Audun Korsæth

Head of Department/Head of Research

(+47) 404 82 560
audun.korsaeth@nibio.no

Place
Apelsvoll

Visiting address
Nylinna 226, 2849 Kapp

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Abstract

Nitrous oxide (N2O) emissions from cultivated soils correlate positively with the amount of N-fertilizer applied, but a large proportion of the annual N2O emission occurs outside the cropping season, potentially blurring this correlation. We measured the effect of split-N application (total N addition varying from 0 to 220 kg N ha−1) on N2O emissions in a spring wheat plot trial in SE Norway from the time of split-N application until harvest, and during the following winter and spring thaw period. N2O emissions were largest in the two highest N-levels, whereas yield-scaled emission (N2O intensity) was highest in the 0 N treatment. Nitrogen yield increased by 23% when adding 80 kg N ha−1 compared to adding 40 kg N ha−1 as split application, while corresponding N2O emissions were reduced by 16%. No differences in measured emissions between the N-fertilization levels were observed during the winter period or during spring thaw. Measurements of soil air composition below the snow pack revealed that N2O production continued throughout winter as the concentration in the soil air increased from 0.37 to 30.0 µL L−1 N2O over the 3 months period with continuous snow cover. However, only 7–28% of the N2O emitted during spring thaw could be ascribed to accumulated N2O, indicating de novo production of N2O in the thawing soil. The direct effect of split-N fertilizer rate on N2O emissions in sub-boreal cereal cropping was limited to the first 15–21 days after N-addition.

Abstract

In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are within a predefined radius from the robot. A central part of the approach is a new Heading Weight Function (HWF), in which the beams within the aperture angle of the LiDAR are virtually weighted in order to generate the best trajectory candidate for the robot. The HWF is designed to find a solution also in the case of a local-minima situation. The function is coupled with the robot’s controller in order to provide both linear and angular velocities. We tested the method both by simulations in a digital environment with a range of different static obstacles, and in a real, experimental environment including static and dynamic obstacles. The results showed that when utilizing the novel HWF, the robot was able to navigate safely toward the target while avoiding all obstacles included in the tests. Our findings thus show that it is possible for a robot to navigate safely in a populated environment using this method, and that sufficient efficiency in navigation may be obtained without basing the method on a global planner. This is particularly promising for navigation challenges occurring in unknown environments where models of the world cannot be obtained.

Abstract

The key factor for autonomous navigation is efficient perception of the surroundings,while being able to move safely from an initial to a final point. We deal in this paper with a wheeled mobile robot working in a GPS-denied environment typical for a greenhouse. The Hector Simultaneous Localization and Mapping (SLAM) approach is used in order to estimate the robots’ pose using a LIght Detection And Ranging (LIDAR) sensor. Waypoint following and obstacle avoidance are ensured by means of a new artificial potential field (APF) controller presented in this paper. The combination of the Hector SLAMand the APF controller allows themobile robot to performperiodic tasks that require autonomous navigation between predefined waypoints. It also provides themobile robot with a robustness to changing conditions thatmay occur inside the greenhouse, caused by the dynamic of plant development through the season. In this study, we show that the robot is safe to operate autonomously with a human presence, and that in contrast to classical odometrymethods, no calibration is needed for repositioning the robot over repetitive runs. We include here both hardware and software descriptions, as well as simulation and experimental results.

Abstract

Today’s modern precision agriculture applications have a huge demand for data with high spatial and temporal resolution. This leads to the need of unmanned aerial vehicles (UAV) as sensor platforms providing both, easy use and a high area coverage. This study shows the successful development of a prototype hybrid UAV for practical applications in precision agriculture. The UAV consists of an off-the-shelf fixed-wing fuselage, which has been enhanced with multi-rotor functionality. It was programmed to perform pre-defined waypoint missions completely autonomously, including vertical take-off, horizontal flight, and vertical landing. The UAV was tested for its return-to-home (RTH) accuracy, power consumption and general flight performance at different wind speeds. The RTH accuracy was 43.7 cm in average, with a root-mean-square error of 39.9 cm. The power consumption raised with an increase in wind speed. An extrapolation of the analysed power consumption to conditions without wind resulted in an estimated 40 km travel range, when we assumed a 25 % safety margin of remaining battery capacity. This translates to a maximal area coverage of 300 ha for a scenario with 18 m/s airspeed, 50 minutes flight time, 120 m AGL altitude, and a desired 70 % of image side-lap and 85 % forward-lap. The ground sample distance with an in-built RGB camera was 3.5 cm, which we consider sufficient for farm-scale mapping missions for most precision agriculture applications.

