Sasakian quiver gauge theory on the Aloff-Wallach space X1,1X_{1,1}

We consider the SU(3)-equivariant dimensional reduction of gauge theories on spaces of the form $M^d \times X_{1,1}$ with d-dimensional Riemannian manifold $M^d$ and the Aloff-Wallach space $X_{1,1}$= SU(3)/U(1) endowed with its Sasaki-Einstein structure. The condition of SU(3)-equivariance of vector bundles, which has already occurred in the studies of Spin(7)-instantons on cones over Aloff-Wallach spaces, is interpreted in terms of quiver diagrams, and we construct the corresponding quiver bundles, using (parts of) the weight diagram of SU(3). We consider three examples thereof explicitly and then compare the results with the quiver gauge theory on $Q_3$ =SU(3)/(U(1) x U(1)), the leaf space underlying the Sasaki-Einstein manifold $X_{1,1}$. Moreover, we study instanton solutions on the metric cone $C(X_{1,1})$ by evaluating the Hermitian Yang-Mills equation. We briefly discuss some features of the moduli space thereof, following the main ideas of a treatment of Hermitian Yang-Mills instantons on cones over generic Sasaki-Einstein manifolds in the literature.Comment: 25 page

Instantons on Calabi-Yau and hyper-Kähler cones

The instanton equations on vector bundles over Calabi-Yau and hyper-Kähler cones can be reduced to matrix equations resembling Nahm’s equations. We complement the discussion of Hermitian Yang-Mills (HYM) equations on Calabi-Yau cones, based on regular semi-simple elements, by a new set of (singular) boundary conditions which have a known instanton solution in one direction. This approach extends the classic results of Kronheimer by probing a relation between generalised Nahm’s equations and nilpotent pairs/tuples. Moreover, we consider quaternionic instantons on hyper-Kähler cones over generic 3-Sasakian manifolds and study the HYM moduli spaces arising in this set-up, using the fact that their analysis can be traced back to the intersection of three Hermitian Yang-Mills conditions. © 2017, The Author(s)

Implementation and improvement of an unmanned aircraft system for precision farming purposes

