfed areas is made, whereas it is necessary to distinguish rainfed and irrigated crops, because crop productivity and water use differ significantly between them.
To support global-scale assessments that are sensitive to agricultural land use, the global data set of Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) was developed by the author. With a spatial resolution of 5 arc-minutes (approximately 9.2 km at the equator), MIRCA2000 provides for the first time, spatially explicit irrigated and rainfed crop areas separately for each of the 26 crop classes for each month of the year, and includes multi-cropping. The data set covers all major food crops as well as cotton, while the remaining crops are grouped into three categories (perennial, annual and fodder grasses). Also for the first time, crop calendars on national or sub-national level were consistently linked to annual values of harvested area at the 5 arc-minutes grid cell level, such that monthly growing areas could be computed that are representative for the time period 1998 to 2002.
The downscaling algorithm maximizes the consistency to the grid-based input data of cropland extent [Ramankutty et al., 2008], crop-specific total annual harvested area [Monfreda et al., 2008], and area equipped for irrigation [Siebert et al., 2007]. In addition to the methodology, this dissertation describes differences to other datasets and standard scaling methods, as well as some applications. For quality assessment independent datasets and newly developed quality parameters are used, and scale effects are discussed.
Supplementary Appendices document crop calendars for irrigated and rainfed crops for each of the 402 spatial units (Appendix I), data sources of harvested area and of cropping periods for irrigated crops, country by country (Appendix K), as well as data quality parameters (Appendix L, including spreadsheet files).
The paper presents a short introduction to the environmental factors, e.g. climate, geology, relief forms and soils of the study area in the southern parts of the Gongola Basin. The study area covers the high mountain range of the Tangale-Waja Uplands and the adjacent pediplain, following in the north. It is asked if the natural factors enforced former inhabitants of the area to develop special land use techniques like field terracing to ensure the essential crop production under insufficient geoecological conditions.
Our daily food consumption is slowly but surely turning into the largest environmental threat. The agricultural sector consumes 70% of the water used by humankind. The production of meat consumes enormous amounts of water compared to plants. Innovations in the area of food production are lately summarized as AgTech, agricultural technology. This encompasses all sorts of areas, ranging from drone-controlled tractors to printed hamburgers. Specifically the challenge of making use of the limited areas available in cities and maximizing crop yields has seen a recent boom in novel approaches – and quite a bit of investor finance.
Climate change imposes severe stress on European forests, with forest degradation already visible in several parts of Europe. Thus adaptation of forestry applications in Mediterranean areas and central Europe is necessary. Proactive forestry management may include the planting of Mediter- ranean oak species in oak-bearing Central European regions. Five replicate common gardens of Greek and Italian provenances of Quercus ilex, Q. pubescens and Q. frainetto seedlings (210 each per plantation) were established in Central Italy, NE Greece (two) and Southern Germany (two, including Q. robur) to assess their performance under different climate conditions. Climate and soil data of the plantation sites are given and seedling establishment was monitored for survival and morphological parameters. After 3 years (2019) survival rates were satisfactory in the German and Italian sites, whereas the Greek sites exerted extremely harsh conditions for the seedlings, including extreme frost and drought events. In Germany, seedlings suffered extreme heat and drought periods in 2018 and 2019 but responded well. Provenances were ranked for each country for their performance after plan- tation. In Greece and Italy, Q. pubescens was the best performing species. In Germany, Q. pubescens and Q. robur performed best. We suggest that Greek or Italian provenances of Q. pubescens may be effectively used for future forestation purposes in Central Europe. For the establishment of Quercus plantations in Northern Greece, irrigation appears to be a crucial factor in seedling establishment.