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Abstract

The aim of the study was to explore whether and how intensification would contribute to more environmentally friendly dairy production in Norway. Three typical farms were envisaged, representing intensive production strategies with regard to milk yield both per cow and per hectare in the three most important regions for dairy production in Norway. The scores on six impact categories for produced milk and meat were compared with corresponding scores obtained with a medium production intensity at a base case farm. Further, six scenario farms were derived from the base case. They were either intensified or made more extensive with regard to management practices that were likely to be varied and implemented under northern temperate conditions. The practices covered the proportion and composition of concentrates in animal diets and the production and feeding of forages with different energy concentration. Processes from cradle to farm gate were incorporated in the assessments, including on-farm activities, capital goods, machinery and production inputs. Compared to milk produced in a base case with an annual yield of 7250 kg energy corrected milk (ECM) per cow, milk from farms with yields of 9000 kg ECM or higher, scored better in terms of global warming potential (GWP). The milk from intensive farms scored more favourably also for terrestrial acidification (TA), fossil depletion (FD) and freshwater eutrophication (FE). However, this was not in all cases directly related to animal yield, but rather to lower burden from forage production. Production of high yields of energy-rich forage contributed substantially to the better scores on farms with higher-yielding animals. The ranking of farms according to score on agricultural land occupation (ALO) depended upon assumptions set for land use in the production of concentrate ingredients. When the Ecoinvent procedure of weighting according to the length of the cropping period was applied, milk and meat produced on diets with a high proportion of concentrates, scored better than milk and meat based on a diet dominated by forages. With regards to terrestrial ecotoxicity (TE), the score was mainly a function of the amount of concentrates fed per functional unit produced, and not of animal yield per se. Overall, the results indicated that an intensification of dairy production by means of higher yields per animal would contribute to more environment-friendly production. For GWP this was also the case when higher yields per head also resulted in higher milk yields and higher N inputs per area of land.

Abstract

In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.

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Abstract

Currently, sugar snap peas are harvested manually. In high-cost countries like Norway, such a labour-intensive practise implies particularly large costs for the farmer. Hence, automated alternatives are highly sought after. This project explored a concept for robotic autonomous identification and tracking of sugar snap pea pods. The approach was based on a combination of visible–near infrared reflection measurements and image analysis, along with visual servoing. A proof-of-concept harvesting platform was implemented by mounting a robotic arm with hand-mounted sensors on a mobile unit. The platform was tested under plastic greenhouse conditions on potted plants of the sugar snap pea variety Cascadia using LED-lights and a partial shade. The results showed that it was feasible to differentiate the pods from the surrounding foliage using the light reflection at the spectral range around 970 nm combined with elementary image segmentation and shape modelling methods. The proof-of-concept harvesting platform was tested on 48 representative agricultural environments comprising dense canopy, varying pod sizes, partial occlusions and different working distances. A set of 104 images were analysed during the teleoperation experiment. The true positive detection rate was 93 and 87% for images acquired at long distances and at close distances, respectively. The robot arm achieved a success rate of 54% for autonomous visual servoing to a pre-grasp pose around targeted pods on 22 untouched scenarios. This study shows the potential of developing a prototype robot for semi-automated sugar snap pea harvesting.

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Abstract

The success of precision agriculture relies largely on our ability to identify how the plants’ growth limiting factors vary in time and space. In the field, several stress factors may occur simultaneously, and it is thus crucial to be able to identify the key limitation, in order to decide upon the correct contra-action, e.g., herbicide application. We performed a pot experiment, in which spring wheat was exposed to water shortage, nitrogen deficiency, weed competition (Sinapis alba L.) and fungal infection (Blumeria graminis f. sp. tritici) in a complete, factorial design. A range of sensor measurements were taken every third day from the two-leaf stage until booting of the wheat (BBCH 12 to 40). Already during the first 10 days after stress induction (DAS), both fluorescence measurements and spectral vegetation indices were able to differentiate between non-stressed and stressed wheat plants exposed to water shortage, weed competition or fungal infection. This meant that water shortage and fungal infection could be detected prior to visible symptoms. Nitrogen shortage was detected on the 11–20 DAS. Differentiation of more than one stress factors with the same index was difficult.

