ABSTRACT:High accuracy with real-time positioning of moving objects has been considered a standard task of engineering geodesy for 10 to 15 years. An absolute positioning accuracy of 1-3 cm is generally possible worldwide and is further used in many areas of machine guidance (machine control and guidance), and farming (precision farming) as well as for various special applications (e.g. railway trolley, mining, etc.). The cost of the measuring instruments required for the use of geodetic L1/L2 receivers with a local reference station amounts to approximately USD 30,000 to 50,000. Therefore, dual frequency RTK GNSS receivers are not used in the mass market.Affordable GPS/GNSS modules have already reached the mass market in various areas such as mobile phones, car navigation, the leisure industry, etc. Kinematic real-time positioning applications with centimetre or decimetre levels could also evolve into a mass product. In order for this to happen, the costs for such systems must lie between USD 1,000 to 2,000. What exactly low-cost means is determined by the precise specifications of the given individual application.Several university studies in geodesy focus on the approach of high-accuracy positioning by means of single frequency receivers for static applications [e.g. GLABSCH et. al. 2009, SCHWIEGER and GLÄSER 2005, ALKAN 2010, REALINI et. al. 2010, KORTH and HOFMANN 2011. Although intelligent approaches have been developed that compute a trajectory in the post-processing mode [REALINI et. al., 2010], at present, there are only a very few GNSS Low-Cost Systems that enable real-time processing.This approach to precise position determination by means of the computation of static raw data with single frequency receivers is currently being explored in a research project at the Beuth Hochschule für Technik Berlin -and is being further developed for kinematic applications. The project is embedded in the European Social Fund. It is a follow-up project in the area of static positioning with single GNSS frequency receivers [KORTH and HOFMANN, 2011]. MOTIVATIONThe exact positioning of moving objects within an accuracy range of a few centimetres has been possible through geodetic measuring sensors for approximately 15-20 years. With this, one can distinguish between the systems for 3D-capture in terrestrial (Total Stations or IMUs) and global processes (GNSS). Nowadays, many applications for exact positioning utilise geodetic RTK GNSS receivers. GPS single frequency receivers with or without code corrections and with lower accuracy standards are used for this. Today and as a rule, kinematic applications at the centimetre level use geodetic 2 frequency receivers for the determination of a 3D-trajectory. Many GNSS applications are not realisable due to the high cost and the heavy weight of the measuring equipment. Therefore today, the usage of RTK GNSS primarily limits itself to applications in the areas of the machine control and guidance as well as Precision Farming. At the Beuth Hochschule für Technik Berlin -University...

Weather derivatives are considered a promising agricultural risk management tool. Station-based meteorological indices typically provide the data underlying these instruments. However, the main shortcoming of these weather derivatives is an imperfect correlation between the weather index and the yield of the insured crop, called basis risk. This paper considers three available remotely sensed vegetation health (VH) indices, namely, the vegetation condition index (VCI), the temperature condition index (TCI), and the vegetation health index (VHI), as indices for weather derivatives in a German case study. We investigated the correlation and period of highest correlation with winter wheat yield. Moreover, we analyzed whether the use of remotely sensed VH indices for weather derivatives can reduce basis risk and thus improve the performance of weather derivatives. The two commonly used meteorological indices, precipitation and temperature sums, were employed as benchmarks. Quantile regression and index value simulation were used for the design and pricing of the weather derivatives. The analysis for the selected farms and corresponding counties in northeastern Germany revealed that, on average, the VHI resulted in the highest correlation with winter wheat yield, and VHI-based weather derivatives were also superior in terms of the hedging effectiveness. The total periods of the highest correlations ranged from the beginning of April to the end of July. VHI- and VCI-based weather derivatives led to statistically significant reductions of basis risk, compared to the benchmarks. Our results indicate that the VHI-based weather derivatives can be useful alternatives to meteorological indices, especially in regions with sparser weather station networks.

Fusarium subglutinans is a plant pathogenic fungus infecting cereal grain crops. In 2011, the species was divided in Fusarium temperatumsp. nov. and F. subglutinans sensu stricto. In order to determine the occurrence and significance of F. temperatum and F. subglutinans on maize, a monitoring of maize ears and stalks was carried out in Germany in 2017 and 2018. Species identification was conducted by analysis of the translation elongation factor 1α (TEF-1α) gene. Ninety-four isolates of F. temperatum and eight isolates of F. subglutinans were obtained during two years of monitoring from 60 sampling sites in nine federal states of Germany. Inoculation of maize ears revealed a superior aggressiveness for F. temperatum, followed by Fusarium graminearum, Fusarium verticillioides, and F. subglutinans. On maize stalks, F. graminearum was the most aggressive species while F. temperatum and F. subglutinans caused only small lesions. The optimal temperature for infection of maize ears with F. temperatum was 24 °C and 21 °C for F. subglutinans. All strains of F. temperatum and F. subglutinans were pathogenic on wheat and capable to cause moderate to severe head blight symptoms. The assessment of mycotoxin production of 60 strains of F. temperatum cultivated on rice revealed that all strains produced beauvericin, moniliformin, fusaric acid, and fusaproliferin. The results demonstrate a higher prevalence and aggressiveness of F. temperatum compared to F. subglutinans in German maize cultivation areas.