Precision farming (PF) is an agricultural concept that accounts for within-field variability by gathering spatial and temporal information with modern sensing technology and performs variable and targeted treatments on a smaller scale than field scale. PF research quickly recognized the possible benefits unmanned aerial vehicles (UAVs) can add to the site-specific management of farms. As UAVs are flexible carrier platforms, they can be equipped with a range of different sensing devices and used in a variety of close-range remote sensing scenarios. Most frequently, UAVs are utilized to gather actual in-season canopy information with imaging sensors that are sensitive to reflected electro-magnetic radiation in the visual (VIS) and near-infrared (NIR) spectrum. They are generally used to infer the crops biophysical and biochemical parameters to support farm management decisions. A current disadvantage of UAVs is that they are not designed to interact with their attached sensor payload. This leads to the need of intensive data post-processing and prohibits the possibility of real-time scenarios, in which UAVs can directly transfer information to field machinery or robots. In consequence, this thesis focused on the development of a smart unmanned aircraft system (UAS), which in the thesis context was regarded as a combination of a UAV carrier platform, an on-board central processing unit for sensor control and data processing, and a remotely connected ground control station. The ground control station was supposed to feature the possibility of flight mission control and the standardized distribution of sensor data with a sensor data infrastructure, serving as a data basis for a farm management information system (FMIS). The UAS was intended to be operated as a flexible monitoring tool for in-season above-ground biomass and nitrogen content estimation as well as crop yield prediction. Therefore, the selection, development, and validation of appropriate imaging sensors and processing routines were key parts to prove the UAS usability in PF scenarios. The individual objectives were (i) to implement an advanced UAV for PF research, providing the possibilities of remotely-controlled and automatic flight mission execution, (ii) to improve the developed UAV to a UAS by implementing sensor control, data processing and communication functionalities, (iii) to select and develop appropriate sensor systems for yield prediction and nitrogen fertilization strategies, (iv) to integrate the sensor systems into the UAS and to test the performance in example use cases, and (v) to embed the UAS into a standardized sensor data infrastructure for data storage and usage in PF applications. This work demonstrated the successful development of a custom rotary-wing UAV carrier platform with an embedded central processing unit. A modular software framework was developed with the ability to control any kind of sensor payload in real-time. The sensors can be triggered and their measurements are retrieved, fused together with the carriers navigation information, logged and broadcasted to a ground control station. The setup was used as basis for further research, focusing on information generation by sophisticated data processing. For a first application of predicting the grain yield of corn (Zea mays L.), a simple RGB camera was selected to acquire a set of aerial imagery of early- and mid-season corn crops. Orthoimages were processed with different ground resolutions and were computed to simple vegetation indices (VI) for a crop/non-crop classification. In addition to that, crop surface models (CSMs) were generated to estimate the crop heights. Linear regressions were performed with the corn grain yield as dependent variable and crop height and crop coverage as independent variable. The analysis showed the best prediction results of a relative root mean square error (RMSE) of 8.8 % at mid-season growth stages and ground resolutions of 4 cm px −1 . Moreover, the results indicate that with on-going canopy closure and homogeneity accounting for high ground resolutions and crop/non-crop classification becomes less and less important. For the estimation of above-ground biomass and nitrogen content in winter wheat (Triticum aestivum L.) a programmable multispectral camera was developed. It is based on an industrial multi-sensor camera, which was equipped with bandpass filters to measure four narrow wavelength bands in the so-called red-edge region. This region is the transition zone in between the VIS and NIR spectrum and known to be sensitive to leaf chlorophyll content and the structural state of the plant. It is often used to estimate biomass and nitrogen content with the help of the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). The camera system was designed to measure