Abstract

It has been long known that thermal imaging may be used to detect stress (e.g. water and nutrient deficiency) in growing crops. Developments in microbolometer thermal cameras, such as the introduction of imaging arrays that may operate without costly active temperature stabilization, have vitalized the interest in thermal imaging for crop measurements. In this study, we have focused on the challenges occurring when temperature stabilization is omitted, including the effects of focal-plane-array (FPA) temperature, camera settings and the environment in which the measurements are performed. Further, we have designed and tested models for providing thermal response from an analog LWIR video signal (typical output from low-cost microbolometer thermal cameras). Finally, we have illustrated and discussed challenges which typically occur under practical use of thermal imaging of crops, by means of three cereal showcases, including proximal and remotely based (UAV) data acquisition. The results showed that changing FPA temperature greatly affected the measurements, and that wind and irradiance also appeared to affect the temperature dynamics considerably. Further, we found that adequate settings of camera gain and offset were crucial for obtaining a reliable result. The model which was considered best in terms of transforming video signals into thermal response data included information on camera FPA temperature, and was based on a priori calibrations using a black-body radiation source under controlled conditions. Very good calibration (r2>0.99, RMSE=0.32°C, n=96) was obtained for a target temperature range of 15-35°C, covering typical daytime crop temperatures in the growing season. However, the three showcases illustrated, that under practical conditions, more factors than FPA temperature may need to be corrected for. In conclusion, this study shows that thermal data acquisition by means of an analog, uncooled thermal camera may represent a possible, cost-efficient method for the detection of crop stress, but appropriate corrections of disturbing factors are required in order to obtain sufficient accuracy.

Abstract

The fauna of surface-active spiders was studied in 12 cereal fields, with two types of subcrop, and in four young (17 months old) perennial leys (grass/clover). The fields were located in the southeastern (A), central (B) and western (C) parts of Norway. In total, 3945 spiders were caught from May to September 2004, using pitfall traps. Linyphidae was the most numerous family, with Erigone atra Blackwall 1833 representing 56% of all trapped individuals. The total numbers of spider species and individuals were significantly higher in leys than in barley at sites where both crops were present (sites A and B), with on average 11 species and 93 specimens in barley, and 20 species and 393 specimens in leys. Thus, young perennial leys appeared to provide a better habitat for spiders than did cereal fields, as has previously been documented for older perennial leys. The use of multi-species crops instead of a single crop species undersown in cereals, tended to result in higher spider species diversity, but it did not influence the total number of specimens. An ordination (DCA) showed a clustering of the spider fauna from the same site, but no clear separation between main crop types (ley vs. barley) was apparent. The main crops, subcrops, and the surrounding environs of the cropped field seem to affect the diversity and abundance of spiders.

Abstract

Mechanistic, multi-compartment decomposition models require that carbon (C) and nitrogen (N) in plant material be distributed among pools of different degradability. For this purpose, measured concentrations of C and N in fractions obtained through stepwise chemical digestion (SCD) and values predicted from near-infrared (NIR) spectra or total plant N concentration were compared. Seventysix cash, forage, green manure and cover crop plant materials representing a wide range in biological origin and chemical quality were incubated in a sandy soil at 15 degrees C and -10 kPa water potential for 217 d. A mechanistic decomposition model was calibrated with data from soil without plant material and initialised by data on amounts of C and N in fractions obtained from SCD directly or C and N in SCD fractions as predicted from NIR spectroscopy or plant N concentration. All model parameters describing C and N flows from plant material were kept at default values as defined in previous, independent works with the same model. When results from SCD were used directly to initialise the decomposition model, C and N mineralisation dynamics were predicted well (r(2) = 0.76 and 0.70 for C mineralisation rates and accumulation of inorganic N, respectively). When a NIR calibration was used to predict the SCD data, this resulted in nearly equally good model performance (r(2) = 0.76 and 0.69 for C and N mineralisation, respectively). This was also the case when SCD data were predicted from plant material N concentration (r(2) = 0.76 and 0.69 for C and N). We conclude that the combined use of a mechanistic decomposition model and quality data from SCD is a highly adequate basis for an a priori description of the mineralisation of both C and N from common agricultural plant materials, and that both NIR spectroscopy and measurement of total N concentration offer good and cost-effective alternatives if they are calibrated with SCD data. (C) 2007 Elsevier Ltd. All rights reserved.