Restrictive irrigation water policies established due to e.g. environmental concerns or water scarcity appear to result in declining farm income and arising risk exposure in terms of yield uncertainty. With this in mind, we investigate the potential of index-based weather insurance, which is also known as weather derivatives, to cope with the economic disadvantages for farmers resulting from a reduction in water quotas and increased water prices. By means of a whole-farm risk programming approach, we systematically compare crop portfolios without and with the possibility of purchasing standardized weather derivatives based on precipitation and temperature indices. In doing so, we allow for crop diversification as well as water reallocation between crops. Thus, overcoming some of the shortcomings inherent to previous studies in this strand of research. In an application to a representative cash crop farm in northern Germany, we found that the use of weather derivatives offsets the loss in the farmer's certainty equivalent resulting from moderate reductions in water quotas and water price increases. Our results also indicate that weather derivatives have the potential to substantially alter farm plans and the optimal irrigation water demand. Far reaching environmental implications might be the consequence which require further attention and careful consideration by policymakers.

We use a business management game to investigate how a pesticide tax and a green nudge affect crop, tillage and pesticide decisions for a virtual farm. Results from German farmers reveal that both policies can reduce pesticide applications. The pesticide tax involves a substantial profit loss. Unlike in the green nudge scenario, participants under pesticide tax adjust their cropping and tillage strategies. We compare farmers’ decisions to those made by a mathematical programming model. Assuming profit maximisation would overestimate farmers’ response to the tax and underestimate the effectiveness of the nudge.

Olive oil yields fluctuate strongly due to their dependence on sufficient precipitation. An interesting option to hedge the yield risk in olive cultivation could be satellite‐based weather index insurance. Therefore, we implement index insurance as a hedging alternative for non‐irrigated olive groves using MODerate‐resolution Imaging Spectroradiometer (MODIS) satellite data. For this purpose, we focus on the Spanish region of Andalusia, given its importance in olive production at the international level. We calculate three satellite indices: the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI) and the Vegetation Health Index (VHI). Meteorological indices related to temperature and precipitation are used as benchmarks. Firstly, we estimate the periods that have the greatest influence on the critical vegetative phase of olives, which extends from March to September. Based on the indices, insurance contracts are designed using a copula approach, which is then employed to evaluate their hedging effectiveness. On average, the hedging effectiveness of VCI‐, VHI‐ and TCI‐based weather index insurance contracts amounts to 38 per cent, 38 per cent and 29 per cent, respectively. Moreover, VCI‐ and VHI‐based weather index insurance contracts outperform traditional weather index insurance contracts based on precipitation (by 29 per cent) and temperature (by 16 per cent) indices.

Despite numerous studies on farmers' responses to changing irrigation water policies, uncertainties remain about the potential of water pricing schemes and water quotas to reduce irrigation. Thus far, policy impact analysis is predominantly based upon rational choice models that assume behavioral assumptions, such as a perfectly rational profit‐maximizing decision maker. Also, econometric techniques are applied which could lack internal validity due to uncontrolled field data. Furthermore, such techniques are not capable of identifying ill‐designed policies prior to their implementation. With this in mind, we apply a business simulation game for ex ante policy impact analysis of irrigation water policies at the farm level. Our approach has the potential to reveal the policy‐induced behavioral change of the participants in a controlled environment. To do so, we investigate how real farmers from Germany, in an economic experiment, respond to a water pricing scheme and a water quota intending to reduce irrigation. In the business simulation game, the participants manage a “virtual” cash‐crop farm for which they make crop allocation and irrigation decisions during several production periods, while facing uncertain product prices and weather conditions. The results reveal that a water quota is able to reduce mean irrigation applications, while a water pricing scheme does not have an impact, even though both policies exhibit equal income effects for the farmers. However, both policies appear to increase the variation of irrigation applications. Compared to a perfectly rational profit‐maximizing decision maker, the participants apply less irrigation on average, both when irrigation is not restricted and when a water pricing scheme applies. Moreover, the participants' risk attitude affects the irrigation decisions.

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