ambient light conditions during the flight mission to set appropriate image acquisition times, which guarantee images with high contrast. It is fully programmable and can be further developed to a real-time image processing system. The analysis relies on semi-automatic orthoimage processing. The NDVI orthoimages were analyzed for the correlation with biomass by means of simple linear regression. These models proved to estimate biomass for all measurements with RMSEs of 12.3 % to 17.6 %. The REIP was used to infer nitrogen content and showed good results with RMSEs of 7.6 % to 11.7 %. Both NDVI and REIP were also tested for the in-season grain yield prediction ability (RMSE = 9.012.1 %), whereas grain protein content could be modeled with the REIP, except for low-fertilized wheat plots. The last part of the thesis comprised the development of a standardized sensor data infrastructure as a first step to a holistic farm management. The UAS was integrated into a real-time sensor data acquisition network with standardized data base storage capabilities. The infrastructure was based on open source software and the geo-data standards of the Open Geospatial Consortium (OGC). A prototype implementation was tested for four exemplary sensor systems and proved to be able to acquire, log, visualize and store the sensor data in a standardized data base via a sensor observation service on-the-fly. The setup is scalable to scenarios, where a multitude of sensors, data bases, and web services interact with each other to exchange and process data. This thesis demonstrates the successful prototype implementation of a smart UAS and a sensor data infrastructure, which offers real-time data processing functionality. The UAS is equipped with appropriate sensor systems for agricultural crop monitoring and has the potential to be used in real-world scenarios.Precision farming (PF) ist ein landwirtschaftliches Konzept, das die Variabilität innerhalb eines Feldes berücksichtigt, indem es mit Hilfe moderner Sensortechnologien räumliche und zeitliche Bestandsinformationen sammelt. Dadurch ist PF in der Lage, gezielte teilflächenspezifische Anwendungen innerhalb eines Feldes durchzuführen. Die Forschung im Bereich von PF hat früh die potenziellen Vorzüge von kleinen Luftfahrzeugen, sogenannten unmanned aerial vehicles (UAVs), für die teilflächenspezifische Bewirtschaftung erkannt. Da UAVs flexible Lastenträger darstellen, können sie mit den verschiedensten Sensoren ausgestattet und in einer Vielzahl von fernerkundlichen Anwendungsfällen in der Landwirtschaft genutzt werden. Dabei werden sie am häufigsten mit bildgebenden Sensoren eingesetzt, um aktuelle Informationen über den Pflanzenbestand in der Vegetationsperiode zu liefern. Die eingesetzten Sensoren sind dabei meist zur Messung elektromagnetischer Strahlung im sichtbaren (VIS) und nahen infraroten (NIR) Bereich ausgelegt. Im Allgemeinen werden sie dazu benutzt auf biophysikalische und biochemische Eigenschaften der Nutzpflanzen zu schließen und damit die Entscheidungsprozesse in der Bestandsführung zu unterstützen. Ein aktueller Nachteil der UAVs ist, dass sie nicht dafür gebaut werden um mit ihrer Nutzlast zu interagieren. Das führt zu einem Bedarf an erheblicher Datennachverarbeitung und verhindert Echtzeitszenarios, in denen UAVs Informationen direkt an Feldmaschinen und Roboter senden können. Aus diesem Grund konzentrierte sich diese Dissertation auf die Entwicklung eines intelligenten fliegenden Systems, eines sogenannten unmanned aircraft system (UAS), welches im Kontext dieser Dissertation als eine Kombination aus UAV Trägerplattform, zentralem Computer zur Sensorsteuerung und Datenverarbeitung, sowie einer entfernt verbundenen Bodenstation betrachtet wurde. Die Bodenstation war zur Flugüberwachung und zur standardisierten Verteilung der Sensordaten über eine Sensordateninfrastruktur bestimmt. Die Sensordateninfrastruktur diente als Basis eines sogenannten farm management information system (FMIS), das die Verwaltung und Bewirtschaftung eines landwirtschaftlichen Betriebs mit Methoden der Informatik unterstützt. Das UAS sollte als flexibles Aufklärungswerkzeug eingesetzt werden, um Schätzungen von Biomasse, Stickstoffgehalt und erwartetem Ertrag während der Vegetationsperiode zu liefern. Daher war die Auswahl, Entwicklung und Validierung geeigneter bildgebender Sensoren und zugehöriger Verarbeitungsmethoden ein zentraler Bestandteil, um die Nutzbarkeit von UAS im PF zu belegen. Die einzelnen Ziele waren (i) der Aufbau eines UAVs für das PF, das sich fernsteuern und automatisch nach Wegpunkten fliegen lässt, (ii) die Erweiterung des UAVs zum UAS, durch die Entwicklung einer zentralen Sensorsteuerung, Datenverarbeitung und Kommunikationsfähigkeit, (iii) die Auswahl und Entwicklung geeigneter Sensorsysteme