Bruk av fjernmåling for økt presisjon i engdyrking

Forage is a key resource for ruminant meat and milk production. More precise information on variation in yields and forage quality can link this information with agricultural practices, and therefore help farmers make appropriate management decisions. Remote sensing technology (with sensors mounted on tractors, drones and satellite or portable sensors) can help quickly estimate forage yields and quality at field or regional/national levels. Optical remote sensing instruments measure the electromagnetic radiant energy reflected or emitted from an object to indirectly determine properties about it. Based on the relationship between reflection from the plant canopy and precisely measured characteristics of yield or quality, models can be developed to estimate these properties by remote sensing measurements only. In this project, we investigated how sensor technology on different platforms, and analysis methodology, can be used to build systems adapted to both regional interests and the individual farmer to acquire useful information for managing forage production. We investigated whether we can distinguish different forage species based on their spectral signatures of reflected electromagnetic radiation (reflectance) measured by the sensors. We used a handheld sensor (ASD FieldSpec3) and a hyperspectral camera (Rikola) mounted on a drone (UAV) to measure reflectance of several grass species and clover just before harvest. Analyses of reflectance of different species showed clover had a different signature than grass species. There were minor differences between grass species, but it was still possible to distinguish timothy from meadow fescue and perennial ryegrass. To investigate whether remote measurements can be used to estimate yield and feed quality, we established field trials at Holt (Tromsø), Kvithamar (Stjørdal) and Apelsvoll (Kapp), with a mixture of timothy, meadow fescue and some red clover at three fertilizer levels. We measured reflectance with a handheld sensor just before harvest in all three trials, and with a sensor mounted on a drone at Apelsvoll and Kvithamar. We measured yield and analysed feed quality. Results showed remote sensing is suitable for estimating crop and feed quality in forage production, but drone-based measurements are better than handhelds. Results show an effect of botanical composition (e.g., clover or herb content) on spectral signature, and further studies on modelling different compositions are recommended. We attempted estimates of field-level forage yields in farms with drone and handheld sensors. Preliminary results with data from drones clearly show the variation within the fields. However, the correlation with yield levels is poorer than for experimental fields, probably due to greater variation in botanical composition, soil conditions and other environmental effects. Preliminary results with the handheld sensor show yields can be estimated satisfactorily using parts of the available spectral range (350-900 nm). Damp weather during measurements (especially in 2017), common in north Norway affected the spectra and created significant noise in the data. The low sun angle in north Norway relative to southern latitudes also seems to affect measurements, requiring further investigation to manage this extra source of error. We investigated whether satellite data can be used to map extent of winter damages. Areas with and without winter damage from Målselv in Troms from 2017 were identified and available Landsat and Sentinel-2 satellite images were downloaded from the start of growing season in 2017. These areas were used as training data for modelling spectral signals associated with fields with and without winter damage. The results showed significant correlation between areas of winter damage estimated with satellite data and areas reported to have winter damage. Satellite data can thus be used to estimate regional extent of winter damage, but cloud free satellite images at start of growing season, and local reporting of date of growth start and some local reference data are necessary. The method cannot be used on historical data without good reference data. We estimated crop and botanical composition in fields of different age in mountain areas in Møre og Romsdal, and in north Norway by measuring field area, counting bales and weighing three bales per field and harvest. Samples for the determination of dry matter and feed quality (NIRS) were also taken. The surveys show a large variation in yields with a clear yield decline with age in the mountains. In Møre og Romsdal there were small differences between the age groups. In north Norway, yield was reduced in older meadows. Sown species declined with increasing age and were replaced with couchgrass and smooth meadowgrass. Feed quality was not affected by age of the fields, but was lowest in Finnmark. Dry matter content and weight of the bales were generally higher than the norms used by SSB or NIBIO.

Forage is a key resource for ruminant meat and milk production. Information on yields and forage quality on the standing crop could help farmers make appropriate management decisions concerning i.e.: harvesting sequence of different fields according to yield and feed quality, sorting according to feed quality, purchasing the appropriate supplements, stocking rate etc. More precise information on variations at field and regional level can give better knowledge on the links between yields and agricultural practices, soil and climate. Remote sensing utilizes the electromagnetic radiant energy reflected or emitted from an object, to indirectly determine its properties. Based on the relationship between reflection from plant canopy and some precisely measured characteristics, i.e. yield or quality, models can be developed to estimate these properties by remote sensing measurements only. The methodology is fast and cost-efficient, once reliable calibration models are established. The remotely sensed data may be acquired with a variety of sensor platforms: hand-held, tractor-mounted, mounted on unmanned aerial vehicles (UAVs), or on airplanes and satellites. Bioforsk has already developed a sensor-based system for estimating yields and quality of spring cereals in Norway. Internationally, related approaches have been reported for grassland too, where both handheld sensor system and remote measurements with satellite data have been used to estimate forage quality, yield and productivity with promising results. In this project we will use sensor technology at different scales in forage production to create systems tailored to both regional interests and the individual farmer under Norwegian conditions.