Abstract

Målet med denne studien var å jamføre risikoen for økologiske, integrerte og convensjonelle dyrkingssystem. Forsøksdata frå eit dyrkingssystem (1991-1999) på Austlandet vart brukte saman med budsjettal frå gardsbruk. Empirisk fordeling av nettoinntekt for ulike dyrkingssystem vart estimert ved hjelp av ein simuleringsmodell. Resultata syner at det økologiske systemet hadde størst variasjon i nettoinntekt, men med gjeldane tilskotsordningar og meirprisar for økologiske varer vert dette det mest økonomiske alternativet.

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Abstract

Six cropping systems, ranging from conventional arable without livestock to organic livestock farming dominated by ley, have been compared in 1990 and 2004 in SE Norway. Ley in the crop rotation increased density and biomass of earthworms and channels in both organic and conventional systems. A ley proportion higher than 25 % only increased the density of channels. Among the arable systems, the organic system had a higher density and biomass of earthworms as compared to the conventional systems. Among the fodder systems, the optimised system had the highest density of earthworms in 2004, but there were no differences between these systems in earthworm biomass or density of earthworm channels.

Abstract

Development of environmentally and economically sound agricultural production systems is an important aim in agricultural policy and has a high priority in agricultural research worldwide. The present work uses results from the first complete crop rotation period (1990-1997) of the Apelsvoll cropping system experiment in south-eastern Norway, to discuss the effect of cropping systems and their management practices on environment, soil fertility, crop yields and the farm economy, and how this knowledge may be used to develop a more sustainable agriculture. The experiment includes conventional arable (CON-A), integrated arable (INT-A), ecological arable (ECO-A), conventional forage (CON-F), integrated forage (INT-F) and ecological forage (ECO-F) cropping systems which were established on model farms of 0.2 ha. On the basis of nutrient runoff, soil erosion and pesticide contamination, the following ranking from the most to the least favourable was made for environmental effects: INT-F> ECO-F> ECO-A> INT-A> CON-F> CON-A. Environmental effects such as N and P runoff losses were very much linked to the proportion of ley in the system. Thus, major improvements to reduce the effects of agriculture on nutrient runoff, cannot be achieved without changing the cropping systems in the direction of more mixed farming with reduced cropping intensity. The nutrient balance calculations showed that there were considerable deficits in the ecological systems, a fact which must be taken into consideration in the development of sustainable ecological cropping systems. The yield reduction experienced with integrated and ecological cropping, relative to conventional cropping, was smaller for forage crops and potatoes than for cereals. This suggests that it is easier to maintain the yield level by reduced cropping intensity in mixed farming systems with livestock than in arable farming systems without livestock. Because of the premium prices and government subsidies to ecological farming, the economic results were equally good in the ecological systems as in the conventional ones. Economically, integrated farming was less favourable than the other systems. It is concluded that, overall, integrated and ecological forage systems results in the least environmental harm, and based upon the present government subsidies, the forage systems also seem the most profitable, along with the ecological arable system.

Abstract

The effect of six cropping systems (rotations of either mainly arable or mainly forage crops) on the soil N content was evaluated using mass balances of total N, and the usefulness of such N balances to predict N runoff (total N losses via drainage and surface water) was investigated. All the arable cropping systems resulted in a net reduction in the calculated soil N pool, and the reduction increased with decreasing N input. Only the forage system with the highest N input maintained the initial soil N content. Mass N balances were found to be a useful tool for predicting N runoff, as up to 87% of the variation in N runoff could be explained.