zur Ertragsschätzung und Stickstoffdüngung, (iv) der Einbau der Sensorsysteme in das UAS und deren Validierung in Beispielanwendungen und (v) die Integration des UAS in eine standardisierte Sensordateninfrastruktur um die Daten für PF-Anwendungen abzuspeichern und verfügbar zu machen. Diese Dissertation präsentiert eine erfolgreiche Entwicklung eines Drehflügler-UAVs mit zentraler Steuereinheit. Dazu passend wurde eine modulare Software entwickelt, die jegliche Sensorik in Echtzeit steuern kann. Messungen können ausgelöst, empfangen, mit den Navigationsdaten des UAVs fusioniert, gespeichert und an eine Bodenstation gesendet werden. Das UAV diente als Basis weiterer Forschung, die die Verarbeitung von Sensordaten zur Erzeugung pflanzenbaulicher Information zum Ziel hatte. Eine erste Anwendung war die Ertragsschätzung von Körnermais (Zea mays L.). Eine einfache RGB Kamera wurde dazu benutzt Luftbilder von Maispflanzen in frühen und mittleren Wachstumsstadien aufzunehmen. Daraus wurden Orthophotos mit unterschiedlichen Bodenauflösungen erzeugt und zu einfachen Vegetationsindizes (VIs) zur Klassifizierung der Pixel als Pflanze oder nicht Pflanze weiterverarbeitet. Zusätzlich wurden Oberflächenmodelle des Pflanzenbestands, sogenannte crop surface models (CSMs), erzeugt, um die Pflanzenhöhen abzuschätzen. Mit dem Ertrag als abhängige Variable, sowie Pflanzenhöhe und Bedeckungsgrad als unabhängige Variablen, wurden lineare Regressionen durchgeführt. Die Analyse ergab beste Vorhersagen mit geringsten Standardabweichungen (SD) von 8.8 % für die Messungen in mittleren Wachstumsstadien mit einer Bodenauflösung von 4 cm px −1 . Darüber hinaus zeigten die Ergebnisse, dass hohe Bodenauflösungen und Klassifizierung mit fortschreitendem Reihenschluss und sich angleichendem Pflanzenbestand immer unwichtiger werden. Zur Schätzung von Biomasse und Stickstoffgehalt von Winterweizen (Triticum aestivum L.) wurde eine programmierbare multispektrale Kamera entwickelt. Sie basiert auf einer Industriekamera mit mehreren Sensorköpfen, von denen jeder mit einem Bandpassfilter bestückt wurde. Die Kamera misst vier schmalbandige Wellenlängen im Übergangsbereich vom VIS- zum NIR-Spektrum, der sogenannten roten Kante red-edge. Dieser Bereich ist dafür bekannt Rückschlüsse auf den Chlorophyllgehalt der Blätter und die Pflanzenstruktur zuzulassen. Mit Hilfe der Formeln zur Berechnung des normalized difference vegetation index (NDVI) und des red-edge inflection point (REIP) wird dieser Bereich oft zur Schätzung von Biomasse und Stickstoffgehalt genutzt. Das Kamerasystem wurde darüber hinaus entworfen, die Lichtverhältnisse während des Fluges zu messen und geeignete Belichtungszeiten festzulegen, um Bilder mit hohem Kontrast zu erzeugen. Die Kamera ist komplett programmierbar und kann zur Echtzeitbildverarbeitung weiterentwickelt werden. Die Untersuchung basiert auf der teilautomatisierten Erzeugung von Orthophotos. Die NDVI Orthophotos wurden mit Hilfe einer einfachen linearen Regression auf ihre Korrelation mit Biomasse getestet. Sie zeigten über alle Messzeitpunkte, dass sie Biomasse mit Standardabweichungen von 12.3 % bis 17.6 % schätzen konnten. Der REIP wurde zur Stickstoffgehaltschätzung heran gezogen und zeigte gute Ergebnisse mit Standardabweichungen von 7.6 % bis 11.7 %. Beide, NDVI und REIP, wurden auch auf ihre Vorhersagefähigkeit des Kornertrags getestet (SD = 9.012.1 %). Überdies konnte, außer in gering gedüngten Parzellen, der Proteingehalt im Korn mit dem REIP abgeschätzt werden. Der letzte Teil der Dissertation beinhaltete die Entwicklung einer standardisierten Sensordateninfrastruktur als Schritt hin zu einem umfassenden Bewirtschaftungskonzept, das möglichst viele Faktoren berücksichtigt. Das UAS wurde in ein echtzeitbasiertes Sensordatennetzwerk integriert, das Sensordaten erfassen und standardisiert in Datenbanken ablegen kann. Die Infrastruktur basiert auf quellcodeoffener open source software und den Geodatenstandards des Open Geospatial Consortiums (OGC). Eine erste Umsetzung einer solchen Infrastruktur wurde mit vier Beispielsensoren getestet und zeigte, dass Sensordaten in Echtzeit erfasst, lokal gespeichert, visualisiert und mittels eines Sensordatendienstes (sensor observation service) standardisiert in einer Datenbank gespeichert werden konnten. Die Umsetzung ist auf eine beliebige Anzahl von Sensoren und Diensten erweiterbar und ermöglicht ihnen den Austausch und die Verarbeitung von Daten. Diese Dissertation zeigt eine erfolgreiche Umsetzung eines intelligenten UAS und einer Sensordateninfrastruktur, die Sensordatenverarbeitung in Echtzeit anbietet. Das UAS ist mit Sensoren ausgestattet, die zur landwirtschaftlichen Beurteilung von Pflanzenbeständen geeignet sind und zeigt Potential auch unter realistischen Bedingungen eingesetzt werden zu können

Sasakian quiver gauge theories and instantons on cones over round and squashed seven-spheres

We study quiver gauge theories on the round and squashed seven-spheres, and orbifolds thereof. They arise by imposing $G$-equivariance on the homogeneous space $G/H=\mathrm{SU}(4)/\mathrm{SU}(3)$ endowed with its Sasaki-Einstein structure, and $G/H=\mathrm{Sp}(2)/\mathrm{Sp}(1)$ as a 3-Sasakian manifold. In both cases we describe the equivariance conditions and the resulting quivers. We further study the moduli spaces of instantons on the metric cones over these spaces by using the known description for Hermitian Yang-Mills instantons on Calabi-Yau cones. It is shown that the moduli space of instantons on the hyper-Kahler cone can be described as the intersection of three Hermitian Yang-Mills moduli spaces. We also study moduli spaces of translationally invariant instantons on the metric cone $\mathbb{R}^8/\mathbb{Z}_k$ over $S^7/\mathbb{Z}_k$.Comment: 44 pages; v2: minor changes, reference added; Final version to appear in Nuclear Physics

Gaussian Process Modeling of In-Season Physiological Parameters of Spring Wheat Based on Airborne Imagery from Two Hyperspectral Cameras and Apparent Soil Electrical Conductivity

The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.75–0.85, RPDP=2.0–2.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2>0.8, RPDP>2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion.publishedVersio

A Semi-Autonomous Multi-Vehicle Architecture for Agricultural Applications

The ageing population, climate change, and labour shortages in the agricultural sector are driving the need to reevaluate current farming practices. To address these challenges, the deployment of robot systems can help reduce environmental footprints and increase productivity. However, convincing farmers to adopt new technologies poses difficulties, considering economic viability and ease of use. In this paper, we introduce a management system based on the Robot Operating System (ROS) that integrates heterogeneous vehicles (conventional tractors and mobile robots). The goal of the proposed work is to ease the adoption of mobile robots in an agricultural context by providing to the farmer the initial tools needed to include them alongside the conventional machinery. We provide a comprehensive overview of the system’s architecture, the control laws implemented for fleet navigation within the field, the development of a user-friendly Graphical User Interface, and the charging infrastructure for the deployed vehicles. Additionally, field tests are conducted to demonstrate the effectiveness of the proposed framework.publishedVersio

NORNE, a process-based grass growth model accounting for within-field soil variation using remote sensing for in-season corrections

A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.publishedVersio

Minkowski4_4 ×\times S2S^2 solutions of IIB supergravity

We classify $\mathcal N = 2$ Minkowski$_4$ solutions of IIB supergravity with an $SU(2)_R$ symmetry geometrically realized by an $S^2$-foliation in the remaining six dimensions. For the various cases of the classification, we reduce the supersymmetric system of equations to PDEs. These cases often accommodate systems of intersecting branes and half-maximally supersymmetric AdS$_{5,6,7}$ solutions when they exist. As an example, we analyze the AdS$_6$ case in more detail, reducing the supersymmetry equations to a single cylindrical Laplace equation. We also recover an already known linear dilaton background dual to the $(1,1)$ Little String Theory (LST) living on NS5-branes, and we find a new Minkowski$_5$ linear dilaton solution from brane intersections. Finally, we also discuss some simple Minkowski$_4$ solutions based on compact conformal Calabi-Yau manifolds.Comment: 43 pages, 1 appendix. v2: typos corrected, references adde

Kartlegging av Presisjonshektaren – Hvordan kartlegge et skifte som skal presisjonsdyrkes?

Som en del av prosjektet Presisjonshektaren ved NIBIO Senter for Presisjonsjordbruk har det gjennom 2021 og 2022 blitt utført et demonstrasjonsforsøk hvor det ble prøvd ulike verktøy til jordkartlegging på et tilsynelatende homogent skifte gjennom en sesong. Denne rapporten oppsummerer dette arbeidet ved å beskrive resultatet fra kartleggingen og ser på sammenhenger mellom kartleggingsresultater og avlingsnivå. Resultatene viser at også innenfor et skifte som ser homogent ut, finnes det en god del variasjon på grunn av topografi (Figur 2) og forskjeller i jordegenskaper (Figur 8 - Figur 11) som kan føre til en betydelig variasjon i avling og proteininnhold (Figur 20 og Figur 21). Dette viser at dersom man skal kartlegge jorda som basis for presisjonstildeling av innsatsfaktorer, kan det være verdt å vurdere å ta jordprøver noe tettere enn den generelle anbefalingen. I dette forsøket viste høsteprøvene et spenn i kornavling tilsvarende 560-800 kg per daa minimum. Proteininnholdet varierte fra 11,2 til 13 %. Siden dette forsøket ble gjennomført som et ettårig forsøk uten gjentak, er det ikke mulig å konkludere med noen årsakssammenheng mellom de forskjellige variablene som er målt. Det er likevel observert interessante samvariasjoner mellom forskjellige typer kartlegging. Det kan være interessant å gjøre mer detaljerte forsøk for å undersøke disse nærmere på et senere tidspunkt. Ved konvensjonell, uniform dyrkingspraksis, vil hele skiftet behandles likt ved for eksempel gjødsling og jordarbeiding. Dette fører til at ikke alle områder blir behandlet etter behov og potensiale. Ved homogen gjødsling vil noen områder få mer gjødsel enn nødvendig. Dette reduserer utnyttelsen av innsatsfaktorene og kan føre til økt miljøbelastning og kostnader. Samtidig vil andre områder få for lite gjødsel, noe som kan begrense avlingspotensialet og produksjonseffektiviteten. Omfanget av variasjonen i dette forsøket illustrerer derfor behovet for stedspesifikk behandling. Med høye priser på innsatsfaktorer er det et stort innsparingspotensial i å behandle de ulike områdene ut fra behov og potensiale. Det finnes mange muligheter for kartlegging av åkeren, og gårdbrukeren møter mange ulike tilbud. Det er svært viktig at gårdbrukeren før hen benytter seg av et slikt tilbud ber om dokumentasjon på kvalitet og kalibrering for norske forhold. Ved omfattende kartlegging genereres også store mengder data som må ivaretas på en god måte for å kunne være til nytte for bonden. Som en del av arbeidet med Presisjonshektaren er det også utarbeidet en oversikt over forskjellige såkalte «Farm Management Information Systems» (FMIS) – informasjonsstyringssystemer for gårdsbruk (NIBIO Rapport – FMIS for norske gårdbrukere).) Et ettårig forsøk slik som det som er gjennomført her gir ikke muligheter for å konkludere om hvilken kartleggingsmetode som egner seg best. Resultatene og erfaringen fra det første året med forsøk på ‘Presisjonshektaren’ viser behovet for utvidet forskning på praktisk anvendelse av metodene for kartlegging for å prøve metodene gjennom flere sesonger og på ulike plasser for å også dekke variasjon i jordtype og klimatiske forhold. NIBIO driver med en rekke spennende forsøk innom presisionslandbruk i grensesnittet mellom agronomi, jordfag, plantedyrking og teknologi og dette blir også tema i nye forsøk i tida framover. Følg med på NIBIO’s aktivitet innenfor fagområdet: www.nibio.no https://precisionag.no/nb/hjem/Kartlegging av Presisjonshektaren – Hvordan kartlegge et skifte som skal presisjonsdyrkes?publishedVersio